Cloud monitoring management method, system and equipment for lithium ion battery diaphragm equipment

Abstract

The invention discloses a cloud monitoring management method, system and equipment for lithium ion battery diaphragm equipment, and relates to the technical field of equipment cloud monitoring management, environmental conditions in a target area are monitored in real time, an abnormal environment in the target area is identified, and if the environmental conditions in the target area are abnormal, multi-stage alarm is sent to the outside; analyzing the influence degree of the environmental condition data on the product production, if the influence degree exceeds the expectation, using the trained adjustment automatic control model to control the environmental improvement equipment, and optimizing the environmental condition in the target area; the operation state of the equipment is identified according to all the operation parameters of the equipment, if the operation state is abnormal, potential faults of the equipment are predicted and identified, and if the potential faults exist, the equipment is maintained when the equipment is in a shutdown state; by constructing a multi-stage alarm mechanism, the production process of the battery diaphragm is adjusted, and monitoring management of battery diaphragm equipment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud-based monitoring and management of equipment, and in particular to a cloud-based monitoring and management method, system and equipment for lithium-ion battery diaphragm equipment. Background Art

[0002] Lithium-ion battery separator equipment, as the core equipment in lithium battery production, integrates all-round functions such as material processing, solution preparation, precision coating, efficient curing, precise cutting and environmentally friendly packaging, ensuring the quality and safety of the separator as the key separator between the positive and negative electrodes of the battery. These equipment not only realize the fully automated production process from raw materials to finished separators, improve production efficiency and cost control, but also meet the growing demand for lithium battery performance in new energy vehicles, energy storage systems and power tools through high-precision technology.

[0003] In the Chinese invention patent with application publication number CN115857397A, a monitoring and management system for aluminum-air battery production and processing is disclosed, the system includes an electrically connected data acquisition module, an equipment online monitoring module and a master control module; the data acquisition module is used to collect dynamic data in the production environment, as well as information on the performance of raw materials and finished batteries, based on the collected environmental dynamic data, as well as information on the performance of raw materials and finished products; the dynamic data in the production environment, as well as information on the performance of raw materials and finished batteries are collected through the data acquisition module, which facilitates the control of the normal operation of the production and processing procedures, and the working status of the environmental management equipment, processing equipment and conveying equipment is monitored by the equipment online monitoring module, providing a prerequisite for the normal operation of the equipment, and the general control module coordinates the work to ensure the orderly progress of production and processing.

[0004] Combined with the above application and the contents of the prior art: When lithium-ion battery separators are produced through production equipment, in order to ensure product quality and reduce pollutant emissions, the operating status of the production equipment is usually monitored and managed in real time, and the various control parameters of the production equipment are adjusted according to changes in actual production processes and production tasks.

[0005] In the existing cloud-based monitoring and management method for lithium-ion battery diaphragm equipment, after real-time monitoring of the equipment's operating data, it is determined whether the equipment has any abnormalities, and when an abnormality occurs, it is repaired to ensure the normal operation of the equipment; however, there are certain abnormalities in the environment in the production area, especially when the temperature and humidity in the area fluctuate greatly, and there is a lot of dust or chemical pollutants in the production area. In this case, if the equipment is still in a long-term high-load operation state, the risk of equipment failure will increase significantly.

[0006] To this end, the present invention provides a cloud-based monitoring and management method, system and device for lithium-ion battery diaphragm equipment. Summary of the invention

[0007] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a cloud-based monitoring and management method, system and equipment for lithium-ion battery diaphragm equipment. The method identifies abnormal environments in a target area. If there are abnormalities in the environmental conditions in the target area, a multi-level alarm is issued to the outside. The impact of environmental condition data on product production volume is analyzed. If the impact exceeds expectations, the trained adjustment automatic control model is used to control the environmental improvement equipment to optimize the environmental conditions in the target area. The operating status of the equipment is identified based on various operating parameters of the equipment. If there are abnormalities in the operating status, the potential faults of the equipment are predicted and identified. If there are potential faults, the equipment is maintained when the equipment is in a shutdown state. By constructing a multi-level alarm mechanism, the production process of the battery diaphragm is adjusted, thereby solving the technical problems raised in the background technology.

