TECHNICAL FIELD

[0001] The present disclosure relates to the field of Internet of things (IoT), and in particular, to a method and apparatus for monitoring production safety for a lithium battery, and a device and storage medium thereof.

BACKGROUND

[0002] The IoT technology is widely applied to monitoring of a production process of a factory. For example, various types of IoT devices are set up in the factory, the IoT device acquires production information of the factory, and uploads the information to a management center by the IoT. In this way, the management staff can acquire the factory's production conditions in real time, and make timely adjustments to abnormal production conditions.

[0003] In the related art, the management center collects video information of a production workshop using a video monitoring device and collects temperature information of the production workshop using a temperature sensor. When the management staff find out that the video information or temperature information is abnormal, they can react to abnormal situations in a timely fashion.

[0004] The factory monitoring measures in the related art are not target-oriented, and hazardous production processes of the factory cannot be monitored in a target-oriented fashion in combination with the type of the factory.

SUMMARY

[0005] Embodiments of the present disclosure provide a method and apparatus for monitoring production safety for a lithium battery, and a computer device and a non- transitory computer-readable storage medium thereof, which can solve the problem in the related art that the factory monitoring measures are not target-oriented, hazardous production processes of the factory cannot be specially monitored in combination with the type of the factory.

[0006] According to one aspect of embodiments of the present disclosure, a method for monitoring production safety for a lithium battery is provided. The method is applicable to an IoT cloud platform connected to production monitoring devices corresponding to at least two production processes. The method includes:

[0007] receiving production information reported by the production monitoring device; [0008] analyzing, based on the production information, whether production materials of the lithium battery are abnormal, wherein the production materials include at least one of an electrolyte raw material for producing the lithium battery, a nitrogen raw material for producing the lithium battery, and a cell of the lithium battery; and [0009] outputting an alarm message when the production materials of the lithium battery are abnormal.

[0010] According to another aspect of embodiments of the present disclosure, a production safety monitoring apparatus for a lithium battery is provided. The apparatus is applicable to an IoT cloud platform connected to production monitoring devices corresponding to at least two production processes. The apparatus includes:

[0011] a receiving module, configured to receive production information reported by the production monitoring device;

[0012] an analyzing module, configured to analyze, based on the production information, whether production materials of the lithium battery are abnormal, wherein the production materials include at least one of an electrolyte raw material for producing the lithium battery, a nitrogen raw material for producing the lithium battery, and a cell of the lithium battery; and

[0013] an alarming module, configured to output an alarm message when the production materials of the lithium battery are abnormal.

[0014] According to another aspect of embodiments of the present disclosure, a computer device is provided. The computer device includes: a processor and a memory storing at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set, when loaded and executed by the processor, causes the processor to perform the method for monitoring production safety for a lithium battery as described above.

[0015] According to another aspect of the present disclosure, a non-transitory computer- readable storage medium is provided. The non-transitory computer-readable storage medium stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set, when loaded and executed by a processor, causes the processor to perform the method for monitoring production safety for a lithium battery as described above.

[0016] The technical solutions according to the embodiments of the present disclosure may achieve the following beneficial effects.

[0017] Whether the lithium battery production materials are abnormal is determined based on the production information of the lithium battery production materials reported by the production monitoring device. If the production materials are abnormal, an alarm message is generated in time to warn the staff of the abnormal situation. By target- oriented monitoring at least one of the electrolyte raw material of lithium batteries, nitrogen raw materials for producing lithium batteries, and cells of the lithium batteries and disposing corresponding monitoring devices for production processes that are prone to hazards or leakage in the production of lithium batteries for key monitoring, the safety monitoring efficiency of lithium battery production is improved, and the production risks are reduced.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to describe the technical solutions in the embodiments of the present more clearly, the following briefly introduces the accompanying drawings required for describing the embodiments. Apparently, the accompanying drawings in the following description show merely some embodiments of the present disclosure, and a person of ordinary skill in the art may also derive other drawings from these accompanying drawings without creative efforts.

[0019] FIG. 1 is a block diagram of an IoT system according to an exemplary embodiment of the present disclosure; [0020] FIG. 2 is a flowchart of a method for monitoring production safety for a lithium battery according to an exemplary embodiment of the present disclosure;

[0021] FIG. 3 is a schematic diagram of a device in a production process using an electrolyte according to another exemplary embodiment of the present disclosure;

[0022] FIG. 4 is a flowchart of a method for monitoring production safety for a lithium battery according to another exemplary embodiment of the present disclosure;

[0023] FIG. 5 is a flowchart of a method for monitoring production safety for a lithium battery according to another exemplary embodiment of the present disclosure;

[0024] FIG. 6 is a flowchart of a method for monitoring production safety for a lithium battery according to another exemplary embodiment of the present disclosure;

[0025] FIG. 7 is a schematic diagram of a device in a production process using nitrogen according to another exemplary embodiment of the present disclosure;

[0026] FIG. 8 is a flowchart of a method for monitoring production safety for a lithium battery according to another exemplary embodiment of the present disclosure;

[0027] FIG. 9 is a flowchart of a method for monitoring production safety for a lithium battery according to another exemplary embodiment of the present disclosure;

[0028] FIG. 10 is a pressure-time diagram of a nitrogen storage container according to another exemplary embodiment of the present disclosure;

[0029] FIG. 11 is a flowchart of a method for monitoring production safety for a lithium battery according to another exemplary embodiment of the present disclosure;

[0030] FIG. 12 is a structural diagram of an apparatus for monitoring production safety for a lithium battery according to another exemplary embodiment of the present disclosure; and

[0031] FIG. 13 is a schematic structural diagram of a server according to an exemplary embodiment of the present disclosure.

DETAILED DESCRIPTION

[0032] The present disclosure is hereinafter described in further detail with reference to the accompanying drawings, to present the objects, technical solutions, and advantages of the present disclosure more clearly. [0033] First, the terms involved in the embodiments of the present disclosure are introduced:

[0034] Internet of things (IoT): The IoT refers to adopt various devices and technologies such as information sensors, radio frequency identification technology, global positioning systems, infrared sensors, laser scanners, and the like to collect any objects or processes that require monitoring, connection and interaction in real time, collect various required information such as sound, light, heat, electricity, mechanics, chemistry, biology, location, etc., and access them over various possible networks to realize ubiquitous connections between things and between things and people to achieve intelligent perception, identification and management of items and processes. The IoT is an information carrier based on the Internet and traditional telecommunications networks, which allows all common physical objects that can be independently addressed to form an interconnected network.

[0035] Alarm: An alarm is an alarm message that is generated when an IoT cloud platform detects abnormalities in production processes, devices, device clusters, systems, products, and production materials. A display device may display an alarm, or a buzzer may issue an alarm, or an indicator may emit a light effect, or an alarm message is sent to a designated device, computer, server, and the like to notify the staff of the alarm.

[0036] FIG. 1 illustrates a schematic diagram of an IoT system according to an embodiment of the present disclosure. The IoT system 100 may include a server cluster 101 and an IoT device. In an exemplary embodiment, the IoT device includes a first IoT device 102, a second IoT device 103, a third IoT device 104, a fourth IoT device 105, and a fifth IoT device 106.

[0037] The server cluster 101 is a cluster that gathers a plurality of servers for computing and storing data information. In the embodiments of the present disclosure, the server cluster 101 includes at least one server. In the embodiments of the present disclosure, the server cluster includes an IoT cloud platform. The IoT cloud platform stores production information analysis methods and alarm rules. The IoT cloud platform may receive production information reported by IoT devices, or other information from IoT devices. In an exemplary embodiment, the IoT cloud platform may also issue alarm messages based on the analysis results. The IoT cloud platform may be deployed in one or more servers, which is not limited in the embodiments of the present disclosure.

