Electric power material management real-time data analysis method and system

Abstract

The invention is applicable to the field of electric power systems and automation thereof, and provides a method and a system for real-time data analysis of electric power material management, and the method comprises the steps: collecting and integrating power grid topology data, equipment real-time operation state data, material main data and real-time fault alarm information in real time to form multi-dimensional data; taking the fault equipment as a starting point, and generating a potential influenced equipment list through downstream topology traversal and equipment state analysis; mapping to form an initial material demand set; querying an inventory, optimizing a warehouse according to the fault position, and locking the materials to generate a first-aid repair material package work order; potential affected device list generation logic is optimized through actual consumption feedback data. According to the invention, data-driven electric power material full-process accurate management is realized, the first-aid repair material allocation efficiency and response speed are improved, and stable operation of a power grid is ensured.

Description

Technical Field

[0001] This invention belongs to the field of power systems and their automation, and particularly relates to a method and system for real-time data analysis of power material management. Background Technology

[0002] As the scale of the power system continues to expand and the number and types of power grid equipment increase, the probability of sudden faults rises accordingly. The timeliness and accuracy of power grid repair are crucial to ensuring the stable operation of the power grid. Currently, power material management is developing towards intelligence and real-time processing. Data-driven, efficient allocation of materials is becoming an industry trend. Integrating power grid operation data, material data, and fault information to improve repair response speed and resource utilization efficiency has become a core industry requirement.

[0003] Existing technologies have failed to achieve deep integration and collaborative analysis of multi-source real-time data. The prediction of the scope of fault impact lacks comprehensiveness and accuracy. The matching of material demand and spare parts codes is not accurate enough. The warehouse optimization and material locking mechanisms lack scientific coordination and have not established a continuous optimization system based on actual feedback. This results in low material allocation efficiency, easy material shortages or redundancies, and affects the progress of emergency repairs and the speed of power grid restoration. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for real-time data analysis of power material management, aiming to solve the technical problems existing in the prior art as identified in the background art.

[0005] This invention is implemented as follows: a method for real-time data analysis of power material management, the method comprising: Real-time collection and integration of power grid topology data, real-time equipment operating status data, material master data, and real-time fault alarm information to form multi-dimensional data; Taking the faulty device specified in the real-time fault alarm information as the fault starting point, the downstream topology is traversed according to the electrical connection relationship defined by the power grid topology data, and combined with the real-time operating status data of the equipment, a list of potentially affected devices is analyzed and generated. Each equipment identifier in the potentially affected equipment list is mapped and associated with the spare parts material code in the material master data to form an initial material requirement set. The system queries the inventory data of spare parts material codes corresponding to the initial material demand set in real time, and selects the best warehouse from the inventory data based on the geographical location of the fault starting point in the real-time fault alarm information, and virtually locks the required spare parts material codes, and integrates and generates an output emergency repair material work order. Receive actual consumption feedback data for spare parts material codes in emergency repair material work orders, and use the actual consumption feedback data to optimize the generation of the potential affected equipment list.

[0006] As a further aspect of the present invention, the formation of multi-dimensional data specifically includes: Continuously acquire power grid topology data containing device hierarchy and electrical connection attributes to establish a complete device association network. The power grid topology data includes device ID, upstream power supply point, downstream load point, and connection line parameters. Real-time data on the equipment's operating status, including switch opening and closing status, line energization status, and current and voltage measurements, is acquired and the equipment operating status snapshot is updated once per second. Batch extract material master data containing the corresponding relationships between equipment models, technical parameters, and spare parts material codes, and establish a complete mapping relationship table between equipment assets and spare parts materials; Continuously monitor real-time fault alarm information, including fault device ID, fault occurrence time, and fault type, and capture fault events; The power grid topology data, real-time equipment operating status data, material master data, and real-time fault alarm information are associated according to the equipment ID and aligned with timestamps to form multi-dimensional data with a unified equipment identifier.

[0007] As a further aspect of the present invention, the generation of the potentially affected device list specifically includes: Using the fault device ID specified in the real-time fault alarm information as the starting node, based on the electrical connection relationship defined by the power grid topology data, and according to the preset topology traversal depth parameter, the downstream devices are traversed layer by layer along the power supply direction to construct a complete fault impact path. During the traversal, the switch opening and closing status and line energization status of the downstream equipment in the real-time operating status data of the equipment are compared in real time. When it is found that the downstream equipment changes from energized to de-energized after the fault time point, the equipment is marked as confirmed affected equipment. The fault type code contained in the real-time fault alarm information is parsed. Based on the typical impact patterns of different fault types in the power grid, a preset fault impact rule library is invoked. Combined with the electrical connection relationships and hierarchies of the devices in the power grid topology data, associated devices with potential damage risks are identified. The confirmed power-out devices and the associated devices with potential damage risks are included in the list of potentially affected devices.