[0008] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: A cloud monitoring and management method for lithium-ion battery diaphragm equipment, comprising: real-time monitoring of environmental conditions in a target area, identifying abnormal environments in the target area, and if there are abnormalities in the environmental conditions in the target area, issuing a multi-level alarm to the outside; Analyze the impact of environmental condition data on product production. If the impact exceeds expectations, use the trained automatic control model to control the environmental improvement equipment and optimize the environmental conditions in the target area. Identify the operating status of the equipment based on its operating parameters. If the operating status is abnormal, predict and identify the potential failure of the equipment. If a potential failure exists, maintain the equipment when it is in a shutdown state.

[0009] Furthermore, using equipment-related data as input, after predicting and obtaining the equipment's operating load, a pre-trained genetic algorithm is used to optimize the equipment's working time and task allocation; Use the digital twin model of battery diaphragm production to simulate and test the production process of battery diaphragms. If the difference between the obtained test emission data and the production emission data is greater than expected, the production process of the battery diaphragms will be optimized.

[0010] Furthermore, a sensor network is arranged in the target area, and the sensor network monitors the environmental conditions in the target area in real time, obtains corresponding environmental condition data, and generates an environmental condition data set after aggregation; Taking environmental condition data as input, the trained abnormal environment recognition model is used to identify abnormal environments. If the environmental conditions in the target area are abnormal, a first-level alarm command is issued to the outside.

[0011] Furthermore, the time node when the first-level alarm instruction is received is regarded as an abnormal node. When the number of abnormal nodes within the abnormal observation period exceeds expectations, the environmental abnormality degree is generated based on the abnormal nodes and the degree of abnormality of the environmental conditions after each alarm instruction is issued; if the obtained environmental abnormality degree exceeds expectations, a second-level alarm instruction is issued to the outside.

[0012] Furthermore, upon receiving a second-level alarm command or receiving several first-level alarm commands in succession, the environmental condition data, equipment operation data and product production data are summarized as an equipment status data set, and each data in the equipment status data set is encrypted and sent to the cloud data analysis platform.

[0013] Furthermore, multiple linear regression analysis was performed with environmental condition data as independent variables and production volume as dependent variable to obtain the corresponding linear regression equation; The sum of the regression coefficients corresponding to the various environmental condition parameters in the linear regression equation is taken as the impact value. If the impact value exceeds the preset impact threshold, an automatic adjustment instruction is issued to the outside.

[0014] Furthermore, environmental improvement equipment is arranged in the target area, and after receiving the automatic adjustment instruction, parameter thresholds are pre-set for various environmental conditions, and the real-time environmental condition data in the target area is used as input to control the environmental improvement equipment.

[0015] Furthermore, various operating parameters of the equipment are recorded in real time, and the operating parameters are used as input to identify the operating status of the equipment using the trained abnormal state recognition model; If there is an abnormality in the operating status of the equipment, the equipment's operating data is used as input, and the trained fault prediction model is used to predict and identify potential faults of the equipment. If the equipment has a potential fault, a maintenance instruction is issued to the outside.

[0016] Furthermore, after receiving the maintenance instruction, the maintenance frequency of the equipment is constrained, the equipment is maintained at the maintenance node that meets the constraint conditions, and the detailed information of each maintenance is recorded and summarized to create an equipment maintenance file.

[0017] Furthermore, the equipment operation data, production task data, and environmental condition data in the target area are collected and used as input. The equipment operation load prediction model after training is used to predict the equipment operation load and obtain the operation load prediction value. Taking reducing the equipment load operation as the optimization goal, the equipment operation load prediction value, operation data, production data and environmental data are used as input, and a pre-trained genetic algorithm is used to optimize the equipment's working time and task allocation.

[0018] Furthermore, after pre-setting the same environmental conditions as the actual production scenarios in the target area and setting the corresponding control parameters for the equipment, the battery diaphragm production digital twin model is used to simulate the production process of the battery diaphragm to obtain the test emission data of the production process.

[0019] The production emission data under the actual production scenario is collected, and after the similarity analysis between the test emission data and the production emission data within the sub-period is performed, the corresponding emission similarity is obtained.

