Electric power omnibearing metadata management system

Abstract

The invention relates to an electric power omnibearing metadata management system which comprises a metadata resource management module, a metadata maintenance module, a metadata evaluation module and a metadata monitoring management module. The metadata resource management module is used for collecting all metadata in a service system and performing classification management on the metadata; the metadata maintenance module is used for updating and maintaining metadata; the metadata evaluation module is used for evaluating metadata by defining an evaluation standard, performing data asset registration on the evaluated metadata and storing the data asset in a digital asset directory; and the metadata monitoring management module is used for monitoring the classified metadata and performing early warning when abnormal data is monitored.

Description

Technical Field

[0001] The present invention relates to a comprehensive power metadata management system in the field of power data management and calculation. Background Art

[0002] Currently, power enterprises have realized the effective collection, storage and management of metadata by adopting metadata management tools. It can automatically obtain metadata in the technical dimension, such as data models, databases, tables, fields, etc., and support manual sorting of metadata in the business dimension, such as data processing strategies, security levels, etc.

[0003] In the power industry, with the continuous improvement of the degree of informatization, the metadata generated and used by power enterprises is increasing day by day; traditional metadata management methods often have problems such as information islands, data redundancy, and inconsistencies, making it difficult to meet the needs of power enterprises for data integration, sharing and management;

[0004] Different power business systems (such as substation monitoring systems, dispatching automation systems, and power metering systems) manage their own metadata respectively, and these data are stored in different databases with various formats, making it difficult to effectively integrate.

[0005] At the same time, due to inconsistent field definitions in different systems, the record formats of the same device in different databases are different, and even the data values are inconsistent. There is a lack of a global data governance strategy, making it difficult to effectively clean and optimize redundant data.

[0006] There is a lack of a comprehensive and systematic metadata management solution in the prior art, resulting in many challenges for power enterprises in data processing and decision support. Summary of the Invention

[0007] In order to solve the problems existing in the above prior art, the present invention proposes a comprehensive power metadata management system.

[0008] The technical solution of the present invention is as follows:

[0009] A comprehensive power metadata management system includes a metadata resource management module, a metadata maintenance module, a metadata evaluation module, and a metadata monitoring and management module;

[0010] The metadata resource management module is used to collect all metadata in the business system and classify and manage the metadata at the same time;

[0011] The metadata maintenance module is used to update and maintain the metadata;

[0012] The metadata evaluation module is used to evaluate the metadata by defining evaluation criteria, and register and store the evaluated metadata in the digital asset directory as data assets;

[0013] The metadata monitoring and management module is used to monitor the classified metadata and give early warnings when abnormal data is detected.

[0014] As a preferred embodiment of the present invention, the system further includes a digital asset catalog management module;

[0015] The digital asset catalog management module is used to generate a data asset ledger based on the digital asset data in the digital asset catalog and manage the data asset ledger.

[0016] As a preferred embodiment of the present invention, the metadata resource management module stores the collected metadata in a database;

[0017] The metadata resource management module creates different sub-databases in the database for storage according to the metadata type.

[0018] As a preferred embodiment of the present invention, the metadata maintenance module includes a metadata update unit and a metadata maintenance unit;

[0019] The metadata update unit is used to update the definition, structure, relationship, and mapping rules of the metadata. At the same time, each time the metadata is updated, the old version will be retained;

[0020] The metadata maintenance unit is used to automatically classify and manage the metadata according to the preset metadata importance, and at the same time allows the preset management account to manually adjust the metadata classification.

[0021] As a preferred embodiment of the present invention, the metadata evaluation module includes an evaluation unit and a digital asset registration unit;

[0022] The evaluation unit evaluates the metadata based on six criteria for lake entry and puts forward corresponding improvement suggestions for the metadata that does not meet the criteria according to the evaluation results;

[0023] The digital asset registration unit is used to register the metadata that meets the criteria as a digital asset and store it in the digital asset catalog.

