Power grid data processing method and device, computer device and storage medium

Abstract

The application relates to a power grid data processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: classifying and grading to-be-processed power grid data to obtain a classification grading result; determining a target compliance requirement and a target business process node matched with the classification grading result based on preset first table association data; screening a target data compliance rule matched with the classification grading result from preset data compliance rules according to the target data compliance requirement and the target business process node; determining a target compliance risk point matched with the classification grading result according to the target data compliance rule and preset second table association data; and screening a data compliance control strategy matched with the target compliance risk point from preset data compliance control strategies. The method can improve the compliance of power grid data.

Description

Technical Field

[0001] This application relates to the field of power grid data management technology, and in particular to a power grid data compliance control method, device, computer equipment, storage medium, and computer program product. Background Technology

[0002] With the development of data element markets across various industries, data compliance issues are becoming increasingly prominent. Taking power grid companies as an example, they have a wide variety of data types, such as public data, personal data, and enterprise data. Furthermore, the scenarios for data sharing and openness are complex and the demand is strong, making data compliance management increasingly urgent.

[0003] Traditional data compliance control methods typically rely on legal knowledge bases. They collect and identify the characteristics of the data to be tested through questionnaires or uploaded data files, and then match the data to be tested with the legal provisions in the legal knowledge base to generate relevant data compliance rules and compliance control strategies.

[0004] However, due to the complexity and diversity of data compliance management, and the fact that the data involved in actual business processes often have different characteristics and sensitivities, the compliance control strategies generated by the above-mentioned traditional solutions may be too broad, and applying such compliance control strategies will result in poor compliance of the processed data. Summary of the Invention

[0005] Therefore, it is necessary to provide a power grid data processing method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve data compliance in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a power grid data processing method. The method includes:

[0007] The power grid data to be processed is classified and graded to obtain the classification and grading results;

[0008] Based on the preset first table of associated data, target compliance requirements and target business process nodes that match the classification and grading results are determined. The first table of associated data includes the relationship between historical classification and grading results, preset data compliance requirements, business process nodes, and preset data compliance rules.

[0009] Based on the target data compliance requirements and the target business process nodes, target data compliance rules that match the classification and grading results are selected from the preset data compliance rules;

[0010] Based on the target data compliance rules and the preset second table association data, target compliance risk points that match the classification and grading results are determined. The preset second table association data includes the relationship between the historical classification and grading results, the preset data compliance rules, the preset data compliance risk points, and the preset data compliance control strategies.

[0011] Select data compliance control strategies that match the target compliance risk points from the preset data compliance control strategies.

[0012] In one embodiment, classifying and grading the power grid data to be processed to obtain classification and grading results includes:

[0013] By using a pre-built data classification and grading model, the power grid data to be processed is classified from the dimensions of legal attributes and organizational operation, and graded from the dimension of data security, thus obtaining the classification and grading results.

[0014] In one embodiment, before classifying the power grid data to be processed from the legal attribute dimension and organizational operation dimension using a pre-built data classification and grading model, and grading the power grid data to be processed from the data security dimension to obtain the classification and grading results, the method further includes:

[0015] Extract entities and relationships between entities from historical power grid data;

[0016] The entities are identified as nodes in the knowledge graph, and the relationships between the entities are identified as edges in the knowledge graph, thus obtaining an initial knowledge graph.

[0017] The historical power grid data is classified from the dimensions of legal attributes and organizational operation, and graded from the dimension of data security to obtain historical classification and grading results;

[0018] Based on the historical classification and grading results, corresponding category labels and level labels are assigned to the nodes in the initial knowledge graph to obtain a data classification and grading knowledge graph. The pre-built data classification and grading model includes the classification and grading knowledge graph.