[0038] In an exemplary embodiment, the server cluster may also be another IoT node having a function of receiving information uploaded by the IoT device and processing the information, such as a router, or a gateway.

[0039] The IoT device refers to a physical device with IoT communication capabilities. For example, the IoT device in the present disclosure refers to a production monitoring device. In an exemplary embodiment, the production monitoring device includes a scanning device, a sensor, a gas monitoring device, a temperature monitoring device, an ammeter, a voltmeter, a flow meter, a video monitoring device, and the like. In another exemplary embodiment, the IoT device may send production information or other information to the IoT cloud platform.

[0040] In an exemplary embodiment, the IoT device and the server cluster 101 are connected over a network. The network may be a wired network or a wireless network. For example, the IoT device and the server cluster 101, and the server cluster 101 and the server cluster 101 may be connected in an IoT device to an IoT device (Ad-Hoc) fashion, or may be connected by the coordination of a base station or a wireless access point (AP), which is not limited in the embodiments of the present disclosure.

[0041] Those skilled in the art may know that the number of the above-mentioned server clusters 101 or IoT devices may be more or less. For example, only one server cluster 101 or IoT device may be deployed, or t dozens or hundreds of server clusters 101 or IoT devices, or more may be deployed. The number and type of the server clusters 101 or the IoT devices are not limited in the embodiments of the present disclosure.

[0042] FIG. 2 illustrates a flowchart of a method for monitoring production safety for a lithium battery according to an exemplary embodiment of the present disclosure. This method may be performed by the server cluster 101 in the IoT system 100 illustrated in FIG. 1. The method is applicable to an IoT cloud platform connected to production monitoring devices corresponding to at least two production processes. The method includes the following steps.

[0043] In step 101, production information reported by the production monitoring device is received. [0044] The IoT cloud platform receives production information reported by the production monitoring device.

[0045] The production monitoring device is a device having a function of monitoring a production process. In an exemplary embodiment, the production monitoring device may monitor at least one of a production environment, a production process, a product, a production material, and a person. For example, the production monitoring device may monitor the temperature of a production workshop, monitor an operating state of a production device in a production process, monitor the current location of a product, monitor the use of production materials, monitor the arrival of production personnel, and the like. In an exemplary embodiment, the production monitoring device may also be a production device provided with a monitoring function. For example, an infrared sensing assembly line or a material adding device equipped with a gravity sensor is provided. In an exemplary embodiment, the production monitoring device is an IoT device connected to the IoT cloud platform. In an exemplary embodiment, the production monitoring device is a device provided in a lithium battery production process. In an exemplary embodiment, the production monitoring device is a device disposed in the production device. Alternatively, the production monitoring device is a device provided outside the production device. In an exemplary embodiment, the production monitoring device is a device provided in a production workshop.

[0046] The production information is monitoring information of a production process. In an exemplary embodiment, the production information includes at least one of quantity information, time information, video information, mass/weight information, temperature information, pressure information, flow information, concentration information, current/voltage information, location information, and alarm message. In an exemplary embodiment, the IoT cloud platform determines, based on the received production information, whether the production process is abnormal . In an exemplary embodiment, the production information is production process information collected by the production monitoring device. In an exemplary embodiment, the production information is monitoring information on the lithium battery production process. [0047] In an exemplary embodiment, when the production monitoring device collects production information, the production monitoring device sends the production information to the IoT cloud platform.

[0048] In step 102, whether production materials of the lithium battery are abnormal is analyzed based on the production information, wherein the production materials includes at least one of an electrolyte raw material for producing the lithium battery, a nitrogen raw material for producing the lithium battery, and a cell of the lithium battery.

[0049] The IoT cloud platform analyzes, based on the production information, whether the production materials of the lithium battery are abnormal.

[0050] The production material is an article used for lithium battery production. In an exemplary embodiment, the production materials are consumables for lithium battery production. In another exemplary embodiment, the production materials include at least one of raw materials used in the production of lithium batteries, products in the production process, semi-finished products, auxiliary raw materials, catalysts, production models, and production conditions.

[0051] In an exemplary embodiment, the production monitoring device reports production information related to the production materials, such as the amount of production materials, the balance of production materials, whether the production materials leak, the purity of production materials, the proportion of production materials, etc. In another exemplary embodiment, the production materials include at least one of an electrolyte raw material, a nitrogen raw material, and a lithium battery cell used in the lithium battery production process.

[0052] In an exemplary embodiment, if the production information is numerical information, the IoT cloud platform may analyze whether the production materials are abnormal by determining whether the production information meets a threshold. For example, the production information is an hourly output of a first process in the lithium battery production process, and the threshold is that the hourly output is greater than 100. When the production information of the first process received by the IoT cloud platform is that the hourly output is 50, the IoT cloud platform determines that the output is abnormal. [0053] In an exemplary embodiment, if the production information is non-numeric information, the IoT cloud platform may analyze whether the production materials are abnormal by determining whether the production information meets a condition. For example, the production information is a monitoring video of a lithium battery production workshop, and the condition is that items are placed in a hazardous area of the workshop. When the IoT cloud platform scans from the monitoring video that items are placed in the hazardous area, the IoT cloud platform determines that the hazardous area is abnormal. [0054] In an exemplary embodiment, when the production material is the electrolyte raw material for producing the lithium battery, the production information includes at least one of the mass of an electrolyte raw material storage container, the mass of the electrolyte each time replenished by an electrolyte replenishment container, and the mass of the electrolyte injected into the cell by the electrolyte injection device.

[0055] In an exemplary embodiment, when the production material is the nitrogen raw material for producing the lithium battery, the production information includes at least one of the pressure of a nitrogen storage container, the nitrogen flow at an output of the nitrogen storage container, the nitrogen concentration in an closed space, and the concentration of oxygen in a nitrogen room.

[0056] In an exemplary embodiment, when the production material is the cell of the lithium battery, the production information includes at least one of time when the cell arrives at each production process, identification of the production process, acceptance information of the cell, the scanning time, and an expected duration needed by the cell to reach the next process.

[0057] In step 103, an alarm message is output when the production materials of the lithium battery are abnormal.

[0058] When the production materials of the lithium battery are abnormal, the IoT cloud platform outputs an alarm message.

[0059] In an exemplary embodiment, the alarm message is an alarm for abnormality in the production materials. In another exemplary embodiment, the production material is abnormal in that the condition of the production material affects normal production or has hidden safety risks. [0060] In an exemplary embodiment, the way by which the IoT cloud platform outputs an alarm message is not limited in the present disclosure. For example, the IoT cloud platform may send an alarm message to a designated computer; send an alarm text message to a designated mobile phone; send an alarm message to a designated device, and cause the device's buzzer to alarm or cause the device's indicator light to flash; and send an alarm message to a designated application server, and send the alarm message to a terminal by an application.

[0061] In an exemplary embodiment, after determining that the production materials are abnormal, the IoT cloud platform may also take specified measures to reduce hidden security risks. For example, after determining that a first workshop is on fire, the IoT cloud platform shuts down the production power of the first workshop and starts fire facilities.

[0062] In summary, in the method according to this embodiment, whether the production materials of the lithium battery are abnormal is determined based on the production information of the lithium battery production materials reported by the production monitoring device, and when the production materials are abnormal, an alarm message is generated in time to warn the staff of the abnormal situation. By target-oriented monitoring at least one of the electrolyte raw material of lithium batteries, nitrogen raw materials for producing lithium batteries, and cells of the lithium batteries and disposing corresponding monitoring devices for production processes that are prone to hazards or leakage in the production of lithium batteries for key monitoring, the safety monitoring efficiency of lithium battery production is improved, and the production risks are reduced. [0063] The present disclosure also provides an exemplary embodiment of monitoring the electrolyte replenishment process.

