Power grid digital balance benefit library management method and system and storage medium

Abstract

The invention relates to the technical field of electric power material management, and particularly discloses a power grid digital balance benefit library management method and system and a storage medium. The method comprises the following steps: constructing a full-quantity material digital resource pool, integrating a central library, a professional warehouse and in-transit / reserved material data, and carrying out standardization and structuring processing; based on a multi-level rule engine and an intelligent recommendation algorithm, in combination with supply chain simulation, a library benefiting strategy is automatically generated and optimized; embedding a strategy into a business process, and realizing closed-loop management from demand triggering, approval to allocation execution by using a process automation engine; and meanwhile, through the lifelong unique identification code of the material, the full-life-cycle traceability is realized, so that the inventory transparency, the cooperation efficiency and the resource utilization level are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power material management, and particularly relates to a power grid digital balance library management method, a system and a storage medium. BACKGROUND

[0002] In the current power grid enterprise material management practice, there are generally problems such as weak inventory management foundation, non-standard warehousing link, fragmented resource information, and lagging library process. The traditional balance library method mainly relies on manual experience judgment, lacks unified data view and intelligent decision support, resulting in inaccurate inventory material identification, low library efficiency, and serious resource waste. Especially in the front-end stage such as project feasibility study and preliminary design, the inventory resource overall planning mechanism is not effectively embedded, resulting in the coexistence of double high phenomena of repeated procurement and inventory accumulation.

[0003] In the prior art, the idle material information is synchronized through a data integration module, and an idle library matching management module is used to realize intelligent matching of project demand and idle inventory, and generate an idle library scheme for approval. Although this scheme improves the utilization rate of idle materials to a certain extent, it still has the following shortcomings:

[0004] (1) Only focusing on idle materials, not covering all inventory materials (including in-stock, in-transit, and project temporary storage), the resource view is incomplete;

[0005] (2) The idle library process is still in the later stage of project implementation, and cannot be moved to the feasibility study or preliminary design stage, so it cannot control the procurement demand from the source;

[0006] (3) Lack of multi-mode idle library strategy, difficult to adapt to the differentiated management needs of different types of materials;

[0007] (4) The material identity management relies on manual input, and does not realize automatic coding and full life cycle tracking, so the collaboration efficiency is limited. SUMMARY

[0008] The purpose of the present application is to provide a power grid digital balance library management method, system and storage medium, by constructing a full material digital resource pool, optimizing the balance library rule system, implementing a multi-mode idle library strategy, and moving the idle library process to the early stage of the project, to realize accurate identification, efficient allocation and full life cycle collaborative management of inventory materials, thereby effectively reducing inventory, activating resources, and improving the overall operation efficiency of the supply chain.

[0009] The technical solution of the present application is as follows:

[0010] According to an aspect of the present application, a power grid digital balance library management method is provided, comprising:

[0011] A full-quantity material digital resource pool is constructed, global inventory data of power grid enterprises is integrated and standardized, a unified material information database is established, and a data basis is provided for subsequent intelligent decision-making;

[0012] Based on the digital resource pool, the business demand is intelligently matched and an optimized stock allocation strategy is generated through a preset multi-level balanced stock allocation rule system;

[0013] The stock allocation strategy is automatically triggered and executed by the process engine, and the closed-loop management from demand identification to resource allocation is completed at the business front end;

[0014] The inventory material is managed for a lifetime, the whole life cycle state change of the material is recorded through a unique identity code, and the traceability of the operation process is ensured.

[0015] As a further selection of the method of the application, the construction of the full-quantity material digital resource pool comprises: loading the global inventory data after standardization to a data warehouse, modeling using a star or snowflake model, taking a material inventory fact table as the core, and correlating material dimensions, time dimensions, warehouse dimensions and state dimensions;

[0016] Among them, the latest inventory state data is stored in real time, the historical inventory snapshot and operation log are stored in historical data, and the query performance is optimized by combining columnar storage technology.