[0019] In one embodiment, the data classification and hierarchical knowledge graph includes a data classification knowledge graph and a data hierarchical knowledge graph;

[0020] The process involves classifying the power grid data to be processed from legal attribute and organizational operation dimensions using a pre-built data classification and grading model, and grading the data from a data security dimension to obtain classification and grading results, including:

[0021] Extract the legal attribute dimension features and organizational operation dimension features of the power grid data to be processed, as well as the first data feature value of the legal attribute dimension features and the second data feature value under the organizational operation dimension features;

[0022] Based on the data classification knowledge graph, the first category of the power grid data to be processed under the legal attribute dimension and the second category under the organization and operation dimension are matched. The first data feature value corresponds to the first category and the second data feature value corresponds to the second category.

[0023] The first data feature value and the second data feature value are compared to obtain a comparison result. Based on the comparison result, the first category or the second category is determined as the category of the power grid data to be processed.

[0024] Based on the data hierarchical knowledge graph, the security level of the power grid data to be processed is matched, and the classification and hierarchical results include the category and the security level.

[0025] In one embodiment, before determining the target compliance requirements and target business process nodes that match the classification and grading results based on preset first table association data, the method further includes:

[0026] For each legal provision in the preset legal knowledge base, based on the legal hierarchy to which the legal provision belongs, the data processing node corresponding to the legal provision, and the legal relationship subject and object corresponding to the legal provision, the legal provision is split and then classified to obtain the classified legal provisions.

[0027] Based on the categorized legal provisions, determine the data compliance requirements;

[0028] The historical classification and grading results, the data compliance requirements, business process nodes, and preset data compliance rules are subjected to table association processing to obtain the preset first table association data.

[0029] In one embodiment, before determining the target compliance risk point matching the classification and grading results based on the target data compliance rules and preset second table associated data, the method further includes:

[0030] Based on a pre-defined legal knowledge base and pre-defined data compliance rules, data compliance risk points are identified. These data compliance risk points are used to characterize the legal risks arising from violations of the pre-defined data compliance rules.

[0031] The historical classification and grading results, the preset data compliance rules, the data compliance risk points, and the preset data compliance control strategies are subjected to table association processing to obtain the preset second table association data.

[0032] Secondly, this application also provides a power grid data processing apparatus. The apparatus includes:

[0033] The data classification and grading module is used to classify and grade the power grid data to be processed, and obtain the classification and grading results.

[0034] The first matching module is used to determine the target compliance requirements and target business process nodes that match the classification and grading results based on the preset first table association data. The first table association data includes the association relationship between historical classification and grading results, preset data compliance requirements, business process nodes and preset data compliance rules.

[0035] The data rule determination module is used to select target data compliance rules that match the classification and grading results from the preset data compliance rules based on the target data compliance requirements and the target business process nodes.

[0036] The second matching module is used to determine the target compliance risk points that match the classification and grading results based on the target data compliance rules and the preset second table association data. The preset second table association data includes the association relationship between the historical classification and grading results, the preset data compliance rules, the preset data compliance risk points, and the preset data compliance control strategies.

[0037] The data compliance strategy determination module is used to select data compliance control strategies that match the target compliance risk points from the preset data compliance control strategies.

[0038] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps described in the above-described power grid data processing method embodiments.

[0039] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps in the above-described embodiments of the power grid data processing method.

[0040] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps described in the above-described power grid data processing method embodiments.

[0041] The aforementioned power grid data compliance control method, device, computer equipment, storage medium, and computer program product acquire power grid data to be processed, classify and grade the data, and obtain classification and grading results. Then, based on the classification and grading results and preset first table association data, data compliance rules for the power grid data to be processed are determined. The first table association data is obtained by associating table classification and grading results, compliance requirements, business process nodes, and preset data compliance rules. Further, based on the classification and grading results, data compliance rules, and preset second table association data, a data compliance control strategy for the power grid data to be processed is determined. The second table association data is obtained by associating table classification and grading results, data compliance rules, data compliance risk points, and preset data compliance control strategy. Unlike traditional data compliance control methods, the above scheme first classifies and grades the power grid data to be processed, thus providing more refined power grid data for determining subsequent compliance control strategies. Furthermore, since the data in the first table includes business process nodes, it allows the classified and graded power grid data to be associated with business process nodes. The data compliance rules determined in this way can be deeply integrated into the power grid's business processes and are more aligned with the power grid's business needs. Therefore, the data compliance control strategy determined by associating data and data compliance rules in the second table is more applicable and more closely matches the actual business processes of the power grid, resulting in better data compliance after processing through this data compliance control strategy. Attached Figure Description