[0024] As a preferred embodiment of the present invention, the metadata monitoring and management module detects abnormal data in the database through a change detection algorithm, as shown in the following formula:

[0025]

[0026] Where: S t represents the change score of the metadata at time t; X t represents the metadata value at time t; represents the time derivative of the metadata change; fi (X t ) represents the i-th feature of the metadata extracted based on the machine learning model; w i represents the weight of the i-th feature; N represents the total number of features of the metadata; X t-1 represents the metadata value at time t - 1; α, β, γ, λ represent the weight parameters of the anomaly detection algorithm;

[0027] A preset anomaly score threshold. When the anomaly score exceeds the preset threshold, it is determined that the metadata at the current moment has an anomaly and a warning is issued.

[0028] As a preferred embodiment of the present invention, the features of the metadata include an anomaly degree feature and a clustering center offset feature;

[0029] The anomaly degree feature is specifically shown as the following formula:

[0030]

[0031] Where: LOF K (X t ) represents the anomaly degree feature of X t under the K-nearest data points; lrd K represents the local reachability density under the K-nearest data points; X k represents the K-th nearest data point of X t ; dis(X t , X k ) represents the distance between X k and X t ; ∈ represents a regulation parameter.

[0032] As a preferred embodiment of the present invention, the calculation formula of the clustering center offset feature is:

[0033] ΔC j (X t ) = (||X t - C j,t || - ||X t-1 - C j,t-1 ||0 × τ(LOF K (X t ))

[0034] Where: ΔC j (X t ) represents the offset of X t from the clustering center; C j,t represents the value of C j at time t; τ represents a balance coefficient.

[0035] The present invention has the following beneficial effects:

[0036] 1. The present invention can comprehensively cover and efficiently manage various databases in the power business system, realize metadata scheduling and collection by adapting to different databases, and ensure the centralized management and dynamic update of technical metadata.

[0037] 2. The present invention can realize real-time monitoring and closed-loop disposal of metadata, and ensure the stability and reliability of data resources.

[0038] 3. The present invention can intelligently evaluate metadata to ensure that the metadata quality meets the enterprise requirements and improve the management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of the system modules of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0041] It should be understood that the step numbers used in the text are only for convenient description and do not limit the execution order of the steps.

[0042] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0043] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0044] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0045] Embodiment 1:

[0046] Referring to Figure 1 , a comprehensive power metadata management system includes a metadata resource management module, a metadata maintenance module, a metadata evaluation module, and a metadata monitoring and management module;

[0047] The metadata resource management module is used to collect all metadata in the business system and classify and manage the metadata at the same time;

[0048] The metadata maintenance module is used to update and maintain the metadata;

[0049] The metadata evaluation module is used to evaluate the metadata by defining evaluation criteria, and register the evaluated metadata as data assets and store them in the digital asset directory;

[0050] The metadata monitoring and management module is used to monitor the classified metadata and give early warnings when abnormal data is detected.

[0051] In this embodiment, the metadata includes technical metadata, business metadata, and management metadata;

[0052] 1. Technical metadata

[0053] Technical metadata is the technical cornerstone in data management. It covers the definitions of data objects and data structures in all business systems, as well as core information such as data relationships and mappings from source data to target data.

[0054] Data object and structure definition: Technical metadata details basic information such as the system standard name and system standard code, as well as the business meanings of the data source name, database name, and database instance name. At the same time, it also clarifies the database type (such as Oracle, MySQL, PostgreSQL, etc.) and the distribution of on-cloud / under-cloud databases. Database object types (such as tables, views, indexes, etc.) and their attributes (such as creation time, last update time) are also detailedly recorded, providing strong support for the lifecycle management of data.