[0064] In an exemplary embodiment, in the lithium battery production process, a step of injecting an electrolyte into the cell of the lithium battery is included. In another exemplary embodiment, the production device in this process is illustrated in FIG. 3. The electrolyte is stored in an electrolyte container 301 and input to a replenishment machine 303 by an input pipe 302. In another exemplary embodiment, the electrolyte is sufficiently stirred and mixed in the replenishment machine 303, and then injected into a cell 306 of each lithium battery on an assembly line 305 by an injection machine 304. In another exemplary embodiment, a high-level sensor 307 and a low-level sensor 308 are disposed in the replenishment machine 303. When the level of the electrolyte in the replenishment machine 303 is lower than the low-level sensor 308 and the low-level sensor generates a low-level signal, the replenishment machine 303 starts to replenish the electrolyte from the electrolyte container 301. When the level of the electrolyte in the replenishment machine 303 is higher than the high-level sensor 307 and the high-level sensor generates high level information, the replenishment machine 303 stops the replenishment of the electrolyte from the electrolyte container 301. In another exemplary embodiment, the mass of the electrolyte each time replenished by the replenishment machine 303 is a fixed value (the error is within a range).

[0065] FIG. 4 illustrates a flowchart of a method for monitoring production safety for a lithium battery according to an exemplary embodiment of the present disclosure. This method may be performed by the server cluster 101 in the IoT system 100 illustrated in FIG. 1. The production materials include the electrolyte raw material. The production monitoring device includes a mass monitoring device disposed under an electrolyte container. The electrolyte container is connected to a replenishment machine by an input pipe. In an exemplary embodiment, the mass monitoring device may be a gravity sensor 309 as illustrated in FIG. 3. The method includes the following steps.

[0066] In step 401, a mass difference reported by the mass monitoring device is received, wherein the mass difference is a mass difference before and after a single replenishment of the electrolyte container.

[0067] The IoT cloud platform receives a mass difference reported by the mass monitoring device, wherein the mass difference is a mass difference before and after a single replenishment of the electrolyte container.

[0068] In an exemplary embodiment, the IoT cloud platform receives production information of the electrolyte raw material reported by the production monitoring device. In another exemplary embodiment, the production monitoring device includes a mass monitoring device disposed under an electrolyte container. In another exemplary embodiment, the mass monitoring device includes at least one of a gravity sensor and an electronic scale. [0069] The electrolyte container is a container for storing an electrolyte. In an exemplary embodiment, the electrolyte container is connected to a replenishment machine. In another exemplary embodiment, the electrolyte container is used for replenishing the electrolyte to the replenishment machine.

[0070] The mass monitoring apparatus is configured to acquire the mass/weight of the electrolyte container. The mass of the output electrolyte is acquired by obtaining a mass difference of the electrolyte container.

[0071] In an exemplary embodiment, the mass monitoring device monitors a mass difference of the electrolyte container before and after each replenishment, i.e., the mass of the electrolyte output by the electrolyte container to the replenishment machine at each replenishment.

[0072] In an exemplary embodiment, step 401 may be replaced with step 4011 to step 4013 illustrated in FIG. 5.

[0073] In step 4011, a first mass of the electrolyte container reported by the mass monitoring device is received, wherein the first mass is a mass sum of the electrolyte in the electrolyte container and the electrolyte container when the level of the replenishment machine is lower than the lowest level and the replenishment machine starts to replenish the electrolyte.

[0074] The IoT cloud platform receives a first mass of the electrolyte container reported by the mass monitoring device.

[0075] The first mass is the mass of the electrolyte container when the replenishment machine starts to replenish. In an exemplary embodiment, the first mass is a mass sum of the electrolyte container and the electrolyte contained in the electrolyte container when the electrolyte container starts to output the electrolyte.

[0076] In an exemplary embodiment, the lowest level is a level at which the replenishment machine starts to replenish. In another exemplary embodiment, the lowest level is the level of the electrolyte in the replenishment machine when the low-level sensor in the replenishment machine issues a low-level signal. In another exemplary embodiment, the lowest level may be a level when no electrolyte is present in the replenishment machine. [0077] In an exemplary embodiment, the mass monitoring device may know the time when the replenishment machine starts to replenish in an arbitrary fashion.

[0078] In an exemplary embodiment, the high-level sensor and the low-level sensor on the replenishment machine are connected to the mass monitoring device. When the level of the electrolyte in the replenishment machine is lower than the low-level sensor, the low-level sensor sends a low-level signal to the injection machine and the mass monitoring device, the replenishment machine starts to replenish the electrolyte, and the mass monitoring device acquires the mass of the electrolyte container at this moment as the first mass, and sends the first mass to the IoT cloud platform.

[0079] In an exemplary embodiment, the mass monitoring device may be connected to a valve of the electrolyte container to acquire a state of the valve of the electrolyte container. In another exemplary embodiment, when the valve of the electrolyte container is opened, the mass monitoring device acquires the mass of the electrolyte container at this time as the first mass, and sends the first mass to the IoT cloud platform.

[0080] In an exemplary embodiment, the mass monitoring device may be further configured to acquire the mass of the electrolyte container when the weight starts to change. For example, when detecting the mass change, the mass monitoring device acquires the mass of the electrolyte container at this moment as the first mass, and sends the first mass to the IoT cloud platform.

[0081] In step 4012, a second mass of the electrolyte container reported by the mass monitoring device is received, wherein the second mass is the mass of the electrolyte container when the level of the replenishment machine is higher than the highest level and the replenishment machine stops replenishing the electrolyte.

[0082] The IoT cloud platform receives a second mass of the electrolyte container reported by the mass monitoring device.

[0083] The second mass is the mass of the electrolyte container when the replenishment machine stops replenishment. In an exemplary embodiment, the second mass is the mass of the electrolyte container when the electrolyte container stops outputting the electrolyte. [0084] In an exemplary embodiment, the highest level is a level at which the replenishment machine stops replenishment. In another exemplary embodiment, the highest level is the level of the electrolyte in the replenishment machine when the high- level sensor in the replenishment machine issues a high level signal. In another exemplary embodiment, the highest level may be a level when the replenishment machine is filled with electrolyte.

[0085] In an exemplary embodiment, the mass monitoring device may know the time at which the replenishment machine stops replenishment in any way.

[0086] In an exemplary embodiment, the mass monitoring device may know the time when the replenishment machine stops replenishment in a way similar to step 4011.

[0087] In step 4013, a mass difference is calculated based on the first mass and the second mass.

[0088] The IoT cloud platform calculates a mass difference based on the first mass and the second mass.

[0089] In an exemplary embodiment, upon receiving the first mass and the second mass, the IoT cloud platform calculates a mass difference between the first mass and the second mass.

[0090] The mass difference is the mass of the electrolyte output by the electrolyte container in a single replenishment.

[0091] In step 402, a first difference between the mass difference and a single replenishment mass of the replenishment machine is calculated.

[0092] The IoT cloud platform calculates a first difference between the mass difference and a single replenishment mass of the replenishment machine.

[0093] In an exemplary embodiment, the IoT cloud platform stores the single replenishment mass of the replenishment machine.

[0094] The single replenishment mass is the mass of each replenishment of the electrolyte preset by the replenishment machine. In an exemplary embodiment, the single replenishment mass is related to the highest level and the lowest level of the replenishment machine. In another exemplary embodiment, the single replenishment mass is a fixed value.