[0008] As a further embodiment of the present invention, the formation of the initial material requirement set specifically includes: Parse each device identifier in the potentially affected equipment list to extract the device's model specifications, technical parameters, and installation location information; Query the spare parts material code corresponding to each equipment identifier in the material master data, and establish a one-to-one or many-to-one correspondence table between equipment and spare parts; Based on the importance of the equipment in the power grid and the scope of the impact of the failure, a demand priority is assigned to each spare part material code. The estimated demand quantity is determined by combining the historical replacement frequency, forming an initial material demand set that includes spare part material code, quantity, and priority.

[0009] As a further aspect of the present invention, the step of selecting the optimal shipping warehouse from the inventory data and virtually locking the required spare parts material codes specifically includes: Based on the spare parts material code list in the initial material demand set, initiate a batch query request to obtain the real-time inventory quantity, inventory status and specific storage location of each spare parts material code in the central warehouse, regional warehouse and mobile warehouse; Based on the latitude and longitude coordinates of the fault starting point in the real-time fault alarm information, the actual road distance and estimated travel time from each spare parts warehouse to the fault starting point are calculated, and a comprehensive score of delivery cost is generated by combining the warehouse inventory adequacy rate. The warehouse with the highest overall delivery cost score is selected as the preferred shipping warehouse. The inventory quantity of spare parts materials in the initial material demand set of the preferred shipping warehouse is reserved and marked through the database transaction lock mechanism, and a virtual lock is implemented to generate a lock certificate.

[0010] As a further embodiment of the present invention, the output of the emergency repair material work order specifically includes: The spare parts material codes and quantities in the initial material demand set, the inventory location information of the preferred shipping warehouse, and the virtual locking certificate are integrated and organized in a structured manner according to the emergency repair material classification rules. After the structured organization is filled into the preset work order template, a repair material package work order is generated, which includes fault information, a list of required spare parts codes, the quantity of each spare parts code, the location of the pick-up warehouse, and the virtual lock status. The work order is then pushed to the warehouse management terminal and the on-site repair mobile terminal simultaneously.

[0011] As a further aspect of the present invention, the optimization of generating the list of potentially affected devices specifically includes: We continuously receive the actual usage quantity, reasons for non-use, and actual on-site equipment conditions of each spare part material code in the emergency repair material package form filled out by the emergency repair personnel on-site, and form an actual consumption feedback dataset. The actual consumption feedback dataset is compared with the list of potentially affected devices to perform a difference analysis, and the prediction accuracy index is calculated. Based on the difference analysis results, the depth parameters of the downstream topology traversal and the judgment rules of the fault impact rule base are adjusted.

[0012] Another object of the present invention is to provide a system for real-time data analysis of power material management, the system comprising: The multi-dimensional data integration module is used to collect and integrate power grid topology data, real-time equipment operating status data, material master data, and real-time fault alarm information in real time to form multi-dimensional data. The Potentially Affected Equipment Analysis Module is used to take the faulty equipment specified in the real-time fault alarm information as the fault starting point, perform downstream topology traversal according to the electrical connection relationship defined by the power grid topology data, and combine the real-time operating status data of the equipment to analyze and generate a list of potentially affected equipment. The material demand set mapping module is used to map and associate each equipment identifier in the potentially affected equipment list with the spare parts material code in the material master data to form an initial material demand set. The inventory data query and optimization module is used to query the inventory data of spare parts material codes corresponding to the initial material demand set in real time, and optimize the shipping warehouse from the inventory data based on the geographical location of the fault starting point in the real-time fault alarm information, and virtually lock the required spare parts material codes, and integrate and generate and output the emergency repair material work order. The material consumption feedback optimization module is used to receive actual consumption feedback data for spare parts material codes in the emergency repair material package, and to optimize the generation of the potentially affected equipment list using the actual consumption feedback data.

[0013] The beneficial effects of this invention are: This invention achieves end-to-end intelligent real-time data analysis for power material management by constructing a complete process encompassing multi-dimensional data integration, precise analysis of equipment affected by faults, intelligent matching of material needs, warehouse optimization and virtual locking, and feedback closed-loop optimization. The comprehensive integration and synchronization of multi-source data ensures the integrity and real-time nature of the analysis foundation; the systematic prediction of the fault impact range and the precise mapping of material needs allow material allocation to closely align with actual fault requirements; the warehouse optimization mechanism and virtual locking guarantee the timeliness and dedicated availability of material supply; and the feedback optimization mechanism continuously improves the accuracy of data analysis. The overall solution breaks down information barriers between power grid operation, fault handling, and material management, significantly improving the efficiency and targeting of power material allocation, reducing resource waste, accelerating fault repair progress, and effectively ensuring the safe and stable operation of the power grid. Attached Figure Description

