N-type battery process key equipment matching system and method based on multi-element monitoring

Abstract

The present invention discloses a system and method for matching key equipment of an N-type battery process based on multivariate monitoring, which specifically relates to the technical field of solar cells, including obtaining a set of oxidation assessment coefficients of i production equipment; inputting historical oxidation assessment coefficients in the oxidation assessment coefficient set into a pre-built equipment prediction model to predict the oxidation assessment coefficient at a future time T; presetting an oxidation assessment threshold, and comparing the oxidation assessment coefficient at the future time T with the preset oxidation assessment threshold to obtain w abnormal production equipment, wherein the w abnormal production equipment include m abnormal first equipment and n abnormal second equipment. The present invention can monitor the operation of equipment in the production process in advance by introducing an abnormal monitoring mechanism, thereby taking corresponding maintenance and repair measures to ensure that the equipment can operate normally, thereby improving the reliability and stability of the production equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of solar cells, and more specifically, to a system and method for matching key equipment of an N-type battery process based on multi-element monitoring. Background Art

[0002] N-type cells are a new type of solar cell technology, characterized by adding a layer of N-type doped area (dominated by negative charge carriers) in the center of the P-type area (dominated by positive charge carriers) of the silicon wafer to improve the performance and efficiency of the cell. Among the key equipment for N-type cells, production equipment is one of the important equipment links that requires many process parameter adjustments. By controlling the gas flow and gas composition used in the oxidation process, the formation rate, thickness and quality of the oxide film can be changed, and a layer of silicon dioxide film can be formed on the surface of the cell. This process can not only improve the photoelectric conversion efficiency of the cell, but also ensure the surface and stability of the cell. However, various abnormal conditions may occur during the oxidation process, resulting in uneven thickness, bubbles, cracks and other phenomena in the silicon dioxide film formed on the surface of the cell, which in turn affects the quality of the N-type cell. In order to ensure the quality of N-type batteries, a model was established through measured data to find that the proportion of NMP in the pole piece is correlated with the phase change and drying morphology on the pole piece surface, and the process parameters were optimized. For example, Chinese patent publication number CN117497709A discloses a coating and drying method for lithium-ion battery pole pieces. Although the above method can ensure the quality of N-type batteries, the inventors have studied and applied the above method and the prior art and found that the above method and the prior art have at least the following defects:

[0003] (1) There is a lack of abnormal monitoring mechanism for production equipment, and it is impossible to predict in advance whether the production equipment can operate normally;

[0004] (2) When an abnormality occurs in the production equipment, there is a lack of adaptive adjustment of the process parameters of the production equipment, which can easily cause the production equipment to produce battery cells of poor quality, thereby affecting the production efficiency of N-type batteries.

[0005] To this end, the present invention provides a system and method for matching key equipment in an N-type battery process based on multivariate monitoring. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a system and method for matching key equipment of an N-type battery process based on multi-element monitoring to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a method for matching key equipment of N-type battery process based on multivariate monitoring, comprising:

[0008] Step 1: Obtain an oxidation assessment coefficient set of i production equipment; input the historical oxidation assessment coefficients in the oxidation assessment coefficient set into a pre-built equipment prediction model to predict the oxidation assessment coefficient at a future time T;

[0009] Step 2: Preset an oxidation assessment threshold, and compare the oxidation assessment coefficient at the future time T with the preset oxidation assessment threshold to obtain w abnormal production equipment, wherein the w abnormal production equipment includes m abnormal first equipment and n abnormal second equipment;

[0010] Step 3: Obtain a first target value of the mth abnormal first equipment, and obtain a second target value of the nth abnormal second equipment;

[0011] Step 4: Analyze based on the first target value and the corresponding oxidation evaluation coefficient to obtain a corresponding first adjustment value;

[0012] Step 5: Analyze based on the second target value and the corresponding oxidation evaluation coefficient to obtain a corresponding second adjustment value;

[0013] Step 6: After adjusting the first target values ​​or the second target values ​​of the w abnormal production equipment, obtain the cell evaluation coefficient corresponding to the w-th abnormal production equipment;

[0014] Step 7: Determine the battery cell evaluation coefficient of the w-th abnormal production equipment to determine whether to generate a production warning instruction; if a production warning instruction is generated, send the generated production warning instruction to the supervisory user end.

[0015] Further, the method of obtaining the oxidation assessment coefficient set includes:

[0016] The oxidation assessment coefficient of the i-th production equipment in the historical monitoring process is extracted from the production equipment database and marked as the historical oxidation assessment coefficient. The extracted historical oxidation assessment coefficients are used to construct an oxidation assessment coefficient set. The oxidation assessment coefficient set includes i historical oxidation assessment coefficients. The time intervals for obtaining the i historical oxidation assessment coefficients are equal. The i historical oxidation assessment coefficients correspond to a monitoring period of a production equipment. The monitoring period is hours, days or weeks.

[0017] Furthermore, the pre-built logic of the equipment prediction model is as follows: presetting a time step J, a sliding step H and a sliding window length C; converting the historical oxidation assessment coefficients in the oxidation assessment coefficient set into multiple training samples using a sliding window method, using the training samples as the input of the equipment prediction model, predicting the oxidation assessment coefficient after the time step J as the output, and using the subsequent oxidation assessment coefficient of each training sample as the prediction target. The equipment prediction model is trained with the prediction accuracy as the training target; generating an equipment prediction model that predicts the oxidation assessment coefficient at the future time T based on the historical oxidation assessment coefficients in the oxidation assessment coefficient set; the equipment prediction model is an RNN neural network model.

