Intelligent detection method and system for battery manufacturing and medium

Abstract

The invention provides an intelligent detection method and system for battery manufacturing and a medium, and belongs to the technical field of artificial intelligence and big data. The method comprises the following steps: acquiring battery raw material attribute information and manufacturing equipment basic information, constructing a battery manufacturing digital twin body, carrying out simulation manufacturing to obtain battery manufacturing tracking information, extracting positive and negative pole piece proportioning pulping information, coating piece making information and winding lamination information, the method comprises the following steps: respectively processing raw material quality data, coating effect evaluation data and preparation forming quality data to obtain raw material quality data, coating effect evaluation data and preparation forming quality data, carrying out evaluation processing to obtain a battery manufacturing reliability index, and correcting according to inter-batch manufacturing information and intra-batch manufacturing information to obtain a battery manufacturing reliability correction index. By constructing the battery manufacturing digital twinborn body, the manufacturing process can be predicted and optimized, battery raw materials and the manufacturing process are monitored and analyzed item by item, and all-around intelligent evaluation and visual representation of the whole battery manufacturing process are achieved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence and big data technology, and more specifically, to a battery manufacturing intelligent detection method, system and medium. Background Art

[0002] With the rapid development of electric vehicles, energy storage systems and other fields, the requirements for battery performance and quality are constantly increasing. The traditional battery manufacturing industry faces huge market opportunities and challenges, and needs to continuously improve product quality and technical level to meet market demand. However, unsafe incidents such as battery fires and explosions are increasing, and current battery manufacturing technology still has many shortcomings, especially in terms of battery raw materials and the processing technology of pole pieces during the manufacturing process. There is a lack of a technical method for intelligent battery manufacturing that can monitor the manufacturing process and improve battery safety.

[0003] In view of the above problems, effective technical solutions are currently awaited. Summary of the invention

[0004] The purpose of this application is to provide a battery manufacturing intelligent detection method, system and medium, which can obtain battery raw material attribute information and basic manufacturing equipment information and construct a battery manufacturing digital twin, simulate battery manufacturing through a preset battery manufacturing process, obtain battery manufacturing tracking information, further extract positive and negative electrode material slurry information, coating film information and winding lamination information, and obtain raw material quality data, coating effect evaluation data and preparation molding quality data after processing respectively, and then process through a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then perform correction processing based on batch manufacturing information and batch manufacturing information to obtain a battery manufacturing reliability correction index. By constructing a battery manufacturing digital twin, this application can predict and optimize the manufacturing process, monitor and analyze battery raw materials and manufacturing processes item by item, and also consider the impact of different batches. The battery manufacturing reliability index is corrected according to the batch and batch reliability indexes, realizing a comprehensive intelligent evaluation and intuitive reflection of the entire battery manufacturing process.

[0005] The present application provides a battery manufacturing intelligent detection method, comprising the following steps: Obtain the battery raw material attribute information and basic manufacturing equipment information for a preset time period and build a battery manufacturing digital twin. Perform battery manufacturing simulation through the preset battery manufacturing process to obtain battery manufacturing tracking information. Extracting the positive and negative electrode sheet material slurrying information, coating sheeting information and winding and lamination information according to the battery manufacturing tracking information; According to the positive and negative electrode ingredient parameter information, raw material quality data is obtained, according to the coating and film making information, coating effect evaluation data is obtained, and according to the winding and lamination information, preparation and molding quality data is obtained; The raw material quality data, coating effect evaluation data and preparation molding quality data are processed through a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then correction processing is performed based on inter-batch manufacturing information and intra-batch manufacturing information to obtain a battery manufacturing reliability correction index.

[0006] Among them, in a battery manufacturing intelligent detection method described in the present application, the battery raw material attribute information and manufacturing equipment basic information of a preset time period are obtained and a battery manufacturing digital twin is constructed, specifically: Obtaining battery raw material property information and basic manufacturing equipment information for a preset time period; Extracting raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data according to the battery raw material attribute information; Extracting equipment type data, equipment basic parameter data and operation status performance data according to the basic information of the manufacturing equipment; A battery manufacturing digital twin is constructed based on the raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data combined with the equipment type data, equipment basic parameter data and operating status performance data.

[0007] Among them, in a battery manufacturing intelligent detection method described in the present application, the raw material quality data is obtained according to the positive and negative electrode ingredient parameter information, specifically: Extracting glue liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data according to the positive and negative electrode ingredient parameter information; The raw material quality data is obtained by processing the glue-liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data through a preset batching evaluation model.

[0008] Among them, in a battery manufacturing intelligent detection method described in the present application, the coating effect evaluation data is obtained according to the coating film information, specifically: Extracting coating thickness consistency data, weight distribution uniformity data and coating adhesion data as well as tableting compaction density data, rebound rate data and elongation rate data according to the coating tableting information; The coating thickness consistency data, weight distribution uniformity data and coating adhesion data as well as the tableting compaction density data, rebound rate data and elongation rate data are processed through a preset coating evaluation model to obtain coating effect evaluation data.

[0009] Among them, in a battery manufacturing intelligent detection method described in the present application, the preparation molding quality data is obtained according to the winding stack information, specifically: Extracting winding tension data and winding speed stability data as well as lamination pressure data and pole piece alignment data according to the winding lamination information; The winding tension data, the winding speed stability data, the lamination pressure data, and the pole piece alignment data are processed through a preset preparation evaluation model to obtain preparation molding quality data.