[0017] As a further selection of the method of the application, the multi-level balanced stock allocation rule system comprises a three-level rule structure of company level, unit level and warehouse level;

[0018] Among them, the company-level rules define global stock allocation principles, the unit-level and warehouse-level rules formulate detailed rules in combination with local material attributes, demand attributes, resource attributes and organizational attributes without violating the company-level rules, and are converted into executable logic statements in the form of IF<condition>THEN<action> through rule description language or decision table.

[0019] As a further selection of the method of the application, the intelligent matching and the stock allocation strategy generation process comprise: when a structured material demand is received, the system automatically analyzes the demand content; when an unstructured demand is received, a natural language processing technology is called to extract key material list information, and an optimized stock allocation strategy containing priority, recommended warehouse and transportation path is generated in real time based on the multi-level balanced stock allocation rule system.

[0020] As a further selection of the method of the application, the process engine automatically triggers and executes the stock allocation strategy, which comprises: BPMN modeling of the balanced stock allocation business process, identification of automatic nodes and manual intervention nodes;

[0021] Among them, the automation node covers automatic matching after demand triggering, strategy recommendation, approval flow initiation and warehouse release instruction issuing, and the manual intervention node includes final approval of special bulk materials and abnormal situation handling;

[0022] The process engine is integrated with a project management system or design software, and the material demand is captured and the warehouse flow process instance is started in the project feasibility study or preliminary design stage.

[0023] As a further choice of the method of the application, the unique identity code is generated according to international standards or enterprise self-defined coding rules, contains material type, warehousing time and serial number fields, and ensures global uniqueness;

[0024] The identification code is physically attached to the material through an RFID tag, a two-dimensional code or an Internet of Things device, and is bound to the standardized information of the material when the material is first warehoused, initializes the digital archives, and records the initial state, warehouse location and technical parameters.

[0025] As a further choice of the method of the application, the lifelong management adopts a data model with a material life cycle fact table as the core, and is associated with time dimension, state dimension, location dimension and operation log dimension; the fact table records the timestamp, change type, business order number and responsible person of each state change, and the dimension table stores the material basic attributes and state definition; the system periodically performs archiving and compression on early life cycle data to reduce storage load and ensure data recoverability.

[0026] As a further choice of the method of the application, the traceability is realized through a multi-dimensional query engine and an audit mechanism: the multi-dimensional query engine supports combined queries based on material identification code, time range and state type, and returns the material full life cycle track; the system automatically generates a standardized traceability report covering the material source, flow path, current state and abnormal events, and supports PDF or Excel format export; all query and modification operations record user identity, operation time and IP address, and ensure that the audit log cannot be tampered with through blockchain technology or digital signature.

[0027] Another aspect of the present application provides a digital balance warehouse management system for power grid, the system comprises:

[0028] A digital resource pool module for storing and managing globally standardized inventory information;

[0029] An intelligent recommendation and balance warehouse engine connected with the digital resource pool module for generating warehouse strategies according to business requirements and rule systems;

[0030] An automatic process control module connected with the intelligent recommendation and balance warehouse engine for automatically executing the business processes corresponding to the warehouse strategies;

[0031] The life-long management module is connected with the digital resource pool module, and is used for managing the unique identity code of the material and the whole life cycle event thereof

[0032] The third aspect of the present application provides a computer readable storage medium, wherein the storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the power grid digital balance library management method as described in the above aspect.

[0033] The present application has the following advantages:

[0034] The traditional inventory management method often relies on manual recording and paper archives, which is prone to errors and omissions. However, the present application improves the visibility and accuracy of inventory information through a digital resource pool, reduces human errors, and thus improves the efficiency and accuracy of inventory management.

[0035] The present application introduces an intelligent recommendation algorithm, which can automatically recommend the best balance library strategy according to different situations. This is more efficient and accurate than traditional manual decision-making. At the same time, multi-mode balance library provides more choices, making inventory management more flexible and adaptable to different situations.