[0018] Further, the method for obtaining the historical oxidation assessment coefficient includes:

[0019] Obtain the oxidation monitoring data of the i-th production equipment; the oxidation monitoring data includes oxidation temperature, atmosphere pressure and gas flow rate, and the oxidation temperature, atmosphere pressure and gas flow rate are marked as Yw respectively i , Qy i and Ql i ;

[0020] The dimensionless calculation is performed based on the oxidation temperature, atmosphere pressure and gas flow rate to obtain the historical oxidation assessment coefficient of the i-th production equipment; the calculation formula is:

[0021]

[0022] Where: Yp i represents the historical oxidation assessment coefficient of the ith production equipment, represents the oxidation temperature weight factor of the ith production equipment; α2 represents the atmosphere pressure weight factor of the ith production equipment; α3 represents the gas flow weight factor of the ith production equipment, and α1, α2 and α3 are all greater than zero.

[0023] Furthermore, the method for obtaining w abnormal production equipment includes:

[0024] Extracting preset oxidation assessment thresholds Yz1 and Yz2 of the production equipment, where Yz1>Yz2, and comparing the oxidation assessment coefficient with the oxidation assessment threshold;

[0025] If Yp i >Yz1, the corresponding production equipment is determined to be the first abnormal equipment;

[0026] If Yz1 ≥ Yp i ≥Yz2, the corresponding production equipment is judged to be normal production equipment;

[0027] If Yp i <Yz2, the corresponding production equipment is determined to be the abnormal second equipment;

[0028] Count the number of abnormal first equipment to obtain m abnormal first equipment;

[0029] Count the number of abnormal second equipment to obtain n abnormal second equipment;

[0030] The sum of the number of m abnormal first equipment and the number of n abnormal second equipment is calculated to obtain w abnormal production equipment.

[0031] Further, the first target value is one of a first abnormal oxidation temperature, a first abnormal atmosphere pressure or a first abnormal gas flow rate; the second target value is one of a second abnormal oxidation temperature, a second abnormal atmosphere pressure or a second abnormal gas flow rate;

[0032] The method of obtaining a first target value of an m-th abnormal first equipment and obtaining a second target value of an n-th abnormal second equipment includes:

[0033] s1: extract the normal value interval, which is the maximum normal value maxY and the minimum normal value minY;

[0034] s2: compare the target optimization data of the mth abnormal first equipment and the target optimization data of the nth abnormal second equipment with the normal value range;

[0035] s3: taking the target optimization data greater than the maximum normal value maxY as the first target value, and taking the target optimization data less than the minimum normal value minY as the second target value;

[0036] The target optimization data include but are not limited to oxidation temperature, atmosphere pressure or gas flow rate; the normal value range includes but is not limited to normal oxidation temperature range, normal atmosphere pressure range or gas flow rate range.

[0037] Further, a method for analyzing the first target value and the corresponding oxidation evaluation coefficient to obtain the corresponding first adjustment value includes:

[0038] Step a1: obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the unit target value of f, and obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the unit target value of fg; f is an integer greater than zero, the initial value of f is the first target value, and g is a constant greater than zero;

[0039] Step a2: using the oxidation evaluation coefficient of the test production equipment corresponding to the f unit target value as the first optimization coefficient, and using the oxidation evaluation coefficient of the test production equipment corresponding to the fg unit target value as the second optimization coefficient;

[0040] Step a3: Compare the first optimization coefficient with the second optimization coefficient to determine whether the first optimization coefficient is less than or equal to the second optimization coefficient; if greater, set f=fg and return to step a1; if less than or equal, obtain the target value f corresponding to the first optimization coefficient;

[0041] Step a4: Calculate the absolute value of the difference between the target value f and the first target value to obtain a first adjustment difference, and use the first adjustment difference as the first adjustment value of the mth abnormal first equipment;

[0042] Step a5: Repeat the above steps a1 to a3 until the first optimization coefficient is less than or equal to the second optimization coefficient, and then end the loop.

[0043] Further, the second target value is one of abnormal oxidation temperature, abnormal atmosphere pressure or abnormal gas flow rate;

[0044] Specifically, the method of analyzing based on the second target value and the corresponding oxidation evaluation coefficient to obtain the corresponding second adjustment value includes:

[0045] Step b1: obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the r unit target value, and obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the r+v unit target value; r is an integer greater than zero, the initial value of r is the second target value, and v is a constant greater than zero;

[0046] Step b2: using the oxidation evaluation coefficient of the test production equipment corresponding to the r unit target value as the third optimization coefficient, and using the oxidation evaluation coefficient of the test production equipment corresponding to the r+v unit target value as the fourth optimization coefficient;

[0047] Step b3: Compare the third optimization coefficient with the fourth optimization coefficient to determine whether the third optimization coefficient is greater than or equal to the fourth optimization coefficient; if less than, set r=r+v and return to step b1; if greater than or equal to, obtain the target value r corresponding to the third optimization coefficient;

[0048] Step b4: Calculate the difference between the target value r and the second target value to obtain a second adjustment difference, and use the second adjustment difference as the second adjustment value of the nth abnormal second equipment;

[0049] Step b5: Repeat the above steps b1 to b3 until the third optimization coefficient is greater than or equal to the fourth optimization coefficient, and then end the loop.

