Battery simulation microcosmic internal physical field graph generation method and system

Abstract

The invention discloses a battery simulation microcosmic internal physical field graph generation method and system, and relates to the field of batteries, The method comprises the steps that a client browser generates set viewing request information according to a viewing request of a user and sends the viewing request information to a server, the server queries a visual toolkit file database after receiving the request, and the visual toolkit file database sends the viewing request information to the server; the method comprises the steps that a server receives a request of a battery simulation microcosmic physical field graph, obtains a corresponding visual toolkit file, analyzes a physical scalar data set related to the request, further processes the data set and generates picture rendering data of the battery simulation microcosmic physical field graph and original data of picture pixel point density, then the server returns the original data to a client browser, and the client browser sends the original data to the battery simulation microcosmic physical field graph. According to the method and the system, the calculation burden of the server can be reduced, the data processing and image generation efficiency is improved, and the expansibility and the flexibility of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of batteries, and in particular to a method and system for generating a microscopic internal physical field diagram of a battery simulation. Background Art

[0002] With the widespread application of lithium-ion batteries in consumer electronics, energy storage systems, electric vehicles and other fields, their performance and safety issues have gradually become the focus of research. In order to deeply understand the working mechanism, performance degradation process and thermal runaway risk of lithium-ion batteries, we increasingly rely on multi-physics field simulation inside the battery. The simulation content includes electrochemical reaction field, thermal field, stress field and ion migration field, which work together on the battery's electrodes, electrolytes and diaphragms and other materials to determine the overall performance of the battery.

[0003] In traditional battery simulation methods, the physical field diagrams inside the battery are mostly generated by dedicated software. In particular, there are many challenges in processing the physical field diagrams inside lithium-ion batteries under the SaaS (Software as a Service) architecture. The most significant problem is that most of the current solutions rely on images pre-generated by the server to display the simulation results, resulting in a waste of server resources: in order to meet the needs of different users, the server needs to pre-generate a large number of simulation images with different parameters and different time steps. These images may only be used by a small number of users, but they take up a lot of storage space and computing resources, greatly increasing the burden on the server and causing a waste of resources. Since the images are pre-generated, users cannot adjust the simulation parameters in real time when browsing the physical field diagrams, resulting in a lack of real-time interactivity in the simulation results. Users can only observe the changes in the physical field according to preset conditions, which limits the in-depth exploration of the behavior of complex systems.

[0004] In view of this, how to effectively solve the problems of resource waste, lack of flexibility and calculation delay faced by existing SaaS-based simulation software when processing internal physical field diagrams of lithium-ion batteries is an urgent problem to be solved in existing technologies. Summary of the invention

[0005] A technical problem to be solved by the embodiments of the present invention is how to effectively solve the problems of resource waste, lack of flexibility and calculation delay faced by existing simulation software based on SaaS architecture when processing internal physical field diagrams of lithium-ion batteries.

[0006] In order to cope with the above scenario, in a first aspect, an embodiment of the present application provides a method for generating a microscopic internal physical field diagram of a battery simulation, comprising:

[0007] The client browser generates setting viewing request information according to the battery simulation microscopic internal physical field diagram viewing request initiated by the user;

[0008] The client browser sends the setting viewing request information to the server;

[0009] After receiving the viewing request information from the client browser, the server queries the visualization toolkit file database according to the setting viewing request information to obtain the visualization toolkit file corresponding to the setting viewing request information;

[0010] The server parses the visualization toolkit file to obtain a physics-related scalar data set corresponding to the set view request information;

[0011] The server processes the physics-related scalar data set to generate image rendering data of a battery simulation microscopic internal physical field diagram corresponding to the setting view request information;

[0012] The server processes the image rendering data by a set method to generate original data of the image pixel density of the battery simulation microscopic internal physical field map;

[0013] The server sends the original data of the pixel density of the image to the client browser;

[0014] The client browser renders and generates a microscopic internal physical field diagram of the battery simulation based on the original data of the image pixel density and the browser settings.

[0015] In combination with the first aspect, in some implementations, after receiving the viewing request information from the client browser, the server queries the visualization toolkit file database according to the setting viewing request information to obtain the visualization toolkit file corresponding to the setting viewing request information, including:

[0016] The server side obtains the visualization toolkit file by setting a Node.js API service, wherein the visualization toolkit file includes geometric mesh information and additional simulation set scalar data information, wherein the Node.js API is an application program interface service pre-set based on the Node.js service for obtaining the visualization toolkit file;

[0017] The grid points of the geometric grid can be attached with scalar data information;

[0018] The additional simulation set scalar data information is the type of geometric grid points, including but not limited to positive and negative electrode materials, conductive bridges, and electrolyte information.

[0019] In combination with the first aspect, in some implementations, the server parses the visualization toolkit file to obtain a physics-related scalar data set corresponding to the setting view request information, including:

[0020] The server uses vtk.js and vtk-xml to read the model information of the positive and negative electrode material particles and the conductive bridge by setting the Node.js API service. The vtk.js is a pre-set JavaScript library for processing and rendering 3D data and scenes. The vtk-xml is a pre-set toolkit for parsing VTK format files, which supports the description of complex 3D geometric structures and physical fields.

[0021] If the positive and negative electrode material particles are circular, the center and radius of the positive and negative electrode material particle models are obtained by analysis;

[0022] If the conductive bridge is a square, the vertices of the conductive bridge model are obtained by analysis to obtain the position information of the conductive bridge;

[0023] The data of each grid point in the positive and negative electrode material particles and the conductive bridge are read to include position information and additional scalar data, and a physically related scalar data set including an array of data of the positive and negative electrode material particles and the conductive bridge is generated; the physically related scalar data set includes but is not limited to a solid phase data set and a liquid phase data set corresponding to the microscopic internal physical field diagram of the battery simulation.

[0024] In combination with the first aspect, in some embodiments, the server processes the image rendering data by a setting method to generate raw data of the image pixel density of the battery simulation microscopic internal physical field map, including:

[0025] Processing the image rendering data through an optimization algorithm according to the set image pixel size to reduce the grid point density, wherein the user-initiated battery simulation microscopic internal physical field diagram viewing request generates a set viewing request information including the set image pixel size information required by the client browser;

[0026] The algorithm is optimized based on the image pixel size to output images at multiple times.

