A digital supervision system and method for battery supply based on digital twin

Abstract

The present invention discloses a digital supervision system and method for battery supply based on digital twin, belonging to the technical field of battery supply chain. By setting up block nodes and allocating a supply chain database in the digital twin cloud platform, the real-time sharing and collaboration of supply chain information are realized; by capturing and analyzing supply characteristics and demand characteristics, an accurate supply characteristic matrix carrier model and a demand task characteristic matrix carrier model are constructed, and a block node feedback matrix model is generated to record the node feedback values of the block nodes, and the supply capacity defect values of the block nodes are regularly evaluated and output, thereby realizing the effective evaluation and optimization of the supply capacity of the supply chain; while improving the collaborative efficiency, response speed and information transparency of the supply chain, the present invention can promote the mutual complement of technologies in the battery supply chain system and help all parties in the battery supply chain optimize production capacity.

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 twin. Background Technique

[0002] In the management of battery supply chain, with the rapid development of the new energy industry, the complexity and dynamics of the battery supply chain have increased day by day, posing higher requirements for 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 lagged information transmission and inconsistent data, making it difficult to effectively monitor and optimize the supply chain.

[0003] In recent years, digital twin technology and blockchain technology have been introduced into the management of battery supply chain. Digital twin technology realizes the mapping and monitoring of the physical supply chain by constructing virtual models, improving the transparency and visualization of the supply chain; while blockchain technology, with its decentralized and immutable characteristics, provides security guarantees for information exchange and sharing in the supply chain.

[0004] However, there are still some technical bottlenecks in the existing digital twin and blockchain technologies in the aspect of 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 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 supervision system and method for battery supply based on digital twin to solve the problems raised in the above background technique.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] A digital supervision system for battery supply based on digital twin, which includes: a digital twin cloud platform module, a digital information collection 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 a supply chain database in the digital twin cloud platform, 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;

[0009] The digital information collection module is used to capture the supply characteristics of each block through the block nodes to generate a block supply characteristic set; input the battery demand index information in the digital twin cloud platform to generate a demand task characteristic set;

[0010] The digital matrix carrier module constructs a supply feature matrix carrier model and a demand task feature matrix carrier model in sequence 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 values of the block nodes, and regularly evaluates and outputs the supply capacity defect values of the block nodes.

[0012] Furthermore, 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 block nodes in the digital twin cloud platform according to the number of battery supply chain parties. Among them, one block node corresponds to one battery supply chain party port. The block nodes are 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. The battery supply information includes several battery specification parameters;

[0014] The supply chain database unit allocates a supply chain database in the digital twin cloud platform based on the number of block nodes. Among them, one block node corresponds to the allocation of one battery supply chain party database, and the battery supply information is stored in real time.

[0015] Furthermore, the digital information collection 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 the battery specification parameters and count the battery supply information identified by the block nodes 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. The battery demand index information includes several battery specification parameter groups to generate a demand task feature set.

[0018] Furthermore, 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. The row number of the supply feature matrix corresponds to the encoding number of the block node, and the column number of the supply feature matrix corresponds to the type number of the battery specification parameters; map the block supply feature set to each row of the supply feature matrix, record the matrix elements of the mapped supply feature matrix as 1, and record the matrix elements of the unmapped supply feature matrix as 0;

[0020] The demand task feature matrix carrier model unit is used to construct a demand task feature matrix carrier model. The row numbers of the demand task feature matrix correspond to the coding numbers of the battery specification parameter groups, and the column numbers of the demand task feature matrix correspond to the type numbers of the battery specification parameters. Map the battery specification parameter groups into each row of the demand task feature matrix. Denote the matrix elements of the obtained mapped demand task feature matrix as 1, and denote the matrix elements of the demand task feature matrix that are not mapped as 0.

[0021] Furthermore, 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 through matrix operations based on the supply feature Boolean matrix and the demand feature Boolean matrix. The node feedback values of the block nodes matching the battery specification parameter groups are recorded in the block node feedback matrix model.

[0023] The digital evaluation unit calibrates the rows and columns in the block node feedback matrix model respectively based on the maximum value condition of the node feedback values to obtain the node feedback values to be compared, and evaluates the supply capacity defect values of the block nodes through the node feedback values 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 values of each block node in real time through the demand task feature set.

[0025] A digital supervision method for battery supply based on digital twin, the method includes the following steps:

[0026] Step S1: Set 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 nodes to generate a block supply characteristic set; input the battery demand index information in the digital twin cloud platform to generate a demand task characteristic set.