[0042] Figure 1 This is an application environment diagram of a power grid data processing method in one embodiment;

[0043] Figure 2 This is a flowchart illustrating a power grid data processing method in one embodiment;

[0044] Figure 3 This is a flowchart illustrating the data classification and grading steps in one embodiment;

[0045] Figure 4 This is a flowchart illustrating a power grid data processing method in another embodiment;

[0046] Figure 5 This is a flowchart illustrating the power grid data processing method in yet another embodiment;

[0047] Figure 6 This is a flowchart illustrating a power grid data processing method in a detailed embodiment;

[0048] Figure 7 This is a structural block diagram of a power grid data processing device in one embodiment;

[0049] Figure 8 This is a structural block diagram of a power grid data processing device in another embodiment;

[0050] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] The power grid data processing method provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown is illustrated. Terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0053] Specifically, relevant personnel can upload the power grid data to be processed to server 104 via terminal 102, or server 104 can retrieve the power grid data to be processed from the data storage system. Then, server 104 classifies and grades the power grid data to be processed, obtaining classification and grading results. Next, based on preset first table association data, server 104 determines the target compliance requirements and target business process nodes that match the classification and grading results. The first table association data includes the relationships between historical classification and grading results, preset data compliance requirements, business process nodes, and preset data compliance rules. Further, based on the target data compliance requirements and target business process nodes, server 104 selects target data compliance rules that match the classification and grading results from the preset data compliance rules. It also determines target compliance risk points that match the classification and grading results based on the target data compliance rules and preset second table association data. The preset second table association data includes the relationships between historical classification and grading results, preset data compliance rules, preset data compliance risk points, and preset data compliance control strategies. Finally, server 104 selects data compliance control strategies that match the target compliance risk points from the preset data compliance control strategies.

[0054] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, etc. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0055] In one embodiment, such as Figure 2 As shown, a power grid data processing method is provided, which can be applied to... Figure 1 Taking server 104 as an example, the following steps are included:

[0056] S100 classifies and grades the power grid data to be processed, and obtains the classification and grading results.

[0057] Among them, the power grid data to be processed refers to various data generated, collected, stored, and processed by power grid companies in their daily operations and management, including but not limited to personal user information of employees, relevant organizational user information, power transaction data, power production data, financial data, human resource data, equipment status data, and network security data. The classification and grading results include power grid data carrying category and level labels.

[0058] Since the power grid data to be processed is usually large and complex, it is necessary to classify and grade the data. For example, this can be done using rules from a pre-defined rule base, clustering algorithms, or trained deep learning models.

[0059] For example, the classification result could be: classifying the power grid data to be processed into user data, business data, and technical data. Personal user information and related organizational user information belong to user data; power transaction data, power production data, financial data, and human resource data belong to business data; and equipment status data and cybersecurity data belong to technical data. The grading result could be: grading the sensitivity or importance of the power grid data to be processed. The higher the sensitivity or importance of the data, the higher its corresponding level. For example, financial data could be classified as Level 4 data because the leakage or misuse of financial data could have a significant impact on individuals or organizations; publicly available business data could be classified as Level 1 data because publicly available business data is publicly accessible and has no sensitivity or confidentiality requirements. Taking financial data as an example, the final classification and grading result could be: classifying financial data as Level 4 business data, where "Level 4" is the level label for financial data and "business data" is the category label for financial data.

[0060] S300, based on the pre-set first table of associated data, determines the target compliance requirements and target business process nodes that match the classification and grading results.

[0061] The first table contains related data including historical classification and grading results, preset data compliance requirements, and the relationships between business process nodes and preset data compliance rules. Data compliance requirements may include, but are not limited to, laws, regulations, and industry standards. Business process nodes may include, but are not limited to, data collection nodes, data storage nodes, data analysis nodes, and data upload nodes. Data compliance rules may include, but are not limited to, requiring user confirmation and compliance with privacy regulations when collecting user information at data collection nodes, and requiring data to be encrypted at data storage nodes and accessible only to specific personnel.