[0027] In combination with the first aspect, in some implementations, the reducing the grid point density by processing the image rendering data through an optimization algorithm according to the set image pixel size includes:

[0028] Initialize the image rendering data and randomly select K initial cluster center points;

[0029] An assignment step, using Euclidean distance or other measurement methods to assign the K initial cluster data points to the cluster center closest to them;

[0030] An updating step is to recalculate the center point of each cluster, where the center point is the mean of all points in the cluster;

[0031] Repeating the allocation step and the update step continuously until the center point of each cluster no longer changes significantly or a predetermined number of iterations is reached;

[0032] Set the center point of each cluster as the representative data point;

[0033] A simplified microscopic internal physical field diagram of battery simulation is constructed through the center point of each cluster, and the client browser can parse and obtain the original data of the pixel density of the image according to the simplified microscopic internal physical field diagram of battery simulation.

[0034] In combination with the first aspect, in some implementations, before the client browser generates setting viewing request information according to the battery simulation microscopic internal physical field diagram viewing request initiated by the user, it includes:

[0035] Determine the type of battery simulation microscopic internal physical field diagram that needs to be viewed;

[0036] Determine the upper and lower limits of the color threshold and the color gamut information that you need to view.

[0037] In combination with the first aspect, in some implementations, the client browser generates a microscopic internal physical field diagram of a battery simulation according to the original data of the pixel density of the image and the settings of the browser, including:

[0038] Generate a canvas according to the set size;

[0039] Generate the circular shape of the positive and negative electrode material particles and the square shape of the conductive bridge according to the size information input by the user;

[0040] Call Web Workers to set the color of pixels according to the upper and lower limits of the color threshold;

[0041] Based on the color of each pixel, the browser renders the canvas and outputs the microscopic internal physical field diagram of the battery simulation.

[0042] In combination with the first aspect, in some implementations, calling Web Workers to set the color of the pixel points according to the upper and lower limits of the color threshold includes:

[0043] Color threshold settings support automatic allocation and custom upper and lower color limits;

[0044] Automatically assign the maximum value in the image as the upper color limit, the minimum value as the lower color limit, and the colors of other pixels are set according to the rules;

[0045] Customization means customizing the upper and lower limit colors. When setting, the lower limit will not exceed the maximum value of the data in the graph, and the upper limit will not be less than the minimum value in the graph.

[0046] In combination with the first aspect, in some implementations, it also includes: the client browser re-renders and generates a battery simulation microscopic internal physical field according to the original data of the image pixel density and the browser settings at the switching moment.

[0047] In a second aspect, an embodiment of the present application provides a battery simulation microscopic internal physical field diagram generation system, including a client browser and a server, wherein the client browser includes a first generation module, a first sending module, a first receiving module, and a first processing module, and the server includes a second receiving module, a query module, a parsing module, a second generation module, a second processing module, and a second sending module.

[0048] The first generating module is used to generate setting viewing request information according to a battery simulation microscopic internal physical field diagram viewing request initiated by a user;

[0049] A first sending module, connected to the first generating module, and configured to send the setting viewing request information to a server;

[0050] A second receiving module, connected to the first sending module, and configured to receive setting viewing request information;

[0051] A query module connected to the second receiving module, configured to query the visualization toolkit file database according to the set viewing request information to obtain the visualization toolkit file corresponding to the set viewing request information after receiving the viewing request information from the client browser;

[0052] A parsing module, connected to the query module, for parsing the visualization toolkit file to obtain a physics-related scalar data set corresponding to the setting view request information;

[0053] A second generating module, connected to the analyzing module, for processing the physics-related scalar data set to generate image rendering data of a microscopic internal physical field diagram of a battery simulation corresponding to the setting viewing request information;

[0054] A second processing module, connected to the second generating module, is used to process the image rendering data by a set method to generate original data of the image pixel density of the battery simulation microscopic internal physical field map;

[0055] The second sending module is connected to the second processing module and is used for the server to send the original data of the pixel density of the image to the client browser.

[0056] A first receiving module, connected to the second sending module, and configured to receive information returned by the server;

[0057] The first processing module is connected to the first receiving module and is used to render and generate a battery simulation microscopic internal physical field diagram according to the original data of the image pixel density and the settings of the browser.

[0058] The method and system for generating a simulated microscopic internal physical field map of a lithium-ion battery provided by the present invention generates a physical field map on the browser side, reduces the computing burden of the server, improves the efficiency of data processing and image generation, and provides stronger future scalability and flexibility. By moving the computationally intensive image generation part to the browser client, the computing burden of the server is reduced and the utilization efficiency of system resources is optimized; users can customize the upper and lower limits of color and the color gamut according to their needs, providing a more flexible image display and analysis method; since the browser side is responsible for generating the image, the server only needs to return the streamlined data, and the entire system has better scalability and rapid response capabilities.

[0059] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings, which constitute a part of the specification, illustrate embodiments of the present invention and, together with the description, serve to explain the principles of the present invention.

[0061] The present invention may be more clearly understood from the following detailed description with reference to the accompanying drawings, in which:

[0062] Figure 1 This is a flow chart of a method for generating a microscopic internal physical field diagram of a battery simulation according to an embodiment of the present invention.

[0063] Figure 2 A structural block diagram of a system for simulating microscopic internal physical field diagrams of a battery according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The present application is further described below in conjunction with specific embodiments and drawings. It is understood that the illustrative embodiments of the present disclosure include but are not limited to related methods, devices and systems, and the specific embodiments described herein are only for the purpose of explaining the present application, rather than limiting the present application. In addition, for ease of description, only some, but not all, structures or processes related to the present application are shown in the drawings.

[0065] The following specific embodiments illustrate the implementation of the present application, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Although the description of the present application will be introduced in conjunction with the preferred embodiment, this does not mean that the features of this invention are limited to this implementation. On the contrary, the purpose of introducing the invention in conjunction with the implementation is to cover other options or modifications that may extend based on the claims of the present application. In order to provide a deep understanding of the present application, the following description will include many specific details. The present application can also be implemented without using these details. In addition, in order to avoid confusion or blurring the focus of the present application, some specific details will be omitted in the description. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0066] Figure 1 This is a flow chart of a method for generating a microscopic internal physical field diagram of a battery simulation according to an embodiment of the present invention. As shown in the figure, the method includes:

[0067] Step S101, the client browser generates setting viewing request information according to a battery simulation microscopic internal physical field diagram viewing request initiated by a user.