[0050] Furthermore, the generation logic of obtaining the cell evaluation coefficient corresponding to the wth abnormal production equipment is as follows:

[0051] Obtain the output power and photoelectric conversion rate of the battery and mark them as Gl w and Zhw ,

[0052] The output power and photoelectric conversion rate of the cell are dimensionlessly processed and calculated to obtain the cell evaluation coefficient, which is calculated as follows:

[0053] Znp=β1×Gl w +β2×Zh w ;

[0054] In the formula, Zx w represents the cell evaluation coefficient of the w-th abnormal production equipment, β1 represents the weight factor of the output power of the cell corresponding to the w-th abnormal production equipment, and β2 represents the weight factor of the oxide layer thickness of the cell corresponding to the w-th abnormal production equipment; β1 and β2 are both greater than zero;

[0055] The method for judging the battery cell evaluation coefficient of the w-th abnormal production equipment and whether to generate a production warning instruction includes:

[0056] Preset a cell evaluation coefficient threshold, and compare the cell evaluation coefficient with the cell evaluation coefficient threshold;

[0057] If the cell evaluation coefficient is less than the cell evaluation coefficient threshold, a production warning instruction is generated;

[0058] If the battery cell evaluation coefficient is greater than or equal to the battery cell evaluation coefficient threshold, no production warning instruction is generated.

[0059] In a second aspect, the present invention provides an N-type battery process key equipment matching system based on multivariate monitoring, which is used to implement the above-mentioned N-type battery process key equipment matching method based on multivariate monitoring, including:

[0060] An equipment prediction module is used to obtain an oxidation assessment coefficient set of i production equipment; based on the historical oxidation assessment coefficients in the oxidation assessment coefficient set, input into a pre-built equipment prediction model to predict the oxidation assessment coefficient at a future time T;

[0061] A first judgment module presets an oxidation assessment threshold, compares the oxidation assessment coefficient at a future time T with the preset oxidation assessment threshold, to obtain w abnormal production equipment, wherein the w abnormal production equipment includes m abnormal first equipment and n abnormal second equipment;

[0062] A first analysis module, used for obtaining a first target value of the mth abnormal first equipment, and performing analysis based on the first target value and a corresponding oxidation assessment coefficient to obtain a corresponding first adjustment value;

[0063] a second analysis module, configured to obtain a second target value of the n-th abnormal second equipment, and to perform analysis based on the second target value and a corresponding oxidation assessment coefficient to obtain a corresponding second adjustment value;

[0064] An evaluation module, configured to obtain a cell evaluation coefficient corresponding to the wth abnormal production equipment after adjusting the first target value or the second target value of the w abnormal production equipment;

[0065] The second judgment module is used to judge the battery cell evaluation coefficient of the wth abnormal production equipment to determine whether to generate a production warning instruction; if a production warning instruction is generated, the generated production warning instruction is sent to the supervision user end.

[0066] Technical effects and advantages of the present invention:

[0067] 1. The present invention obtains a set of oxidation assessment coefficients of i production equipment; inputs the historical oxidation assessment coefficients in the oxidation assessment coefficient set into a pre-built equipment prediction model to predict the oxidation assessment coefficient at a future time T; presets an oxidation assessment threshold, and compares the oxidation assessment coefficient at a future time T with the preset oxidation assessment threshold to obtain w abnormal production equipment, wherein the w abnormal production equipment include m abnormal first equipment and n abnormal second equipment; obtains a first target value of the mth abnormal first equipment, and analyzes based on the first target value and the corresponding oxidation assessment coefficient to obtain a corresponding first adjustment value; obtains a second target value of the nth abnormal second equipment, and analyzes based on the second target value and the corresponding oxidation assessment coefficient to obtain Take the corresponding second adjustment value; after adjusting the first target value or the second target value of the w abnormal production equipment, obtain the battery cell evaluation coefficient corresponding to the w-th abnormal production equipment; judge the battery cell evaluation coefficient of the w-th abnormal production equipment to determine whether to generate a production warning instruction; if a production warning instruction is generated, send the generated production warning instruction to the supervision user end so that the staff can further inspect and repair the w-th abnormal production equipment; by introducing the abnormal monitoring mechanism, the equipment operation status in the production process can be monitored in advance, so as to take corresponding maintenance and repair measures to ensure the normal operation of the equipment, improve the reliability and stability of the production equipment, and reduce the downtime caused by equipment failure, thereby improving production efficiency and product quality;

[0068] 2. The present invention also provides a function of adaptively adjusting process parameters when an abnormal situation occurs in the production equipment. By real-time monitoring of abnormal situations in the production process and automatically adjusting process parameters according to actual conditions, the production process can respond in time and adapt to changes in the operating status of the equipment. Even when an abnormal situation occurs in the equipment, the stability and consistency of the production process can be guaranteed, thus avoiding quality problems of N-type battery cells caused by abnormal production equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flow chart of a method for matching key equipment of an N-type battery process based on multivariate monitoring in Example 1;

[0070] Figure 2 This is a schematic diagram of a key equipment matching system for N-type battery process based on multivariate monitoring in Example 2;

[0071] Figure 3 This is a schematic diagram of an electronic device according to Embodiment 3;

[0072] Figure 4 A schematic diagram of a computer-readable storage medium of Example 4. DETAILED DESCRIPTION

[0073] 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.

[0074] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0075] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and a similar second unit may be referred to as a first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0076] Example 1

[0077] See also Figure 1 As shown, this embodiment discloses a method for matching key equipment of N-type battery process based on multivariate monitoring:

[0078] Step 1: Obtain an oxidation assessment coefficient set of i production equipment; input the historical oxidation assessment coefficients in the oxidation assessment coefficient set into a pre-built equipment prediction model to predict the oxidation assessment coefficient at a future time T;

[0079] It should be noted that, in an N-type battery factory, i is an integer greater than zero, and the i production equipment includes but is not limited to annealing equipment, drying equipment and oxidation equipment, etc., and this embodiment is described with the parameter optimization of oxidation equipment; the production equipment has multiple sensors built in, and the production equipment is equipped with a monitoring device, and the sensors include but are not limited to infrared temperature sensors, flow sensors, pressure sensors, etc.;

[0080] Further, the method of obtaining the oxidation assessment coefficient set includes:

[0081] The oxidation assessment coefficient of the i-th production equipment in the historical monitoring process is extracted from the production equipment database and marked as the historical oxidation assessment coefficient. The extracted historical oxidation assessment coefficients are used to construct an oxidation assessment coefficient set. The oxidation assessment coefficient set includes i historical oxidation assessment coefficients. The i historical oxidation assessment coefficients are obtained at equal time intervals. The i historical oxidation assessment coefficients correspond to a monitoring cycle of a production equipment. The monitoring cycle can be hours, days or weeks. The specific monitoring cycle is determined by the staff according to the specific circumstances.