[0062] Taking financial data as an example, based on the pre-set first table of related data, the category of financial data is business data, and the level is 4. Therefore, the corresponding target compliance requirements can be: financial data needs to be kept for a certain number of years in accordance with regulations, and only specific personnel can access it; the corresponding business process nodes can be: data collection node, data storage node, data analysis node, and data upload node.

[0063] S500 selects target data compliance rules that match the classification and grading results from the preset data compliance rules based on the target data compliance requirements and target business process nodes.

[0064] Following the steps above, target data compliance rules that match the financial data are selected from the preset data compliance rules. Specifically, the selected target compliance rules may include: at the data collection node, the collected financial data must be retained for a certain number of years; at the data storage node, the stored financial data is encrypted; at the data analysis node, only specific professionals have the authority to analyze and review the financial data; and at the data upload node, financial data can only be uploaded to user terminals with authorized access.

[0065] S700 determines target compliance risk points that match the classification and grading results based on the target data compliance rules and the pre-set associated data in the second table.

[0066] The pre-defined second table contains the relationships between historical classification and grading results, pre-defined data compliance rules, pre-defined data compliance risk points, and pre-defined data compliance control strategies. Data compliance risk points refer to the potential risks arising from violations of data compliance rules. For example, if financial data is not encrypted at data storage nodes, it may lead to financial data leakage.

[0067] Data compliance control strategies refer to specific measures developed to ensure that power grid data complies with laws, regulations, and industry rules during business processing. For example, regarding Level 4 personal user information data, only the "administrator" role within the power grid company possesses account compliance management permissions such as account registration, activation, cancellation, permission adjustment, and account behavior recording. Furthermore, the "administrator" has the authority to approve data requests submitted by data users, as well as access, write, and execute permissions for Level 4 resources within the management business domain.

[0068] By linking data through a pre-defined second table, target compliance risk points that match the classification and grading results can be identified. Taking financial data as an example, the matching target compliance risk points could be: data breach and data tampering.

[0069] S900 selects data compliance control strategies from preset data compliance control strategies that match the target compliance risk points.

[0070] Following the steps above, again taking financial data as an example, after identifying the corresponding target compliance risk points, the data compliance control strategy for financial data can be: to formulate an encryption strategy for financial data, to encrypt financial data using hash algorithms or other encryption algorithms, and to monitor financial data in real time, saving all access and operation records of financial data.

[0071] The aforementioned power grid data compliance control method involves acquiring the power grid data to be processed, classifying and grading it to obtain classification and grading results. Then, based on the classification and grading results and pre-set first table association data, data compliance rules for the power grid data to be processed are determined. The first table association data is obtained by associating table association classification and grading results, compliance requirements, business process nodes, and pre-set data compliance rules. Further, based on the classification and grading results, data compliance rules, and pre-set second table association data, a data compliance control strategy for the power grid data to be processed is determined. The second table association data is obtained by associating table association classification and grading results, data compliance rules, data compliance risk points, and the pre-set data compliance control strategy. Unlike traditional data compliance control methods, the above scheme first classifies and grades the power grid data to be processed, thus providing more refined power grid data for determining subsequent compliance control strategies. Furthermore, since the data in the first table includes business process nodes, it allows the classified and graded power grid data to be associated with business process nodes. The data compliance rules determined in this way can be deeply integrated into the power grid's business processes and are more aligned with the power grid's business needs. Therefore, the data compliance control strategy determined by associating data and data compliance rules in the second table is more applicable and more closely matches the actual business processes of the power grid, resulting in better data compliance after processing through this data compliance control strategy.

[0072] In one embodiment, S100 includes: classifying the power grid data to be processed from the dimensions of legal attributes and organizational operation through a pre-built data classification and grading model, and grading the power grid data to be processed from the dimension of data security, to obtain classification and grading results.

