Automatic modeling method and device for modular power grid material supply chain model

Abstract

The invention discloses an automatic modeling method and device for a modular power grid material supply chain model. The method comprises the following steps: collecting material management scene information; screening each supply chain node module based on an analysis result of the material management scene information, and determining a plurality of target supply chain node modules; matching node module elements of each target supply chain node module in combination with scene priorities in the material management scene information and an association relationship of each target supply chain node module, and loading each target supply chain node module; through the standard supply chain model framework, each target supply chain node module is assembled, construction of the power grid material supply chain model is completed, on the basis of simplifying the material supply chain model, the flexibility of the material supply chain model is improved, and material management of various supply chain links is adapted.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of material supply chain model construction, and particularly relates to an automatic modeling method and device for a modular power grid material supply chain model. BACKGROUND

[0002] Electric power materials are the basic guarantee in the process of power grid construction, but the current material management work presents the status of "material data fragmentation, material reserve mechanization, and subject responsibility fuzzification", which directly affects the fine management of electric power materials by power grid companies.

[0003] With the development of technologies such as the Internet of Things, big data, and artificial intelligence, data acquisition and utilization are more convenient and convenient, so how to fully utilize the data generated by electric power materials in the supply chain process, reduce enterprise operating costs, and improve the management level of electric power materials is a problem to be solved at present.

[0004] Patent application CN117952351A provides an integrated management and control platform for electric power materials. The platform includes a platform integration module, a warehouse management module, an early warning management module, and a permission management module. Through the platform integration module, data from different platforms are obtained and integrated, and through comprehensive analysis of the data, cross-professional platform data integration is achieved, data cross-professional barriers are eliminated, and multi-dimensional monitoring and management services are provided. The warehouse management module visually displays the full inventory resources, realizes the visual interconnection of various electric power material resources, and dynamically queries the warehouse materials, so as to timely and accurately find the warehouse and material inventory of the materials. The early warning management module provides material early warning, realizes intelligent online reporting of emergency repair material demand, and intelligently recommends suitable material sources through the procurement scheme designation function, ensuring the timeliness of material allocation. The permission management module realizes the allocation of access permissions of platform users, ensuring the security of platform data access.

[0005] The above related technology manages material data from various aspects by constructing modules, but does not consider the relationship between the data of each link of the material supply chain. How to model the management of electric power materials according to the relationship between each link of the material supply chain, improve the flexibility of the material supply chain model on the basis of simplifying the material supply chain model, and adapt to the material management of various supply chain links is a problem to be solved at present. SUMMARY

[0006] In view of the defects in the prior art, the application provides an automatic modeling method and device for a modular power grid material supply chain model, which comprises the following steps: collecting material management scene information; screening each supply chain node module based on the analysis result of the material management scene information, and determining a plurality of target supply chain node modules; matching the node module elements of each target supply chain node module in combination with the scene priority in the material management scene information and the correlation of each target supply chain node module, and loading each target supply chain node module; assembling each target supply chain node module through a standard supply chain model framework, and completing the construction of the power grid material supply chain model.

[0007] In the first aspect, the application provides an automatic modeling method for a modular power grid material supply chain model, which specifically comprises the following steps: Collecting material management scene information; Screening each supply chain node module based on the analysis result of the material management scene information, and determining a plurality of target supply chain node modules; Matching the node module elements of each target supply chain node module in combination with the scene priority in the material management scene information and the correlation of each target supply chain node module, and loading each target supply chain node module; Assembling each target supply chain node module through a standard supply chain model framework, and completing the construction of the power grid material supply chain model.

[0008] Further, screening each supply chain node module based on the analysis result of the material management scene information, and determining a plurality of target supply chain node modules specifically comprises the following steps: Extracting the scene keywords in the material management scene information; Performing keyword matching on the scene keywords in a pre-constructed scene demand library to obtain standard scene demands; Screening the supply chain node modules based on the mapping relationship between the standard scene demands and the supply chain nodes to obtain the target supply chain node modules.

[0009] Further, the supply chain node module is obtained through the following steps: Constructing a node directed graph according to the flow order of the power grid materials at each business flow node in the supply chain and the flow dependency relationship between the business flow nodes; Dividing each business flow node in the node directed graph to obtain a plurality of first primary submodules; Adjusting the ownership relationship between the business flow nodes and the first primary submodules, analyzing the total modular primary gain items of all the first primary submodules before and after the adjustment, and determining target primary submodules; According to the flow transfer dependency relationship of each business flow transfer node in the target primary sub-module, a primary association relationship between each target primary sub-module is established; Based on the primary association relationship between each target primary sub-module, each target primary sub-module is divided to determine the target final sub-module; Adjust the ownership relationship of the target final sub-module and the target primary sub-module, analyze the total modular final gain item of all target final sub-modules before and after adjustment, and determine the supply chain node module.

[0010] Further, the ownership relationship of the business flow transfer node and the first primary sub-module is adjusted, the total modular primary gain item of all first primary sub-modules before and after adjustment is analyzed, and the target primary sub-module is determined, specifically including: Based on each business flow transfer node, the adjacent nodes of the business flow transfer node are determined; The business flow transfer node is removed from the current first primary sub-module and added to the first primary sub-module corresponding to the adjacent node; Calculate the modularity of the business flow transfer node before and after adjustment and give the total modular primary gain item corresponding to each adjacent node; Sort the total modular primary gain item corresponding to each adjacent node to determine the maximum value of the primary gain item; If the maximum value of the primary gain item is positive, the business flow transfer node is added to the first primary sub-module of the adjacent node corresponding to the maximum value of the primary gain item, and the first primary sub-module is updated; When all business flow transfer nodes are traversed and the total modular primary gain item is within the preset range, the first primary sub-module is determined as the target primary sub-module.

[0011] Further, the total modular primary gain item is obtained by the following steps: Based on the weight corresponding to the directed edge of each business flow transfer node in the node directed graph, and combining the sum of the weights corresponding to the directed edges between each business flow transfer node in the first primary sub-module, a weight fusion item is given; Based on the weight corresponding to the directed edge of each business flow transfer node in the node directed graph, and combining the sum of the node degrees of all business flow transfer nodes in the first primary sub-module, a node degree fusion item is given; According to the difference between the weight fusion item and the node degree fusion item, the total modular primary gain item is determined.

