Battery cell production equipment parameter adjustment method, device and equipment and readable storage medium

Abstract

The invention provides a cell production equipment parameter adjustment method, device and equipment and a readable storage medium, and belongs to the field of cell production. The method comprises the following steps: obtaining an input parameter sequence, a production equipment parameter sequence and an output parameter sequence; obtaining a plurality of first stable fluctuation intervals, a plurality of second stable fluctuation intervals and a plurality of third stable fluctuation intervals; obtaining the category of the optimal target stable fluctuation interval; and according to the historical production equipment parameter database, the category of the optimal target stable fluctuation interval and the expected category, adjusting the production equipment parameters to obtain new production equipment parameters. According to the method, the historical production equipment parameter database is referred, and the battery cell production equipment parameters are adjusted by meeting the expected categories and the categories of the optimal target stable fluctuation interval, so that the production equipment parameters required by stable production of the battery cell are quickly achieved.

Description

Technical Field

[0001] The present application relates to the field of battery cell production, and in particular to a method, device, equipment and readable storage medium for adjusting parameters of battery cell production equipment. Background Art

[0002] The cell production process includes many steps such as slurrying, coating and winding. The output quality of each step depends on the control of the process production equipment parameters and the quality of the input of the current step (the output of the previous step). If the input quality of the current step fluctuates or the environment changes, the equipment parameters need to be adjusted appropriately so that the output of the step quickly reaches a stable level.

[0003] The regulation of existing equipment parameters mostly relies on the operator's experience. During manual regulation, different operators have different regulation strategies and strengths, resulting in unstable production output quality. Summary of the invention

[0004] In order to solve the above technical problems, the embodiments of the present application provide a method, device, equipment and readable storage medium for adjusting parameters of battery cell production equipment.

[0005] In a first aspect, an embodiment of the present application provides a method for adjusting parameters of a cell production device, the method comprising:

[0006] Collect input parameters, production equipment parameters and output parameters within a preset time range;

[0007] Performing time correlation alignment on the input parameters, the production equipment parameters and the output parameters to obtain an input parameter sequence, a production equipment parameter sequence and an output parameter sequence respectively;

[0008] Respectively obtaining a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence, and a plurality of third stable fluctuation intervals of the output parameter sequence;

[0009] According to the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval and each third stable fluctuation interval, a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals and a plurality of third target stable fluctuation intervals having the same time interval are obtained;

[0010] Performing cluster analysis on a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals, and a plurality of third target stable fluctuation intervals, respectively, to obtain a category of each first target stable fluctuation interval, a category of each second target stable fluctuation interval, and a category of each third target stable fluctuation interval;

[0011] Associating the category of the first target stable fluctuation interval, the category of the second target stable fluctuation interval, and the category of the third target stable fluctuation interval that belong to the same time interval to generate a plurality of associated categories of different time intervals;

[0012] Acquire, according to the plurality of associated categories, categories of optimal target stable fluctuation intervals corresponding to the plurality of second target stable fluctuation intervals respectively;

[0013] If there is no category of the third target stable fluctuation range in the multiple associated categories that meets the expected category, the production equipment parameters are adjusted according to the historical production equipment parameter database, the category of the optimal target stable fluctuation range and the expected category to obtain new production equipment parameters.

[0014] In one embodiment, the step of performing time correlation alignment on the input parameter, the production equipment parameter, and the output parameter comprises:

[0015] If the sampling frequencies are the same, a first interval time from when the input parameter is detected to when the production equipment receives the input parameter is obtained, and a second interval time from when the production equipment ends production to when the output parameter is detected is obtained, and the input parameter, the production equipment parameter, and the output parameter are time-correlated and aligned according to the sampling frequency, the first interval time, and the second interval time;

[0016] If the sampling frequencies are different, the data with the lower sampling frequency will be interpolated and expanded, and the input parameters, production equipment parameters and output parameters after the interpolation and expansion will be time-correlated and aligned.

[0017] In one embodiment, the step of respectively acquiring a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence, and a plurality of third stable fluctuation intervals of the output parameter sequence comprises:

[0018] Identifying transition points in the input parameter sequence, the production equipment parameter sequence, and the output parameter sequence;

[0019] Divide the input parameter sequence, the production equipment parameter sequence and the output parameter sequence based on the jump point to obtain a plurality of first fluctuation intervals, a plurality of second fluctuation intervals and a plurality of third fluctuation intervals respectively;

[0020] A variance threshold is preset, and according to the variance threshold, a plurality of first stable fluctuation intervals, a plurality of second stable fluctuation intervals, and a plurality of third stable fluctuation intervals having variances less than the variance threshold are screened out from the plurality of first fluctuation intervals, the plurality of second fluctuation intervals, and the plurality of third fluctuation intervals.

[0021] In one embodiment, the method of obtaining a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals, and a plurality of third target stable fluctuation intervals having the same time interval according to the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval, and each third stable fluctuation interval, respectively, comprises:

[0022] Respectively obtain the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval, and each third stable fluctuation interval;

[0023] The intersection of all time intervals is obtained to obtain a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals and a plurality of third target stable fluctuation intervals having the same time interval.

[0024] In one embodiment, cluster analysis is performed on the plurality of first target stable fluctuation intervals, the plurality of second target stable fluctuation intervals, and the plurality of third target stable fluctuation intervals, respectively, including:

[0025] Respectively obtain means and variances corresponding to a plurality of the first target stable fluctuation intervals, a plurality of the second target stable fluctuation intervals, and a plurality of the third target stable fluctuation intervals;

[0026] Based on the mean and the variance, a plurality of the first target stable fluctuation intervals, a plurality of the second target stable fluctuation intervals, and a plurality of the third target stable fluctuation intervals are clustered respectively.

