Electric power storage resource global collaborative management method and system

Abstract

The invention relates to the technical field of electric power storage, and particularly discloses an electric power storage resource global collaborative management method and system, and the method comprises the steps: constructing an electric power professional warehouse data graph with an Internet of Things physical ID as a core node, the Internet of Things physical ID being a unique physical identifier of an electric power storage resource; associating material data of a safety supervision warehouse, a regulation and control warehouse and a logistics warehouse, and integrating key data entities of a safety tool verification system, a scheduling equipment management system and a work order management system; a unified data format and a data mapping rule are defined, and standardized interaction of cross-system heterogeneous data is achieved. According to the method, standardized real-time synchronization of cross-system data is realized by constructing a power professional warehouse data graph and relying on an internet of things entity ID active synchronization engine; by using a multi-dimensional rule engine, based on a professional warehouse type-material attribute-business scene three-dimensional rule base, accurate management and control are provided for warehouse-in, warehouse-in, warehouse-out and exception handling of materials, and the asset utilization efficiency is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power storage, in particular to a power storage resource global collaborative management method and system. BACKGROUND

[0002] As an asset-intensive field, the power industry's storage system covers multiple types of professional warehouses, such as safety tool warehouses (insulation tools, electroscopes, etc.), dispatching warehouses (RTU equipment, communication modules, etc.), and logistics warehouses (emergency power supplies, repair tents, etc.). The materials in these warehouses are diverse and have strong time efficiency requirements. They not only need to meet the precise allocation of daily operation and maintenance, but also need to support emergency response in scenarios such as typhoons and substation failures.

[0003] With the advancement of supply chain digital transformation, some enterprises have introduced WMS, ERP, and other systems to attempt information management, but they have not yet broken through the core bottlenecks of traditional modes:

[0004] Each professional warehouse management system operates independently, and the data standards are fragmented. The field definitions and formats of the same materials differ in different systems, causing information blockage. Data synchronization relies on manual triggering or traditional interface development, which has problems such as long processes and serious delays, making it difficult for provincial companies to real-time monitor global inventory dynamics and providing accurate data support for decision-making. SUMMARY

[0005] The present application aims to at least solve one of the technical problems in the related art. To this end, the present application aims to provide a power storage resource global collaborative management method and system to improve asset utilization efficiency.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a power storage resource global collaborative management method, comprising the following steps:

[0007] Step one, build a power professional warehouse data graph with Internet of Things physical ID as the core node. The Internet of Things physical ID is the unique physical identifier of the power storage resource. Associate the material data of safety tool warehouses, dispatching warehouses, and logistics warehouses, and integrate key data entities of safety tool verification systems, dispatching equipment management systems, and work order management systems. Define a unified data format and data mapping rule to realize standardized interaction of cross-system heterogeneous data.

[0008] Step two, based on the active synchronization engine, use the event-driven synchronization mechanism, pre-synchronization strategy, and delay early warning self-healing mechanism to realize real-time synchronization of the associated data of the Internet of Things physical ID between the power professional warehouse data graph and each professional business system.

[0009] Step three, using a multi-dimensional rule engine, based on the preset professional warehouse type-material attribute-business scenario three-dimensional rule base, the warehousing, in-warehouse, out-of-warehouse and abnormal disposal operation of the power storage resource are matched with the real-time rule, and the decision instruction is output;

[0010] Step four, in the power grid emergency scene, based on the constructed emergency scene-professional warehouse resource coupling graph, combined with the real-time working condition of the power grid, the real-time state of the material and the external environment data, the quantitative priority scheduling engine is used for dynamic scheduling decision of the emergency resource across the professional warehouse, and the intelligent resolution of the multi-order resource conflict is realized;

[0011] Step five, for the old material in the power storage resource, the automatic identification, multi-dimensional compliance evaluation are carried out based on the whole life cycle data, and the optimal disposal scheme is generated according to the cost-driven intelligent disposal path recommendation mechanism.

[0012] In order to achieve the above purpose, the second aspect of the present application provides a kind of power storage resource global collaborative management system, comprising:

[0013] Data graph construction module, for building the power professional warehouse data graph with the Internet of Things real object ID as the core node, and according to the unified data format and data mapping rule, the heterogeneous data from safety supervision warehouse, control warehouse, logistics warehouse and other associated business systems are associated and standardized;

[0014] Data real-time synchronization module is configured with active synchronization engine, which uses event-driven synchronization mechanism, pre-synchronization strategy and delay early warning self-healing mechanism to ensure the real-time and accurate synchronization of data between the data graph and each professional business system;

[0015] In-warehouse collaborative management module, a multi-dimensional rule engine is built in, which is used for real-time rule matching and output decision instruction of the warehousing, in-warehouse and out-of-warehouse operation of the power storage resource based on the preset professional warehouse type-material attribute-business scenario three-dimensional rule base;

[0016] Emergency resource intelligent scheduling module is used for constructing and using emergency scene-professional warehouse resource coupling graph in the power grid emergency scene, and through the quantitative priority scheduling engine built in it, combined with the real-time working condition of the power grid, the real-time state of the material and the external environment data, the optimal emergency resource scheduling scheme across the professional warehouse is calculated and generated;

[0017] Old material intelligent disposal module is used for automatic identification of old material based on material whole life cycle data, and multi-dimensional compliance evaluation is carried out, and the optimal disposal scheme is generated according to the disposal priority of different disposal paths calculated by cost-driven model;

[0018] The old material intelligent disposal module is further used for: when calculating the disposal priority of the disposal path involving cross-warehouse allocation or transformation reuse, instead of the preset static risk coefficient, a dynamic risk coefficient is calculated by real-time acquisition and quantification of the environmental risk factor, the technical guarantee factor, the power grid criticality factor and the demand urgency factor of the target receiving unit of the disposal path, and the dynamic risk coefficient is substituted into the disposal priority calculation formula, so that the generated disposal scheme can accurately reflect the specific risks of the target receiving unit.

[0019] To achieve the above object, a third aspect of the embodiment of the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory, wherein the computer program is executed by the processor to realize the power warehouse resource global collaborative management method.

[0020] The power warehouse resource global collaborative management method and system provided by the embodiment of the present application can realize the standardization of real-time synchronization of cross-system data by constructing a power professional warehouse data graph and relying on a physical ID active synchronization engine of the Internet of Things; accurate control can be provided for material warehousing, in-warehouse, out-of-warehouse and abnormal disposal by using a multi-dimensional rule engine and based on a three-dimensional rule library of professional warehouse type-material attribute-business scenario; meanwhile, the cross-professional warehouse emergency resource dynamic scheduling mechanism can make intelligent decisions in combination with multi-dimensional data such as real-time working conditions of the power grid and external environment, realize efficient resolution of multi-professional warehouse resource collaboration and conflicts, and improve the emergency response capability; and the power professional warehouse old material whole life cycle intelligent disposal can automatically identify old materials, carry out multi-dimensional compliance evaluation and recommend the optimal disposal path, thereby effectively improving the asset utilization efficiency while ensuring compliance, and overall promoting the transformation of power warehouse resources from dispersed management to global collaborative intelligent management and control. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 is a flowchart of the power warehouse resource global collaborative management method provided by the present application;

[0022] Figure 2 is a structural schematic diagram of the power warehouse resource global collaborative management system provided by the present application;

[0023] Figure 3 is a structural schematic diagram of the electronic device provided by the present application. DETAILED DESCRIPTION

[0024] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0025] An electric power storage resource global collaborative management method, system and electronic device are described below with reference to the accompanying drawings.

[0026] Embodiment one:

[0027] Figure 1 is a flowchart of an electric power storage resource global collaborative management method according to an embodiment of the present application. The method aims to solve a series of pain points such as strict professional barriers and inconsistent data standards in existing electric power storage management by building a unified data bottom layer and intelligent application on the upper layer. As an organic whole, the method can be specifically decomposed into the following five core steps, which are collaboratively executed by the electric power storage resource global collaborative management system proposed by the present application.

[0028] As shown in Figure 2 , an electric power storage resource global collaborative management system includes the following modules:

[0029] A data graph construction module is configured to construct an electric power professional warehouse data graph with Internet of Things physical ID as the core node, and to associate and standardize heterogeneous data from safety monitoring warehouses, control warehouses, logistics warehouses and other associated business systems according to a unified data format and data mapping rules.

