Power big data management optimization method in combination with data owner

Abstract

The invention relates to an electric power big data management optimization method in combination with a data owner, and the method comprises the following steps: S1, collecting electric power big data, and extracting context features; s2, identifying an initial owner according to metadata ownership information or a data uploader; s3, constructing a data owner identification model, realizing accurate identification and dynamic updating of the data owner based on the extracted context features, and correcting the initial owner to obtain a final data owner; s4, automatically writing a result output by the data owner identification model into a data governance platform to form a structured data ownership record, and recording an identification process by adopting a block chain technology; s5, detecting a data problem by using a rule base detection and anomaly detection model; generating a work order according to the detected data problem; and S6, after receiving the problem, the data owner optimizes and repairs the problem data and verifies whether the data repairs are compliant, and if the data repairs are not compliant, a reverse work order is returned to continue processing. According to the invention, high quality, high efficiency and high credibility of data management are realized.

Description

Technical Field

[0001] The present invention relates to the field of big data management, and in particular to an optimization method for power big data management combined with data owners. Background Art

[0002] In recent years, with the digital transformation and intelligent development of the power industry, power big data has become an important foundation for supporting applications such as power grid dispatch optimization, load forecasting, fault diagnosis and user behavior analysis. However, power big data faces many technical challenges in the process of collection, storage, processing and circulation: First, the data sources are diverse (such as sensor data, dispatch logs, user electricity behavior data, etc.), resulting in uneven data quality, missing fields, duplications, outliers and other problems; second, data ownership is often vague, such as it is difficult to accurately divide data responsibilities in a data sharing environment with multi-party collaboration; in addition, power data governance needs to ensure the authenticity, security and traceability of data to meet industry regulations and support multi-party collaboration. Summary of the invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide a power big data management optimization method combined with the data owner, so as to realize the automation of data management, the precision of responsibility transfer and the security and reliability of transparency.

[0004] To achieve the above object, the present invention adopts the following technical solutions: A method for optimizing power big data management in combination with data owners, comprising the following steps: S1: Collect power big data and extract context features; S2: Identify the initial owner based on metadata ownership information or data uploader; S3: Build a data owner identification model to accurately identify and dynamically update the data owner based on the extracted context features, modify the initial owner, and obtain the final data owner; S4: Automatically write the output of the data owner identification model into the data governance platform to form a structured data ownership record, and use blockchain technology to record the identification process; S5: Use the rule base detection and anomaly detection model to detect data problems; generate work orders for the detected data problems, and push the work orders immediately based on the data owner identified by the data owner identification model; S6: After receiving the problem, the data owner optimizes and repairs the problematic data and verifies whether the data repair is compliant. If not, a reverse work order is returned for further processing.

[0005] Furthermore, power big data includes power consumption behavior data, user operation behavior records and metadata; the power consumption behavior data includes user power consumption P(t), power grid equipment operation data, time series type data and special scenario records; the user operation behavior records are audit logs, including access, modification and reading frequency data; the metadata includes the table structure, data fields and field description information of the power internal management system.

[0006] Furthermore, context features include power consumption behavior pattern values, operation behavior features, and metadata characteristics, as follows: The power consumption behavior pattern value includes peak load period and abnormal fluctuation. The daily load value is calculated from the user's power consumption P(t) and the peak value P is extracted. peak and the corresponding period t peak : ; Calculate the difference between the user's electricity consumption at time t+1 and the user's electricity consumption at time t , if ΔP>T threshold , then the corresponding time t is marked as an abnormal fluctuation point, where T threshold To preset the threshold:

[0007] Among them, mean and std are the fluctuation mean and standard deviation, and k is the adjustment coefficient; The operation behavior characteristics include data usage frequency F access (u) and modify preferences : ; ; Among them, N access (u) is the number of times user u accesses the data within time T; For user u for field f i The number of all modification operations, The total number of modifications made by user u on all fields; The metadata features include extracting field semantic relevance and naming patterns extracted from table names or field names; the field semantic relevance is calculated using cosine similarity: ; Among them, V( f 1 ),V( f 2 ) are fields f 1 ,f 2 The embedding vector of They are V( f 1 ),V( f 2 )'s mold length.