[0027] In one embodiment, the acquiring, according to the plurality of associated categories, categories of the optimal target stable fluctuation intervals corresponding to the plurality of second target stable fluctuation intervals respectively comprises:

[0028] Counting the categories of the third target stable fluctuation range that meet the target associated categories of the expected categories;

[0029] The category of the second target stable fluctuation interval whose second target stable fluctuation interval meets the frequency threshold, the mean threshold and the second variance threshold is selected from the target association category, and the category of the second target stable fluctuation interval is the category of the optimal intersection stable fluctuation interval.

[0030] In one embodiment, the adjusting the production equipment parameters according to the historical production equipment parameter database and the category of the optimal target stable fluctuation range to obtain new production equipment parameters includes:

[0031] Select new production equipment parameters from the historical production equipment parameter database;

[0032] According to the new production equipment parameters, a plurality of new associated categories are obtained, and a plurality of second target stable fluctuation ranges are selected from the new multiple associated categories, wherein the categories of the optimal target stable fluctuation range and the categories of the third target stable fluctuation range meet the expected category, and a new associated category is selected;

[0033] A confidence threshold is preset, and the confidence between the new association category and any association category before adjusting the production equipment parameters is calculated. If the confidence is greater than the confidence threshold, the production equipment parameters are adjusted to the new production equipment parameters.

[0034] In a second aspect, an embodiment of the present application provides a device for adjusting parameters of a cell production device, the device for adjusting parameters of a cell production device comprising:

[0035] A collection module is used to collect input parameters, production equipment parameters and output parameters within a preset time range;

[0036] A time correlation module, used to perform time correlation alignment on the input parameters, the production equipment parameters and the output parameters to obtain an input parameter sequence, a production equipment parameter sequence and an output parameter sequence respectively;

[0037] A first acquisition module, used to respectively acquire a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence, and a plurality of third stable fluctuation intervals of the output parameter sequence;

[0038] A second acquisition module is used to acquire a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals, and a plurality of third target stable fluctuation intervals having the same time interval according to the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval, and each third stable fluctuation interval respectively;

[0039] A clustering module, used to perform cluster analysis on a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals, and a plurality of third target stable fluctuation intervals, respectively, to obtain a category of each first target stable fluctuation interval, a category of each second target stable fluctuation interval, and a category of each third target stable fluctuation interval;

[0040] A category association module, used to associate the category of the first target stable fluctuation interval, the category of the second target stable fluctuation interval, and the category of the third target stable fluctuation interval belonging to the same time interval, so as to generate a plurality of associated categories of different time intervals;

[0041] A third acquisition module, configured to acquire, according to the plurality of associated categories, categories of optimal target stable fluctuation intervals corresponding to the plurality of second target stable fluctuation intervals;

[0042] The adjustment module is used to determine if there is no category of the third target stable fluctuation range in the multiple associated categories that meets the expected category, and then adjust the production equipment parameters according to the historical production equipment parameter database, the category of the optimal target stable fluctuation range and the expected category to obtain new production equipment parameters.

[0043] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is run by the processor, the method for adjusting the parameters of the battery cell production equipment provided in the first aspect is executed.

[0044] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when running on a processor, executes the method for adjusting parameters of battery cell production equipment provided in the first aspect.

[0045] The battery cell production equipment parameter adjustment method provided by the present application collects input parameters, production equipment parameters and output parameters within a preset time range; performs time correlation alignment on the input parameters, the production equipment parameters and the output parameters to obtain an input parameter sequence, a production equipment parameter sequence and an output parameter sequence respectively; obtains a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence and a plurality of third stable fluctuation intervals of the output parameter sequence respectively; obtains a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals and a plurality of third target stable fluctuation intervals having the same time interval according to the time intervals corresponding to the first stable fluctuation intervals, the second stable fluctuation intervals and the third stable fluctuation intervals respectively; and performs time correlation alignment on the plurality of first target stable fluctuation intervals, the plurality of second target stable fluctuation intervals and the plurality of The first target stable fluctuation interval and multiple third target stable fluctuation intervals are clustered and analyzed respectively to obtain the categories of each first target stable fluctuation interval, the categories of each second target stable fluctuation interval and the categories of each third target stable fluctuation interval; the categories of the first target stable fluctuation interval, the categories of the second target stable fluctuation interval and the categories of the third target stable fluctuation interval belonging to the same time interval are associated to generate multiple associated categories of different time intervals; the categories of the optimal target stable fluctuation intervals corresponding to the multiple second target stable fluctuation intervals are obtained according to the multiple associated categories; if there is no category of the third target stable fluctuation interval in the multiple associated categories that meets the expected category, the production equipment parameters are adjusted according to the historical production equipment parameter database, the category of the optimal target stable fluctuation interval and the expected category to obtain new production equipment parameters. This application refers to the historical production equipment parameter database, and adjusts the battery cell production equipment parameters by meeting the expected category and the category of the optimal target stable fluctuation interval, so that it can quickly reach the production equipment parameters required for stable production of battery cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and should not be regarded as limiting the scope of protection of the present application. In each of the drawings, similar components are numbered similarly.