[0020] Furthermore, after obtaining several consecutive emission similarities, a similarity coefficient is constructed. If the similarity coefficient is lower than a similarity threshold, various parameters of the battery separator production process are optimized.

[0021] A cloud monitoring and management system for lithium-ion battery diaphragm equipment, comprising an environmental anomaly recognition unit, which monitors the environmental conditions in a target area in real time, recognizes the abnormal environment in the target area, and sends a multi-level alarm to the outside if the environmental conditions in the target area are abnormal; The environmental adjustment unit analyzes the impact of environmental condition data on product production. If the impact exceeds expectations, the trained automatic control model is used to control the environmental improvement equipment to optimize the environmental conditions in the target area. The fault prediction unit identifies the operating status of the equipment based on the various operating parameters of the equipment. If the operating status is abnormal, the potential faults of the equipment are predicted and identified. If there is a potential fault, the equipment is maintained. The load optimization unit takes the equipment-related data as input, and after predicting the equipment's operating load, uses a pre-trained genetic algorithm to optimize the equipment's working time and task allocation; The test unit uses the digital twin model of battery diaphragm production to simulate the production process of battery diaphragms. If the difference between the obtained test emission data and the production emission data is greater than expected, the production process of the battery diaphragms is optimized.

[0022] A cloud monitoring and management device for lithium-ion battery diaphragm equipment, comprising at least one processor; The memory is used to store executable instructions, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the cloud-based monitoring and management method for lithium-ion battery diaphragm equipment.

[0023] A computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform steps of a cloud-based monitoring and management method for lithium-ion battery diaphragm equipment.

[0024] (III) Beneficial effects The present invention provides a cloud monitoring and management method, system and device for lithium-ion battery diaphragm equipment, which has the following beneficial effects:

[0025] 1. When abnormal environmental conditions in the target area occur, handle the abnormal environmental conditions in a timely manner to avoid the impact of abnormal environmental conditions on the production process; build a second-level alarm instruction based on the continuous receipt of several first-level alarm instructions to implement a multi-level alarm mechanism. When abnormal environmental conditions in the target area occur, handle them in a hierarchical manner to avoid long-term impact of abnormal environmental conditions on the production status.

[0026] 2. Determine whether the environmental conditions have a certain degree of impact on the production status of the equipment. If there is an impact, the environmental conditions in the target area can be adjusted to ensure the production status.

[0027] 3. Use the trained adjustment automatic control model to control the environmental improvement equipment, automatically adjust the various environmental condition parameters in the target area, and realize automatic response when receiving alarm instructions. This can reduce the impact of abnormal environmental conditions on battery diaphragms and realize automatic adjustment of the battery diaphragm production environment.

[0028] 4. By monitoring the changes in operating parameters, the abnormal operating status of the equipment can be identified, and when abnormalities occur in the equipment operation, they can be dealt with in a timely manner.

[0029] 5. Based on the prediction data, possible operating failures of the equipment can be identified. If potential failures are identified, maintenance can be carried out in a timely manner to ensure the normal operation of the equipment. The maintenance frequency of the equipment can be constrained by the degree of failure, making the maintenance frequency of the equipment more reasonable, thus avoiding the negative impact of excessive maintenance on the actual production process.

[0030] 6. Use the trained equipment operation load prediction model to predict the equipment's operation load. If the equipment is still operating at a high load, the abnormal operation state of the equipment will continue. When executing the preset control conditions, it will be difficult to achieve the expected results, which will affect the actual production of the equipment.

[0031] 7. By allocating and adjusting the original production tasks, reducing the operating load of the current equipment and optimizing the working status of the equipment, the risk of equipment abnormalities can be reduced and the continuity of production status can be guaranteed; judging the necessity of process optimization, reducing ineffective optimization processes, optimizing the battery diaphragm production process, and realizing automatic control and adjustment of battery diaphragm production equipment.