[0014] Figure 1 A flowchart illustrating a method for real-time data analysis of power material management provided in an embodiment of the present invention; Figure 2 A flowchart for forming multi-dimensional data provided in an embodiment of the present invention; Figure 3 A flowchart for generating a list of potentially affected devices provided in an embodiment of the present invention; Figure 4A flowchart for forming an initial material requirements set is provided as an embodiment of the present invention; Figure 5 A flowchart for performing virtual locking and integrating the generation and output of emergency repair material work orders is provided for embodiments of the present invention; Figure 6 A flowchart for optimizing the generation of the potentially affected device list provided in this embodiment of the invention; Figure 7 The structural block diagram of the system for real-time data analysis of power material management provided in the embodiments of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] Figure 1 A flowchart of a method for real-time data analysis of power material management provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method includes: S100 collects and integrates power grid topology data, real-time equipment operating status data, material master data, and real-time fault alarm information in real time to form multi-dimensional data. The acquisition of power grid topology data focuses on the hierarchical relationships and electrical connection attributes of equipment, building a complete equipment association network by continuously acquiring equipment association information. The acquisition of real-time equipment operating status data covers key indicators such as switch opening and closing status, line energization status, and current and voltage measurements. Frequent updates of equipment operating status snapshots capture the operating status of equipment at different time points. The acquisition of material master data revolves around the correspondence between equipment model technical parameters and spare parts material codes, extracting relevant information in batches and establishing a mapping table to create a connection channel between equipment assets and spare parts materials. The acquisition of real-time fault alarm information involves continuously monitoring key fault-related information to capture fault events in the first instance and determine the trigger point for the entire data analysis process. In the data integration phase, equipment IDs are used as unified association identifiers to associate four types of data from different sources. Simultaneously, timestamp alignment is used to eliminate data deviations in the time dimension, forming a multi-dimensional data system with unified identification, time synchronization, and complete content.

[0017] S200 takes the faulty device specified in the real-time fault alarm information as the fault starting point, performs downstream topology traversal according to the electrical connection relationship defined by the power grid topology data, and analyzes and generates a list of potentially affected devices in combination with the real-time operating status data of the devices. Starting with the faulty device specified in the real-time fault alarm information, and relying on the device association network constructed based on the power grid topology data, the downstream topology traversal is carried out according to the electrical connection relationship. During the traversal, the downstream devices are extended layer by layer along the power supply direction strictly according to the preset topology traversal depth parameters, thus constructing a complete fault impact path. The real-time operating status data of synchronously linked devices are traversed, and the switch opening and closing status and line energization status of downstream devices are dynamically compared. Focusing on the status changes after the fault time point, devices that have changed from energized to de-energized and whose switches are in the normal closed state are accurately identified and included in the scope of confirmed affected devices.

[0018] Simultaneously, the fault type in the fault alarm information is analyzed, the preset fault impact rule base is called, and the hierarchical relationship and electrical connection characteristics of the equipment in the power grid topology data are combined to screen related equipment in the same electrical circuit, the same voltage level or the same feeder. Through the typical impact mode and damage probability threshold corresponding to the fault type, the equipment damage risk is assessed, and the confirmed affected equipment and related equipment with damage risk are integrated to form a comprehensive list of potentially affected equipment.

[0019] S300, map and associate each equipment identifier in the potentially affected equipment list with the spare parts material code in the material master data to form an initial material requirement set; Each device identifier in the potentially affected equipment list is analyzed in depth to extract core information such as the device's model, specifications, technical parameters, and installation location. This information is the basis for ensuring accurate matching of spare parts and can distinguish the specific requirements of different devices for spare parts.

[0020] Based on the existing mapping table of equipment assets and spare parts materials in the master data, the corresponding spare parts material code is queried according to the parsed equipment information, and a flexible correspondence between equipment and spare parts is established. This not only meets the situation where a single piece of equipment corresponds to a specific spare part, but also adapts to the scenario where multiple different pieces of equipment share the same type of spare parts, thus achieving efficient association of spare parts resources.

[0021] Based on this, the importance of the equipment is determined by its functional positioning in the power grid, and the scope of the fault impact is also considered. Demand priority is set for each spare part material code, so that material allocation can focus on core needs. At the same time, the frequency of equipment replacement corresponding to the spare parts in the recent period is taken into account to comprehensively determine the estimated demand quantity, and finally form an initial material demand set that includes the quantity priority of spare part material codes.