[0082] Furthermore, the pre-construction logic of the equipment prediction model is as follows: preset the time step J, sliding step H and sliding window length C according to the actual experience of the staff; convert the historical oxidation assessment coefficients in the oxidation assessment coefficient set into multiple training samples using the sliding window method, use the training samples as the input of the equipment prediction model, predict the oxidation assessment coefficient after the time step J as the output, and use the subsequent oxidation assessment coefficient of each training sample as the prediction target. The equipment prediction model is trained with the prediction accuracy as the training target; generate an equipment prediction model that predicts the oxidation assessment coefficient at the future time T according to the historical oxidation assessment coefficients in the oxidation assessment coefficient set; wherein the equipment prediction model is an RNN neural network model; by using the oxidation assessment coefficient set, the oxidation assessment coefficients of i production equipment at the future time T are predicted, thereby realizing the oxidation assessment coefficient based on the future time T, predicting the situation of the production equipment to be predicted, and predicting the oxidation assessment coefficient from multiple aspects;

[0083] It should be noted that the oxidation evaluation coefficient set is divided into Z sliding windows of equal size, the oxidation evaluation coefficient in each window is taken as a sample, and the oxidation evaluation coefficient of the window at the future T time is taken as a digital label. It should be noted that one sample corresponds to one digital label, and one sample and the corresponding digital label constitute a training sample, and multiple groups of training samples constitute a coefficient training set;

[0084] For further exemplary explanation, it is assumed that the oxidation evaluation coefficient set K includes 10 sets of historical oxidation evaluation coefficients, K = {K1, K2, K3, K4, K5, ..., K 10}, K a For the a-th group of historical oxidation evaluation coefficients, multiple training samples are constructed using a sliding window, and the prediction time step J is set to 1, the length of the sliding window H is set to 5, and the sliding step C is set to 1. Then, each training sample is generated to contain 5 consecutive historical oxidation evaluation coefficients, and the next oxidation evaluation coefficient of the 5 consecutive historical oxidation evaluation coefficients is used as the prediction target; for example:

[0085] {K1, K2, K3, K4, K5} are used as training samples, and the corresponding prediction target is K6;

[0086] {K2, K3, K4, K5, K6} are used as training samples, and the corresponding prediction target is K7;

[0087] By analogy, the equipment prediction model is used to train the oxidation evaluation coefficient at future times.

[0088] In practice, methods for obtaining historical oxidation assessment coefficients include:

[0089] Obtain the oxidation monitoring data of the i-th production equipment; the oxidation monitoring data includes oxidation temperature, atmosphere pressure and gas flow rate, and the oxidation temperature, atmosphere pressure and gas flow rate are marked as Yw respectively i , Qy i and Ql i ;

[0090] The dimensionless calculation is performed based on the oxidation temperature, atmosphere pressure and gas flow rate to obtain the historical oxidation assessment coefficient of the i-th production equipment; the calculation formula is:

[0091]

[0092] Where: Yp i represents the historical oxidation assessment coefficient of the i-th production equipment, α1 represents the oxidation temperature weight factor of the i-th production equipment; α2 represents the atmosphere pressure weight factor of the i-th production equipment; α3 represents the gas flow weight factor of the i-th production equipment, and α1, α2 and α3 are all greater than zero;

[0093] It should be noted that: the oxidation temperature, atmosphere pressure and gas flow rate are respectively collected and obtained through pre-processing by temperature sensors, pressure sensors and flow sensors installed in production equipment, wherein the various sensors include but are not limited to temperature sensors, vibration sensors and voltage sensors, and the pre-processing includes but is not limited to data cleaning, noise removal, missing value processing or error data repair;

[0094] It is further explained that: during the monitoring period, the greater the absolute value of the difference between the oxidation temperature and the standard value of the oxidation temperature, the greater the absolute value of the difference between the atmosphere pressure and the standard value of the atmosphere pressure, or the greater the absolute value of the difference between the gas flow rate and the standard value of the gas flow rate, the smaller the oxidation evaluation coefficient is, which means the worse the oxidation effect of the production equipment is, and the greater the possibility that the production equipment needs parameter matching; conversely, the smaller the absolute value of the difference between the oxidation temperature and the standard value of the oxidation temperature, the smaller the absolute value of the difference between the atmosphere pressure and the standard value of the atmosphere pressure, or the smaller the absolute value of the difference between the gas flow rate and the standard value of the gas flow rate, the greater the oxidation evaluation coefficient is, which means the better the oxidation effect of the production equipment is, and the less likely the production equipment needs parameter matching;

[0095] Step 2: Preset an oxidation assessment threshold, and compare the oxidation assessment coefficient at the future time T with the preset oxidation assessment threshold to obtain w abnormal production equipment, wherein the w abnormal production equipment includes m abnormal first equipment and n abnormal second equipment;

[0096] During implementation, the method of obtaining w abnormal production equipment includes:

[0097] Extracting preset oxidation assessment thresholds Yz1 and Yz2 of the production equipment, where Yz1>Yz2, and comparing the oxidation assessment coefficient with the oxidation assessment threshold;