[0068] Step S102: The client browser sends the setting viewing request information to the server.

[0069] Step S103, after receiving the viewing request information from the client browser, the server queries the visualization toolkit file database according to the setting viewing request information to obtain the visualization toolkit file corresponding to the setting viewing request information.

[0070] Step S104: the server parses the visualization toolkit file to obtain a physics-related scalar data set corresponding to the setting view request information.

[0071] Step S105 : the server processes the physics-related scalar data set to generate image rendering data of a microscopic internal physical field diagram of a battery simulation corresponding to the setting view request information.

[0072] Step S106: The server processes the image rendering data by a set method to generate original data of the image pixel density of the battery simulation microscopic internal physical field map.

[0073] Step S107: The server sends the original data of the pixel density of the image to the client browser.

[0074] Step S108, the client browser renders and generates a battery simulation microscopic internal physical field diagram according to the original data of the image pixel density and the browser settings.

[0075] The method for generating a microscopic internal physical field diagram of a lithium-ion battery simulation proposed in the embodiment of the present invention generates a physical field diagram on the browser side, reduces the computing burden of the server, improves the efficiency of data processing and image generation, and provides stronger future scalability and flexibility. By moving the computationally intensive image generation part to the browser client, the computing burden of the server is reduced and the utilization efficiency of system resources is optimized; users can customize the upper and lower limits of color and the color gamut according to their needs, providing a more flexible image display and analysis method; since the browser side is responsible for generating the image, the server only needs to return the streamlined data, and the entire system has better scalability and rapid response capabilities.

[0076] In one embodiment, querying a visualization toolkit file database according to the set view request information to obtain a visualization toolkit file corresponding to the set view request information includes: the server side obtains the visualization toolkit file by setting a Node.js API service, the visualization toolkit file includes geometric mesh information and additional simulation set scalar data information, wherein the Node.js API is an application program interface service pre-set based on the Node.js service for obtaining the visualization toolkit file; the grid points of the geometric mesh can be attached with scalar data information; the additional simulation set scalar data information is the type of geometric mesh points, including but not limited to positive electrode materials, negative electrode materials, conductive bridges, and electrolyte information.

[0077] In a specific embodiment of the present invention, the system can query the visualization toolkit file database and obtain the visualization toolkit file corresponding to the request by processing the user's set viewing request information. In this embodiment, the server side operates through the set Node.js API service, which may specifically include:

[0078] (1) Parsing of setting viewing request information: When a user issues a setting viewing request, the system first parses the request to determine the type of lithium-ion battery simulation results that the user needs to view (such as geometric mesh, positive and negative electrode material particle distribution, conductive bridge, electrolyte, etc.).

[0079] (2) Obtaining the visualization toolkit file: After parsing is completed, the server calls a pre-set Node.js API service. The Node.js API is based on the Node.js service architecture and is specifically designed for obtaining visualization toolkit files. The API service retrieves the visualization toolkit file that matches the user request from the database. The visualization toolkit file contains geometric mesh information and additional simulation set scalar data information.

[0080] (3) Processing of geometric mesh and scalar data: In the retrieved visualization toolkit file, the geometric mesh file describes the mesh structure of the internal materials of the battery, including the coordinates of each mesh point. The additional simulation set scalar data information is associated with these geometric mesh points. The types of scalar data include positive electrode materials, negative electrode materials, conductive bridges, and electrolyte information.

[0081] The embodiment of the present invention obtains the visualization toolkit file on demand through the server-side Node.js API service, avoiding the waste of resources caused by a large number of pre-generated images under the traditional SaaS architecture. The corresponding simulation data is generated and obtained only when the user requests it, thereby saving the server's storage and computing resources. Through the architectural design of the Node.js API service, the system has good scalability, and new types of simulation data or analysis tools can be easily integrated into existing services without a large number of modifications to the system architecture. It supports continuously expanding data types and simulation dimensions, and geometric mesh information and scalar data are dynamically loaded when the user requests it. Flexible and interactive visualization operations are achieved through visualization tools, and users can adjust simulation parameters and observe results in real time, which improves the understanding and exploration of the complex internal physical fields of batteries.

[0082] In one embodiment, the server parses the visualization toolkit file to obtain a physics-related scalar data set corresponding to the set viewing request information, including: the server uses vtk.js and vtk-xml to read the model information of the positive and negative electrode material particles and the conductive bridge by setting the Node.js API service, wherein the vtk.js is a pre-set JavaScript library for processing and rendering 3D data and scenes, and the vtk-xml is a pre-set toolkit for parsing VTK (Visualization Toolkit, visualization toolkit file) format files, which supports the description of complex 3D geometric structures and physical fields; the positive and negative electrode material particles are circular, and the center and radius of the positive and negative electrode material particle models are obtained; the conductive bridge is square, and the vertices of the conductive bridge model are obtained to obtain the position information of the conductive bridge; the data of each grid point in the positive and negative electrode material particles and the conductive bridge are read to include the position information and additional scalar data, and a physics-related scalar data set including the positive and negative electrode material particles and the conductive bridge data array is generated; the physics-related scalar data set includes but is not limited to a solid phase data set and a liquid phase data set corresponding to the microscopic internal physical field diagram of the battery simulation.

[0083] In one embodiment, VTK files can be set to store various types of data, including geometric data (points, lines, surfaces, volumes), image data, scalar data (such as temperature, density), vector data (such as velocity field), etc.; VTK file settings support reading and processing on different operating systems and software platforms. VTK settings support custom data types and attributes, have strong scalability, support direct use in 3D visualization, and display complex data structures and fields.

[0084] The present invention combines Node.js API service with vtk.js and vtk-xml to achieve efficient reading and parsing of complex 3D models, and can dynamically generate physics-related scalar data sets corresponding to user requests, which enables the system to respond to user requests in real time, provide accurate simulation data, and improve the processing efficiency of the server.

