Battery supply digital supervision system and method based on digital twinning

Abstract

The invention discloses a battery supply digital supervision system and method based on digital twinning, and belongs to the technical field of battery supply chains. Block nodes are arranged in a digital twin cloud platform and a supply chain database is distributed, so that real-time sharing and collaboration of supply chain information are realized; by capturing and analyzing supply features and demand features, an accurate supply feature matrix carrier model and a demand task feature matrix carrier model are constructed, a block node feedback matrix model is generated to record node feedback values of block nodes, and supply capability defect values of the block nodes are evaluated and output regularly. Therefore, effective evaluation and optimization of the supply capacity of the supply chain are realized. According to the method, the collaborative efficiency, the response speed and the information transparency of the supply chain are improved, the technology mutual compensation of the battery supply chain system can be promoted, and the capacity optimization of each side of the battery supply chain can be helped.

Description

Technical Field

[0001] The present invention relates to the technical field of battery supply chain, and specifically to a digital supervision system and method for battery supply based on digital twins. Background Art

[0002] In battery supply chain management, with the rapid development of the new energy industry, the complexity and dynamism of the battery supply chain are increasing, which puts higher requirements on the collaborative efficiency, response speed and information transparency of the supply chain; traditional management methods rely on manual coordination and paper document circulation, resulting in delayed information transmission and inconsistent data, making it difficult to achieve effective monitoring and optimization of the supply chain.

[0003] In recent years, digital twin technology and blockchain technology have been introduced into battery supply chain management. Digital twin technology builds a virtual model to map and monitor the physical supply chain, thereby improving the transparency and visualization of the supply chain. Blockchain technology, with its decentralized and tamper-proof characteristics, provides security for information exchange and sharing in the supply chain.

[0004] However, the existing digital twin and blockchain technologies still have some technical bottlenecks in battery supply chain collaboration: for example, how to efficiently integrate digital twin models and blockchain nodes to achieve real-time sharing and collaboration of supply chain information; how to accurately capture and analyze the supply characteristics and demand characteristics in the supply chain to build accurate supply and demand models; how to effectively evaluate and optimize the supply capacity of the supply chain based on these models. Summary of the invention

[0005] The purpose of the present invention is to provide a digital twin-based battery supply digital supervision system and method to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A digital twin-based battery supply digital supervision system, the system includes: a digital twin cloud platform module, a digital information acquisition module, a digital matrix carrier module and a digital analysis and processing module;

[0008] The digital twin cloud platform module is used to set up block nodes and allocate supply chain databases in the digital twin cloud platform, connect to the ERP software of each battery supply chain party through the block nodes, and obtain and store the battery supply information reported by the battery supply chain party;

[0009] The digital information acquisition module is used to capture the supply characteristics of each block through the block node to generate a block supply feature set; enter the battery demand index information in the digital twin cloud platform to generate a demand task feature set;

[0010] The digital matrix carrier module sequentially constructs a supply feature matrix carrier model and a demand task feature matrix carrier model based on the block supply feature set and the demand task feature set;

[0011] The digital analysis and processing module generates a block node feedback matrix model based on the supply feature Boolean matrix and the demand feature Boolean matrix to record the node feedback value of the block node, and regularly evaluates and outputs the supply capacity defect value of the block node.

[0012] Further, the digital twin cloud platform module includes a port connection unit and a supply chain database unit;

[0013] The port connection unit is used to set a block node on the digital twin cloud platform according to the number of battery supply chain parties, wherein one block node corresponds to one battery supply chain party port, and the block node is connected to the ERP software of the battery supply chain party through the interface protocol, and the battery supply information reported by the battery supply chain party is identified, and the battery supply information includes a number of battery specification parameters;

[0014] The supply chain database unit allocates the supply chain database in the digital twin cloud platform based on the number of block nodes, wherein one block node corresponds to one battery supply chain database and stores the battery supply information in real time.

[0015] Further, the digital information acquisition module includes a block supply feature generation unit and a demand task feature generation unit;

[0016] The block supply feature generation unit is used to uniformly encode battery specification parameters and count battery supply information identified by the block node to generate a block supply feature set;

[0017] The demand task feature generation unit is used to input battery demand index information in the digital twin cloud platform, where the battery demand index information includes several battery specification parameter groups to generate a demand task feature set.