[0012] Further, the node module element includes a module decision variable and a module constraint condition; According to the scene priority in the material management scene information and the association relationship of each target supply chain node module, the node module elements of each target supply chain node module are matched, and each target supply chain node module is loaded, specifically including: According to the scene priority of the material management scene information, node module elements corresponding to the scene priority of the material management scene information are sequentially screened out from a multi-dimensional matching library, wherein the multi-dimensional matching library is constructed through operation characteristics of the power material supply chain and includes a mapping relationship between the scene priority and the node module elements. According to the node module element loading sequence, module decision variables and module constraint conditions are sequentially loaded and embedded into each target supply chain node module, so as to complete loading of each target supply chain node module.

[0013] Further, the supply chain node module is divided into a decision node module and a support node module, and the target supply chain node module includes at least one decision node module and at least one support node module.

[0014] Further, each target supply chain node module is assembled through a standard supply chain model framework, so as to complete construction of the power material supply chain model, and the construction specifically includes: Based on the standard supply chain model framework, associated decision variables and associated constraint conditions are determined; According to a mapping relationship between the associated decision variables and the target supply chain node module, a variable transmission channel is established between the target supply chain node modules; According to a mapping relationship between the associated constraint conditions and the target supply chain node module, a constraint judgment channel is established between the target supply chain node modules; Through the variable transmission channel and the constraint judgment channel, material information is transmitted between the target supply chain node modules, the target supply chain node modules are assembled, and the power material supply chain model is obtained.

[0015] Further, each decision node module corresponds to a standard supply chain model framework, and the construction is completed through the following steps: Based on decision characteristics of each decision node module, a corresponding module objective function is set from at least one dimension; The module constraint conditions and the module decision variables in the decision node module are fused, and construction of the standard supply chain model framework is completed.

[0016] In a second aspect, the application further provides an automatic modeling device of a modular power material supply chain model, which adopts the automatic modeling method of the modular power material supply chain model according to any one of the above aspects, and includes: A data acquisition unit is configured to acquire material management scene information; A module determination unit is configured to screen each supply chain node module based on an analysis result of the material management scene information, and determine a plurality of target supply chain node modules. The module loading unit is used for matching node module elements of each target supply chain node module in combination with a scene priority in the material management scene information and an association relationship of each target supply chain node module, and loading each target supply chain node module. The module combination unit is used for assembling each target supply chain node module through a standard supply chain model framework, and completing construction of the power grid material supply chain model.

[0017] The application provides a modular power grid material supply chain model automatic modeling method and device, and at least has the following beneficial effects: The material supply chain node is modularly divided, the complex process of the material supply chain is disassembled, the formation mode of the complex system is simplified, the modeling speed is improved, meanwhile, the relationship of each module is maximally simplified, the flexibility of the power grid material supply chain model is improved, and the model is convenient to modify and expand. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A flowchart of the modular power grid material supply chain model automatic modeling method provided by the embodiment of the application is provided. Figure 2 A flowchart of determining the supply chain node module is provided for the embodiment of the application. Figure 3 A flowchart of loading the target supply chain node module is provided for the embodiment of the application. Figure 4 A structural block diagram of the modular power grid material supply chain model automatic modeling device provided by the embodiment of the application is provided.

[0019] Among them, 201, data acquisition unit;202, module determination unit;203, module loading unit;204, module combination unit. DETAILED DESCRIPTION

[0020] In order to better understand the above technical solutions, the above technical solutions will be described in detail in combination with the description of the drawings and specific embodiments. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the application.

[0021] The terms used in the embodiments of the application are only for the purpose of describing specific embodiments, and are not intended to limit the application. The singular forms "a", "said" and "the" used in the embodiments of the application and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise. "Multiple" generally includes at least two.

[0022] It is also to be noted that the terms "comprising", "including", and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product or process that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such product or process. An element proceeded by "comprises a" does not, without further restriction, exclude the existence of additional elements of the same kind in the product or process.

[0023] The power grid material supply chain nodes include procurement demand formulation, demand submission, procurement demand review, procurement document preparation, bidding and bid selection, contract signing, supplier qualification verification, panoramic quality control, supplier evaluation, performance execution, material transportation and distribution, material warehousing, and material installation and debugging. Among them, procurement demand formulation is to determine the coverage of the company's procurement catalog and to formulate an annual demand plan based on it. Demand submission is to form and submit a demand plan based on material classification standards, material master data, and other basic information, combined with system data such as technical specification ID and project WBS information. The procurement demand review will verify the demand submission plan based on project approval information, procurement catalog, and other ERP data, and make compliance and reasonableness judgments in combination with ECP system information of the procurement plan. Procurement document preparation needs to convert the demand plan into procurement documents containing technical specifications and procurement plans in combination with evaluation rules, qualification conditions, and sub-contracting principles. Bidding and bid selection rely on the ECP system to complete the processes of bidding document preparation, bidding / tendering, and bid opening, and carry out prequalification and bid evaluation in combination with prequalification results and professional opinions in expert management, and finally convert the material, commercial requirements corresponding to the demand decision into the bid-winning result through the bid selection link. Contract signing will solidify the material, technical, and commercial requirements determined in the demand decision into contract terms. Performance execution is to track project progress, supply plan, delivery information, settlement information, and performance records through the ECP system based on the demand content determined at the time of contract signing, which is a dynamic tracking of the demand decision landing to ensure that the material supply, project implementation, and other links are synchronized with the plan in the demand submission stage. Qualification verification is that the business party will verify the information such as the qualifications and performance of the supplier according to the qualification and capability verification standards in the ECP system. Panoramic quality control includes supervision and sampling inspection. Supervision relies on the ECP system to track product design, production progress, and other whole-process information to form a supervision report to ensure that the material production meets the technical specifications in the demand submission. Sampling inspection is based on the sampling inspection specifications in the ECP system, combined with the procurement contract and key component parameters to carry out quality verification. Supplier evaluation is to integrate the information such as qualification and capability in the ECP system, production and supply, and the cost and installation quality data in the ERP system to evaluate the supplier from the dimensions of external credit, performance, and capacity, and to associate the results of bad behavior handling and integrity. Material transportation and distribution will synchronize equipment status information, distribution plan, road and weather information, etc. through the ECP system to ensure that the time and quantity of material transportation are consistent with the project progress requirements in the demand plan. Material warehousing is to record the in-out of the material after transportation and distribution based on the panoramic inventory information and other ERP and T system data for storage management. The installation and debugging node will associate the quality information, delivery information, and other data in the infrastructure system to confirm whether the material supply and project implementation fully meet the technical and functional requirements in the demand submission stage through the feedback of installation and debugging results, and complete the whole-process closed loop from demand submission to landing.