[0010] Among them, in a battery manufacturing intelligent detection method described in the present application, the raw material quality data, coating effect evaluation data and preparation molding quality data are processed by a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then correction processing is performed according to the batch manufacturing information and the batch manufacturing information to obtain a battery manufacturing reliability correction index, which is specifically: The raw material quality data, coating effect evaluation data and preparation molding quality data are processed by a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index; According to the inter-batch manufacturing information and the intra-batch manufacturing information, the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index are extracted respectively; The battery manufacturing reliability index is corrected according to the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index to obtain a battery manufacturing reliability correction index.

[0011] In a second aspect, the present application provides a battery manufacturing intelligent detection system, the system comprising: a memory and a processor, the memory comprising a program of a battery manufacturing intelligent detection method, the program of the battery manufacturing intelligent detection method being executed by the processor to implement the following steps: Obtain the battery raw material attribute information and basic manufacturing equipment information for a preset time period and build a battery manufacturing digital twin. Perform battery manufacturing simulation through the preset battery manufacturing process to obtain battery manufacturing tracking information. Extracting the positive and negative electrode sheet material slurrying information, coating sheeting information and winding and lamination information according to the battery manufacturing tracking information; According to the positive and negative electrode ingredient parameter information, raw material quality data is obtained, according to the coating and film making information, coating effect evaluation data is obtained, and according to the winding and lamination information, preparation and molding quality data is obtained; The raw material quality data, coating effect evaluation data and preparation molding quality data are processed through a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then correction processing is performed based on inter-batch manufacturing information and intra-batch manufacturing information to obtain a battery manufacturing reliability correction index.

[0012] Among them, in a battery manufacturing intelligent detection system described in the present application, the battery raw material attribute information and manufacturing equipment basic information of a preset time period are obtained and a battery manufacturing digital twin is constructed, specifically: Obtaining battery raw material property information and basic manufacturing equipment information for a preset time period; Extracting raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data according to the battery raw material attribute information; Extracting equipment type data, equipment basic parameter data and operation status performance data according to the basic information of the manufacturing equipment; A battery manufacturing digital twin is constructed based on the raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data combined with the equipment type data, equipment basic parameter data and operating status performance data.

[0013] Among them, in a battery manufacturing intelligent detection system described in the present application, the raw material quality data is obtained according to the positive and negative electrode ingredient parameter information, specifically: Extracting glue liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data according to the positive and negative electrode ingredient parameter information; The raw material quality data is obtained by processing the glue-liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data through a preset batching evaluation model.

[0014] In a third aspect, the present application also provides a computer-readable storage medium, which includes a battery manufacturing intelligent detection method program. When the battery manufacturing intelligent detection method program is executed by a processor, it implements the steps of a battery manufacturing intelligent detection method as described in any one of the above items.

[0015] As can be seen from the above, the embodiment of the present application provides an intelligent detection method, system and medium for battery manufacturing. By acquiring the battery raw material attribute information and basic information of manufacturing equipment in a preset time period and constructing a digital twin of battery manufacturing, the battery manufacturing simulation is performed through a preset battery manufacturing process to obtain battery manufacturing tracking information, and the positive and negative electrode material slurry information, coating film information and winding lamination information are extracted respectively. According to the positive and negative electrode material parameter information, the raw material quality data is obtained; according to the coating film information, the coating effect evaluation data is obtained; according to the winding lamination information, the preparation molding quality data is obtained. Then, the battery manufacturing reliability index is obtained by processing through a preset manufacturing effect evaluation model, and the battery manufacturing reliability index is corrected according to the batch manufacturing information and the batch manufacturing information to obtain the battery manufacturing reliability correction index. By constructing a digital twin of battery manufacturing, the present application can simulate the battery manufacturing process in a virtual environment, predict and optimize the manufacturing process without actual production, thereby improving production efficiency, while reducing human errors and improving the accuracy of the manufacturing process. This application can comprehensively consider multiple factors such as raw material quality, coating effect and preparation and molding quality, and objectively evaluate the reliability of battery manufacturing. Through the correction and processing of inter-batch manufacturing information and intra-batch manufacturing information, the accuracy and reliability of the evaluation results can be further improved, the cost and risk can be reduced, and strong support can be provided for the sustainable development of the battery manufacturing industry.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flowchart of a battery manufacturing intelligent detection method provided in an embodiment of the present application; Figure 2 A flowchart of building a battery manufacturing digital twin for a battery manufacturing intelligent detection method provided in an embodiment of the present application; Figure 3 A flowchart of obtaining raw material quality data of a battery manufacturing intelligent detection method provided in an embodiment of the present application; Figure 4A flowchart of obtaining coating effect evaluation data of a battery manufacturing intelligent detection method provided in an embodiment of the present application; Figure 5 A flowchart of obtaining preparation molding quality data of a battery manufacturing intelligent detection method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different 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.