[0018] Further, the digital matrix carrier module includes a supply feature matrix carrier model unit and a demand task feature matrix carrier model unit;

[0019] The supply feature matrix carrier model unit is used to construct a supply feature matrix carrier model, wherein the row number of the supply feature matrix corresponds to the code number of the block node, and the column number of the supply feature matrix corresponds to the type number of the battery specification parameter; the block supply feature set is mapped to each row of the supply feature matrix, and the matrix elements of the supply feature matrix that are mapped are recorded as 1, and the matrix elements of the supply feature matrix that are not mapped are recorded as 0;

[0020] The demand task feature matrix carrier model unit is used to construct a demand task feature matrix carrier model, the row number of the demand task feature matrix corresponds to the coding number of the battery specification parameter group, and the column number of the demand task feature matrix corresponds to the type number of the battery specification parameter; the battery specification parameter group is mapped to each row of the demand task feature matrix, the matrix elements of the demand task feature matrix that are mapped are recorded as 1, and the matrix elements of the demand task feature matrix that are not mapped are recorded as 0.

[0021] Further, the digital analysis and processing module includes a block node feedback matrix model unit, a digital evaluation unit and a digital update unit;

[0022] The block node feedback matrix model unit constructs a block node feedback matrix model by matrix operation based on the supply feature Boolean matrix and the demand feature Boolean matrix, wherein the block node feedback matrix model records the node feedback value of the block node matching battery specification parameter group;

[0023] The digital evaluation unit calibrates rows and columns in the block node feedback matrix model based on the maximum value of the node feedback value to obtain the node feedback value to be compared, and evaluates the supply capacity defect value of the block node through the node feedback value to be compared;

[0024] The digital update unit is used to regularly update the supply chain database and output and feedback the supply capacity defect value of each block node in real time through the demand task feature set.

[0025] A digital twin-based battery supply digital supervision method, the method comprising the following steps:

[0026] Step S1: Set up block nodes in the digital twin cloud platform and allocate a supply chain database, connect the ERP software of each battery supply chain party through the block nodes, and obtain and store the battery supply information reported by the battery supply chain party;

[0027] Step S2: Capture the supply characteristics of each block through the block node to generate a block supply feature set; enter the battery demand index information in the digital twin cloud platform to generate a demand task feature set;

[0028] Step S3: Based on the block supply feature set and the demand task feature set, respectively construct a supply feature matrix carrier model and a demand task feature matrix carrier model;

[0029] Step S4: Based on the supply feature Boolean matrix and the demand feature Boolean matrix, a block node feedback matrix model is generated to record the node feedback value of the block node, and to regularly evaluate and output the supply capacity defect value of the block node.

[0030] Furthermore, the specific implementation process of step S1 includes:

[0031] Establish a digital twin cloud platform, in which a number of block nodes are set, wherein one block node corresponds to a battery supply chain party port, and the block node is connected to the ERP software of the battery supply chain party through an interface protocol, and the battery supply information reported by the battery supply chain party is identified, and the battery supply information includes a number of battery specification parameters;

[0032] Based on the number of block nodes, a supply chain database is allocated in the digital twin cloud platform, wherein one block node corresponds to a battery supply chain database, and the battery supply information is stored in real time.

[0033] Furthermore, the specific implementation process of step S2 includes:

[0034] The battery specification parameters are uniformly coded, and the xth battery specification parameter is recorded as BS x ; Count the battery supply information identified by the block node and generate a block supply feature set, recorded as B(C i )={BS x |x∈[1,Y]}, where Y represents the total number of battery specification parameters, C i Represents the i-th block node;

[0035] Input the battery demand index information into the digital twin cloud platform, the battery demand index information includes several battery specification parameter groups, and generate a demand task feature set, denoted as D = {F e |e∈[1,E]}, where F e represents the e-th battery specification parameter group, E represents the total number of battery specification parameter groups, and F E ={BS x |x∈[1,Y]}.

