Production data tracing method, device and computer equipment

Abstract

The application relates to a production data tracing method, device and computer equipment. The method comprises the following steps: acquiring identification information of a battery product to be traced; according to the identification information, searching a production path matched with the identification information and production data corresponding to each flow node on the production path from a graph database; wherein the production path comprises a plurality of flow nodes with a preset sequence; and the flow nodes and the production data are stored in a one-to-one corresponding manner in the graph database. The production path or the production data corresponding to the flow nodes of a certain section of the production path can be conveniently and quickly searched, and the tracing query time is shortened from minutes to seconds.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, storage medium, and computer program product for tracing production data. Background Technology

[0002] Traceability systems have been widely applied across various industries. Essentially, they are production control systems capable of forward, reverse, or non-directional product tracking, applicable to various types of process and production control systems. In the lithium battery industry, the mainstream approach to traceability of production data is through Manufacturing Execution Systems (MES). MES decomposes the lithium battery production process into several traceability sub-units, from the slurry mixing stage to the packaging stage. Each traceability sub-unit corresponds to a single traceability identifier, linking all production-related data for that sub-unit. For example, the slurry mixing stage includes all data related to the slurry mixing stage of all products, and the packaging stage includes all data related to the packaging stage of all products.

[0003] Because the manufacturing process of lithium batteries is quite complex, it typically generates dozens of database tables corresponding to traceability sub-units. To trace lithium battery production data, these dozens of tables need to be linked and queried. Since lithium battery cells contain a vast amount of data, a single table can contain hundreds of millions of records after six months of mass production. Such linked queries across dozens of tables result in lengthy database search processing times, and may even cause data searches to freeze, preventing timely results from being obtained for traceability queries. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, storage medium, and computer program product for tracing production data to address the aforementioned technical problems.

[0005] Firstly, this application provides a method for tracing production data. The method includes:

[0006] A method for tracing production data, applied to a server, the method comprising:

[0007] Obtain the identification information of the battery products to be traced;

[0008] Based on the identification information, the production path matching the identification information and the production data corresponding to each process node on the production path are searched from the graph database; wherein, the production path includes multiple process nodes with a preset order; the process nodes and the production data are stored in a one-to-one correspondence in the graph database.

[0009] In one possible implementation, the production data includes deterministic production data and non-deterministic production data. The step of searching the graph database for a production path matching the identification information and the production data corresponding to each process node on the production path, based on the identification information, includes:

[0010] Based on the identification information, search the graph database for the production path that matches the identification information and the deterministic production data corresponding to each process node on the production path;

[0011] Based on the start and end times of each process node, nondeterministic production data matching the process node and the start and end times are retrieved from the time series database.

[0012] In one possible implementation, the process node records the start and end times of the production of the battery product, and the time-series database stores the correspondence between each time point within a preset time period of the process node and non-deterministic data; based on the start and end times of each process node, non-deterministic production data matching the process node and the start and end times is retrieved from the time-series database, including:

[0013] Based on the start and end times of the battery product recorded at each process node, a sub-period matching the start and end times is queried from a preset period in the time series database;

[0014] Obtain the nondeterministic production data corresponding to the sub-period from the time-series database.

[0015] One possible implementation also includes:

[0016] The production data is returned to the client; wherein the client is used to display the production data in a visualization scenario of the digital twin.

[0017] In one possible implementation, the client is used to display the production data in a visualization scenario of the digital twin, including:

[0018] The client is used to display the production data corresponding to each process node on the production path of the battery product; and in response to the query operation of the production time, to display the data changes in the time period adjacent to the production time.

[0019] In one possible implementation, the deterministic production data includes at least one of the following:

[0020] Feeding data, process data, and result parameters;

[0021] The nondeterministic production data includes at least one of the following:

[0022] Personnel operation, environmental parameters, and process parameters.

[0023] Secondly, this application also provides a production data traceability device applied to a server, the device comprising:

[0024] The acquisition module is used to acquire the identification information of the battery products to be traced.

[0025] The traceability module is used to search the graph database for the production path that matches the identification information and the production data corresponding to each process node on the production path, based on the identification information; wherein, the production path includes multiple process nodes with a preset order; the process nodes and the production data are stored in a one-to-one correspondence in the graph database.

