Electric power material supply chain risk early warning method and device based on digital mirror image

Abstract

The invention discloses an electric power material supply chain risk early warning method and device based on a digital mirror image, and belongs to the field of electric power material supply chain management, and the method specifically comprises the steps: mapping WBS data of a target electric power engineering task into BOM data based on an electric power engineering knowledge graph, and querying a risk threshold value; the supply chain system starts material transfer operation based on the BOM data, and updates the digital mirror image according to the generated first logistics data; the digital mirror image performs risk assessment on a target business event occurring in the material transfer operation execution process based on the risk threshold, and generates and outputs a coping strategy; the supply chain system starts material transportation operation based on an execution instruction corresponding to the coping strategy, and updates the digital mirror image according to generated second logistics data; and the digital mirror image compares the execution effect data corresponding to the material transportation operation with the expected data corresponding to the coping strategy, and generates and outputs an early warning signal. Therefore, the accuracy of risk early warning of the electric power material supply chain can be improved through implementation of the method.

Description

Technical Field

[0001] This invention relates to the field of power supply chain management, and in particular to a method and apparatus for early warning of power supply chain risks based on digital mirroring. Background Technology

[0002] In the management of the power supply chain, the timeliness of risk warnings and the scientific nature of decision-making directly determine the project progress and the quality of power grid commissioning. Among these, the logistics of large components such as main transformers are prone to risks such as transportation delays, inventory shortages, and abnormal supplier performance. If not properly managed, these risks can lead to project delays, increased costs, and even equipment damage.

[0003] Digital mirroring, as a core carrier connecting the physical world and digital space of the power material supply chain, plays a crucial role in achieving accurate risk warnings and scientifically optimizing decision-making through virtual-physical mapping and dynamic simulation. It is key to solving supply chain management pain points and driving the supply chain's transformation from "passive response" to "proactive service." However, in the current field of power material management, a mature digital mirroring application solution has not yet been formed. The closest existing technology is the "geometric model visualization + static data display" model, which has the following shortcomings: First, this model can only visualize static information such as the location and inventory of power materials, without embedding risk identification logic and early warning mechanisms, thus failing to provide early warnings of risks. Second, this model lacks multi-scenario simulation and algorithmic support, serving only as a simple "visualization tool," unable to generate optimal response decisions for risk events, and still relying on human experience. Third, this model fails to achieve the linkage of "early warning-decision-execution-feedback-optimization," unable to utilize actual execution data to optimize its own model, leading to the recurrence of similar risks. Fourth, this model is a generalized design, without incorporating specific functions tailored to the transportation constraints and technical parameters (such as moisture protection and load limits) of large materials like main transformers, resulting in its display and simulation results being detached from the reality of the power material supply chain. Summary of the Invention

[0004] This invention provides a method and apparatus for early warning of risks in the power supply chain based on digital mirroring, which can improve the accuracy of early warning of risks in the power supply chain.

[0005] This invention provides a method for early warning of power material supply chain risks based on digital mirroring, including: Obtain the WBS data of the target power engineering task, map the WBS data to BOM data based on the pre-built power engineering knowledge graph, and query the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph; The BOM data is sent to the supply chain system so that the supply chain system can start material flow operations based on the BOM data and update the pre-built digital image according to the first logistics data generated during the material flow operation. When a target business event is detected during the execution of the material flow operation, the target business event and the risk threshold are input into the updated digital mirror, so that the digital mirror can perform a risk assessment on the target business event based on the risk threshold, and generate and output a response strategy based on the risk assessment result; The response strategy is converted into an execution instruction and sent to the supply chain system, so that the supply chain system can start material transportation operations based on the execution instruction, and update the digital image according to the second logistics data generated during the material transportation operation. The execution effect data corresponding to the material transportation operation is input into the updated digital mirror, so that the digital mirror compares the execution effect data with the expected data corresponding to the response strategy, and generates and outputs a warning signal based on the comparison result.

[0006] This invention, through obtaining the Work Breakdown Structure (WBS) data of the target power engineering task and mapping it to Bill of Materials (BOM) data based on a power engineering knowledge graph, can generate a material requirement list based on the inherent mapping relationship between WBS and BOM data, ensuring the accuracy of early warning data from the data source. By pushing the BOM data to the supply chain system to initiate material flow operations and updating the digital mirror based on the first logistics data generated during the flow process, the material status in the digital mirror can be kept consistent with reality, providing accurate dynamic data support for subsequent risk identification. By inputting the target business events and risk thresholds into the digital mirror for risk assessment and outputting response strategies, risks can be automatically identified based on preset quantitative standards and real-time data, avoiding the subjectivity of human judgment. By converting the response strategies into execution instructions and issuing them to the supply chain system to initiate material transportation operations and updating the digital mirror based on the second logistics data generated during transportation, the digital mirror can be kept synchronized with the physical transportation process, providing accurate transportation process data for subsequent comparison of execution effects. By inputting the execution effect data of transportation operations into the digital mirror for comparison with expected data and outputting early warning signals based on the comparison results, the execution results and expected targets can be quantitatively compared, enabling the identification and feedback of execution risks. Compared to existing technologies that lack dynamic simulation capabilities, this application can improve the accuracy of risk warnings in the power supply chain.

[0007] Furthermore, the step of obtaining the WBS data of the target power engineering task, mapping the WBS data to BOM data based on a pre-built power engineering knowledge graph, and querying the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph includes: Analyze the WBS data of the target power engineering task and identify the engineering nodes contained in the WBS data; Based on the WBS-BOM conversion rules, the power engineering knowledge graph is traversed to query the engineering attribute information that matches each engineering node, and the corresponding material demand feature information is matched according to each engineering attribute information to generate BOM data level by level. Based on the project type and material category of the target power engineering task, corresponding historical risk data is matched from the power engineering knowledge graph, and a risk threshold is generated based on the historical risk data.