[0008] Furthermore, S2 is specifically: Use SQL to query the system's metadata table to collect metadata ownership and field description information; use the ELK log collection and processing framework to collect user operation logs in real time; Initialize the unified field structure of log data and metadata, associate table data by time and user ID, parse and classify field names, and classify field names into specific responsibility types; Prioritize matching the owner_id information in the metadata. If there is no owner record, match the metadata field description information or creator identity. If the metadata cannot be identified, identify the initial owner based on the upload log and the most recent upload operation.

[0009] Furthermore, the construction of the data owner identification model is as follows: The context features are converted into structured vectors for model input, and the input feature sequence X is constructed. Each master candidate corresponds to a context feature sequence, and each context feature sequence is represented as a time-dependent multidimensional vector: ; Among them, x t is the feature at the tth moment, including a multi-dimensional context feature vector; ; in, The owner information in the metadata; Matching scores between fields and responsibilities; Timestamp for data upload; Use the embedding layer to transform x t Encoded as a fixed-length vector e t , then the input feature sequence X is transformed into the embedded feature sequence E: ; The input sequence is the embedded feature sequence E, and the temporal context information is added through the positional encoding P to obtain the final input Z=E+P; Use the Transformer's self-attention mechanism to model the relationship between context features and calculate the weight of each time step : ; Where Q = ZW Q is the query matrix, K=ZW Kis the bond matrix, V=ZW V is the value matrix; d k is the dimension of the query / key; G is a dense matrix; W Q、 W K、 W V are the weights of the query matrix, key matrix, and value matrix, respectively; Capturing dependencies between different features through a multi-head mechanism: ; in, is the weight coefficient; For the Attention head; represents the concatenation function; Output for the multi-head mechanism; ; in, For the attention head A trainable matrix; after l layers of Transformer encoding, the output feature sequence is obtained ; And use global pooling to aggregate sequence information to obtain a comprehensive recognition vector H = Pool (Z l ); The output is mapped to the category space of all candidate responsible persons through a fully connected layer: ; in, and are the weights and biases of the fully connected layer respectively; is the probability that the responsible person is u; Then, the ultimate person responsible for identification for: .

[0010] Furthermore, the dense matrix G is constructed based on GNN as follows: The input feature sequence X is regarded as a node representation of the graph, and each time step feature x t Corresponding to a node; the initial adjacency matrix A is a fully connected graph, initialized to the unit matrix; Define the node update formula in the m-th layer GNN: ; in, is the hidden feature of node a in the mth layer; is the hidden feature of node b in the mth layer; Indicates the strength of the relationship between node a and node b; is the adjacent node of node a; , are the learnable weights and biases in GNN respectively; The node hidden features are updated in each layer, and the adjacency matrix is ​​adjusted according to the feature changes to obtain a dynamically updated adjacency matrix: ; in, is the activation function; is the similarity function; Finally, after the M-layer GNN update, the output adjacency matrix is ​​the dynamically generated dense matrix .

[0011] Furthermore, the S4 is specifically: The data owner identification model processes the input data, generates ownership results, interacts with the data governance platform through interfaces, and transmits structured ownership data; The data governance platform completes data storage and permission allocation updates, and triggers the blockchain writing process; The blockchain records the identification process and ownership changes, and supports traceability and historical record query; The blockchain and the data governance platform database are synchronized to ensure that all ownership changes in the data governance platform are reflected on the chain, and the consistency of the platform data is verified from the historical records on the chain.

[0012] Furthermore, S5 is specifically Build a rule base to store predefined detection rules and meta-information, configure anomaly detection models, and run them adaptively after parameterized training; Based on the rule base and anomaly detection model, data problems can be detected for scheduled or real-time scenarios; Generate a structured problem record for each detection problem, and organize the detected data problems into work orders that are readable by the data owner; The data owner is determined through the output of the data owner identification model, and the work order is pushed to the data owner to be responsible for repair or review.

[0013] Furthermore, S6 is specifically: The data owner receives the problem ticket through the ticket receiving platform, including the problem description, impact scope and repair suggestions; After the data owner repairs the data, he submits the repair results to the data governance platform; the data governance platform calls the compliance verification module to verify the repair results: If the repair is compliant, the work order is closed; if the repair is not compliant, a reverse work order is generated, and the problem and verification feedback are sent to the data owner for further processing.