[0024] The related technology manages the material data from various aspects by constructing a platform integration module, a warehouse management module, an early warning management module and a permission management module, but does not consider the relationship between the data of each link of the material supply chain, how to model the power material management according to the relationship between each link of the material supply chain, and on the basis of simplifying the material supply chain model, improve the flexibility of the material supply chain model, adapt to the material management of various supply chain links, which is the problem to be solved at present.

[0025] Based on this, the application provides an automatic modeling method of a modular power grid material supply chain model, which comprises the following steps:

[0026] As shown in the figure, the embodiment of the application provides an automatic modeling method of a modular power grid material supply chain model, and the specific steps are as follows: Figure 1 S101: Collect material management scene information.

[0027] It should be understood that the material management scene information represents the relevant information of the material application scene, and the material management scene information includes normal supply scene, peak winter / summer scene and emergency repair scene, etc. Among them, the normal supply scene has stable demand for materials, smooth supply and no sudden situation. The peak winter / summer scene has a sharp increase in demand for materials (such as equipment load increase caused by cooling / warming), and has great supply pressure. The emergency repair scene is caused by equipment failure, which leads to power failure, and has urgent demand for materials and short time window.

[0028] The material management scene information is input by the staff according to the actual situation, and different material management scene information is input according to different actual situations.

[0029] ​S102: Based on the analysis result of the material management scene information, each supply chain node module is screened to determine a plurality of target supply chain node modules.

[0030] Further, based on the analysis result of the material management scene information, each supply chain node module is screened to determine a plurality of target supply chain node modules, specifically including: Extract the scene keywords in the material management scene information; According to the keyword matching in the pre-constructed scene demand library, the standard scene demand is obtained; Based on the mapping relationship between the standard scene demand and the supply chain node, the supply chain node module is screened to obtain the target supply chain node module.

[0031] It can be understood that the material management scene information is input according to the actual situation, and the description may not be perfect, and the current material demand cannot be accurately provided. Based on this, according to various application scenarios of material management, a scene demand library is constructed, which includes the demand, constraint and core keyword of the corresponding scene of various scenes. Through natural language processing (NLP) technology, the scene keywords in the material management scene information are extracted, the scene keywords and the core keywords in the scene demand library are matched, and at least one standard scene demand is output according to the matching result. According to the mapping relationship between the standard scene demand and the supply chain node, each supply chain node module is screened to obtain the target supply chain node module.

[0032] In a specific example, the material management scene information is "control inventory cost and ensure peak supply", combined with the preset scene demand library, the scene information can be improved, and the implicit demand in the material management scene information is obtained through the standard scene demand. For example, the implicit demand of "emergency repair scene" is "zero out of stock".

[0033] It should be noted that the above supply chain node module is not directly set according to each link of the supply chain, and each link needs to be combined and split through analysis of each link of the material supply chain and the flow transfer dependence relationship between each link. While ensuring the flexibility of the material supply chain model, the number of supply chain modules is maximized to ensure high cohesion within each supply chain node module and low coupling between each supply chain node module.

[0034] Specifically, the supply chain node module, with reference to Figure 2 is obtained by the following steps: According to the flow transfer order of the power grid materials at each business flow transfer node in the supply chain and the flow transfer dependence relationship between each business flow transfer node, a node directed graph is constructed; The node directed graph is divided to obtain a plurality of first primary sub-modules; The relationship between the business flow transfer node and the first primary sub-module is adjusted, the total modular primary gain item of all first primary sub-modules before and after adjustment is analyzed, and a target primary sub-module is determined; According to the transfer dependency relationship of each business flow transfer node in the target primary sub-module, a primary association relationship between each target primary sub-module is established; Based on the primary association relationship between each target primary sub-module, each target primary sub-module is divided to determine a target final sub-module; The relationship between the target final sub-module and the target primary sub-module is adjusted, the total modular final gain item of all target final sub-modules before and after adjustment is analyzed, and a supply chain node module is determined.

[0035] Further, the relationship between the business flow transfer node and the first primary sub-module is adjusted, the total modular primary gain item of all first primary sub-modules before and after adjustment is analyzed, and a target primary sub-module is determined, which specifically includes: Based on each business flow transfer node, the adjacent nodes of the business flow transfer node are determined; The business flow transfer node is removed from the current first primary sub-module and added to the first primary sub-module corresponding to the adjacent node; The modularity of the business flow transfer node before and after adjustment is calculated, and the total modular primary gain item corresponding to each adjacent node is given; The total modular primary gain item corresponding to each adjacent node is sorted to determine the maximum value of the primary gain item; If the maximum value of the primary gain item is positive, the business flow transfer node is added to the first primary sub-module of the adjacent node corresponding to the maximum value of the primary gain item, and the first primary sub-module is updated; When all business flow transfer nodes are traversed and the total modular primary gain item is within a predetermined range, the first primary sub-module is determined as a target primary sub-module.

[0036] It should be understood that the predetermined range is (-∞, 0], i.e. when all total modular primary gain items are negative or 0, the first primary sub-module is no longer updated, and the current first primary sub-module is taken as a target primary sub-module.

[0037] Specifically, the total modular primary gain item is obtained by the following steps: Based on the weight corresponding to the directed edge of each business flow transfer node in the node directed graph, and combining the sum of the weights corresponding to the directed edges between each business flow transfer node in the first primary sub-module, a weight fusion item is given; The node degree fusion term is given based on the weight of the directed edge between each business flow transfer node in the node directed graph and the sum of the node degrees of all business flow transfer nodes in the first primary sub-module. The total modular primary gain term is determined according to the difference between the weight fusion term and the node degree fusion term.

[0038] The total modular primary gain term is specifically expressed as:

[0039] wherein, △Q(i, C) is the total modular primary gain term corresponding to the first primary sub-module C entered by the business flow transfer node i, k i,in (C) is the sum of the weights of the directed edges between each business flow transfer node in the first primary sub-module C, w ij is the weight of the directed edge between the business flow transfer node i and the business flow transfer node j, k i is the sum of the weights of the directed edges between the business flow transfer node i and all business flow transfer nodes, V is the set of all business flow transfer nodes, ∑ tot (C) is the sum of the node degrees of all business flow transfer nodes in the first primary sub-module C, d j is the node degree of the business flow transfer node j, m is half of the sum of the weights of the directed edges in the node directed graph, w uv is the weight of the directed edge between the business flow transfer node u and the business flow transfer node v in the node directed graph.