[0030] A data real-time synchronization module is configured with an active synchronization engine. The engine uses an event-driven synchronization mechanism, a pre-synchronization strategy and a delay early warning self-healing mechanism to ensure real-time and accurate synchronization of data between the data graph and various professional business systems.

[0031] An in-warehouse collaborative management module is internally provided with a multi-dimensional rule engine. The engine is used to perform real-time rule matching and output decision instructions for the warehousing, in-warehouse and out-of-warehouse operations of electric power storage resources based on a pre-set three-dimensional rule library of professional warehouse type-material attribute-business scenario.

[0032] An emergency resource intelligent scheduling module is configured to construct and utilize an emergency scenario-professional warehouse resource coupling graph in the power grid emergency scenario, and to calculate and generate an optimal emergency resource scheduling scheme across professional warehouses through a built-in quantitative priority scheduling engine, combined with real-time operating conditions of the power grid, real-time status of materials and external environmental data.

[0033] An old material intelligent disposal module is configured to automatically identify old materials based on material life cycle data, perform multi-dimensional compliance evaluation, calculate disposal priorities of different disposal paths according to a cost-driven model to generate an optimal disposal scheme.

[0034] The intelligent disposal module for old materials is further used to replace the preset static risk coefficient when calculating the disposal priority of disposal paths involving cross-warehouse transfer or renovation and reuse. It calculates a dynamic risk coefficient by acquiring and quantifying the environmental risk factors, technical support factors, power grid criticality factors, and demand urgency factors of the target receiving unit in real time, and substitutes the dynamic risk coefficient into the disposal priority calculation formula, so that the generated disposal plan can accurately reflect the specific risks of the target receiving unit.

[0035] Specifically, the method for comprehensive collaborative management of power storage resources includes the following steps:

[0036] Step 1: Data Map Construction and Standardization

[0037] This step is executed by the data graph construction module in the system, and its core task is to achieve the aggregation and standardization of data across the entire domain. Specifically, the implementation of this step includes the following key technical details:

[0038] 1. Unique Identifier for Core Nodes: Establishment and Application of Physical IDs for IoT Devices

[0039] To fundamentally solve the persistent problems of multiple codes for a single item and discrepancies between records and actual inventory, this invention assigns a unique and tamper-proof Internet of Things (IoT) physical ID to every power storage resource included in the management scope, whether it is a large, valuable transformer or a numerous, frequently changing safety tool. This ID is strongly bound to the physical item through an embedded chip or optical code, becoming its unique identity credential in the digital world.

[0040] For example, for 20kV insulating poles stored in safety monitoring warehouses that require frequent individual identification and status tracking, their IoT physical IDs can take the form of passive NFC electronic tags. These tags are encapsulated in epoxy resin and embedded at the end of the pole. Maintenance personnel can accurately read the unique ID (e.g., INSROD-SA-20KV-SN202501A034) by bringing the tag close to the pole and update its status. This method ensures accurate individual identification even when the poles are densely packed.

[0041] For emergency repair lighting fixtures stored in the logistics warehouse that require rapid batch inventory checks, ultra-high frequency (UHF) RFID flexible tags are the preferred IoT physical ID. These tags are affixed to the fixture's casing, and through a UHF RFID reader access control system deployed at the warehouse entrance, dozens or even hundreds of fixtures can be identified and recorded in batches without opening the boxes or requiring manual intervention, greatly improving inventory and circulation efficiency.

[0042] 2. Graph-based association and integration of cross-domain data

[0043] After establishing the IoT physical ID as the core node, the data graph construction module uses graph database technologies such as Neo4j and JanusGraph to use heterogeneous data entities from different professional warehouses and different business systems as related nodes, and weaves them into a vast and logically clear knowledge network of power storage resources by defining rich relationships.

[0044] The data graph is constructed by deeply integrating data entities from multiple key business systems, including:

[0045] (1) Deeply integrated safety supervision warehouse materials and safety tool verification system:

[0046] Taking an insulating rod with ID INSROD-SA-20KV-SN202501A034 as an example, its core node in the graph is connected to an aggregate node via an edge defined as HAS_VERIFICATION_HISTORY (possessing verification history). This aggregate node is then connected to a series of nodes representing verification task sheets (such as VT-2025-Q3-001) and test reports (such as VR-2025-Q3-001-RESULT) via edges INCLUDES_TASK (containing task sheets) and GENERATED_REPORT (generating reports). Each test report node carries its own detailed attributes. Through this graph structure, managers can instantly traverse the complete verification record chain of the insulating rod since its commissioning, leaving no room for any missed inspections or non-compliance records to escape detection.

[0047] (2) Deeply integrated control warehouse material and scheduling equipment management system:

[0048] Taking DTU with ID DTU-DA-SJVENDOR-SNB789C005 as an example, its core node is connected to a device ledger parameter node via an edge defined as HAS_TECHNICAL_PROFILE (possessing technical documentation). This node aggregates dozens of key technical parameters synchronized from the dispatching equipment management system. When the power grid needs to urgently replace a faulty DTU, the dispatcher no longer needs to search through cumbersome Excel spreadsheets; instead, they can directly issue a query in the graph: find all DTUs in inventory with firmware versions higher than V3.0 and supporting the IEC104 protocol. The system can return all matching materials and their precise storage location information within seconds.

[0049] (3) Deeply integrate with the entire material and work order management system:

[0050] Whether it's the safety monitoring insulated rod or the control DTU, its usage throughout its lifecycle is closely linked to work orders. When an operations and maintenance personnel (employee number E123, Mr. Liu) receives the aforementioned DTU to execute a work order (work order number WO-IM-20251011-007) for the intelligent transformation of the #3 ring main unit, this record will be captured in the work order management system. The data graph construction module will establish an ASSIGNED_TO_WO (assigned to work order) edge between the DTU node and the work order node, and simultaneously establish an EXECUTED_BY (executed by...) edge between the work order node and Mr. Liu's node. This association constructs a complete traceability chain: materials -> what work to use -> who executes it -> when and where. This is of positive significance for subsequent fault analysis, responsibility determination, and even the accurate calculation of consumption quotas.

[0051] 3. Standardized interaction of heterogeneous data: unified data format and data mapping rules

[0052] To seamlessly integrate system data from different manufacturers, with varying technical architectures, and developed in different eras into a unified graph, data standardization is a crucial hurdle to overcome. This invention addresses this problem by defining a rigorous, unified data format and a flexible set of data mapping rules.

[0053] Data mapping rules are a set of pre-defined rules used to convert data fields from various professional business systems to corresponding fields of IoT physical object IDs.

[0054] (1) Definition of unified data format: At the beginning of map construction, the system defined a standard data model. For example, it is stipulated that all nodes must contain creation_timestamp and last_update_timestamp attributes; the values ​​of all attributes representing status must come from a predefined enumeration list; and all geographic location information uniformly adopts the latitude and longitude format under the WGS84 coordinate system. This ensures the homogeneity and computability of the data within the map.

[0055] (2) Configuration and execution of data mapping rules: The data graph construction module provides a visual interface for configuring mapping rules. For each heterogeneous system that needs to be connected, the administrator can define how its data fields are translated into standard fields of the graph, just like configuring a table.

[0056] Step 2: Real-time data synchronization

[0057] To achieve this goal, the system deploys a high-performance real-time data synchronization module, the core of which is an active synchronization engine. Based on the active synchronization engine, an event-driven synchronization mechanism, a pre-synchronization strategy, and a delay warning and self-healing mechanism are adopted to realize the real-time synchronization of IoT physical ID-related data between the power professional warehouse data map and various professional business systems.

[0058] The engine utilizes three mechanisms in a coordinated manner, including:

[0059] 1. Event-Driven Synchronization Mechanism: This is the preferred and primary mode for data synchronization. The event-driven synchronization mechanism involves setting synchronization trigger events in a specialized business system. When a trigger event occurs, the interface of the active synchronization engine is automatically invoked, pushing the changed data to the data graph. Compared to traditional timed polling, this reduces data latency from hours or minutes to sub-seconds.

[0060] For example, in a modern business system, after executing the core business logic of database write operations, a piece of code is embedded to call the RESTful API provided by the active synchronization engine. For instance, when a warehouse keeper marks a batch of newly arrived cables as qualified for inspection and formally put them into storage in the system, the system, after writing the status to its own database, will immediately construct a JSON message containing a list of physical IDs of the cables and the new status, and send it to the synchronization engine via an HTTP POST request.

