A cross-database collaborative updating method and device of an electric power database, a terminal device, and a storage medium

Abstract

This invention discloses a method, apparatus, terminal device, and storage medium for cross-database collaborative updating of power databases, relating to the field of data management technology. The method includes: acquiring the entities and content to be updated from the target power database; updating the target power database; determining associated entities based on a dynamic association graph; and updating the remaining power databases. The construction process of the dynamic association graph involves: acquiring the native data and historical interaction records of each power database; extracting native semantic tags; determining dynamic mapping rules based on a preset ontology mapping rule base, native semantic tags, and historical interaction records; determining global semantic identifiers based on the dynamic mapping rules; reconstructing the native data to generate standardized data elements; and constructing the dynamic association graph. By implementing this invention, the problem of inconsistent native semantic tags among power databases in the prior art, leading to low efficiency in manual cross-database updates, is solved, thus achieving automatic collaborative updating of power databases.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and in particular to a method, apparatus, terminal equipment, and storage medium for cross-database collaborative updating of an electric power database. Background Technology

[0002] In the field of power system operation and management, power data is widely distributed across multiple business systems such as production, dispatching, operation and maintenance, and metering, forming a multi-source, heterogeneous power database system. During the construction and operation and maintenance of various power databases, when a power database is updated due to business needs, the corresponding or sourced data in other related databases needs to be updated synchronously.

[0003] However, the native semantic tags of different power databases are significantly different, which means that existing power data updates mostly rely on manual updates. When data updates occur, operation and maintenance personnel need to check each related database one by one, manually identify and update the corresponding data entries, which is inefficient. Summary of the Invention

[0004] This invention provides a method, apparatus, terminal device, and storage medium for cross-database collaborative updates of power databases. It can solve the problem of low efficiency in manual cross-database updates caused by inconsistent native semantic tags among various power databases in the prior art, and realize automatic collaborative updates of power databases.

[0005] An embodiment of the present invention provides a cross-database collaborative update method for a power database, comprising: Obtain the entities to be updated and their contents to be updated from the target power database; After updating the target power database based on the entity to be updated and its content to be updated, the associated entities corresponding to the entity to be updated are determined based on the dynamic association graph. Based on the associated entities and the corresponding content to be updated, the remaining power databases are updated collaboratively. The dynamic association graph is constructed in the following way: Obtain all native data from each power database and historical interaction records between the power databases; Extract the corresponding native semantic tags from the native data; Based on the preset ontology mapping rule base, the native semantic tags corresponding to the native data of each power database, and the historical interaction records between each power database, dynamic mapping rules are determined; the dynamic mapping rules include the correspondence between native semantic tags and global semantic identifiers; Based on the dynamic mapping rules, determine the global semantic identifier corresponding to the native semantic tag; Based on the global semantic identifiers corresponding to the original semantic tags, the original data is reconstructed to generate standardized data elements; A dynamic association graph is constructed based on all standardized data elements.

[0006] Furthermore, after acquiring all the raw data from each power database and the historical interaction records between the power databases, it also includes: Data cleaning and unit normalization are performed on all raw data from various power databases to obtain preprocessed raw data.

[0007] Furthermore, the corresponding native semantic tags are extracted from the native data, including: For each piece of raw data in each power database, perform modal classification on the raw data to determine the corresponding data type: Extract the native semantic tags corresponding to the native data based on the data type of the native data.

[0008] Furthermore, data types include: structured data, semi-structured data, or unstructured data; Based on the data type of the original data, extract the corresponding native semantic tags, including: Raw data whose data type belongs to structured data is defined as the first raw data; Raw data whose data type belongs to semi-structured data is defined as second raw data; Raw data whose data type belongs to unstructured data is defined as third raw data; For the first piece of raw data, read the field names, field meanings, or data attributes of the first piece of raw data, and extract the corresponding raw semantic tags based on the field names, field meanings, or data attributes of the first piece of raw data; For the second native data, read the tag fields, node attributes, or hierarchical structure of the second native data, and extract the native semantic tags corresponding to the second native data based on the tag fields, node attributes, or hierarchical structure of the second native data; For third-party native data, read the text content, key information fragments, or content themes of the third-party native data, and extract the corresponding native semantic tags based on the text content, key information fragments, or content themes of the third-party native data.

[0009] Furthermore, standardized data elements include: entity ID, attributes, values, and global semantic identifiers; Based on all standardized data elements, a dynamic association graph is constructed, including: Extract the entity ID from all standardized data elements; Use the entity ID as a node in the dynamic association graph; The attributes, values, and global semantic identifiers corresponding to the entity ID are used as the associated attributes of the corresponding node; Based on the global semantic identifier of each standardized data element and the historical interaction records between each power database, the association relationship between nodes corresponding to different entity IDs is identified, and the association relationship is used as a directed edge in the dynamic association graph. Construct a dynamic association graph based on nodes, association attributes, and directed edges.

[0010] Furthermore, after constructing the dynamic association graph, it also includes: Obtain the data timestamp for each standardized data element; Get the current time in real time; Calculate the time decay coefficient based on the time difference between the data timestamp and the current time; By using the time decay coefficient as the weight of the directed edges in the dynamic association graph, a dynamic association graph with real-time decay is obtained.

[0011] Furthermore, before performing collaborative updates on the remaining power databases based on the associated entities and their corresponding content to be updated, the following steps are taken: Real-time monitoring of the access frequency and associated trigger count of all related entities; For each associated entity, calculate the corresponding dynamic activity value based on the entity's access frequency and the number of association triggers; Calculate the corresponding comprehensive score based on the preset entity update priority and dynamic activity value; Sort all comprehensive scores from high to low, and determine the update order of related entities based on the sorting results; Based on the associated entities and the corresponding content to be updated, the remaining power database is updated collaboratively, including: Based on the associated entities, the corresponding update order, and the corresponding content to be updated, the remaining power databases are updated collaboratively.