[0040] The transfer order of each business flow transfer node refers to the operation order of each business flow transfer node in the material supply chain, and the transfer dependency relationship between each business flow transfer node includes a sequential dependency relationship and a resource dependency relationship. The sequential dependency relationship refers to the fact that one business flow transfer node ends and another business flow transfer node can start, and there is a strong dependency relationship between the two business flow transfer nodes. For example, the supplier must first produce the materials before the materials can be distributed, and the order cannot be changed. The resource dependency relationship is that the output of one business flow transfer node is the input of another business flow transfer node, and the two business flow transfer nodes have a supply and demand relationship in information, resources, personnel, etc. For example, the current total inventory information output after the materials are warehoused and the framework agreement quota information output by the contract performance execution process will be used as the input of the demand reporting activity.

[0041] In a specific embodiment, the business flow transfer nodes are taken as nodes in the node directed graph, and a directed edge between each business flow transfer node is established according to the transfer order of the power grid materials in each business flow transfer node in the supply chain and the transfer dependency relationship between each business flow transfer node, i.e., a directed edge is established between the business flow transfer nodes with a transfer dependency relationship. Connecting each business flow transfer node according to the transfer order can improve the construction efficiency of the node directed graph. The weight corresponding to each directed edge is the strength of the dependency relationship between the two business flow transfer nodes, which is set according to actual conditions.

[0042] In a specific example, each business flow transfer node is first taken as a first primary submodule, each business flow transfer node is traversed, and the business flow transfer node is adjusted according to each adjacent node. The business flow transfer node is removed from the current first primary submodule and added to the first primary submodule corresponding to the adjacent node. At the same time, the modularity of the business flow transfer node before and after adjustment is calculated, and the total modular primary gain item is given. According to the total modular primary gain item corresponding to each adjacent node, the relationship between the business flow transfer node and each first primary submodule is determined and each first primary submodule is updated. When all business flow transfer nodes are traversed and the total modularity does not change, each first primary submodule is determined as a target primary submodule.

[0043] For each business flow transfer node i and all corresponding adjacent nodes j, each business flow transfer node i is removed from the original first primary submodule and added to the first primary submodule of the adjacent node j, and the total modular primary gain item corresponding to the adjacent node j is calculated. The maximum value of the total modular primary gain item corresponding to all adjacent nodes j is selected as the primary gain item maximum value, and it is judged whether the primary gain item maximum value is positive. If yes, the business flow transfer node i is added to the first primary submodule of the adjacent node j; otherwise, if the maximum value is negative, the business flow transfer node j is kept in the current first primary submodule. Each business flow transfer node is traversed in the node directed graph and is cycled, each first primary submodule is updated, until the movement of all business flow transfer nodes cannot produce a total modular primary gain item maximum value that is positive, and the current first primary submodule is determined as a target primary submodule.

[0044] According to the flow transfer dependency relationship of each business flow transfer node in the target primary sub-module, a primary association relationship between each target primary sub-module is established, the primary association relationship represents a directed edge weight between the target primary sub-modules, and the directed edge weight between the target primary sub-modules is the sum of the directed edge weights of all business flow transfer nodes in two target final sub-modules. After obtaining each target primary sub-module and its primary association relationship, each primary target sub-module is taken as a new node, and the same update iteration process as the business flow transfer node is repeated. At the beginning of iteration, each target primary sub-module is regarded as a target final sub-module, that is, the edge weight between each target final sub-module is the sum of the directed edge weights of all business flow transfer nodes in two target final sub-modules. Taking the target primary sub-module a as an example, a set of adjacent sub-modules connected with the target primary sub-module a is determined, the target primary sub-module a is removed from the current target final sub-module and added to the target final sub-module corresponding to the adjacent target primary sub-module, each total modular final gain term of the adjacent target primary sub-module is calculated, and the maximum value is determined. If the maximum value is positive, the target primary sub-module is moved into the target final sub-module corresponding to the maximum value, and the update of the target final sub-module is completed, otherwise, the target primary sub-module is kept in the current target final sub-module. When all target primary sub-modules cannot produce new movement, the current target final sub-module is determined as a supply chain node module. It can be understood that the calculation process of the total modular final gain term is the same as that of the total modular primary gain term, and will not be described here.

[0045] It can be understood that, in the update iteration process of the business flow transfer node and the target primary sub-module, the traversal order of the business flow transfer node and the target primary sub-module will have a certain influence on the update efficiency. Therefore, in the embodiments provided by the present application, the traversal order is determined according to the degree centrality of the business flow transfer node and the target primary sub-module, and the degree centrality represents the number of edges connected to the business flow transfer node or the target primary sub-module. The higher the degree centrality of a node means that it has more contact with other nodes, indicating that its position in the node directed graph is more core and the node is more important. Therefore, the degree centrality can be used to optimize the update order of the business flow transfer node and the target primary sub-module.

[0046] Firstly, the degree centrality of the business flow transfer node or the target primary sub-module is calculated, and the business flow transfer node and the target primary sub-module are sorted according to the degree centrality. Based on the sorting result, the update process is optimized, the process of the supply chain node module can be determined, the efficiency is improved, and the stability of the supply chain node module is increased. The degree centrality of the business flow transfer node i is specifically represented as:

[0047] Wherein, d w(i) is the degree centrality of the business flow transfer node i, N(i) is the adjacent node set of the business flow transfer node i, and w(i,j) is the weight of the directed edge connecting the business flow transfer node i and the business flow transfer node j.

[0048] It can be understood that the degree centrality of the target primary sub-module can also be calculated by converting the business flow transfer node i into the target primary sub-module.

[0049] The power grid material supply chain is divided into a plurality of supply chain node modules through the above process, the supply chain node modules are divided into decision node modules and support node modules, the decision node modules include a procurement decision module, a delivery decision module and an inventory decision module, wherein the procurement decision module formulates a globally optimal procurement strategy according to a demand plan, a real-time inventory level and a cost target, comprehensively considers the importance of materials, the urgency of demand and the comprehensive performance of suppliers, and finally decides the variety, quantity, timing and optimal supplier selection of procurement; the delivery decision module determines an optimal actual delivery time window based on an inventory strategy, demand urgency and warehousing cost on the basis of a delivery time promised by a supplier; and the inventory decision module formulates and dynamically adjusts inventory control strategies (such as a safety inventory level and a reorder point) of various materials, and monitors an inventory state in real time, so as to optimize a replenishment triggering time and quantity on the premise of meeting a preset service level.