[0036] The present application realizes an automated process, reducing the need for manual intervention and manual operation, thereby saving time and human resources. This makes inventory management more efficient and reduces costs.

[0037] The life-long management ensures the consistency and traceability of material information throughout the life cycle, reduces the risk of information loss and inconsistency, and improves the quality and reliability of data.

[0038] In summary, the present application has significant advantages in digital resource management, intelligent decision-making, automated processes and information consistency compared to the prior art, and is expected to improve the efficiency, accuracy and reliability of inventory management. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 It is a whole flowchart of a power grid digital balance library management method;

[0040] Figure 2 It is a detailed flowchart of step S100 of a power grid digital balance library management method;

[0041] Figure 3 It is a detailed flowchart of step S200 of a power grid digital balance library management method;

[0042] Figure 4A grid digital balance library management method S300 step detailed flow chart;

[0043] Figure 5 A grid digital balance library management method S400 step detailed flow chart. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0045] Embodiment 1

[0046] As the core link of power supply chain operation, the efficiency and accuracy of grid inventory management are directly related to the cost control and resource utilization efficiency of grid construction and operation. In the grid inventory system with large scale, various types and wide distribution, the balance library management is faced with long-term challenges such as low data transparency, difficult cross-departmental cooperation, decision-making relying on experience, and low process efficiency. The theoretical basis of the present application is established on the three pillars of resource optimization allocation theory, system simulation theory and intelligent decision-making theory. The transparent management of inventory resources is realized by constructing a full-quantity material digital resource pool. The optimization generation of balance library strategy is realized by using multi-level rule engine and intelligent recommendation algorithm. The business efficiency is significantly improved by combining with the process automation engine. The traceability of the whole life cycle information is realized by using the unique material life-long identification code. Please refer to Figure 1 , which shows a general flow chart of a grid digital balance library management method provided by an embodiment of the present application. The method comprises:

[0047] S100: Construct a full-quantity material digital resource pool.

[0048] S200: Establish and execute intelligent recommendation and multi-mode balance library.

[0049] S300: Realize balance library process automation.

[0050] S400: Implement material lifelong management.

[0051] The specific scheme is as follows:

[0052] The purpose of S100 is to establish a full-quantity material digital resource pool. A unified digital resource pool is built through a supply chain platform. Various material information is standardized, including material code, name, specification, unit, price, and a corresponding database is established, providing an accurate data basis for subsequent intelligent decision-making and automatic processing.

[0053] Reference is made to Figure 2 which shows a flow chart of an exemplary construction of a digital resource pool, the contents of which include:

[0054] S110: Extract inventory data from scattered business systems and perform preliminary cleaning and integration.

[0055] S110 is responsible for extracting inventory-related data from scattered business systems and performing preliminary cleaning and integration.

[0056] In an optional implementation, central warehouse data, professional warehouse data, and in-transit and reserved material data can be collected.

[0057] Specifically, central warehouse data collection is achieved by database interface or file exchange, and the material inventory data of the central warehouse is obtained in a timely or real-time manner, including material code, name, specification, inventory quantity, location information, storage time, and value. Professional warehouse data collection is achieved by means of Internet of Things gateway or edge computing device to collect material data of on-site professional warehouses such as transformer substations and power supply stations. In-transit and reserved material data collection is achieved by obtaining in-transit material information that has been purchased but not yet arrived, and material information that has been reserved by a project, from a supply chain management system or a project management system.

[0058] S120: Standardize and structure the material information, including code unification, attribute completion, and data verification.

[0059] In an optional implementation, the standardization and structuring of material information includes material code unification and mapping, key attribute completion and verification, data quality evaluation and correction, and unstructured data association.

[0060] Specifically, material code unification and mapping is to map and unify the same material with different codes according to the material master data. Key attribute completion and verification is to complete the missing key technical parameters by associating with a technical database or initiating a manual verification process. Data quality evaluation and correction is to perform reasonableness verification on core numerical fields such as inventory quantity and amount, and to identify and freeze obviously abnormal data. Unstructured data association is to associate non-structured documents such as drawings, instructions, field photos, and factory test reports with material code and structured data to form a complete digital material archive.