[0014] The present invention has the following beneficial effects: 1. The present invention realizes high quality, high efficiency and high reliability of data management, which can not only strengthen data quality control, but also improve the overall data usage value of the power industry, laying a solid foundation for the efficient operation of smart grids; 2. The present invention combines contextual features to dynamically construct a data owner identification model, more accurately divides data owners, and corrects from the initial owner to the context-aware final owner, effectively solving the problem of complexity in power data ownership (such as ambiguity in the ownership of multi-source data and shared data), and adopts a dynamic update mechanism to ensure real-time identification and ownership adjustment during data generation, circulation and use; combined with blockchain technology, key information such as the data owner identification process, data ownership records, and work orders are stored on the chain to ensure that they cannot be tampered with and the credibility of the entire process record; 3. The present invention uses rule base detection (field missing, duplicate value and other problems) combined with anomaly detection models (distribution anomalies, cross-dimensional anomalies, time series anomalies, etc.) to achieve comprehensive coverage and intelligent detection of data problems, and based on the data owner identification model combined with fast work order push, each problem can accurately find the person responsible, maximize the shortening of problem response time and repair time, automatically verify compliance after the repair is completed, establish a benign circulation mechanism, and reduce friction in the responsibility attribution and repair process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0016] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments: refer to Figure 1 In this embodiment, a method for optimizing power big data management in combination with a data owner is provided, comprising the following steps: S1: Collect power big data and extract context features; S2: Identify the initial owner based on metadata ownership information or data uploader; S3: Build a data owner identification model to accurately identify and dynamically update the data owner based on the extracted context features, modify the initial owner, and obtain the final data owner; S4: Automatically write the output of the data owner identification model into the data governance platform to form a structured data ownership record. Use blockchain technology to record the identification process to ensure that it cannot be tampered with and achieve traceability. S5: Use rule base detection (such as missing fields, duplicate values) and anomaly detection models to detect data problems; generate work orders for detected data problems (the work order content includes problem description, impact scope, etc.), and push the work order immediately based on the data owner identified by the data owner identification model; S6: After receiving the problem, the data owner optimizes and repairs the problematic data and verifies whether the data repair is compliant. If not, a reverse work order is returned for further processing.

[0017] In this embodiment, the power big data includes power consumption behavior data, user operation behavior records and metadata; the power consumption behavior data includes user power consumption P(t), power grid equipment operation data (load power, abnormal fluctuation records), time series type data (such as voltage, current, power and other fluctuation data) and special scenario records (marking faults, load peaks, power outages, etc.); the user operation behavior records are audit logs, including access, modification, and reading frequency data; the metadata includes the table structure, data fields, and field description information of the power internal management system.

[0018] In this embodiment, the context features include power consumption behavior pattern values ​​(such as peak load period, abnormal fluctuation characteristics), operation behavior characteristics (such as data usage frequency, modification preference) and metadata characteristics (semantic association, naming rules, etc.), as follows: The power consumption behavior pattern value includes peak load period and abnormal fluctuation. The daily load value is calculated from the user's power consumption P(t) and the peak value P is extracted. peak and the corresponding period t peak : ; Calculate the difference between the user's electricity consumption at time t+1 and the user's electricity consumption at time t , if ΔP>T threshold , then the corresponding time t is marked as an abnormal fluctuation point, where T threshold To preset the threshold:

[0019] Among them, mean and std are the fluctuation mean and standard deviation, and k is the adjustment coefficient; The operation behavior characteristics include data usage frequency F access (u) and modify preferences : ; ; Among them, N access (u) is the number of times user u accesses the data within time T; For user u for field f i The number of all modification operations, The total number of modifications made by user u on all fields; The metadata features include extracting field semantic relevance and naming patterns extracted from table names or field names; the field semantic relevance is calculated using cosine similarity: ; Among them, V( f 1 ),V( f 2 ) are fields f 1 ,f 2 The embedding vector of They are V( f 1 ),V( f 2 )'s mold length.

[0020] In this embodiment, S2 is specifically: Use SQL to query the system's metadata table to collect metadata ownership and field description information; use the ELK log collection and processing framework to collect user operation logs in real time; Initialize the unified field structure of log data and metadata, associate table data by time and user ID, parse and classify field names, and classify field names into specific responsibility types; Prioritize matching the owner_id information (owner information) in the metadata. If there is no owner record, match the metadata field description information or creator identity. If the metadata cannot be identified, identify the initial owner based on the upload log and the most recent upload operation.