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

[0029] Step S4: Generate a block node feedback matrix model based on the supply feature Boolean matrix and the demand feature Boolean matrix to record the node feedback values of the block nodes, and regularly evaluate and output the supply capacity defect values of the block nodes.

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

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

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

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

[0034] Perform unified coding on the battery specification parameters, and denote the xth battery specification parameter as BS x ; Count the battery supply information identified by the block nodes, and generate a block supply feature set, denoted as B(C i ) = {BS x |x ∈ [1, Y]}, where Y represents the total number of types of battery specification parameters, and C i represents the ith block node;

[0035] Input 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 eth battery specification parameter group, E represents the total number of battery specification parameter groups, and F e = {BS x |x ∈ [1, Y]}.

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

[0037] Construct a supply feature matrix carrier model. The row number of the supply feature matrix corresponds to the coding number of the block node, and the column number of the supply feature matrix corresponds to the type number of the battery specification parameter; Map the block supply feature set B(C i ) to the ith row of the supply feature matrix. The matrix element corresponding to the ith row and the xth column of the obtained mapped supply feature matrix is denoted as BS x = 1, and the matrix element corresponding to the ith row and the xth column of the supply feature matrix that has not been mapped is denoted as BS xIf it is equal to 0, then output the supply feature Boolean matrix, denoted as R(I×Y), where I represents the total number of block nodes;

[0038] Construct a demand task feature matrix carrier model. The row numbers of the demand task feature matrix correspond to the coding numbers of the battery specification parameter groups, and the column numbers of the demand task feature matrix correspond to the type numbers of the battery specification parameters; Map the battery specification parameter group F e Map the e-th row of the demand task feature matrix, and represent the matrix element corresponding to the x-th column of the e-th row of the mapped demand task feature matrix as BS x If it is equal to 1, represent the matrix element corresponding to the x-th column of the e-th row of the unmapped demand task feature matrix as BS x If it is equal to 0, then output the demand feature Boolean matrix, 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, construct a block node feedback matrix model R(I×E) = R(I×Y)×R(E×Y) T , where T represents the transpose symbol;

[0041] Denote the matrix element in the i-th row and e-th column of the block node feedback matrix model as FV ie , and FV ie represents the node feedback value of block node C i matching the battery specification parameter group F e ;

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

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

[0044] According to the above method, through matrix transpose 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 battery specification parameters for communication; after determining a row in the block node feedback matrix model, that is, the system automatically selects the types of batteries that a supply chain party can produce, and this type of description can be characterized by a battery specification parameter group. By determining the row and first selecting the maximum node feedback value of this supply chain party, this is the demand for the types of batteries that this supply chain party could adapt to in history. But when other supply chain parties make production line adjustments, the other supply chain parties can produce more types of batteries, and then the maximum node feedback value in the column is calibrated again. Through the supply capacity defect value, the production technology ability of any battery supply chain can be judged. The larger the supply capacity defect value, the higher the defect degree of the production technology ability of this battery supply chain party, and the greater the gap from the technological innovation in 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 a supply chain database in the digital twin cloud platform, real-time sharing and collaboration of supply chain information are realized; by capturing and analyzing supply characteristics and demand characteristics, an accurate supply characteristic matrix carrier model and a demand task characteristic 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 realizing the effective evaluation and optimization of the supply capacity of the supply chain; while improving the collaborative efficiency, response speed and information transparency of the supply chain, the present invention can promote the mutual compensation of technologies in the battery supply chain system and help all parties in the battery supply chain optimize production capacity. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0049] In the first embodiment: A digitalized supervision system for battery supply based on digital twin is provided. The system includes: a digital twin cloud platform module, a digital information collection module, a digital matrix carrier module, and a digital analysis and processing module;

[0050] The digital twin cloud platform module is used to set 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;

[0051] Among them, 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 block nodes in the digital twin cloud platform according to the number of battery supply chain parties. Among them, one block node corresponds to one battery supply chain party port, connect the block nodes to the ERP software of the battery supply chain party through the interface protocol, and identify the battery supply information reported by the battery supply chain party. The battery supply information includes several battery specification parameters;

[0053] The supply chain database unit allocates a supply chain database in the digital twin cloud platform based on the number of block nodes. Among them, one block node corresponds to the allocation of one battery supply chain party database, and the battery supply information is stored in real time.