[0036] Furthermore, the specific implementation process of step S3 includes:

[0037] Construct a supply feature matrix carrier model, wherein the row number of the supply feature matrix corresponds to the code number of the block node, and the column number of the supply feature matrix corresponds to the type number of the battery specification parameter; i ) is mapped to the i-th row of the supply feature matrix, and the matrix element corresponding to the i-th row and x-th column of the mapped supply feature matrix is ​​represented as BS x =1, the matrix element corresponding to the i-th row and x-th column of the supply feature matrix that has not been mapped is represented as BS x=0, then the supply feature Boolean matrix is ​​output, recorded as R(I×Y), where I represents the total number of block nodes;

[0038] Construct a demand task feature matrix carrier model, wherein the row number of the demand task feature matrix corresponds to the code number of the battery specification parameter group, and the column number of the demand task feature matrix corresponds to the type number of the battery specification parameter; e Map the e-th row of the demand task feature matrix, and represent the matrix element corresponding to the e-th row and x-th column of the mapped demand task feature matrix as BS x =1, the matrix element corresponding to the e-th row and x-th column of the demand task feature matrix that has not been mapped is represented as BS x =0, then the demand feature Boolean matrix is ​​output, denoted as R(E×Y).

[0039] Furthermore, the specific implementation process of step S4 includes:

[0040] Based on the supply feature Boolean matrix and the demand feature Boolean matrix, the block node feedback matrix model R(I×E)=R(I×Y)×R(E×Y) is constructed. T , where T represents the transposition symbol;

[0041] The matrix element in the i-th row and e-th column of the block node feedback matrix model is denoted as FV ie , and FV ie Represents block node C i Matching battery specification parameter group F e Node feedback value of

[0042] In the i-th row of the block node feedback matrix model, the maximum node feedback value FV is first calibrated. iv The corresponding column number v, and in the vth column of the block node feedback matrix model, the maximum node feedback value FV is again calibrated jv The corresponding row number j;

[0043] Based on node feedback value FV iv and node feedback value FV jv , evaluate block node C i Supply capacity deficit value Regularly update the supply chain database, and output and feedback the supply capacity defect value of each block node in real time through the demand task feature set;

[0044] According to the above method, through the matrix transposition operation, effective collaborative cross-matching can be achieved between different suppliers and demanders. The larger the node feedback value, the higher the matching degree between the supplier and the demander. However, it should be noted that this matching method is based on the battery specification parameters. After the row is determined in the block node feedback matrix model, the system automatically selects a type of battery that a supply chain party can produce. The description of this type can be represented by a battery specification parameter group. The largest node feedback value of the supply chain party is selected for the first time through the row determination. This is the battery type demand that the supply chain party can adapt to in history. However, when other supply chain parties make production line adjustments, other supply chain parties can produce more types of batteries, and then the maximum node feedback value in the column is calibrated again. The production technology capability of any battery supply chain can be judged through the supply capacity defect value. The larger the supply capacity defect value, the higher the defect degree of the production technology capability of the battery supply chain party, and the greater the gap in technological innovation with the market.

[0045] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in a digital twin-based battery supply digital supervision system and method provided by the present invention, by setting block nodes and allocating supply chain databases in the digital twin cloud platform, real-time sharing and collaboration of supply chain information is achieved; by capturing and analyzing supply characteristics and demand characteristics, an accurate supply feature matrix carrier model and a demand task feature matrix carrier model are constructed, and a block node feedback matrix model is generated to record the node feedback values ​​of block nodes, and regularly evaluate and output the supply capacity defect values ​​of block nodes, thereby achieving effective evaluation and optimization of the supply chain supply capacity; while improving the collaborative efficiency, response speed and information transparency of the supply chain, the present invention can promote the mutual complementation of technologies in the battery supply chain system, and can help all parties in the battery supply chain optimize production capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0047] Figure 1 It is a schematic diagram of the steps of a digital twin-based battery supply digital supervision method of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In the first embodiment of the present invention: a digital twin-based battery supply digital supervision system is provided, the system comprising: a digital twin cloud platform module, a digital information acquisition module, a digital matrix carrier module and a digital analysis and processing module;