[0061] 2. Pre-synchronization Strategy: In certain foreseeable, business-critical scenarios, passively waiting for event pushes may pose risks. This invention introduces a proactive pre-synchronization strategy to actively increase the synchronization frequency and verification strength of specific data, ensuring that core data is absolutely secure at critical moments. The pre-synchronization strategy refers to identifying highly correlated operations in advance for high-frequency emergency scenarios such as power grid fault repair, and automatically updating the latest data of corresponding materials in the professional business system.

[0062] For example, when the system receives a warning from the provincial meteorological bureau that the region will experience heavy rainstorms within the next 24 hours, triggering a Level IV flood control emergency response, it will automatically activate the flood control emergency pre-synchronization strategy. This strategy executes the following process:

[0063] First, identify key materials: Based on the contingency plan knowledge base, immediately identify all categories of materials related to flood control, such as: large submersible pumps, waterproof cables, emergency drainage vehicles, sandbags, rubber boats, etc.

[0064] Then, the synchronization frequency is increased: For these locked critical materials, the engine will temporarily change the synchronization mode, switching from passively waiting for events to high-frequency active polling (for example, actively querying the latest status, location, and available quantity of these materials in the logistics warehouse management system and emergency material management platform every 3 minutes via API).

[0065] Finally, proactive verification is triggered: the engine can even send instructions to the warehouse's IoT system to trigger a mandatory inventory of these critical materials and compare the inventory results with the map data to ensure that the accounts and actual materials are completely consistent.

[0066] In this way, when the emergency repair order is actually issued, the emergency resource information that the commanders see is real-time data that has been repeatedly confirmed and is extremely reliable.

[0067] 3. Delay Warning and Self-Healing Mechanism: The stability of the data synchronization link is the cornerstone of the reliable operation of the entire system. This invention designs an intelligent delay warning and self-healing mechanism to monitor the synchronization health in real time and issue alarms and automatically repair when anomalies occur. The delay warning and self-healing mechanism refers to setting a data synchronization timeout threshold. When a synchronization timeout is detected, an early warning is automatically triggered, the cause of the delay is diagnosed, and self-healing operations are performed based on the diagnosis results, such as switching to a backup interface or automatically correcting the data format. The closed-loop process of monitoring, early warning, diagnosis, and self-healing is as follows:

[0068] Configuration and Monitoring: For each data source to the graph synchronization link, set a Service Level Agreement (SLA), for example: latency <1 second. The monitoring module of the active synchronization engine will behave like an electrocardiogram. Figure 1 Similarly, continuously monitor the time taken for each synchronization transaction.

[0069] Early Warning and Diagnosis: The mechanism is activated when there are five consecutive instances of data synchronization delays exceeding one second from the control warehouse system. It first sends a Level 1 alert to the operations team via DingTalk, WeChat Work, or SMS. Simultaneously, a diagnostic program runs automatically, attempting to ping the source system server, checking the HTTP status codes returned by the interface, and analyzing error messages in the logs to preliminarily determine whether the problem is network jitter, excessive load on the source system, or a program error in the interface itself.

[0070] The self-healing process specifically includes:

[0071] Backup interface switching: If the diagnostics find that the primary synchronization interface continuously returns a 502 BadGateway error, and this interface has a backup disaster recovery interface configured during the configuration, the self-healing program will immediately and automatically modify the routing configuration, seamlessly switch all subsequent synchronization requests to the backup interface, and send a recovery notification after the switch is successful.

[0072] Automatic data format correction: If the diagnostic reveals an error due to a data format validation failure—for example, after a source system upgrade, the value of a critical field `isAvailable` in the pushed JSON changed from a boolean `true / false` to a string `1 / 0`—the self-healing mechanism will attempt to temporarily convert `1` and `0` to `true / false` based on built-in common format compatibility rules. If the conversion and synchronization are successful, it will temporarily apply this compatibility rule and generate a support ticket requiring developers to permanently update the data mapping rules, thus achieving immunity to minor incompatibility issues.

[0073] Step 3: Collaborative Management of Inventory

[0074] Utilizing a multi-dimensional rule engine, based on a pre-defined three-dimensional rule base of professional warehouse type, material attributes, and business scenarios, the system performs real-time rule matching for the inbound, in-warehouse, outbound, and anomaly handling operations of power storage resources, and outputs decision instructions. This step is executed by the in-warehouse collaborative management module within the system, the core of which is a flexibly configurable multi-dimensional rule engine. The engine's decision-making is based on a structured three-dimensional rule base of professional warehouse type, material attributes, and business scenarios.

[0075] 1. Construction and connotation of the three-dimensional rule base: The design of the rule base fully considers the complexity and professionalism of power storage management, and constructs a set of refined and practical management rules.

[0076] (1) First dimension: Specialized warehouse type. This dimension ensures the relevance and professional compliance of the rules, as the management focus and red lines are completely different for different warehouses.

[0077] Safety Supervision Warehouse Rules Subset: Absolute safety is the first principle. Rule Example: For all protective covers used for live-line work, if the leakage current value in the associated test report exceeds 80% of the standard limit, they are prohibited from leaving the warehouse.

[0078] Control Warehouse Rule Subset: Focuses on precise technical matching. Rule Example: An early warning rule states that when a 220kV substation uses a standby protection device, if the selected device's setting version is inconsistent with the setting version of the currently operating main protection device, a pop-up window will indicate a setting version mismatch, requiring reconfiguration. Please confirm.

[0079] Logistics Warehouse Rules Subset: Aiming at ensuring strong support and controllable costs. Rule Example: For non-urgent materials with a unit price exceeding 5,000 yuan, a pre-approved procurement plan or project work order must be associated with the requisition; otherwise, the outbound application cannot be submitted.

[0080] (2) Second dimension: Material attributes. This dimension allows the rules to delve into the intrinsic technical details and legal qualifications of the materials.

[0081] Material Qualification Validity Period Rules: These are general rules for managing materials with time-sensitive requirements. For example: The blocking rule requires that all vehicle-mounted fire extinguishers have their expiration date checked before leaving the warehouse; if expired, they are prohibited from leaving. Another example: The reminder rule automatically generates a pending task for all prepaid electricity meters when their estimated battery life is less than 6 months, reminding the warehouse manager to perform batch replacements.

[0082] Technical parameter matching rules: For example, if a line renovation work order requests a batch of LGJ-400 / 35 model wires, and the warehouse scans the barcode when issuing the wires, if the system recognizes that the IoT physical ID associated with the wires is LGJ-300 / 25, the operation will be immediately blocked, and a message will be displayed indicating that the wire model does not match, and the wires will not be issued.

[0083] (3) Third dimension: Business scenario. This dimension gives the rule engine flexibility and intelligence, enabling it to understand the nature of the current work and dynamically adjust the control strategy.

[0084] Emergency Dispatch Scenario Rules: When the system identifies the work order associated with the current operation as a high-priority scenario such as power grid fault repair or natural disaster emergency, some regular restrictions can be temporarily exempted or simplified. For example, in a Level 1 power grid emergency response scenario, emergency repair personnel are allowed to dispatch the nearest emergency generator vehicle across units and regions using an electronic approval form, without having to follow the regular paper-based dispatch process. However, all operation traces will be highlighted and recorded, and the formalities must be completed within 24 hours afterwards.

[0085] Routine Business Scenario Rules: In scenarios such as planned maintenance, routine inspections, and infrastructure projects, all rules will be strictly enforced. For example, in the annual planned maintenance scenario, all material requisitions must strictly adhere to the First-In, First-Out (FIFO) principle. When an operator attempts to retrieve a later-arrived batch, if earlier-arrived materials of the same model exist, the system will prompt and guide the operator to the correct storage location.

[0086] 2. Closed-loop process of real-time rule matching and decision command output: When any operation involving warehousing resources occurs, the operation type, IoT physical ID and current business scenario information are extracted, matched with the above three-dimensional rule base, and decision commands of allow, block or warning are output.

[0087] Step 4: Intelligent Dispatch of Emergency Resources

[0088] This step is executed by the system's emergency resource intelligent scheduling module, whose goal is to provide the most suitable resource support to the repair site in the fastest and most optimal way under chaotic and high-pressure emergency conditions.