[0032] 8. By building a multi-level alarm mechanism and a multi-level emergency response mechanism, the current battery diaphragm production process can be adjusted in different aspects and levels to achieve monitoring and management of battery diaphragm equipment, resulting in less pollution emissions, lower risk of equipment abnormalities, and better actual monitoring and management effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flow chart of the cloud monitoring management method of the present invention; Figure 2 This is a schematic diagram of the structure of the cloud monitoring and management system of the present invention; Figure 3 It is a schematic diagram of the structure of the cloud monitoring and management device of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] See also Figure 1 The present invention provides a cloud monitoring and management method for lithium-ion battery diaphragm equipment, comprising: Step 1: monitor the environmental conditions in the target area in real time, identify the abnormal environment in the target area, and send a multi-level alarm to the outside if there is an abnormality in the environmental conditions in the target area; The step 1 includes the following contents: Step 101: The area where the equipment is located is taken as the target area, and a sensor network is arranged in the target area, wherein the sensor network includes a temperature sensor, a humidity sensor, an air quality sensor, etc.: the sensor network performs real-time monitoring of environmental conditions in the target area, and obtains corresponding environmental condition data, such as environmental temperature, environmental humidity, dust concentration, VOCs, light intensity, noise level, equipment vibration and air pressure, etc., and after aggregating the obtained environmental condition data, generates an environmental condition data set; Step 102: train a convolutional neural network with the labeled sample data to obtain a trained abnormal environment recognition model, use the real-time environmental condition data as input, use the trained abnormal environment recognition model to perform abnormal environment recognition, and if the environmental conditions in the target area are abnormal, issue a first-level alarm instruction to the outside; When in use, after real-time monitoring and obtaining environmental condition data in the target area, when abnormal environmental conditions in the target area occur, the abnormal environmental conditions are promptly handled to avoid the impact of abnormal environmental conditions on the production process.

[0036] Step 103, pre-set an abnormal observation period, and take the time node when the first-level alarm instruction is received as an abnormal node. When the number of abnormal nodes in the abnormal observation period exceeds expectations, the abnormal degree of the environmental conditions after the abnormal nodes and each alarm instruction is issued is determined by the sum of the difference ratios between each environmental condition parameter and the corresponding environmental parameter threshold; the environmental abnormality degree is generated in the following manner: :

[0037] Where: n is the number of environmental parameters, It is i Environmental parameters at time t The actual measured value on It is i The qualified values ​​of environmental parameters, For the i The weight of the environmental parameter, the value falls into , is the time decay factor, , For the j The Fourier transform of the environmental parameters, m is the number of environmental parameters that need to be Fourier transformed; The value falls into ; K It represents the length of the time window used to calculate the abnormality of the environment; Based on historical data and management expectations of environmental conditions, abnormal thresholds are set in advance; if the obtained environmental abnormality If the abnormality threshold is exceeded, it means that the frequency and degree of environmental abnormality in the target area at the current stage are high. At this time, a secondary alarm command is issued to the outside. When using, combine the contents in steps 101 to 103: Considering that production personnel cannot immediately adjust the abnormal environment in the target area, or the abnormal environmental conditions last for a short time and the negative impact is relatively small, in this scenario, a second-level alarm instruction is constructed based on the continuous receipt of several first-level alarm instructions to implement a multi-level alarm mechanism. When the environmental conditions in the target area are abnormal, they are handled in a hierarchical manner to avoid long-term impact of abnormal environmental conditions on the production status.

[0038] In the existing cloud-based monitoring and management method for lithium-ion battery diaphragm equipment, after real-time monitoring of the equipment's operating data, it is determined whether the equipment has any abnormalities, and when an abnormality occurs, it is repaired to ensure the normal operation of the equipment. However, there are certain abnormalities in the environment in the production area, especially when the temperature and humidity in the area fluctuate greatly, and there is a lot of dust or chemical pollutants in the production area. In this case, if the equipment is still in a long-term high-load operation state, the risk of equipment failure will increase significantly, and even the production quality and pollution emissions of the final product will be affected to a certain extent.

[0039] Step 2: Analyze the impact of environmental condition data on product production. If the impact exceeds expectations, use the trained automatic control model to control the environmental improvement equipment and optimize the environmental conditions in the target area. The step 2 includes the following contents: Step 201: upon receiving a secondary alarm command or receiving several primary alarm commands in succession, the environmental condition data, equipment operation data and product production data collected by the sensor network are aggregated as an equipment status data set, and after encrypting each data in the equipment status data set, the encrypted data is sent to a cloud data analysis platform; When in use, after completing the collection of various data, the collected data can be encrypted to ensure data security.