[0095] The first difference is a mass loss of the electrolyte during the input from the electrolyte container to the replenishment machine. In an exemplary embodiment, the first difference value is used to monitor whether an electrolyte leakage occurs during the electrolyte replenishment process. In another exemplary embodiment, if the electrolyte leaks during the replenishment process, the first difference is generated.

[0096] In step 403, whether the first difference is greater than a first error threshold is determined.

[0097] The IoT cloud platform determines whether the first difference value is greater than a first error threshold. When the first difference value is greater than the first error threshold, step 404 is performed; and otherwise, step 405 is performed.

[0098] In step 404, when the first difference is greater than the first error threshold, it is determined that electrolyte leakage occurs in the input pipe.

[0099] When the first difference is greater than the first error threshold, the IoT cloud platform determines that electrolyte leakage occurs in the input pipe.

[00100] The first error threshold is a preset error range. In an exemplary embodiment, in consideration of errors in the mass monitoring device, the high-level sensor, and the low-level sensor, or inevitable leakage of the electrolyte, the first error threshold is set. When the first difference is less than or equal to the first error threshold, it is determined that the first difference is within a range allowed by the error, and the possibility of leakage of the electrolyte is low. When the first difference is greater than the first error threshold, it is determined that the electrolyte leaks during the replenishment process.

[00101] In an exemplary embodiment, the first error threshold may be zero or any value.

[00102] In an exemplary embodiment, when the first difference value is greater than the first error threshold, it is determined that leakage occurs in the input pipe, or it is determined that electrolyte leakage occurs during the replenishment process.

[00103] In step 405, when the first difference is less than or equal to the first error threshold, it is determined that the input pipe is normal.

[00104] When the first difference is less than or equal to the first error threshold, the IoT cloud platform determines that the input pipe is normal.

[00105] In step 203, when the production materials of the lithium battery are abnormal, an alarm message is output. [00106] In an exemplary embodiment, when determining that the electrolyte replenishment is abnormal, the IoT cloud platform outputs an alarm message corresponding to the abnormal electrolyte replenishment.

[00107] In summary, in the method according to this embodiment, the mass monitoring device is disposed under the electrolyte container. The IoT cloud platform acquires the first mass and the second mass of the electrolyte container reported by the mass monitoring device before and after each replenishment, calculates the mass of the electrolyte output from the electrolyte container for each replenishment, and calculates the first difference between the mass of the electrolyte output from the electrolyte container and a single replenishment mass of the replenishment machine. By determining the size of the first difference, whether electrolyte leakage occurs in the input pipe is determined. When the first difference is greater than the first error threshold, the IoT cloud platform determines that electrolyte leakage occurs in the input pipe, and generates an alarm message in time to issue an alarm.

[00108] The present disclosure also provides an exemplary embodiment of monitoring the electrolyte injection process.

[00109] FIG. 6 illustrates a flowchart of a method for monitoring production safety for a lithium battery according to an exemplary embodiment of the present disclosure. This method may be performed by the server cluster 101 in the IoT system 100 illustrated in FIG. 1. The production materials include the electrolyte raw material. The production monitoring device includes a flow detector disposed on an output pipe of the injection machine. In an exemplary embodiment, as illustrated in FIG. 3, a flow detector 310 is disposed on an output pipe of the injection machine 304. The method includes the following steps.

[00110] In step 201, production information reported by the production monitoring device is received.

[00111] In an exemplary embodiment, the production monitoring device includes a flow detector disposed on an output pipe of the injection machine. In another exemplary embodiment, the flow detector may detect the mass of the electrolyte each time injected by the injection machine to the cell. In another exemplary embodiment, the flow detector may detect the flow of the electrolyte each time injected by the injection machine to the cell, and the IoT cloud platform may calculate the mass of the electrolyte based on the flow.

[00112] In an exemplary embodiment, the production information is an injection mass of the injection machine from the last replenishment to the current replenishment. [00113] In step 404, an injection mass of the injection machine from the last replenishment to the current replenishment is accumulated, wherein the injection mass is the mass of the electrolyte injected into the cell by the injection machine.

[00114] The IoT cloud platform accumulates an injection mass of the injection machine from the last replenishment to the current replenishment.

[00115] In an exemplary embodiment, the IoT cloud platform calculates the mass of the electrolyte output by the injection machine from the end of the last replenishment to the beginning of the current replenishment. For example, the replenishment machine ends the last replenishment at 00:00, and starts the current replenishment at 00:10 when the electrolyte level in the replenishment machine is lower than the lowest level, the replenishment machines injects electrolyte to the cell three times from 00:00 to 00:10, and the injection mass reported by the flow detector each time is 1 kg, 1.01 kg, and 1.05 kg respectively. Then the IoT cloud platform calculates the sum of the three injection masses reported by the flow detector from 00:00 to 00: 10, that is, 1+1.01+1.05=3.06 kg. [00116] In step 405, a second difference between the injection mass and a single replenishment mass of the replenishment machine is calculated.

[00117] The IoT cloud platform calculates a second difference between the injection mass and a single replenishment mass of the replenishment machine.

[00118] The second difference is a mass loss of the electrolyte input from the replenishment machine to the injection machine. In an exemplary embodiment, the second difference value is used to monitor whether an electrolyte leakage occurs during the electrolyte injection process. In another exemplary embodiment, if the electrolyte leaks during the injection process, the second difference is generated.

[00119] In step 406, whether the second difference is greater than a second error threshold is determined. [00120] The IoT cloud platform determines whether the second difference value is greater than a second error threshold. The second difference value is greater than the second error threshold, step 407 is performed; and otherwise, step 408 is performed. [00121] In step 407, when the second difference value is greater than the second error threshold, it is determined that electrolyte leakage occurs in the output pipe.

[00122] When the second difference is greater than the second error threshold, the IoT cloud platform determines that electrolyte leakage occurs in the output pipe.

[00123] The second error threshold is a preset error range. In an exemplary embodiment, in consideration of errors in the high-level sensor, the low-level sensor, or the flow detector, or inevitable leakage of the electrolyte, the second error threshold is set. When the second difference is less than or equal to the second error threshold, it is determined that the second difference is within a range allowed by the error, and the possibility of leakage of the electrolyte is low. When the second difference is greater than the second error threshold, it is determined that the electrolyte leaks during the injection process.

[00124] In an exemplary embodiment, the second error threshold may be zero or any value.

[00125] In an exemplary embodiment, when the second difference value is greater than the second error threshold, it is determined that leakage occurs in the output pipe, or it is determined that leakage occurs in the injection process.

[00126] In step 408, when the second difference is less than or equal to the second error threshold, it is determined that the output pipe is normal.

[00127] When the second difference is less than or equal to the second error threshold, the IoT cloud platform determines that the output pipe is normal.

[00128] In step 203, when the production materials of the lithium battery are abnormal, an alarm message is output.

[00129] In an exemplary embodiment, when determining that the electrolyte injection is abnormal, the IoT cloud platform outputs an alarm message corresponding to the electrolyte injection abnormality.

[00130] In summary, in the method according to this embodiment, a flow detector is disposed on the output pipe of the injection machine to detect the mass of the electrolyte each time injected into the cell by injection machine, and the mass of the electrolyte injected into the cell by the injection machine is compared with the single replenishment mass of the replenishment machine to determine whether output leakage occurs to the electrolyte between the replenishment machine and the injection machine. When the second difference is greater than the second error threshold, the IoT cloud platform determines that electrolyte leakage occurs in the output pipe, and generates an alarm message in time to issue an alarm.

[00131] The present disclosure also provides an exemplary embodiment for monitoring nitrogen.