[0020] It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0021] Please refer to Figure 1 , Figure 1 1 is a flow chart of a battery manufacturing intelligent detection method in some embodiments of the present application. The battery manufacturing intelligent detection method is used in terminal equipment, such as computers, mobile phone terminals, etc. The battery manufacturing intelligent detection method includes the following steps: S101, obtaining battery raw material attribute information and basic manufacturing equipment information for a preset time period and constructing a battery manufacturing digital twin, performing battery manufacturing simulation through a preset battery manufacturing process, and obtaining battery manufacturing tracking information; S102, extracting the positive and negative electrode material pulping information, coating and lamination information, and winding and lamination information according to the battery manufacturing tracking information; S103, obtaining raw material quality data according to the positive and negative electrode ingredient parameter information, obtaining coating effect evaluation data according to the coating and film making information, and obtaining preparation and molding quality data according to the winding and lamination information; S104, processing the raw material quality data, coating effect evaluation data and preparation molding quality data through a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then performing correction processing based on inter-batch manufacturing information and intra-batch manufacturing information to obtain a battery manufacturing reliability correction index.

[0022] Among them, the present application obtains the battery raw material attribute information and basic information of manufacturing equipment in a preset time period and constructs a digital twin of battery manufacturing, performs battery manufacturing simulation through a preset battery manufacturing process, obtains battery manufacturing tracking information, and extracts positive and negative electrode material slurry information, coating film information, and winding lamination information respectively. According to the positive and negative electrode material parameter information, the raw material quality data is obtained; according to the coating film information, the coating effect evaluation data is obtained; according to the winding lamination information, the preparation molding quality data is obtained. Then, the battery manufacturing reliability index is obtained by processing through a preset manufacturing effect evaluation model, and the battery manufacturing reliability index is corrected according to the batch manufacturing information and the batch manufacturing information to obtain the battery manufacturing reliability correction index. By constructing a digital twin of battery manufacturing, the present application can simulate the battery manufacturing process in a virtual environment, predict and optimize the manufacturing process without actual production, thereby improving production efficiency, while reducing human errors and improving the accuracy of the manufacturing process. This application can comprehensively consider multiple factors such as raw material quality, coating effect and preparation and molding quality, and objectively evaluate the reliability of battery manufacturing. Through the correction and processing of inter-batch manufacturing information and intra-batch manufacturing information, the accuracy and reliability of the evaluation results can be further improved, the cost and risk can be reduced, and strong support can be provided for the sustainable development of the battery manufacturing industry.

[0023] Please refer to Figure 2 , Figure 2 It is a flowchart of constructing a battery manufacturing digital twin of a battery manufacturing intelligent detection method in some embodiments of the present application. According to an embodiment of the present invention, the battery raw material attribute information and basic information of manufacturing equipment in a preset time period are obtained and the battery manufacturing digital twin is constructed, specifically: S201, obtaining battery raw material attribute information and basic manufacturing equipment information for a preset time period; S202, extracting raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data according to the battery raw material attribute information; S203, extracting equipment type data, equipment basic parameter data and operation status performance data according to the basic information of the manufacturing equipment; S204. Construct a battery manufacturing digital twin based on the raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data in combination with the equipment type data, equipment basic parameter data and operating status performance data.

[0024] Among them, in order to build a digital twin of battery manufacturing, the battery raw material attribute information and basic information of manufacturing equipment in a preset time period are obtained. According to the battery raw material attribute information, the raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data are extracted; according to the basic information of manufacturing equipment, the equipment type data, basic equipment parameter data and operation status performance data are extracted. The raw material type data records the main types of raw materials used, such as lithium, cobalt, nickel, etc.; the chemical composition data represents the chemical composition ratio or specific composition of each raw material; the physical property characteristic data refers to the physical properties of the raw materials such as density, melting point, hardness, etc.; the electrochemical performance data refers to the reaction activity and ion conductivity of the raw materials in the battery; the equipment type data refers to the type of equipment, such as mixer, coater, winder, etc.; the basic equipment parameter data refers to the specifications, models, design capacity, etc. of the equipment; the operation status performance data refers to the real-time operation data of the equipment, such as temperature, pressure, speed, running time, etc. By building a digital twin of battery manufacturing with the above data, the battery can be simulated and manufactured and the relevant parameters can be monitored and obtained in real time.

[0025] Please refer to Figure 3 , Figure 3 This is a flow chart of obtaining raw material quality data of a battery manufacturing intelligent detection method in some embodiments of the present application. According to an embodiment of the present invention, the raw material quality data is obtained according to the positive and negative electrode ingredient parameter information, specifically: S301, extracting glue liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data according to the positive and negative electrode ingredient parameter information; S302, processing the glue-liquid ratio configuration data, stirring speed data, slurry viscosity data and deionized water conductivity data through a preset batching evaluation model to obtain raw material quality data.

[0026] Among them, in order to obtain raw material quality data, the glue ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data are extracted according to the positive and negative electrode batching parameter information. The glue ratio configuration data refers to the ratio of glue to other ingredients (such as active substances, conductive agents, etc.) in the positive and negative electrode batching. This ratio has a significant impact on the performance of the battery because it determines the viscosity and coating performance of the slurry; the stirring rate data refers to the speed at which the agitator rotates during the batching process, which affects the uniformity and particle size of the slurry, and thus affects the electrochemical performance of the battery; the slurry viscosity data is a measure of the slurry flow resistance, which directly affects the coating quality and battery consistency; the deionized water conductivity data reflects the number of ions dissolved in the water. The raw material quality data is then processed through the preset batching evaluation model; The calculation formula of the ingredient evaluation model is: ; in, For raw material quality data, They are the glue-liquid ratio configuration data, stirring speed data, slurry viscosity data and deionized water conductivity data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset battery manufacturing database).