[0061] S130: Load the processed standard data into the data warehouse model to construct a digital resource pool.

[0062] The processed standard data is loaded into a target database, and is modeled using a star or snowflake model of a data warehouse. Specifically, taking a material inventory fact table as a core, a material dimension, a time dimension, a warehouse dimension, a state dimension, etc. are associated to form a digital resource pool.

[0063] In an optional embodiment, the latest inventory status in the database is updated in real time, and historical inventory snapshots, operation logs, etc. are partitioned as historical data.

[0064] S200, based on the digital resource pool, analyzes user demand and inventory resources, identifies a best inventory recommendation scheme in real time, and automatically generates a balancing inventory and warehouse strategy, so that the balancing inventory and warehouse decision is changed from relying on personal experience to a scientific and efficient intelligent process dominated by the system.

[0065] Please refer to Figure 3 which shows an exemplary intelligent recommendation and multi-mode balancing inventory and warehouse flowchart, the content of which includes:

[0066] S210: combing and quantifying business rules into a computer executable balancing inventory and warehouse rule system.

[0067] The complex business rules are combed, quantified and converted into a computer understandable and executable form, i.e. a balancing inventory and warehouse rule system.

[0068] In an optional embodiment, the balancing inventory and warehouse rule system step includes:

[0069] Determining the elements on which the rule system depends, including material attributes, demand attributes, resource attributes and organization attributes.

[0070] Establishing a company-level, unit-level and warehouse-level balancing inventory and warehouse rule system. Specifically, the company-level rules specify global principles, and the unit-level and warehouse-level rules formulate local actual situation details without violating the company-level rules.

[0071] Using a rule description language or a decision table, the business rules of the balancing inventory and warehouse rule system are converted into IF<condition>THEN<action> logical statements. For example: IF material age > 24 months AND demand project is the unit AND transportation distance < 100 kilometers THEN recommend balancing inventory, with high priority.

[0072] S220: automatically analyzing demand and matching inventory resources, and generating and recommending balancing inventory strategies through a rule engine.

[0073] When receiving a front-end business demand, an automatic matching process is started, which includes:

[0074] Analyzing the demand plan, extracting key features, including the code, quantity, required arrival time, demand project type, and reporting unit of the required materials.

[0075] According to the demand characteristics, call the full-quantity material digital resource pool, and search for available inventory materials that meet the conditions within the company to form an initial candidate resource list.

[0076] The candidate resource list is sent to the rule engine together with the demand information. The rule engine scores each candidate resource based on the rule base constructed in S210, and filters out resources that meet the hard conditions.

[0077] For candidate resources filtered by rules, the system generates one or more library strategies from the perspective of library efficiency, economic benefit, and inventory optimization. Each strategy clearly indicates which warehouse to call, what materials to call, how much quantity, and which demand to meet, with an estimated utility score for the strategy.

[0078] The generated library strategy is simulated by the supply chain simulation, simulating its execution process, checking whether there are potential conflicts, bottlenecks or violations of constraints. The library strategy that passes the simulation is sorted according to the utility score, and the first recommended library strategy is ranked first.

[0079] S300 realizes the automation of the balance library process, embeds the intelligent recommendation strategy into the power grid business front-end process, automatically triggers the balance library process in the project feasibility study or preliminary design stage, and drives the strategy approval, material allocation instruction issuance and execution state tracking through the process engine, reduces manual intervention, forms a closed-loop automated management from demand identification to resource allocation, and significantly improves business response speed and operation efficiency.

[0080] Please refer to Figure 4 , which shows an exemplary balance library process automation flowchart, the content of which includes:

[0081] S310: Use business process modeling standards to define the balance library process and clearly define automated and manual nodes.

[0082] Use business process modeling standards to model the complete balance library business process and identify key links in the balance library business process. Specifically, the key links include: demand triggering, system matching, strategy recommendation, scheme approval, material reservation, warehouse instruction issuance, logistics tracking, and warehouse confirmation.