[0026] In one possible implementation, the production data includes deterministic production data and non-deterministic production data, and the traceability module includes:

[0027] The search submodule is used to search the graph database for the production path that matches the identification information and the deterministic production data corresponding to each process node on the production path, based on the identification information.

[0028] The traceability submodule is used to search for nondeterministic production data that matches the process node and the start and end times from the time series database based on the start and end times of each process node.

[0029] In one possible implementation, the process node records the start and end times of the production of the battery product, and the time-series database stores the correspondence between each time point within a preset time period of the process node and non-deterministic data; the traceability submodule includes:

[0030] The query unit is used to query a sub-time period that matches the start time and end time of the battery product recorded at each process node from a preset time period in the time series database.

[0031] The acquisition unit is used to acquire nondeterministic production data corresponding to the sub-period from the time series database.

[0032] One possible implementation also includes:

[0033] A return module is used to return the production data to the client; wherein the client is used to display the production data in a visualization scenario of the digital twin.

[0034] In one possible implementation, the client in the return module is also used to respond to a query operation at the production time and display the data changes in the time period adjacent to the production time.

[0035] In one possible implementation, the deterministic production data includes at least one of the following:

[0036] Feeding data, process data, and result parameters;

[0037] The nondeterministic production data includes at least one of the following:

[0038] Personnel operation, environmental parameters, and process parameters.

[0039] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the production data traceability method as described in any one of the embodiments of this disclosure.

[0040] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the production data traceability method as described in any one of the embodiments of this disclosure.

[0041] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the production data traceability method as described in any of the embodiments of this disclosure.

[0042] Compared to traditional methods of storing production data for each traceability sub-unit (process node), where each process node includes production data for all battery products within that sub-unit, querying the production data for a specific battery product in the next traceability sub-unit presents significant challenges. Since the positions of battery products within different traceability sub-units are uncertain (e.g., battery product A's production data is stored at location 'a' in the first traceability sub-unit and location 'b' in the second), it's necessary to determine the next traceability sub-unit's production data based on the correspondence between 'a' and 'b', resulting in high traceability complexity. In this embodiment, by storing the battery product production path and the corresponding production data for each process node along that path in a graph database, the production path contains the relationships between process nodes, allowing for convenient and rapid retrieval of production data corresponding to process nodes within or within a specific segment of the production path. This reduces traceability query wait times from minutes to seconds. Attached Figure Description

[0043] Figure 1This is an application environment diagram of a production data traceability method in one embodiment;

[0044] Figure 2 This is a schematic diagram of the first process of a production data traceability method in one embodiment;

[0045] Figure 3 This is a second process diagram of a production data traceability method in another embodiment;

[0046] Figure 4 This is a schematic diagram of the third process of a production data traceability method in another embodiment;

[0047] Figure 5 A mapping diagram between a digital twin and a graph database for a production data traceability method in another embodiment;

[0048] Figure 6 This is a structural block diagram of a production data traceability device in one embodiment;

[0049] Figure 7 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0052] The production data traceability method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0053] In one embodiment, such as Figure 2 As shown, a method for tracing production data is provided, which can be applied to... Figure 1 Taking the server in the example, the following steps are included:

[0054] Step S201: Obtain the identification information of the battery product to be traced.

[0055] Specifically, the battery products include, but are not limited to, lithium batteries, alkaline batteries, and acid batteries. The identification information can uniquely identify the product, and its form may include, for example, a QR code, barcode, serial number, or characters. In an exemplary embodiment, the user inputs the identification information of the battery product to be traced in the client, which is then sent to the server to obtain the identification information of the battery product to be traced.

[0056] Step S203: Based on the identification information, search the graph database for the production path that matches the identification information and the production data corresponding to each process node on the production path; wherein, the production path includes multiple process nodes with a preset order; the process nodes and the production data are stored in a one-to-one correspondence in the graph database.