[0012] Based on the above method embodiments, the present invention provides corresponding device embodiments, including: a module for determining entities to be updated, a module for determining associated entities, and a cross-database collaborative update module; The module for determining entities to be updated is used to obtain the entities to be updated and their content to be updated from the target power database. The associated entity determination module is used to determine the associated entities corresponding to the entities to be updated based on the entities to be updated and their content to be updated, after updating the target power database, according to the dynamic association graph. The cross-database collaborative update module is used to collaboratively update other power databases based on related entities and the corresponding content to be updated. The associated entity determination module includes: an associated graph construction submodule; The association graph construction submodule includes: a native data acquisition unit, a semantic tag extraction unit, a mapping rule generation unit, a global semantic mapping unit, a data element generation unit, and a graph structure construction unit; The native data acquisition unit is used to acquire all native data from each power database and historical interaction records between the power databases. The semantic tag extraction unit is used to extract the corresponding native semantic tags from the native data; The mapping rule generation unit is used to determine dynamic mapping rules based on the preset ontology mapping rule library, the native semantic tags corresponding to the native data of each power database, and the historical interaction records between each power database; the dynamic mapping rules include the correspondence between native semantic tags and global semantic identifiers; The global semantic mapping unit is used to determine the global semantic identifier corresponding to the native semantic tag according to the dynamic mapping rules; The data element generation unit is used to reconstruct the original data based on the global semantic identifiers corresponding to the original semantic tags, and generate standardized data elements. The graph structure building unit is used to construct a dynamic association graph based on all standardized data elements.

[0013] Based on the above method embodiments, the present invention provides a corresponding terminal device embodiment, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the cross-database collaborative update method for the power database as described in the present invention.

[0014] Based on the above method embodiments, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to execute the cross-database collaborative update method for the power database as described in the present invention.

[0015] Compared with the prior art, the beneficial effects of this embodiment are as follows: This invention obtains the entities to be updated and their contents to be updated in the target power database; after updating the target power database based on the entities to be updated and their contents, it determines the associated entities corresponding to the entities to be updated based on the dynamic association graph, and performs collaborative updates on the remaining power databases based on the associated entities and their corresponding contents to be updated. The dynamic association graph is constructed as follows: All native data from each power database and historical interaction records between them are acquired; corresponding native semantic tags are extracted from the native data; based on a preset ontology mapping rule base, the native semantic tags corresponding to the native data of each power database, and the historical interaction records between them, dynamic mapping rules are determined, including the correspondence between native semantic tags and global semantic identifiers, thereby eliminating semantic ambiguity across databases; according to the dynamic mapping rules, the inconsistent native semantic tags of each database are converted into global semantic identifiers, aligning the data at the semantic level; based on the global semantic identifiers corresponding to the native semantic tags, the native data is reconstructed to generate standardized data elements; based on all standardized data elements, a dynamic association graph is constructed, thus solving the problem of low efficiency in manual cross-database updates due to inconsistent native semantic tags among power databases in existing technologies, and realizing automatic collaborative updates of power databases. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a cross-database collaborative update method for a power database according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a cross-database collaborative update device for a power database provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In the description of this invention, it should be understood that 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 technical features indicated.

[0019] like Figure 1 As shown, in order to solve the problem of low efficiency in manual cross-database updates due to the inconsistency of native semantic tags among various power databases in the prior art, an embodiment of the present invention provides a cross-database collaborative update method for power databases, which includes at least the following steps: Step S1: Obtain the entities to be updated and their contents to be updated from the target power database; For step S1, in power data management and operation and maintenance, the power database to be updated is taken as the target power database. According to the preset update requirements, the business entities that will be added, modified or deleted in the target power database are determined. For example, the database table rows or objects corresponding to power businesses such as power generation equipment ledger, electricity metering data, and power grid operation parameter records.

[0020] Based on this, according to the update operations to be executed, the names of the fields that need to be changed, the new data content to be written, and the corresponding update methods in each business entity are determined. At the same time, the triggering conditions, operation subjects, data sources and other metadata information of this update operation are recorded, and finally a structured list of content to be updated that can be directly executed for the target power database is formed.

[0021] It should be noted that the power database described in this embodiment includes a customer database, a product database, a service database, and a knowledge database. The customer database stores basic information, electricity usage records, electricity usage behavior, and customer relationships related to power users, providing data support for user management, electricity services, and demand response. The product database records the attributes, parameters, specifications, configurations, and lifecycle information of power company-related products, equipment, materials, and supporting services, supporting product management, material scheduling, and equipment operation and maintenance. The service database collects various service work orders, service records, service processes, fault handling, and customer requests in the power business, enabling traceability, monitoring, and optimization of the service process. The knowledge database integrates power industry standards, technical standards, fault diagnosis knowledge, operation and maintenance experience, solutions, and professional knowledge base content, providing knowledge support for equipment operation and maintenance, fault handling, and business decision-making. These four types of power databases are interconnected and together constitute a power database system covering the entire power business process.

[0022] Step S2: After updating the target power database based on the entity to be updated and its content to be updated, determine the associated entity corresponding to the entity to be updated based on the dynamic association graph. The dynamic association graph is constructed in the following way: Obtain all native data from each power database and historical interaction records between the power databases; Extract the corresponding native semantic tags from the native data; Based on the preset ontology mapping rule base, the native semantic tags corresponding to the native data of each power database, and the historical interaction records between each power database, dynamic mapping rules are determined; the dynamic mapping rules include the correspondence between native semantic tags and global semantic identifiers; Based on the dynamic mapping rules, determine the global semantic identifier corresponding to the native semantic tag; Based on the global semantic identifiers corresponding to the original semantic tags, the original data is reconstructed to generate standardized data elements; Based on all standardized data elements, a dynamic association graph is constructed; For step S2, based on the entity to be updated and its content to be updated, a data update operation is performed on the target power database to complete the addition, modification or deletion of the corresponding business entity in the target power database, thereby realizing the data synchronization and update of the target power database.

[0023] After completing the database update, other related entities that are directly or indirectly related to the entity to be updated are queried and extracted from the pre-built dynamic association graph. These entities are then used as the associated entities corresponding to the entity to be updated, thereby clarifying the scope of association affected by the data update.