[0008] This invention identifies engineering nodes by parsing WBS data, matches engineering attribute information by traversing the knowledge graph based on transformation rules, and generates BOM data level by level. It can accurately match the material requirements corresponding to each node according to the hierarchical relationship of the engineering decomposition structure, ensuring the precise correspondence between BOM data and engineering tasks.

[0009] Furthermore, before obtaining the WBS data of the target power engineering task, mapping the WBS data to BOM data based on a pre-built power engineering knowledge graph, and querying the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph, the method further includes: The power engineering knowledge graph is constructed, and the WBS-BOM conversion rules are configured using a rule engine.

[0010] This invention, through pre-constructing a power engineering knowledge graph and configuring WBS-BOM conversion rules using a rule engine, enables the structured storage of power engineering knowledge and fixes the mapping logic in the form of configurable rules, ensuring that each subsequent mapping is executed according to unified and accurate rules.

[0011] Furthermore, the step of distributing the BOM data to the supply chain system, enabling the supply chain system to initiate material flow operations based on the BOM data, and updating the pre-built digital image according to the first logistics data generated during the execution of the material flow operations, includes: The BOM data is sent to the supply chain system, triggering the supply chain system to execute the procurement and warehousing operations for the target materials; Based on a preset update frequency, material procurement progress, inventory change data, and material outbound status are collected from the supply chain system through a real-time data acquisition interface as the first logistics data. The first logistics data is synchronized to the digital mirror to update the virtual inventory status and procurement progress status of the target material in the digital mirror.

[0012] This invention pushes BOM data to the supply chain system to trigger procurement and warehousing operations, collects first logistics data based on update frequency, and synchronously updates the virtual inventory status and procurement progress status of target materials in the digital mirror. This enables real-time tracking of the entire process from procurement to warehousing, keeping the material status in the digital mirror synchronized with the actual status, and providing real-time and accurate data for risk identification.

[0013] Furthermore, before distributing the BOM data to the supply chain system to enable the supply chain system to initiate material flow operations based on the BOM data, and before updating the pre-built digital mirror according to the first logistics data generated during the execution of the material flow operations, the method further includes: The digital mirror is constructed, and based on the update frequency, the collected real-time environmental data and real-time traffic data are synchronized to the digital mirror.

[0014] The embodiments of the present invention, by pre-constructing a digital mirror and synchronizing real-time environmental data and real-time traffic data based on the update frequency, can ensure that the external conditions in the digital mirror are consistent with the actual environment, providing a basis for risk simulation that conforms to the real world.

[0015] Furthermore, when a target business event is detected during the execution of the material flow operation, the target business event and the risk threshold are input into the updated digital mirror, so that the digital mirror performs a risk assessment on the target business event based on the risk threshold, and generates and outputs a response strategy based on the risk assessment result, including: Identify the target value in the target business event, compare the target value with the risk threshold, and if the target value is greater than or equal to the risk threshold, determine that the target business event has a risk and generate multiple candidate response strategies. The multiple candidate response strategies are simulated and deduced using a multi-objective optimization algorithm. The simulation results are compared according to preset weights, and the response strategy is determined and output based on the comparison results.

[0016] The embodiments of the present invention use a multi-objective optimization algorithm to perform simulation and deduction, and determine the optimal strategy after comparing the effects of each scheme according to a preset weight. This ensures that the decision-making process is based on quantitative calculation rather than empirical estimation, thereby improving the accuracy of risk response.

[0017] Further, the step of converting the response strategy into execution instructions and issuing them to the supply chain system, so that the supply chain system can initiate material transportation operations based on the execution instructions, and update the digital mirror according to the second logistics data generated during the material transportation operation, includes: The response strategy is converted into an execution instruction and sent to the supply chain system, triggering the supply chain system to perform the transportation operation of the target materials based on the execution instruction; Based on the update frequency, transportation operation execution data is collected from the supply chain system through the real-time data acquisition interface as second logistics data. The second logistics data is synchronized to the digital mirror to update the transportation status of the target materials in the digital mirror.

[0018] This invention converts response strategies into execution instructions and sends them to the supply chain system to trigger transportation operations. It also collects second logistics data based on update frequency and synchronously updates the transportation status of target materials in the digital mirror. This enables real-time tracking of the location and progress of transport vehicles, keeping the digital mirror synchronized with the physical transportation process and providing accurate transportation process data for subsequent comparison of execution effects.

[0019] Another embodiment of the present invention provides a power material supply chain risk early warning device based on digital mirroring, including: a data mapping module, a first digital mirroring update module, a risk assessment module, a second digital mirroring update module, and a risk early warning module; The data mapping module is used to obtain the WBS data of the target power engineering task, map the WBS data to BOM data based on the pre-built power engineering knowledge graph, and query the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph. The first digital mirror update module is used to send the BOM data to the supply chain system so that the supply chain system can start material flow operations based on the BOM data, and update the pre-built digital mirror according to the first logistics data generated during the execution of the material flow operations; The risk assessment module is used to input the target business event and the risk threshold into the updated digital mirror when a target business event is detected during the execution of the material flow operation, so that the digital mirror can perform a risk assessment on the target business event based on the risk threshold, and generate and output a response strategy based on the risk assessment result; The second digital mirror update module is used to convert the response strategy into an execution instruction and send it to the supply chain system, so that the supply chain system can start material transportation operations based on the execution instruction, and update the digital mirror according to the second logistics data generated during the material transportation operation. The risk warning module is used to input the execution effect data corresponding to the material transportation operation into the updated digital mirror, so that the digital mirror compares the execution effect data with the expected data corresponding to the response strategy, and generates and outputs a warning signal based on the comparison result.