[0050] The procurement decision module realizes the balance between total cost optimization and supply risk from the source. The delivery decision module smoothes inventory fluctuations by flexibly adjusting the delivery time, thereby effectively reducing inventory holding costs and warehouse backlog risks while responding to demand. The inventory decision module minimizes the conflict between inventory holding costs and stockout loss costs, ensuring the resilience and economy of material supply.

[0051] The support node modules include a demand management module, a supplier management module and a sampling inspection management module. The demand management module is responsible for predicting and simulating future material demand, and generating basic data driving all core decisions; the supplier management module is used for maintaining a qualified supplier list and historical performance data thereof, such as a quality qualification rate, an average delivery on-time rate, a production capacity and the like, simulating production completion time of an order and logistics in-transit time, thereby determining a total delivery time; and the sampling inspection management module simulates “sampling inspection decisions” for batches of incoming materials according to established sampling inspection rules (such as a sampling inspection proportion and exemption conditions), simulates the time consumed in the inspection process for materials that need to be sampled, and randomly simulates sampling inspection results.

[0052] The demand management module quantifies the uncertainty of prediction to provide key parameters for the decision node modules to formulate safety inventory strategies. The support node modules do not have decision-making functions, but the information outputted thereby is an important parameter for constructing a material supply chain model.

[0053] The six supply chain node modules are connected to each other through clear input-output interfaces, forming an organic whole that works cooperatively. Among them, the demand management module outputs demand prediction data to the procurement decision module, supporting procurement variety, quantity, and timing decisions; and provides demand volatility rate to the inventory decision module, assisting in setting safety stock and reordering point. Through the order plan of the procurement decision module, it indirectly affects the demand urgency judgment of the delivery decision module. The supplier management module outputs supplier performance data (quality pass rate, on-time rate) to the procurement decision module, supporting optimal supplier selection; and provides the total estimated delivery time (production + in-transit) to the delivery decision module as a benchmark for negotiating the actual delivery window. Through the delivery time parameter, it affects the replenishment lead time calculation of the inventory decision module. The sampling inspection management module outputs sampling inspection results and time consumption to the inventory decision module, triggering the qualified material storage or unqualified material processing process; and feeds back the supplier sampling inspection pass rate to the procurement decision module, optimizing the subsequent supplier selection strategy. Through the change of inventory status, it indirectly affects the replenishment timing adjustment of the procurement decision module.

[0054] The procurement decision module outputs procurement order details (variety, quantity, supplier), providing a negotiation basis for the delivery decision module; the delivery decision module feeds back the actual delivery time window after negotiation, serving as the basis for the procurement decision module to adjust the order execution pace. The procurement decision module outputs procurement arrival plan, supporting the inventory decision module's storage planning and capacity reservation; the inventory decision module outputs real-time inventory level and inventory gap, serving as the core basis for the procurement decision module to trigger replenishment and adjust procurement quantity. The delivery decision module outputs arrival time window and arrival batch, assisting the inventory decision module in optimizing warehouse space allocation and storage scheduling; the inventory decision module outputs current inventory occupancy rate and warehouse remaining capacity, constraining the delivery decision module's arrival time adjustment (e.g., delaying delivery when inventory is saturated). The delivery decision module outputs arrival material batch information (batch number, quantity, arrival time), triggering the sampling inspection decision and process simulation of the sampling inspection management module; the sampling inspection management module outputs sampling inspection time consumption, assisting the delivery decision module in optimizing the reserved buffer of the subsequent arrival time window. The supplier management module outputs supplier historical sampling inspection pass rate, assisting the sampling inspection management module in adjusting the sampling inspection proportion (e.g., increasing sampling frequency when the pass rate is low); the sampling inspection management module outputs the latest batch sampling inspection result, updating the performance data of the supplier management module. The demand size data of the demand management module assists the supplier management module in evaluating the supplier's capacity matching degree, providing a reference for procurement decisions.

[0055] Each supply chain node module interacts through standardized interfaces, ensuring "low coupling" and "high cohesion".

[0056] S103: Match the node module elements of each target supply chain node module according to the scenario priority in the material management scene information and the association relationship of each target supply chain node module, and load each target supply chain node module.

[0057] Further, the node module elements of each target supply chain node module are matched according to the scenario priority in the material management scene information and the association relationship of each target supply chain node module, and each target supply chain node module is loaded, referring to Figure 3 , specifically including: According to the scenario priority of the material management scene information, the node module elements corresponding to the scenario priority of the material management scene information are sequentially filtered out from the multi-dimensional matching library, wherein the multi-dimensional matching library is constructed through the operation characteristics of the power grid material supply chain, including the mapping relationship between the scenario priority and the node module elements; According to the node module element loading order, the module decision variables and the module constraint conditions are sequentially loaded and embedded into each target supply chain node module, and the loading of each target supply chain node module is completed.

[0058] Specifically, the supply chain node module is divided into a decision node module and a support node module, and the target supply chain node module includes at least one decision node module and at least one support node module.

[0059] The node module elements include module decision variables and module constraint conditions. The node module elements represent the module decision variables and the module constraint conditions required for the internal operation of the target supply chain node module. It should be understood that the node module elements of each supply chain node module are pre-constructed according to the business flow node business requirements.

[0060] The module decision variables corresponding to the procurement decision module include a procurement quantity variable, a supplier selection variable, and a batch decision variable. The procurement quantity variable represents the quantity of material y purchased from supplier x at time t. The supplier selection variable is a 0-1 variable. If supplier x is selected for supply at time t, the supplier selection variable is 1, otherwise the supplier selection variable is 0. The batch decision variable is an integer variable, representing the batch quantity of material y purchased from supplier x at time t. The module decision variables corresponding to the inventory decision module include existing inventory level, safety inventory decision, shortage quantity variable, and replenishment trigger point. The existing inventory level is a continuous variable, representing the actual storage quantity of material y in the warehouse at the end of time t. The safety inventory decision is a decision variable, representing the minimum inventory alert line set for the demand fluctuation of material y. The shortage quantity variable is a continuous variable, representing the unmet demand of item y at time t. Reorder point is a continuous variable, used to trigger a reorder instruction when the inventory is below the reorder point. The module decision variables corresponding to the delivery decision module include item allocation, path selection variable, and vehicle scheduling variable. Item allocation is a continuous variable, representing the amount of item y transported from warehouse k to demand point l at time t. Path selection variable is a 0-1 variable, equal to 1 if the transportation path from warehouse k to demand point l is enabled, otherwise equal to 0. Vehicle scheduling variable is a 0-1 or integer variable, representing whether vehicle v is enabled at time t, or the number of vehicles enabled.