[0024] The dynamic association graph used to achieve accurate matching of related entities is constructed in the following steps: S201 to S206: Step S201: Obtain all raw data from each power database and historical interaction records between each power database; For step S201, firstly, through the cross-database data acquisition interface of the power data management and operation system, all native data in various power databases such as customer database, product database, service database and knowledge database are extracted, including basic attribute data, business characteristic data and identification data of various business entities.

[0025] Simultaneously, the system collects interaction records between various power databases generated during historical business operations, such as work order association records between the customer database and the service database, and equipment maintenance knowledge matching records between the product database and the knowledge database.

[0026] In a preferred embodiment, after obtaining all the raw data of each power database and the historical interaction records between the power databases, the method further includes: Data cleaning and unit normalization are performed on all raw data from various power databases to obtain preprocessed raw data.

[0027] In one embodiment of the present invention, in order to improve the accuracy and reliability of dynamic correlation graph construction, data cleaning and unit normalization are performed on the raw data in various power databases. Specifically, data cleaning removes missing values, outliers, duplicate values, and invalid redundant data from the raw data, while correcting format errors and character garbled characters generated during data entry to ensure the integrity, accuracy, and uniqueness of the raw data. Unit normalization unifies the measurement units of various power business data. For example, voltage, current, power, and other power parameters are uniformly converted to international standard units, and units for time, quantity, and other dimensions that are consistent across different databases are standardized and aligned, thereby eliminating data matching errors caused by inconsistent units. Finally, preprocessed raw data with standardized format, accurate data, and unified units is obtained.

[0028] Step S202: Extract the corresponding native semantic tags from the native data; For step S202, corresponding native semantic tags are extracted from the preprocessed native data. Specifically, this is done by parsing the field attributes, business meanings, and data characteristics of various types of power business data to match exclusive semantic tags for different types of native data. For example, equipment-related semantic tags such as "equipment type," "equipment number," and "substation" are extracted from the power generation equipment ledger data; metering-related semantic tags such as "user number," "metering period," and "electricity consumption" are extracted from the electricity metering data; and service-related semantic tags such as "work order type," "fault level," and "service object" are extracted from the service work order data. This transforms the structured native data into feature data with business semantic identifiers, giving the scattered native data a unified basis for association and identification.

[0029] In a preferred embodiment, extracting corresponding native semantic tags from native data includes: For each piece of raw data in each power database, perform modal classification on the raw data to determine the corresponding data type: Extract the native semantic tags corresponding to the native data based on the data type of the native data.

[0030] In a preferred embodiment, the data type includes: structured data, semi-structured data, or unstructured data; Based on the data type of the original data, extract the corresponding native semantic tags, including: Raw data whose data type belongs to structured data is defined as the first raw data; Raw data whose data type belongs to semi-structured data is defined as second raw data; Raw data whose data type belongs to unstructured data is defined as third raw data; For the first piece of raw data, read the field names, field meanings, or data attributes of the first piece of raw data, and extract the corresponding raw semantic tags based on the field names, field meanings, or data attributes of the first piece of raw data; For the second native data, read the tag fields, node attributes, or hierarchical structure of the second native data, and extract the native semantic tags corresponding to the second native data based on the tag fields, node attributes, or hierarchical structure of the second native data; For third-party native data, read the text content, key information fragments, or content themes of the third-party native data, and extract the corresponding native semantic tags based on the text content, key information fragments, or content themes of the third-party native data.

[0031] In one embodiment of the present invention, in order to further improve the accuracy and completeness of the extraction of native semantic tags, the present invention provides a preferred implementation method for the step of extracting the corresponding native semantic tags from the native data. That is, the native data is classified into modalities based on the data type, and a differentiated extraction method adapted to each data type is adopted to adapt to the semantic features of power data with different structural forms.

[0032] Specifically, firstly, each piece of raw data in each power database is modally classified. Based on the data organization form and structural characteristics, the raw data is divided into three categories: structured data, semi-structured data, and unstructured data. Among them, structured data refers to data with a fixed data table structure, clearly defined fields, standardized data format, and can be stored and read in a standardized manner through row and column relationships. Semi-structured data refers to data without a unified fixed data table structure, but with parsable identifiers, nodes, or hierarchical features. Unstructured data refers to data without a fixed structure, without standardized fields, and existing in free text or unformatted form. This achieves accurate classification of raw data with different organizational forms, providing a classification basis for subsequent extraction of raw semantic tags using differentiated methods.

[0033] In this embodiment, the data type distribution of each power database is shown in Table 1. As shown in Table 1, the data type distribution of each power database is not uniform, and there are significant differences in the data modalities contained in the customer database, product database, service database, and knowledge database. If a single semantic tag extraction method is used, there may be problems with the adaptation to certain data types. For example, directly extracting unstructured text by structured fields will yield no results, and extracting semi-structured data by keywords alone will lose hierarchical relationships, leading to missing tags or semantic deviations.

[0034] Table 1. Distribution of Data Types in Various Power Databases Based on the above classification results of data types, different types of raw data are defined. Specifically, raw data with structured data type is uniformly defined as first raw data, raw data with semi-structured data type is uniformly defined as second raw data, and raw data with unstructured data type is uniformly defined as third raw data.

[0035] For the first type of raw data, which is raw data with a structured data type, the field names, field meanings and data attributes of the first type of raw data can be read directly because it has clear field definitions and standardized storage structure. For example, from the structured records of the power generation equipment ledger, the corresponding equipment category semantic tags can be directly extracted based on the field names such as "equipment model", "rated capacity" and "commissioning date". For the second type of raw data, which is raw data with a semi-structured data type, although there is no fixed data structure, fields, types or relationships, there are parsable tag fields, node attributes and hierarchical relationships. Therefore, semantic tags with hierarchical logic, such as "fault level" and "processing priority", can be extracted by parsing the node names, attribute values ​​and nesting levels in formats such as JSON and XML. For third-generation raw data, which is unstructured raw data such as operation and maintenance logs and fault reports, natural language processing technology is used to read the text content, key information fragments and content themes of the third-generation raw data, and extract semantic tags such as "equipment fault phenomena" and "handling measures" from the free text.