[0054] The digital information collection module is used to capture the supply characteristics of each block through the block nodes to generate a block supply characteristic set; input battery demand index information in the digital twin cloud platform to generate a demand task characteristic set;

[0055] Among them, the digital information collection module includes a block supply characteristic generation unit and a demand task characteristic generation unit;

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

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

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

[0059] Among them, the digital matrix carrier module includes a supply characteristic matrix carrier model unit and a demand task characteristic matrix carrier model unit;

[0060] The supply feature matrix carrier model unit is used to construct a supply feature matrix carrier model. The row numbers of the supply feature matrix correspond to the coding numbers of block nodes, and the column numbers of the supply feature matrix correspond to the type numbers of battery specification parameters. Map the block supply feature set into each row of the supply feature matrix, mark the matrix elements of the obtained mapped supply feature matrix as 1, and mark the matrix elements of the supply feature matrix that are not mapped as 0.

[0061] The demand task feature matrix carrier model unit is used to construct a demand task feature matrix carrier model. The row numbers of the demand task feature matrix correspond to the coding numbers of battery specification parameter groups, and the column numbers of the demand task feature matrix correspond to the type numbers of battery specification parameters. Map the battery specification parameter groups into each row of the demand task feature matrix, mark the matrix elements of the obtained mapped demand task feature matrix as 1, and mark the matrix elements of the demand task feature matrix that are not mapped 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 values of block nodes, and regularly evaluates and outputs the supply capacity defect values of block nodes.

[0063] Among them, 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 through matrix operations based on the supply feature Boolean matrix and the demand feature Boolean matrix. The node feedback values of block nodes matching battery specification parameter groups are recorded in the block node feedback matrix model.

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

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

[0067] Please refer to Figure 1 , in the second embodiment: Provide a digital twin-based digital supervision method for battery supply, and this 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 parties.

[0069] Exemplarily, establish a digital twin cloud platform. A number of block nodes are set up in the digital twin cloud platform. Among them, one block node corresponds to one battery supply chain party port. Connect the block nodes to the ERP software of the battery supply chain parties through an interface protocol, and identify the battery supply information reported by the battery supply chain parties. The battery supply information includes a number of battery specification parameters.

[0070] Allocate a supply chain database in the digital twin cloud platform based on the number of block nodes. Among them, one block node corresponds to the allocation of one battery supply chain party database, and the battery supply information is stored in real time.

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

[0072] Exemplarily, uniformly encode the battery specification parameters, and denote the xth battery specification parameter as BS x ; Count the battery supply information recognized by the block nodes and generate a block supply characteristic set, denoted as B(C i ) = {BS x |x ∈ [1, Y]}, where Y represents the total number of types of battery specification parameters, and C i represents the ith block node.

[0073] Input the battery demand index information in the digital twin cloud platform. The battery demand index information includes a number of battery specification parameter groups, and generate a demand task characteristic set, denoted as D = {F e |e ∈ [1, E]}, where F e represents the eth 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 characteristic set and the demand task characteristic set, construct a supply characteristic matrix carrier model and a demand task characteristic matrix carrier model respectively in sequence.

[0075] Exemplarily, construct a supply characteristic matrix carrier model. The row number of the supply characteristic matrix corresponds to the encoding number of the block node, and the column number of the supply characteristic matrix corresponds to the type number of the battery specification parameters; The block supply characteristic set B(C i)Map to the i-th row of the supply feature matrix, and denote the matrix element corresponding to the x-th column of the i-th row of the mapped supply feature matrix as BS x = 1, and denote the matrix element corresponding to the x-th column of the i-th row of the unmapped supply feature matrix as BS x = 0, then output the supply feature boolean matrix, denoted as R(I×Y), where I represents the total number of block nodes;

[0076] Construct a demand task feature matrix carrier model, where 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; For the battery specification parameter group F e Map the e-th row of the demand task feature matrix, and denote the matrix element corresponding to the x-th column of the e-th row of the mapped demand task feature matrix as BS x = 1, and denote the matrix element corresponding to the x-th column of the e-th row of the unmapped demand task feature matrix as BS x = 0, then output the demand feature boolean matrix, denoted as R(E×Y).