[0047] Figure 1 A schematic diagram of a process of adjusting parameters of a battery cell production device provided in an embodiment of the present application is shown;

[0048] Figure 2 Another schematic diagram of the process of adjusting parameters of a battery cell production device provided in an embodiment of the present application is shown;

[0049] Figure 3 Another schematic diagram of a process of adjusting parameters of a battery cell production device provided in an embodiment of the present application is shown;

[0050] Figure 4 A schematic diagram of a third fluctuation range of an output parameter provided in an embodiment of the present application is shown;

[0051] Figure 5 A schematic diagram of a second stable fluctuation range of a production equipment parameter provided in an embodiment of the present application is shown;

[0052] Figure 6 A schematic diagram showing a second stable fluctuation range of another production equipment parameter provided in an embodiment of the present application is shown;

[0053] Figure 7 Another schematic diagram of a process of adjusting parameters of a battery cell production device provided in an embodiment of the present application is shown;

[0054] Figure 8 A structural schematic diagram of a battery cell production equipment parameter adjustment device provided in an embodiment of the present application is shown.

[0055] Icons: 800 - battery cell production equipment parameter adjustment device, 801 - acquisition module, 802 - time association module, 803 - first acquisition module, 804 - second acquisition module, 805 - clustering module, 806 - category association module, 807 - third acquisition module, 808 - adjustment module. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0057] The components of the embodiments of the present application generally described and shown in the drawings herein may be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0058] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present application, are intended only to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing items or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations of the foregoing items.

[0059] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0060] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present application.

[0061] Example 1

[0062] An embodiment of the present application provides a method for adjusting parameters of battery cell production equipment.

[0063] See also Figure 1 The method for adjusting parameters of battery cell production equipment includes steps S101 to S107:

[0064] S101: Collect input parameters, production equipment parameters and output parameters within a preset time range.

[0065] In this embodiment, the process of cell production includes coating process, rolling process, slitting process and baking process, etc. If the relevant recorded data of the coating process is taken as the research object, the data to be collected include slurry data, including solid content, moisture content, etc. (input parameters), thickness data (output parameters) and regulated equipment parameter data, among which important equipment parameter data include intermittent coating die gap value and intermittent liquid supply pump speed value. If the relevant recorded data of the rolling process is taken as the research object, the data to be collected include coating thickness (input parameter) rolling thickness (output parameter), and important production equipment parameters include operating side hydraulic master cylinder pressure and transmission test hydraulic master cylinder pressure.

[0066] S102: performing time correlation alignment on the input parameters, the production equipment parameters and the output parameters to obtain an input parameter sequence, a production equipment parameter sequence and an output parameter sequence respectively.

[0067] In this embodiment, when processing input parameters, production equipment parameters and output parameters, due to the existence of delay time, such as the time granularity of slurry data (input parameters) is at the hour level, while the time granularity of equipment parameters and output parameters can reach the second level, it is necessary to interpolate the parameters with large time granularity to ensure the consistency of sampling frequency. There is also a delay time (interval time) in the transmission between parameters, so time correlation alignment is required to ensure that these parameters can correspond correctly.

[0068] See also Figure 2 In one embodiment, step S102 includes steps S1021-S1022:

[0069] S1021: If the sampling frequencies are the same, obtain the first interval time from when the input parameter is detected to when the production equipment receives the input parameter, obtain the second interval time from when the production equipment ends production to when the output parameter is detected, and perform time correlation alignment on the input parameter, the production equipment parameter and the output parameter according to the sampling frequency, the first interval time and the second interval time.

[0070] In this embodiment, it is assumed that the slurry parameter detection time t1 needs to pass Δt1 to reach the equipment, and the parameter value on the equipment at this time t2 is corresponding, and then it passes Δt2 to reach the thickness detection time t3. Therefore, it is necessary to form a set of associated data with the input parameters at time t1, the production equipment parameters at time t2, and the output parameters at time t3.

[0071] Exemplarily, if the first time interval is Δt1=2 seconds (from slurry parameter detection to production equipment reception), and the second time interval is Δt2=3 seconds (from production equipment to output parameter detection), the first set of associated data (t1=0 seconds, the collected input parameter is A1) and (t2=2 seconds, production equipment parameters are (B1, C1)) and (t3=5 seconds, output parameter is D1) form a set of associated data points. Since the acquisition frequency is once per second (i.e., 1Hz), the second set of associated data, (t1=1 second, the collected input parameter is A2) and (t2=3 seconds, production equipment parameters are (B2, C2)) and (t3=6 seconds, output parameter is D2) form another set of associated data points. By continuously sampling, we can obtain the input parameter sequence [A1, A2, …, An], the production equipment parameter sequence [B1, B2, …, Bn], [C1, C2, …, Cn] and the output parameter sequence [D1, D2, …, Dn].

[0072] S1022: If the sampling frequencies are different, the data with the lower sampling frequency are interpolated and expanded, and the input parameters, production equipment parameters and output parameters after the interpolation and expansion are time-correlated and aligned.

[0073] In this embodiment, if the sampling frequencies are different, the data with the lower sampling frequency is interpolated and expanded, and after the interpolation and expansion, there will be no data missing when associating data due to different sampling frequencies. Data association is performed according to the method of the same sampling frequency in step S1021.

[0074] S103: Acquire a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence, and a plurality of third stable fluctuation intervals of the output parameter sequence respectively.

[0075] In this embodiment, a change point detection algorithm is used to segment each data sequence. Optionally, the method in the ruptures package is used for segmentation, firstly the thickness data (output parameter) is segmented to obtain multiple third stable fluctuation intervals, then the production equipment parameter data is segmented to obtain multiple second stable fluctuation intervals, and finally the slurry data (input parameter) is segmented to obtain multiple first stable fluctuation intervals.