[0098] It should be noted that the calculation process of the preset oxidation assessment threshold is the same as the generation process of the oxidation assessment coefficient, and the details are referred to above, and the preset oxidation assessment thresholds Yz1 and Yz2 are determined by the management personnel, which will not be described in detail here;

[0099] If Yp i >Yz1, the corresponding production equipment is determined to be the first abnormal equipment;

[0100] If Yz1 ≥ Yp i ≥Yz2, the corresponding production equipment is judged to be normal production equipment;

[0101] If Yp i <Yz2, the corresponding production equipment is determined to be the abnormal second equipment;

[0102] Count the number of abnormal first equipment to obtain m abnormal first equipment;

[0103] Count the number of abnormal second equipment to obtain n abnormal second equipment;

[0104] The sum of the number of m abnormal first equipment and the number of n abnormal second equipment is calculated to obtain w abnormal production equipment.

[0105] Step 3: Obtain a first target value of the mth abnormal first equipment, and obtain a second target value of the nth abnormal second equipment;

[0106] Specifically, the first target value is one of a first abnormal oxidation temperature, a first abnormal atmosphere pressure, or a first abnormal gas flow rate;

[0107] The second target value is one of a second abnormal oxidation temperature, a second abnormal atmosphere pressure, or a second abnormal gas flow rate;

[0108] In a preferred embodiment, a method for obtaining a first target value of an m-th abnormal first device and a method for obtaining a second target value of an n-th abnormal second device includes:

[0109] s1: extract the normal value interval, which is the maximum normal value maxY and the minimum normal value minY;

[0110] s2: compare the target optimization data of the mth abnormal first equipment and the target optimization data of the nth abnormal second equipment with the normal value range;

[0111] s3: taking the target optimization data greater than the maximum normal value maxY as the first target value, and taking the target optimization data less than the minimum normal value minY as the second target value;

[0112] It is additionally noted that the target optimization data includes but is not limited to oxidation temperature, atmosphere pressure or gas flow rate; therefore, the normal value interval includes but is not limited to the normal oxidation temperature interval, the normal atmosphere pressure interval or the gas flow rate interval; the acquisition logic of the first target value or the second target value in other forms is the same as the above acquisition logic, which will not be described in detail;

[0113] Step 4: Analyze based on the first target value and the corresponding oxidation evaluation coefficient to obtain a corresponding first adjustment value;

[0114] Specifically, the method of analyzing based on the first target value and the corresponding oxidation evaluation coefficient to obtain the corresponding first adjustment value includes:

[0115] Step a1: obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the unit target value of f, and obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the unit target value of fg; f is an integer greater than zero, the initial value of f is the first target value, and g is a constant greater than zero;

[0116] Step a2: using the oxidation evaluation coefficient of the test production equipment corresponding to the f unit target value as the first optimization coefficient, and using the oxidation evaluation coefficient of the test production equipment corresponding to the fg unit target value as the second optimization coefficient;

[0117] Step a3: Compare the first optimization coefficient with the second optimization coefficient to determine whether the first optimization coefficient is less than or equal to the second optimization coefficient; if greater, set f=fg and return to step a1; if less than or equal, obtain the target value f corresponding to the first optimization coefficient;

[0118] Step a4: Calculate the absolute value of the difference between the target value f and the first target value to obtain a first adjustment difference, and use the first adjustment difference as the first adjustment value of the mth abnormal first equipment;

[0119] Step a5: repeat the above steps a1 to a3 until the first optimization coefficient is less than or equal to the second optimization coefficient, and then end the loop;

[0120] Step 5: Analyze based on the second target value and the corresponding oxidation evaluation coefficient to obtain a corresponding second adjustment value;

[0121] Specifically, the method of analyzing based on the second target value and the corresponding oxidation evaluation coefficient to obtain the corresponding second adjustment value includes:

[0122] Step b1: obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the r unit target value, and obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the r+v unit target value; r is an integer greater than zero, the initial value of r is the second target value, and v is a constant greater than zero;

[0123] Step b2: using the oxidation evaluation coefficient of the test production equipment corresponding to the r unit target value as the third optimization coefficient, and using the oxidation evaluation coefficient of the test production equipment corresponding to the r+v unit target value as the fourth optimization coefficient;

[0124] Step b3: Compare the third optimization coefficient with the fourth optimization coefficient to determine whether the third optimization coefficient is greater than or equal to the fourth optimization coefficient; if less than, set r=r+v and return to step b1; if greater than or equal to, obtain the target value r corresponding to the third optimization coefficient;

[0125] Step b4: Calculate the difference between the target value r and the second target value to obtain a second adjustment difference, and use the second adjustment difference as the second adjustment value of the nth abnormal second equipment;

[0126] Step b5: repeat the above steps b1 to b3 until the third optimization coefficient is greater than or equal to the fourth optimization coefficient, and then end the loop;

[0127] Step 6: After adjusting the first target values ​​or the second target values ​​of the w abnormal production equipment, obtain the cell evaluation coefficient corresponding to the w-th abnormal production equipment;

[0128] Furthermore, the generation logic of obtaining the cell evaluation coefficient corresponding to the wth abnormal production equipment is as follows:

[0129] Obtain the output power and photoelectric conversion rate of the battery and mark them as Gl w and Zh w ,

[0130] The output power and photoelectric conversion rate of the cell are dimensionlessly processed and calculated to obtain the cell evaluation coefficient, which is calculated as follows:

[0131] Znp=β1×Gl w +β2×Zh w ;

[0132] In the formula, Zx w represents the cell evaluation coefficient of the w-th abnormal production equipment, β1 represents the weight factor of the output power of the cell corresponding to the w-th abnormal production equipment, and β2 represents the weight factor of the oxide layer thickness of the cell corresponding to the w-th abnormal production equipment; β1 and β2 are both greater than zero;