[0057] Specifically, the process nodes can include nodes in the production process that the battery product undergoes, such as slurry mixing, coating, rolling, baking, slitting, winding, and packaging. It should be noted that the types and order of process nodes experienced by the battery product may differ in different scenarios; therefore, different types of production paths may differ. In this embodiment, the graph database pre-stores each process node on the production path, and the production data corresponding to each process node is stored, for example, in the form of attributes: baking corresponds to data A; slitting corresponds to data B; winding corresponds to data C, etc. The graph database also pre-stores the association relationships between process nodes; for example, the slurry mixing process node points to the coating process node, the coating process node points to the rolling process node, the rolling process node points to the baking process node, and so on. In an exemplary embodiment, the above-mentioned pointing association relationships can be implemented using pointers.

[0058] In one exemplary embodiment, reference is made to Figure 3 As shown, for example, the battery product to be traced is IDXX098, and the production data to be traced is from packaging (process node) to cell (process node). The graph database pre-stores the process nodes from cell to packaging, i.e., the production path, and the process nodes are related to each other. The attributes of each process node can store the corresponding production data. Therefore, by inputting the identification information of the battery product to be traced and the process node to be traced, the corresponding production data can be quickly provided. Similarly, in another exemplary embodiment, for example, if the production data to be traced for the battery product is from cell (process node) to positive and negative electrode raw materials (process node), the graph database pre-stores the production path from positive and negative electrode raw materials to cell, and the corresponding production data can be quickly provided. It should be noted that when tracing the production data of the battery product to be traced, from the cell to the positive and negative electrode raw materials, if the identification information, i.e., the ID in the diagram, changes from rolling (process node) to coating (process node) or from coating (process node) to slurry mixing (process node), for example, the former changes from XX005 to XX003, this identification information can indicate that the batch has changed. Since the graph database stores the relationship between two process nodes, for example... Figure 3 The arrows in the diagram indicate that when data is inserted into the graph database, the output ID of the previous process becomes the input ID of the next process. Therefore, changes in the identification information do not affect the traceable production path. Thus, all production data can be retrieved from a single cell ID through node relationships.

[0059] Compared to traditional methods of storing production data for each traceability sub-unit (process node), where each process node includes production data for all battery products within that sub-unit, querying the production data for a specific battery product in the next traceability sub-unit presents significant challenges. Since the positions of battery products within different traceability sub-units are uncertain (e.g., battery product A's production data is stored at location 'a' in the first traceability sub-unit and location 'b' in the second), it's necessary to determine the next traceability sub-unit's production data based on the correspondence between 'a' and 'b', resulting in high traceability complexity. In this embodiment, by storing the battery product production path and the corresponding production data for each process node along that path in a graph database, the production path contains the relationships between process nodes, allowing for convenient and rapid retrieval of production data corresponding to process nodes within or within a specific segment of the production path. This reduces traceability query wait times from minutes to seconds.

[0060] In one possible implementation, refer to Figure 4 As shown, the production data includes deterministic production data and non-deterministic production data. Step S203, based on the identification information, involves searching the graph database for the production path matching the identification information and the production data corresponding to each process node on the production path, including:

[0061] Step S401: Based on the identification information, search the graph database for the production path that matches the identification information and the deterministic production data corresponding to each process node on the production path;

[0062] Step S403: Based on the start and end times of each process node, search the time series database for nondeterministic production data that matches the process node and the start and end times.

[0063] Specifically, the deterministic data can include data that does not change over time, including but not limited to, for example: material input data, process data, and result parameters. Material input data can include the type of material, its weight, and its proportion; process data can include product batches and product processes; and result parameters can include the specifications of the intermediate parts after processing. The non-deterministic data can include data that changes over time, including but not limited to, for example: personnel operations, environmental parameters, and process parameters. Personnel operations can include personnel names, the object being operated on, and the amount of data on the object over time; environmental parameters can include data related to temperature, humidity, and pH in the production environment; and process parameters can include data on the intermediate parts during processing. In an exemplary embodiment, deterministic production data is stored in a graph database, for example, stored as attributes, with baking corresponding to deterministic data h; slitting corresponding to deterministic data j; and winding corresponding to deterministic data c, etc. Non-deterministic production data is stored in a time-series database. Based on the production time of the battery product to be traced at the process node, such as the start and end times (including the start and end times), non-deterministic production data matching the process node and the start and end times is searched from the time-series database.