[0061] The module constraints corresponding to the inventory decision module include inventory flow balance constraints and stockout rate constraints. The module constraints corresponding to the delivery decision module include warehouse capacity constraints and transportation capacity constraints. The module constraints corresponding to the procurement decision module include supplier capacity constraints, supplier number constraints, and minimum order quantity constraints.

[0062] The inventory flow balance constraint is used to ensure the balance of item in and out of each period, which is specifically represented as:

[0063] where, is the inventory of item y at the end of time t, is the external demand of item y at time t, is the amount of item y purchased from supplier x at time t, is the amount of item y transported to demand point l at time t.

[0064] The stockout rate constraint is used to force the stockout rate to be controlled within the allowed range α in emergency or critical item guarantee scenarios, which is specifically represented as:

[0065] where, is the unmet demand of item y at time t, is the external demand of item y at time t.

[0066] The warehouse capacity constraint is used to limit the total space (or total weight) occupied by all items in the warehouse to not exceed the upper limit of the warehouse, which is specifically represented as:

[0067] where, v y is the volume / occupied area of unit item y, The actual storage amount of material y in the warehouse at the end of time t, The warehouse capacity threshold of warehouse k.

[0068] The transportation capacity constraint is used to limit the total amount of transportation from warehouse k to demand point l to be no more than the upper limit of vehicle capacity, which is specifically expressed as:

[0069] wherein, The quantity of material y transported to demand point l at time t, v y The volume / occupancy area of unit material y, The transportation capacity threshold of vehicle v, Whether vehicle v is enabled or the number of enabled vehicles at time t.

[0070] The supplier capacity constraint is used to limit the procurement quantity to the supplier to be no more than its maximum available quantity (or remaining capacity), which is specifically expressed as:

[0071] wherein, The quantity of material y procured from supplier x at time t, The maximum available quantity or remaining capacity of supplier x at time t.

[0072] The supplier quantity constraint is used to limit the number of suppliers to ensure the safety of the supply chain, which is specifically expressed as:

[0073] wherein, The supplier selection variable, if supplier x is selected to supply at time t, the supplier selection variable is 1, otherwise the supplier selection variable is 0, N max The threshold value of the number of suppliers, and a logical association needs to be established: M is a large number to ensure that if not selected, it cannot be procured.

[0074] The minimum order quantity constraint is used to limit the procurement quantity to be no less than the procurement threshold when procurement occurs, which is specifically expressed as:

[0075] wherein, The quantity of material y procured from supplier x at time t, The procurement decision variable, if supplier x is used to supply material y at time t, the procurement decision variable is 1, otherwise the procurement decision variable is 0, MOQ xy The minimum order quantity of supplier x to supply material y.

[0076] S104: Assembling each target supply chain node module through a standard supply chain model framework to complete the construction of the power grid material supply chain model.

[0077] Further, the construction of the power grid material supply chain model is completed by assembling each target supply chain node module through a standard supply chain model framework, and specifically includes: Based on the standard supply chain model framework, determine each associated decision variable and associated constraint condition; According to the mapping relationship between the associated decision variable and the target supply chain node module, a variable transmission channel is established between the target supply chain node modules; According to the mapping relationship between the associated constraint condition and the target supply chain node module, a constraint judgment channel is established between the target supply chain node modules; Through the variable transmission channel and the constraint judgment channel, the material information is transmitted between the target supply chain node modules, the target supply chain node modules are assembled, and the power grid material supply chain model is obtained.

[0078] Further, each decision node module corresponds to a standard supply chain model framework, which is constructed by the following steps: Based on the decision characteristics of each decision node module, set the corresponding module objective function from at least one dimension; Fusion of module constraint conditions and module decision variables in the decision node module to complete the construction of the standard supply chain model framework.

[0079] The standard supply chain model framework is a core logic of "decision variable + constraint condition + objective function", combined with the functional boundaries and interface standards of each supply chain node module, to construct a standardized and reusable basic model, to ensure that a single module can be solved independently, and multiple modules can be integrated through interfaces.

[0080] It can be understood that the module objective function, the module decision variable and the module constraint condition are included in the standard supply chain model framework of each module, if the module decision variable appears in two or more target supply chain node modules, the module decision variable is a related decision variable. Similarly, if the module decision variable of other target supply chain node modules exists in the module constraint condition, the module constraint condition is a related constraint condition. Through the standard supply chain module framework, the related decision variable and the related constraint condition contained therein can be obtained. Through the related decision variable, the variable transmission channel between the corresponding target supply chain node modules is established. Through the related constraint condition, the constraint judgment channel between the corresponding target supply chain node modules can be suggested. According to the variable transmission channel and the constraint judgment channel, the transmission of material information between each target supply chain node module can be completed, and the construction of the power material supply chain model is realized. According to different scenes and needs, through the function coefficients corresponding to the scene, the module objective functions of different target supply chain node modules in the power material supply chain model are fused to determine the scene objective function, and the corresponding material demand result can be obtained by solving in combination with the module constraint condition.

[0081] In a specific example, when there is "considering supplier selection restriction" in the material management scene information, scan each module decision variable, identify the variable with the label and add it to the decision vector space of the current power grid material supply chain model. When the user switches the model granularity from "monthly" to "weekly", the set range of the time index t of all activated variables is automatically updated. The dependent relationship between variables is automatically maintained, for example: once the purchase quantity variable is activated, the existing inventory level will be activated by default, because purchase will inevitably lead to inventory change.

[0082] The scene objective function represents the demand for the material supply chain under the current scene, including but not limited to cost, efficiency, safety, etc. In the embodiments provided by the present application, the module objective functions in each standard supply chain model framework are fused, the module objective functions are combined according to the actual scene (such as "reducing cost and increasing efficiency" or "ensuring supply priority"), the scene objective function is formed, and the flexible configuration of the optimization target is realized.

[0083] For example, the purchase cost objective function, the warehouse cost objective function and the shortage penalty objective function are fused to obtain the scene objective function, which is suitable for the annual planning or daily inventory replenishment scene of regular materials, and the core is to minimize the total cost of the supply chain. For another example, the response time objective function and the supply satisfaction objective function are fused to obtain the scene objective function, which is suitable for the material guarantee scene during emergency rescue (such as typhoon, ice disaster) or peak summer.

[0084] The procurement decision-making module is based on a standard supply chain model framework where demand forecast data is known and its fluctuations conform to a preset distribution, supplier performance data is accurate, valid, and quantifiable, and constraints such as procurement budgets and warehousing capacity are clearly defined. It does not consider unforeseen policy changes or extreme supply disruptions. By constructing objective functions for the module from the dimensions of procurement cost and supply satisfaction, a multi-objective optimization function is formed, balancing cost and supply risk.