[0020] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of a digital mirror-based power material supply chain risk early warning method as described in the present invention.

[0021] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of a digital mirror-based power material supply chain risk early warning method of the present invention. Attached Figure Description

[0022] Figure 1 A flowchart illustrating an embodiment of the power material supply chain risk early warning method based on digital mirroring provided by the present invention; Figure 2 This is a schematic diagram of one embodiment of the power material supply chain risk early warning device based on digital mirroring provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0025] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0028] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0029] See Figure 1 To address the lack of dynamic simulation capabilities in existing technologies, an embodiment of the present invention provides a method for early warning of power material supply chain risks based on digital mirroring, including steps S101 to S105: Step S101: Obtain the WBS data of the target power engineering task, map the WBS data to BOM data based on the pre-built power engineering knowledge graph, and query the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph.

[0030] It should be noted that obtaining the WBS data of the target power engineering task, mapping the WBS data to BOM data based on a pre-built power engineering knowledge graph, and querying the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph means: During the target power engineering task initiation phase, the WBS data of the target power engineering task is first received or read. The WBS data contains the specific work packages required to complete the project and their hierarchical relationships. Subsequently, using the WBS data as input, the pre-generated power engineering knowledge graph is traversed. Based on preset WBS-BOM conversion rules, corresponding material requirement feature information is matched for each WBS node, and complete BOM data is generated by summarizing at each level. During this process, preset risk thresholds associated with the target power engineering task are simultaneously retrieved from the power engineering knowledge graph according to the project type and material category of the target power engineering task.

[0031] Preferably, the step of obtaining the WBS data of the target power engineering task, mapping the WBS data to BOM data based on a pre-built power engineering knowledge graph, and querying the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph includes: Analyze the WBS data of the target power engineering task and identify the engineering nodes contained in the WBS data; Based on the WBS-BOM conversion rules, the power engineering knowledge graph is traversed to query the engineering attribute information that matches each engineering node, and the corresponding material demand feature information is matched according to each engineering attribute information to generate BOM data level by level. Based on the project type and material category of the target power engineering task, corresponding historical risk data is matched from the power engineering knowledge graph, and a risk threshold is generated based on the historical risk data.

[0032] For example, assuming the target power engineering task is "110kV main transformer transportation and installation", the project management system can be accessed via an interface to subscribe to progress events and obtain relevant WBS data. When the target power engineering task is started, BOM data containing material codes, quantities, technical requirements, and required time (such as 1 main transformer, 1 set of iron core, or 4 sets of bushings) is automatically generated through a pre-built power engineering knowledge graph, and risk thresholds (such as delayed delivery exceeding 7 days or inventory less than 1 unit) are obtained simultaneously.

[0033] Preferably, before obtaining the WBS data of the target power engineering task, mapping the WBS data to BOM data based on a pre-built power engineering knowledge graph, and querying the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph, the method further includes: The power engineering knowledge graph is constructed, and the WBS-BOM conversion rules are configured using a rule engine.

[0034] For example, assuming the target power engineering task is "110kV main transformer transportation and installation", a power engineering knowledge graph can be constructed by connecting to engineering design documents, historical material supply data, and material management systems to obtain information on the main transformer specifications, required iron cores and bushings, and historical risk data such as delayed deliveries in previous years. This knowledge graph covers main transformer classification, adaptation logic, supply chain rules, and risk thresholds. Simultaneously, based on this power engineering knowledge graph, the Drools rule engine, adapted for power grids, is used to configure WBS-BOM conversion rules.

[0035] Step S102: The BOM data is sent to the supply chain system so that the supply chain system can start material flow operations based on the BOM data and update the pre-built digital image according to the first logistics data generated during the material flow operation.

[0036] It should be noted that sending the BOM data to the supply chain system so that the supply chain system can initiate material flow operations based on the BOM data, and updating the pre-built digital mirror according to the first logistics data generated during the execution of the material flow operations, means that after generating the BOM data for the target power engineering task, the BOM data is pushed to the supply chain system, triggering the supply chain system to execute the procurement and warehousing operations of the target materials based on the BOM data; during the execution of procurement and warehousing operations, based on a preset update frequency, material procurement progress, inventory change data, and material outbound status are collected from the supply chain system through a real-time data acquisition interface, and the collected data is synchronized to the pre-built digital mirror as the first logistics data to update the virtual inventory status and procurement progress status of the target materials in the digital mirror, so that the digital mirror keeps real-time synchronized with the actual execution of the material flow operations.

[0037] Preferably, the step of sending the BOM data to the supply chain system so that the supply chain system can initiate material flow operations based on the BOM data, and updating the pre-built digital image according to the first logistics data generated during the execution of the material flow operations, includes: The BOM data is sent to the supply chain system, triggering the supply chain system to execute the procurement and warehousing operations for the target materials; Based on a preset update frequency, material procurement progress, inventory change data, and material outbound status are collected from the supply chain system through a real-time data acquisition interface as the first logistics data. The first logistics data is synchronized to the digital mirror to update the virtual inventory status and procurement progress status of the target material in the digital mirror.

[0038] For example, after generating BOM data, the BOM data is sent to the supply chain system, triggering the supply chain system to execute procurement and warehousing operations for the target materials based on the BOM data. For example, it generates purchase orders for main transformers, iron cores, and bushings and makes inventory reservations. During the execution of procurement and warehousing operations, at a preset update frequency (e.g., every 30 seconds), the supply chain system collects material procurement progress (e.g., order status and estimated arrival time), inventory change data (e.g., current inventory and reservation quantity), and material outbound status (e.g., quantity and time of outbound) from the supply chain system through a real-time data acquisition interface (e.g., a dedicated Kafka data acquisition tool). The collected data is then synchronized as the first logistics data to a pre-built digital mirror to update the virtual inventory status (e.g., remaining inventory) and procurement progress status (e.g., procurement completion percentage) of the target materials in the digital mirror.