[00132] In an exemplary embodiment, in the lithium battery production process, a production process that requires the use of nitrogen is needed. In another exemplary embodiment, the production device in this process is illustrated in FIG. 7. In a lithium battery production workshop using nitrogen, a room 700 is present. A nitrogen storage container 701 is placed in the room 700. For example, the nitrogen storage container may be a nitrogen tank. The nitrogen storage container 701 inputs nitrogen into a closed space 702 by a pipe. In another exemplary embodiment, the nitrogen in the nitrogen storage container 701 does not leak into the room 700 under normal circumstances.

[00133] FIG. 8 illustrates a flowchart of a method for monitoring production safety for a lithium battery according to an exemplary embodiment of the present disclosure. This method may be performed by the server cluster 101 in the IoT system 100 illustrated in FIG. 1. The production materials include the nitrogen raw material. The production monitoring device includes an oxygen concentration sensor disposed in a room using the nitrogen raw material and a pressure gauge disposed on a nitrogen storage container. In an exemplary embodiment, as illustrated in FIG. 7, an oxygen concentration sensor 703 is disposed in the room 700 of the nitrogen raw material, and a pressure gauge 704 is disposed on the nitrogen storage container 701. In another exemplary embodiment, the oxygen concentration sensor 703 may acquire the concentration of oxygen in the air, and the pressure gauge 704 may acquire the pressure in the nitrogen storage container. In another exemplary embodiment, the room 700 may be a closed room or a ventilated room. The method includes the following steps. [00134] In step 201, production information reported by the production monitoring device is received.

[00135] In an exemplary embodiment, the production monitoring device includes an oxygen concentration sensor disposed in a room using the nitrogen raw material and a pressure gauge disposed on a nitrogen storage container.

[00136] In an exemplary embodiment, the production monitoring device in the nitrogen storage container may be adjusted by an inspection device disposed in the nitrogen storage container. For example, some nitrogen storage containers are not provided with a pressure gauge in the container, but are provided with a flow meter at the output port. For example, a flow meter 705 is provided at the outlet of the nitrogen storage container 701 as illustrated in FIG. 7. Then, the production monitoring device may also be a flow meter provided at the outlet of the nitrogen storage container.

[00137] The production information includes oxygen concentration information reported by the oxygen concentration sensor, pressure information in the nitrogen storage container reported by the pressure gauge, or nitrogen flow information reported by the flow meter.

[00138] In step 501, whether the nitrogen raw material is abnormal is determined based on at least one of an oxygen concentration reported by the oxygen concentration sensor and a nitrogen pressure value reported by the pressure gauge.

[00139] The IoT cloud platform determines, based on at least one of an oxygen concentration reported by the oxygen concentration sensor and a nitrogen pressure value reported by the pressure gauge, whether the nitrogen raw material is abnormal .

[00140] In an exemplary embodiment, the IoT cloud platform determines whether the oxygen concentration in the room is within a tolerance range of the human body by determining the oxygen concentration in the room. For example, an excessively low oxygen concentration may cause a person to be hypoxic. In another exemplary embodiment, the IoT cloud platform determines whether the nitrogen leaks into the room by determining the oxygen concentration in the room.

[00141] In an exemplary embodiment, the IoT cloud platform determines, by determining the pressure in the nitrogen storage container, whether leakage occurs in the nitrogen storage container. In another exemplary embodiment, the IoT cloud platform determines whether the nitrogen in the nitrogen storage container is about to run out by determining the pressure in the nitrogen storage container.

[00142] In an exemplary embodiment, step 501 may be replaced with step 5011 to step 5013 illustrated in FIG. 9.

[00143] In step 5011, when the oxygen concentration is less than a first concentration threshold, it is determined that low oxygen occurs in the room.

[00144] When the oxygen concentration is less than the first concentration threshold, the IoT cloud platform determines that low oxygen occurs in the room.

[00145] In an exemplary embodiment, when nitrogen leaks or the room is closed for a long time, the oxygen concentration in the room will decrease. When the oxygen concentration is too low, people entering the room are subject to hidden safety hazards or even life dangers. Therefore, when the oxygen concentration in the room is too low, the IoT cloud platform determines that low oxygen occurs in the room.

[00146] The first concentration threshold is the lowest oxygen concentration threshold. In an exemplary embodiment, the first concentration is the lowest concentration that can ensure human health. In another exemplary embodiment, the first concentration threshold may take any value from 19% to 24%.

[00147] In an exemplary embodiment, when the oxygen concentration is greater than or equal to the first concentration threshold, the IoT cloud platform determines that the oxygen concentration is normal.

[00148] In step 5012, when the oxygen concentration is greater than a second concentration threshold and a falling acceleration of the nitrogen pressure value is greater than an acceleration threshold, it is determined that leakage occurs in the nitrogen storage container.

[00149] When the oxygen concentration is greater than a second concentration threshold and a falling acceleration of the nitrogen pressure value is greater than an acceleration threshold, the IoT cloud platform determines that leakage occurs in the nitrogen storage container.

[00150] In an exemplary embodiment, the second concentration threshold is an oxygen concentration threshold. In another exemplary embodiment, the human body's activities in an oxygen environment greater than the second concentration threshold will not endanger health. In another exemplary embodiment, the second concentration threshold is a threshold equal to the first concentration threshold, or the second concentration threshold is a threshold different from the first concentration threshold. In another exemplary embodiment, the second concentration threshold may take any value from 19% to 24%.

[00151] In an exemplary embodiment, in the nitrogen storage container, the pressure drop rate is different. When the nitrogen storage container is filled with nitrogen, the pressure drop rate is faster. When the nitrogen storage container is insufficient, the pressure drop rate is slower. That is, under normal circumstances, the acceleration of the pressure drop in the nitrogen storage container gradually decreases. When the nitrogen leaks, the pressure in the nitrogen storage container suddenly drops, and the acceleration of the pressure drop suddenly increases.

[00152] In an exemplary embodiment, graph (1) in FIG. 10 indicates the pressure change with time in the nitrogen storage container filled with nitrogen under normal circumstances. As time goes by, the pressure is getting lower and lower, and the pressure drop rate is getting slower and slower, the acceleration is negative, and the amplitude of change is small. As illustrated in graph (2) in FIG. 10, leakage occurs in the nitrogen storage container at time tl, the pressure in the nitrogen storage container drops rapidly, the pressure drop rate suddenly increases, and its acceleration becomes a positive value. [00153] The acceleration threshold is a threshold used to determine whether the pressure drop rate in the nitrogen storage container suddenly changes significantly. When the pressure in the nitrogen storage container suddenly drops and the pressure drop acceleration is greater than the acceleration threshold, the IoT cloud platform determines that leakage occurs in the nitrogen storage container.

[00154] In an exemplary embodiment, the acceleration threshold is 0. That is, when the oxygen concentration is greater than the second concentration threshold and the falling acceleration of the nitrogen pressure value is greater than 0, it is determined that leakage occurs in nitrogen storage container.

[00155] In an exemplary embodiment, the IoT cloud platform may also determine whether the nitrogen leaks by monitoring the pressure drop rate in the nitrogen storage container. In another exemplary embodiment, when the pressure drop rate in the nitrogen storage container becomes large, it is determined that leakage occurs in the nitrogen storage container.

[00156] In an exemplary embodiment, the flow of the nitrogen output from the nitrogen storage container measured by the flow meter may be used instead of the pressure measured by the pressure gauge. In an exemplary embodiment, the method of determining whether leakage occurs in the nitrogen storage container by using the flow is similar to the pressure method.

[00157] In an exemplary embodiment, when the oxygen concentration is less than or equal to the second concentration threshold, the IoT cloud platform determines that low oxygen occurs in the room. When the oxygen concentration is greater than the second concentration threshold and the drop acceleration of the nitrogen pressure value is less than or equal to the acceleration threshold, the IoT cloud platform determines that the nitrogen storage container is normal.