[0076] See also Figure 3 In one embodiment, the step S103 includes steps S1031-S1033:

[0077] S1031: Identify the transition points in the input parameter sequence, the production equipment parameter sequence, and the output parameter sequence.

[0078] In this embodiment, the ruptures package is used to perform change point detection on the jump points in the input parameter sequence, production equipment parameter sequence and output parameter sequence generated in step S102. It should be noted that the ruptures package is a Python library for detecting change points in time series data (i.e., points where data properties change significantly). It provides a variety of algorithms or techniques to analyze and segment non-stationary signals, thereby identifying change points in the data.

[0079] S1032: Divide the input parameter sequence, the production equipment parameter sequence, and the output parameter sequence based on the jump point to obtain a plurality of first fluctuation intervals, a plurality of second fluctuation intervals, and a plurality of third fluctuation intervals, respectively.

[0080] In this embodiment, it is assumed that there is an input parameter sequence consisting of n points (x1, x2, ..., x n ), where (x1,x2,…,x n )There are m transition points (x k1 ,x k2 ,…,x km ), after identifying all the jump points, the data segment between adjacent jump points is defined as an interval, and the original data sequence is divided into m+1 first fluctuation intervals ([x1,x k1 ],[x k2 ,x k3 ],…,[x km ,x n ]), each sub-interval represents a stage of change of the input parameter data. According to the same steps as above, multiple second fluctuation intervals and multiple third fluctuation intervals are obtained.

[0081] S1033: Preset a variance threshold, and select, according to the variance threshold, from the plurality of first fluctuation intervals, the plurality of second fluctuation intervals, and the plurality of third fluctuation intervals to obtain a plurality of first stable fluctuation intervals, a plurality of second stable fluctuation intervals, and a plurality of third stable fluctuation intervals whose variances are less than the variance threshold.

[0082] In this embodiment, the variances of the first fluctuation interval, the second fluctuation interval, and the third fluctuation interval are calculated respectively and compared with the preset variance threshold. The interval with a variance less than the variance threshold is considered to be a stable fluctuation interval. Because the variance reflects the degree of fluctuation of the data around its mean, the smaller the variance, the more stable the data. Figure 4 , the third stable fluctuation range of the obtained output parameter (thickness parameter), see Figure 5 , the second stable fluctuation range of the obtained production equipment parameters (intermittent coating die gap), see Figure 6, the second stable fluctuation range of the production equipment parameters (intermittent liquid supply pump speed) is obtained.

[0083] S104: Acquire multiple first target stable fluctuation intervals, multiple second target stable fluctuation intervals, and multiple third target stable fluctuation intervals having the same time interval according to the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval, and each third stable fluctuation interval.

[0084] In this embodiment, the stable fluctuation intervals of the common time periods of the first stable fluctuation intervals, the second stable fluctuation intervals and the third stable fluctuation intervals corresponding to the input parameters, the production equipment parameters and the output parameters are obtained by adding a process of obtaining the time intersection of the intervals on the basis of a single stable fluctuation interval.

[0085] In one embodiment, step S104 includes: respectively obtaining the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval and each third stable fluctuation interval; finding the intersection of all time intervals to obtain multiple first target stable fluctuation intervals, multiple second target stable fluctuation intervals and multiple third target stable fluctuation intervals with the same time interval.

[0086] In this embodiment, two time intervals corresponding to the input parameter and the output parameter are obtained, which are respectively a first stable fluctuation interval [x t ,x t+1 ,…,x t+n ] (the subscript x corresponds to the time when the data is generated), the time interval is (t, t+n) and a third stable fluctuation interval [y t+2 ,y t+3 ,…,y t+n ,y t+n+1 ], whose time interval is (t+2, t+n+1), then according to their common time intersection interval (t+2, t+n), the first target stable fluctuation interval corresponding to is [x t+2 ,…,x t+n ] and the third target stable fluctuation range [y t+2 ,…,y t+n ].

[0087] S105: Perform cluster analysis on multiple first target stable fluctuation intervals, multiple second target stable fluctuation intervals, and multiple third target stable fluctuation intervals respectively to obtain categories of each first target stable fluctuation interval, categories of each second target stable fluctuation interval, and categories of each third target stable fluctuation interval.

[0088] In this embodiment, cluster analysis is performed on multiple first target stable fluctuation intervals, multiple second target stable fluctuation intervals, and multiple third target stable fluctuation intervals, respectively, and the input parameters, production equipment parameters, and output parameters are divided into multiple categories. Cluster analysis is an unsupervised learning method used to divide samples in a data set into several groups or "clusters" so that the samples in the same cluster have a high similarity, while the samples in different clusters have a low similarity.

[0089] In one embodiment, step S105 includes: respectively obtaining the means and variances corresponding to the multiple first target stable fluctuation intervals, the multiple second target stable fluctuation intervals, and the multiple third target stable fluctuation intervals; based on the means and the variances, respectively clustering the multiple first target stable fluctuation intervals, the multiple second target stable fluctuation intervals, and the multiple third target stable fluctuation intervals.

[0090] In this embodiment, for the first target stable fluctuation interval, the second target stable fluctuation interval, and the third target stable fluctuation interval, the mean and variance of each target stable fluctuation interval under each type of target stable fluctuation interval are first calculated respectively, and the central trend and fluctuation range of the interval are preliminarily judged, which are important input features of cluster analysis. Through the k-means clustering algorithm, the number of clusters to be divided for each type of target stable fluctuation interval is determined in advance. For example, the thickness interval (the target stable fluctuation interval of the output parameter) is set to 7 categories, the intermittent coating die gap value interval and the intermittent liquid supply pump speed value interval (the target stable fluctuation interval of the production equipment parameter) are both set to 9 categories, and the slurry interval (the target stable fluctuation interval of the input parameter) is set to 3 categories. Using the calculated mean and variance as features, k-means clustering is performed for each type of target stable fluctuation interval. The algorithm iteratively updates the centroid of the cluster until the maximum number of iterations is reached, and then clustering is stopped.