[0039] Preferably, before sending the BOM data to the supply chain system to enable the supply chain system to initiate material flow operations based on the BOM data, and updating the pre-built digital mirror according to the first logistics data generated during the execution of the material flow operations, the method further includes: The digital mirror is constructed, and based on the update frequency, the collected real-time environmental data and real-time traffic data are synchronized to the digital mirror.

[0040] For example, the AVEVA E3D engine can be used to construct a 3D digital mirror containing various power material storage areas, cross-regional transportation routes, and various engineering construction sites. Entities such as power materials, storage nodes, logistics vehicles, and construction equipment are abstracted into computable objects, completing the virtual-real mapping. Specifically, the 3D digital mirror can be loaded with intelligent algorithms such as optimization algorithms specifically for large-item logistics of power materials, multi-dimensional risk fusion identification algorithms, and intelligent early warning level determination algorithms. Furthermore, it can set deduction objectives such as "lowest cost, no construction delays, and controllable risks," and clarify constraints such as transportation restrictions and material storage protection.

[0041] Step S103: When a target business event is detected during the execution of the material flow operation, the target business event and the risk threshold are input into the updated digital mirror so that the digital mirror performs a risk assessment on the target business event based on the risk threshold, and generates and outputs a response strategy based on the risk assessment result.

[0042] It should be noted that when a target business event is detected during the execution of the material flow operation, the target business event and the risk threshold are input into the updated digital mirror. This allows the digital mirror to perform a risk assessment on the target business event based on the risk threshold, and generate and output a response strategy based on the risk assessment result. This means that during the execution of the material flow operation, when a target business event related to the target material is detected (such as a supplier reporting a delay in the delivery of iron cores), the target value (such as the number of days of delay) and the corresponding risk threshold (such as a delay exceeding 7 days) contained in the target business event are input into the real-time synchronized digital mirror. In the digital mirror, the target value is compared with the risk threshold based on the preset risk identification logic. If the target value reaches or exceeds the risk threshold, the target business event is determined to constitute a risk. Based on the event type and the current supply chain status, multiple candidate response strategies are automatically generated (such as emergency procurement of backup suppliers, adjustment of construction sequence, etc.). Subsequently, the digital mirror simulates and deduces each candidate strategy through the built-in multi-objective optimization algorithm, evaluates the effectiveness of each solution in terms of execution cost, schedule impact and risk control, and compares them according to preset weights. Finally, the optimal strategy is determined and output as the response plan for this risk event.

[0043] Preferably, when a target business event is detected during the execution of the material flow operation, the target business event and the risk threshold are input into the updated digital mirror, so that the digital mirror performs a risk assessment on the target business event based on the risk threshold, and generates and outputs a response strategy based on the risk assessment result, including: Identify the target value in the target business event, compare the target value with the risk threshold, and if the target value is greater than or equal to the risk threshold, determine that the target business event has a risk and generate multiple candidate response strategies. The multiple candidate response strategies are simulated and deduced using a multi-objective optimization algorithm. The simulation results are compared according to preset weights, and the response strategy is determined and output based on the comparison results.

[0044] For example, suppose the target business event is "delayed delivery of core components (iron core) of 110kV main transformer by 14 days". The digital mirror can automatically identify the risk by comparing it with the previously set risk threshold (delayed delivery exceeding 7 days), send warnings to the procurement manager and construction manager, and explain the impact of the risk (such as the main transformer not being able to be assembled on time or the project schedule being delayed). Then, it automatically generates two solutions: Solution A (such as finding an alternative supplier for emergency procurement, dedicated transportation, and no delay in the project schedule) and Solution B (such as adjusting the construction sequence, laying cables first, and waiting for the original supplier to deliver, thus reducing costs). The digital mirror automatically extrapolates the effects of the two solutions: for example, Solution A has a cost of 96,000 yuan, no delay in the project schedule, and low risk; Solution B has no additional cost, a 7-day delay in the project schedule, and moderate risk. After comparing according to the set weights, Solution A is automatically selected as the response strategy.

[0045] Step S104: The response strategy is converted into an execution instruction and sent to the supply chain system, so that the supply chain system can start material transportation operations based on the execution instruction and update the digital mirror according to the second logistics data generated during the material transportation operation.

[0046] It should be noted that converting the response strategy into an execution instruction and sending it to the supply chain system, so that the supply chain system can initiate material transportation operations based on the execution instruction, and updating the digital mirror based on the second logistics data generated during the material transportation operation, means: after determining the optimal response strategy, converting the response strategy into an executable instruction (such as a transportation task order containing transportation route, transportation mode, and arrival time limit), and sending the execution instruction to the supply chain system, triggering the supply chain system to execute the transportation operation of the target material based on the execution instruction; during the transportation operation, based on a preset update frequency, real-time data such as the real-time location, transportation status, estimated arrival time, and transportation anomalies of the transportation vehicles are collected from the supply chain system and transportation vehicle terminals through a real-time data acquisition interface as second logistics data; the second logistics data is synchronized to the digital mirror to update the transportation trajectory and transportation status of the target material in the digital mirror, so that the digital mirror keeps real-time synchronized with the actual execution of the material transportation operation.

[0047] Preferably, the step of converting the response strategy into an execution instruction and issuing it to the supply chain system, so that the supply chain system can initiate material transportation operations based on the execution instruction, and update the digital mirror according to the second logistics data generated during the material transportation operation, includes: The response strategy is converted into an execution instruction and sent to the supply chain system, triggering the supply chain system to perform the transportation operation of the target materials based on the execution instruction; Based on the update frequency, transportation operation execution data is collected from the supply chain system through the real-time data acquisition interface as second logistics data. The second logistics data is synchronized to the digital mirror to update the transportation status of the target materials in the digital mirror.