[0021] In this embodiment, the data owner identification model is constructed as follows: The context features are converted into structured vectors for model input, and the input feature sequence X is constructed. Each master candidate corresponds to a context feature sequence, and each context feature sequence is represented as a time-dependent multidimensional vector: ; Among them, x t is the feature at the tth moment, including a multi-dimensional context feature vector; ; in, The owner information in the metadata; Matching scores between fields and responsibilities; Timestamp for data upload; Use the embedding layer to transform x t Encoded as a fixed-length vector e t , then the input feature sequence X is transformed into the embedded feature sequence E: ; The input sequence is the embedded feature sequence E, and the temporal context information is added through the positional encoding P to obtain the final input Z=E+P; Use the Transformer's self-attention mechanism to model the relationship between context features and calculate the weight of each time step : ; Where Q = ZW Q is the query matrix, K=ZW K is the bond matrix, V=ZW V is the value matrix; d k is the dimension of the query / key; G is a dense matrix; W Q、 W K、 W V are the weights of the query matrix, key matrix, and value matrix, respectively; Capturing dependencies between different features through a multi-head mechanism: ; in, is the weight coefficient; For the Attention head; represents the concatenation function; Output for the multi-head mechanism; ; in, For the attention head The trainable matrix of After l layers of Transformer encoding, the output feature sequence is obtained ; And use global pooling Pool to aggregate sequence information to obtain a comprehensive recognition vector H = Pool (Z l ); Through the fully connected layer Map the output to the class space of all candidate responsible persons: ; in, and are the weights and biases of the fully connected layer respectively; is the probability that the responsible person is u; Then, the ultimate person responsible for identification for: .

[0022] In this embodiment, the dense matrix G is constructed based on GNN, as follows: The input feature sequence X is regarded as a node representation of the graph, and each time step feature x t Corresponding to a node; the initial adjacency matrix A is a fully connected graph, initialized to the unit matrix; Define the node update formula in the m-th layer GNN: ; in, is the hidden feature of node a in the mth layer; is the hidden feature of node b in the mth layer; Indicates the strength of the relationship between node a and node b; is the adjacent node of node a; , are the learnable weights and biases in GNN respectively; The node hidden features are updated in each layer, and the adjacency matrix is ​​adjusted according to the feature changes to obtain a dynamically updated adjacency matrix: ; in, is the activation function; is the similarity function; Finally, after the M-layer GNN update, the output adjacency matrix is ​​the dynamically generated dense matrix .

[0023] In this embodiment, S4 is specifically: The data owner identification model processes the input data, generates ownership results, interacts with the data governance platform through interfaces, and transmits structured ownership data; The data governance platform completes data storage and permission allocation updates, and triggers the blockchain writing process; The blockchain records the identification process and ownership changes, and supports traceability and historical record query; The blockchain and the data governance platform database are synchronized to ensure that all ownership changes in the data governance platform are reflected on the chain, and the consistency of the platform data is verified from the historical records on the chain.

[0024] In this embodiment, S5 is specifically Build a rule base to store predefined detection rules and meta-information (such as field constraints and data distribution), configure anomaly detection models, and perform adaptive operation after parameterized training; The rule base uses predefined rules to implement field-level or table-level problem detection based on specific business requirements and data governance specifications.

[0025] Missing fields: Check whether the corresponding fields in the table are empty or some field values ​​are not filled in.

[0026] Duplicate Values: Checks the table for primary key violations or duplicate records.

[0027] Data constraint checking: Checks whether the fields conform to the defined range (such as date field format, numeric range, character length limit).

[0028] Business logic verification: Define rules based on specific business logic (such as transaction amount must be > 0, time sequence must be increasing).

[0029] Rule implementation method: Define the rule template (rule name, target field, detection conditions, etc.) according to the business.

[0030] Direct detection based on SQL queries or through scripts; For problems that cannot be set with preset rules and require dynamic judgment (such as outliers, distribution changes, etc.), an anomaly detection model is used, and the anomaly detection model is constructed based on One-Class SVM.