[0091] S106: Associating the category of the first target stable fluctuation interval, the category of the second target stable fluctuation interval, and the category of the third target stable fluctuation interval belonging to the same time interval to generate a plurality of associated categories of different time intervals.

[0092] In this embodiment, it is assumed that there are N target stable fluctuation ranges of input parameter data, production equipment parameter data, and output parameter data, which are [X1, X2, …, X N ], [Y1,Y2,…,Y N ],in The i-th stable fluctuation interval of the j-th parameter of the production equipment parameter is represented. The N target stable fluctuation intervals of the input parameter, the production equipment parameter and the output parameter are divided into a, (b1, b2, ..., bm ), c categories, where a, b, c represent the number of categories of input parameters, the number of categories of production equipment parameters and the number of categories of output parameters respectively. Among them, there are multiple production equipment parameters, b1 means that there are b1 categories of production equipment parameter 1, and bm means that there are bm categories of production equipment parameter m.

[0093] Exemplarily, the category of the first target stable fluctuation interval, the category of the second target stable fluctuation interval, and the category of the third target stable fluctuation interval belonging to the same time interval are associated to obtain an associated interval Converted into associated categories [1, (3, 2, ..., 7), 1], where the numbers represent the corresponding target stability intervals. For example, the category number of X1 is 1, and the target stability interval of production equipment parameter 1 The category is 3.

[0094] S107: Obtaining categories of optimal target stable fluctuation intervals corresponding to a plurality of second target stable fluctuation intervals according to a plurality of associated categories.

[0095] In this embodiment, the category of the optimal target stable fluctuation range of the production equipment parameters (such as the intermittent coating die gap value and the intermittent liquid supply pump speed value) is obtained by associating categories and statistical features. This category can make the output parameters (such as coating thickness) quickly reach the expected category, that is, the production standard process.

[0096] In one embodiment, step S107 includes: counting the categories of the third target stable fluctuation interval that meet the target association categories of the expected categories; screening the categories of the second target stable fluctuation interval that meet the frequency threshold, the mean threshold and the second variance threshold from the target association categories, and the category of the second target stable fluctuation interval is the category of the optimal intersection stable fluctuation interval.

[0097] In this embodiment, the categories in the third target stable fluctuation range are counted to make them meet the target associated categories of the expected categories of the output parameters, and the expected categories refer to the process requirements that the thickness mean is within the set value range and the fluctuation is relatively small. It is necessary to count the frequency of occurrence of the categories of the second target stable fluctuation range of all associated categories that meet the conditions, the mean and variance of the data of all target stable fluctuation ranges under each category, set the frequency threshold, mean threshold and second variance threshold of each category according to the historical production data, screen the categories of the second target stable fluctuation range that meet the frequency requirements, mean and variance requirements, and use them as the categories of the optimal intersection stable fluctuation range.

[0098] S108: If there is no category of the third target stable fluctuation range in the multiple associated categories that meets the expected category, the production equipment parameters are adjusted according to the historical production equipment parameter database, the category of the optimal target stable fluctuation range and the expected category to obtain new production equipment parameters.

[0099] In this embodiment, the current slurry category is 1, the intermittent coating die gap value category is 2, the intermittent liquid supply pump speed value is 7, and the thickness category is 5 (poor, that is, the category of the third target stable fluctuation interval does not meet the expected category). By adjusting the production equipment parameters, assuming that the slurry target stable fluctuation interval category is 1, by adjusting the die gap value to interval 2 (the category of the optimal intersection stable fluctuation interval, mean 208, variance 16, meeting the preset conditions) and the liquid supply pump speed value to interval 1 (the category of the optimal intersection stable fluctuation interval, mean 156.27, variance 0.0467, meeting the preset conditions), a good output with a coating thickness interval of 1 (meets the expected category) can be obtained, and the newly adjusted equipment parameters meet the requirements.

[0100] See also Figure 7 In one embodiment, the step of adjusting the production equipment parameters according to the historical production equipment parameter database and the category of the optimal target stable fluctuation range to obtain new production equipment parameters includes steps S701-703:

[0101] S701: Select new production equipment parameters from the historical adjustment plan database.

[0102] In this embodiment, a production equipment parameter adjustment plan that has been successful or tried in the past should be selected from the historical database, and an effective production equipment parameter combination should be selected from the database according to the current production situation and the expected category that needs to be adjusted.

[0103] S702: Obtain multiple new associated categories based on the new production equipment parameters, and select a new associated category from the multiple new associated categories, wherein multiple second target stable fluctuation ranges meet the category of the optimal target stable fluctuation range, and the category of the third target stable fluctuation range meets the expected category.

[0104] In this embodiment, multiple new association categories will also be generated under the new device parameters, and the category that meets the second target stable fluctuation range at the same time in the new association categories will be screened out as the optimal target stable fluctuation range category, and the category of the third target stable fluctuation range will be a new association category that meets the expected category.

[0105] S703: Preset a confidence threshold, calculate the confidence between the new association category and any association category before adjusting the production equipment parameters, and if the confidence is greater than the confidence threshold, adjust the production equipment parameters to the new production equipment parameters.