[0048] For example, after the digital mirror converts Solution A into execution instructions, it sends the execution instructions to the procurement system (for emergency procurement of iron cores) and the logistics system (for arranging dedicated transportation) via API. At the same time, through the edge GPS device, the ERP system's preset data acquisition interface, and the warehouse management system's acceptance data synchronization rules, it automatically tracks the procurement approval, vehicle location, and iron core arrival and acceptance, and feeds the data back to the digital mirror.

[0049] Step S105: Input the execution effect data corresponding to the material transportation operation into the updated digital mirror, so that the digital mirror compares the execution effect data with the expected data corresponding to the response strategy, and generates and outputs a warning signal based on the comparison result.

[0050] It should be noted that inputting the execution effect data corresponding to the material transportation operation into the updated digital mirror, so that the digital mirror compares the execution effect data with the expected data corresponding to the response strategy, and generates and outputs an early warning signal based on the comparison result, means that after the material transportation operation is completed or during its execution, the actual execution effect data (such as actual arrival time, actual transportation cost, and actual material status) is input into the digital mirror that has been synchronized in real time; the digital mirror compares the execution effect data with the expected data (such as expected arrival time, expected transportation cost, and expected material status) corresponding to the previous response strategy item by item; when the deviation of any comparison result exceeds the preset range (such as the actual arrival time being later than the expected arrival time by more than the allowed number of days, or the actual transportation cost exceeding the expected cost by a certain percentage), the digital mirror automatically generates and outputs the corresponding early warning signal to remind relevant personnel that there is a deviation between the execution effect and the expected target, providing a basis for subsequent decision-making and model optimization.

[0051] For example, the digital mirror can use a built-in deviation comparison algorithm to compare the differences between the actual execution results and the expected data (such as actual transportation costs being higher than expected costs), automatically analyze the reasons (such as not calculating nighttime toll fees), and generate corresponding early warning signals. In particular, it can automatically optimize the algorithm based on the analysis results (such as supplementing the nighttime toll fee calculation rules) and adjust the risk standards (such as fine-tuning the delay warning time to 8 days and reserving nighttime passage time), while updating relevant models and the digital mirror, making subsequent early warnings and decisions for the main variable logistics more accurate.

[0052] This invention, through obtaining the Work Breakdown Structure (WBS) data of the target power engineering task and mapping it to Bill of Materials (BOM) data based on a power engineering knowledge graph, can generate a material requirement list based on the inherent mapping relationship between WBS and BOM data, ensuring the accuracy of early warning data from the data source. By pushing the BOM data to the supply chain system to initiate material flow operations and updating the digital mirror based on the first logistics data generated during the flow process, the material status in the digital mirror can be kept consistent with reality, providing accurate dynamic data support for subsequent risk identification. By inputting the target business events and risk thresholds into the digital mirror for risk assessment and outputting response strategies, risks can be automatically identified based on preset quantitative standards and real-time data, avoiding the subjectivity of human judgment. By converting the response strategies into execution instructions and issuing them to the supply chain system to initiate material transportation operations and updating the digital mirror based on the second logistics data generated during transportation, the digital mirror can be kept synchronized with the physical transportation process, providing accurate transportation process data for subsequent comparison of execution effects. By inputting the execution effect data of transportation operations into the digital mirror for comparison with expected data and outputting early warning signals based on the comparison results, the execution results and expected targets can be quantitatively compared, enabling the identification and feedback of execution risks. Compared to existing technologies that lack dynamic simulation capabilities, this application can improve the accuracy of risk warnings in the power supply chain.

[0053] Optionally, in this embodiment of the invention, the step of obtaining the WBS data of the target power engineering task, mapping the WBS data to BOM data based on a pre-built power engineering knowledge graph, and querying the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph includes: Analyze the WBS data of the target power engineering task and identify the engineering nodes contained in the WBS data; Based on the WBS-BOM conversion rules, the power engineering knowledge graph is traversed to query the engineering attribute information that matches each engineering node, and the corresponding material demand feature information is matched according to each engineering attribute information to generate BOM data level by level. Based on the project type and material category of the target power engineering task, corresponding historical risk data is matched from the power engineering knowledge graph, and a risk threshold is generated based on the historical risk data.

[0054] This invention identifies engineering nodes by parsing WBS data, matches engineering attribute information by traversing the knowledge graph based on transformation rules, and generates BOM data level by level. It can accurately match the material requirements corresponding to each node according to the hierarchical relationship of the engineering decomposition structure, ensuring the precise correspondence between BOM data and engineering tasks.

[0055] Optionally, in this embodiment of the invention, before obtaining the WBS data of the target power engineering task, mapping the WBS data to BOM data based on a pre-built power engineering knowledge graph, and querying the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph, the method further includes: The power engineering knowledge graph is constructed, and the WBS-BOM conversion rules are configured using a rule engine.

[0056] This invention, through pre-constructing a power engineering knowledge graph and configuring WBS-BOM conversion rules using a rule engine, enables the structured storage of power engineering knowledge and fixes the mapping logic in the form of configurable rules, ensuring that each subsequent mapping is executed according to unified and accurate rules.

[0057] Optionally, in this embodiment of the invention, the step of sending the BOM data to the supply chain system so that the supply chain system can initiate material flow operations based on the BOM data, and updating the pre-built digital image according to the first logistics data generated during the execution of the material flow operations, includes: The BOM data is sent to the supply chain system, triggering the supply chain system to execute the procurement and warehousing operations for the target materials; Based on a preset update frequency, material procurement progress, inventory change data, and material outbound status are collected from the supply chain system through a real-time data acquisition interface as the first logistics data. The first logistics data is synchronized to the digital mirror to update the virtual inventory status and procurement progress status of the target material in the digital mirror.