[0085] The objective function for procurement costs is specifically expressed as follows:

[0086] The supply satisfies the objective function, specifically expressed as:

[0087] Module constraints include budget constraints, storage capacity constraints, and supplier capacity constraints. Budget constraints are specifically expressed as follows:

[0088] Storage capacity constraints are specifically expressed as follows:

[0089] Supplier capacity constraints are specifically expressed as follows:

[0090] Among them, P x Let Q be the unit price of material x. x,reg Q represents the regular procurement volume of material x. x,emer For the emergency reserve of material x, k emer C represents the emergency procurement premium factor. x,ord Let B be the cost of materials x per purchase order. x For the procurement batch of material x, I x,curr Let D be the current inventory of material x. x,pred Let x be the monthly demand forecast for material. Indicates taking non-negative values, C xy B represents the monthly production capacity limit of supplier y for material x. total For the total procurement budget, S total Let n be the total storage capacity and n be the total number of different types of goods.

[0091] The basis of the delivery decision module corresponding to the standard supply chain model framework is that the delivery time promised by the supplier is known and negotiable, the inventory occupation state is updated in real time, the demand urgency level is clear, and the logistics transportation sudden interruption is not considered. By constructing the module objective function from the warehouse cost dimension and the response time dimension, the warehouse cost objective function and the response time objective function are obtained, and the core of "minimum inventory cost + maximum demand response timeliness" is realized.

[0092] The warehouse cost objective function is specifically represented as:

[0093] The response time objective function is specifically represented as:

[0094] The module constraint conditions include delivery time window constraint, warehouse capacity constraint and demand urgency constraint. The delivery time window constraint is specifically represented as:

[0095] The warehouse capacity constraint is specifically represented as:

[0096] The demand urgency constraint is specifically represented as:

[0097] Wherein, H x is the daily inventory holding cost of material x, Q x,t is the arrival quantity of material x on the tth day, D x,t is the demand quantity of material x on the tth day, T x,urgent is the demand urgency time limit of material x, T curr is the current date, S x is the warehouse capacity of material x, T s,y,min is the earliest delivery day of supplier y, T s,y,max is the latest delivery day of supplier y, T start is the delivery start day, T end is the delivery deadline.

[0098] The basis of the standard supply chain model framework corresponding to the inventory decision module is that the demand fluctuation rate and the delivery delay probability are known, the replenishment lead time is fixed, the warehouse capacity and the capital occupation rate constraint are clear, and the material loss and expiration are not considered. By constructing the module objective function from the dimension of shortage penalty, the shortage penalty objective function is obtained, which can balance the inventory cost and the shortage loss, and is specifically represented as:

[0099] The module constraints include service level constraints, fund occupation constraints, and warehouse capacity constraints. The service level constraints are specifically represented as:

[0100] The fund occupation constraints are specifically represented as:

[0101] The warehouse capacity constraints are specifically represented as:

[0102] wherein P x is the unit price of the material x, SS x is the safety stock of the material x, S x is the unit shortage loss of the material x, L x is the reordering lead time of the material x, O x is the single reordering order cost of the material x, Q x,replenish is the reordering batch of the material x, represents rounding up, SL x is the target service level of the material x, F total is the maximum fund occupation limit of the warehouse.

[0103] In a specific embodiment, in combination with the operation characteristics of the power grid material supply chain, core business scenarios are divided, the decision target priorities, constraint conditions, and key influence factors of each scenario are clarified, a four-dimensional matching rule library of "scenario target function, module decision variable, and module constraint condition" is established, and precise calling and combination of elements are realized. Taking the normal supply scenario as an example, the procurement decision module, delivery decision module, and inventory decision module are fused, the procurement cost target function and warehouse cost target function are fused, the scenario target function is obtained, and the minimization of total procurement cost, the minimization of inventory holding cost, and the minimization of total inventory cost are considered. The decision variables include regular procurement quantity, procurement batch, safety stock, regular delivery time window, and normal distribution batch. The constraint conditions include budget constraints, warehouse capacity constraints, inventory turnover rate constraints, and supplier regular capacity constraints. The module decision variables of the support node module include regular demand forecast value, supplier normal performance data, and standard sampling ratio.

[0104] Taking the winter / summer peak demand scenario as an example, the procurement decision module, the delivery decision module and the inventory decision module are fused, and the supply satisfaction target function, the response time target function and the shortage penalty target function are combined to maximize the supply satisfaction rate, maximize the demand response rate and minimize the shortage loss. The module decision variables include emergency reserve, procurement trigger point downward shift (advance replenishment), safety stock upward floating coefficient (1.3 times), delivery time window compression (advance delivery), emergency transportation quantity. The constraint conditions include emergency budget relaxation, temporary expansion of warehouse capacity, upper limit constraint of distribution capacity, emergency arrival time. The module decision variables of the supporting node block include demand peak value, demand fluctuation rate, emergency production capacity of suppliers, and sampling proportion.

[0105] Taking the emergency repair scenario as an example, the procurement decision module, the delivery decision module and the inventory decision module are fused, and the response time target function is taken as the scenario target function to maximize the emergency response rate. The module decision variables include emergency reserve, emergency procurement quantity, delay tolerance threshold tightening, safety stock upward floating coefficient, emergency transportation path and priority delivery batch. The module constraint conditions include zero shortage constraint, emergency arrival time, transportation priority constraint and no upper limit of emergency budget. The module decision variables of the supporting node module include emergency demand batch, demand priority, supplier emergency production time and inspection exemption identifier (trigger).

[0106] Based on the standard supply chain model framework of each module, the matched "variable assignment constraint embedded target function integration" sequence is dynamically assembled. First, the module decision variable is assigned, and the default value of each module decision variable is loaded, then the module constraint conditions are embedded, according to the principle of "hard constraint priority, soft constraint adaptation", the module constraint conditions are converted into mathematical expressions and embedded into the model, the target functions are integrated by weight coefficients, and the scenario target function is obtained. Finally, the cross-module interface is connected, the interface variables between modules are identified, the data flow logic is established (such as the "procurement quantity" of the procurement module as the input of the inventory module), and the construction of the power grid material supply chain model is completed.