[0055] Data relationship and mapping: Technical metadata also involves the association relationships between data tables, such as foreign key constraints and index relationships. These relationships provide important bases for data query, analysis, and integration. In addition, the mapping rules from source data to target data are also an important part of technical metadata, which ensures the accurate transmission and conversion of data between different systems or different levels.

[0056] Metadata attributes: Technical metadata also includes metadata attributes such as the number of table records, the number of fields, and the table size (MB). These attributes provide an intuitive description of the data scale. The anomaly monitoring flag is used to identify whether the data has undergone abnormal changes, so as to take timely measures for correction.

[0057] 2. Business metadata

[0058] Business metadata is the bridge connecting technology and business. It mainly focuses on the business meanings and uses of data, providing important support for the business understanding and application of data.

[0059] Business Terms and Descriptions: Business metadata details basic information such as the Chinese name of the table and the business description of the table, providing strong support for the intuitive understanding of data. At the same time, attributes such as table type (such as transaction table, statistical table, etc.) and whether it is a valid table identify the nature and usage status of the data.

[0060] Business Rules and Classifications: Business metadata also involves key information such as business rules and information classifications. Table type codes and business themes, etc., define the business classification and attribution of the data; function menu page paths and directories, etc., provide access paths and navigation support for the data. These information helps users quickly locate and understand the data, improving the usage efficiency of the data.

[0061] Data Security and Compliance: Business metadata also contains sensitive information such as data security classifications and data levels, as well as compliance information such as whether it is on the negative list, the type of negative list and its coding. These information are of great significance for ensuring the compliance and security of the data. At the same time, attributes such as professional fields and table-level business departments provide information on the business attribution and division of responsibilities of the data.

[0062] Business Associations and Tags: Business metadata also involves key information such as data tags and business tags. These data tags and business tags provide strong support for the multi-dimensional classification and retrieval of the data. At the same time, attributes such as whether it is an authoritative data source and the authoritative source system ensure the accuracy and reliability of the data.

[0063] 3. Management Metadata

[0064] Management metadata is the baton in data management, which mainly focuses on data management processes, division of responsibilities and rule-making.

[0065] Data Management Responsibilities: Management metadata clarifies the division of responsibilities and management authorities of the data management owner and the data production owner. At the same time, attributes such as the data fetching logic of the production owner provide the acquisition method and processing rules of the data, providing strong support for the production and maintenance of the data.

[0066] Data Structure and Source: Management metadata also involves key information such as data structure classification and data source classification. These classification information helps users quickly understand and use the data, improving the usability and maintainability of the data. At the same time, attributes such as whether it is connected to the middle platform identify whether the data is integrated into the enterprise's data platform system, providing strong support for data integration and sharing.

[0067] Data hierarchy and publishing: Management metadata also includes the storage and access information of data such as the source layer project space and the source layer table name, as well as the storage and access information of shared data such as the shared layer project space and the shared layer table name. At the same time, attributes such as the data supermarket release path provide data release and distribution channels, providing strong support for data sharing and reuse.

[0068] Data status and maintenance: Management metadata also records key information such as the data registration status and table-level maintenance progress. This information helps users quickly understand the current status and maintenance progress of the data, and improves the efficiency and maintainability of data use. At the same time, attributes such as the lake entry status indicate whether the data has been included in the enterprise's data lake or data warehouse storage system, providing strong support for long-term data preservation and query.

[0069] In this embodiment, when the system adapts to the source business system, it can fully support a variety of mainstream relational databases including Oracle, Mysql, Postgresql, as well as database types such as Odps (Open Data Processing Service) for big data scenarios, ensuring wide compatibility and flexibility.

[0070] As a preferred implementation of this embodiment, the system further includes a data digital asset catalog management module;

[0071] The digital asset catalog management module is used to generate a data asset ledger based on the digital asset data in the digital asset catalog and manage the data asset ledger. The ledger information includes but is not limited to the asset name, asset type, business system, metadata description, value assessment results, etc.