[0083] In the business process model, identify nodes that can be automatically completed by the system. Specifically, the automated nodes include: automatic triggering of matching, automatic generation of recommended solutions, automatic initiation of approval flow, automatic issuance of warehouse instructions, etc.

[0084] In the business process model, the node that must be determined by artificial judgment or operation, specifically, the node of artificial judgment or operation includes: final approval of special bulk materials, handling of abnormal situations, etc.

[0085] S320: The integrated project management system automatically parses the demand and creates a balanced inventory process instance.

[0086] Integrate the inventory strategy with the company's project management system or design software. In the project feasibility study or preliminary design phase, when the designer selects a certain equipment or material, the system can automatically push the material demand information to the balanced inventory system in real time or near real time.

[0087] The demand plan received by the balanced inventory system is automatically parsed if it is structured data; if it is unstructured data, the key material list information is extracted using natural language processing technology. Once the valid material demand is parsed, the system immediately creates a balanced inventory process instance in the background and sets its state to be matched.

[0088] S330: Drive the inventory process instance through the process engine to handle automated and manual tasks.

[0089] The process engine instantiates and drives the inventory process according to the model defined in S310. The process engine is responsible for assigning tasks to different processing nodes and monitoring the execution status of the entire process.

[0090] When the process flows to the automated node, the engine calls the corresponding service or script. For example, in the automatic matching node, the balanced inventory strategy of S200 is called; in the automatic approval node, for the low-risk and high-benefit strategies preset in the rule base, the system simulates the approval person to automatically complete the approval and flows to the next link. When the process flows to the manual node, the engine accurately pushes the task to the designated person through the to-do list, email, SMS, etc. and sets up a timeout reminder mechanism to ensure that the process does not block.

[0091] S400 implements life-long management of inventory materials, assigns a unique identity code to each material, and records the full life cycle state changes from warehousing, use, maintenance to scrap, ensuring that all operation processes are traceable and auditable. This step relies on the digital resource pool built in S100 and the process automation engine in S300 to realize continuous updating and closed-loop management of material data.

[0092] Please refer to Figure 5 , which shows an exemplary material life-long management process diagram of the present application, which includes:

[0093] S410: Assign a unique identity code to each material and initialize a digital file.

[0094] Assign a unique identity code to each inventory item and initialize its digital profile as the foundation for life cycle management. The identity code is generated when the item first enters inventory and remains throughout the item's entire life cycle.

[0095] In an optional implementation, the unique identity code assignment and initialization includes identity code generation rule definition, identity code binding and activation, and initial state recording. Specifically, the identity code generation rule definition is to generate an identity code with uniqueness based on international standards or enterprise-defined coding rules, containing elements such as item type, entry time, serial number, etc., to ensure global uniqueness. Identity code binding and activation is to physically attach the identity code to the item through RFID tags, two-dimensional codes or Internet of Things devices, and associate the identity code with the standardized information of the item through the supply chain platform. Initial state recording is to automatically record the initial state, entry time, warehouse location, technical parameters, etc. of the item when it enters the warehouse, and store it in the item dimension table of the digital resource pool, forming the starting point of the item digital profile.

[0096] S420: Real-time tracking and recording of item life cycle state changes based on unique identity code.

[0097] Based on the unique identity code, real-time tracking and recording of item state changes in the entire life cycle, including key links such as entry, exit, allocation, use, maintenance, and scrap, to ensure the timeliness and accuracy of state changes.

[0098] In an optional implementation, the life cycle state change record includes state definition and trigger mechanism, change data collection and verification, and abnormal state handling. Specifically, the state definition and trigger mechanism is to define the possible states of the item in advance in the system, and automatically trigger state changes through business processes or Internet of Things sensors. For example, when the item exits, the process engine automatically updates the state to exit and records the exit time, destination and responsible person. Change data collection and verification is to collect state change data in real time through integrated Internet of Things devices, business system interfaces or manual mobile terminals, and perform logical verification on key fields to prevent data conflicts or errors. Abnormal state handling is to freeze the item record when the system detects abnormal changes and trigger an alarm process for manual intervention.