[0064] In the above embodiments, due to the non-deterministic nature of the production data, each timestamp may correspond to data as it changes over time, resulting in a large data volume. This embodiment stores the non-deterministic data in a time-series database and associates it with the start and end times of process nodes. Therefore, this embodiment can achieve convenient and quick querying even for large volumes of non-deterministic production data.

[0065] In one possible implementation, the process node records the start and end times of the production of the battery product, and the time-series database stores the correspondence between each time point within a preset time period of the process node and non-deterministic data; based on the start and end times of each process node, non-deterministic production data matching the process node and the start and end times is retrieved from the time-series database, including:

[0066] Based on the start and end times of the battery product recorded at each process node, a sub-period matching the start and end times is queried from a preset period in the time series database;

[0067] Obtain the nondeterministic production data corresponding to the sub-period from the time-series database.

[0068] Specifically, the start and end times can be stored as attributes. For example, process node 1 corresponds to deterministic production data a, with start time t1 and end time t2; process node 2 corresponds to deterministic production data b, with start time t3 and end time t4; process node 3 corresponds to deterministic production data c, with start time t5 and end time t6, and so on. In this embodiment, the correspondence between each moment of the process node within a preset time period and non-deterministic data can be as follows: For example, the correspondence between each moment of process node S and non-deterministic data between 2019 and 2022 may include: XX month XX day XX hour XX minute 01 second, the rotation speed of equipment A is 1500 r / min, and the ambient temperature is 23℃; XX month XX day XX hour XX minute 02 second, the rotation speed of equipment A is 1499 r / min, and the ambient temperature is 25℃; XX month XX day XX hour XX minute 02 second, the rotation speed of equipment A is 1485 r / min, and the ambient temperature is 24℃, and so on. Based on the start time t1 and end time t2 recorded in the process node, the sub-period corresponding to the start time and end time can be determined in the time series database. For example, if t1 is 8:30:25 AM on March 5, 2020, and t2 is 3:30:16 PM on March 5, 2020, then the sub-period is from 8:30:25 AM on March 5, 2020 to 3:30:16 PM on March 5, 2020. The deterministic production data corresponding to the sub-period can then be queried from the time series database.

[0069] Production data in this embodiment can be obtained in the following way: by collecting material input data, manual operation data, production process data, environmental data, and quality inspection data related to equipment production through the data acquisition module, integrating the production process data of MES (Manufacturing Execution System), and storing all data in a graph database and a time-series database.

[0070] In this embodiment, the start and end times of battery product production at process nodes are selected, and the association between the graph database and the time-series database is established. This conforms to the data storage rules of the time-series database and allows for convenient and quick querying of large amounts of non-deterministic production data that match the process nodes and identification information.

[0071] One possible implementation also includes:

[0072] The production data is returned to the client; wherein the client is used to display the production data in a visualization scenario of the digital twin.

[0073] For details, please refer to Figure 5The preset sequence of process nodes in the graph database can correspond to the production line in the digital twin model. The production data of the process nodes in the graph database provides data support for the digital twin model, and the production data of the production line is displayed in the client's digital twin model display interface. In an exemplary embodiment, by inputting the identification information of the battery product to be traced in the client, the time is located to the production time of the battery product, and the production data related to each process of the entire production line can be continuously displayed through the client. In an exemplary embodiment, the digital twin visualization scenario supports the playback of the production links of each process node and the analysis of the entire production process. In an exemplary embodiment, in response to the query operation of the production time, the data changes of the time period adjacent to the production time are displayed, which facilitates statistical analysis of the data of interest and assists in the location of quality problems.

[0074] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0075] Based on the same inventive concept, this application also provides a production data traceability device for implementing the production data traceability method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more production data traceability device embodiments provided below can be found in the limitations of the production data traceability method described above, and will not be repeated here.

[0076] In one embodiment, such as Figure 6 As shown, a production data traceability device 600 is provided, applied to a server, and includes:

[0077] The acquisition module 601 is used to acquire the identification information of the battery product to be traced;

[0078] The traceability module 603 is used to search the graph database for the production path that matches the identification information and the production data corresponding to each process node on the production path, based on the identification information; wherein, the production path includes multiple process nodes with a preset order; the process nodes and the production data are stored in a one-to-one correspondence in the graph database.