[0040] Step 202: Perform multiple linear regression analysis with environmental condition data as independent variables and product production volume as dependent variable to obtain corresponding linear regression equations; take the sum of regression coefficients corresponding to various environmental condition parameters in the linear regression equation as the impact value; if the impact value exceeds a preset impact threshold, it means that the environmental condition has a greater impact on the production volume, and an automatic adjustment instruction is issued to the outside; When in use, multiple linear regression analysis is used to determine whether environmental conditions have a certain degree of impact on the production status of the equipment. If there is an impact, the environmental conditions in the target area can be adjusted to ensure the production status; Step 203: Arrange air purification equipment in the target area, such as high-efficiency air filters, activated carbon adsorption devices and photocatalytic oxidation equipment, and equip them with high-efficiency air conditioning systems and humidification / dehumidification equipment, etc.; use the above equipment as environmental improvement equipment, and mark their locations in the target area; The convolutional neural network is trained by the labeled sample data to obtain the trained automatic control model; After receiving the automatic adjustment instruction, the corresponding parameter thresholds are pre-set for each environmental condition in the target area, and the real-time environmental condition data in the target area is used as input. The trained automatic adjustment control model is used to control the environmental improvement equipment to optimize the environmental conditions in the target area. When using, combine the contents in steps 201 to 203: When the environmental conditions in the target area become abnormal and affect the production status of the equipment, the trained adjustment automatic control model is used to control the environmental improvement equipment based on the pre-set parameter thresholds, and the various environmental condition parameters in the target area are automatically adjusted. When an alarm instruction is received, an automatic response is achieved, which can reduce the impact of abnormal environmental conditions on battery diaphragms and realize automatic adjustment of the battery diaphragm production environment.

[0041] Step 3: Identify the operating status of the equipment based on the various operating parameters of the equipment. If the operating status is abnormal, predict and identify the potential failure of the equipment. If there is a potential failure, maintain the equipment when the equipment is in a shutdown state; The step three includes the following contents: Step 301: Record various operating parameters of the equipment in real time within a preset observation period, such as start time, stop time, load rate, temperature, current, voltage and other parameters; train a convolutional neural network with the labeled sample data to obtain a trained abnormal state recognition model; Taking the operating parameters as input, the trained abnormal state recognition model is used to identify the operating state of the equipment. If the operating state of the equipment is abnormal, an external fault prediction instruction is issued; When in use, on the basis of controlling the abnormal environmental conditions in the target area, by monitoring the changes in the operating parameters, the abnormal operating status of the equipment can be identified, and when there is an abnormality in the operation of the equipment, it can be handled in time; Step 302: Use the labeled sample data to train the machine learning algorithm to obtain a trained fault prediction model; after receiving the fault prediction instruction, use the equipment operation data as input and use the trained fault prediction model to predict and identify potential faults of the equipment. If the equipment has a potential fault, send a maintenance instruction to the outside to facilitate maintenance in advance; Step 303: After receiving the maintenance instruction, perform maintenance on the equipment when the equipment is in a shutdown state, including regular inspection, lubrication, replacement of wearing parts, etc.; the maintenance frequency of the equipment Constraints are as follows:

[0042] Where: is the weight coefficient, , n To predict the number of faulty nodes that will cause failures, It is i The faulty node to j The time interval between failed nodes, is the time interval average; Maintain the equipment at the maintenance nodes that meet the constraints, and record the detailed information of each maintenance, such as maintenance date, maintenance reason, maintenance content, repair measures, test results and equipment health status, etc., and create equipment maintenance files after summarizing; When using, combine the contents in steps 301 to 303: When the equipment produces several abnormalities continuously, it means that the risk of equipment failure is relatively high. At this time, the possible operating failures of the equipment are identified based on the prediction data. If potential failures are identified, maintenance can be carried out in a timely manner to ensure the normal operation of the equipment. At the same time, the maintenance frequency of the equipment is constrained by the degree of failure, making the maintenance frequency of the equipment more reasonable, and avoiding the negative impact of excessive maintenance on the actual production process.