[0036] The data type-based classification extraction method can avoid the omission of labels or semantic deviations caused by using a single extraction method for mixed modal data, thereby ensuring that the extracted native semantic labels fully cover the meaning of the data, and providing high-quality semantic support for the semantic construction of entity nodes and the mining of association relationships in the subsequent dynamic association graph.

[0037] Step S203: Determine dynamic mapping rules based on the preset ontology mapping rule base, the native semantic tags corresponding to the native data of each power database, and the historical interaction records between each power database; the dynamic mapping rules include the correspondence between native semantic tags and global semantic identifiers; For step S203, dynamic mapping rules are determined based on a pre-set ontology mapping rule base, the native semantic tags corresponding to the native data of each power database, and the historical interaction records between the power databases. Specifically, firstly, based on the power marketing business scenario, an ontology mapping rule base covering customer attributes, product attributes, service attributes, and knowledge attributes is pre-set. The ontology mapping rule base is a set of semantic specifications in the power business domain, used to define the standard semantic expressions of various business entities, attributes, and relationships.

[0038] Subsequently, by combining the native semantic tags corresponding to the native data of each power database, a deep analysis of historical interaction records was conducted to extract frequently occurring semantic association pairs and to explore the co-occurrence patterns and business association logic of native semantic tags between different databases.

[0039] Based on this, a domain ontology framework is constructed. Specifically, the pre-set ontology mapping rule base, various native semantic tags, and semantic associations mined from historical interaction records are integrated and summarized. According to the actual meaning of power marketing business, various business entities such as customers, products, services, and knowledge are classified and sorted. The subordinate and inclusion relationships of different entities under the same category are clarified. At the same time, according to the actual business process, the correspondence, association, and constraint relationships between different entities are determined, thereby forming hierarchical relationships and semantic associations between entities.

[0040] Based on the domain ontology framework, the native semantic tags of each power database are mapped to globally unified semantic identifiers, forming a correspondence between native semantic tags and global semantic identifiers, i.e., dynamic mapping rules.

[0041] This invention uses a domain ontology framework and dynamic mapping rules to uniformly map heterogeneous native semantic tags to a global semantic identifier system, achieving cross-library semantic consistency and interoperability.

[0042] Step S204: Determine the global semantic identifier corresponding to the native semantic tag according to the dynamic mapping rules; For step S204, the native semantic tags of each native data in each power database are used as matching inputs. The correspondence between the native semantic tags and the global semantic identifiers in the constructed dynamic mapping rules is compared one by one. Thus, for native semantic tags from different power databases that have different expression forms but consistent core business semantics, they are directly matched to the same global semantic identifier according to the dynamic mapping rules, thereby realizing the semantic alignment of native semantic tags across databases.

[0043] Step S205: Reconstruct the original data based on the global semantic identifiers corresponding to the original semantic tags to generate standardized data elements; For step S205, the original data is reconstructed based on the global semantic identifiers corresponding to the original semantic tags to generate standardized data elements. Specifically, based on the determined global semantic identifiers, format information unrelated to business semantics in the original data is stripped away, such as table structure constraints of structured data, nesting hierarchy identifiers of semi-structured data, and formatting of unstructured data, retaining only the core business content.

[0044] Subsequently, the core content is reconstructed according to a pre-set unified format, and each piece of raw data is converted into a standardized data element containing four elements: entity ID, attribute, value and global semantic identifier. The entity ID is used to uniquely identify the business object, the attribute and value correspond to the specific characteristics of the business object, and the global semantic identifier ensures that the semantics of the attribute remain consistent in cross-database scenarios.

[0045] Through the above data reconstruction, a unified representation of four types of power databases—customer database, product database, service database, and knowledge database—was achieved, transforming the originally heterogeneous and multimodal raw data into standardized data elements with unified semantics and structure.

[0046] Step S206: Construct a dynamic association graph based on all standardized data elements; In a preferred embodiment, the standardized data elements include: entity ID, attribute, value, and global semantic identifier; Based on all standardized data elements, a dynamic association graph is constructed, including: Extract the entity ID from all standardized data elements; Use the entity ID as a node in the dynamic association graph; The attributes, values, and global semantic identifiers corresponding to the entity ID are used as the associated attributes of the corresponding node; Based on the global semantic identifier of each standardized data element and the historical interaction records between each power database, the association relationship between nodes corresponding to different entity IDs is identified, and the association relationship is used as a directed edge in the dynamic association graph. Construct a dynamic association graph based on nodes, association attributes, and directed edges.

[0047] For step S206, a dynamic association graph is constructed based on all standardized data elements. Specifically, entity IDs are extracted from each standardized data element, and the entity IDs are used as independent nodes in the dynamic association graph. At the same time, the attributes, values, and global semantic identifiers corresponding to the entity IDs are used as the association attributes of the node to describe the business characteristics and semantic orientation of the node.

[0048] Subsequently, using global semantic identifiers as anchors, the historical interaction records between various power databases are traversed to filter out interaction data containing the same or related global semantic identifiers. Potentially related entity ID combinations are identified, and business behavior analysis is performed on the filtered interaction data. Combined with pre-defined power business logic, the corresponding business behavior type is determined. Based on the analyzed business behavior type, the association relationships between nodes corresponding to different entity IDs are defined, and these relationships are treated as directed edges in a dynamic association graph. For example, the association relationship between "customer entity ID" and "product entity ID" is defined as "ordering," with a directed edge pointing from the customer node to the product node; the association relationship between "service entity ID" and "customer entity ID" is defined as "response."

[0049] Finally, by integrating all nodes, associated attributes, and directed edges, a dynamic association graph covering all business scenarios of electricity marketing is constructed.

[0050] In a preferred embodiment, after constructing the dynamic association map in step S206, the method further includes: Obtain the data timestamp for each standardized data element; Get the current time in real time; Calculate the time decay coefficient based on the time difference between the data timestamp and the current time; By using the time decay coefficient as the weight of the directed edges in the dynamic association graph, a dynamic association graph with real-time decay is obtained.