[0089] The core workflow of this module is as follows: First, based on the historical emergency case library and the historical material consumption feature library, an emergency scenario-specialized warehouse resource coupling map is established through machine learning algorithms (such as association rule mining) to analyze different fault types, such as conductor ice breakage and transformer overload burnout, and the required specialized warehouses and materials, such as de-icing devices, backup transformers, and insulated bucket trucks.

[0090] Secondly, when a real-world power grid emergency occurs, such as a large-scale power outage reported by the SCADA system, the emergency resource intelligent scheduling module will immediately activate its built-in quantitative priority scheduling engine. This engine will comprehensively analyze the following inputs from multiple dimensions in real time:

[0091] Real-time power grid operating conditions: severity of faults, number of affected users, load level, etc.;

[0092] Real-time status of supplies: The precise location, availability, and technical parameters of all relevant emergency supplies across the entire region, obtained from data maps;

[0093] External environmental data: real-time traffic information, weather conditions (such as whether the roads are icy), etc.

[0094] Finally, the engine uses a complex optimization algorithm to calculate a dynamic scheduling priority score for each potentially available resource. The resource or combination of resources with the highest score is the optimal scheduling solution recommended by the system across specialized warehouses. For example, it might recommend using the backup transformer in warehouse A, which is slightly farther away but has better performance, instead of the transformer in warehouse B, which is closer but has a minor defect. Furthermore, when multiple urgent work orders simultaneously require the same scarce resource, this module can intelligently resolve resource conflicts between multiple work orders, automatically adjudicating and allocating resources according to preset preemption rules.

[0095] Step 5: Intelligent Disposal of Old Materials

[0096] This step is handled by the intelligent disposal module for old materials in the system, which transforms the handling of old materials from a passive and extensive management process into an active and intelligent asset management process.

[0097] The module's workflow is as follows: First, based on its comprehensive lifecycle data of materials, such as years of service, cumulative usage counts, maintenance records, and technical performance degradation curves, the system automatically identifies aging materials in power storage resources, combined with pre-set identification rules for different specialized warehouses. For example, safety belts in the safety monitoring warehouse may be marked based on the rule that their initial service time exceeds 5 years; relay protection devices in the control warehouse may be marked based on a composite rule that their design service life has expired and that they have experienced two unexplained malfunctions within the past year.

[0098] Secondly, for the old materials that are automatically identified, the system will call a rule base that embeds national standards, industry procedures and technical specifications to conduct a multi-dimensional compliance assessment to determine whether they still have the potential for downgrading, modification and reuse.

[0099] Finally, based on a cost-driven intelligent disposal path recommendation mechanism, the system will virtually calculate the comprehensive benefits of multiple disposal paths for each piece of old material, including:

[0100] Cross-warehouse transfer: Allocate to non-critical positions where performance requirements are not high for continued use;

[0101] Upgrade and reuse: Upgrade and repurpose by replacing some aging parts;

[0102] Disposal: Recycled as waste or auctioned off in an environmentally friendly manner;

[0103] Donation disposal: Donated to scientific research institutions or vocational colleges for teaching and research purposes.

[0104] The system will comprehensively consider factors such as new purchase cost, disposal cost, disposal cycle, residual value and potential risks, and finally generate an optimal disposal plan, which will be pushed to the asset management department for decision-making.

[0105] In summary, this embodiment, through the five interconnected and progressively advancing steps described above, comprehensively constructs a powerful, logically rigorous, and all-encompassing method and system for the collaborative management of power storage resources. Starting with the most basic data standardization and process normalization, it extends to advanced emergency intelligent dispatching and intelligent asset disposal, providing a complete and innovative technical solution for the digital transformation and lean management of power enterprises.

[0106] Example 2:

[0107] The technical solution in this embodiment mainly corresponds to step four of embodiment one, and its function is executed by the emergency resource intelligent scheduling module in the system. When a power grid fails, especially in an emergency that causes a large-scale power outage or threatens important users, time is of the essence, and rapid and accurate resource scheduling is the core factor determining the success or failure of repairs. The emergency resource intelligent scheduling method proposed in this invention aims to transform the traditional scheduling mode that relies on human experience and telephone coordination into an intelligent and automated scheduling mode driven by data and algorithmic decision-making.

[0108] 1. Specialized Warehouse Resource Coupling Diagram

[0109] The primary challenge in emergency dispatch is quickly identifying what is needed amidst chaos. Traditional methods rely on the personal experience of dispatchers or repair experts, which is often fragmented, incomplete, and difficult to pass on. To address this issue, this invention first constructs an emergency scenario-specialized warehouse resource coupling map. This map is a pre-trained knowledge network that stores expert-level knowledge in a structured manner about what types of faults and what materials are most likely to be needed.

[0110] The specific process of constructing this coupling map is as follows:

[0111] (1) Data Foundation: In-depth mining of historical data. The emergency resource intelligent scheduling module will first access and deeply analyze two major historical databases, including:

[0112] Historical Emergency Response Case Database: This database records detailed files on various power grid fault repair incidents that have occurred over the past few years. Each file includes fault tags, such as {Fault Type: Cable Joint Breakdown, Voltage Level: 10kV, Cause: External Damage, Weather: Sunny, Location: City Center Cable Tunnel}. More importantly, the file is linked to the complete work order record for that repair, which details all materials consumed.

[0113] Historical Material Consumption Characteristics Database: This database is a collection of material outbound records from all professional warehouses across the entire region. It records when, by which unit, and for what type of work order each material was used.

[0114] (2) Knowledge Extraction: Coupled Weight Training Based on Random Forest Algorithm. This invention uses the random forest algorithm to extract strong correlations between scenarios and resources from the massive historical data mentioned above, and quantifies them into coupled weights.

[0115] Feature engineering: This involves using various tags from historical emergency cases, such as fault type, voltage level, geographical environment, and weather, as input features. The prediction target is whether each specific emergency supply was used (yes / no).

[0116] Model Training: Random forest models learn the relationships between features and objectives by building hundreds or thousands of decision trees. Each tree learns based on a subset of randomly selected samples and features, thus avoiding overfitting. For example, a decision tree might learn a rule like this: if the fault type is a cable fault and the voltage level is 10kV, then the probability of using a 10kV cold-shrink cable joint is very high.

[0117] Weighted Output: After all decision trees are trained, for a new and specific emergency scenario, the model will synthesize the voting results of all trees and output a probability value between 0 and 1. This probability value, after normalization, becomes the coupling weight between the scenario and the specific resource. The higher the weight, the stronger the certainty of the demand for that resource in that scenario.

[0118] (3) Visualization and application of the graph. All the high-weight relationships obtained from training are eventually stored in a graph database to form an emergency scenario-specialized warehouse resource coupling graph. This graph not only shows what is needed, but also directly indicates which specialized warehouse these resources belong to, providing clear guidance for subsequent cross-warehouse scheduling.

[0119] 2. Dynamic scheduling decision based on a quantized priority scheduling engine

[0120] When multiple storage points and various types of similar materials exist across the network, making globally optimal decisions poses a significant challenge to emergency command. This invention addresses this issue through a built-in quantitative priority scheduling engine, which serves as the decision-making core of the entire emergency dispatch module.

[0121] When a real-world emergency occurs, the engine executes a dynamic scheduling decision-making process that integrates multi-source real-time data and is based on quantization model calculations, including the following:

[0122] (1) Triggering and Information Fusion. Triggering: The emergency dispatch process is triggered by a real power grid failure event. For example, the power grid dispatch automation system (SCADA / EMS) detects a loss of voltage at a 110kV substation A, and the relevant line protections are activated, initially judging that a serious fault has occurred in the main transformer B within the substation.

[0123] Information Fusion: Once activated, the engine will instantly capture and fuse the following four types of information, much like an octopus:

[0124] ① Coupling diagram information: Based on the preliminary fault diagnosis, the 110kV main transformer is seriously faulty. The list of materials with high coupling degree is obtained from the coupling diagram. The 110kV standby main transformer is the first priority.

[0125] ② Real-time power grid operating conditions: Detailed fault information is obtained from SCADA and CIS user management systems to calculate fault severity scores. For example: This fault resulted in a power outage affecting a number of industrial users. The number of affected facilities is 25, including a large hospital and a semiconductor factory; based on historical data of similar failures, the system predicts the failure recovery time. Approximately 48 hours.

[0126] ③ Real-time status of materials: From the comprehensive data map constructed in Example 1, query the real-time status of all 110kV standby main transformers in the province. The query result may show two transformers:

[0127] Material A: Stored in Warehouse C, 120 kilometers from the fault point A. It is in good condition and passed the most recent test.