[0058] This invention pushes BOM data to the supply chain system to trigger procurement and warehousing operations, collects first logistics data based on update frequency, and synchronously updates the virtual inventory status and procurement progress status of target materials in the digital mirror. This enables real-time tracking of the entire process from procurement to warehousing, keeping the material status in the digital mirror synchronized with the actual status, and providing real-time and accurate data for risk identification.

[0059] Optionally, in this embodiment of the invention, before sending the BOM data to the supply chain system so that the supply chain system can initiate material flow operations based on the BOM data, and updating the pre-built digital image according to the first logistics data generated during the execution of the material flow operations, the method further includes: The digital mirror is constructed, and based on the update frequency, the collected real-time environmental data and real-time traffic data are synchronized to the digital mirror.

[0060] The embodiments of the present invention, by pre-constructing a digital mirror and synchronizing real-time environmental data and real-time traffic data based on the update frequency, can ensure that the external conditions in the digital mirror are consistent with the actual environment, providing a basis for risk simulation that conforms to the real world.

[0061] Optionally, in this embodiment of the invention, when a target business event is detected during the execution of the material flow operation, the target business event and the risk threshold are input into the updated digital mirror, so that the digital mirror performs a risk assessment on the target business event based on the risk threshold, and generates and outputs a response strategy based on the risk assessment result, including: Identify the target value in the target business event, compare the target value with the risk threshold, and if the target value is greater than or equal to the risk threshold, determine that the target business event has a risk and generate multiple candidate response strategies. The multiple candidate response strategies are simulated and deduced using a multi-objective optimization algorithm. The simulation results are compared according to preset weights, and the response strategy is determined and output based on the comparison results.

[0062] The embodiments of the present invention use a multi-objective optimization algorithm to perform simulation and deduction, and determine the optimal strategy after comparing the effects of each scheme according to a preset weight. This ensures that the decision-making process is based on quantitative calculation rather than empirical estimation, thereby improving the accuracy of risk response.

[0063] Optionally, in this embodiment of the invention, the step of converting the response strategy into an execution instruction and issuing it to the supply chain system, so that the supply chain system can initiate material transportation operations based on the execution instruction, and update the digital mirror according to the second logistics data generated during the execution of the material transportation operations, includes: The response strategy is converted into an execution instruction and sent to the supply chain system, triggering the supply chain system to perform the transportation operation of the target materials based on the execution instruction; Based on the update frequency, transportation operation execution data is collected from the supply chain system through the real-time data acquisition interface as second logistics data. The second logistics data is synchronized to the digital mirror to update the transportation status of the target materials in the digital mirror.

[0064] This invention converts response strategies into execution instructions and sends them to the supply chain system to trigger transportation operations. It also collects second logistics data based on update frequency and synchronously updates the transportation status of target materials in the digital mirror. This enables real-time tracking of the location and progress of transport vehicles, keeping the digital mirror synchronized with the physical transportation process and providing accurate transportation process data for subsequent comparison of execution effects.

[0065] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; An embodiment of the present invention provides a risk early warning device for the power material supply chain based on digital mirroring, comprising: a data mapping module 201, a first digital mirroring update module 202, a risk assessment module 203, a second digital mirroring update module 204, and a risk early warning module 205; The data mapping module 201 is used to obtain the WBS data of the target power engineering task, map the WBS data to BOM data based on the pre-built power engineering knowledge graph, and query the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph. The first digital mirror update module 202 is used to send the BOM data to the supply chain system so that the supply chain system can start material flow operations based on the BOM data and update the pre-built digital mirror according to the first logistics data generated during the execution of the material flow operations. The risk assessment module 203 is used to input the target business event and the risk threshold into the updated digital mirror when a target business event is detected during the execution of the material flow operation, so that the digital mirror can perform a risk assessment on the target business event based on the risk threshold, and generate and output a response strategy based on the risk assessment result; The second digital mirror update module 204 is used to convert the response strategy into an execution instruction and send it to the supply chain system, so that the supply chain system can start material transportation operations based on the execution instruction and update the digital mirror according to the second logistics data generated during the material transportation operation. The risk warning module 205 is used to input the execution effect data corresponding to the material transportation operation into the updated digital mirror, so that the digital mirror compares the execution effect data with the expected data corresponding to the response strategy, and generates and outputs a warning signal based on the comparison result.

[0066] Optionally, in this embodiment of the invention, the data mapping module 201 includes: a data parsing submodule, a data query submodule, and a threshold generation submodule; The data parsing submodule is used to parse the WBS data of the target power engineering task and identify the engineering nodes contained in the WBS data; The data query submodule is used to traverse the power engineering knowledge graph based on WBS-BOM conversion rules, query the engineering attribute information that matches each engineering node, and generate BOM data level by level by matching the corresponding material demand feature information according to each engineering attribute information. The threshold generation submodule is used to match corresponding historical risk data from the power engineering knowledge graph according to the engineering type and material category of the target power engineering task, and generate a risk threshold based on the historical risk data.

[0067] This invention identifies engineering nodes by parsing WBS data, matches engineering attribute information by traversing the knowledge graph based on transformation rules, and generates BOM data level by level. It can accurately match the material requirements corresponding to each node according to the hierarchical relationship of the engineering decomposition structure, ensuring the precise correspondence between BOM data and engineering tasks.

[0068] Optionally, in this embodiment of the invention, a knowledge graph construction submodule is further included before the data mapping module 201; The knowledge graph construction submodule is used to construct the power engineering knowledge graph and to configure the WBS-BOM conversion rules using a rule engine.