[0085] The method provided by the present invention can extract geometric parameters such as the center and radius of the positive and negative electrode material particles, the vertices of the conductive bridge, etc. from the simulation file, and integrate this information into the physically related scalar data set, ensuring an accurate description of the internal microstructure of the battery, which helps to deeply understand the physical processes inside the battery. With the real-time 3D rendering function of vtk.js, users can flexibly view and operate the simulation results with high interactivity. The system is modularly designed to be easy to expand and integrate more simulation data and physical field analysis to meet different simulation needs.

[0086] In one embodiment, the server processes the image rendering data by a setting method to generate raw data of the image pixel density of the battery simulation microscopic internal physical field map, including:

[0087] Step A: processing the image rendering data through an optimization algorithm according to the set image pixel size to reduce the grid point density, wherein a user-initiated battery simulation microscopic internal physical field diagram viewing request generates a set viewing request information including the set image pixel size information required by the client browser.

[0088] Step B: Output images at multiple moments according to the image pixel size optimization algorithm.

[0089] Since the number of simulation grid points is very large, such a large number of grid points are not needed when generating images, so the density of grid points needs to be reduced; according to the set image pixel size, the density of grid points can be reduced through fast processing by optimization algorithm without affecting the macro numerical changes; the simulation image normally outputs images at multiple moments, so step B needs to be repeated to obtain the results at each moment.

[0090] In one embodiment, the reducing the grid point density by processing the image rendering data through an optimization algorithm according to the set image pixel size includes:

[0091] Step 201: Initialize the image rendering data and randomly select K initial cluster center points.

[0092] Step 202: an assignment step, using Euclidean distance or other measurement methods to assign the K initial cluster data points to the cluster center closest to them.

[0093] Step 203, an updating step, recalculates the center point of each cluster, where the center point is the mean value of all points in the cluster.

[0094] Step 204: Repeat the allocation step and the update step continuously until the center point of each cluster no longer changes significantly or a predetermined number of iterations is reached.

[0095] Step 205: Set the center point of each cluster as the representative data point.

[0096] Step 206: construct a simplified battery simulation microscopic internal physical field map through the center point of each cluster, and the client browser can parse and obtain the original data of the image pixel density according to the simplified battery simulation microscopic internal physical field map.

[0097] Compared with the prior art, the data point simplification method provided by the present invention effectively reduces the data points in large-scale data sets by optimizing the initialization and iteration process of the algorithm, thereby significantly improving data processing efficiency; the denoising and outlier processing algorithms are introduced in the data preprocessing stage, further improving the robustness and stability of the algorithm.

[0098] In a specific embodiment, a trained K-means clustering algorithm can be used to simplify data points. The simplification algorithm divides the data set into K clusters to minimize the distance from the points in the cluster to the cluster center. The following are the steps to reduce the number of data points using K-means clustering:

[0099] Step 21, Initialization: Select K initial cluster centers. You can randomly select data points as initial cluster centers, or use a more optimized initialization method such as K-means++ to improve the algorithm effect.

[0100] Step 22, assign points to clusters: Assign each data point to the cluster center that is closest to it. The distance is usually measured using Euclidean distance, but other metrics can also be used.

[0101] Step 23, Update cluster center: Recalculate the center point of each cluster, which is the mean of all points in the cluster.

[0102] Step 24: Repeat: Repeat steps 22 and 23 until the cluster center no longer changes significantly or a predetermined number of iterations is reached.

[0103] Step 25, output result: The center point of each cluster is the representative of the simplified data point. These cluster centers can be used to construct a simplified physical field diagram.

[0104] The K-means algorithm with simplified data point settings provided by the present invention is simple to understand, easy to implement, has high computational efficiency, and is suitable for processing large data sets.

[0105] In a specific embodiment, a dynamic K value adjustment mechanism can be used to optimize the K value in real time according to the data distribution. Adaptiveness emphasizes that the system is dynamically adjusted according to the environment or conditions. In combination with the elbow method and the silhouette coefficient, the K value is dynamically adjusted in each iteration, and the optimal K value is automatically found by calculating the error function and the similarity within the cluster. Specifically, the total error function can be set to J(c,μ), and the K value can be adjusted in real time by calculating the error function under different K values ​​and combining the silhouette coefficient S(k), where S(k) = (b(i)-a(i)) / max(a(i), b(i)), where a(i) represents the average distance of the cluster to which point i belongs, and b(i) represents the distance from point i to the nearest other cluster.

[0106] In one embodiment, a semi-supervised clustering feedback mechanism may be introduced, and semi-supervised learning may be combined with a K-means algorithm to use known label data as the initialization of the guiding cluster center, thereby enhancing the accuracy and stability of the model.

[0107] In one embodiment, the noise and outliers are sensitive and a preprocessing step and an outlier detection algorithm can be introduced. For example, noise and outliers can be removed before performing K-means clustering. The specific steps include: (1) Preprocessing: Identify and remove abnormal data points through density or distance analysis to avoid interference with K-means results. (1) Before K-means is performed, the DBSCAN method is applied to remove outliers to make clustering more stable. The DBSCAN algorithm formula can be: N i (p) = {q∈D|d(p,q)≤∈}, this formula can be used to eliminate outliers and then perform K-means clustering.

[0108] In one embodiment, before the client browser generates a set viewing request information based on a battery simulation microscopic internal physical field diagram viewing request initiated by a user, it includes: determining a set type of battery simulation microscopic internal physical field diagram that needs to be viewed; determining the upper and lower limits of the color threshold and the color gamut information that needs to be viewed.

[0109] In one embodiment, the client browser generates a microscopic internal physical field diagram of a battery simulation according to the original data of the pixel density of the image and the settings of the browser, including:

[0110] Step 301, generating a canvas according to a set size.

[0111] Specifically, the client browser first generates a canvas of a specified size according to the size information set by the user. The elements of the Canvas are used to draw and render the internal physical field diagram of the battery simulation in the browser, providing a display space for the image.

[0112] Step 302, generating circular shapes of positive and negative electrode material particles and square shapes of conductive bridges according to the size information input by the user.

[0113] Specifically, the browser generates the geometric shapes of the positive and negative electrode material particles in the lithium-ion battery simulation based on the size information entered by the user. The positive and negative electrode material particles are represented as circular structures, and the radius and center position of each particle are calculated and generated based on the input information. At the same time, the conductive bridge structure is represented as a square, and a square conductive bridge model is generated based on the input geometric parameters and placed in the canvas.