[0073] The legal attributes dimension involves identifying the types of legally related objects in power grid data, such as personal information, publicly available corporate data, and private corporate data, thereby enabling detailed classification of power grid data. The organizational and operational dimension involves classifying power grid data into user data, business data, operational management data, system operation and security data, etc., according to the internal data classification standards of power grid enterprises.

[0074] For example, classifying power grid data by taking "organizational operation dimension" as the first classification dimension can classify it into user data, business data, operation and management data, and system operation and security data. Furthermore, on this basis, a legal attribute dimension can be added. Taking "user data" as an example, it can be further subdivided into personal user information and organizational user information, such as basic organizational information, organizational account information, and organizational credit information.

[0075] In addition, the pre-built data classification and grading model also grades the power grid data to be processed from the perspective of data security. For example, the power grid data is graded according to the objects, scope and degree of impact of the power grid data. For example, the data level is divided into four data security levels: "severe damage, moderate damage, minor damage, and weak impact / none". The data security level usually determines the strictness of data protection measures, such as encryption requirements and access control.

[0076] In this embodiment, the power grid data to be processed is classified from the dimensions of legal attributes and organizational operation through a data classification and grading model, and the power grid data to be processed is graded from the dimension of data security. This can quickly and accurately identify sensitive data. Introducing the dimension of legal attributes can establish a more comprehensive and accurate correlation between the power grid data domain and laws and regulations, so that the final data compliance strategy can fully take into account the legal compliance of the power grid data.

[0077] In one embodiment, such as Figure 3 As shown, the method uses a pre-built data classification and grading model to classify the power grid data to be processed from the dimensions of legal attributes and organizational operation, and to grade the power grid data to be processed from the dimension of data security. Before obtaining the classification and grading results, the method also includes:

[0078] S112, extract entities and relationships between entities from historical power grid data, identify entities as nodes of the knowledge graph, and identify relationships between entities as edges of the knowledge graph to obtain the initial knowledge graph.

[0079] S114 classifies historical power grid data from the dimensions of legal attributes and organizational operation, and performs hierarchical processing of historical power grid data from the dimension of data security, resulting in historical classification and grading results.

[0080] S116. Based on the historical classification and grading results, assign corresponding category labels and level labels to the nodes in the initial knowledge graph to obtain the data classification and grading knowledge graph.

[0081] In the process of constructing a knowledge graph, an entity refers to an object or concept that has independent existence and meaning, and the relationship between entities refers to the semantic connection or relationship between two entities, representing a specific association between the two entities.

[0082] For example, meaningful entities such as "substations," "transmission lines," "user electricity consumption," and "equipment status" are identified from historical power grid data, along with relationships between these entities, such as "substations contain transmission lines" and "user electricity consumption is recorded in equipment status." Then, by defining entities as nodes in a knowledge graph and relationships between entities as edges, the extracted entities and relationships are structurally represented, resulting in an initial knowledge graph. Further, historical power grid data is classified and graded in multiple dimensions. For example, from a legal attribute perspective, historical power grid data is divided into personal information, trade secrets, and public data; from an organizational and operational perspective, it is divided into user data, business data, operational management data, and system operation and security data. The data is then graded according to its importance and sensitivity, such as four data security levels: severe damage, moderate damage, minor damage, and minimal / no impact, resulting in a historical classification and grading result.

[0083] Furthermore, clustering algorithms such as K-Means can be used to classify historical power grid data in other dimensions, resulting in more comprehensive historical classification and grading results. Further, based on these historical classification and grading results, specific category and level labels are assigned to nodes in the knowledge graph, enabling the knowledge graph to reflect the data's classification and grading information. For example, the node "user electricity consumption data" might be assigned the labels "user data" and "moderate damage." In this way, a data classification and grading knowledge graph containing detailed classification and grading information can be obtained. Based on this knowledge graph and deep learning algorithms, a data classification and grading model can be constructed. Moreover, as new power grid data is added, the aforementioned data classification and grading knowledge graph can be dynamically updated and expanded.

[0084] In this embodiment, by constructing a data classification and grading knowledge graph, power grid data can be centralized in a structured network. Based on category and level labels, the category and level of power grid data can be quickly and accurately located and retrieved, improving the efficiency and accuracy of data classification and grading, thereby improving the efficiency and accuracy of data compliance control.