[0022] S400: Real-time query of the inventory data of spare parts material codes corresponding to the initial material demand set; and based on the geographical location of the fault starting point in the real-time fault alarm information, select the best warehouse from the inventory data and virtually lock the required spare parts material codes, and integrate and generate an output emergency repair material work order. Based on the spare parts material code list in the initial material demand set, a batch query request is initiated to comprehensively obtain the real-time inventory quantity, inventory status and specific storage location of various spare parts in warehouses at different levels, covering central warehouses, regional warehouses and mobile warehouses, to ensure that no possible spare parts supply source is missed.

[0023] By combining the latitude and longitude coordinates of the fault origin in the real-time fault alarm information, the actual road distance and estimated travel time from each warehouse to the fault site are accurately calculated. At the same time, the inventory adequacy rate of the required spare parts in the warehouse is taken into account. Through a comprehensive scoring mechanism, the inventory availability and delivery efficiency are balanced, and the optimal shipping warehouse is selected.

[0024] For the selected warehouses, a database transaction locking mechanism is used to reserve and mark the required spare parts inventory quantities, implement virtual locking, and generate locking credentials to ensure that the spare parts are exclusively used for the current fault repair. Based on this, the spare parts information, inventory location information of the selected warehouses, and virtual locking credentials from the initial material demand set are integrated, structured according to the emergency repair material classification rules, and populated into a preset work order template. This generates an emergency repair material package work order containing key information such as fault information, spare parts list, quantity, pickup location, and locking status, and is simultaneously pushed to the warehouse management terminal and the on-site emergency repair mobile terminal to achieve real-time information synchronization.

[0025] S500 receives actual consumption feedback data for spare parts material codes in the emergency repair material package order, and uses the actual consumption feedback data to optimize the generation of the potentially affected equipment list.

[0026] We continuously collect various key information reported by emergency repair personnel on-site, covering the actual usage quantity of each spare part material code, the reason for not using it, and the actual condition of the equipment on-site. This information directly reflects the degree of consistency between the prediction of the list of potentially affected equipment in the early stage and the actual situation. We integrate these scattered on-site data into a structured actual consumption feedback dataset.

[0027] A comprehensive difference analysis was conducted between this dataset and the previously generated list of potentially affected devices. By calculating the prediction accuracy index, the omissions and misjudgments in the prediction process were accurately located, and it was determined whether the previous topology traversal depth parameter settings were reasonable and whether the judgment rules of the fault impact rule base fit the actual power grid fault scenario.

[0028] Based on the difference analysis results, the depth parameters of the downstream topology traversal are dynamically adjusted. At the same time, the judgment rules of the fault impact rule base are optimized, the uncovered fault impact patterns are supplemented, and the biased judgment logic is corrected, so that the list of potentially affected equipment generated later can better match the actual impact of power grid faults.

[0029] like Figure 2 As shown, the formation of multi-dimensional data specifically includes: S110, continuously acquire power grid topology data containing device hierarchy and electrical connection attributes, and establish a complete device association network. The power grid topology data includes device ID, upstream power supply point, downstream load point and connection line parameters. S120 acquires real-time equipment operating status data, including switch opening and closing status, line energization status, and current and voltage measurement values, and updates the equipment operating status snapshot once per second. S130, batch extract material master data containing equipment model, technical parameters, and spare parts material code correspondence, and establish a complete mapping relationship table between equipment assets and spare parts materials; S140 continuously monitors real-time fault alarm information, including fault device ID, fault occurrence time, and fault type, and captures fault events. S150, the power grid topology data, real-time equipment operating status data, material master data and real-time fault alarm information are associated according to the equipment ID, and the data is aligned with timestamps to form multi-dimensional data with unified equipment identification.

[0030] like Figure 3 As shown, generating the list of potentially affected devices specifically includes: S210, using the fault device ID specified in the real-time fault alarm information as the starting node, based on the electrical connection relationship defined by the power grid topology data, and according to the preset topology traversal depth parameter, traverse the downstream devices layer by layer along the power supply direction to construct a complete fault impact path. Starting with the faulty device ID in the real-time fault alarm information, the upstream power point, downstream load point and connection line parameters corresponding to the device are extracted from multi-dimensional data to clarify the initial electrical connection relationship.

[0031] Based on the grid voltage level and equipment density, the topology traversal depth parameters are set, and the first-level traversal is initiated: All direct subordinate load points and connecting lines of the starting node are queried, and the ID of each subordinate device, the connecting line number, and the line parameters are recorded. The second-level traversal then begins: Each subordinate device obtained in the first-level traversal is used as a new node, and its corresponding subordinate load points and connecting lines are queried. Simultaneously, the electrical connectivity of each connecting line is verified (excluding disconnected backup lines) to avoid path breaks.