[0106] In this embodiment, the association rule algorithm Apriori is used to calculate the confidence of the new association category and any association category before adjusting the production equipment parameters. If the confidence is greater than a preset confidence threshold, it is considered that the new production equipment parameters are associated with the previous association category and can be used for future production.

[0107] The battery cell production equipment parameter adjustment method provided in the present embodiment collects input parameters, production equipment parameters and output parameters within a preset time range; performs time correlation alignment on the input parameters, the production equipment parameters and the output parameters to obtain an input parameter sequence, a production equipment parameter sequence and an output parameter sequence respectively; obtains a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence and a plurality of third stable fluctuation intervals of the output parameter sequence respectively; obtains a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals and a plurality of third target stable fluctuation intervals having the same time interval according to the time intervals corresponding to the first stable fluctuation intervals, the second stable fluctuation intervals and the third stable fluctuation intervals respectively; and performs time correlation alignment on the plurality of first target stable fluctuation intervals, the plurality of second target stable fluctuation intervals and the plurality of third target stable fluctuation intervals. The first target stable fluctuation interval and multiple third target stable fluctuation intervals are clustered and analyzed respectively to obtain the categories of each first target stable fluctuation interval, the categories of each second target stable fluctuation interval and the categories of each third target stable fluctuation interval; the categories of the first target stable fluctuation interval, the categories of the second target stable fluctuation interval and the categories of the third target stable fluctuation interval belonging to the same time interval are associated to generate multiple associated categories of different time intervals; the categories of the optimal target stable fluctuation intervals corresponding to the multiple second target stable fluctuation intervals are obtained according to the multiple associated categories; if there is no category of the third target stable fluctuation interval in the multiple associated categories that meets the expected category, the production equipment parameters are adjusted according to the historical production equipment parameter database, the category of the optimal target stable fluctuation interval and the expected category to obtain new production equipment parameters. This application refers to the historical production equipment parameter database, and adjusts the battery cell production equipment parameters through the expected category and the category that meets the optimal target stable fluctuation interval, so that it can quickly reach the production equipment parameters required for stable production of battery cells.

[0108] Example 2

[0109] In addition, an embodiment of the present application provides a battery cell production equipment parameter adjustment device, which is applied to electronic equipment.

[0110] like Figure 8 As shown, the battery cell production equipment parameter adjustment device 800 includes:

[0111] The collection module 801 is used to collect input parameters, production equipment parameters and output parameters within a preset time range;

[0112] A time correlation module 802 is used to perform time correlation alignment on the input parameters, the production equipment parameters and the output parameters to obtain an input parameter sequence, a production equipment parameter sequence and an output parameter sequence respectively;

[0113] A first acquisition module 803, used to respectively acquire a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence, and a plurality of third stable fluctuation intervals of the output parameter sequence;

[0114] The second acquisition module 804 is used to acquire a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals, and a plurality of third target stable fluctuation intervals having the same time interval according to the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval, and each third stable fluctuation interval respectively;

[0115] The clustering module 805 is used to perform cluster analysis on the multiple first target stable fluctuation intervals, the multiple second target stable fluctuation intervals and the multiple third target stable fluctuation intervals, respectively, to obtain the category of each first target stable fluctuation interval, the category of each second target stable fluctuation interval and the category of each third target stable fluctuation interval;

[0116] A category association module 806 is used to associate the category of the first target stable fluctuation interval, the category of the second target stable fluctuation interval, and the category of the third target stable fluctuation interval belonging to the same time interval to generate a plurality of associated categories of different time intervals;

[0117] A third acquisition module 807 is used to acquire the categories of the optimal target stable fluctuation intervals corresponding to the plurality of second target stable fluctuation intervals according to the plurality of associated categories;

[0118] The adjustment module 808 is used to determine if there is no category of the third target stable fluctuation range in the multiple associated categories that meets the expected category, and then adjust the production equipment parameters according to the historical production equipment parameter database, the category of the optimal target stable fluctuation range and the expected category to obtain new production equipment parameters.

[0119] Optionally, the time association module 802 shown is also used to determine if the sampling frequencies are the same, then obtain the first interval time from the time the input parameter is detected to the time the production device receives the input parameter, obtain the second interval time from the time the production device ends production to the time the output parameter is detected, and perform time association alignment on the input parameter, the production equipment parameter and the output parameter according to the sampling frequency, the first interval time and the second interval time; if the sampling frequencies are different, then interpolate and expand the data with a lower sampling frequency, and perform time association alignment on the input parameters, production equipment parameters and output parameters after the interpolation and expansion.

[0120] Optionally, the first acquisition module 803 shown is also used to identify jump points in the input parameter sequence, the production equipment parameter sequence and the output parameter sequence; divide the input parameter sequence, the production equipment parameter sequence and the output parameter sequence based on the jump points to obtain multiple first fluctuation intervals, multiple second fluctuation intervals and multiple third fluctuation intervals respectively; preset a variance threshold, and select, according to the variance threshold, from the multiple first fluctuation intervals, the multiple second fluctuation intervals and the multiple third fluctuation intervals to obtain multiple first stable fluctuation intervals, multiple second stable fluctuation intervals and multiple third stable fluctuation intervals whose variances are less than the variance threshold.

[0121] Optionally, the second acquisition module 804 shown is also used to respectively obtain the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval and each third stable fluctuation interval; and to find the intersection of all time intervals to obtain multiple first target stable fluctuation intervals, multiple second target stable fluctuation intervals and multiple third target stable fluctuation intervals with the same time interval.