[0051] In one embodiment of the present invention, in order to make the dynamic association graph better reflect the time-sensitive characteristics of business operations, time decay is introduced to dynamically adjust the weights of the association relationships. The specific process is as follows: First, obtain the timestamp of each standardized data element. This timestamp represents the time when the corresponding business event occurred, such as the time a customer orders a product, the time a service ticket is created, or the time a knowledge document is updated. Simultaneously, obtain the current time in real time as the benchmark for calculating the time difference.

[0052] Next, the time decay coefficient is calculated based on the time difference between the data timestamp and the current time. The larger the time difference, the further back in time the business event occurred, and the lower its impact on current business decisions; therefore, the smaller the corresponding time decay coefficient. Conversely, the smaller the time difference, the larger the time decay coefficient, thus reflecting the timeliness value of business connections.

[0053] Finally, the calculated time decay coefficient is used as the weight of the directed edges in the dynamic association graph to update the association relationships in the original graph in a weighted manner, thus obtaining a dynamic association graph with real-time decay.

[0054] In this embodiment, by introducing a weight adjustment based on the time dimension, the dynamic association graph can reflect the timeliness changes of business associations, avoiding interference from old data on current business analysis and enhancing the guiding significance of the dynamic association graph.

[0055] Step S3: Based on the associated entities and the corresponding content to be updated, perform collaborative updates on the remaining power database.

[0056] For step S3, when the entity information in any power database changes, the associated entities that are associated with the entity are located based on the dynamic association graph in step S2, and the associated power databases and associated entities that need to be updated synchronously are identified. Then, the content to be updated is converted into content that conforms to the format and semantic specifications of the associated power database according to the preset cross-database data synchronization rules.

[0057] It should be noted that the cross-database data synchronization rules are a set of pre-defined specifications for achieving data synchronization between different power databases. They define the triggering conditions for data updates, the conversion method of updated content, the writing format of the target database, and the semantic correspondence, ensuring that data from different databases can maintain a unified format and semantic consistency when updated.

[0058] Finally, the content to be updated is synchronously pushed to the corresponding associated power database to complete the batch update of associated entity information and ensure that the information of the same business entity in each power database remains consistent.

[0059] In this invention, when entity information in a certain database is updated, the corresponding information in other related databases can be automatically updated synchronously, avoiding the lag and error rate caused by manual maintenance, and effectively improving the overall accuracy and reliability of power marketing business data.

[0060] In a preferred embodiment, before collaboratively updating the remaining power database based on the associated entities and their corresponding content to be updated, the process includes: Real-time monitoring of the access frequency and associated trigger count of all related entities; For each associated entity, calculate the corresponding dynamic activity value based on the entity's access frequency and the number of association triggers; Calculate the corresponding comprehensive score based on the preset entity update priority and dynamic activity value; Sort all comprehensive scores from high to low, and determine the update order of related entities based on the sorting results; Based on the associated entities and the corresponding content to be updated, the remaining power database is updated collaboratively, including: Based on the associated entities, the corresponding update order, and the corresponding content to be updated, the remaining power databases are updated collaboratively.

[0061] In one embodiment of the present invention, to improve the efficiency of collaborative updates, a dynamic priority sorting is added before collaboratively updating the remaining power databases based on the associated entities and their corresponding content to be updated. The specific process is as follows: First, the access frequency and association trigger count of all related entities are monitored in real time. The access frequency reflects how often the entity is queried and called in the business scenario, while the association trigger count represents the frequency of business associations between the entity and other entities. Together, they reflect the activity level of the entity in the business process.

[0062] For each associated entity, the access frequency and the number of associated triggers are weighted and summed to calculate the corresponding dynamic activity value. The higher the access frequency and the more associated triggers, the larger the dynamic activity value, which means that the entity is more important and has a higher level of attention in the current business scenario.

[0063] Next, a comprehensive score is calculated by weighting and summing the preset entity update priority and the dynamic activity value. It should be noted that the preset entity update priority is used to distinguish the basic importance of different types of entities. For example, the basic priority of core business entities (such as customers and products) is higher than that of auxiliary entities (such as knowledge documents), while the dynamic activity value is used to reflect the real-time business popularity of the entity. The comprehensive score obtained by weighting the two can fully reflect the urgency of the entity's update.

[0064] Then, the overall scores of all related entities are sorted from high to low, and the update order of related entities is determined according to the sorting results. The higher the overall score, the earlier the update order, ensuring that the information of highly important entities can be updated first.

[0065] Finally, based on the associated entities, the corresponding update order, and the corresponding content to be updated, the remaining power databases are updated collaboratively. That is, according to the determined update order, the content to be updated is pushed to the corresponding associated power databases in sequence to complete the batch update.

[0066] In this embodiment, intelligent scheduling of collaborative updates is achieved through dynamic priority sorting, which avoids system resource consumption and business blockage caused by simultaneous updates of a large number of related entities. At the same time, it ensures that information of highly important entities can be synchronized first, improves the real-time performance of business data and decision support capabilities, and makes the collaborative update process more efficient and reasonable.

[0067] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided; One embodiment of the present invention provides a cross-database collaborative update device for a power database, comprising: a module for determining entities to be updated, a module for determining associated entities, and a cross-database collaborative update module; The module for determining entities to be updated is used to obtain the entities to be updated and their content to be updated from the target power database. The associated entity determination module is used to determine the associated entities corresponding to the entities to be updated based on the entities to be updated and their content to be updated, after updating the target power database, according to the dynamic association graph. The cross-database collaborative update module is used to collaboratively update other power databases based on related entities and the corresponding content to be updated. The associated entity determination module includes: an associated graph construction submodule; The association graph construction submodule includes: a native data acquisition unit, a semantic tag extraction unit, a mapping rule generation unit, a global semantic mapping unit, a data element generation unit, and a graph structure construction unit; The native data acquisition unit is used to acquire all native data from each power database and historical interaction records between the power databases. The semantic tag extraction unit is used to extract the corresponding native semantic tags from the native data; The mapping rule generation unit is used to determine dynamic mapping rules based on the preset ontology mapping rule library, the native semantic tags corresponding to the native data of each power database, and the historical interaction records between each power database; the dynamic mapping rules include the correspondence between native semantic tags and global semantic identifiers; The global semantic mapping unit is used to determine the global semantic identifier corresponding to the native semantic tag according to the dynamic mapping rules; The data element generation unit is used to reconstruct the original data based on the global semantic identifiers corresponding to the original semantic tags, and generate standardized data elements. The graph structure building unit is used to construct a dynamic association graph based on all standardized data elements.