[0031] Based on the rule base and anomaly detection model, it detects data problems in scheduled (such as daily / hourly) or real-time (when data is updated) scenarios; Generate a structured problem record for each detection problem, and organize the detected data problems into work orders that are readable by the data owner; Preferably, the work order content includes: Basic information; Work order number: unique identifier of the work order; Submission time and submitter: the time and system when the work order was generated; Data information: ID of the problem data, metadata information (such as generation time, data source, table name, etc.); Details of the problem; Problem type: missing fields, duplicate values, outliers, etc. Problem description: Detailed description of the detected problem (e.g. "The number of missing values ​​in the field exceeds 120 rows, with a coverage ratio of 25%)" Problem impact: clearly indicate which business logic or data links the problem may affect; Impact scope: specific data and fields affected; Restoration plan; Suggested data repair or remediation options.

[0032] The data owner is determined through the output of the data owner identification model, and the work order is pushed to the data owner to be responsible for repair or review.

[0033] The push method is as follows: Obtain data owner information; Call the data owner identification model and query the unique identifier of the responsible person based on the data ID; The model returns the data owner's Owner ID and other meta-information (such as permission scope) to identify the specific person in charge.

[0034] Push work order content: Online instant push: Push the work order directly to the user notification interface of the data manager through the message system of the data governance platform; External system integration: The work order is delivered to the corresponding business system through REST API or message queue (such as Kafka, RabbitMQ, etc.).

[0035] Notify the person in charge of relevant work order tasks via email or SMS.

[0036] In this embodiment, S6 is specifically: The data owner receives the problem ticket through the ticket receiving platform, including the problem description, impact scope and repair suggestions; After the data owner repairs the data, he submits the repair results to the data governance platform; the data governance platform calls the compliance verification module to verify the repair results: If the repair is compliant, the work order is closed; if the repair is not compliant, a reverse work order is generated, and the problem and verification feedback are sent to the data owner for further processing.

[0037] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0038] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0039] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0040] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0041] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. A method for optimizing power big data management in combination with data owners, characterized in that: The following steps are involved: S1: Collect power big data and extract context features; S2: Identify the initial owner based on metadata ownership information or data uploader; S3: Build a data owner identification model to accurately identify and dynamically update the data owner based on the extracted context features, modify the initial owner, and obtain the final data owner; S4: Automatically write the output of the data owner identification model into the data governance platform to form a structured data ownership record, and use blockchain technology to record the identification process; S5: Use the rule base detection and anomaly detection model to detect data problems; generate work orders for the detected data problems, and push the work orders immediately based on the data owner identified by the data owner identification model; S6: After receiving the problem, the data owner optimizes and repairs the problematic data and verifies whether the data repair is compliant. If not, a reverse work order is returned for further processing.

2. The method for optimizing power big data management in combination with data owners according to claim 1, characterized in that: The power big data includes power consumption behavior data, user operation behavior records and metadata; the power consumption behavior data includes user power consumption P(t), power grid equipment operation data, time series type data and special scenario records; the user operation behavior records are audit logs, including access, modification and reading frequency data; the metadata includes the table structure, data fields and field description information of the power internal management system.

3. The method for optimizing power big data management in combination with data owners according to claim 2 is characterized in that: The context features include power consumption behavior pattern values, operation behavior features and metadata characteristics, as follows: The power consumption behavior pattern value includes peak load period and abnormal fluctuation. The daily load value is calculated from the user's power consumption P(t) and the peak value P is extracted. peak and the corresponding period t peak : ； Calculate the difference between the user's electricity consumption at time t+1 and the user's electricity consumption at time t , if ΔP>T threshold , then the corresponding time t is marked as an abnormal fluctuation point, where T threshold To preset the threshold: Among them, mean and std are the fluctuation mean and standard deviation, and k is the adjustment coefficient; The operation behavior characteristics include data usage frequency F access (u) and modify preferences : ； ； Among them, N access (u) is the number of times user u accesses the data within time T; For user u for field f i The number of all modification operations, The total number of modifications made by user u on all fields; The metadata features include extracting field semantic relevance and naming patterns extracted from table names or field names; the field semantic relevance is calculated using cosine similarity: ； Among them, V( f 1 ),V( f 2 ) are fields f 1 ,f 2 The embedding vector of They are V( f 1 ),V( f 2 )'s mold length.

4. The method for optimizing power big data management in combination with data owners according to claim 1, characterized in that: The S2 is specifically: Use SQL to query the system's metadata table to collect metadata ownership and field description information; use the ELK log collection and processing framework to collect user operation logs in real time; Initialize the unified field structure of log data and metadata, associate table data by time and user ID, parse and classify field names, and classify field names into specific responsibility types; Prioritize matching the owner_id information in the metadata. If there is no owner record, match the metadata field description information or creator identity. If the metadata cannot be identified, identify the initial owner based on the upload log and the most recent upload operation.