[0043] Step 4: Using the equipment-related data as input, after predicting the equipment's operating load, use the pre-trained genetic algorithm to optimize the equipment's working time and task allocation; The step 4 includes the following contents: Step 401: Collect the operation data of the equipment, including parameters such as start time, stop time, load rate, temperature, current, voltage, etc.; collect the production task data of the equipment, including production plan, task completion time, product quantity and quality; collect the environmental condition data in the target area, including temperature, humidity and air quality, etc.; The LSTM neural network is trained with the labeled sample data to obtain the trained equipment operation load prediction model; the equipment operation data, environmental condition data and production task data are used as input, and the equipment operation load is predicted using the trained equipment operation load prediction model to obtain the operation load prediction value; During use, when there is an abnormality in the operating status of the equipment, considering that long-term high-load operation of the equipment will increase the risk of abnormal operation of the equipment, the trained equipment operation load prediction model is used to predict the operating load of the equipment. If the equipment is still in high-load operation, the abnormal operating state of the equipment will continue. When executing the preset control conditions, it is difficult to achieve the expected effect, which affects the actual production of the equipment.

[0044] Step 402: Taking the fluctuation of the equipment's operating load within a preset range and avoiding long-term high-load operation as the optimization goal; taking the equipment's operating load prediction value, operating data, production data and environmental data as input, using a pre-trained genetic algorithm to optimize the equipment's working time and task allocation, adjusting the production plan and equipment operating time in real time, and ensuring the flexibility and response speed of the production process; When using, combine the contents in steps 401 to 402: By optimizing the work tasks and working hours of the equipment through the optimization algorithm, when there are several similar equipment, the original production tasks can be allocated and adjusted to reduce the operating load of the current equipment and optimize the working status of the equipment. This can reduce the risk of equipment abnormalities and ensure the continuity of production status.

[0045] Step 5: Use the digital twin model of battery separator production to simulate the production process of battery separators. If the difference between the obtained test emission data and the production emission data is greater than expected, optimize the production process of battery separators. The step five includes the following contents: Step 501: train a machine learning algorithm with sample data to construct a digital twin model for battery diaphragm production; pre-set environmental conditions that are the same as those in the actual production scenario in the target area, and set corresponding control parameters for the equipment, and then use the digital twin model for battery diaphragm production to simulate the production process of the battery diaphragm and obtain test emission data in the production process, for example: real-time monitoring of emission data such as waste gas and wastewater, and real-time monitoring of the emission of harmful substances in the production process, such as waste gas, wastewater and solid waste; Collect production emission data under actual production scenarios, and obtain the corresponding emission similarity after analyzing the similarity between the test emission data and the production emission data within the sub-period; When using it, it is necessary to take into account that abnormal environment and equipment failure will have a certain degree of impact on the production status, especially when the various control parameters of the equipment are difficult to keep consistent with the actual settings. There will also be a certain degree of difference in the production products during the production process. For example, more pollutants will be produced than expected. Therefore, after improving the environmental conditions and equipment, the production process also needs to be adjusted.

[0046] Step 502: construct a similarity coefficient after obtaining several consecutive emission similarities , under the preset control conditions, the similarity between expected emissions and actual emissions is judged based on the similarity coefficient, where: Emission similarity All are linearly normalized and the corresponding data values ​​are mapped to the interval According to the following formula:

[0047] in, For the i The emission similarity within a sub-period is is the corresponding mean, is the number of sub-periods, is the weight coefficient, ;The weight coefficient remains the same as the previous value; Pre-set similar thresholds based on historical data and expectations for pollutant emission management when the equipment produces battery separators; If the similarity coefficient If the value is lower than the similarity threshold, it means that under the preset control conditions, the pollutant emissions of the battery diaphragm are inconsistent with expectations and may be more than expected, and the emission pollution is more serious. In addition to controlling the equipment, the current production process should be optimized. For example, through multi-objective optimization algorithm or simulated annealing algorithm, on the basis of the digital twin model of battery diaphragm production, the various parameters of the battery diaphragm production process are optimized. By optimizing the production process, the emission pollution caused by the production of battery diaphragms can be ultimately reduced.