[0068] In one embodiment of the present invention, the entity to be updated determination module takes the power database to be updated as the target power database and determines the business entities that will be added, modified or deleted in the target power database according to the preset update requirements. For example, database table rows or objects corresponding to power businesses such as power generation equipment ledgers, electricity metering data, and power grid operation parameter records.

[0069] Based on this, according to the update operations to be executed, the names of the fields that need to be changed, the new data content to be written, and the corresponding update methods in each business entity are determined. At the same time, the triggering conditions, operation subjects, data sources and other metadata information of this update operation are recorded, and finally a structured list of content to be updated that can be directly executed for the target power database is formed.

[0070] It should be noted that the power database described in this embodiment includes a customer database, a product database, a service database, and a knowledge database. The customer database stores basic information, electricity usage records, electricity usage behavior, and customer relationships related to power users, providing data support for user management, electricity services, and demand response. The product database records the attributes, parameters, specifications, configurations, and lifecycle information of power company-related products, equipment, materials, and supporting services, supporting product management, material scheduling, and equipment operation and maintenance. The service database collects various service work orders, service records, service processes, fault handling, and customer requests in the power business, enabling traceability, monitoring, and optimization of the service process. The knowledge database integrates power industry standards, technical standards, fault diagnosis knowledge, operation and maintenance experience, solutions, and professional knowledge base content, providing knowledge support for equipment operation and maintenance, fault handling, and business decision-making. These four types of power databases are interconnected and together constitute a power database system covering the entire power business process.

[0071] The associated entity determination module performs data update operations on the target power database based on the entity to be updated and its content to be updated, completing the addition, modification or deletion of the corresponding business entity in the target power database, and realizing data synchronization and updating of the target power database.

[0072] After completing the database update, other related entities that are directly or indirectly related to the entity to be updated are queried and extracted from the pre-built dynamic association graph. These entities are then used as the associated entities corresponding to the entity to be updated, thereby clarifying the scope of association affected by the data update.

[0073] In the associated entity determination module, the dynamic association graph used to achieve accurate matching of associated entities is constructed through the association graph construction submodule.

[0074] In the association graph construction submodule, the native data acquisition unit extracts all native data from various power databases, including customer database, product database, service database, and knowledge database, including basic attribute data, business characteristic data, and identification data of various business entities.

[0075] Simultaneously, the system collects interaction records between various power databases generated during historical business operations, such as work order association records between the customer database and the service database, and equipment maintenance knowledge matching records between the product database and the knowledge database.

[0076] The semantic tag extraction unit extracts corresponding native semantic tags from the preprocessed native data. Specifically, it analyzes the field attributes, business meanings, and data characteristics of various types of power business data to match exclusive semantic tags for different types of native data. For example, it extracts equipment-related semantic tags such as "equipment type," "equipment number," and "substation" from power generation equipment ledger data; it extracts metering-related semantic tags such as "user number," "metering period," and "electricity consumption" from electricity metering data; and it extracts service-related semantic tags such as "work order type," "fault level," and "service recipient" from service work order data. This transforms the structured native data into feature data with business semantic identifiers, giving the scattered native data a unified basis for association and identification.

[0077] Through the mapping rule generation unit, dynamic mapping rules are determined based on a preset ontology mapping rule library, native semantic tags corresponding to native data in each power database, and historical interaction records between power databases. Specifically, firstly, based on the power marketing business scenario, an ontology mapping rule library covering customer attributes, product attributes, service attributes, and knowledge attributes is pre-set. The ontology mapping rule library is a set of semantic specifications in the power business domain, used to define standard semantic expressions for various business entities, attributes, and relationships.

[0078] Subsequently, by combining the native semantic tags corresponding to the native data of each power database, a deep analysis of historical interaction records was conducted to extract frequently occurring semantic association pairs and to explore the co-occurrence patterns and business association logic of native semantic tags between different databases.

[0079] Based on this, a domain ontology framework is constructed. Specifically, the pre-set ontology mapping rule base, various native semantic tags, and semantic associations mined from historical interaction records are integrated and summarized. According to the actual meaning of power marketing business, various business entities such as customers, products, services, and knowledge are classified and sorted. The subordinate and inclusion relationships of different entities under the same category are clarified. At the same time, according to the actual business process, the correspondence, association, and constraint relationships between different entities are determined, thereby forming hierarchical relationships and semantic associations between entities.

[0080] Based on the domain ontology framework, the native semantic tags of each power database are mapped to globally unified semantic identifiers, forming a correspondence between native semantic tags and global semantic identifiers, i.e., dynamic mapping rules.

[0081] The mapping rule generation unit uses the domain ontology framework and dynamic mapping rules to uniformly map heterogeneous native semantic tags to the global semantic identifier system, achieving cross-library semantic consistency and interoperability.

[0082] By using the global semantic mapping unit, the native semantic tags of each native data in each power database are used as matching inputs. The correspondence between the native semantic tags and the global semantic identifier in the constructed dynamic mapping rules is compared one by one. Thus, for native semantic tags from different power databases that have different forms of expression but consistent core business semantics, they are directly matched to the same global semantic identifier according to the dynamic mapping rules, thereby realizing the semantic alignment of native semantic tags across databases.

[0083] Through the data element generation unit, the original data is reconstructed based on the global semantic identifiers corresponding to the original semantic tags to generate standardized data elements. Specifically, based on the determined global semantic identifiers, format information unrelated to business semantics in the original data is stripped away, such as table structure constraints of structured data, nesting hierarchy identifiers of semi-structured data, and formatting of unstructured data, retaining only the core business content.