[0032] Following this logic, the process traverses downwards layer by layer until a preset traversal depth is reached or a terminal device with no downstream load is encountered. During the traversal, the device ID, connection relationship with the parent node, line parameters, and traversal level of each node are recorded in real time, forming a tree structure data with the faulty device as the root node and downstream devices as child nodes. This tree structure represents the complete fault impact path, where each branch corresponds to a power supply link from the fault origin to the terminal device.

[0033] S220, During the traversal, the switch opening and closing status of downstream equipment and the energized status of the line are compared in real time with the real-time operating status data of the equipment. When it is found that the downstream equipment changes from the energized state to the de-energized state after the fault time point, the equipment is marked as confirmed affected equipment. The power supply to equipment in the power grid depends on the transmission of electrical energy from upstream equipment through connecting lines. A faulty device will directly cause the interruption of its power supply link. The energized state in the real-time operating status data of the equipment is collected in real time by voltage and current sensors. When the power supply link is normal, the downstream equipment remains energized; when a fault occurs and the power supply link is interrupted, the downstream equipment will change from energized to de-energized due to the loss of power supply.

[0034] The switch's open / closed status is used to assist in judgment: if the downstream equipment switch is in the closed state (normal power supply state), and the energized state changes to de-energized state after the fault, it indicates that the power interruption is caused by the fault; if the switch is in the open state, the power loss may be caused by human operation or other non-fault factors, which need to be ruled out.

[0035] By simultaneously comparing the switch opening and closing status (confirming that the equipment is in normal power supply configuration) and changes in the energized state (confirming power interruption), equipment that directly causes power interruption due to faults is screened out and marked as affected equipment to ensure the accuracy of fault impact range judgment.

[0036] S230, parse the fault type code contained in the real-time fault alarm information, and based on the typical impact patterns of different fault types in the power grid, call the preset fault impact rule library, and combine the electrical connection relationship and hierarchy of the equipment in the power grid topology data to identify the associated equipment with damage risk, and include the confirmed power failure equipment and the associated equipment with damage risk into the list of potentially affected equipment.

[0037] 1. Parse the fault type code in the real-time fault alarm information and retrieve the typical impact pattern corresponding to the fault type from the preset fault impact rule base.

[0038] 2. Based on the electrical connection relationships of devices in the power grid topology data, screen the associated devices that are in the same electrical circuit as the faulty device: for series circuits, the switching equipment and protection devices downstream of the faulty device are directly associated devices; for parallel circuits, other parallel branch devices that share the same upstream power supply with the faulty device are indirectly associated devices.

[0039] 3. Based on the equipment hierarchy attributes, narrow down the scope of related equipment: prioritize screening equipment that is at the same voltage level and on the same feeder as the faulty equipment, as such equipment is more likely to be affected by the fault.

[0040] 4. Based on the failure impact rule base, the failure type damage probability threshold for different devices is used to conduct a risk assessment on the screened related devices: if the device type matches the typical failure impact pattern and is located in a critical power supply position, it is determined to be a related device with a risk of damage.

[0041] like Figure 4 As shown, the formation of the initial material demand set specifically includes: S310, parse each device identifier in the potentially affected device list and extract the device's model specifications, technical parameters, and installation location information; S320, query the spare parts material code corresponding to each equipment identifier in the material master data, and establish a one-to-one or many-to-one correspondence table between equipment and spare parts; S330 assigns a demand priority to each spare part material code based on the importance of the equipment in the power grid and the scope of the fault impact. It also determines the estimated demand quantity by combining the historical replacement frequency, forming an initial material demand set that includes spare part material code, quantity, and priority.

[0042] ; in, The priority of spare parts material codes ranges from 0 to 10, with higher scores indicating higher priority. , , These are the weighting coefficients. Adjustments will be made based on the power grid operation and maintenance strategy. The importance of equipment is quantified based on its functional positioning within the power grid. The quantification of the impact range of the fault is calculated by comprehensively considering the number of affected devices, the number of affected users, and the affected load capacity. ,in This refers to the number of devices affected by the malfunction. This represents the maximum number of devices that may be affected by a single power grid fault in the region. For the number of users affected by this outage, This represents the maximum number of users that may be affected by a single power grid fault in the region. The load capacity affected by this fault This represents the maximum load capacity that may be affected by a single fault in the power grid of this region. This is a quantified value of historical replacement frequency, ranging from 0 to 10, which represents the number of times the corresponding equipment for this spare part has been replaced in the past year. ,according to calculate.