[0072] The digital asset catalog management module also supports the management of middle-end data ledgers, including the following functions:

[0073] Classification display: This system supports classification display of the middle office structured data ledger information according to dimensions such as professional departments, business systems, and middle office project spaces, helping users quickly locate the required data resources.

[0074] Resource distribution analysis: This system clarifies the distribution of data resources in the middle platform by analyzing the structured data ledger information of the middle platform, including information on data volume, data type, data quality, etc., to provide decision support for the optimization and management of data resources.

[0075] The digital asset catalog management module also provides data service account management, specifically including the following functions:

[0076] Data service sorting: Sort out all data service results such as tags, metrics, reports, and data sets, and clarify information such as the name, description, type, and business scenario to which the data service belongs.

[0077] Service catalog construction: Based on the sorted data service results, this system supports the construction of a data service catalog, providing a unified management and query entry for data services. The catalog information should include but not be limited to service name, service description, service type, department to which it belongs, responsible person, etc.

[0078] Data supermarket publishing: Publish the constructed data service catalog to the data supermarket to achieve visual display and online application of data services. At the same time, provide an evaluation and feedback mechanism for data services to promote the continuous optimization and improvement of data services.

[0079] Service application and approval: Establish an application and approval process for data services to standardize the use and management of data services. Users can apply for the required data services in the data supermarket according to business needs and obtain corresponding data access permissions after approval.

[0080] As a preferred implementation manner of this embodiment, the metadata resource management module stores the collected metadata in a database, and at the same time supports only collecting new or updated data that has changed since the last collection, thereby greatly reducing the time cost and resource consumption of data synchronization.

[0081] The metadata resource management module creates different sub-databases in the database for storage according to the metadata type;

[0082] The data table field information covers field names, data types (such as integer, floating point, string, etc.), value ranges (such as date format, numerical range), detailed descriptions of fields (i.e., the business meaning and use of fields), and crucial primary and foreign key relationships, which define the association logic between data entities and provide a solid foundation for data governance and analysis.

[0083] As a preferred implementation manner of this embodiment, the metadata maintenance module includes a metadata update unit and a metadata maintenance unit;

[0084] The metadata update unit is used to update the definition, structure, relationships, and mapping rules of metadata. At the same time, each time metadata is updated, the old version is retained for backtracking or comparison when needed;

[0085] The metadata maintenance unit is used to automatically classify and manage metadata according to the preset metadata importance (which can also be sensitivity, business value, etc.), and at the same time allows a preset management account to manually adjust the metadata classification (by restricting account login, it helps to ensure data accuracy and consistency, and at the same time improves data maintainability);

[0086] Hierarchical classification helps users quickly find the required data while ensuring that sensitive data is properly protected;

[0087] Meanwhile, the metadata maintenance module also supports users in identifying authoritative data sources, i.e., the most accurate and reliable sources of data; this helps avoid data conflicts and duplications while enhancing the credibility of the data.

[0088] As a preferred implementation manner of this embodiment, the metadata evaluation module includes an evaluation unit and a digital asset registration unit;

[0089] The evaluation unit evaluates the metadata based on six criteria for data lake entry and provides corresponding improvement suggestions for the metadata that does not meet the criteria according to the evaluation results;

[0090] The six criteria for data lake entry are specifically as follows:

[0091] 1) Data owner responsibility

[0092] Standard requirement: Clearly define the data owner for each data asset, i.e., the person or team responsible for data maintenance, update, interpretation, and quality control.

[0093] Evaluation method: Through the data owner information recorded in the system, combined with data usage and feedback, verify whether the data owner fulfills their responsibilities to ensure the accuracy and timeliness of the data.

[0094] 2) Negative list management

[0095] Standard requirement: Establish a negative list to clarify which data is sensitive or restricted, as well as the corresponding access and usage rules.

[0096] Evaluation method: Check whether the system has set up a negative list and verify whether the negative list is strictly enforced to ensure the security and compliance of sensitive data.