[0114] Step 303, calling Web Workers to set the color of the pixel points according to the upper and lower limits of the color threshold.

[0115] Specifically, in order to improve rendering efficiency, the browser calls Web Workers for parallel computing. Web Workers is a multi-threaded technology in the browser that can handle complex calculations in the background without blocking the operation of the main thread. In WebWorkers, the system processes the raw data of each pixel according to the upper and lower limits of the color threshold set by the user. According to the density of the pixel and the threshold range, the color of each pixel is set to correspond to different states of the battery simulation physical field, such as temperature field, stress field and other physical quantities.

[0116] Step 304: According to the color of each pixel, the browser renders the canvas and outputs a microscopic internal physical field diagram of the battery simulation.

[0117] Specifically, the browser renders a complete battery simulation microscopic internal physical field diagram on the canvas based on the color information of each pixel. The physical field diagram uses different colors and geometric shapes to show the internal microstructure of the battery, such as the distribution of positive and negative electrode material particles, the location and connection of the conductive bridge, and the corresponding physical field data.

[0118] The method of the embodiment of the present invention can effectively share the pressure of the main thread by calling Web Workers to realize parallel computing, greatly improve the efficiency of rendering large-scale simulation data, ensure the smooth presentation of battery simulation images at high resolution, and avoid the problem of browser freeze or response delay. By accurately mapping the density and color value of each pixel, the physical field distribution inside the battery can be intuitively presented to help users understand the behavior of the battery under the microstructure. The shape and position of key structures such as positive and negative electrode material particles and conductive bridges are accurately depicted, which helps to optimize battery design and performance analysis.

[0119] Users can flexibly adjust the generation process of simulation images by inputting parameters such as size and color threshold. This interactivity not only improves the user experience, but also supports users to explore the battery physics field under different simulation conditions, thereby gaining deeper insights. With the help of the browser's built-in canvas and Web Workers technology, simulation results can be generated and displayed in real time on the client without relying on the server for complex calculations. This allows users to quickly view simulation results and make adjustments, significantly shortening the feedback time of simulation results.

[0120] In one embodiment, Web Workers are called to set the color of pixels according to the upper and lower limits of the color threshold, including: the color threshold setting supports automatic allocation and custom upper and lower limit color domains; automatic allocation sets the maximum value in the image as the upper color limit, the minimum value as the lower color limit, and other pixels are set to color according to a rule; custom means setting the upper and lower limit colors by yourself, and when setting, the lower limit will be restricted to not exceed the maximum value of the data in the image, and the upper limit will not be less than the minimum value in the image.

[0121] Specifically, the method of the present invention supports two color threshold setting modes: automatic allocation and custom color upper and lower limits. These settings are used to determine the color corresponding to each pixel, reflecting the distribution of the microscopic internal physical field of the lithium-ion battery simulation. In the automatic allocation mode, the system automatically determines the color upper and lower limits according to the range of the image pixel data: the maximum value can be set as the color upper limit, representing the maximum value of the physical field data in the simulation diagram (such as the highest temperature, the maximum stress, etc.). The minimum value can be set as the color lower limit, representing the minimum value of the physical field data (such as the lowest temperature, the minimum stress, etc.). The colors of other pixels will be automatically allocated in proportion according to the relative position of their physical field data between the minimum and maximum values. The system will generate a color transition based on the color gradient to show the change from low value to high value. In the custom mode, the user can manually set the upper and lower limits of the color. The upper and lower limits set by the user must meet the following constraints: the color lower limit may be the maximum value of the data in the figure that must not exceed, that is, the lower limit must be less than or equal to the maximum value in the simulation data. The color upper limit may be the minimum value of the data in the figure that must not be lower than, that is, the upper limit must be greater than or equal to the minimum value in the simulation data. By customizing the upper and lower color limits, users can more precisely control the color distribution in the physics field diagram and focus on a specific range of values, making it easier to analyze and interpret key data.

[0122] In one embodiment, the client browser generates a battery simulation microscopic internal physical field by re-rendering according to the original data of the image pixel density and the browser settings at the switching moment.

[0123] Specifically, the client browser supports users to manually or automatically switch the simulation data of different time points in the battery simulation. Users can select the simulation data at a specific moment through a graphical interface, such as selecting different time points in the battery discharge process, or switching to different stages of the charge and discharge cycle. When the user selects a certain moment, the browser obtains the original data of the pixel density of the image corresponding to that moment from the server or local cache. These original data usually include the distribution of different physical fields (such as current density, temperature field, stress field, etc.) inside the battery. The present invention supports the browser to re-render the battery simulation microscopic physical field diagram in real time according to the simulation data at different time points. Through fast switching and re-rendering, users can instantly view the simulation results of multiple time points, and conveniently compare and analyze the performance changes of the battery in different time periods.

[0124] In a specific embodiment of the present invention, the process flow of the browser and the server cooperating to process the physical scene graph includes the following steps:

[0125] Step 41: The browser initiates a request to view the physical scene graph.

[0126] Step 411: The browser determines the physical scene to be viewed. For example, the user selects a physical scene diagram (such as a temperature field, a stress field, etc.) at a specific time or state in a battery simulation.

[0127] Step 412: The user determines the upper and lower limits and color gamut range of the Color Bar on the browser side. This step is used to set the data distribution color in the physical field so that the user can observe the simulation results according to different threshold ranges.

[0128] Step 42: The server obtains the VTK file through the Node.js API.

[0129] The server side obtains the VTK file related to the selected physical scenario through the preset Node.js API service request. The VTK file contains the geometric mesh information in the battery simulation and additional simulation scalar data.

[0130] Step 43: The server parses the VTK file.

[0131] Step 431: The server parses the VTK file to obtain the position and size information of the positive electrode, negative electrode material particles and the conductive bridge, as well as the scalar data related thereto (such as temperature or potential value).

[0132] Step 432: The server will also obtain a set of relevant positions and scalar data of the liquid phase to ensure that the interaction between the solid phase and the liquid phase can be accurately displayed in the physical scene graph.

[0133] Step 44: The server generates processing results.