[0027] Please refer to Figure 4 , Figure 4 The flowchart of obtaining coating effect evaluation data of a battery manufacturing intelligent detection method in some embodiments of the present application. According to the embodiment of the present invention, the coating effect evaluation data is obtained according to the coating film production information, specifically: S401, extracting coating thickness consistency data, weight distribution uniformity data and coating adhesion data as well as tableting compaction density data, rebound rate data and elongation rate data according to the coating filming information; S402, processing the coating thickness consistency data, weight distribution uniformity data and coating adhesion data as well as the tableting compaction density data, rebound rate data and elongation rate data through a preset coating evaluation model to obtain coating effect evaluation data.

[0028] Among them, in order to obtain coating effect evaluation data, coating thickness consistency data, weight distribution uniformity data and coating adhesion data, as well as tablet compaction density data, rebound rate data and elongation data are extracted according to the coating film information. Coating thickness consistency data refers to the uniformity of coating thickness during the coating process. The consistency of thickness has an important influence on the performance of the battery because it determines the capacity and cycle life of the battery; weight distribution uniformity data refers to whether the weight distribution of the coating on the substrate is uniform. Uneven weight distribution may lead to inconsistent battery performance; coating adhesion data The adhesion between the coating and the substrate is an important indicator for evaluating the quality of the coating. Insufficient adhesion may cause the coating to fall off during subsequent processing or use; tablet compaction density data is the density of the material obtained by compaction, and the compaction density has a direct impact on the capacity and energy density of the battery; rebound rate data refers to the material's ability to recover after being subjected to external force. Too high a rebound rate may mean that the material is insufficiently tough and easily deformed during processing or use; elongation data reflects the flexibility and plasticity of the material. Then, the coating effect evaluation data is obtained through processing using the preset coating evaluation model; The calculation formula of the coating evaluation model is: ; in, To evaluate the coating effect, They are coating thickness consistency data, weight distribution uniformity data and coating adhesion data, as well as tablet compaction density data, rebound rate data and elongation data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset battery manufacturing database).

[0029] Please refer to Figure 5 , Figure 5 The flowchart of obtaining the preparation molding quality data of a battery manufacturing intelligent detection method in some embodiments of the present application. According to an embodiment of the present invention, the preparation molding quality data is obtained according to the winding stack information, specifically: S501, extracting winding tension data, winding speed stability data, lamination pressure data and pole piece alignment data according to the winding lamination information; S502, processing the winding tension data and the winding speed stability data as well as the lamination pressure data and the pole piece alignment data through a preset preparation evaluation model to obtain preparation molding quality data.

[0030] Among them, in order to obtain the preparation and molding quality data, the winding tension data and winding speed stability data as well as the stacking pressure data and the pole piece alignment data are extracted according to the winding stacking information. The winding tension data refers to the tension applied between the pole piece and the diaphragm. The magnitude and stability of the tension have an important influence on the winding quality and final performance of the battery; the winding speed stability data determines the uniformity and efficiency of the winding; the stacking pressure data refers to the pressure applied to the pole piece and the diaphragm. The magnitude and uniformity of the pressure have a direct impact on the quality of the stacking and the final performance of the battery; the pole piece alignment data refers to the degree of alignment between the pole pieces. The alignment determines the uniformity and stability of the internal structure of the battery. Then, the preparation and molding quality data is obtained through processing through the preset preparation evaluation model; The calculation formula of the preparation evaluation model is: ; in, To prepare molding quality data, They are winding tension data, winding speed stability data, lamination pressure data, and pole piece alignment data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset battery manufacturing database).

[0031] According to an embodiment of the present invention, the raw material quality data, coating effect evaluation data and preparation molding quality data are processed by a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then correction processing is performed according to inter-batch manufacturing information and intra-batch manufacturing information to obtain a battery manufacturing reliability correction index, specifically: The raw material quality data, coating effect evaluation data and preparation molding quality data are processed by a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index; According to the inter-batch manufacturing information and the intra-batch manufacturing information, the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index are extracted respectively; The battery manufacturing reliability index is corrected according to the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index to obtain a battery manufacturing reliability correction index.

[0032] Among them, in order to obtain the battery manufacturing reliability correction index, the raw material quality data, coating effect evaluation data and preparation molding quality data are processed through a preset manufacturing effect evaluation model to obtain the battery manufacturing reliability index; the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index are extracted according to the inter-batch manufacturing information and the intra-batch manufacturing information respectively; the battery manufacturing reliability index is corrected according to the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index to obtain the battery manufacturing reliability correction index; The calculation formula of the manufacturing effect evaluation model and the battery manufacturing reliability correction index is: ; ; in, To create a reliability index for batteries, is the average reliability index of battery manufacturing between batches, is the average reliability index of battery manufacturing within a batch, To create a reliability calibration index for batteries, They are raw material quality data, coating effect evaluation data and preparation molding quality data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset battery manufacturing database).

[0033] According to an embodiment of the present invention, it also includes: Comparing the battery manufacturing reliability correction index with a preset reliability index threshold to obtain a reliability deviation rate; comparing the reliability deviation rate with a preset reliability deviation rate threshold; If the reliability deviation rate is greater than or equal to the reliability deviation rate threshold, a battery manufacturing reliability failure message is sent; If the reliability deviation rate is less than the reliability deviation rate threshold, the battery manufacturing reliability qualification information is sent.