[0050] The digital twin cloud platform module is used to set up block nodes and allocate supply chain databases in the digital twin cloud platform, connect to the ERP software of each battery supply chain party through the block nodes, and obtain and store the battery supply information reported by the battery supply chain party;

[0051] Wherein, the digital twin cloud platform module includes a port connection unit and a supply chain database unit;

[0052] The port connection unit is used to set a block node on the digital twin cloud platform according to the number of battery supply chain parties, wherein one block node corresponds to one battery supply chain party port, and the block node is connected to the ERP software of the battery supply chain party through the interface protocol, and the battery supply information reported by the battery supply chain party is identified, and the battery supply information includes a number of battery specification parameters;

[0053] The supply chain database unit allocates the supply chain database in the digital twin cloud platform based on the number of block nodes, wherein one block node corresponds to one battery supply chain database and stores the battery supply information in real time.

[0054] The digital information acquisition module is used to capture the supply characteristics of each block through the block node to generate a block supply feature set; enter the battery demand index information in the digital twin cloud platform to generate a demand task feature set;

[0055] Wherein, the digital information acquisition module includes a block supply feature generation unit and a demand task feature generation unit;

[0056] The block supply feature generation unit is used to uniformly encode battery specification parameters and count battery supply information identified by the block node to generate a block supply feature set;

[0057] The demand task feature generation unit is used to input battery demand index information in the digital twin cloud platform, where the battery demand index information includes several battery specification parameter groups to generate a demand task feature set.

[0058] The digital matrix carrier module sequentially constructs a supply feature matrix carrier model and a demand task feature matrix carrier model based on the block supply feature set and the demand task feature set;

[0059] Wherein, the digital matrix carrier module includes a supply feature matrix carrier model unit and a demand task feature matrix carrier model unit;

[0060] The supply feature matrix carrier model unit is used to construct a supply feature matrix carrier model, wherein the row number of the supply feature matrix corresponds to the code number of the block node, and the column number of the supply feature matrix corresponds to the type number of the battery specification parameter; the block supply feature set is mapped to each row of the supply feature matrix, and the matrix elements of the supply feature matrix that are mapped are recorded as 1, and the matrix elements of the supply feature matrix that are not mapped are recorded as 0;

[0061] The demand task feature matrix carrier model unit is used to construct a demand task feature matrix carrier model, the row number of the demand task feature matrix corresponds to the coding number of the battery specification parameter group, and the column number of the demand task feature matrix corresponds to the type number of the battery specification parameter; the battery specification parameter group is mapped to each row of the demand task feature matrix, the matrix elements of the demand task feature matrix that are mapped are recorded as 1, and the matrix elements of the demand task feature matrix that are not mapped are recorded as 0.

[0062] The digital analysis and processing module generates a block node feedback matrix model based on the supply feature Boolean matrix and the demand feature Boolean matrix to record the node feedback value of the block node, and regularly evaluates and outputs the supply capacity defect value of the block node;

[0063] Wherein, the digital analysis and processing module includes a block node feedback matrix model unit, a digital evaluation unit and a digital update unit;

[0064] The block node feedback matrix model unit constructs a block node feedback matrix model by matrix operation based on the supply feature Boolean matrix and the demand feature Boolean matrix, wherein the block node feedback matrix model records the node feedback value of the block node matching battery specification parameter group;

[0065] The digital evaluation unit calibrates rows and columns in the block node feedback matrix model based on the maximum value of the node feedback value to obtain the node feedback value to be compared, and evaluates the supply capacity defect value of the block node through the node feedback value to be compared;

[0066] The digital update unit is used to regularly update the supply chain database and output and feedback the supply capacity defect value of each block node in real time through the demand task feature set.