[0133] It should be understood that the output power and photoelectric conversion rate of the cell are determined by those skilled in the art through a large number of experiments; the smaller the output power and photoelectric conversion rate of the cell, the smaller the value of the cell evaluation coefficient; on the contrary, the larger the output power and photoelectric conversion rate of the cell, the larger the value of the cell evaluation coefficient;

[0134] Step 7: Determine whether to generate a production warning instruction for the cell evaluation coefficient of the w-th abnormal production equipment; if a production warning instruction is generated, send the generated production warning instruction to the supervision user end so that the staff can further inspect and repair the w-th abnormal production equipment;

[0135] Specifically, the method for judging the battery cell evaluation coefficient of the w-th abnormal production equipment and whether to generate a production warning instruction includes:

[0136] Preset a cell evaluation coefficient threshold, and compare the cell evaluation coefficient with the cell evaluation coefficient threshold;

[0137] If the cell evaluation coefficient is less than the cell evaluation coefficient threshold, a production warning instruction is generated; indicating that the first target value or the second target value of the w-th abnormal production equipment is not properly adjusted, and the staff is required to further inspect and repair the w-th abnormal production equipment;

[0138] If the cell evaluation coefficient is greater than or equal to the cell evaluation coefficient threshold, no production warning instruction is generated; it means that the first target value or the second target value of the w-th abnormal production equipment is adjusted appropriately, and there is no need to inspect and repair the w-th abnormal production equipment;

[0139] In this embodiment, a set of oxidation assessment coefficients of i production equipment is obtained; the oxidation assessment coefficients in the oxidation assessment coefficient set are input into a pre-built equipment prediction model to predict the oxidation assessment coefficient at a future time T; an oxidation assessment threshold is preset, and the oxidation assessment coefficient at a future time T is compared with the preset oxidation assessment threshold to obtain w abnormal production equipment, wherein the w abnormal production equipment include m abnormal first equipment and n abnormal second equipment; a first target value of the mth abnormal first equipment is obtained, and an analysis is performed based on the first target value and the corresponding oxidation assessment coefficient to obtain a corresponding first adjustment value; a second target value of the nth abnormal second equipment is obtained, and an analysis is performed based on the second target value and the corresponding oxidation assessment coefficient to obtain a corresponding second adjustment value; after adjusting the first target value or the second target value of w abnormal production equipment, obtain the battery cell evaluation coefficient corresponding to the w-th abnormal production equipment; judge the battery cell evaluation coefficient of the w-th abnormal production equipment to determine whether to generate a production warning instruction; if a production warning instruction is generated, send the generated production warning instruction to the supervision user end so that the staff can further inspect and repair the w-th abnormal production equipment; this embodiment introduces an abnormal monitoring mechanism to monitor the equipment operation status in the production process in advance, so as to take corresponding maintenance and repair measures to ensure the normal operation of the equipment, improve the reliability and stability of the production equipment, and reduce the downtime caused by equipment failure, thereby improving production efficiency and product quality;

[0140] In this embodiment, when an abnormal situation occurs in the production equipment, a process parameter adaptive adjustment function is also provided. By real-time monitoring of abnormal conditions in the production process and automatically adjusting the process parameters according to actual conditions, the production process can respond in time and adapt to changes in the equipment's operating status. Even when an abnormality occurs in the equipment, the stability and consistency of the production process can be guaranteed, thereby avoiding N-type battery quality problems caused by abnormal production equipment.

[0141] The formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The weight factors in the formula and the preset thresholds in the analysis process are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data; the size of the weight factor is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the weight factor depends on the amount of sample data and the corresponding processing coefficient initially set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0142] Example 2

[0143] See also Figure 2 As shown, this embodiment provides an N-type battery process key equipment matching system based on multivariate monitoring, including:

[0144] An equipment prediction module is used to obtain an oxidation assessment coefficient set of i production equipment; based on the historical oxidation assessment coefficients in the oxidation assessment coefficient set, input into a pre-built equipment prediction model to predict the oxidation assessment coefficient at a future time T;

[0145] A first judgment module presets an oxidation assessment threshold, compares the oxidation assessment coefficient at a future time T with the preset oxidation assessment threshold, to obtain w abnormal production equipment, wherein the w abnormal production equipment includes m abnormal first equipment and n abnormal second equipment;

[0146] A first analysis module, used for obtaining a first target value of the mth abnormal first equipment, and performing analysis based on the first target value and a corresponding oxidation assessment coefficient to obtain a corresponding first adjustment value;

[0147] a second analysis module, configured to obtain a second target value of the n-th abnormal second equipment, and to perform analysis based on the second target value and a corresponding oxidation assessment coefficient to obtain a corresponding second adjustment value;

[0148] An evaluation module, configured to obtain a cell evaluation coefficient corresponding to the wth abnormal production equipment after adjusting the first target value or the second target value of the w abnormal production equipment;

[0149] The second judgment module is used to judge the battery cell evaluation coefficient of the wth abnormal production equipment to determine whether to generate a production warning instruction; if a production warning instruction is generated, the generated production warning instruction is sent to the supervision user end.

[0150] Example 3

[0151] See also Figure 3As shown, this embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0152] The processor executes the method for matching key equipment of N-type battery process based on multivariate monitoring of Example 1 by calling the computer program stored in the memory.

[0153] Example 4

[0154] See also Figure 4 As shown, this embodiment provides a computer-readable storage medium storing instructions. When the instructions are executed on a computer, the computer executes the method for matching key equipment of an N-type battery process based on multivariate monitoring of Embodiment 1.