[0099] S430: Persistently store life cycle data in data models to support queries and traceability.

[0100] Persistently store life cycle state data and build a dedicated data model to support efficient querying, analysis and traceability. This step is based on the digital resource pool model of S130, extending the item life cycle dimension.

[0101] In an optional embodiment, the life cycle data storage and modeling includes life cycle data model design, storage partitioning strategy, and data archiving and compression. Specifically, the life cycle data model design adopts a star model of data warehouse, with a material life cycle fact table as the core, and associated time dimension, state dimension, location dimension, and operation log dimension. The fact table records state change timestamp, change type, related business order number, and responsible person, and the dimension table stores material basic attributes, state definition, and warehouse information. The storage partitioning strategy stores real-time state data in a high-performance database partition to support fast update and query; historical state snapshots and operation logs are stored in a historical data partition, and columnar storage technology is used to optimize query performance. The data archiving and compression archives and compresses early life cycle data on a regular basis to reduce storage load while ensuring data recoverability.

[0102] S440: Provide multi-dimensional query and traceability tools to ensure operation auditability and transparency.

[0103] Provide powerful query and traceability tools to enable users to quickly retrieve the full life cycle records of materials and ensure the transparency and auditability of the operation process. This step integrates with the process engine of S300 to realize real-time pushing and visualization of traceability data.

[0104] In an optional embodiment, the traceability guarantee and query mechanism includes multi-dimensional query engine design, traceability report generation, and audit log management. Specifically, the multi-dimensional query engine design supports users to conduct combined queries based on material identification code, time range, state type, and other multi-dimensional conditions through a graphical interface or API interface, and the system returns the complete historical track of the material, including all state changes, related documents, and operation personnel. The traceability report generation automatically generates standardized traceability reports, covering material sources, flow path, current state, and abnormal events, and supports export to PDF or Excel format. The audit log management records all queries and modification operations, including user identity, operation time, and IP address, and ensures that the logs are tamper-proof through blockchain technology or digital signature, meeting internal audit and compliance requirements.

[0105] Embodiment 2

[0106] The present application has been fully deployed and verified in a specific power company. The implementation scope covers 1 central warehouse, 14 city-level warehouses, and more than 200 professional warehouses, manages more than 5000 types of materials, and the inventory value exceeds several billion yuan.

[0107] Key configurations during implementation:

[0108] Basic platform: deployed based on the company's unified cloud platform, using microservice architecture.

[0109] Data Integration: Integrates a total of 8 core business systems, including ERP, WMS, and PMS, through the Enterprise Service Bus, processing over one million data exchange records daily.

[0110] Intelligent Algorithm: The rule engine uses Drools, and the similarity matching module is implemented based on SparkMLlib.

[0111] Identification technology: UHF RFID tags are used for high-value and important equipment, while QR code tags are used for ordinary materials.

[0112] Test and running results:

[0113] After a year of operation, the effectiveness of this invention has been significantly demonstrated by comparing key business data before and after implementation:

[0114] It has enabled real-time visual query of inventory information across the entire domain, and the accuracy of inventory data has increased from about 91% before implementation to over 99.5%, basically eliminating the discrepancy between accounts and actual inventory.

[0115] The average processing time for balanced inventory management has been reduced from weeks to hours, improving process efficiency by over 80%. The adoption rate of the system-recommended inventory management solution is as high as 85%.

[0116] By actively utilizing its inventory, the company reduced the value of dormant materials by approximately 35% and increased inventory turnover by approximately 28%, effectively freeing up storage space and reducing capital occupation.

[0117] This broke down inventory barriers between different units, and the number of cross-unit material transfers increased threefold year-on-year, achieving optimized allocation of resources within the company.