[0043] like Figure 5As shown, the step of selecting the optimal shipping warehouse from the inventory data and virtually locking the required spare parts material codes specifically includes: S410, based on the spare parts material code list in the initial material demand set, initiate a batch query request to obtain the real-time inventory quantity, inventory status and specific storage location of each spare parts material code in the central warehouse, regional warehouse and mobile warehouse; S420, based on the latitude and longitude coordinates of the fault starting point in the real-time fault alarm information, calculate the actual road distance and estimated travel time from each spare parts warehouse to the fault starting point, and generate a comprehensive score for delivery cost by combining the warehouse inventory adequacy rate. ; in, The delivery cost is comprehensively scored, with a value ranging from 0 to 10. The higher the score, the higher the priority. , , These are the weighting coefficients. ; To ensure warehouse inventory adequacy, , For the first in the warehouse Real-time inventory of various spare parts For the first The required quantity of various spare parts; The actual road distance is the optimal path distance obtained through the map navigation interface based on the latitude and longitude coordinates of the warehouse and the fault origin. The maximum road distance from all candidate warehouses to the fault origin; The estimated travel time is calculated by the map navigation interface in conjunction with real-time traffic conditions, reflecting the actual transportation time of materials from the warehouse to the fault site; The maximum estimated travel time from all candidate warehouses to the point of failure.

[0044] S430 selects the warehouse with the highest comprehensive distribution cost score as the preferred shipping warehouse. Through the database transaction lock mechanism, it reserves and marks the inventory quantity of spare parts material codes in the initial material demand set of the preferred shipping warehouse, implements virtual locking, and generates locking vouchers.

[0045] Virtual locking uses a database transaction locking mechanism to reserve and mark the inventory quantity of required spare parts in the preferred shipping warehouse, ensuring the dedicated availability of emergency repair materials.

[0046] Power emergency repairs are extremely time-sensitive. Without locking, the same batch of spare parts might be used by other non-urgent work orders, leading to material shortages at the repair site and delaying fault restoration. Virtual locking is not an actual release from the warehouse; it only restricts the use of that inventory by other work orders. This ensures that emergency repair needs are met first, without affecting the warehouse's normal management and circulation of other spare parts.

[0047] Generating a lock voucher is to establish a unique identifier for reserved inventory. The voucher contains information such as spare parts material code, locked quantity, locked time, and associated emergency repair material work order number. On the one hand, it is used by warehouse personnel to verify delivery permissions and ensure that goods are delivered according to the order. On the other hand, it is used for subsequent inventory write-off. After the emergency repair is completed, unused spare parts inventory can be unlocked according to the actual consumption, or the inventory of consumed spare parts can be deducted, ensuring the accuracy and consistency of inventory data and avoiding inventory chaos.

[0048] The output emergency repair material work order specifically includes: S440, integrate the spare parts material codes and quantities in the initial material demand set, the inventory location information of the preferred shipping warehouse, and the virtual locking certificate, and organize them in a structured manner according to the emergency repair material classification rules; S450 After the structured organization is filled into the preset work order template, a repair material package work order is generated, which includes fault information, a list of required spare parts material codes, the quantity of each spare parts material code, the location of the pick-up warehouse, and the virtual lock status. The work order is then pushed to the warehouse management terminal and the on-site repair mobile terminal simultaneously.

[0049] like Figure 6 As shown, the optimization of generating the list of potentially affected devices specifically includes: S510 continuously receives the actual usage quantity, reasons for non-use, and actual on-site equipment conditions of each spare part material code in the emergency repair material package form filled out by the emergency repair personnel on-site, forming an actual consumption feedback dataset. S520, perform a difference analysis between the actual consumption feedback dataset and the list of potentially affected devices, calculate the prediction accuracy index, and adjust the depth parameters of the downstream topology traversal and the judgment rules of the fault impact rule base based on the difference analysis results.

[0050] 1. Calculation of prediction accuracy index: The prediction accuracy is calculated based on the actual consumption feedback dataset and the list of potentially affected devices, using the following formula: ; in, To predict accuracy, the value range is 0-1; To accurately predict the number of equipment, that is, the number of potentially affected equipment that matches the actual number of affected equipment on site; This refers to the total number of devices in the potentially affected equipment list, i.e., the total number of affected devices predicted in the early stages. This represents the total number of equipment actually affected on-site, determined based on actual consumption feedback data and the condition of the equipment on-site.