[0048] When using, combine the contents in steps 501 and 502: By constructing a similarity coefficient based on several consecutive emission similarities , based on the simulation test, according to the similarity coefficient It can judge the necessity of process optimization, reduce invalid optimization processes, and finally optimize the battery separator production process and realize automatic control and adjustment of battery separator production equipment.

[0049] In combination with the above content, taking into account the possibility of more pollutants in the current production process, by constructing a multi-level alarm mechanism and a multi-level emergency response mechanism, the current battery diaphragm production process is adjusted in different aspects and levels, and the battery diaphragm equipment is monitored and managed, resulting in less pollution emissions, lower risk of equipment abnormalities, and better actual monitoring and management effects.

[0050] See also Figure 2 The present invention provides a cloud monitoring and management system for lithium-ion battery diaphragm equipment, comprising: The environmental anomaly recognition unit monitors the environmental conditions in the target area in real time and identifies the abnormal environment in the target area. If the environmental conditions in the target area are abnormal, a multi-level alarm is issued to the outside. The environmental adjustment unit analyzes the impact of environmental condition data on product production. If the impact exceeds expectations, the trained automatic adjustment control model is used to control the environmental improvement equipment to optimize the environmental conditions in the target area. The fault prediction unit identifies the operating status of the equipment based on the various operating parameters of the equipment. If the operating status is abnormal, the potential faults of the equipment are predicted and identified. If there is a potential fault, the equipment is maintained. The load optimization unit takes the equipment-related data as input, and after predicting the equipment's operating load, uses a pre-trained genetic algorithm to optimize the equipment's working time and task allocation; The test unit uses the digital twin model of battery diaphragm production to simulate the production process of battery diaphragms. If the difference between the obtained test emission data and the production emission data is greater than expected, the production process of the battery diaphragms is optimized.

[0051] See also Figure 3 , Based on the same inventive concept, according to another aspect of the present invention, the present invention also provides a cloud monitoring and management device for lithium-ion battery diaphragm equipment, including, at least one processor; The memory is used to store executable instructions, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the cloud-based monitoring and management method for lithium-ion battery diaphragm equipment.

[0052] See also Figure 3 Based on the same inventive concept, according to another aspect of the present invention, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform the steps of a cloud-based monitoring and management method for lithium-ion battery diaphragm equipment.

[0053] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0054] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0055] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0056] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0057] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0058] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0059] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0060] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A cloud monitoring and management method for lithium-ion battery diaphragm equipment, characterized in that: include, Monitor the environmental conditions in the target area in real time, identify abnormal environments in the target area, and send out multi-level alarms to the outside if there are abnormal environmental conditions in the target area; Analyze the impact of environmental condition data on product production. If the impact exceeds expectations, use the trained automatic control model to control the environmental improvement equipment and optimize the environmental conditions in the target area. Identify the operating status of the equipment based on its operating parameters. If the operating status is abnormal, predict and identify the potential failure of the equipment. If there is a potential failure, maintain the equipment when it is in shutdown state. Taking equipment-related data as input, after predicting the equipment's operating load, a pre-trained genetic algorithm is used to optimize the equipment's working time and task allocation; Use the digital twin model of battery diaphragm production to simulate and test the production process of battery diaphragms. If the difference between the obtained test emission data and the production emission data is greater than expected, the production process of battery diaphragms is optimized.

2. The cloud monitoring management method according to claim 1, characterized in that: A sensor network is arranged in the target area, and the sensor network monitors the environmental conditions in the target area in real time, obtains corresponding environmental condition data, and generates an environmental condition data set after aggregation; Taking environmental condition data as input, the trained abnormal environment recognition model is used to identify abnormal environments. If the environmental conditions in the target area are abnormal, a first-level alarm command is issued to the outside.

3. The cloud monitoring management method according to claim 2, characterized in that: The time node when the first-level alarm instruction is received is regarded as an abnormal node. When the number of abnormal nodes within the abnormal observation period exceeds expectations, the environmental abnormality degree is generated based on the abnormal nodes and the degree of abnormality of environmental conditions after each alarm instruction is issued; if the obtained environmental abnormality degree exceeds expectations, a second-level alarm instruction is issued to the outside.