[0034] Among them, in order to improve the reliability of the battery manufacturing process, the battery manufacturing reliability correction index is compared with the preset reliability index threshold to obtain the reliability deviation rate, and then compared with the preset reliability deviation rate threshold. If the reliability deviation rate is greater than or equal to the reliability deviation rate threshold, the battery manufacturing reliability unqualified information is sent, indicating that further inspection or adjustment of the manufacturing process is required; if the reliability deviation rate is less than the reliability deviation rate threshold, the battery manufacturing reliability qualified information is sent, indicating that the current manufacturing process and raw material selection are reliable.

[0035] According to an embodiment of the present invention, it also includes: Extracting product defect rate data based on the battery manufacturing tracking information; Comparing the product defect rate data with a preset first product defect rate threshold and a second product defect rate threshold respectively; The first product defect rate threshold is less than the second product defect rate threshold; If the product defect rate data is less than or equal to the first product defect rate threshold, product quality information is sent; If the product defect rate data is greater than the first product defect rate threshold and less than or equal to the second product defect rate threshold, product optimization information is sent; If the product defect rate data is greater than the second product defect rate threshold, product unqualified information is sent.

[0036] Among them, in order to reduce the defective rate of batteries, product defective rate data is extracted according to the battery manufacturing tracking information, and compared with the preset first product defective rate threshold and the second product defective rate threshold, respectively, and the first product defective rate threshold is less than the second product defective rate threshold. If the product defective rate data is less than or equal to the first product defective rate threshold, product excellent information is sent, indicating that the product quality is excellent and meets or exceeds the expected standards; if the product defective rate data is greater than the first product defective rate threshold and less than or equal to the second product defective rate threshold, product optimization information is sent, indicating that the product quality is acceptable but there is room for improvement, and it is recommended to take optimization measures to improve the product quality; if the product defective rate data is greater than the second product defective rate threshold, product unqualified information is sent, indicating that the product quality does not meet the standards and immediate measures need to be taken for rectification to avoid further losses or impacts.

[0037] The present invention also discloses a battery manufacturing intelligent detection system, including a memory and a processor, wherein the memory includes a battery manufacturing intelligent detection method program, and when the battery manufacturing intelligent detection method program is executed by the processor, the following steps are implemented: Obtain the battery raw material attribute information and basic manufacturing equipment information for a preset time period and build a battery manufacturing digital twin. Perform battery manufacturing simulation through the preset battery manufacturing process to obtain battery manufacturing tracking information. Extracting the positive and negative electrode sheet material slurrying information, coating sheeting information and winding and lamination information according to the battery manufacturing tracking information; According to the positive and negative electrode ingredient parameter information, raw material quality data is obtained, according to the coating and film making information, coating effect evaluation data is obtained, and according to the winding and lamination information, preparation and molding quality data is obtained; The raw material quality data, coating effect evaluation data and preparation molding quality data are processed through a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then correction processing is performed based on inter-batch manufacturing information and intra-batch manufacturing information to obtain a battery manufacturing reliability correction index.

[0038] Among them, the present application obtains the battery raw material attribute information and basic information of manufacturing equipment in a preset time period and constructs a digital twin of battery manufacturing, performs battery manufacturing simulation through a preset battery manufacturing process, obtains battery manufacturing tracking information, and extracts positive and negative electrode material slurry information, coating film information, and winding lamination information respectively. According to the positive and negative electrode material parameter information, the raw material quality data is obtained; according to the coating film information, the coating effect evaluation data is obtained; according to the winding lamination information, the preparation molding quality data is obtained. Then, the battery manufacturing reliability index is obtained by processing through a preset manufacturing effect evaluation model, and the battery manufacturing reliability index is corrected according to the batch manufacturing information and the batch manufacturing information to obtain the battery manufacturing reliability correction index. By constructing a digital twin of battery manufacturing, the present application can simulate the battery manufacturing process in a virtual environment, predict and optimize the manufacturing process without actual production, thereby improving production efficiency, while reducing human errors and improving the accuracy of the manufacturing process. This application can comprehensively consider multiple factors such as raw material quality, coating effect and preparation and molding quality, and objectively evaluate the reliability of battery manufacturing. Through the correction and processing of inter-batch manufacturing information and intra-batch manufacturing information, the accuracy and reliability of the evaluation results can be further improved, the cost and risk can be reduced, and strong support can be provided for the sustainable development of the battery manufacturing industry.

[0039] According to an embodiment of the present invention, the process of obtaining the battery raw material attribute information and the basic information of the manufacturing equipment in a preset time period and constructing the battery manufacturing digital twin is as follows: Obtaining battery raw material property information and basic manufacturing equipment information for a preset time period; Extracting raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data according to the battery raw material attribute information; Extracting equipment type data, equipment basic parameter data and operation status performance data according to the basic information of the manufacturing equipment; A battery manufacturing digital twin is constructed based on the raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data combined with the equipment type data, equipment basic parameter data and operating status performance data.

[0040] Among them, in order to build a digital twin of battery manufacturing, the battery raw material attribute information and basic information of manufacturing equipment in a preset time period are obtained. According to the battery raw material attribute information, the raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data are extracted; according to the basic information of manufacturing equipment, the equipment type data, basic equipment parameter data and operation status performance data are extracted. The raw material type data records the main types of raw materials used, such as lithium, cobalt, nickel, etc.; the chemical composition data represents the chemical composition ratio or specific composition of each raw material; the physical property characteristic data refers to the physical properties of the raw materials such as density, melting point, hardness, etc.; the electrochemical performance data refers to the reaction activity and ion conductivity of the raw materials in the battery; the equipment type data refers to the type of equipment, such as mixer, coater, winder, etc.; the basic equipment parameter data refers to the specifications, models, design capacity, etc. of the equipment; the operation status performance data refers to the real-time operation data of the equipment, such as temperature, pressure, speed, running time, etc. By building a digital twin of battery manufacturing with the above data, the battery can be simulated and manufactured and the relevant parameters can be monitored and obtained in real time.