[0067] See also Figure 1 In the second embodiment, a digital twin-based battery supply digital supervision method is provided, and the method includes the following steps:

[0068] Step S1: Set up block nodes in the digital twin cloud platform and allocate a supply chain database, connect the ERP software of each battery supply chain party through the block nodes, and obtain and store the battery supply information reported by the battery supply chain party;

[0069] Exemplarily, a digital twin cloud platform is established, in which a plurality of block nodes are set, wherein one block node corresponds to a battery supply chain party port, and the block node is connected to the ERP software of the battery supply chain party through an interface protocol, and the battery supply information reported by the battery supply chain party is identified, and the battery supply information includes a plurality of battery specification parameters;

[0070] Based on the number of block nodes, a supply chain database is allocated in the digital twin cloud platform, wherein one block node corresponds to a battery supply chain database, and the battery supply information is stored in real time.

[0071] Step S2: Capture the supply characteristics of each block through the block node to generate a block supply feature set; enter the battery demand index information in the digital twin cloud platform to generate a demand task feature set;

[0072] For example, the battery specification parameters are uniformly coded, and the xth battery specification parameter is recorded as BS x ; Count the battery supply information identified by the block node and generate a block supply feature set, recorded as B(C i )={BS x |x∈[1,Y]}, where Y represents the total number of battery specification parameters, C i Represents the i-th block node;

[0073] Input the battery demand index information into the digital twin cloud platform, the battery demand index information includes several battery specification parameter groups, and generate a demand task feature set, denoted as D = {F e |e∈[1,E]}, where F e represents the e-th battery specification parameter group, E represents the total number of battery specification parameter groups, and F E ={BS x |x∈[1,Y]}.

[0074] Step S3: Based on the block supply feature set and the demand task feature set, respectively construct a supply feature matrix carrier model and a demand task feature matrix carrier model;

[0075] Exemplarily, a supply feature matrix carrier model is constructed, wherein the row number of the supply feature matrix corresponds to the code number of the block node, and the column number of the supply feature matrix corresponds to the type number of the battery specification parameter; the block supply feature set B(C i) is mapped to the i-th row of the supply feature matrix, and the matrix element corresponding to the i-th row and x-th column of the mapped supply feature matrix is ​​represented as BS x =1, the matrix element corresponding to the i-th row and x-th column of the supply feature matrix that has not been mapped is represented as BS x =0, then the supply feature Boolean matrix is ​​output, recorded as R(I×Y), where I represents the total number of block nodes;

[0076] Construct a demand task feature matrix carrier model, wherein the row number of the demand task feature matrix corresponds to the code number of the battery specification parameter group, and the column number of the demand task feature matrix corresponds to the type number of the battery specification parameter; e Map the e-th row of the demand task feature matrix, and represent the matrix element corresponding to the e-th row and x-th column of the mapped demand task feature matrix as BS x =1, the matrix element corresponding to the e-th row and x-th column of the demand task feature matrix that has not been mapped is represented as BS x =0, then the demand feature Boolean matrix is ​​output, denoted as R(E×Y).

[0077] Step S4: Based on the supply feature Boolean matrix and the demand feature Boolean matrix, a block node feedback matrix model is generated to record the node feedback value of the block node, and the supply capacity defect value of the block node is regularly evaluated and output;

[0078] Exemplarily, based on the supply feature Boolean matrix and the demand feature Boolean matrix, a block node feedback matrix model R(I×E)=R(I×Y)×R(E×Y) is constructed: T , where T represents the transposition symbol;

[0079] The matrix element in the i-th row and e-th column of the block node feedback matrix model is denoted as FV ie , and FV ie Represents block node C i Matching battery specification parameter group F e Node feedback value of

[0080] In the i-th row of the block node feedback matrix model, the maximum node feedback value FV is first calibrated. iv The corresponding column number v, and in the vth column of the block node feedback matrix model, the maximum node feedback value FV is again calibrated jv The corresponding row number j;

[0081] Based on node feedback value FV iv and node feedback value FV jv , evaluate block node C i Supply capacity deficit value The supply chain database is updated regularly, and the supply capacity defect value of each block node is fed back in real time through the demand task feature set.