[0128] Material B: Stored in warehouse D, 80 kilometers from the fault point A. It is in good condition, but historically this model has been in high demand in the region.

[0129] ④ External environment data: Call the third-party map service API to obtain real-time traffic conditions from warehouses C and D to fault point A, and calculate the estimated average transportation speed. For example, although warehouse D is closer, the road leading to point A is under construction. The speed limit is only 40 km / h; while warehouse C, although far away, is entirely on the highway. It can reach speeds of up to 80 km / h.

[0130] (2) Quantitative priority calculation: The core of the engine is to calculate the scheduling priority score for each candidate material (material A, material B). Optionally, the formula for calculating the scheduling priority score can be:

[0131]

[0132] in: The final scheduling priority score;

[0133] The severity score is calculated as follows: ,in The number of industrial users experiencing power outages. For fault recovery time prediction, and These are the corresponding weighting coefficients;

[0134] The response timeliness score is calculated as follows: ,in The distance from the warehouse to the fault location. This represents the average transport speed.

[0135] The resource utilization rate score is calculated as follows: ,in This refers to the current inventory level in the warehouse. This is the historical demand for the same period.

[0136] , , These are preset weights for fault severity, response time, and resource utilization, respectively, and the sum of the three is 1.

[0137] Regarding the aforementioned materials A and B, the following evaluation will be conducted:

[0138] Assume the preset weights are: fault severity weights Response time weight Resource utilization rate weight .

[0139] First, calculate the scheduling priority score for material A. :

[0140] Calculate the severity score of the fault The severity of the fault is the same for all candidate materials. Assume the weighting coefficients... , . To combine values ​​with different dimensions, the system first normalizes them (e.g., maps them to a score of 0-100). For example, after normalization, the scores for 25 key industrial users are 90 points, and the 48-hour recovery time is 85 points. point.

[0141] Calculate response timeliness score : It's important to note here that this formula should be used directly, and to avoid negative scores, the result will be the maximum value between zero and the given value. The estimated transportation time for Material A. Hour. Take 0, so point.

[0142] Calculate resource utilization score : The current inventory of Material A in its warehouse ( The figure is 1 unit. Assume historical demand for the same period is... The figure is 0.1 units (indicating that this model is not commonly used and there is ample inventory). Points. To prevent the denominator from being 0 or the value from being too large, the system sets an upper limit for the score, for example, 500 points. Therefore, point.

[0143] Calculate the final score : point.

[0144] Then calculate the scheduling priority score for material B. :

[0145] Fault Severity Score Same as A, 88 points.

[0146] Calculate response timeliness score : Estimated transportation time for Goods B Hour. Take 0, so point.

[0147] Calculate resource utilization score The current inventory of Material B in warehouse The figure is 1 unit. However, assuming this model is a commonly used model, the historical demand during the same period is... It is 0.8 units. point.

[0148] Calculate the final score : point.

[0149] By comparison, Therefore, the quantification priority scheduling engine will ultimately make a decision: schedule resource A.

[0150] This decision may seem to contradict the intuitive principle of proximity, but it is a scientific decision that integrates information from multiple dimensions. It takes into account that although material B is closer, its transportation time is actually longer due to traffic conditions; more importantly, using material B would deplete the inventory of a commonly used material, potentially affecting the handling of other subsequent malfunctions. The significant difference in scores highlights the importance of utilizing resource A, which has a more abundant inventory. Therefore, deploying resource A is the globally optimal solution with lower risk and greater rationality for the entire power grid resource pool.

[0151] Finally, the system will automatically generate a detailed dispatch instruction, including instructions to the corresponding warehouse to immediately send the 110kV standby main transformer to Substation A. The recommended transport route is as follows, and the estimated arrival time is as follows.

[0152] 3. Intelligent resolution of resource conflicts across multiple work orders

[0153] In extreme cases, such as widespread natural disasters causing multiple concurrent failures, multiple emergency work orders of equal priority may compete for the same scarce resource. This invention provides a three-tiered strategy for efficiently resolving such resource conflicts.

[0154] Scenario: A coastal area is hit by a super typhoon, causing critical substations in cities E and F to be flooded simultaneously. Both cities urgently need the province's only high-power emergency drainage vehicle.

[0155] Level 1 Strategy: Priority Assignment. The emergency resource intelligent scheduling module will independently calculate the scheduling priority score for the repair tasks in City E and City F. and Assuming calculations reveal that the substation in City E is crucial for supplying power to the city's central hospital and railway hub, its... The score was extremely high, which ultimately led to The substation in City F primarily affects ordinary residential areas. Based on the priority preemption principle, the system will automatically assign the drainage truck to the work order in City E, and mark the resource status of the work order in City F as waiting for resources.

[0156] Level 2 Strategy: Geographic Proximity Allocation. After allocating the sole resource to City E, the system will not leave City F's work order waiting indefinitely. It will immediately initiate an alternative search. The system will broaden the search criteria, for example, searching for drainage pumps with slightly lower power output, or searching for resources in non-electrical systems. It might find two medium-sized drainage pumps in a flood control warehouse of a water conservancy department 30 kilometers from City F. The system will assess whether the total drainage capacity of these two pumps can meet the emergency requirements. If so, the system will generate a collaborative scheduling suggestion: Emergency Work Order for City F: The primary resource is already occupied by a higher-priority task. It is recommended to initiate cross-industry collaboration to allocate two medium-sized drainage pumps from the XX water conservancy warehouse as an alternative. The expected drainage efficiency is 80% of the primary resource. Should it be executed?

[0157] Level 3 Strategy: Emergency Procurement as a Backup. If the Level 2 strategy fails to find any available internal or collaborative resources, the system will activate its final safeguard. The emergency resource intelligent scheduling module will connect to a pre-established database of qualified emergency suppliers. This database contains information on companies in the surrounding area that can provide emergency equipment rental services and may have a preliminary inventory lookup via API. The system will automatically check if there are any drainage trucks of the correct specifications available for emergency rental and calculate their deployment costs (rental fee + transportation fee). If a suitable supplier is found, the system will immediately generate an emergency procurement / rental application form, detailing the reasons, needs, supplier information, and budget, and will expedited the process to relevant leaders for approval.

[0158] Through the aforementioned three-tiered progressive strategy, this invention transforms the resource conflict problem, which previously tested the wisdom and coordination capabilities of emergency commanders, into an automated process, maximizing the guarantee of resource supply under extreme conditions.

[0159] In summary, the intelligent emergency resource scheduling scheme described in this embodiment elevates power emergency resource scheduling to an unprecedented level of intelligence, scientific rigor, and efficiency, providing solid technical support for ensuring the safe and stable operation of the power grid and producing significant technical benefits.

[0160] Example 3:

[0161] In traditional power asset management practices, the disposal of aging equipment is often a rigid process with rudimentary decision-making. It is primarily based on simple design service life or purchase dates in equipment ledgers, and the disposal method is mostly uniform scrapping upon reaching the end of the service life. This model has obvious drawbacks: on the one hand, a large number of devices that, although past their service life, are operating in good condition and performing well are prematurely deemed obsolete, resulting in huge sunk costs and resource waste; on the other hand, some devices that, although not yet of service life, have suffered severe performance degradation due to long-term operation under high loads and harsh environments, continue to serve because they have not reached their service life threshold, becoming time bombs threatening the safe operation of the power grid.

[0162] The core objective of the technical solution proposed in this embodiment is to transform the disposal of obsolete materials from a passive termination process into a proactive asset management decision-making process driven by massive real-time data and deeply integrated with economic value assessment and operational risk management. This advanced function is executed by the intelligent disposal module for obsolete materials in the system. The entire process can be broken down into three progressive stages: automatic identification of obsolete materials, pre-disposal compliance assessment of obsolete materials, and final decision path recommendation.

[0163] Phase 1: Automatic identification of old and worn-out materials.

[0164] The starting point of the disposal process is how to accurately and promptly identify, from among millions of equipment assets in the power grid, those old materials that truly need to enter the disposal assessment channel. This embodiment abandons the traditional approach of relying solely on static, isolated ledger information for judgment, and instead fully utilizes the powerful capabilities of the full-domain data map constructed in Embodiment 1 to achieve a multi-dimensional, dynamic, and deeply business-scenario-integrated automated identification mechanism.