[0097] 3) Authoritative data source identification

[0098] Standard requirement: Identify the authoritative source of the data to ensure the accuracy and reliability of the data.

[0099] Evaluation method: Through the data source information recorded in the system, combined with the results of data quality monitoring, verify whether the data source is authoritative and whether the data transmission between different systems is consistent.

[0100] 4) Classification and hierarchical management

[0101] Standard requirement: Classify and hierarchically manage the data according to the business value, sensitivity, and usage scenarios of the data.

[0102] Evaluation method: Check whether the system has established a classification and grading system, and verify the accuracy and rationality of the classification and grading to ensure the effective management and utilization of data resources.

[0103] 5) Maintenance of the relationship between business data and technical data

[0104] Standard requirements: Maintain the association relationship between business metadata and technical metadata to ensure the consistency between the business meaning of data and its technical implementation.

[0105] Evaluation method: Through the business-data relationship information recorded by the system, combined with data usage and feedback, verify whether the business-data relationship is accurate, complete, and consistent.

[0106] 6) Standardization of table descriptions

[0107] Standard requirements: Provide clear, accurate, and standardized table descriptions, including the Chinese name of the table, business description, field meaning, etc.

[0108] Evaluation method: Check the table description information recorded by the system and verify whether it meets the standardization requirements to ensure that users can quickly understand the meaning and use of the data.

[0109] The digital asset registration unit is used to register the metadata that meets the standards as digital assets and store them in the digital asset catalog. The data asset catalog can provide clear classification and search functions to facilitate users to quickly find the required data resources.

[0110] As a preferred implementation manner of this embodiment, the metadata monitoring and management module detects abnormal data in the database through a change detection algorithm, as shown in the following formula:

[0111]

[0112] Where: S t represents the change score of the metadata at time t; X t represents the metadata value at time t; represents the time derivative of the metadata change; f i (X t ) represents the i-th feature of the metadata extracted based on the machine learning model; w i represents the weight of the i-th feature; N represents the total number of metadata features; X t-1 represents the metadata value at time t-1; α, β, γ, λ represent the weight parameters of the change detection algorithm;

[0113] Set a preset abnormal score threshold. When the abnormal score exceeds the preset threshold, it is determined that the metadata at the current moment has an abnormality, and a warning is issued. Through various methods such as emails, text messages, and system messages, the data operation personnel are notified in a timely manner. The warning information includes the detailed information of the abnormality, the possible impacts, and the recommended handling measures.

[0114] To facilitate the data operation personnel to quickly understand and respond to the abnormal information, this system provides a visual display interface, including an abnormal list, an abnormal trend chart, data comparison, etc., so that the operation personnel can intuitively understand the abnormality of the metadata.

[0115] To improve the pertinence and effectiveness of the notification, this system supports the function of information customization. The operation personnel can set the content, format, frequency, etc. of the notification according to their own needs and preferences.

[0116] To help the operation personnel better understand and handle the metadata abnormality, the system can provide relevant knowledge bases and training resources. This includes the abnormal handling process, common question answers, case sharing, etc., so that the operation personnel can quickly get started and efficiently handle the abnormality.

[0117] As a preferred implementation manner of this embodiment, the characteristics of the metadata include an abnormal degree characteristic and a clustering center offset characteristic;

[0118] The abnormal degree characteristic is specifically shown by the following formula:

[0119]

[0120] Where: LOF K (X t ) represents the abnormal degree characteristic of X t under the K-nearest data points; lrd K represents the local reachability density under the K-nearest data points; X k represents X t 's K-nearest data point; dis(X t , X k ) represents the distance between X k and X t ; ∈ represents an adjustment parameter.