[0079] In one possible implementation, the production data includes deterministic production data and non-deterministic production data, and the traceability module includes:

[0080] The search submodule is used to search the graph database for the production path that matches the identification information and the deterministic production data corresponding to each process node on the production path, based on the identification information.

[0081] The traceability submodule is used to search for nondeterministic production data that matches the process node and the start and end times from the time series database based on the start and end times of each process node.

[0082] In one possible implementation, the process node records the start and end times of the production of the battery product, and the time-series database stores the correspondence between each time point within a preset time period of the process node and non-deterministic data; the traceability submodule includes:

[0083] The query unit is used to query a sub-time period that matches the start time and end time of the battery product recorded at each process node from a preset time period in the time series database.

[0084] The acquisition unit is used to acquire nondeterministic production data corresponding to the sub-period from the time series database.

[0085] One possible implementation also includes:

[0086] A return module is used to return the production data to the client; wherein the client is used to display the production data in a visualization scenario of the digital twin.

[0087] In one possible implementation, the client in the return module is also used to respond to a query operation at the production time and display the data changes in the time period adjacent to the production time.

[0088] In one possible implementation, the deterministic production data includes at least one of the following:

[0089] Feeding data, process data, and result parameters;

[0090] The nondeterministic production data includes at least one of the following:

[0091] Personnel operation, environmental parameters, and process parameters.

[0092] Each module in the aforementioned production data traceability device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0093] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The computer device's graph database and time-series database store traceable production data. The network interface allows communication with external terminals via a network connection. When executed by the processor, the computer program implements a production data traceability method.

[0094] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0095] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0096] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for tracing production data, characterized in that, Applied to a server, the method includes: Obtain the identification information of the battery products to be traced; Based on the identification information, search the graph database for the production path that matches the identification information and the deterministic production data corresponding to each process node on the production path; Based on the start and end times of the battery product recorded at each process node, a sub-period matching the start and end times is queried from a preset time period in the time-series database; non-deterministic production data corresponding to the sub-period is obtained from the time-series database; wherein, the production path includes multiple process nodes with a preset order; the process nodes and the deterministic production data are stored in a one-to-one correspondence in the graph database; the deterministic production data includes production data that does not change over time, and the non-deterministic production data includes production data that changes over time; the process nodes record the start and end times of the production of the battery product, and the time-series database stores the correspondence between each moment within the preset time period of the process node and the non-deterministic data; the deterministic production data includes at least one of the following: material input data, process data, and result parameters; the non-deterministic production data includes at least one of the following: personnel operation, environmental parameters, and process parameters.

2. The method according to claim 1, characterized in that, Also includes: The production data is returned to the client; wherein the client is used to display the production data in a visualization scenario of the digital twin.

3. The method according to claim 2, wherein, The client is used to display the production data in a digital twin visualization scenario, including: The client is used to display the production data corresponding to each process node on the production path of the battery product; and in response to the query operation of the production time, to display the data changes in the time period adjacent to the production time.

4. A production data traceability device, characterized in that, Applied to a server, the device includes: The acquisition module is used to acquire the identification information of the battery products to be traced. The traceability module includes a search submodule and a traceability submodule. The search submodule is used to search the graph database for a production path that matches the identification information and the deterministic production data corresponding to each process node on the production path, based on the identification information. The query unit is used to query a sub-time period that matches the start time and end time of the battery product recorded at each process node from a preset time period in the time series database. An acquisition unit is used to acquire nondeterministic production data corresponding to the sub-period from the time-series database; wherein, the production path includes multiple process nodes with a preset order; the process nodes and the deterministic production data are stored in a one-to-one correspondence in the graph database; the deterministic production data includes production data that does not change over time, and the nondeterministic production data includes production data that changes over time; the process nodes record the start time and end time of the production of the battery product, and the time-series database stores the correspondence between each moment within the preset period of the process node and the nondeterministic data; the deterministic production data includes at least one of the following: material input data, process data, and result parameters; the nondeterministic production data includes at least one of the following: personnel operation, environmental parameters, and process parameters.

5. The apparatus according to claim 4, characterized in that, Also includes: A return module is used to return the production data to the client; wherein the client is used to display the production data in a visualization scenario of the digital twin.

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