4. The cloud monitoring management method according to claim 3, characterized in that: If a level 2 alarm command is received or several level 1 alarm commands are received consecutively, the environmental condition data, equipment operation data and product production data are summarized as an equipment status data set, and each data in the equipment status data set is encrypted and sent to the cloud data analysis platform.

5. The cloud monitoring management method according to claim 4, characterized in that: Multiple linear regression analysis is performed with environmental condition data as the independent variable and production volume as the dependent variable to obtain the corresponding linear regression equation. The sum of the regression coefficients corresponding to each environmental condition parameter in the linear regression equation is taken as the impact value. If the impact value exceeds the preset impact threshold, an automatic adjustment instruction is issued to the outside.

6. The cloud monitoring management method according to claim 5, characterized in that: Arrange environmental improvement equipment in the target area, and after receiving the automatic adjustment command, pre-set parameter thresholds for various environmental conditions, and use the real-time environmental condition data in the target area as input to control the environmental improvement equipment; The various operating parameters of the equipment are recorded in real time, and the operating parameters are used as input to use the trained abnormal state recognition model to identify the operating status of the equipment; if the operating status of the equipment is abnormal, the operating data of the equipment is used as input to use the trained fault prediction model to predict and identify the potential faults of the equipment. If the equipment has a potential fault, a maintenance instruction is issued to the outside.

7. The cloud monitoring management method according to claim 6, characterized in that: After receiving the maintenance instruction, the maintenance frequency of the equipment is constrained, the equipment is maintained at the maintenance node that meets the constraint conditions, and the detailed information of each maintenance is recorded and summarized to create the equipment maintenance file; Collect equipment operation data, production task data, and environmental condition data in the target area, use them as input, use the trained equipment operation load prediction model to predict the equipment operation load, and obtain the operation load prediction value; Taking reducing equipment load as the optimization goal, the equipment's operating load forecast value, operating data, production data and environmental data are used as input, and a pre-trained genetic algorithm is used to optimize the equipment's working time and task allocation.

8. The cloud monitoring management method according to claim 7, characterized in that: After pre-setting the same environmental conditions as the actual production scenario in the target area and setting the corresponding control parameters for the equipment, the battery separator production digital twin model is used to simulate the production process of the battery separator to obtain the test emission data during the production process; Collect production emission data under actual production scenarios, and obtain the corresponding emission similarity after analyzing the similarity between the test emission data and the production emission data within the sub-period; After obtaining several consecutive emission similarities, a similarity coefficient is constructed. If the similarity coefficient is lower than the similarity threshold, various parameters of the battery separator production process are optimized.

9. A cloud monitoring and management system for lithium-ion battery diaphragm equipment, characterized in that: include, The environmental anomaly recognition unit monitors the environmental conditions in the target area in real time and identifies the abnormal environment in the target area. If the environmental conditions in the target area are abnormal, a multi-level alarm is issued to the outside. The environmental adjustment unit analyzes the impact of environmental condition data on product production. If the impact exceeds expectations, the trained automatic adjustment control model is used to control the environmental improvement equipment to optimize the environmental conditions in the target area. The fault prediction unit identifies the operating status of the equipment based on the various operating parameters of the equipment. If the operating status is abnormal, the potential fault of the equipment is predicted and identified. If there is a potential fault, the equipment is maintained; The load optimization unit takes the equipment-related data as input, and after predicting the equipment's operating load, uses a pre-trained genetic algorithm to optimize the equipment's working time and task allocation; The test unit uses the digital twin model of battery diaphragm production to simulate the production process of battery diaphragms. If the difference between the obtained test emission data and the production emission data is greater than expected, the production process of the battery diaphragms is optimized.

10. A cloud monitoring and management device for lithium-ion battery diaphragm equipment, characterized in that: include, at least one processor; A memory for storing executable instructions, wherein the instructions are executed by at least one of the processors to enable at least one of the processors to perform the steps of the method according to any one of claims 1 to 8.

Images