[0051] 2. Method for adjusting the downstream topology traversal depth parameter: Set accuracy threshold ,when If the difference analysis shows (There are omissions; the actual number of affected devices on-site is greater than predicted), indicating that the current traversal depth is insufficient and does not cover all affected devices. The traversal depth parameter needs to be increased, and the formula should be adjusted as follows: ; in, This is the adjusted traversal depth; This is the traversal depth before adjustment, i.e., the traversal depth currently in use; The step size is set according to the complexity of the power grid structure for in-depth adjustment; The percentage of missed diagnoses reflects the severity of the missed diagnoses; a higher percentage indicates a greater need for adjustment. If the difference analysis shows... (There was a misjudgment; more devices were predicted to be affected than were actually affected), indicating that the traversal depth was too large, including unaffected devices. The traversal depth parameter needs to be reduced, and the formula should be adjusted as follows: ; in, The percentage of misjudgments reflects the severity of the misjudgment; a higher percentage indicates a greater need for adjustment. After adjustment, it is necessary to ensure... That is, at least the direct downstream devices must be traversed.

[0052] 3. Methods for adjusting rules for judging the impact of faults on the rule base: For misjudged equipment (predicted to be at risk of damage but not actually damaged on-site) and missed-judged equipment (damaged on-site but not predicted) identified in the difference analysis, attribution analysis is performed: For misjudged equipment, information such as its equipment type, connection relationship with the faulty equipment, and fault type is extracted. The influence patterns of the corresponding fault types in the fault influence rule base are checked for biases. The damage probability threshold for this type of equipment under the corresponding fault type is lowered, or constraints are added. For missed-judged equipment, the reasons why it is not covered by the rule base are analyzed, new judgment rules are added, or the scope of application of existing rules is adjusted. After adjustment, the new rule parameters are updated to the fault influence rule base for use in generating a list of potentially affected equipment in the future.

[0053] Figure 7 A structural block diagram of a system for real-time data analysis of power material management provided in an embodiment of the present invention is shown below. Figure 7As shown, the system includes: The multi-dimensional data integration module 100 is used to collect and integrate power grid topology data, real-time equipment operating status data, material master data and real-time fault alarm information in real time to form multi-dimensional data. The Potentially Affected Equipment Analysis Module 200 is used to take the faulty equipment specified in the real-time fault alarm information as the fault starting point, perform downstream topology traversal according to the electrical connection relationship defined by the power grid topology data, and combine the real-time operating status data of the equipment to analyze and generate a list of potentially affected equipment. The material demand set mapping module 300 is used to map and associate each equipment identifier in the potentially affected equipment list with the spare parts material code in the material master data to form an initial material demand set. The inventory data query and optimization module 400 is used to query the inventory data of spare parts material codes corresponding to the initial material demand set in real time, and optimize the shipping warehouse from the inventory data based on the geographical location of the fault starting point in the real-time fault alarm information, and virtually lock the required spare parts material codes, and integrate and generate and output the emergency repair material work order. The material consumption feedback optimization module 500 is used to receive actual consumption feedback data for the spare parts material codes in the emergency repair material package, and to optimize the generation of the potentially affected equipment list using the actual consumption feedback data.

[0054] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0055] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for real-time data analysis of power material management, characterized in that, The method includes: Real-time collection and integration of power grid topology data, real-time equipment operating status data, material master data, and real-time fault alarm information to form multi-dimensional data; Taking the faulty device specified in the real-time fault alarm information as the fault starting point, the downstream topology is traversed according to the electrical connection relationship defined by the power grid topology data, and combined with the real-time operating status data of the equipment, a list of potentially affected devices is analyzed and generated. Each equipment identifier in the potentially affected equipment list is mapped and associated with the spare parts material code in the material master data to form an initial material requirement set. The system queries the inventory data of spare parts material codes corresponding to the initial material demand set in real time, and selects the best warehouse from the inventory data based on the geographical location of the fault starting point in the real-time fault alarm information, and virtually locks the required spare parts material codes, and integrates and generates an output emergency repair material work order. Receive actual consumption feedback data for spare parts material codes in emergency repair material work orders, and use the actual consumption feedback data to optimize the generation of the potential affected equipment list.

2. The method according to claim 1, characterized in that, The formation of multi-dimensional data specifically includes: Continuously acquire power grid topology data containing device hierarchy and electrical connection attributes to establish a complete device association network. The power grid topology data includes device ID, upstream power supply point, downstream load point, and connection line parameters. Real-time data on the equipment's operating status, including switch opening and closing status, line energization status, and current and voltage measurements, is acquired and the equipment operating status snapshot is updated once per second. Batch extract material master data containing the corresponding relationships between equipment models, technical parameters, and spare parts material codes, and establish a complete mapping relationship table between equipment assets and spare parts materials; Continuously monitor real-time fault alarm information, including fault device ID, fault occurrence time, and fault type, and capture fault events; The power grid topology data, real-time equipment operating status data, material master data, and real-time fault alarm information are associated according to the equipment ID and aligned with timestamps to form multi-dimensional data with a unified equipment identifier.