[0041] According to an embodiment of the present invention, the raw material quality data is obtained according to the positive and negative electrode ingredient parameter information, specifically: Extracting glue liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data according to the positive and negative electrode ingredient parameter information; The raw material quality data is obtained by processing the glue-liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data through a preset batching evaluation model.

[0042] Among them, in order to obtain raw material quality data, the glue ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data are extracted according to the positive and negative electrode batching parameter information. The glue ratio configuration data refers to the ratio of glue to other ingredients (such as active substances, conductive agents, etc.) in the positive and negative electrode batching. This ratio has a significant impact on the performance of the battery because it determines the viscosity and coating performance of the slurry; the stirring rate data refers to the speed at which the agitator rotates during the batching process, which affects the uniformity and particle size of the slurry, and thus affects the electrochemical performance of the battery; the slurry viscosity data is a measure of the slurry flow resistance, which directly affects the coating quality and battery consistency; the deionized water conductivity data reflects the number of ions dissolved in the water. The raw material quality data is then processed through the preset batching evaluation model; The calculation formula of the ingredient evaluation model is: ; in, For raw material quality data, They are the glue-liquid ratio configuration data, stirring speed data, slurry viscosity data and deionized water conductivity data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset battery manufacturing database).

[0043] According to an embodiment of the present invention, the coating effect evaluation data is obtained according to the coating film information, specifically: Extracting coating thickness consistency data, weight distribution uniformity data and coating adhesion data as well as tableting compaction density data, rebound rate data and elongation rate data according to the coating tableting information; The coating thickness consistency data, weight distribution uniformity data and coating adhesion data as well as the tableting compaction density data, rebound rate data and elongation rate data are processed through a preset coating evaluation model to obtain coating effect evaluation data.

[0044] Among them, in order to obtain coating effect evaluation data, coating thickness consistency data, weight distribution uniformity data and coating adhesion data, as well as tablet compaction density data, rebound rate data and elongation data are extracted according to the coating film information. Coating thickness consistency data refers to the uniformity of coating thickness during the coating process. The consistency of thickness has an important influence on the performance of the battery because it determines the capacity and cycle life of the battery; weight distribution uniformity data refers to whether the weight distribution of the coating on the substrate is uniform. Uneven weight distribution may lead to inconsistent battery performance; coating adhesion data The adhesion between the coating and the substrate is an important indicator for evaluating the quality of the coating. Insufficient adhesion may cause the coating to fall off during subsequent processing or use; tablet compaction density data is the density of the material obtained by compaction, and the compaction density has a direct impact on the capacity and energy density of the battery; rebound rate data refers to the material's ability to recover after being subjected to external force. Too high a rebound rate may mean that the material is insufficiently tough and easily deformed during processing or use; elongation data reflects the flexibility and plasticity of the material. Then, the coating effect evaluation data is obtained through processing using the preset coating evaluation model; The calculation formula of the coating evaluation model is: ; in, To evaluate the coating effect, They are coating thickness consistency data, weight distribution uniformity data and coating adhesion data, as well as tablet compaction density data, rebound rate data and elongation data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset battery manufacturing database).

[0045] According to an embodiment of the present invention, the preparation molding quality data is obtained according to the winding lamination information, specifically: Extracting winding tension data and winding speed stability data as well as lamination pressure data and pole piece alignment data according to the winding lamination information; The winding tension data, the winding speed stability data, the lamination pressure data, and the pole piece alignment data are processed through a preset preparation evaluation model to obtain preparation molding quality data.

[0046] Among them, in order to obtain the preparation and molding quality data, the winding tension data and winding speed stability data as well as the stacking pressure data and the pole piece alignment data are extracted according to the winding stacking information. The winding tension data refers to the tension applied between the pole piece and the diaphragm. The magnitude and stability of the tension have an important influence on the winding quality and final performance of the battery; the winding speed stability data determines the uniformity and efficiency of the winding; the stacking pressure data refers to the pressure applied to the pole piece and the diaphragm. The magnitude and uniformity of the pressure have a direct impact on the quality of the stacking and the final performance of the battery; the pole piece alignment data refers to the degree of alignment between the pole pieces. The alignment determines the uniformity and stability of the internal structure of the battery. Then, the preparation and molding quality data is obtained through processing through the preset preparation evaluation model; The calculation formula of the preparation evaluation model is: ; in, To prepare molding quality data, They are winding tension data, winding speed stability data, lamination pressure data, and pole piece alignment data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset battery manufacturing database).

[0047] According to an embodiment of the present invention, the raw material quality data, coating effect evaluation data and preparation molding quality data are processed by a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then correction processing is performed according to inter-batch manufacturing information and intra-batch manufacturing information to obtain a battery manufacturing reliability correction index, specifically: The raw material quality data, coating effect evaluation data and preparation molding quality data are processed by a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index; According to the inter-batch manufacturing information and the intra-batch manufacturing information, the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index are extracted respectively; The battery manufacturing reliability index is corrected according to the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index to obtain a battery manufacturing reliability correction index.