[0121] As a preferred implementation manner of this embodiment, the calculation formula of the clustering center offset characteristic is:

[0122] ΔC j (X t ) = (||X t - C j,t || - ||X t-1 - C j,t-1 ||0 × τ(LOF K (Xt ))

[0123] Where: ΔC j (X t ) represents the offset of X t from the cluster center; C j,t represents the value of C j at time t; τ represents the balance coefficient.

[0124] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent the case where A exists alone, A and B exist simultaneously, or B exists alone. Where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0125] Those of ordinary skill in the art can realize that the units and algorithm steps described in the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0126] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0127] In several embodiments provided by the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0128] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A comprehensive metadata management system for electric power, characterized in that: It includes metadata resource management module, metadata maintenance module, metadata evaluation module and metadata monitoring management module; The metadata resource management module is used to collect all metadata in the business system and classify and manage the metadata; The metadata maintenance module is used to update and maintain metadata; The metadata evaluation module is used to evaluate metadata by defining evaluation criteria, and register the evaluated metadata as a data asset and store it in the digital asset catalog; The metadata monitoring and management module is used to monitor the classified metadata and issue an early warning when abnormal data is detected.

2. The electric power comprehensive metadata management system according to claim 1, characterized in that: The system also includes a digital asset catalog management module; The digital asset catalog management module is used to generate a data asset ledger based on the digital asset data in the digital asset catalog and manage the data asset ledger.

3. The electric power comprehensive metadata management system according to claim 1, characterized in that: The metadata resource management module stores the collected metadata in a database; The metadata resource management module creates different sub-libraries in the database for storage according to the metadata type.

4. The electric power comprehensive metadata management system according to claim 1, characterized in that: The metadata maintenance module includes a metadata updating unit and a metadata maintenance unit; The metadata updating unit is used to update the definition, structure, relationship and mapping rules of the metadata, and each time the metadata is updated, the old version will be retained; The metadata maintenance unit is used to automatically manage metadata in a hierarchical manner according to preset metadata importance, and allows a preset management account to manually adjust metadata classification.

5. The electric power comprehensive metadata management system according to claim 1, characterized in that: The metadata evaluation module includes an evaluation unit and a digital asset registration unit; The evaluation unit evaluates the metadata based on the six standards for entering the lake, and makes corresponding improvement suggestions for the metadata that does not meet the standards according to the evaluation results; The digital asset registration unit is used to register digital assets with metadata that meets the standards and store them in a digital asset catalog.

6. A comprehensive metadata management system for electric power according to claim 1, characterized in that: The metadata monitoring and management module detects abnormal data in the database through an abnormality detection algorithm, as shown in the following formula: Where: S t represents the change score of metadata at time t; X t Represents the metadata value at time t; represents the time derivative of metadata changes; f i (X t ) represents the i-th feature of metadata extracted based on the machine learning model; w i represents the weight of the i-th feature; N represents the total number of metadata features; X t-1 represents the metadata value at time t-1; α, β, γ, and λ represent the weight parameters of the anomaly detection algorithm; A change score threshold is preset. When the change score exceeds the preset threshold, it is determined that the metadata at the current moment has changed, and an early warning is issued.

7. A comprehensive metadata management system for electric power according to claim 6, characterized in that: The features of the metadata include features of abnormality degree and features of cluster center shift; The abnormality degree characteristics are specifically shown in the following formula: Among them: LOF K (X t ) indicates X t Abnormality characteristics under K neighboring data points; lrd K represents the local reachable density under K neighboring data points; X k Represents X t The Kth neighboring data point of t ,X k ) indicates X k With X t The distance between ∈ and ∈ represents the adjustment parameter.

8. A comprehensive metadata management system for electric power according to claim 7, characterized in that: The calculation formula of the cluster center offset feature is: ΔC j (X t )=(||X t -C j,t ||-||X t-1 -C j,t-1 ||)×τ(LOF K (X t )) Where: ΔC j (X t ) indicates X t The offset from the cluster center; C j,t Represents C j The value at time t; τ represents the balance coefficient.

Images