[0155] The formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The weight factors in the formula and the preset thresholds in the analysis process are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data; the size of the weight factor is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the weight factor depends on the amount of sample data and the corresponding processing coefficient initially set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0156] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using 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 devices. 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 site, computer, server or data center to another website site, computer, server or data center via a wired network. The computer-readable storage medium can be any available medium that a computer can access 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.

[0157] 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.

[0158] 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.

[0159] 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 one. There may be other division methods in actual implementation. For example, 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.

[0160] 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.

[0161] 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.

[0162] If the functions are implemented in the form of software functional units and sold or used as independent products, they 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, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0163] 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.

[0164] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for matching key equipment of N-type battery process based on multivariate monitoring, characterized in that: include: Step 1: Obtain the oxidation assessment coefficient set of i production equipment; Based on the historical oxidation assessment coefficients in the oxidation assessment coefficient set, the historical oxidation assessment coefficients are input into a pre-built equipment prediction model to predict the oxidation assessment coefficient at a future time T; Methods for obtaining a set of oxidation assessment coefficients include: Extracting the oxidation assessment coefficient of the i-th production equipment in the historical monitoring process from the production equipment database and marking it as the historical oxidation assessment coefficient, constructing an oxidation assessment coefficient set with the extracted historical oxidation assessment coefficient, the oxidation assessment coefficient set includes i historical oxidation assessment coefficients, the time intervals for obtaining the i historical oxidation assessment coefficients are equal, and the i historical oxidation assessment coefficients correspond to a monitoring cycle of the production equipment; the monitoring cycle is hours, days or weeks; The pre-construction logic of the equipment prediction model is as follows: presetting a time step J, a sliding step H, and a sliding window length C; converting the historical oxidation assessment coefficients in the oxidation assessment coefficient set into multiple training samples using a sliding window method, taking the training samples as the input of the equipment prediction model, predicting the oxidation assessment coefficients after the time step J as the output, taking the subsequent oxidation assessment coefficients of each training sample as the prediction target, taking the prediction accuracy as the training target, and training the equipment prediction model; generating an equipment prediction model that predicts the oxidation assessment coefficients at the future time T according to the historical oxidation assessment coefficients in the oxidation assessment coefficient set; the equipment prediction model is an RNN neural network model; Methods for obtaining historical oxidation assessment factors include: Obtain the oxidation monitoring data of the i-th production equipment; the oxidation monitoring data includes oxidation temperature, atmosphere pressure and gas flow rate, and the oxidation temperature, atmosphere pressure and gas flow rate are marked as Yw respectively i , Qy i and Ql i ; The dimensionless calculation is performed based on the oxidation temperature, atmosphere pressure and gas flow rate to obtain the historical oxidation assessment coefficient of the i-th production equipment; the calculation formula is: Where: Yp i represents the historical oxidation assessment coefficient of the i-th production equipment, α1 represents the oxidation temperature weight factor of the i-th production equipment; α2 represents the atmosphere pressure weight factor of the i-th production equipment; α3 represents the gas flow weight factor of the i-th production equipment, and α1, α2 and α3 are all greater than zero; Step 2: Preset an oxidation assessment threshold, and compare the oxidation assessment coefficient at the future time T with the preset oxidation assessment threshold to obtain w abnormal production equipment, wherein the w abnormal production equipment includes m abnormal first equipment and n abnormal second equipment; Step 3: Obtain a first target value of the mth abnormal first equipment, and obtain a second target value of the nth abnormal second equipment; Step 4: Analyze based on the first target value and the corresponding oxidation evaluation coefficient to obtain a corresponding first adjustment value; Step 5: Analyze based on the second target value and the corresponding oxidation evaluation coefficient to obtain a corresponding second adjustment value; Step 6: After adjusting the first target values ​​or the second target values ​​of the w abnormal production equipment, obtain the cell evaluation coefficient corresponding to the w-th abnormal production equipment; The generation logic of obtaining the cell evaluation coefficient corresponding to the wth abnormal production equipment is as follows: Obtain the output power and photoelectric conversion rate of the battery and mark them as Gl w and Zh w , The output power and photoelectric conversion rate of the cell are dimensionlessly processed and calculated to obtain the cell evaluation coefficient, which is calculated as follows: Znp=β1×Gl w +β2×Zh w ; In the formula, Zx w represents the cell evaluation coefficient of the w-th abnormal production equipment, β1 represents the weight factor of the output power of the cell corresponding to the w-th abnormal production equipment, and β2 represents the weight factor of the oxide layer thickness of the cell corresponding to the w-th abnormal production equipment; β1 and β2 are both greater than zero; Step 7: Determine the battery cell evaluation coefficient of the w-th abnormal production equipment to determine whether to generate a production warning instruction; if a production warning instruction is generated, send the generated production warning instruction to the supervisory user end.

2. The method for matching key equipment of N-type battery process based on multivariate monitoring according to claim 1 is characterized in that: Methods for obtaining w abnormal production equipment include: Extracting preset oxidation assessment thresholds Yz1 and Yz2 of the production equipment, where Yz1>Yz2, and comparing the oxidation assessment coefficient with the oxidation assessment threshold; If Yp i >Yz1, the corresponding production equipment is determined to be the first abnormal equipment; If Yz1 ≥ Yp i ≥Yz2, the corresponding production equipment is judged to be normal production equipment; If Yp i <Yz2, the corresponding production equipment is determined to be the abnormal second equipment; Count the number of abnormal first equipment to obtain m abnormal first equipment; Count the number of abnormal second equipment to obtain n abnormal second equipment; The sum of the number of m abnormal first equipment and the number of n abnormal second equipment is calculated to obtain w abnormal production equipment.