[0082] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0083] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A digital supervision method for battery supply based on digital twin, characterized in that: The method includes: Step S1: Set up block nodes in the digital twin cloud platform and allocate a supply chain database, connect the ERP software of each battery supply chain party through the block nodes, and obtain and store the battery supply information reported by the battery supply chain party; Step S2: Capture the supply characteristics of each block through the block node to generate a block supply feature set; enter the battery demand index information in the digital twin cloud platform to generate a demand task feature set; Step S3: Based on the block supply feature set and the demand task feature set, respectively construct a supply feature matrix carrier model and a demand task feature matrix carrier model; Step S4: Based on the supply feature Boolean matrix and the demand feature Boolean matrix, a block node feedback matrix model is generated to record the node feedback value of the block node, and to regularly evaluate and output the supply capacity defect value of the block node.

2. According to claim 1, a digital twin-based battery supply digital supervision method is characterized in that: The specific implementation process of step S1 includes: Establish a digital twin cloud platform, in which a number of block nodes are set, wherein one block node corresponds to a battery supply chain party port, and the block node is connected to the ERP software of the battery supply chain party through an interface protocol, and the battery supply information reported by the battery supply chain party is identified, and the battery supply information includes a number of battery specification parameters; Based on the number of block nodes, a supply chain database is allocated in the digital twin cloud platform, wherein one block node corresponds to a battery supply chain database, and the battery supply information is stored in real time.

3. According to claim 2, a digital twin-based battery supply digital supervision method is characterized in that: The specific implementation process of step S2 includes: The battery specification parameters are uniformly coded, and the xth battery specification parameter is recorded as BS x ; Count the battery supply information identified by the block node and generate a block supply feature set, recorded as B(C i )={BS x |x∈[1,Y]}, where Y represents the total number of battery specification parameters, C i Represents the i-th block node; Input the battery demand index information into the digital twin cloud platform, the battery demand index information includes several battery specification parameter groups, and generate a demand task feature set, denoted as D = {F e |e∈[1,E]}, where F e represents the e-th battery specification parameter group, E represents the total number of battery specification parameter groups, and F E ={BS x |x∈[1,Y]}.

4. According to claim 3, a digital twin-based battery supply digital supervision method is characterized in that: The specific implementation process of step S3 includes: Construct a supply feature matrix carrier model, wherein the row number of the supply feature matrix corresponds to the code number of the block node, and the column number of the supply feature matrix corresponds to the type number of the battery specification parameter; i ) is mapped to the i-th row of the supply feature matrix, and the matrix element corresponding to the i-th row and x-th column of the mapped supply feature matrix is ​​represented as BS x =1, the matrix element corresponding to the i-th row and x-th column of the supply feature matrix that has not been mapped is represented as BS x =0, then the supply feature Boolean matrix is ​​output, recorded as R(I×Y), where I represents the total number of block nodes; Construct a demand task feature matrix carrier model, wherein the row number of the demand task feature matrix corresponds to the code number of the battery specification parameter group, and the column number of the demand task feature matrix corresponds to the type number of the battery specification parameter; e Map the e-th row of the demand task feature matrix, and represent the matrix element corresponding to the e-th row and x-th column of the mapped demand task feature matrix as BS x =1, the matrix element corresponding to the e-th row and x-th column of the demand task feature matrix that has not been mapped is represented as BS x =0, then the demand feature Boolean matrix is ​​output, denoted as R(E×Y).

5. According to claim 4, a digital twin-based battery supply digital supervision method is characterized in that: The specific implementation process of step S4 includes: Based on the supply feature Boolean matrix and the demand feature Boolean matrix, the block node feedback matrix model R(I×E)=R(I×Y)×R(E×Y) is constructed. T , where T represents the transposition symbol; The matrix element in the i-th row and e-th column of the block node feedback matrix model is denoted as FV ie , and FV ie Represents block node C i Matching battery specification parameter group F e Node feedback value of In the i-th row of the block node feedback matrix model, the maximum node feedback value FV is first calibrated. iv The corresponding column number v, and in the vth column of the block node feedback matrix model, the maximum node feedback value FV is again calibrated jv The corresponding row number j; Based on node feedback value FV iv and node feedback value FV jv , evaluate block node C i Supply capacity deficit value The supply chain database is updated regularly, and the supply capacity defect value of each block node is fed back in real time through the demand task feature set.