[00158] In step 5013, when the oxygen concentration is greater than a third concentration threshold and the nitrogen pressure value is less than a pressure value threshold, it is determined that material shortage occurs to the nitrogen raw material. [00159] When the oxygen concentration is greater than a third concentration threshold and the nitrogen pressure value is less than a pressure value threshold, the IoT cloud platform determines that material shortage occurs to the nitrogen raw material. [00160] In an exemplary embodiment, the third concentration threshold is an oxygen concentration threshold. In another exemplary embodiment, the human body's activities in an oxygen environment greater than the third concentration threshold will not endanger health. In another exemplary embodiment, the third concentration threshold is a threshold equal to the first concentration threshold and the second concentration threshold, or the third concentration threshold is a threshold different from the first concentration threshold and the second concentration threshold. In another exemplary embodiment, the third concentration threshold may take any value from 19% to 24%. [00161] The pressure threshold is the pressure in the nitrogen storage container when the nitrogen balance in the nitrogen storage container is low.

[00162] In an exemplary embodiment, when the pressure in the nitrogen storage container is less than the pressure threshold, the nitrogen in the nitrogen storage container is insufficient, and the nitrogen storage container filled with nitrogen needs to be replaced in order to maintain the normal progress of the production process.

[00163] In an exemplary embodiment, when the oxygen concentration is less than or equal to the third concentration threshold, the IoT cloud platform determines that low oxygen concentration occurs in the room. When the oxygen concentration is greater than the third concentration threshold and the nitrogen pressure value is greater than or equal to the pressure value threshold, it is determined that the nitrogen raw material is sufficient. [00164] In step 203, when the production materials of the lithium battery are abnormal, an alarm message is output.

[00165] In an exemplary embodiment, when determining that at least one of low oxygen, leakage, and material shortage occurs in the production process using nitrogen, the IoT cloud platform outputs an alarm message corresponding to the abnormality. [00166] In summary, in the method according to this embodiment, the oxygen concentration and nitrogen pressure in the production process using nitrogen are monitored to acquire the nitrogen usage in real time. When the oxygen concentration or nitrogen pressure is abnormal, an alarm message corresponding to the abnormality is generated in time, such that the staff can take corresponding countermeasures for different abnormal situations, and reduce production safety risks.

[00167] The present disclosure also provides an exemplary embodiment of monitoring a cell process.

[00168] In an exemplary embodiment, in the lithium battery production process, the cells are tracked and monitored, and the production processes where the cells are located are monitored in real time.

[00169] FIG. 11 illustrates a flowchart of a method for monitoring production safety for a lithium battery according to an exemplary embodiment of the present disclosure. This method may be performed by the server cluster 101 in the IoT system 100 illustrated in FIG. 1. The production materials include the cell. The production monitoring device includes production devices disposed in at least two production processes. Graphic code scanning components are disposed on the production device. In an exemplary embodiment, the production device provided with a graphic code scanning component includes: at least one of an injection machine, a forming machine, a volume dividing machine, an open-circuit voltage testing machine, a heat sealing integrated machine, a thickness measuring machine, an external size testing machine, and a charging and discharging testing machine, a short-circuit detector, a normal-temperature aging device, a high-temperature aging device, and a scanning gun. The method includes the following steps.

[00170] In step 601, first scanning information of a target cell reported by a first production device in a first production process is received, wherein the first scanning information includes first scanning time.

[00171] The IoT cloud platform receives first scanning information of a target cell reported by a first production device in a first production process, wherein the first scanning information includes the first scanning time.

[00172] In an exemplary embodiment, the production monitoring device is a device having a function of scanning a graphic code.

[00173] In an exemplary embodiment, the production information includes scanning information. In another exemplary embodiment, the scanning information includes at least one of scanning time, a scanning production process, battery identification, and battery acceptance information.

[00174] In an exemplary embodiment, when the target cell reaches the first production device in the first production process, the first production device will scan a graphic code on the target cell. The graphic code includes the identification of the target cell, which is used to identify the target cell. In another exemplary embodiment, the first device also records the first scanning time of the target cell, and reports the first scanning time, identification of the target cell, and identification of the first production device as the first scanning information to the IoT cloud platform.

[00175] In an exemplary embodiment, the first production process is a production process in the lithium battery production process.

[00176] In an exemplary embodiment, the first production device is a device having a function of scanning a graphic code.

[00177] In an exemplary embodiment, the target cell is a lithium battery in the lithium battery production process. [00178] In an exemplary embodiment, the first scanning information is scanning information generated by the first production device scanning the graphic code of the target cell. In another exemplary embodiment, the first scanning information includes at least one of identification of the first production device, identification of the target cell, the first scanning time, and cell acceptance information of the target cell.

[00179] In step 602, an expected completion time of the target cell from a first production process to a second production process is determined, wherein the second production process is a production process after the first production process in the production process.

[00180] The IoT cloud platform determines an expected completion time of the target cell from a first production process to a second production process, wherein the second production process is a production process after the first production process in the production process.

[00181] In an exemplary embodiment, the second production process is a production process of a production device having a function of scanning a graphic code after the first production process in the production process.

[00182] In an exemplary embodiment, the IoT cloud platform acquires an expected completion time required for the target cell from a first production device in the first production process to a second production device in the second production process. In another exemplary embodiment, the IoT cloud platform acquires an expected duration required for the target cell from the first production device in the first production process to the second production device in the second production process, and calculates the expected completion time based on the expected duration and the first scanning time. [00183] The expected completion time is the time at which the target cell is expected to reach the second production process. For example, the expected completion time is the time when the target cell is expected to reach the second production device in the second production process.

[00184] In step 603, whether second scanning information is received before the expected completion time is determined.

[00185] The IoT cloud platform determines whether the target cell receives second scanning information before the expected completion time. If the second scanning information is received, step 605 is performed. If the second scanning information is not received, step 604 is performed.

[00186] In step 604, when the second scanning information reported by the second production device in the second production process is not received at the expected completion time, it is determined that the target cell is abnormal.

[00187] When the second scanning information reported by the second production device in the second production process is not received at the expected completion time, the IoT cloud platform determines that the target cell is abnormal.

[00188] In an exemplary embodiment, if the target cell does not reach the second production device in the second production process at the expected completion time, the position of the target cell on the production line is abnormal. For example, the target cell disappears from the line, or the target cell is stuck in a position by a production device. [00189] In an exemplary embodiment, the second scanning information is scanning information generated by the second production device scanning the graphic code of the target cell. In another exemplary embodiment, the second scanning information includes at least one of identification of the second production device, identification of the target cell, second scanning time, and battery acceptance information of the target cell.

[00190] In step 605, when the second scanning information reported by the second production device in the second production process is received before the expected completion time, second scanning time in the second scanning information is acquired. [00191] When the second scanning information reported by the second production device in the second production process is received before the expected completion time, the IoT cloud platform acquires second scanning time in the second scanning information. [00192] In an exemplary embodiment, when the target cell reaches the second production device, the second production device scans the graphic code of the target cell to generate second scanning information, and the second production device reports the second scanning information to the IoT cloud platform.

[00193] In step 606, when a difference between the second scanning time and the expected completion time is greater than a duration threshold, it is determined that the production duration of the target cell is abnormal. [00194] When a difference between the second scanning time and the expected completion time is greater than a duration threshold, the IoT cloud platform determines that the production duration of the target cell is abnormal.

[00195] In an exemplary embodiment, the IoT cloud platform calculates a difference between the second scanning time and the expected completion time. When the difference is greater than the duration threshold, the target cell reaches the second production device early, and then the production process of the target cell may be incomplete and abnormal.