[0134] Step 441: The server generates rendering information of the solid phase geometry based on the data of the positive and negative electrode materials and the conductive bridge to ensure that its shape, size and position can be accurately displayed.

[0135] Step 442: The server simultaneously generates rendering information of the liquid phase to ensure that the physical interaction between the liquid phase and the solid phase is reflected.

[0136] Step 45: The server generates image pixel density data using a K-means algorithm.

[0137] The server processes the scalar information in the VTK data through a K-means algorithm and generates raw data of the pixel density of the image for rendering. This data is used to represent the density distribution of each pixel in the physical field, thereby reflecting the changes in the physical field in the simulation.

[0138] Step 46: The client browser receives and generates a physical scene graph.

[0139] Step 461: The browser generates a canvas according to the set size for displaying the physical scene graph on the web page.

[0140] Step 462: The browser generates the geometric shapes of the electrodes and conductive bridges based on the size and position information of the solid phase, ensuring consistency with the geometric rendering information processed by the server.

[0141] Step 463: The browser calls Web Workers to set a suitable color for each pixel according to the upper and lower limits of the Color Bar and the pixel density data provided by the server.

[0142] Step 47: Browser Rendering Physical Scene Graph

[0143] The browser generates and displays a complete microscopic internal physical field diagram of the battery simulation through real-time rendering of the canvas, showing the distribution of the solid phase, liquid phase and its physical field inside the battery.

[0144] The browser and server of the present invention collaborate to process the physical scene graph method, the K-means algorithm efficiently extracts simulation data, generates pixel density data, reduces data complexity, and enables the client to quickly receive and render. The solid and liquid phase geometric rendering data generated by the server ensures accurate display of the battery simulation microstructure and improves the visualization effect. The browser supports user-defined Color Bar range to enhance the flexibility of simulation analysis. With the help of Web Workers, the browser calculates pixel colors in parallel to improve rendering speed, which is suitable for high-resolution scenes. Lightweight data transmission reduces server pressure, improves system scalability, and supports multiple users to view simulation results at the same time.

[0145] Figure 2 FIG. 1 is a structural block diagram of a system for generating a microscopic internal physical field diagram of a battery simulation according to an embodiment of the present invention. Figure 2 As shown, the system includes a client browser 11 and a server 12, wherein the client browser includes a first generating module 1101, a first sending module 1102, a first receiving module 1103, and a first processing module 1104, and the server includes a second receiving module 1105, a query module 1106, a parsing module 1107, a second generating module 1108, a second processing module 1109, and a second sending module 1110.

[0146] The first generating module 1101 is used to generate setting viewing request information according to a battery simulation microscopic internal physical field diagram viewing request initiated by a user;

[0147] A first sending module 1102, connected to the first generating module 1101, configured to send the setting viewing request information to a server;

[0148] A second receiving module 1105, connected to the first sending module 1102, for receiving setting viewing request information;

[0149] The query module 1106 is connected to the second receiving module 1105 and is used to query the visualization toolkit file database according to the set viewing request information to obtain the visualization toolkit file corresponding to the set viewing request information after receiving the viewing request information from the client browser;

[0150] A parsing module 1107, connected to the query module 1106, for parsing the visualization toolkit file to obtain a physics-related scalar data set corresponding to the setting view request information;

[0151] A second generating module 1108, connected to the analyzing module 1107, is used to process the physics-related scalar data set and generate image rendering data of a microscopic internal physical field diagram of a battery simulation corresponding to the setting viewing request information;

[0152] The second processing module 1109 is connected to the second generating module 1108 and is used to process the image rendering data by a set method to generate original data of the image pixel density of the battery simulation microscopic internal physical field map;

[0153] The second sending module 1110 is connected to the second processing module 1109 and is used for the server to send the original data of the pixel density of the image to the client browser.

[0154] A first receiving module 1103, connected to the second sending module 1110, for receiving information returned by the server;

[0155] The first processing module 1104 is connected to the first receiving module 1103, and is used for rendering and generating a battery simulation microscopic internal physical field diagram according to the original data of the image pixel density and the settings of the browser.

[0156] The battery simulation microscopic internal physical field image generation system of the present invention can reduce the computing burden of the server, improve the efficiency of data processing and image generation, and enhance the scalability and flexibility of the system.

[0157] In one embodiment, the query module 1106 is also used to obtain a visualization toolkit file by setting a Node.js API service, wherein the visualization toolkit file includes geometric mesh information and additional simulation set scalar data information, wherein the Node.js API is an application program interface service pre-set based on the Node.js service for obtaining a visualization toolkit file; the grid points of the geometric mesh can be supplemented with scalar data information; the additional simulation set scalar data information is the type of the geometric mesh points, including but not limited to positive and negative electrode materials, conductive bridges, and electrolyte information.

[0158] In one embodiment, the parsing module 1107 is further used to use vtk.js and vtk-xml to read the model information of the positive and negative electrode material particles and the conductive bridge by setting the Node.js API service, wherein the vtk.js is a pre-set JavaScript library for processing and rendering 3D data and scenes, and the vtk-xml is a pre-set toolkit for parsing VTK format files, which supports the description of complex 3D geometric structures and physical fields;

[0159] In one embodiment, if the positive and negative electrode material particles are circular, the analysis module 1107 is used to analyze and obtain the center and radius of the positive and negative electrode material particle model.

[0160] In one embodiment, if the conductive bridge is a square, the parsing module 1107 is used to parse and obtain the vertices of the conductive bridge model to obtain the position information of the conductive bridge.

[0161] The analysis module 1107 is used to read the data of each grid point in the positive and negative electrode material particles and the conductive bridge, including position information and additional scalar data, and generate a physically related scalar data set containing an array of positive and negative electrode material particles and conductive bridge data; the physically related scalar data set includes but is not limited to a solid phase data set and a liquid phase data set corresponding to the microscopic internal physical field diagram of the battery simulation.

[0162] The second processing module 1109 is also used to process the image rendering data through an optimization algorithm according to the set image pixel size to reduce the grid point density, wherein a battery simulation microscopic internal physical field diagram viewing request initiated by a user generates a set viewing request information including the set image pixel size information required by the client browser; and outputs diagrams at multiple times according to the image pixel size optimization algorithm.