[0048] Among them, in order to obtain the battery manufacturing reliability correction index, the raw material quality data, coating effect evaluation data and preparation molding quality data are processed through a preset manufacturing effect evaluation model to obtain the battery manufacturing reliability index; the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index are extracted according to the inter-batch manufacturing information and the intra-batch manufacturing information respectively; the battery manufacturing reliability index is corrected according to the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index to obtain the battery manufacturing reliability correction index; The calculation formula of the manufacturing effect evaluation model and the battery manufacturing reliability correction index is: ; ; in, To create a reliability index for batteries, is the average reliability index of battery manufacturing between batches, is the average reliability index of battery manufacturing within a batch, To create a reliability calibration index for batteries, They are raw material quality data, coating effect evaluation data and preparation molding quality data. It is a preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset battery manufacturing database).

[0049] According to an embodiment of the present invention, it also includes: Comparing the battery manufacturing reliability correction index with a preset reliability index threshold to obtain a reliability deviation rate; comparing the reliability deviation rate with a preset reliability deviation rate threshold; If the reliability deviation rate is greater than or equal to the reliability deviation rate threshold, a battery manufacturing reliability failure message is sent; If the reliability deviation rate is less than the reliability deviation rate threshold, the battery manufacturing reliability qualification information is sent.

[0050] Among them, in order to improve the reliability of the battery manufacturing process, the battery manufacturing reliability correction index is compared with the preset reliability index threshold to obtain the reliability deviation rate, and then compared with the preset reliability deviation rate threshold. If the reliability deviation rate is greater than or equal to the reliability deviation rate threshold, the battery manufacturing reliability unqualified information is sent, indicating that further inspection or adjustment of the manufacturing process is required; if the reliability deviation rate is less than the reliability deviation rate threshold, the battery manufacturing reliability qualified information is sent, indicating that the current manufacturing process and raw material selection are reliable.

[0051] According to an embodiment of the present invention, it also includes: Extracting product defect rate data based on the battery manufacturing tracking information; Comparing the product defect rate data with a preset first product defect rate threshold and a second product defect rate threshold respectively; The first product defect rate threshold is less than the second product defect rate threshold; If the product defect rate data is less than or equal to the first product defect rate threshold, product quality information is sent; If the product defect rate data is greater than the first product defect rate threshold and less than or equal to the second product defect rate threshold, product optimization information is sent; If the product defect rate data is greater than the second product defect rate threshold, product unqualified information is sent.

[0052] Among them, in order to reduce the defective rate of batteries, product defective rate data is extracted according to the battery manufacturing tracking information, and compared with the preset first product defective rate threshold and the second product defective rate threshold, respectively, and the first product defective rate threshold is less than the second product defective rate threshold. If the product defective rate data is less than or equal to the first product defective rate threshold, product excellent information is sent, indicating that the product quality is excellent and meets or exceeds the expected standards; if the product defective rate data is greater than the first product defective rate threshold and less than or equal to the second product defective rate threshold, product optimization information is sent, indicating that the product quality is acceptable but there is room for improvement, and it is recommended to take optimization measures to improve the product quality; if the product defective rate data is greater than the second product defective rate threshold, product unqualified information is sent, indicating that the product quality does not meet the standards and immediate measures need to be taken for rectification to avoid further losses or impacts.

[0053] A third aspect of the present invention provides a computer-readable storage medium, which includes a battery manufacturing intelligent detection method program. When the battery manufacturing intelligent detection method program is executed by a processor, the steps of a battery manufacturing intelligent detection method as described in any one of the above items are implemented.

[0054] The present invention discloses an intelligent detection method, system and medium for battery manufacturing. By acquiring the battery raw material attribute information and basic information of manufacturing equipment in a preset time period and constructing a digital twin of battery manufacturing, the battery manufacturing simulation is performed through a preset battery manufacturing process to obtain battery manufacturing tracking information, and the positive and negative electrode material slurrying information, coating film making information and winding lamination information are extracted respectively. According to the positive and negative electrode material parameter information, the raw material quality data is obtained; according to the coating film making information, the coating effect evaluation data is obtained; according to the winding lamination information, the preparation molding quality data is obtained. Then, the battery manufacturing reliability index is obtained by processing through a preset manufacturing effect evaluation model, and the battery manufacturing reliability index is corrected according to the batch manufacturing information and the batch manufacturing information to obtain the battery manufacturing reliability correction index. By constructing a digital twin of battery manufacturing, the present application can simulate the battery manufacturing process in a virtual environment, predict and optimize the manufacturing process without actual production, thereby improving production efficiency, while being able to reduce human errors and improve the accuracy of the manufacturing process. This application can comprehensively consider multiple factors such as raw material quality, coating effect and preparation and molding quality, and objectively evaluate the reliability of battery manufacturing. Through the correction and processing of inter-batch manufacturing information and intra-batch manufacturing information, the accuracy and reliability of the evaluation results can be further improved, the cost and risk can be reduced, and strong support can be provided for the sustainable development of the battery manufacturing industry.

[0055] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0056] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; 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 present embodiment.