[0122] Optionally, the clustering module 805 shown is also used to obtain the means and variances corresponding to multiple first target stable fluctuation intervals, multiple second target stable fluctuation intervals and multiple third target stable fluctuation intervals, respectively; based on the means and the variances, cluster the multiple first target stable fluctuation intervals, multiple second target stable fluctuation intervals and multiple third target stable fluctuation intervals, respectively.

[0123] Optionally, the third acquisition module 807 shown is also used to count the categories of the third target stable fluctuation interval that meet the target association categories of the expected categories; and to select the categories of the second target stable fluctuation interval that meet the frequency threshold, the mean threshold and the second variance threshold from the target association categories, and the category of the second target stable fluctuation interval is the category of the optimal intersection stable fluctuation interval.

[0124] Optionally, the adjustment module 808 shown is also used to select new production equipment parameters from a historical production equipment parameter database; obtain multiple new association categories based on the new production equipment parameters, and screen multiple second target stable fluctuation range categories that meet the categories of the optimal target stable fluctuation range from the new multiple association categories, and a new association category whose category of the third target stable fluctuation range meets the expected category; pre-set a confidence threshold, calculate the confidence of the new association category and any association category before adjusting the production equipment parameters, and if the confidence is greater than the confidence threshold, adjust the production equipment parameters to the new production equipment parameters.

[0125] The battery cell production equipment parameter adjustment device 800 provided in this embodiment can implement the battery cell production equipment parameter adjustment method provided in Example 1, and will not be described again here to avoid repetition.

[0126] The battery cell production equipment parameter adjustment device provided in the present embodiment collects input parameters, production equipment parameters and output parameters within a preset time range; performs time correlation alignment on the input parameters, the production equipment parameters and the output parameters to obtain an input parameter sequence, a production equipment parameter sequence and an output parameter sequence respectively; obtains a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence and a plurality of third stable fluctuation intervals of the output parameter sequence respectively; obtains a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals and a plurality of third target stable fluctuation intervals having the same time interval according to the time intervals corresponding to the first stable fluctuation intervals, the second stable fluctuation intervals and the third stable fluctuation intervals respectively; and performs time correlation alignment on the plurality of first target stable fluctuation intervals, the plurality of second target stable fluctuation intervals and the plurality of third target stable fluctuation intervals. The first target stable fluctuation interval and multiple third target stable fluctuation intervals are clustered and analyzed respectively to obtain the categories of each first target stable fluctuation interval, the categories of each second target stable fluctuation interval and the categories of each third target stable fluctuation interval; the categories of the first target stable fluctuation interval, the categories of the second target stable fluctuation interval and the categories of the third target stable fluctuation interval belonging to the same time interval are associated to generate multiple associated categories of different time intervals; the categories of the optimal target stable fluctuation intervals corresponding to the multiple second target stable fluctuation intervals are obtained according to the multiple associated categories; if there is no category of the third target stable fluctuation interval in the multiple associated categories that meets the expected category, the production equipment parameters are adjusted according to the historical production equipment parameter database, the category of the optimal target stable fluctuation interval and the expected category to obtain new production equipment parameters. This application refers to the historical production equipment parameter database, and adjusts the battery cell production equipment parameters through the expected category and the category that meets the optimal target stable fluctuation interval, so that it can quickly reach the production equipment parameters required for stable production of battery cells.

[0127] Example 3

[0128] In addition, an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program runs on the processor, the method for adjusting the parameters of the battery cell production equipment provided in Example 1 is executed.

[0129] The electronic device provided in the embodiment of the present invention can execute the steps of the method for adjusting the parameters of the cell production equipment provided in the above method embodiment 1, which will not be described again here to avoid repetition.

[0130] Example 4

[0131] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for adjusting parameters of battery cell production equipment provided in Example 1 is implemented.

[0132] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0133] The computer-readable storage medium provided in this embodiment can implement the battery cell production equipment parameter adjustment method provided in Example 1, and will not be described again here to avoid repetition.

[0134] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or terminal including the element.

[0135] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0136] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for adjusting parameters of battery cell production equipment, characterized in that: The method comprises: Collect input parameters, production equipment parameters and output parameters within a preset time range; Performing time correlation alignment on the input parameters, the production equipment parameters and the output parameters to obtain an input parameter sequence, a production equipment parameter sequence and an output parameter sequence respectively; Respectively obtaining a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence, and a plurality of third stable fluctuation intervals of the output parameter sequence; According to the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval and each third stable fluctuation interval, a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals and a plurality of third target stable fluctuation intervals having the same time interval are obtained; Performing cluster analysis on a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals, and a plurality of third target stable fluctuation intervals, respectively, to obtain a category of each first target stable fluctuation interval, a category of each second target stable fluctuation interval, and a category of each third target stable fluctuation interval; Associating the category of the first target stable fluctuation interval, the category of the second target stable fluctuation interval, and the category of the third target stable fluctuation interval that belong to the same time interval to generate a plurality of associated categories of different time intervals; Acquire, according to the plurality of associated categories, categories of optimal target stable fluctuation intervals corresponding to the plurality of second target stable fluctuation intervals respectively; If there is no category of the third target stable fluctuation range in the multiple associated categories that meets the expected category, the production equipment parameters are adjusted according to the historical production equipment parameter database, the category of the optimal target stable fluctuation range and the expected category to obtain new production equipment parameters.