[0084] Subsequently, the core content is reconstructed according to a pre-set unified format, and each piece of raw data is converted into a standardized data element containing four elements: entity ID, attribute, value and global semantic identifier. The entity ID is used to uniquely identify the business object, the attribute and value correspond to the specific characteristics of the business object, and the global semantic identifier ensures that the semantics of the attribute remain consistent in cross-database scenarios.

[0085] Through data reconstruction using data element generation units, a unified representation of four types of power databases—customer database, product database, service database, and knowledge database—is achieved, transforming the originally heterogeneous and multimodal raw data into standardized data elements with unified semantics and structure.

[0086] By constructing graph structure units, a dynamic association graph is built based on all standardized data elements.

[0087] In the cross-database collaborative update module, when the entity information in any power database changes, the associated entity determination module locates the associated entities based on the dynamic association graph, identifies the associated power databases and associated entities that need to be updated synchronously, and then in the cross-database collaborative update module, the content to be updated is converted into adapted content that conforms to the format and semantic specifications of the associated power database according to the preset cross-database data synchronization rules.

[0088] It should be noted that the cross-database data synchronization rules are a set of pre-defined specifications for achieving data synchronization between different power databases. They define the triggering conditions for data updates, the conversion method of updated content, the writing format of the target database, and the semantic correspondence, ensuring that data from different databases can maintain a unified format and semantic consistency when updated.

[0089] Finally, the content to be updated is synchronously pushed to the corresponding associated power database to complete the batch update of associated entity information and ensure that the information of the same business entity in each power database remains consistent.

[0090] Through the cross-database collaborative update module, when the entity information in one database is updated, the corresponding information in other related databases can be automatically updated synchronously, avoiding the lag and error rate caused by manual maintenance, and effectively improving the overall accuracy and reliability of power marketing business data.

[0091] In a preferred embodiment, the association map construction submodule further includes: a raw data preprocessing unit; The raw data preprocessing unit is used to perform data cleaning and unit normalization on all raw data from various power databases to obtain preprocessed raw data.

[0092] In a preferred embodiment, the semantic tag extraction unit includes: a data classification subunit and a classification extraction subunit; The data classification subunit is used to perform modal classification on each piece of raw data in each power database to determine the corresponding data type. The classification extraction sub-unit is used to extract the native semantic tags corresponding to the native data based on the data type of the native data.

[0093] In a preferred embodiment, the data type includes: structured data, semi-structured data, or unstructured data; The classification extraction sub-unit includes: a first data definition component, a second data definition component, a third data definition component, a first data label extraction component, a second data label extraction component, and a third data label extraction component; The first data definition component is used to define raw data whose data type belongs to structured data as first raw data; The second data definition component is used to define raw data whose data type belongs to semi-structured data as second raw data; The third data definition component is used to define raw data whose data type belongs to unstructured data as third raw data; The first data tag extraction component is used to read the field names, field meanings, or data attributes of the first raw data, and extract the corresponding raw semantic tags based on the field names, field meanings, or data attributes of the first raw data. The second data tag extraction component is used to read the tag fields, node attributes, or hierarchical structure of the second raw data, and extract the corresponding native semantic tags based on the tag fields, node attributes, or hierarchical structure of the second raw data. The third data tag extraction component is used to read the text content, key information fragments, or content themes of the third native data, and extract the corresponding native semantic tags based on the text content, key information fragments, or content themes of the third native data.

[0094] In a preferred embodiment, the standardized data elements include: entity ID, attribute, value, and global semantic identifier; The graph structure construction unit includes: entity extraction subunit, graph node determination subunit, graph attribute determination subunit, graph relationship determination subunit, and graph integration subunit; The entity extraction subunit is used to extract entity IDs from all standardized data elements; Graph nodes determine sub-units, which are used to use entity IDs as nodes in the dynamic association graph; The graph attribute determination sub-unit is used to use the attributes, values, and global semantic identifiers corresponding to the entity ID as the associated attributes of the corresponding node; The graph relationship determination subunit is used to identify the association between nodes corresponding to different entity IDs based on the global semantic identifier of each standardized data element and the historical interaction records between each power database, and to treat the association as directed edges in the dynamic association graph. The graph integration subunit is used to construct a dynamic association graph based on nodes, association attributes, and directed edges.

[0095] In a preferred embodiment, the spectral structure building unit further includes: a time decay weighting subunit; The time decay weighting subunit includes: a timestamp acquisition component, a current time acquisition component, a time decay calculation component, and a decay weighting component; The timestamp acquisition component is used to obtain the data timestamp of each standardized data element; The current time acquisition component is used to obtain the current time in real time. The time decay calculation component is used to calculate the time decay coefficient based on the time difference between the data timestamp and the current time. The decay weighting component is used to apply the time decay coefficient as the weight of the directed edges in the dynamic association graph, thereby obtaining a dynamic association graph with real-time decay.

[0096] In a preferred embodiment, the cross-database collaborative update apparatus further includes: an update order determination module; The update order determination module includes: associated entity monitoring submodule, activity value calculation submodule, comprehensive score calculation submodule, and update order sorting submodule; The associated entity monitoring submodule is used to monitor the access frequency and association trigger count of all associated entities in real time. The activity value calculation submodule is used to calculate the corresponding dynamic activity value for each associated entity based on the entity's access frequency and the number of associated triggers. The comprehensive score calculation submodule is used to calculate the corresponding comprehensive score based on the preset entity update priority and dynamic activity value; The update order sorting submodule is used to sort all comprehensive scores from high to low, and determine the update order of related entities based on the sorting results.

[0097] In a preferred embodiment, the remaining power database is collaboratively updated based on the associated entities and the corresponding content to be updated, including: Based on the associated entities, the corresponding update order, and the corresponding content to be updated, the remaining power databases are updated collaboratively.

[0098] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the cross-database collaborative update method for the power database provided by any of the above-described method embodiments of the present invention.

[0099] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0100] Based on the above embodiments of the cross-database collaborative update method for power databases, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the cross-database collaborative update method for power databases according to any embodiment of the present invention.

[0101] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0102] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0103] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting various parts of the terminal device via various interfaces and lines.

[0104] Based on the above-described method embodiments, another embodiment is provided: another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the cross-database collaborative update method for the power database described in any of the above-described method embodiments of the present invention.