5. The method for optimizing power big data management in combination with data owners according to claim 3 is characterized in that: The construction of the data owner identification model is as follows: The context features are converted into structured vectors for model input, and the input feature sequence X is constructed. Each master candidate corresponds to a context feature sequence, and each context feature sequence is represented as a time-dependent multidimensional vector: ； Among them, x t is the feature at the tth moment, including a multi-dimensional context feature vector; ； in, The owner information in the metadata; Matching scores between fields and responsibilities; Timestamp for data upload; Use the embedding layer to transform x t Encoded as a fixed-length vector e t , then the input feature sequence X is transformed into the embedded feature sequence E: ； The input sequence is the embedded feature sequence E, and the temporal context information is added through the positional encoding P to obtain the final input Z=E+P; Use the Transformer's self-attention mechanism to model the relationship between context features and calculate the weight of each time step : ； Where, Q = ZW Q is the query matrix, K=ZW K is the bond matrix, V=ZW V is the value matrix; d k is the dimension of the query / key; G is a dense matrix; W Q、 W K、 W V are the weights of the query matrix, key matrix, and value matrix, respectively; Capturing dependencies between different features through a multi-head mechanism: ； in, is the weight coefficient; For the Attention head; represents the concatenation function; Output for the multi-head mechanism; ； in, For the attention head The trainable matrix of After l layers of Transformer encoding, the output feature sequence is obtained ; And use global pooling to aggregate sequence information to obtain a comprehensive recognition vector H = Pool (Z l ); The output is mapped to the category space of all candidate responsible persons through a fully connected layer: ; in, and are the weights and biases of the fully connected layer respectively; is the probability that the responsible person is u; Then, the ultimate person responsible for identification for: .

6. The method for optimizing power big data management in combination with data owners according to claim 5, characterized in that: The dense matrix G is constructed based on GNN, as follows: The input feature sequence X is regarded as a node representation of the graph, and each time step feature x t Corresponding to a node; the initial adjacency matrix A is a fully connected graph, initialized to the unit matrix; Define the node update formula in the m-th layer GNN: ； in, is the hidden feature of node a in the mth layer; is the hidden feature of node b in the mth layer; Indicates the strength of the relationship between node a and node b; is the adjacent node of node a; , They are the learnable weights and biases in GNN respectively; The node hidden features are updated in each layer, and the adjacency matrix is ​​adjusted according to the feature changes to obtain a dynamically updated adjacency matrix: ; in, is the activation function; is the similarity function; Finally, after the M-layer GNN update, the output adjacency matrix is ​​the dynamically generated dense matrix .

7. The method for optimizing power big data management in combination with data owners according to claim 1, characterized in that: The S4 is specifically: The data owner identification model processes the input data, generates ownership results, interacts with the data governance platform through interfaces, and transmits structured ownership data; The data governance platform completes data storage and permission allocation updates, and triggers the blockchain writing process; The blockchain records the identification process and ownership changes, and supports traceability and historical record query; The blockchain and the data governance platform database are synchronized to ensure that all ownership changes in the data governance platform are reflected on the chain, and the consistency of the platform data is verified from the historical records on the chain.

8. The method for optimizing power big data management in combination with data owners according to claim 1, characterized in that: The S5 is specifically: Build a rule base to store predefined detection rules and meta-information, configure anomaly detection models, and run them adaptively after parameterized training; Based on the rule base and anomaly detection model, data problems can be detected for scheduled or real-time scenarios; Generate a structured problem record for each detection problem, and organize the detected data problems into work orders that are readable by the data owner; The data owner is determined through the output of the data owner identification model, and the work order is pushed to the data owner to be responsible for repair or review.

9. The method for optimizing power big data management in combination with data owners according to claim 1, characterized in that: The S6 is specifically: The data owner receives the problem ticket through the ticket receiving platform, including the problem description, impact scope and repair suggestions; After the data owner repairs the data, he / she submits the repair results to the data governance platform; The data governance platform calls the compliance verification module to verify the repair results: If the repair is compliant, the work order is closed; if the repair is not compliant, a reverse work order is generated, and the problem and verification feedback are sent to the data owner for further processing.

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