2. The method according to claim 1, characterized in that The step of performing time correlation alignment on the input parameter, the production equipment parameter and the output parameter comprises: If the sampling frequencies are the same, a first interval time from when the input parameter is detected to when the production equipment receives the input parameter is obtained, and a second interval time from when the production equipment ends production to when the output parameter is detected is obtained, and the input parameter, the production equipment parameter, and the output parameter are time-correlated and aligned according to the sampling frequency, the first interval time, and the second interval time; If the sampling frequencies are different, the data with the lower sampling frequency will be interpolated and expanded, and the input parameters, production equipment parameters and output parameters after the interpolation and expansion will be time-correlated and aligned.

3. The method according to claim 1, characterized in that The step of respectively acquiring a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence, and a plurality of third stable fluctuation intervals of the output parameter sequence comprises: Identifying transition points in the input parameter sequence, the production equipment parameter sequence, and the output parameter sequence; Divide the input parameter sequence, the production equipment parameter sequence and the output parameter sequence based on the jump point to obtain a plurality of first fluctuation intervals, a plurality of second fluctuation intervals and a plurality of third fluctuation intervals respectively; A variance threshold is preset, and according to the variance threshold, a plurality of first stable fluctuation intervals, a plurality of second stable fluctuation intervals, and a plurality of third stable fluctuation intervals having variances less than the variance threshold are screened out from the plurality of first fluctuation intervals, the plurality of second fluctuation intervals, and the plurality of third fluctuation intervals.

4. The method according to claim 1, characterized in that: The method of obtaining a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals, and a plurality of third target stable fluctuation intervals having the same time interval according to the time intervals respectively corresponding to the first stable fluctuation intervals, the second stable fluctuation intervals, and the third stable fluctuation intervals comprises: Respectively obtain the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval, and each third stable fluctuation interval; The intersection of all time intervals is obtained to obtain a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals and a plurality of third target stable fluctuation intervals having the same time interval.

5. The method according to claim 1, characterized in that The cluster analysis is performed on the plurality of first target stable fluctuation intervals, the plurality of second target stable fluctuation intervals, and the plurality of third target stable fluctuation intervals respectively, including: Respectively obtain means and variances corresponding to a plurality of the first target stable fluctuation intervals, a plurality of the second target stable fluctuation intervals, and a plurality of the third target stable fluctuation intervals; Based on the mean and the variance, a plurality of the first target stable fluctuation intervals, a plurality of the second target stable fluctuation intervals, and a plurality of the third target stable fluctuation intervals are clustered respectively.

6. The method according to claim 1, characterized in that The step of acquiring the categories of the optimal target stable fluctuation intervals corresponding to the plurality of second target stable fluctuation intervals respectively according to the plurality of associated categories comprises: Counting the categories of the third target stable fluctuation range that meet the target associated categories of the expected categories; The category of the second target stable fluctuation interval whose second target stable fluctuation interval meets the frequency threshold, the mean threshold and the second variance threshold is selected from the target association category, and the category of the second target stable fluctuation interval is the category of the optimal intersection stable fluctuation interval.

7. The method according to claim 1, characterized in that The adjusting the production equipment parameters according to the historical production equipment parameter database and the category of the optimal target stable fluctuation range to obtain new production equipment parameters includes: Select new production equipment parameters from the historical production equipment parameter database; According to the new production equipment parameters, a plurality of new associated categories are obtained, and a plurality of second target stable fluctuation ranges are selected from the new multiple associated categories, wherein the categories of the optimal target stable fluctuation range and the categories of the third target stable fluctuation range meet the expected category, and a new associated category is selected; A confidence threshold is preset, and the confidence between the new association category and any association category before adjusting the production equipment parameters is calculated. If the confidence is greater than the confidence threshold, the production equipment parameters are adjusted to the new production equipment parameters.

8. A battery cell production equipment parameter adjustment device, characterized in that: The device comprises: A collection module is used to collect input parameters, production equipment parameters and output parameters within a preset time range; A time correlation module, used to perform time correlation alignment on the input parameters, the production equipment parameters and the output parameters to obtain an input parameter sequence, a production equipment parameter sequence and an output parameter sequence respectively; A first acquisition module, used to respectively acquire a plurality of first stable fluctuation intervals of the input parameter sequence, a plurality of second stable fluctuation intervals of the production equipment parameter sequence, and a plurality of third stable fluctuation intervals of the output parameter sequence; A second acquisition module is used to acquire a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals, and a plurality of third target stable fluctuation intervals having the same time interval according to the time intervals corresponding to each first stable fluctuation interval, each second stable fluctuation interval, and each third stable fluctuation interval respectively; A clustering module, used to perform cluster analysis on a plurality of first target stable fluctuation intervals, a plurality of second target stable fluctuation intervals, and a plurality of third target stable fluctuation intervals, respectively, to obtain a category of each first target stable fluctuation interval, a category of each second target stable fluctuation interval, and a category of each third target stable fluctuation interval; A category association module, used to associate the category of the first target stable fluctuation interval, the category of the second target stable fluctuation interval, and the category of the third target stable fluctuation interval belonging to the same time interval, so as to generate a plurality of associated categories of different time intervals; A third acquisition module, configured to acquire, according to the plurality of associated categories, categories of optimal target stable fluctuation intervals corresponding to the plurality of second target stable fluctuation intervals; The adjustment module is used to determine if there is no category of the third target stable fluctuation range in the multiple associated categories that meets the expected category, and then adjust the production equipment parameters according to the historical production equipment parameter database, the category of the optimal target stable fluctuation range and the expected category to obtain new production equipment parameters.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program, the method for adjusting parameters of a battery cell production equipment according to any one of claims 1 to 7 is executed.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when running on a processor, executes the method for adjusting parameters of battery cell production equipment according to any one of claims 1 to 7.

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