[0165] The automatic identification is based on preset aging material identification parameters in the IoT physical ID, and these parameters are bound to the professional warehouse type. This means that the system of this invention has tailored differentiated aging or disposal assessment models for materials with different professional attributes, different security levels, and different management objectives. These models are no longer a single age scale, but a complex set of rules composed of multiple parameters.

[0166] For materials within the safety supervision warehouse (a specialized safety supervision warehouse), the identification model prioritizes absolute safety, focusing on hard constraints such as statutory lifespan, usage intensity, and condition monitoring.

[0167] For example, consider a safety belt with a unique IoT physical ID in the system. Traditional management methods might only record its purchase date and set a fixed scrap age. The system of this invention, however, constructs a dynamic profile of its entire lifecycle. Its preset identification rules might be a complex logic or combination: the initial use date has exceeded the mandatory scrap age stipulated by national standards, or the cumulative number of high-altitude operations recorded in the work order system has reached the manufacturer's recommended fatigue limit, or the most recent preventative test report conducted by the safety supervision department showed a failure or the presence of minor cracks. The system's background daemon continuously monitors changes in all data nodes related to this safety belt.

[0168] Once any of the above conditions are met, the system will immediately and automatically trigger the identification mechanism, changing the digital twin status of the seat belt from "in use" or "in stock" to "pending disposal assessment," and automatically generating a work order with detailed trigger reasons. This work order will be accurately pushed to the work log of the asset specialist in the corresponding safety management department, prompting them to initiate the offline verification and disposal process.

[0169] For materials within the control and dispatch warehouse (specialized dispatch and control warehouse), the identification model focuses more on the degradation of technical performance, system compatibility, and key impacts on power grid operation.

[0170] For example, consider a line protection device operating on the grid, whose design life may be more than ten years. Judging solely by its age, it is easy to overlook the gradual degradation of its performance. The identification model in this embodiment will comprehensively consider the following factors: the device's design service life has reached a preset threshold (e.g., 90%), and the number of unexplained alarms or self-test anomalies captured from its operation logs has shown a significant upward trend in the past year; or its firmware version has not been updated for several consecutive upgrade cycles, resulting in known communication compatibility issues with the newly built smart substation master station system; or the system, through the equipment aging curve algorithm, calculates that the theoretical aging degree of the core electronic components has exceeded the safety threshold based on parameters such as the temperature and humidity data of its historical operating environment, the cumulative energized time, and the historical load curve.

[0171] This composite identification logic can more accurately and proactively identify critical equipment that, although it appears to be still working, has become a weak link in the power grid operation and is in a sub-healthy state.

[0172] For materials within logistics warehouses (specialized logistics support warehouses), the identification model emphasizes the principle of economy and focuses on the assessment of cost-effectiveness throughout the entire life cycle.

[0173] For example, taking a large emergency diesel generator as an example, the system is not only concerned with its age, but also with its economic lifespan. The identification rule may be defined as follows: the generator's cumulative service time has exceeded ten years and its average annual maintenance man-hours and spare parts costs in work orders over the past three years have exceeded the annual fuel savings that could be achieved by purchasing a new generator of the same power but with higher fuel efficiency, or the number of major failures of its core components (such as the engine block) has reached more than three.

[0174] This rule aims to help managers identify negative assets that have lost their repair value and whose continued investment will only cause greater losses through precise data calculations, thereby providing a scientific basis for timely loss mitigation and optimization of asset structure.

[0175] Phase Two: Pre-construction Compliance Assessment for the Disposal of Obsolete Materials

[0176] Once a resource is identified and tagged as requiring disposal assessment in the first phase of the mechanism, the system does not hastily proceed to recommend disposal solutions. Instead, it first initiates a crucial pre-approval process: a compliance assessment. This aims to conduct a rigorous eligibility review of the resource's current status from legal, regulatory, and technical standards perspectives, determining whether it still possesses the legal status to be used in any scenario.

[0177] The compliance assessment, based on a multi-dimensional compliance assessment rule base embedded with professional technical standards, evaluates the compliance of identified obsolete materials. This rule base serves as the legal code for the disposal module of this invention; it is not merely a simple collection of documents, but a structured knowledge base deeply integrated with the material model and automatically invoked by computer programs. This base contains and is regularly updated with a large number of national mandatory standards (GB series), power industry standards (DL / T series), enterprise technical supervision regulations of State Grid and China Southern Power Grid, safety announcements from equipment manufacturers, and more.

[0178] Taking the aforementioned identified sub-health circuit protection device as an example, when it enters the compliance assessment process, the system will automatically perform the following series of operations:

[0179] First, the system accurately identifies the specific model, batch, and technical specifications of the device using its IoT physical ID, and automatically matches all relevant currently valid standard clauses from the rule base. Next, the system retrieves all historical files of the device from the global data map, especially the most recent preventative test report or scheduled inspection data record uploaded by professional technicians.

[0180] Then, the system, like an experienced technical supervisor, compares each key performance indicator in the report with the specified limits in the standard library. For example, the rule library clearly stipulates that the operating time error of a certain type of protection device under a specific fault current should not exceed ±5%, while the system parses from the test report that the actual test error of the device is ±6.2%.

[0181] Finally, based on the comparison results, the system will automatically generate a structured compliance assessment conclusion. This conclusion will clearly state that the assessed object is not fully compliant. Specifically, while indicators such as insulation resistance and dielectric loss meet standard requirements, the accuracy of the core indicator's action time does not comply with Article X, posing a potential safety hazard. This conclusion will serve as an insurmountable constraint, passed to the next decision-making stage. For example, it will directly lead to the automatic filtering out of options such as cross-warehouse allocation to critical trunk lines or direct reuse of existing equipment without repair in subsequent route recommendations.

[0182] Phase 3: Final Decision Path Recommendation

[0183] This is the core decision-making stage of the entire obsolete materials disposal process, and also the most innovative and intelligent part of this embodiment. For materials that have passed (or partially passed) the compliance assessment, the system designs and evaluates multiple possible disposal paths and recommends the optimal solution with the highest overall benefit. This embodiment creatively divides this decision-making process into two levels: first, preliminary screening based on traditional economic principles, and second, refined decision optimization by introducing future-oriented dynamic risk assessment.

[0184] Level 1: The Cornerstone of Decision Making - Preliminary Path Prioritization Based on Cost Drivers

[0185] In the initial decision-making phase, the system acts as a rational economic analyst. Based on a quantitative model centered on economic efficiency, it calculates an initial disposal priority for all potential disposal paths, such as end-of-life disposal, supplier recycling, local downgrading, reuse after upgrading, and cross-regional allocation and support. .

[0186] For example, the formula for calculating the disposal priority can be expressed as:

[0187]

[0188] in: To prioritize processing, the higher the score, the higher the recommendation rate. The new procurement costs for acquiring similar new materials; The cost of implementing the current disposal path; In order to execute the current handling cycle, The compliance risk coefficient is a risk adjustment parameter preset according to different handling paths.

[0189] Understanding the above formula, the difference between the new purchase cost and the disposal cost measures the direct net economic benefit of a disposal plan. For example, compared to spending 100,000 yuan to purchase a new piece of equipment, if it is renovated and reused at a cost of 10,000 yuan, the net economic benefit is 90,000 yuan. The larger this difference, the stronger the economic attractiveness of the plan. The disposal cycle represents the time cost and opportunity cost of a disposal plan. A complex renovation project that takes three months to complete will occupy technical personnel, space, and funds for a long time, and its overall cost is far higher than that of an inter-unit transfer that can be completed in just one week. Therefore, the shorter the disposal cycle, the higher the execution efficiency of the plan, and the higher its priority.

[0190] The compliance risk coefficient is the part of this preliminary model used to reflect risk considerations. It is a static, pre-set, experience-based adjustment factor. The system assigns a fixed coefficient value to different disposal paths based on their inherent risk attributes. For example, the scrapping disposal path, although its economic benefits may be low, can fundamentally eliminate all potential hazards of the old materials, posing zero risk to the power grid. Therefore, the system may assign it a higher risk coefficient value to provide some encouragement in decision-making. Conversely, the cross-warehouse transfer or renovation and reuse path, because it involves the continued service of old equipment in a new environment, inherently involves certain uncertainties and unknown risks, and therefore may be assigned a relatively lower risk coefficient value.