[0077] Step S4: Based on the supply feature boolean matrix and the demand feature boolean matrix, generate a block node feedback matrix model to record the node feedback values of the block nodes, and regularly evaluate and output the supply capacity defect values of the block nodes;

[0078] Exemplarily, based on the supply feature boolean matrix and the demand feature boolean matrix, construct a block node feedback matrix model R(I×E) = R(I×Y) × R(E×Y) T , where T represents the transpose symbol;

[0079] Denote the matrix element at the i-th row and e-th column in the block node feedback matrix model as FV ie , and FV ie represents the node feedback value of block node C i matching the battery specification parameter group F e ;

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

[0081] Based on the node feedback value FV iv and the node feedback value FV jv , evaluate the supply capacity defect value of block node C i ; Regularly update the supply chain database and output and feedback the supply capacity defect values of each block node 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 are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0083] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within 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 parties. Step S2: Capture the supply characteristics of each block through the block nodes to generate a block supply characteristic set; enter the battery demand index information in the digital twin cloud platform to generate a demand task characteristic set. Step S3: Based on the block supply characteristic set and the demand task characteristic set, construct a supply characteristic matrix carrier model and a demand task characteristic matrix carrier model respectively. Step S4: Based on the supply characteristic Boolean matrix and the demand characteristic Boolean matrix, generate a block node feedback matrix model to record the node feedback values of the block nodes, and regularly evaluate and output the supply capacity defect values of the block nodes. The specific implementation process of step S4 includes: Based on the supply feature Boolean matrix and the demand feature Boolean matrix, construct a block node feedback matrix model \(R(I\times E)=R(I\times Y)\times R(E\times Y)\) T , where \(T\) represents the transpose symbol; Denote the matrix element in the \(i\)-th row and \(e\)-th column of the block node feedback matrix model as FV ie , and FV ie represents the node feedback value of block node C i matching the battery specification parameter group F e ; In the i-th row of the block node feedback matrix model, the largest node feedback value FV is marked for the first time iv The corresponding column number v, and in the v-th column of the block node feedback matrix model, the largest node feedback value FV is marked again 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 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; R(I×Y) and R(E×Y) represent the supply characteristic Boolean matrix and the demand characteristic Boolean matrix respectively, and the supply characteristic Boolean matrix and the demand characteristic Boolean matrix are obtained from the supply characteristic matrix carrier model and the demand task characteristic matrix carrier model of step S3 respectively. I represents the total number of block nodes, Y represents the total number of types of battery specification parameters, and E represents the total number of battery specification parameter groups.

2. The digital supervision method for battery supply based on digital twin according to claim 1, wherein The specific implementation process of step S1 includes: Establish a digital twin cloud platform. A number of block nodes are set in the digital twin cloud platform. Among them, one block node corresponds to one battery supply chain party port. Connect the ERP software of the battery supply chain party through the interface protocol, and identify the battery supply information reported by the battery supply chain party. The battery supply information includes a number of battery specification parameters. Allocate a supply chain database in the digital twin cloud platform based on the number of block nodes. Among them, one block node corresponds to the allocation of one battery supply chain party database, and the battery supply information is stored in real time.

3. The digital supervision method for battery supply based on digital twin according to claim 2, characterized in that, The specific implementation process of step S2 includes: Uniformly encode the battery specification parameters, and denote the x-th type of battery specification parameters as BS x ; Count the battery supply information recognized by the block node and generate a block supply feature set, denoted as B(C i ) = {BS x |x ∈ [1, Y]}, where C i represents the i-th block node; Input battery demand index information in the digital twin cloud platform. The battery demand index information includes several battery specification parameter groups and generates a demand task feature set, denoted as D = {F e | e ∈ [1, E]}, where F e represents the e-th battery specification parameter group, and F e = {BS x | x ∈ [1, Y]}.

4. The digital supervision method for battery supply based on digital twin according to claim 3, wherein, The specific implementation process of step S3 includes: Construct a carrier model for the supply feature matrix, where the row number of the supply feature matrix corresponds to the coding number of the block node, and the column number of the supply feature matrix corresponds to the type number of the battery specification parameters; map the block supply feature set B(C i ) to the i-th row of the supply feature matrix, and represent the matrix element corresponding to the i-th row and x-th column of the obtained mapped supply feature matrix as BS x = 1, and represent the matrix element corresponding to the i-th row and x-th column of the supply feature matrix that has not been mapped as BS x = 0, then output the supply feature boolean matrix, denoted as R(I×Y); Construct a carrier model of the demand task feature matrix, where 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; For the battery specification parameter group F e Map the e-th row of the demand task feature matrix, and represent the matrix element corresponding to the x-th column of the e-th row of the mapped demand task feature matrix as BS x = 1, and represent the matrix element corresponding to the x-th column of the e-th row of the demand task feature matrix that has not been mapped as BS x = 0, then output the demand feature boolean matrix, denoted as R(E×Y).