[0069] This invention, through pre-constructing a power engineering knowledge graph and configuring WBS-BOM conversion rules using a rule engine, enables the structured storage of power engineering knowledge and fixes the mapping logic in the form of configurable rules, ensuring that each subsequent mapping is executed according to unified and accurate rules.

[0070] Optionally, in this embodiment of the invention, the first digital mirror update module 202 includes: a data distribution submodule, a first data acquisition submodule, and a first data synchronization submodule; The data distribution submodule is used to distribute the BOM data to the supply chain system, triggering the supply chain system to execute the procurement and warehousing operations of the target materials; The first data acquisition submodule is used to collect material procurement progress, inventory change data and material outbound status from the supply chain system through a real-time data acquisition interface based on a preset update frequency, as the first logistics data; The first data synchronization submodule is used to synchronize the first logistics data to the digital mirror to update the virtual inventory status and procurement progress status of the target material in the digital mirror.

[0071] This invention pushes BOM data to the supply chain system to trigger procurement and warehousing operations, collects first logistics data based on update frequency, and synchronously updates the virtual inventory status and procurement progress status of target materials in the digital mirror. This enables real-time tracking of the entire process from procurement to warehousing, keeping the material status in the digital mirror synchronized with the actual status, and providing real-time and accurate data for risk identification.

[0072] Optionally, in this embodiment of the invention, a digital image construction submodule is further included before the first digital image update module 202; The digital mirror construction submodule is used to construct the digital mirror and, based on the update frequency, synchronize the collected real-time environmental data and real-time traffic data to the digital mirror.

[0073] The embodiments of the present invention, by pre-constructing a digital mirror and synchronizing real-time environmental data and real-time traffic data based on the update frequency, can ensure that the external conditions in the digital mirror are consistent with the actual environment, providing a basis for risk simulation that conforms to the real world.

[0074] Optionally, in this embodiment of the invention, the risk assessment module 203 includes: a risk assessment submodule and a strategy deduction submodule; The risk assessment submodule is used to identify the target value in the target business event, compare the target value with the risk threshold, and if the target value is greater than or equal to the risk threshold, determine that the target business event has a risk and generate multiple candidate response strategies. The strategy deduction submodule is used to simulate and deduce the multiple candidate response strategies through a multi-objective optimization algorithm, compare the simulation results according to preset weights, determine the response strategy based on the comparison results, and output the result.

[0075] The embodiments of the present invention use a multi-objective optimization algorithm to perform simulation and deduction, and determine the optimal strategy after comparing the effects of each scheme according to a preset weight. This ensures that the decision-making process is based on quantitative calculation rather than empirical estimation, thereby improving the accuracy of risk response.

[0076] Optionally, in this embodiment of the invention, the second digital mirror update module 204 includes: an instruction issuing submodule, a second data acquisition submodule, and a second data synchronization submodule; The instruction issuing submodule is used to convert the response strategy into an execution instruction and issue it to the supply chain system, triggering the supply chain system to perform the transportation operation of the target material based on the execution instruction; The second data acquisition submodule is used to acquire transportation operation execution data from the supply chain system through the real-time data acquisition interface based on the update frequency, as second logistics data; The second data synchronization submodule is used to synchronize the second logistics data to the digital mirror to update the transportation status of the target materials in the digital mirror.

[0077] This invention converts response strategies into execution instructions and sends them to the supply chain system to trigger transportation operations. It also collects second logistics data based on update frequency and synchronously updates the transportation status of target materials in the digital mirror. This enables real-time tracking of the location and progress of transport vehicles, keeping the digital mirror synchronized with the physical transportation process and providing accurate transportation process data for subsequent comparison of execution effects.

[0078] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the power material supply chain risk early warning method based on digital mirroring provided by any of the above-described method embodiments of the present invention.

[0079] This invention, in its embodiments, acquires the Work Breakdown Structure (WBS) data of the target power engineering task through a data mapping module 201 and maps it to Bill of Materials (BOM) data based on a power engineering knowledge graph. This generates a material requirements list based on the inherent mapping relationship between WBS and BOM data, ensuring the accuracy of early warning data from the data source. A first digital mirror update module 202 pushes the BOM data to the supply chain system to initiate material flow operations and updates the digital mirror based on the first logistics data generated during the flow process. This ensures that the material status in the digital mirror remains consistent with reality, providing accurate dynamic data support for subsequent risk identification. Finally, a risk assessment module 203 inputs the target business event and risk threshold into the digital mirror for risk assessment. The system assesses risks and outputs response strategies, automatically identifying risks based on preset quantitative standards and real-time data, avoiding the subjectivity of human judgment. The second digital mirror update module 204 converts these strategies into execution instructions and sends them to the supply chain system to initiate material transportation operations. It also updates the digital mirror based on second logistics data generated during transportation, ensuring synchronization between the digital mirror and the physical transportation process and providing accurate transportation process data for subsequent performance comparison. The risk warning module 205 inputs the performance data of the transportation operation into the digital mirror for comparison with expected data, outputting warning signals based on the comparison results. This allows for quantitative comparison of execution results with expected targets, enabling the identification and feedback of execution risks. Compared to existing technologies that lack dynamic extrapolation capabilities, this application improves the accuracy of risk warnings in the power material supply chain.

[0080] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0081] Based on the above embodiment of a power supply chain risk early warning method based on digital mirroring, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a power supply chain risk early warning method based on digital mirroring according to any embodiment of the present invention.

[0082] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0083] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0084] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0085] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power material supply chain risk early warning method based on digital mirroring described in any of the above-described method embodiments of the present invention.