[0191] However, this static risk coefficient has a significant limitation: it's too one-size-fits-all and fails to differentiate analysis based on specific application scenarios. It assumes that the risk is the same regardless of where an outdated device is placed. This clearly contradicts objective reality and lays the groundwork for deeper, more sophisticated intelligent decision-making optimizations later on.

[0192] Level Two: Refined Decision Optimization through Dynamic Risk Assessment

[0193] To overcome the limitations of static risk models and achieve truly intelligent and context-based decision-making, this embodiment proposes that when the system's assessed disposal path involves options with future operational risks, such as cross-warehouse transfers or repurposed systems, the system will no longer use the crude, static compliance risk coefficient. Instead, it calculates a dynamic risk coefficient in real time that is strongly correlated with the specific circumstances of the target receiving unit or future application scenario. express.

[0194] Specifically, dynamic risk coefficient The calculation steps include:

[0195] Target profile data acquisition: When the disposal path is cross-warehouse transfer or renovation and reuse, the real-time status data of the target receiving unit of the disposal path is automatically acquired. The real-time status data includes at least: environmental monitoring data of the target location, historical operation and maintenance data of the target unit, criticality level of the target node in the power grid topology, and work order priority of the current material demand.

[0196] Multidimensional risk factor quantification: Based on preset quantification rules, real-time status data is normalized and quantified into environmental risk factors. Technical support factor Power grid criticality factors and demand urgency factor ;

[0197] For example, dynamic risk coefficient The calculation formula can be:

[0198]

[0199] in: The basic risk coefficient is preset according to the type of disposal path;

[0200] These are environmental risk factors quantified from environmental monitoring data, reflecting the degree of impact of the target location environment on the lifespan and stability of old materials.

[0201] The technical support factor is quantified from historical operation and maintenance data, reflecting the target unit's ability to install, maintain, and troubleshoot the material.

[0202] The power grid criticality factor is quantified from the power grid topology criticality level, reflecting the scope and severity of the impact that the failure of the material at the target node may have on the power grid.

[0203] The urgency factor is a requirement quantified by work order priority, reflecting the degree of urgency in meeting the requirement;

[0204] , , , These are preset weighting coefficients for environment, technology, power grid criticality, and demand urgency, respectively, and the sum of the four is 1.

[0205] This assumes the system is recommending disposal routes for a 10kV vacuum circuit breaker (material Z) that has been in service for eleven years. After passing a compliance assessment, the system finds that two substations have submitted relocation requests, thus forming two potential disposal routes: Route C is to relocate it to Substation A, a well-managed and environmentally sound regional central substation; Route Q is to relocate it to Substation B, a terminal substation located in a remote mountainous area with high humidity year-round and which is the only power source in the area.

[0206] When calculating the dynamic risk of path Q, the system performs the following intelligent profiling and risk quantification:

[0207] Environmental risk factors Assessment: The system will immediately query the geographic information and historical environmental monitoring data associated with Station B in the global data map. It will find that the station's historical humidity records consistently exceed 90%, and there are reports of salt spray corrosion risk. These harsh environmental conditions will undoubtedly significantly accelerate the decline in insulation performance and the corrosion of the mechanism for an old circuit breaker with signs of mechanical aging, thereby greatly increasing the probability of future failure. Therefore, the system will assign a very high environmental risk factor to path Q. Score.

[0208] Technical support factor Assessment: The system will further query the human resources information and maintenance work order history associated with Station B. It may discover that the maintenance team at this station is understaffed, and the team members' training records lack in-depth maintenance experience for the specific older model of Material Z. This means that if Material Z experiences atypical failures after being put into operation at Station B, the local team may not be able to quickly and accurately diagnose and handle the problem, potentially turning a small issue into a major incident. Therefore, the system will assign a similarly high technical assurance factor to path Q. Score.

[0209] Power grid criticality factor Assessment: The system analyzes its location and role in the power grid topology diagram. The analysis shows that station B is the only power source supplying power to a critical area containing hospitals, schools, and communication base stations, and lacks any backup or transfer power supply capabilities. This means that if material Z fails to operate or malfunctions at this location, it will directly lead to a power outage event with significant social impact. The severity of its consequences is unparalleled. Therefore, the system assigns a very high power grid criticality factor to path Q. Score.

[0210] Demand urgency factor Assessment: The system will analyze the background of Station B's application to determine whether the adjustment is to cope with emergency fault repairs or a planned equipment rotation. Different levels of urgency correspond to different levels of risk tolerance.

[0211] In contrast, when calculating the dynamic risk of path C, the system produces a completely different profile:

[0212] Station A is an indoor station with a controllable environment. The score was extremely low; Station A's technical team is the expert center for the entire region. The score was extremely low; Station A is located within a robust ring network structure, meaning that even if there is a Z-type failure in the material supply, it can be instantly isolated without affecting downstream power supply. The score was low.

[0213] Ultimately, the system scores the risk factors across these four dimensions and assigns them to preset weights, such as the power grid criticality factor, which has the greatest impact on safety. It may have the highest weighting coefficient. Perform a weighted summation, then multiply by a base risk coefficient. This resulted in a very high dynamic risk coefficient for path Q. The dynamic risk coefficient calculated for path C is very low.

[0214] When this dynamic risk coefficient can provide profound insights into future risks It was incorporated into the processing priority. When the calculation formula is used, a disruptive decision-making result is produced: although from a purely economic point of view, the cost of transferring to station B (route Q) may be similar to that of transferring to station A (route C) due to factors such as transportation distance, after the punitive adjustment of dynamic risk, the final disposal priority score of route Q will plummet, far lower than that of route C, and may even be lower than local renovation and reuse or direct scrapping.

[0215] The intelligent disposal solution described in this embodiment, through its unique, progressive identification-assessment-decision process, and especially through the creative construction and application of a dynamic risk assessment model based on multi-dimensional real-time data, achieves a fundamental leap in power asset disposal decision-making, from experience-driven to data-driven, and from economic orientation to a balance of value and risk. It enables the system to clearly calculate current economic benefits and, more importantly, to clearly see potential future risks, thereby scientifically and rationally exploring and utilizing the residual value of every aging asset while ensuring the safe operation of the power grid. This not only brings significant economic benefits to power companies but also provides a solid and innovative technical guarantee for improving their inherent safety level and enhancing their sustainable development capabilities.

[0216] Example 4:

[0217] Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0218] like Figure 3 The diagram shows a structural schematic of an electronic device according to the present invention. The electronic device 200 includes a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, for example, via a bus 202. Optionally, the electronic device 200 may further include a transceiver 204. It should be noted that in practical applications, the transceiver 204 is not limited to one unit, and the structure of this electronic device 200 does not constitute a limitation on the embodiments of the present invention.

[0219] Processor 201 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 201 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0220] Bus 202 may include a path for transmitting information between the aforementioned components. Bus 202 may be a PCI bus or an EISA bus, etc. Bus 202 may be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0221] The memory 203 stores a computer program corresponding to the power storage resource full-domain collaborative management method of the above embodiments of the present invention. The computer program is controlled and executed by the processor 201. The processor 201 executes the computer program stored in the memory 203 to implement the content shown in the aforementioned method embodiments.

[0222] Among them, electronic devices 200 include, but are not limited to: mobile terminals such as laptops and tablets, as well as fixed terminals such as desktop computers. Figure 3 The electronic device 200 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0223] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, a computer-readable medium can be any means that can contain, store, electrically store, propagate, or transmit a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0224] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0225] In the description of this specification, references to the terms "an embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0226] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0227] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for comprehensive collaborative management of power storage resources, characterized in that, Includes the following steps: Step 1: Construct a power professional warehouse data map with IoT physical IDs as the core nodes. The IoT physical IDs are the unique physical identifiers of power storage resources. Link the material data of safety supervision warehouses, control warehouses, and logistics warehouses, and integrate the key data entities of safety tool verification system, dispatching equipment management system, and work order management system. Define a unified data format and data mapping rules to achieve standardized interaction of heterogeneous data across systems. Step 2: Based on the active synchronization engine, an event-driven synchronization mechanism, a pre-synchronization strategy, and a delay warning self-healing mechanism are adopted to realize the real-time synchronization of the associated data of the IoT physical IDs between the power professional warehouse data map and various professional business systems. Step 3: Using a multi-dimensional rule engine, based on a preset three-dimensional rule library of professional warehouse type, material attributes, and business scenarios, perform real-time rule matching on the inbound, in-warehouse, outbound, and abnormal handling operations of power storage resources, and output decision instructions. Step 4: In the power grid emergency scenario, based on the constructed emergency scenario-specialized warehouse resource coupling graph, combined with real-time power grid operating conditions, real-time material status and external environment data, the quantitative priority scheduling engine makes dynamic scheduling decisions for emergency resources across specialized warehouses and realizes intelligent resolution of resource conflicts among multiple work orders. Step 5: For old materials in power storage resources, perform automatic identification and multi-dimensional compliance assessment based on their full life cycle data, and generate the optimal disposal plan based on a cost-driven intelligent disposal path recommendation mechanism.