[0118] Approximately 70% of the routine balance inventory process has been automated end-to-end, requiring no manual intervention, which greatly reduces the workload of frontline warehouse and project management personnel.

[0119] Typical application scenarios:

[0120] In a 330kV substation expansion project, the designer selected the required SF6 circuit breakers using the Product Management System (PMS) during the preliminary design phase. The demand information automatically triggered a stock balancing process. Within 5 seconds, the system completed a full-domain search and intelligent matching, recommending a circuit breaker of the same model in good technical condition from the warehouse of another decommissioned substation. After automatic system approval, the instruction was sent directly to the source warehouse's Warehouse Management System (WMS). From demand triggering to material issuance, the entire process required no manual searching, coordination, or form filling, taking only 4 hours, saving approximately 500,000 yuan in procurement funds, and revitalizing idle assets.

[0121] In summary, this invention provides power grid companies with a complete, efficient, and accurate solution for balancing inventory management by systematically integrating digital, intelligent, and automated technologies, achieving significant economic benefits and management improvements in practice.

[0122] Example 3

[0123] According to Embodiment 1 of this application, a digital power grid balancing and resource utilization management system is provided, the system comprising:

[0124] The multi-source data acquisition and vector construction module is used to collect multi-source heterogeneous data from distributed monitoring nodes within new energy power plants, and to construct a multi-dimensional monitoring resource vector for each distributed monitoring node based on the multi-source heterogeneous data.

[0125] The functional zoning and risk prediction module is used to calculate the resource vector similarity between nodes based on the monitoring resource vector of each monitoring node, and calculate the monitoring feature density of each node based on the similarity; further, based on the monitoring feature density, the entire site is automatically divided into multiple monitoring functional zones, and the overall load change and failure risk probability of each zone in the future short time window are predicted.

[0126] The task optimization and deployment module is used to model the allocation of monitoring tasks as an optimization model based on the monitoring function partitions and prediction results; solve the optimization model to obtain the optimal deployment probability of each monitoring task, and allocate the corresponding monitoring nodes to the monitoring tasks according to the optimal deployment probability;

[0127] The dynamic sharding and redeployment module continuously monitors the resource usage of each monitoring function partition and monitoring node after task deployment. When it identifies a monitoring node as being at risk of resource contention due to task overload, it activates the dynamic sharding mechanism, sharding the monitoring tasks on that node into multiple parallel processing sub-task groups. These sub-task groups are then input into the task optimization and deployment module, where the tasks are redeployed according to the optimization model.

[0128] Example 4

[0129] According to Embodiment 1 of this application, a computer read storage medium is provided, wherein the storage medium stores at least one instruction, at least one program, code set or instruction set, wherein the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to realize a digital power grid balancing resource management method as described above.

[0130] Those skilled in the art will understand that the embodiments of this application are provided as methods, systems, or computer program products. Therefore, this application takes the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application takes the form of a computer program product implemented on one or more computer storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer program code. The solutions in the embodiments of this application are implemented using various computer languages, exemplified by the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0131] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, are implemented by computer program instructions. These computer program instructions are provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions are also stored in a computer read-memory memory (CROM) that can direct a computer or other programmed data processing device to operate in a specific manner, such that the instructions stored in the CROM produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions are also loaded onto a computer or other programmed data processing device, causing a series of operational steps to be performed on the computer or other programmed device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmed device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0135] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for digital power grid balancing and resource management, characterized in that, include: By integrating and standardizing the inventory data of power grid companies, a digital resource pool of all materials is constructed. Based on a digital resource pool, and through a pre-defined multi-level balance rules system, business needs are intelligently matched and optimized resource strategies are generated. The Liku strategy is automatically triggered and executed through the process engine, completing closed-loop management from demand identification to resource allocation at the business front end; The inventory materials are managed on a lifelong basis, and the status changes of the materials throughout their entire life cycle are recorded through a unique identification code to ensure the traceability of the operation process.