[00196] In an exemplary embodiment, the duration threshold is used to determine the duration in which the target cell reaches the second production device early. When the duration is too long, it is determined that the production duration of the target cell is abnormal.

[00197] In step 607, when the battery acceptance information of the target cell indicates non-acceptance, it is determined that the battery property of the target cell is abnormal.

[00198] When the battery acceptance information of the target cell indicates non- acceptance, the IoT cloud platform determines that the battery property of the target cell is abnormal.

[00199] In an exemplary embodiment, the first production device is a cell quality detection device, and the first scanning information further includes the cell acceptance information of the target cell.

[00200] In an exemplary embodiment, some devices in the lithium battery production process may determine whether the lithium battery is qualified.

[00201] In an exemplary embodiment, when acquiring the cell acceptance information of the cell, the first production device uploads the cell acceptance information, the first scanning time, and the like to the IoT cloud platform as the first scanning information.

[00202] In an exemplary embodiment, the cell acceptance information is used to describe whether the cell of the lithium battery is an accepted product. In another exemplary embodiment, the cell acceptance information indicates acceptance and non- acceptance. [00203] In an exemplary embodiment, the cell acceptance information is information describing the quality level of the cell, which includes multiple levels such as first, second, and third levels. When the cell acceptance information of the target cell is higher or lower than a specific level, the IoT cloud platform determines that the cell property of the target cell is abnormal.

[00204] In step 203, when the production materials of the lithium battery are abnormal, an alarm message is output.

[00205] In an exemplary embodiment, when determining that at least the production process using the lithium battery is abnormal, the production duration is abnormal, or the property of the cell is abnormal, the IoT cloud platform outputs an alarm message corresponding to any of the abnormal situations.

[00206] In summary, in the method according to this embodiment, a component that scans a graphic code is disposed on each production device of a cell production process, thereby acquiring the state of the cell in each production process in real time. When a cell does not reach the next production process in accordance with the expected time, an alarm of the abnormal position of the cell is issued to prevent the abnormal position of the cell from affecting the normal operation of the entire production process, and the position of each product is monitored in real time.

[00207] The present disclosure also provides an exemplary embodiment of monitoring battery storage.

[00208] In an exemplary embodiment, in a lithium battery finished product storage warehouse, the staff may scan a graphic code on a lithium battery with a scanning gun to acquire third scanning information. The third scanning information includes identification of the scanning gun, identification of the lithium battery, and third scanning time. The IoT cloud platform determines that the lithium battery reaches the finished product storage warehouse based on the third scanning information. In another exemplary embodiment, the IoT cloud platform acquires the number of all lithium batteries in the lithium battery finished product storage warehouse based on the third scanning information. When the quantity is greater than a storage quantity threshold, it is determined that the number of lithium batteries in the lithium battery finished product storage warehouse is too large and there are security risks. [00209] Moreover, the staff scan the graphic codes of all lithium batteries in the finished product storage warehouse with a scanning gun at intervals to acquire fourth scanning information. The fourth scanning information includes identification of the scanning gun, identification of the lithium battery, and fourth scanning time. When the IoT cloud platform acquires the same lithium battery identification from the third scanning information and the fourth scanning information, and a time difference between the third scanning time and the fourth scanning time is greater than a storage duration threshold, the IoT cloud platform determines that the storage time of the lithium battery is too long.

[00210] In an exemplary embodiment, in a waste lithium battery processing warehouse, the staff scan a graphic code on a lithium battery with a scanning gun to acquire fifth scanning information. The fifth scanning information includes identification of the scanning gun, identification of the lithium battery and fifth scanning time. The IoT cloud platform determines that the lithium battery has reached the waste lithium battery processing warehouse based on the fifth scanning information. In another exemplary embodiment, the IoT cloud platform acquires the quantity of all lithium batteries in the waste lithium battery processing warehouse based on the fourth scanning information. When the quantity is greater than a quantity threshold, it is determined that too many lithium batteries are present in the waste lithium battery processing warehouse and there are security risks.

[00211] Moreover, the staff scan the graphic codes of all lithium batteries in the waste lithium battery processing warehouse with a scanning gun at intervals to acquire sixth scanning information. The sixth scanning information includes identification of the scanning gun, identification of the lithium battery, and sixth scanning time. When the IoT cloud platform acquires the same lithium battery identification from the fifth scanning information and the sixth scanning information, and a time difference between the fifth scanning time and the sixth scanning time is greater than a duration threshold, the IoT cloud platform determines that the lithium battery has been left for too long, or determines that the lithium battery has not been dealt with on time.

[00212] In summary, in the method according to in this embodiment, the quantity and storage time of lithium batteries in a warehouse are monitored by scanning a lithium battery graphic code with a scanning gun in a lithium battery finished product storage warehouse and a waste lithium battery disposal warehouse. When the number of lithium batteries is too large or the storage time is too long, a corresponding alarm message is issued to prompt the lithium battery storage abnormality, so as to remind the staff to deal with it in time to reduce the hidden hazards of lithium battery storage.

[00213] An apparatus embodiment of the present disclosure is described hereinafter. For details that are not described in detail in the device embodiment, reference may be made to the corresponding records in the method embodiments, and details are not described herein again.

[00214] FIG. 12 illustrates a schematic structural diagram of an apparatus for monitoring production safety for a lithium battery according to an exemplary embodiment of the present disclosure. The apparatus is applicable to an IoT cloud platform which is connected to production monitoring devices corresponding to at least two production processes. The apparatus includes:

[00215] a receiving module 801, configured to receive production information reported by the production monitoring device;

[00216] an analyzing module 802, configured to analyze, based on the production information, whether production materials of the lithium battery are abnormal, wherein the production materials include at least one of an electrolyte raw material for producing the lithium battery, a nitrogen raw material for producing the lithium battery and a cell of the lithium battery; and

[00217] an alarming module 803, configured to output alarm message when the production materials of the lithium battery are abnormal.

[00218] In an exemplary embodiment, the production materials include the electrolyte raw material, the production monitoring device includes a mass monitoring device disposed under an electrolyte container, and the electrolyte container is connected to a replenishment machine by an input pipe.

[00219] The analyzing module 802 includes a second calculation sub-module 806 and a determination sub-module 805. [00220] The receiving module 801 is configured to receive a mass difference reported by the mass monitoring device, wherein the mass difference is a mass difference before and after a single replenishment of the electrolyte container.

[00221] The second calculating sub-module 806 is configured to calculate a first difference between the mass difference and a single replenishment mass of the replenishment machine.

[00222] The determining sub-module 805 is configured to determine that electrolyte leakage occurs in the input pipe when the first difference is greater than a first error threshold.

[00223] In an exemplary embodiment, the receiving module 801 further includes a first calculating sub-module 809.

[00224] The receiving module 801 is further configured to receive a first mass of the electrolyte container reported by the mass monitoring device, wherein the first mass is a mass sum of the electrolyte in the electrolyte container and the electrolyte container when the level of the replenishment machine is lower than the lowest level and the replenishment machine starts to replenish the electrolyte.

[00225] The receiving module 801 is further configured to receive a second mass of the electrolyte container reported by the mass monitoring device, wherein the second mass is a mass sum of the electrolyte in the electrolyte container and the electrolyte container when the level of the replenishment machine is higher than the highest level and the replenishment machine stops replenishing the electrolyte.

[00226] The first calculating sub-module 809 is further configured to calculate the mass difference based on the first mass and the second mass.