[0057] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0058] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0059] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A battery manufacturing intelligent detection method, characterized in that: The following steps are involved: Obtain the battery raw material attribute information and basic manufacturing equipment information for a preset time period and build a battery manufacturing digital twin. Perform battery manufacturing simulation through the preset battery manufacturing process to obtain battery manufacturing tracking information. Extracting the positive and negative electrode sheet material slurrying information, coating sheeting information and winding and lamination information according to the battery manufacturing tracking information; According to the positive and negative electrode ingredient parameter information, raw material quality data is obtained, according to the coating and film making information, coating effect evaluation data is obtained, and according to the winding and lamination information, preparation and molding quality data is obtained; The raw material quality data, coating effect evaluation data and preparation molding quality data are processed through a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then correction processing is performed based on inter-batch manufacturing information and intra-batch manufacturing information to obtain a battery manufacturing reliability correction index.

2. A battery manufacturing intelligent detection method according to claim 1, characterized in that: The obtaining of the battery raw material attribute information and the basic information of the manufacturing equipment in the preset time period and the construction of the battery manufacturing digital twin is specifically as follows: Obtaining battery raw material property information and basic manufacturing equipment information for a preset time period; Extracting raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data according to the battery raw material attribute information; Extracting equipment type data, equipment basic parameter data and operation status performance data according to the basic information of the manufacturing equipment; A battery manufacturing digital twin is constructed based on the raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data combined with the equipment type data, equipment basic parameter data and operating status performance data.

3. A battery manufacturing intelligent detection method according to claim 2, characterized in that: The raw material quality data is obtained according to the positive and negative electrode ingredient parameter information, specifically: Extracting glue liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data according to the positive and negative electrode ingredient parameter information; The raw material quality data is obtained by processing the glue-liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data through a preset batching evaluation model.

4. A battery manufacturing intelligent detection method according to claim 3, characterized in that: The coating effect evaluation data is obtained according to the coating film information, specifically: Extracting coating thickness consistency data, weight distribution uniformity data and coating adhesion data as well as tableting compaction density data, rebound rate data and elongation rate data according to the coating tableting information; The coating thickness consistency data, weight distribution uniformity data and coating adhesion data as well as the tableting compaction density data, rebound rate data and elongation rate data are processed through a preset coating evaluation model to obtain coating effect evaluation data.

5. A battery manufacturing intelligent detection method according to claim 4, characterized in that: The preparation molding quality data is obtained according to the winding lamination information, specifically: Extracting winding tension data and winding speed stability data as well as lamination pressure data and pole piece alignment data according to the winding lamination information; The winding tension data, the winding speed stability data, the lamination pressure data, and the pole piece alignment data are processed through a preset preparation evaluation model to obtain preparation molding quality data.

6. A battery manufacturing intelligent detection method according to claim 5, characterized in that: The raw material quality data, coating effect evaluation data and preparation molding quality data are processed by a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then correction processing is performed according to the inter-batch manufacturing information and the intra-batch manufacturing information to obtain a battery manufacturing reliability correction index, which is specifically: The raw material quality data, coating effect evaluation data and preparation molding quality data are processed by a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index; According to the inter-batch manufacturing information and the intra-batch manufacturing information, the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index are extracted respectively; The battery manufacturing reliability index is corrected according to the inter-batch battery manufacturing reliability average index and the intra-batch battery manufacturing reliability average index to obtain a battery manufacturing reliability correction index.

7. A battery manufacturing intelligent detection system, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a battery manufacturing intelligent detection method program, and when the battery manufacturing intelligent detection method program is executed by the processor, the following steps are implemented: Obtain the battery raw material attribute information and basic manufacturing equipment information for a preset time period and build a battery manufacturing digital twin. Perform battery manufacturing simulation through the preset battery manufacturing process to obtain battery manufacturing tracking information. Extracting the positive and negative electrode sheet material slurrying information, coating sheeting information and winding and lamination information according to the battery manufacturing tracking information; According to the positive and negative electrode ingredient parameter information, raw material quality data is obtained, according to the coating and film making information, coating effect evaluation data is obtained, and according to the winding and lamination information, preparation and molding quality data is obtained; The raw material quality data, coating effect evaluation data and preparation molding quality data are processed through a preset manufacturing effect evaluation model to obtain a battery manufacturing reliability index, and then correction processing is performed based on inter-batch manufacturing information and intra-batch manufacturing information to obtain a battery manufacturing reliability correction index.

8. A battery manufacturing intelligent detection system according to claim 7, characterized in that: The obtaining of the battery raw material attribute information and the basic information of the manufacturing equipment in the preset time period and the construction of the battery manufacturing digital twin is specifically as follows: Obtaining battery raw material property information and basic manufacturing equipment information for a preset time period; Extracting raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data according to the battery raw material attribute information; Extracting equipment type data, equipment basic parameter data and operation status performance data according to the basic information of the manufacturing equipment; A battery manufacturing digital twin is constructed based on the raw material type data, chemical composition data, physical property characteristic data and electrochemical performance data combined with the equipment type data, equipment basic parameter data and operating status performance data.

9. A battery manufacturing intelligent detection system according to claim 8, characterized in that: The raw material quality data is obtained according to the positive and negative electrode ingredient parameter information, specifically: Extracting glue liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data according to the positive and negative electrode ingredient parameter information; The raw material quality data is obtained by processing the glue-liquid ratio configuration data, stirring rate data, slurry viscosity data and deionized water conductivity data through a preset batching evaluation model.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a battery manufacturing intelligent detection method program, and when the battery manufacturing intelligent detection method program is executed by a processor, the steps of a battery manufacturing intelligent detection method as described in any one of claims 1 to 6 are implemented.

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