[0163] The second processing module 1109 is also used for: initializing the image rendering data, randomly selecting K initial cluster center points; an allocation step, using Euclidean distance or other measurement methods to allocate the K initial cluster data points to the cluster center closest to them; an update step, recalculating the center point of each cluster, wherein the center point is the mean of all points in the cluster; continuously repeating the allocation step and the update step until the center point of each cluster no longer changes significantly or reaches a predetermined number of iterations; setting the center point of each cluster as a representative data point; and constructing a simplified battery simulation microscopic internal physical field diagram through the center point of each cluster, and the client browser can parse and obtain the original data of the image pixel density according to the simplified battery simulation microscopic internal physical field diagram.

[0164] The first generating module 1101 is further used to: determine the battery simulation microscopic internal physical field diagram of a set type that needs to be viewed; and determine the upper and lower limits of the color threshold and the color gamut information that needs to be viewed.

[0165] The first processing module 1104 is also used to: generate a canvas according to the set size; generate the circular shape of the positive and negative electrode material particles and the square shape of the conductive bridge according to the size information input by the user; call Web Workers to set the color of the pixels according to the upper and lower limits of the color threshold; according to the color of each pixel, the browser renders the canvas to output the microscopic internal physical field diagram of the battery simulation.

[0166] The first processing module 1104 is also used for: color threshold setting supports automatic allocation and customized color upper and lower limit color domains; automatic allocation sets the maximum value in the image as the color upper limit, the minimum value as the color lower limit, and other pixels are set according to the rules; customization means setting the upper and lower limit colors by yourself, and when setting, the lower limit will be restricted to not exceed the maximum value of the data in the image, and the upper limit will not be less than the minimum value in the image.

[0167] The first processing module 1104 is also used to generate a battery simulation microscopic internal physical field by re-rendering according to the original data of the image pixel density and the settings of the browser at the switching moment.

[0168] In one embodiment, the server processing includes:

[0169] 1.According to the browser request parameters, obtain the VTK file of the simulation results. The VTK file contains geometric information and simulation results.

[0170] (1) The main purpose of using VTK files is to attach scalar data to each grid point to store other information.

[0171] (2) Additional scalar data mainly include the types of grid points (positive and negative electrode materials, conductive bridge, and electrolyte).

[0172] 2.Node.js parses VTK file:

[0173] (1) Node.js uses vtk.js and vtk-xml packages to read the model information of positive and negative electrode material particles and conductive bridges.

[0174] (2) The positive and negative electrode materials are circular, and the center and radius of the model are obtained (which can be obtained by calculation);

[0175] (3) The conductive bridge is a square, and the model vertices are obtained to obtain the position information of the conductive bridge;

[0176] (4) Reading the data of each grid point in the positive and negative electrode materials and the conductive bridge, including position information and additional scalar data, to generate a data array of the positive electrode, negative electrode, and conductive bridge;

[0177] (5) generating a liquid phase data array;

[0178] 3. Grid point data processing in simulation:

[0179] (1) Because the number of simulation grid points is very large and is not needed in image generation, the density of grid points needs to be reduced;

[0180] (2) According to the set image pixel size, the K-means clustering algorithm can quickly process and reduce the grid point density without affecting the macro-value changes;

[0181] (3) The simulation graph normally outputs graphs at multiple moments, so it is necessary to repeat step 2 to obtain the results at each moment;

[0182] In one embodiment, the client browser obtains data and then processes it, including:

[0183] 1. Set the canvas size according to requirements and initially generate a canvas.

[0184] 2. Process data: Generate positive and negative pole circles and conductive bridge squares based on the position information in the data.

[0185] 3. Color settings

[0186] (1) Because the amount of pixel data in the image is large, consider using Web Works to set the pixel color.

[0187] (2) ColorBar color settings support automatic allocation and custom upper and lower color limits;

[0188] (3) Automatically assign the maximum value in the image to be the upper color limit, the minimum value to be the lower color limit, and the colors of other pixels are set according to the rules;

[0189] (4) Customization means customizing the upper and lower limit colors. When setting, the lower limit shall not exceed the maximum value of the data in the figure, and the upper limit shall not be less than the minimum value in the figure;

[0190] In one embodiment, the client browser generates a result after acquiring data, including: generating the color of each pixel according to the browser data acquisition post-processing step, and the browser rendering canvas. By switching the time, the browser data acquisition post-processing can be reset and the canvas can be re-rendered.

[0191] The lithium-ion battery simulation microscopic physical field diagram generation system proposed in the present invention reduces the computing burden of the server, improves the efficiency of data processing and image generation, and enhances the scalability and flexibility of the system by generating physical field diagrams on the browser side. The computationally intensive image generation tasks are moved to the client, optimizing the use of server resources. Users can customize the upper and lower color limits and color gamut to achieve more flexible image display and analysis. Since the server only needs to return streamlined data, the system has stronger scalability and rapid response performance.

[0192] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server, data center, etc. that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0193] The various illustrative logic units and circuits described in the embodiments of the present application can be implemented or operated by a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, a discrete hardware component, or the design of any combination of the above functions. The general-purpose processor can be a microprocessor, and optionally, the general-purpose processor can also be any traditional processor, control device, micro or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration to implement.

[0194] The steps of the method or algorithm described in the embodiments of the present application can be directly embedded in the software unit executed by the hardware, the processor, or a combination of the two. The software unit can be stored in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM or other storage media of any form in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Alternatively, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC.

[0195] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0196] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0197] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0198] The description of the present invention is given for the purpose of illustration and description, and is not intended to be exhaustive or to limit the invention to the disclosed form. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for particular uses.

Claims

1. A method for generating a microscopic internal physical field diagram of a battery simulation, characterized in that: include: The client browser generates setting viewing request information according to the battery simulation microscopic internal physical field diagram viewing request initiated by the user; The client browser sends the setting viewing request information to the server; After receiving the viewing request information from the client browser, the server queries the visualization toolkit file database according to the setting viewing request information to obtain the visualization toolkit file corresponding to the setting viewing request information; The server parses the visualization toolkit file to obtain a physics-related scalar data set corresponding to the set view request information; The server processes the physics-related scalar data set to generate image rendering data of a battery simulation microscopic internal physical field diagram corresponding to the setting view request information; The server processes the image rendering data by a set method to generate original data of the image pixel density of the battery simulation microscopic internal physical field map; The server sends the original data of the pixel density of the image to the client browser; The client browser generates a battery simulation microscopic internal physical field diagram based on the original data of the image pixel density and the browser settings.