[00227] In an exemplary embodiment, the production materials include the electrolyte raw material, the production monitoring device includes a flow detector disposed on an output pipe of an injection machine, and the analyzing module 802 includes an accumulating sub-module 807, a second calculating sub-module 806, and a determining sub-module 805.

[00228] The accumulating sub-module 807 is configured to accumulate an injection mass of the replenishment machine after the last replenishment and before the current replenishment, wherein the injection mass is the mass of the electrolyte injected by the replenishment machine into the cell.

[00229] The second calculating sub-module 806 is further configured to calculate a second difference between the injection mass and a single replenishment mass of the replenishment machine.

[00230] The determining sub-module 805 is further configured to determine that electrolyte leakage occurs in the output pipe when the second difference is greater than a second error threshold.

[00231] In an exemplary embodiment, the production material includes the nitrogen raw material, and the production monitoring device includes an oxygen concentration sensor disposed in a room using the nitrogen raw material and a pressure gauge disposed on the nitrogen storage container.

[00232] The analyzing module 802 includes a determining sub-module 805.

[00233] The receiving module 801 is configured to receive at least one of an oxygen concentration reported by the oxygen concentration sensor and a nitrogen pressure value reported by the pressure gauge.

[00234] The determining sub-module 805 is configured to determine, based on at least one of the oxygen concentration reported by the oxygen concentration sensor and the nitrogen pressure value reported by the pressure gauge, whether the nitrogen raw material is abnormal .

[00235] In an exemplary embodiment, the determining sub-module 805 is further configured to determine that low oxygen occurs in the room when the oxygen concentration is less than a first concentration threshold.

[00236] In an exemplary embodiment, the determining sub-module 805 is further configured to determine that leakage occurs in the nitrogen storage container when the oxygen concentration is greater than a second concentration threshold and a falling acceleration of the nitrogen pressure value is greater than an acceleration threshold; [00237] In an exemplary embodiment, the determining sub-module 805 is further configured to determine that material shortage occurs to the nitrogen raw material when the oxygen concentration is greater than a third concentration threshold and the nitrogen pressure value is less than a pressure value threshold. [00238] In an exemplary embodiment, the production material includes the cell, the production monitoring device includes production devices disposed in at least two production processes, and a graphic code scanning component is disposed on the production device.

[00239] The analyzing module 802 includes a calculating sub-module 806 and a determining sub-module 805.

[00240] The receiving module 801 is configured to receive first scanning information of a target cell reported by a first production device in a first production process, wherein the first scanning information includes first scanning time.

[00241] The determining sub-module 805 is configured to determine an expected completion time of the target cell from the first production process to a second production process, wherein the second production process is a production process after the first production process in the production process.

[00242] The determining sub-module 805 is further configured to determine that the target cell is abnormal when the second scanning information reported by a second production device in the second production process has not been received at the expected completion time.

[00243] In an exemplary embodiment, the first production device is a cell quality detection device, and the first scanning information further includes cell acceptance information of the target cell.

[00244] The determining sub-module 805 is further configured to determine that the cell property of the target cell is abnormal when the cell acceptance information of the target cell indicates non-acceptance.

[00245] In an exemplary embodiment, the device further includes an acquiring module 808.

[00246] The acquiring module 808 is configured to acquire second scanning time in the second scanning information when the second scanning information reported by the second production device in the second production process is received before the expected completion time.

[00247] The determining sub-module 805 is further configured to determine that the production duration of the target cell is abnormal when a difference between the second scanning time and the expected completion time is greater than a duration threshold.

[00248] FIG. 13 illustrates a structural block diagram of a server according to an embodiment of the present disclosure. The server cluster formed by the servers can be used to perform the IoT-based method for monitoring production safety for a lithium battery according to the above embodiments. For example, the server may be all or a part of the server cluster 101 in the application environment illustrated in FIG. 1.

[00249] Specifically, the server 1000 includes a processing unit 1001, such as a central processing unit (CPU), a graphics processing unit (GPU) and a field- programmable gate array (FPGA), a system memory 1004 including a random-access memory (RAM) 1002 and a read-only memory (ROM) 1003, and a system bus 1005 connecting the system memory 1004 and the central processing unit 1001. The server 1000 further includes a basic input/output system (I/O system) 1006 which helps transmit information between various components within the server, and a high-capacity storage device 1007 for storing an operating system 1013, an application 1014, and other program modules 1015.

[00250] The basic input/output system 1006 includes a display 1008 for displaying information and an input device 1009, such as a mouse and a keyboard, for inputting information by the user. Both the display 1008 and the input device 1009 are connected to the central processing unit 1001 by an input/output controller 1010 connected to the system bus 1005. The basic input/output system 1006 may also include the input/output controller 1010 for receiving and processing input from a plurality of other devices, such as the keyboard, the mouse, or an electronic stylus. Similarly, the input/output controller 1010 further provides output to the display, a printer or other types of output devices. [00251] The high-capacity storage device 1007 is connected to the central processing unit 1001 by a high-capacity storage controller (not illustrated) connected to the system bus 1005. The high -capacity storage device 1007 and a server-readable medium associated therewith provide non-volatile storage for the server 1000. That is, the high -capacity storage device 1007 may include the server-readable medium (not illustrated), such as a hard disk, or a compact disc HYPERUINK "https://en.wikipedia.org/wiki/Read-only_memory" \o "" read-only memory (CD- ROM) driver.

[00252] Without loss of generality, the server-readable medium may include a server storage medium and a communication medium. The server storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as a server-readable instruction, a data structure, a program module or other data. The server storage medium includes a RAM, a ROM, an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory or other solid- state storage technologies; a CD-ROM, a digital versatile disc (DVD), or other optical storage devices; and a tape cartridge, a magnetic tape, a disk storage, or other magnetic storage devices. It will be known by a person skilled in the art that the server storage medium is not limited to above. The system memory 1004 and the high -capacity storage device 1007 may be collectively referred to as the memory.

[00253] According to the various embodiments of the present disclosure, the server 1000 may also be run by a remote server connected to a network via a network, such as the Internet. That is, the server 1000 may be connected to the network 1012 by a network interface unit 1011 connected to the system bus 1005, or may be connected to other types of networks or remote server systems (not illustrated) with the network interface unit 1011.

[00254] The memory includes at least one instruction, at least one program, a code set, or an instruction set stored therein. The at least one instruction, the at least one program, the code set, or the instruction set, when loaded and executed by one or more processors, causes the one or more processors to perform the IoT-based method for monitoring production safety for a lithium battery.

[00255] An embodiment of the present disclosure further provides an IoT device. The IoT device includes a processor and a memory storing at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set, when loaded and executed by a processor, causes the processor to perform the IoT-based method for monitoring production safety for a lithium battery as described above. [00256] An embodiment of the present disclosure further provides a non-transitory computer-readable storage medium storing at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, the at least one program, the code set, or the instruction set, when loaded and executed by a processor, causes the processor to perform the IoT-based method for monitoring production safety for a lithium battery as described above.

[00257] Understandably, the term "a plurality of' herein refers to two or more, and the term "and/or" herein describes the correspondence of the corresponding objects, indicating three kinds of relationship. For example, A and/or B, can be expressed as: A exists alone, A and B exist concurrently, B exists alone. The character "/" generally indicates that the context object is an "OR" relationship.

[00258] Persons of ordinary skill in the art can understand that all or part of the steps described in the above embodiments can be completed by hardware, or by relevant hardware instructed by applications stored in a non-transitory computer readable storage medium, such as a read-only memory, a disk, or a compact disc (CD).

[00259] Described above are merely optional embodiments of the present disclosure, and are not intended to limit the present disclosure. Within the spirit and principles of the disclosure, any modifications, equivalent substitutions, improvements, and the like are within the protection scope of the present disclosure.