5. A digital supervision system for battery supply based on digital twin, characterized in that, The system includes a digital twin cloud platform module, a digital information collection 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 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 parties. The digital information collection module is used to capture the supply characteristics of each block through the block nodes to generate a block supply characteristic set. Enter the battery demand index information in the digital twin cloud platform to generate a demand task characteristic set. The digital matrix carrier module constructs a supply characteristic matrix carrier model and a demand task characteristic matrix carrier model respectively based on the block supply characteristic set and the demand task characteristic set. The digital analysis and processing module generates a block node feedback matrix model based on the supply characteristic Boolean matrix and the demand characteristic Boolean matrix to record the node feedback values of the block nodes, and regularly evaluates and outputs the supply capacity defect values of the block nodes. 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 R(I×E) = R(I×Y) × R(E×Y) based on the supply characteristic Boolean matrix and the demand characteristic Boolean matrix through matrix operations T , where T represents the transpose symbol, and the node feedback values of the block node matching the battery specification parameter group are recorded in the block node feedback matrix model; The digital evaluation unit, based on the maximum value condition of the node feedback value, calibrates the rows and columns in the block node feedback matrix model respectively to obtain the node feedback values to be compared, and evaluates the supply capacity defect value of the block node through the node feedback values to be compared. The specific process is as follows: Denote the matrix element in the $i$-th row and $e$-th column of the block node feedback matrix model as FV ie , and FV ie represents the node feedback value of block node C i matching the battery specification parameter group F e ; In the $i$-th row of the block node feedback matrix model, the largest node feedback value $FV$ is first marked. iv The corresponding column number $v$ is obtained, and in the $v$-th column of the block node feedback matrix model, the largest node feedback value $FV$ is marked again. jv The corresponding row number $j$ is obtained. Based on the node feedback value FV iv and the node feedback value FV jv , evaluate the supply capacity defect value of the block node C i ​ R(I×Y) and R(E×Y) respectively represent the supply characteristic Boolean matrix and the demand characteristic Boolean matrix in sequence, and the supply characteristic Boolean matrix and the demand characteristic Boolean matrix are obtained from the supply characteristic matrix carrier model and the demand task characteristic matrix carrier model of the digital matrix carrier module respectively. I represents the total number of block nodes, Y represents the total number of types of battery specification parameters, and E represents the total number of battery specification parameter groups; The digital update unit is used to regularly update the supply chain database and output and feedback the supply capacity defect values of each block node in real time through the demand task feature set.

6. The digital supervision system for battery supply based on digital twin according to claim 5, 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 block nodes in the digital twin cloud platform according to the number of battery supply chain parties. Among them, one block node corresponds to one battery supply chain party port. 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. The battery supply information includes a number of battery specification parameters; The supply chain database unit allocates a supply chain database in the digital twin cloud platform based on the number of block nodes. Among them, one block node corresponds to the allocation of one battery supply chain party database, and the battery supply information is stored in real time.

7. A digital supervision system for battery supply based on digital twin according to claim 6, characterized in that, The digital information collection module includes a block supply characteristic generation unit and a demand task characteristic generation unit; The block supply feature generation unit is used to uniformly encode the battery specification parameters, and record the x-th battery specification parameter as BS x , and count the battery supply information recognized by the block nodes to generate a block supply feature set B(C i ) = {BS x | x ∈ [1, Y]}, where C i represents the i-th block node; The demand task feature generation unit is used to input battery demand index information in the digital twin cloud platform. The battery demand index information includes several battery specification parameter groups to generate a demand task feature set D = {F e |e ∈ [1, E]}, where F e represents the e-th battery specification parameter group, and F e = {BS x |x ∈ [1, Y]}.

8. A digital supervision system for battery supply based on digital twin according to claim 7, characterized in that, The digital matrix carrier module includes a supply characteristic matrix carrier model unit and a demand task characteristic matrix carrier model unit; The supply feature matrix carrier model unit is used to construct a supply feature matrix carrier model. The row number of the supply feature matrix corresponds to the coding number of the block node, and the column number of the supply feature matrix corresponds to the type number of the battery specification parameters; mapping the block supply feature set B(C i ) to the i-th row of the supply feature matrix, and representing the matrix element corresponding to the i-th row and the x-th column of the obtained mapped supply feature matrix as BS x = 1, and representing the matrix element corresponding to the i-th row and the x-th column of the supply feature matrix that has not been mapped as BS x = 0, then output the supply feature boolean matrix, denoted as R(I×Y); 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 F e is mapped to the e-th row of the demand task feature matrix, and the matrix element corresponding to the x-th column of the e-th row of the obtained mapped demand task feature matrix is denoted as BS x = 1, and the matrix element corresponding to the x-th column of the e-th row of the demand task feature matrix that is not mapped is denoted as BS x = 0, then the demand feature boolean matrix is output, denoted as R(E×Y).

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