2. The method according to claim 1, characterized in that After receiving the viewing request information from the client browser, the server queries the visualization toolkit file database according to the setting viewing request information to obtain the visualization toolkit file corresponding to the setting viewing request information, including: The server side obtains the visualization toolkit file by setting a Node.js API service, wherein the visualization toolkit file includes geometric mesh information and additional simulation set scalar data information, wherein the Node.js API service is an application program interface service pre-set based on the Node.js service for obtaining the visualization toolkit file; The grid points of the geometric grid can be attached with scalar data information; The additional simulation set scalar data information is the type of geometric grid points, including but not limited to positive and negative electrode materials, conductive bridges, and electrolyte information.

3. The method according to claim 1 or 2, characterized in that: The server parses the visualization toolkit file to obtain a physics-related scalar data set corresponding to the setting view request information, including: The server uses vtk.js and vtk-xml to read the model information of the positive and negative electrode material particles and the conductive bridge by setting the Node.js API service. The vtk.js is a pre-set JavaScript library for processing and rendering 3D data and scenes. The vtk-xml is a pre-set toolkit for parsing VTK format files, which supports the description of complex 3D geometric structures and physical fields. If the positive and negative electrode material particles are circular, the center and radius of the positive and negative electrode material particle models are obtained by analysis; If the conductive bridge is a square, the vertices of the conductive bridge model are obtained by analysis to obtain the position information of the conductive bridge; The data of each grid point in the positive and negative electrode material particles and the conductive bridge are read to include position information and additional scalar data, and a physically related scalar data set including an array of data of the positive and negative electrode material particles and the conductive bridge is generated; the physically related scalar data set includes but is not limited to a solid phase data set and a liquid phase data set corresponding to the microscopic internal physical field diagram of the battery simulation.

4. The method according to claim 1, characterized in that: The server processes the image rendering data by a setting method to generate original data of the image pixel density of the battery simulation microscopic internal physical field map, including: Processing the image rendering data through an optimization algorithm according to the set image pixel size to reduce the grid point density, wherein the user-initiated battery simulation microscopic internal physical field diagram viewing request generates a set viewing request information including the set image pixel size information required by the client browser; The algorithm is optimized based on the image pixel size to output images at multiple times.

5. The method according to claim 4, characterized in that The step of processing the image rendering data by an optimization algorithm according to the set image pixel size to reduce the grid point density includes: Initialize the image rendering data and randomly select K initial cluster center points; An assignment step, using Euclidean distance or other measurement methods to assign the K initial cluster data points to the cluster center closest to them; An updating step is to recalculate the center point of each cluster, where the center point is the mean of all points in the cluster; Repeating the allocation step and the update step continuously until the center point of each cluster no longer changes significantly or a predetermined number of iterations is reached; Set the center point of each cluster as the representative data point; A simplified microscopic internal physical field diagram of battery simulation is constructed through the center point of each cluster, and the client browser can parse and obtain the original data of the pixel density of the image according to the simplified microscopic internal physical field diagram of battery simulation.

6. The method according to claim 1, characterized in that Before the client browser generates and sets the viewing request information based on the battery simulation microscopic internal physical field diagram viewing request initiated by the user, it includes: Determine the type of battery simulation microscopic internal physical field diagram that needs to be viewed; Determine the upper and lower limits of the color threshold and the color gamut information that you need to view.

7. The method according to claim 1, characterized in that The client browser generates a battery simulation microscopic internal physical field diagram according to the original data of the image pixel density and the settings of the browser, including: Generate a canvas according to the set size; Generate the circular shape of the positive and negative electrode material particles and the square shape of the conductive bridge according to the size information input by the user; Call Web Workers to set the color of pixels according to the upper and lower limits of the color threshold; Based on the color of each pixel, the browser renders the canvas and outputs the microscopic internal physical field diagram of the battery simulation.

8. The method according to claim 7, characterized in that The calling of Web Workers to set the color of the pixel points according to the upper and lower limits of the color threshold includes: Color threshold settings support automatic allocation and custom upper and lower color limits; Automatically assign the maximum value in the image as the upper color limit, the minimum value as the lower color limit, and the colors of other pixels are set according to the rules; Customization means customizing the upper and lower limit colors. When setting, the lower limit will not exceed the maximum value of the data in the graph, and the upper limit will not be less than the minimum value in the graph.

9. The method according to claim 1, characterized in that: Also includes: The client browser re-renders the battery simulation microscopic internal physical field according to the original data of the image pixel density and the browser settings at the switching moment.

10. A battery simulation microscopic internal physical field diagram generation system, characterized in that: The invention comprises a client browser and a server, wherein the client browser comprises a first generating module, a first sending module, a first receiving module and a first processing module, and the server comprises a second receiving module, a query module, a parsing module, a second generating module, a second processing module and a second sending module. The first generating module is used to generate setting viewing request information according to a battery simulation microscopic internal physical field diagram viewing request initiated by a user; A first sending module, connected to the first generating module, and configured to send the setting viewing request information to a server; A second receiving module, connected to the first sending module, and configured to receive setting viewing request information; A query module connected to the second receiving module, configured to query the visualization toolkit file database according to the set viewing request information to obtain the visualization toolkit file corresponding to the set viewing request information after receiving the viewing request information from the client browser; A parsing module, connected to the query module, for parsing the visualization toolkit file to obtain a physics-related scalar data set corresponding to the setting view request information; A second generating module, connected to the analyzing module, for processing the physics-related scalar data set to generate image rendering data of a microscopic internal physical field diagram of a battery simulation corresponding to the setting viewing request information; A second processing module, connected to the second generating module, is used to process the image rendering data by a set method to generate original data of the image pixel density of the battery simulation microscopic internal physical field map; The second sending module is connected to the second processing module and is used for the server to send the original data of the pixel density of the image to the client browser. A first receiving module, connected to the second sending module, and configured to receive information returned by the server; The first processing module is connected to the first receiving module and is used to render and generate a battery simulation microscopic internal physical field diagram according to the original data of the image pixel density and the settings of the browser.

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