6. A digital twin-based battery supply digital supervision system, which executes a digital twin-based battery supply digital supervision method as described in any one of claims 1 to 5, characterized in that: The system includes a digital twin cloud platform module, a digital information acquisition module, a digital matrix carrier module and a digital analysis and processing module; The digital twin cloud platform module is used to set up block nodes and allocate supply chain databases in the digital twin cloud platform, connect to the ERP software of each battery supply chain party through the block nodes, and obtain and store the battery supply information reported by the battery supply chain party; The digital information acquisition module is used to capture the supply characteristics of each block through the block node to generate a block supply characteristic set; Enter the battery demand index information into the digital twin cloud platform to generate a demand task feature set; The digital matrix carrier module sequentially constructs a supply feature matrix carrier model and a demand task feature matrix carrier model based on the block supply feature set and the demand task feature set; The digital analysis and processing module generates a block node feedback matrix model based on the supply feature Boolean matrix and the demand feature Boolean matrix to record the node feedback value of the block node, and regularly evaluates and outputs the supply capacity defect value of the block node.

7. According to claim 6, a digital twin-based battery supply digital supervision system is characterized in that: The digital twin cloud platform module includes a port connection unit and a supply chain database unit; The port connection unit is used to set a block node on the digital twin cloud platform according to the number of battery supply chain parties, wherein one block node corresponds to one battery supply chain party port, and the block node is connected to the ERP software of the battery supply chain party through the interface protocol, and the battery supply information reported by the battery supply chain party is identified, and the battery supply information includes a number of battery specification parameters; The supply chain database unit allocates the supply chain database in the digital twin cloud platform based on the number of block nodes, wherein one block node corresponds to one battery supply chain database and stores the battery supply information in real time.

8. According to claim 7, a digital twin-based battery supply digital supervision system is characterized in that: The digital information acquisition module includes a block supply feature generation unit and a demand task feature generation unit; The block supply feature generation unit is used to uniformly encode battery specification parameters and count battery supply information identified by the block node to generate a block supply feature set; The demand task feature generation unit is used to input battery demand index information in the digital twin cloud platform, where the battery demand index information includes several battery specification parameter groups to generate a demand task feature set.

9. A digital twin-based battery supply digital supervision system according to claim 8, characterized in that: The digital matrix carrier module includes a supply feature matrix carrier model unit and a demand task feature matrix carrier model unit; The supply feature matrix carrier model unit is used to construct a supply feature matrix carrier model, wherein the row number of the supply feature matrix corresponds to the code number of the block node, and the column number of the supply feature matrix corresponds to the type number of the battery specification parameter; the block supply feature set is mapped to each row of the supply feature matrix, and the matrix elements of the supply feature matrix that are mapped are recorded as 1, and the matrix elements of the supply feature matrix that are not mapped are recorded as 0; The demand task feature matrix carrier model unit is used to construct a demand task feature matrix carrier model, the row number of the demand task feature matrix corresponds to the coding number of the battery specification parameter group, and the column number of the demand task feature matrix corresponds to the type number of the battery specification parameter; the battery specification parameter group is mapped to each row of the demand task feature matrix, the matrix elements of the demand task feature matrix that are mapped are recorded as 1, and the matrix elements of the demand task feature matrix that are not mapped are recorded as 0.

10. A digital twin-based battery supply digital supervision system according to claim 9, characterized in that: The digital analysis and processing module includes a block node feedback matrix model unit, a digital evaluation unit and a digital update unit; The block node feedback matrix model unit constructs a block node feedback matrix model by matrix operation based on the supply feature Boolean matrix and the demand feature Boolean matrix, wherein the block node feedback matrix model records the node feedback value of the block node matching battery specification parameter group; The digital evaluation unit calibrates rows and columns in the block node feedback matrix model based on the maximum value of the node feedback value to obtain the node feedback value to be compared, and evaluates the supply capacity defect value of the block node through the node feedback value to be compared; The digital update unit is used to regularly update the supply chain database and output and feedback the supply capacity defect value of each block node in real time through the demand task feature set.

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