[0107] Referring to Figure 4 The embodiment of the application provides a kind of modular power grid material supply chain model automatic modeling device, comprising: Data acquisition unit 201 is used to collect material management scenario information; Module determination unit 202 is used to filter each supply chain node module based on the analysis result of material management scenario information, and determine multiple target supply chain node modules; Module loading unit 203 is used to match the node module element of each target supply chain node module in combination with the scenario priority in material management scenario information and the association of each target supply chain node module, and load each target supply chain node module. The module assembling unit 204 is configured to assemble each target supply chain node module through a standard supply chain model framework, and complete construction of the power grid material supply chain model.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the foregoing method embodiments, and will not be described herein.

[0109] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to the embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for automatic modeling of a modular power grid supply chain model, characterized in that, The method comprises the following steps: Collecting material management scene information; Based on the analysis result of the material management scene information, each supply chain node module is screened to determine a plurality of target supply chain node modules; Based on the scene priority in the material management scene information and the association relationship of each target supply chain node module, the node module elements of each target supply chain node module are matched, and each target supply chain node module is loaded; Through the standard supply chain model framework, each target supply chain node module is assembled to complete the construction of the power grid material supply chain model.

2. The method of automatically modeling a modular power grid asset supply chain model of claim 1, wherein, Based on the analysis result of the material management scene information, each supply chain node module is screened to determine a plurality of target supply chain node modules, which specifically comprises: Extracting the scene keywords in the material management scene information; According to the keyword matching in the pre-constructed scene demand library, the standard scene demand is obtained; Based on the mapping relationship between the standard scene demand and the supply chain node, the supply chain node module is screened to obtain the target supply chain node module.

3. The method of automatically modeling a modular power grid asset supply chain model of claim 1, wherein, The supply chain node module is obtained by the following steps: According to the flow sequence of the power grid material at each business flow node in the supply chain and the flow dependence relationship between each business flow node, a node directed graph is constructed; Each business flow node in the node directed graph is divided to obtain a plurality of first primary sub-modules; Adjusting the ownership relationship between the business flow node and the first primary sub-module, analyzing the total modular primary gain item of all first primary sub-modules before and after adjustment, and determining the target primary sub-module; According to the flow dependence relationship of each business flow node in the target primary sub-module, the primary association relationship between each target primary sub-module is established; Based on the primary association relationship between each target primary sub-module, each target primary sub-module is divided to determine the target final sub-module; Adjusting the ownership relationship between the target final sub-module and the target primary sub-module, analyzing the total modular final gain item of all target final sub-modules before and after adjustment, and determining the supply chain node module.

4. The method of automatically modeling a modular power grid asset supply chain model of claim 3, wherein, Adjusting the ownership relationship between the business flow node and the first primary sub-module, analyzing the total modular primary gain item of all first primary sub-modules before and after adjustment, and determining the target primary sub-module, specifically comprising: Based on each business flow node, the adjacent nodes of the business flow node are determined; The business flow node is removed from the current first primary sub-module and added to the first primary sub-module corresponding to the adjacent node; The modularity of the business flow node before and after adjustment is calculated, and the total modular primary gain item corresponding to each adjacent node is given; The total modular primary gain item corresponding to each adjacent node is sorted to determine the maximum primary gain item; If the maximum primary gain item is positive, the business flow node is added to the first primary sub-module of the adjacent node corresponding to the maximum primary gain item, and the first primary sub-module is updated; When all business flow nodes are traversed and the total modular primary gain item is within the preset range, the first primary sub-module is determined as the target primary sub-module.

5. The method of automatically modeling a modular power grid asset supply chain model of claim 3, wherein, The total modular primary gain item is obtained by the following steps: Based on the weight corresponding to the directed edge of each business flow transfer node in the node directed graph, and combined with the sum of the weights corresponding to the directed edges between each business flow transfer node in the first primary sub-module, a weight fusion term is given; Based on the weight corresponding to the directed edge of each business flow transfer node in the node directed graph, and combined with the sum of the degrees of all business flow transfer nodes in the first primary sub-module, a node degree fusion term is given; The total modular primary gain term is determined according to the difference between the weight fusion term and the node degree fusion term.

6. The method of automatically modeling a modular power grid asset supply chain model of claim 1, wherein, The node module element includes a module decision variable and a module constraint condition; According to the scene priority of the material management scene information, the node module elements corresponding to the scene priority of the material management scene information are sequentially screened from the multi-dimensional matching library, wherein the multi-dimensional matching library is constructed by the operation characteristics of the power grid material supply chain, and includes the mapping relationship between the scene priority and the node module element. According to the node module element loading sequence, the module decision variable and the module constraint condition are sequentially loaded and embedded into each target supply chain node module, and the loading of each target supply chain node module is completed. The supply chain node module is divided into a decision node module and a support node module, and the target supply chain node module includes at least one decision node module and at least one support node module.

7. The method of automatically modeling a modular power grid asset supply chain model of claim 6, wherein, Through the standard supply chain model framework, each target supply chain node module is assembled, and the construction of the power grid material supply chain model is completed, specifically including:

8. The method of automatically modeling a modular power grid asset supply chain model of claim 1, wherein, Based on the standard supply chain model framework, the associated decision variables and the associated constraint conditions are determined; According to the mapping relationship between the associated decision variables and the target supply chain node module, a variable transmission channel is established between the target supply chain node modules; According to the mapping relationship between the associated constraint conditions and the target supply chain node module, a constraint judgment channel is established between the target supply chain node modules; Through the variable transmission channel and the constraint judgment channel, the material information is transmitted between the target supply chain node modules, the target supply chain node modules are assembled, and the power grid material supply chain model is obtained. Each decision node module corresponds to a standard supply chain model framework, which is constructed by the following steps:

9. The method of claim 8, wherein the modular power grid supply chain model is automatically built by: Based on the decision characteristics of each decision node module, a corresponding module objective function is set from at least one dimension; The module constraint conditions and the module decision variables in the decision node module are fused to complete the construction of the standard supply chain model framework. An automatic modeling method of the modular power grid material supply chain model according to any one of claims 1-9 is adopted, including:

10. An apparatus for automatic modeling of a modular power grid supply chain model, characterized in that, A data acquisition unit is used to acquire material management scene information; A module determination unit is used to screen each supply chain node module based on the analysis result of the material management scene information, and determine a plurality of target supply chain node modules; A module loading unit is used to match the node module elements of each target supply chain node module according to the scene priority in the material management scene information and the association relationship of each target supply chain node module, and load each target supply chain node module. ​ A module assembling unit is configured to assemble each target supply chain node module through a standard supply chain model framework to complete construction of the power grid material supply chain model.

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