2. The method according to claim 1, characterized in that, In step one, the construction of the power industry warehouse data map specifically involves: Using the IoT physical ID as the core node, it is associated with the verification task sheet and test report of the safety tool verification system, the equipment ledger parameters of the scheduling equipment management system, and the work order associated material information of the work order management system. The data mapping rule is a set of preset rules used to convert data fields from various professional business systems to the corresponding fields of the IoT physical ID.

3. The method according to claim 1, characterized in that, In step two, the event-driven synchronization mechanism refers to setting up synchronization trigger events in the professional business system. When a trigger event occurs, the interface of the active synchronization engine is automatically called to push the changed data to the data graph. The pre-synchronization strategy refers to identifying highly correlated operations in advance and automatically polling the professional business system for the latest data of corresponding materials in high-frequency emergency scenarios such as power grid fault repair. The aforementioned delay warning self-healing mechanism refers to setting a data synchronization timeliness threshold, automatically triggering a warning and diagnosing the cause of the delay when a synchronization timeout is detected, and performing self-healing operations such as switching to a backup interface or automatically correcting the data format based on the diagnosis results.

4. The method according to claim 1, characterized in that, In step three, the three-dimensional rule base of specialized warehouse type-material attribute-business scenario is a set of rules divided according to the following dimensions: Specialized warehouse type dimension: divided into safety supervision warehouse rule subset, control warehouse rule subset, and logistics warehouse rule subset; Material attribute dimensions: divided into material qualification validity period rules and core technical parameter matching rules; Business scenario dimension: divided into emergency call scenario rules and daily business scenario rules; The real-time rule matching refers to extracting the operation type, IoT physical ID, and current business scenario information when warehouse resources are operated, matching them with the three-dimensional rule base, and outputting decision instructions to allow, block, or warn.

5. The method according to claim 1, characterized in that, In step four, the dynamic scheduling decision-making for emergency resources across specialized warehouses specifically includes: Coupling graph construction: Based on the historical emergency case library and the historical material consumption feature library, the coupling relationship between the fault type and the required specialized warehouses and materials is established, and the coupling weight is obtained by training through the random forest algorithm to form an emergency scenario-specialized warehouse resource coupling graph; Scheduling scheme generation: Based on the coupling graph, combined with real-time power grid operating data, real-time material status data and external environment data, the scheduling priority score of the materials to be selected in each professional warehouse is calculated using the quantitative priority scheduling engine, and the optimal scheduling scheme across professional warehouses is generated. Resource conflict resolution: When multiple emergency work orders call for the same material and resource conflicts occur, the conflict is resolved according to a three-level strategy of priority grabbing, geographical proximity adjustment, and emergency procurement to fill the gap.

6. The method according to claim 5, characterized in that, The formula for calculating the scheduling priority score is as follows: ； in: The final scheduling priority score; The severity score is calculated as follows: ,in The number of industrial users experiencing power outages. For fault recovery time prediction, and These are the corresponding weighting coefficients; The response timeliness score is calculated as follows: ,in The distance from the warehouse to the fault location. This represents the average transport speed. The resource utilization rate score is calculated as follows: ,in This refers to the current inventory level in the warehouse. This is the historical demand for the same period. , , These are preset weights for fault severity, response time, and resource utilization, respectively, and the sum of the three is 1.

7. The method according to claim 1, characterized in that, Step five specifically includes the intelligent disposal of the old materials: Automatic identification: Automatic identification is performed based on the pre-set old material identification parameters in the IoT physical ID. The identification parameters are bound to the professional warehouse type, including the first use time and cumulative number of uses of materials in the safety supervision warehouse, the design service life and aging degree of core components of materials in the control warehouse, and the purchase time and cumulative number of repairs of materials in the logistics warehouse. Compliance assessment: Based on a multi-dimensional compliance assessment rule base embedded with professional technical standards, compliance assessments are conducted on identified obsolete materials; Path recommendation: Based on a cost-driven intelligent disposal path recommendation mechanism, it calculates and recommends the path with the highest disposal priority among cross-warehouse transfer, renovation and reuse, scrap disposal, and donation disposal.

8. The method according to claim 7, characterized in that, The formula for calculating the processing priority is: ； in: To prioritize processing, the higher the score, the higher the recommendation rate. The new procurement costs for acquiring similar new materials; The cost of implementing the current disposal path; In order to execute the current handling cycle, The compliance risk coefficient is a risk adjustment parameter preset according to different handling paths.

9. The method according to claim 8, characterized in that, The compliance risk coefficient Refined into a dynamic risk coefficient Dynamic risk coefficient The calculation steps include: Target profile data acquisition: When the disposal path is cross-warehouse transfer or renovation and reuse, the real-time status data of the target receiving unit of the disposal path is automatically acquired. The real-time status data includes at least: environmental monitoring data of the target location, historical operation and maintenance data of the target unit, criticality level of the target node in the power grid topology, and work order priority of the current material demand. Multidimensional risk factor quantification: Based on preset quantification rules, the real-time status data is normalized and quantified into environmental risk factors. Technical support factor Power grid criticality factors and demand urgency factor ; Dynamic risk coefficient The calculation formula is: ； in: The basic risk coefficient is preset according to the type of disposal path; These are environmental risk factors quantified from environmental monitoring data, reflecting the degree of impact of the target location environment on the lifespan and stability of old materials. The technical support factor is quantified from historical operation and maintenance data, reflecting the target unit's ability to install, maintain, and troubleshoot the material. The power grid criticality factor is quantified from the power grid topology criticality level, reflecting the scope and severity of the impact that the failure of the material at the target node may have on the power grid. The urgency factor is a requirement quantified by work order priority, reflecting the degree of urgency in meeting the requirement; , , , These are preset weighting coefficients for environment, technology, power grid criticality, and demand urgency, respectively, and the sum of the four is 1.

10. A comprehensive collaborative management system for power storage resources, characterized in that, include: The data graph construction module is used to build a power professional warehouse data graph with IoT physical IDs as the core nodes, and to associate and standardize heterogeneous data from safety supervision warehouse, control warehouse, logistics warehouse and other related business systems according to a unified data format and data mapping rules. The real-time data synchronization module is equipped with an active synchronization engine. This engine adopts an event-driven synchronization mechanism, a pre-synchronization strategy, and a delay warning and self-healing mechanism to ensure real-time and accurate data synchronization between the data map and various professional business systems. The in-warehouse collaborative management module has a built-in multi-dimensional rule engine. This engine is used to perform real-time rule matching and output decision instructions for the inbound, in-warehouse and outbound operations of power storage resources based on a preset three-dimensional rule base of professional warehouse type, material attribute and business scenario. The emergency resource intelligent scheduling module is used to construct and utilize the emergency scenario-specialized warehouse resource coupling graph in power grid emergency scenarios. Through its built-in quantitative priority scheduling engine, combined with real-time power grid operating conditions, real-time material status and external environmental data, it calculates and generates the optimal emergency resource scheduling scheme across specialized warehouses. The intelligent disposal module for old materials is used to automatically identify old materials based on the full life cycle data of materials, conduct multi-dimensional compliance assessments, and calculate the disposal priority of different disposal paths based on the cost-driven model to generate the optimal disposal plan. The intelligent disposal module for old materials is further used to: replace the preset static risk coefficient when calculating the disposal priority of disposal paths involving cross-warehouse transfer or renovation and reuse, and calculate a dynamic risk coefficient by acquiring and quantifying the environmental risk factor, technical support factor, power grid criticality factor and demand urgency factor of the target receiving unit of the disposal path in real time, and substitute the dynamic risk coefficient into the disposal priority calculation formula, so that the generated disposal plan can accurately reflect the specific risks of the target receiving unit.

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