3. The method according to claim 2, characterized in that, The generation of the potentially affected device list specifically includes: Using the fault device ID specified in the real-time fault alarm information as the starting node, based on the electrical connection relationship defined by the power grid topology data, and according to the preset topology traversal depth parameter, the downstream devices are traversed layer by layer along the power supply direction to construct a complete fault impact path. During the traversal, the switch opening and closing status and line energization status of the downstream equipment in the real-time operating status data of the equipment are compared in real time. When it is found that the downstream equipment changes from energized to de-energized after the fault time point, the equipment is marked as confirmed affected equipment. The fault type code contained in the real-time fault alarm information is parsed. Based on the typical impact patterns of different fault types in the power grid, a preset fault impact rule library is invoked. Combined with the electrical connection relationships and hierarchies of the devices in the power grid topology data, associated devices with potential damage risks are identified. The confirmed power-out devices and the associated devices with potential damage risks are included in the list of potentially affected devices.

4. The method according to claim 3, characterized in that, The formation of the initial set of material requirements specifically includes: Parse each device identifier in the potentially affected equipment list to extract the device's model specifications, technical parameters, and installation location information; Query the spare parts material code corresponding to each equipment identifier in the material master data, and establish a one-to-one or many-to-one correspondence table between equipment and spare parts; Based on the importance of the equipment in the power grid and the scope of the impact of the failure, a demand priority is assigned to each spare part material code. The estimated demand quantity is determined by combining the historical replacement frequency, forming an initial material demand set that includes spare part material code, quantity, and priority.

5. The method according to claim 4, characterized in that, The process of selecting the optimal shipping warehouse from inventory data and virtually locking the required spare parts material codes specifically includes: Based on the spare parts material code list in the initial material demand set, initiate a batch query request to obtain the real-time inventory quantity, inventory status and specific storage location of each spare parts material code in the central warehouse, regional warehouse and mobile warehouse; Based on the latitude and longitude coordinates of the fault starting point in the real-time fault alarm information, the actual road distance and estimated travel time from each spare parts warehouse to the fault starting point are calculated, and a comprehensive score of delivery cost is generated by combining the warehouse inventory adequacy rate. The warehouse with the highest overall delivery cost score is selected as the preferred shipping warehouse. The inventory quantity of spare parts materials in the initial material demand set of the preferred shipping warehouse is reserved and marked through the database transaction lock mechanism, and a virtual lock is implemented to generate a lock certificate.

6. The method according to claim 5, characterized in that, The output emergency repair material work order specifically includes: The spare parts material codes and quantities in the initial material demand set, the inventory location information of the preferred shipping warehouse, and the virtual locking certificate are integrated and organized in a structured manner according to the emergency repair material classification rules. After the structured organization is filled into the preset work order template, a repair material package work order is generated, which includes fault information, a list of required spare parts codes, the quantity of each spare parts code, the location of the pick-up warehouse, and the virtual lock status. The work order is then pushed to the warehouse management terminal and the on-site repair mobile terminal simultaneously.

7. The method according to claim 6, characterized in that, The optimization of generating the list of potentially affected devices specifically includes: We continuously receive the actual usage quantity, reasons for non-use, and actual on-site equipment conditions of each spare part material code in the emergency repair material package form filled out by the emergency repair personnel on-site, and form an actual consumption feedback dataset. The actual consumption feedback dataset is compared with the list of potentially affected devices to perform a difference analysis, and the prediction accuracy index is calculated. Based on the difference analysis results, the depth parameters of the downstream topology traversal and the judgment rules of the fault impact rule base are adjusted.

8. A system for real-time data analysis of power material management, characterized in that, The system includes: The multi-dimensional data integration module is used to collect and integrate power grid topology data, real-time equipment operating status data, material master data, and real-time fault alarm information in real time to form multi-dimensional data. The Potentially Affected Equipment Analysis Module is used to take the faulty equipment specified in the real-time fault alarm information as the fault starting point, perform downstream topology traversal according to the electrical connection relationship defined by the power grid topology data, and combine the real-time operating status data of the equipment to analyze and generate a list of potentially affected equipment. The material demand set mapping module is used to map and associate each equipment identifier in the potentially affected equipment list with the spare parts material code in the material master data to form an initial material demand set. The inventory data query and optimization module is used to query the inventory data of spare parts material codes corresponding to the initial material demand set in real time, and optimize the shipping warehouse from the inventory data based on the geographical location of the fault starting point in the real-time fault alarm information, and virtually lock the required spare parts material codes, and integrate and generate and output the emergency repair material work order. The material consumption feedback optimization module is used to receive actual consumption feedback data for spare parts material codes in the emergency repair material package, and to optimize the generation of the potentially affected equipment list using the actual consumption feedback data.

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