2. The power grid digital balancing and resource management method as described in claim 1, characterized in that, The construction of a full-scale digital resource pool for materials includes: loading standardized full-domain inventory data into a data warehouse, using a star or snowflake schema for modeling, with the material inventory fact table as the core, and associating material dimension, time dimension, warehouse dimension and status dimension; Specifically, the latest inventory status data is stored in real-time partitions, historical inventory snapshots and operation logs are stored in historical data partitions, and columnar storage technology is used to optimize query performance.

3. The power grid digital balancing and resource management method as described in claim 1 or 2, characterized in that, The multi-level balanced profit-sharing rule system includes a three-level rule structure: company level, unit level, and warehouse level. Among them, the company-level rules define the global principle of resource utilization, while the unit-level and warehouse-level rules, without violating the company-level rules, formulate detailed rules based on local material attributes, demand attributes, resource attributes and organizational attributes, and are transformed into executable logical statements in the form of IF<condition>THEN<action> through rule description language or decision table.

4. The power grid digital balancing and resource management method as described in claim 3, characterized in that, The intelligent matching and inventory management strategy generation process includes: when a structured material demand is received, the system automatically parses the demand content; when an unstructured demand is received, natural language processing technology is used to extract key material list information, and an optimized inventory management strategy including priority, recommended warehouses, and transportation routes is generated in real time based on the multi-level balanced inventory management rule system.

5. The power grid digital balancing and resource management method as described in claim 1, characterized in that, The process engine automatically triggers and executes the profit strategy, including: performing BPMN modeling on the balanced profit business process and identifying automated nodes and manual intervention nodes; Among them, the automated nodes cover automatic matching, strategy recommendation, approval flow initiation and outbound instruction issuance after demand is triggered, while the manual intervention nodes include final approval of special bulk materials and handling of abnormal situations. The process engine is integrated with project management systems or design software to capture material requirements and initiate resource utilization process instances during the project feasibility study or preliminary design phase.

6. The power grid digital balancing and resource management method as described in claim 1, characterized in that, The unique identification code is generated according to international standards or enterprise-defined coding rules, and includes fields such as material type, warehousing time and serial number to ensure global uniqueness; The identification code is physically attached to the materials via RFID tags, QR codes, or IoT devices, and its standardized information is bound to the materials when they first enter the warehouse, initializing the digital file and recording the initial state, warehouse location, and technical parameters.

7. The power grid digital balancing and resource management method as described in claim 6, characterized in that, The lifelong management system adopts a data model centered on a material lifecycle fact table, which is associated with time, status, location, and operation log dimensions. The fact table records the timestamp, change type, business order number, and responsible person for each status change, while the dimension table stores the basic attributes and status definitions of the materials. The system periodically archives and compresses early lifecycle data to reduce storage load and ensure data recoverability.

8. The power grid digital balancing and resource management method as described in claim 7, characterized in that, The traceability is achieved through a multi-dimensional query engine and an auditing mechanism: the multi-dimensional query engine supports combined queries based on material identification codes, time ranges, and status types, returning the entire lifecycle trajectory of materials; the system automatically generates standardized traceability reports covering material sources, circulation paths, current status, and abnormal events, and supports export in PDF or Excel format; All query and modification operations record user identity, operation time, and IP address, and the audit logs are ensured to be tamper-proof through blockchain technology or digital signatures.

9. A digital power grid balancing and resource management system, characterized in that, The system is used to implement a digital power grid balancing and resource management method as described in any one of claims 1 to 8, the system comprising: The digital resource pool module is used to store and manage standardized inventory information across the entire domain; The intelligent recommendation and balancing resource engine is connected to the digital resource pool module and is used to generate resource strategies based on business needs and rule system. An automated process control module is connected to the intelligent recommendation and balanced resource engine and is used to automatically execute the business process corresponding to the resource strategy. The lifetime management module, connected to the digital resource pool module, is used to manage the unique identification code of materials and their entire life cycle events.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of a digital power grid balancing resource management method as described in any one of claims 1 to 8.

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