3. The method for matching key equipment of N-type battery process based on multivariate monitoring according to claim 2 is characterized in that: The first target value is one of a first abnormal oxidation temperature, a first abnormal atmosphere pressure, or a first abnormal gas flow rate; the second target value is one of a second abnormal oxidation temperature, a second abnormal atmosphere pressure, or a second abnormal gas flow rate; The method of obtaining a first target value of an m-th abnormal first equipment and obtaining a second target value of an n-th abnormal second equipment includes: s1: extract the normal value interval, which is the maximum normal value maxY and the minimum normal value minY; s2: compare the target optimization data of the mth abnormal first equipment and the target optimization data of the nth abnormal second equipment with the normal value range; s3: taking the target optimization data greater than the maximum normal value maxY as the first target value, and taking the target optimization data less than the minimum normal value minY as the second target value; The target optimization data include but are not limited to oxidation temperature, atmosphere pressure or gas flow rate; the normal value range includes but is not limited to normal oxidation temperature range, normal atmosphere pressure range or gas flow rate range.

4. The method for matching key equipment of N-type battery process based on multivariate monitoring according to claim 3 is characterized in that: The method for analyzing the first target value and the corresponding oxidation evaluation coefficient to obtain the corresponding first adjustment value includes: Step a1: obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the unit target value of f, and obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the unit target value of fg; f is an integer greater than zero, the initial value of f is the first target value, and g is a constant greater than zero; Step a2: using the oxidation evaluation coefficient of the test production equipment corresponding to the target value of the unit f as the first optimization coefficient, and using the oxidation evaluation coefficient of the test production equipment corresponding to the target value of the unit fg as the second optimization coefficient; Step a3: Compare the first optimization coefficient with the second optimization coefficient to determine whether the first optimization coefficient is less than or equal to the second optimization coefficient; if greater, set f=fg and return to step a1; if less than or equal, obtain the target value f corresponding to the first optimization coefficient; Step a4: Calculate the absolute value of the difference between the target value f and the first target value to obtain a first adjustment difference, and use the first adjustment difference as the first adjustment value of the mth abnormal first equipment; Step a5: Repeat the above steps a1 to a3 until the first optimization coefficient is less than or equal to the second optimization coefficient, and then end the loop.

5. The method for matching key equipment of N-type battery process based on multivariate monitoring according to claim 4 is characterized in that: The second target value is one of abnormal oxidation temperature, abnormal atmosphere pressure or abnormal gas flow rate; Specifically, the method of analyzing based on the second target value and the corresponding oxidation evaluation coefficient to obtain the corresponding second adjustment value includes: Step b1: obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the r unit target value, and obtaining the oxidation evaluation coefficient of the test production equipment corresponding to the r+v unit target value; r is an integer greater than zero, the initial value of r is the second target value, and v is a constant greater than zero; Step b2: using the oxidation evaluation coefficient of the test production equipment corresponding to the r unit target value as the third optimization coefficient, and using the oxidation evaluation coefficient of the test production equipment corresponding to the r+v unit target value as the fourth optimization coefficient; Step b3: Compare the third optimization coefficient with the fourth optimization coefficient to determine whether the third optimization coefficient is greater than or equal to the fourth optimization coefficient; if less than, set r=r+v and return to step b1; if greater than or equal to, obtain the target value r corresponding to the third optimization coefficient; Step b4: Calculate the difference between the target value r and the second target value to obtain a second adjustment difference, and use the second adjustment difference as the second adjustment value of the nth abnormal second equipment; Step b5: Repeat the above steps b1 to b3 until the third optimization coefficient is greater than or equal to the fourth optimization coefficient, and then end the loop.

6. The method for matching key equipment of N-type battery process based on multivariate monitoring according to claim 5 is characterized in that: The method for judging the battery cell evaluation coefficient of the w-th abnormal production equipment and whether to generate a production warning instruction includes: Preset a cell evaluation coefficient threshold, and compare the cell evaluation coefficient with the cell evaluation coefficient threshold; If the battery cell evaluation coefficient is less than the battery cell evaluation coefficient threshold, a production warning instruction is generated; if the battery cell evaluation coefficient is greater than or equal to the battery cell evaluation coefficient threshold, no production warning instruction is generated.

7. A system for matching key equipment of N-type battery process based on multivariate monitoring, used to implement the method for matching key equipment of N-type battery process based on multivariate monitoring as claimed in any one of claims 1 to 6, characterized in that: include: Equipment prediction module, used to obtain the oxidation assessment coefficient set of i production equipment; Based on the historical oxidation assessment coefficients in the oxidation assessment coefficient set, the historical oxidation assessment coefficients are input into a pre-built equipment prediction model to predict the oxidation assessment coefficient at a future time T; A first judgment module presets an oxidation assessment threshold, compares the oxidation assessment coefficient at a future time T with the preset oxidation assessment threshold, to obtain w abnormal production equipment, wherein the w abnormal production equipment includes m abnormal first equipment and n abnormal second equipment; A first analysis module, used for obtaining a first target value of the mth abnormal first equipment, and performing analysis based on the first target value and a corresponding oxidation assessment coefficient to obtain a corresponding first adjustment value; a second analysis module, configured to obtain a second target value of the nth abnormal second equipment, and to perform analysis based on the second target value and a corresponding oxidation assessment coefficient to obtain a corresponding second adjustment value; An evaluation module, configured to obtain a cell evaluation coefficient corresponding to the wth abnormal production equipment after adjusting the first target value or the second target value of the w abnormal production equipment; The second judgment module is used to judge the battery cell evaluation coefficient of the wth abnormal production equipment and whether to generate a production warning instruction; If a production warning instruction is generated, the generated production warning instruction will be sent to the supervision user end.

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