[0086] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0087] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for early warning of risks in the power material supply chain based on digital mirroring, characterized in that, include: Obtain the WBS data of the target power engineering task, map the WBS data to BOM data based on the pre-built power engineering knowledge graph, and query the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph; The BOM data is sent to the supply chain system so that the supply chain system can start material flow operations based on the BOM data and update the pre-built digital image according to the first logistics data generated during the material flow operation. When a target business event is detected during the execution of the material flow operation, the target business event and the risk threshold are input into the updated digital mirror, so that the digital mirror can perform a risk assessment on the target business event based on the risk threshold, and generate and output a response strategy based on the risk assessment result; The response strategy is converted into an execution instruction and sent to the supply chain system, so that the supply chain system can start material transportation operations based on the execution instruction, and update the digital image according to the second logistics data generated during the material transportation operation. The execution effect data corresponding to the material transportation operation is input into the updated digital mirror, so that the digital mirror compares the execution effect data with the expected data corresponding to the response strategy, and generates and outputs a warning signal based on the comparison result.

2. The method for early warning of power material supply chain risks based on digital mirroring as described in claim 1, characterized in that, The process of obtaining the WBS data for the target power engineering task involves mapping the WBS data to BOM data based on a pre-constructed power engineering knowledge graph, and querying the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph, including: Analyze the WBS data of the target power engineering task and identify the engineering nodes contained in the WBS data; Based on the WBS-BOM conversion rules, the power engineering knowledge graph is traversed to query the engineering attribute information that matches each engineering node, and the corresponding material demand feature information is matched according to each engineering attribute information to generate BOM data level by level. Based on the project type and material category of the target power engineering task, corresponding historical risk data is matched from the power engineering knowledge graph, and a risk threshold is generated based on the historical risk data.

3. The method for early warning of power material supply chain risks based on digital mirroring as described in claim 2, characterized in that, Before obtaining the WBS data of the target power engineering task, mapping the WBS data to BOM data based on a pre-built power engineering knowledge graph, and querying the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph, the process further includes: The power engineering knowledge graph is constructed, and the WBS-BOM conversion rules are configured using a rule engine.

4. The method for early warning of power material supply chain risks based on digital mirroring as described in claim 1, characterized in that, The step of sending the BOM data to the supply chain system so that the supply chain system can initiate material flow operations based on the BOM data, and updating the pre-built digital image according to the first logistics data generated during the execution of the material flow operations, includes: The BOM data is sent to the supply chain system, triggering the supply chain system to execute the procurement and warehousing operations for the target materials; Based on a preset update frequency, material procurement progress, inventory change data, and material outbound status are collected from the supply chain system through a real-time data acquisition interface as the first logistics data. The first logistics data is synchronized to the digital mirror to update the virtual inventory status and procurement progress status of the target material in the digital mirror.

5. The method for early warning of power material supply chain risks based on digital mirroring as described in claim 4, characterized in that, Before distributing the BOM data to the supply chain system, enabling the supply chain system to initiate material flow operations based on the BOM data, and updating the pre-built digital image according to the first logistics data generated during the material flow operations, the process further includes: The digital mirror is constructed, and based on the update frequency, the collected real-time environmental data and real-time traffic data are synchronized to the digital mirror.

6. The method for early warning of power material supply chain risks based on digital mirroring as described in claim 1, characterized in that, When a target business event is detected during the execution of the material flow operation, the target business event and the risk threshold are input into the updated digital mirror, so that the digital mirror performs a risk assessment on the target business event based on the risk threshold, and generates and outputs a response strategy based on the risk assessment result, including: Identify the target value in the target business event, compare the target value with the risk threshold, and if the target value is greater than or equal to the risk threshold, determine that the target business event has a risk and generate multiple candidate response strategies. The multiple candidate response strategies are simulated and deduced using a multi-objective optimization algorithm. The simulation results are compared according to preset weights, and the response strategy is determined and output based on the comparison results.

7. The method for early warning of power material supply chain risks based on digital mirroring as described in claim 4, characterized in that, The step of converting the response strategy into execution instructions and issuing them to the supply chain system, so that the supply chain system can initiate material transportation operations based on the execution instructions, and update the digital mirror according to the second logistics data generated during the material transportation operations, includes: The response strategy is converted into an execution instruction and sent to the supply chain system, triggering the supply chain system to perform the transportation operation of the target materials based on the execution instruction; Based on the update frequency, transportation operation execution data is collected from the supply chain system through the real-time data acquisition interface as second logistics data. The second logistics data is synchronized to the digital mirror to update the transportation status of the target materials in the digital mirror.

8. A power supply chain risk early warning device based on digital mirroring, characterized in that, include: The system includes a data mapping module, a first digital mirror update module, a risk assessment module, a second digital mirror update module, and a risk warning module. The data mapping module is used to obtain the WBS data of the target power engineering task, map the WBS data to BOM data based on the pre-built power engineering knowledge graph, and query the risk threshold corresponding to the target power engineering task from the power engineering knowledge graph. The first digital mirror update module is used to send the BOM data to the supply chain system so that the supply chain system can start material flow operations based on the BOM data, and update the pre-built digital mirror according to the first logistics data generated during the execution of the material flow operations; The risk assessment module is used to input the target business event and the risk threshold into the updated digital mirror when a target business event is detected during the execution of the material flow operation, so that the digital mirror can perform a risk assessment on the target business event based on the risk threshold, and generate and output a response strategy based on the risk assessment result; The second digital mirror update module is used to convert the response strategy into an execution instruction and send it to the supply chain system, so that the supply chain system can start material transportation operations based on the execution instruction, and update the digital mirror according to the second logistics data generated during the material transportation operation. The risk warning module is used to input the execution effect data corresponding to the material transportation operation into the updated digital mirror, so that the digital mirror compares the execution effect data with the expected data corresponding to the response strategy, and generates and outputs a warning signal based on the comparison result.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a digital mirror-based power material supply chain risk early warning method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a power material supply chain risk early warning method as described in any one of claims 1-7.

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