[0105] The modules / units integrated into the cross-database collaborative update device / terminal equipment of the power database, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0106] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A cross-database collaborative update method for an electricity database, characterized in that, include: Obtain the entities to be updated and their contents to be updated from the target power database; After updating the target power database based on the entity to be updated and its content to be updated, the associated entities corresponding to the entity to be updated are determined based on the dynamic association graph. Based on the associated entities and the corresponding content to be updated, the remaining power databases are updated collaboratively. The dynamic association graph is constructed in the following way: Obtain all native data from each power database and historical interaction records between the power databases; Extract the corresponding native semantic tags from the native data; Based on the preset ontology mapping rule base, the native semantic tags corresponding to the native data of each power database, and the historical interaction records between each power database, dynamic mapping rules are determined; the dynamic mapping rules include the correspondence between native semantic tags and global semantic identifiers; Based on the dynamic mapping rules, determine the global semantic identifier corresponding to the native semantic tag; Based on the global semantic identifiers corresponding to the original semantic tags, the original data is reconstructed to generate standardized data elements; A dynamic association graph is constructed based on all standardized data elements.

2. The cross-database collaborative update method for power databases according to claim 1, characterized in that, After obtaining all the raw data from each power database and the historical interaction records between the power databases, it also includes: Data cleaning and unit normalization are performed on all raw data from various power databases to obtain preprocessed raw data.

3. The cross-database collaborative update method for a power database according to claim 1, characterized in that, Extract the corresponding native semantic tags from the native data, including: For each piece of raw data in each power database, perform modal classification on the raw data to determine the corresponding data type: Extract the native semantic tags corresponding to the native data based on the data type of the native data.

4. The cross-database collaborative update method for power databases according to claim 3, characterized in that, The data types include: structured data, semi-structured data, or unstructured data; The step of extracting native semantic tags corresponding to the native data based on the data type of the native data includes: Raw data whose data type belongs to structured data is defined as the first raw data; Raw data whose data type belongs to semi-structured data is defined as second raw data; Raw data whose data type belongs to unstructured data is defined as third raw data; For the first piece of raw data, read the field names, field meanings, or data attributes of the first piece of raw data, and extract the corresponding raw semantic tags based on the field names, field meanings, or data attributes of the first piece of raw data; For the second native data, read the tag fields, node attributes, or hierarchical structure of the second native data, and extract the native semantic tags corresponding to the second native data based on the tag fields, node attributes, or hierarchical structure of the second native data; For third-party native data, read the text content, key information fragments, or content themes of the third-party native data, and extract the corresponding native semantic tags based on the text content, key information fragments, or content themes of the third-party native data.

5. The cross-database collaborative update method for a power database according to claim 1, characterized in that, The standardized data elements include: entity ID, attributes, values, and global semantic identifiers; The construction of a dynamic association graph based on all standardized data elements includes: Extract the entity ID from all standardized data elements; Use the entity ID as a node in the dynamic association graph; The attributes, values, and global semantic identifiers corresponding to the entity ID are used as the associated attributes of the corresponding node; Based on the global semantic identifier of each standardized data element and the historical interaction records between each power database, the association relationship between nodes corresponding to different entity IDs is identified, and the association relationship is used as a directed edge in the dynamic association graph. Construct a dynamic association graph based on nodes, association attributes, and directed edges.

6. The cross-database collaborative update method for a power database according to claim 5, characterized in that, After constructing the dynamic association graph, the following is also included: Obtain the data timestamp for each standardized data element; Get the current time in real time; Calculate the time decay coefficient based on the time difference between the data timestamp and the current time; Using the time decay coefficient as the weight of the directed edge in the dynamic association graph, a dynamic association graph with real-time decay is obtained.

7. The cross-database collaborative update method for a power database according to claim 1, characterized in that, Before performing collaborative updates on the remaining power database based on the associated entities and their corresponding content to be updated, the following steps are included: Real-time monitoring of the access frequency and associated trigger count of all related entities; For each associated entity, calculate the corresponding dynamic activity value based on the entity's access frequency and the number of association triggers; Calculate the corresponding comprehensive score based on the preset entity update priority and dynamic activity value; Sort all comprehensive scores from high to low, and determine the update order of related entities based on the sorting results; The step of collaboratively updating the remaining power database based on associated entities and corresponding content to be updated includes: Based on the associated entities, the corresponding update order, and the corresponding content to be updated, the remaining power databases are updated collaboratively.

8. A cross-database collaborative update device for an electricity database, characterized in that, include: The module for determining entities to be updated, the module for determining associated entities, and the module for cross-database collaborative updates; The entity to be updated determination module is used to obtain the entities to be updated and their content to be updated from the target power database. The associated entity determination module is used to update the target power database based on the entity to be updated and its content to be updated, and then determine the associated entity corresponding to the entity to be updated based on the dynamic association graph. The cross-database collaborative update module is used to collaboratively update other power databases based on associated entities and corresponding content to be updated. The associated entity determination module includes: an association graph construction submodule; The association graph construction submodule includes: a native data acquisition unit, a semantic tag extraction unit, a mapping rule generation unit, a global semantic mapping unit, a data element generation unit, and a graph structure construction unit; The native data acquisition unit is used to acquire all native data from each power database and historical interaction records between each power database. The semantic tag extraction unit is used to extract the corresponding native semantic tags from the native data; The mapping rule generation unit is used to determine dynamic mapping rules based on a preset ontology mapping rule library, native semantic tags corresponding to the native data of each power database, and historical interaction records between each power database; the dynamic mapping rules include the correspondence between native semantic tags and global semantic identifiers; The global semantic mapping unit is used to determine the global semantic identifier corresponding to the native semantic tag according to the dynamic mapping rules; The data element generation unit is used to reconstruct the original data based on the global semantic identifier corresponding to the original semantic tag and generate standardized data elements. The graph structure construction unit is used to construct a dynamic association graph based on all standardized data elements.

9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the cross-database collaborative update method for a power database as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the cross-database collaborative update method for the power database as described in any one of claims 1-7.

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