[0085] In one embodiment, such as Figure 3 As shown, using a pre-built data classification and grading model, the power grid data to be processed is classified from the dimensions of legal attributes and organizational operation, and graded from the dimension of data security, resulting in classification and grading results, including:

[0086] S122, extract the legal attribute dimension features and organizational operation dimension features of the power grid data to be processed, as well as the first data feature value of the legal attribute dimension features and the second data feature value under the organizational operation dimension features.

[0087] S124, based on the data classification knowledge graph, matches the power grid data to be processed into the first category under the legal attribute dimension and the second category under the organizational operation dimension. The first data feature value corresponds to the first category, and the second data feature value corresponds to the second category.

[0088] S126, compare the first data feature value and the second data feature value to obtain the comparison result, and based on the comparison result, determine the first category or the second category as the category of the power grid data to be processed.

[0089] S128, based on a data hierarchical knowledge graph, matches the security level of the power grid data to be processed.

[0090] Following the above embodiments, the data classification and grading knowledge graph includes a data classification knowledge graph and a data grading knowledge graph, and the classification and grading results include categories and security levels.

[0091] Here, the first data feature value refers to the weight value of the legal attribute dimension feature, and the second data feature value refers to the weight value of the organizational operation dimension feature. In the process of constructing the data classification and grading model, the weight values ​​of historical power grid data under different classification dimensions can be preset by human, and the historical power grid data can be used as training data to train the data classification and grading model. Therefore, the data classification and grading model can extract the legal attribute dimension feature and organizational operation dimension feature of the power grid data to be processed, as well as the first data feature value of the legal attribute dimension feature and the second data feature value under the organizational operation dimension feature.

[0092] Furthermore, through a data classification knowledge graph, the first category of the power grid data under the legal attribute dimension can be "personal information" or "trade secrets," and the data category under the organizational operation dimension can be "user data" or "business data." The first data feature value corresponds to the first category, and the second data feature value corresponds to the second category. If the first data feature value is greater than the second data feature value, it indicates that the first category under the legal attribute dimension has a higher weight value, and therefore the first category is determined as the category of the power grid data to be processed. Similarly, if the first data feature value is less than the second data feature value, then the second category is determined as the category of the power grid data to be processed. For example, the data classification and grading model will assign high weight values ​​to type features such as "user data," "marketing business data," and "high-security technology." If the power grid data to be processed is a user's electricity consumption analysis data, the data classification knowledge graph determines the first category as "user data" and the second category as "marketing business data." If the first data feature value corresponding to "user data" is greater than the second data feature value corresponding to "marketing business data," then the power grid data is classified as user data. Furthermore, after determining that the power grid data is categorized as "user data", a deeper level of classification can be achieved through a data classification knowledge graph, such as classifying the power grid data as personal user information data under user data.

[0093] After classifying the power grid data, it is necessary to perform hierarchical processing. Specifically, taking personal user information data under the user data category as an example, based on the data hierarchical knowledge graph, the security level of the power grid data can be matched to level 4. This means that the data can only be accessed with authorization and may require encryption. Therefore, the final data classification and hierarchical result is "level 4 personal user information data".

[0094] In this embodiment, power grid data is classified and graded using a data classification knowledge graph. Data feature values ​​are introduced during the classification process, which can quickly and accurately classify and grade power grid data, thereby providing a more accurate basis for subsequently determining data compliance rules and data compliance strategies.

[0095] In one embodiment, such as Figure 4 As shown, prior to S300, the method also includes:

[0096] S220: For each legal provision in the preset legal knowledge base, based on the legal hierarchy to which the legal provision belongs, the data processing node corresponding to the legal provision, and the legal relationship subject and object corresponding to the legal provision, the legal provision is split and the split legal provisions are classified to obtain the classified legal provisions.

[0097] S240, based on the classified legal provisions, determines the data compliance requirements, performs table association processing on the historical classification and grading results, data compliance requirements, business process nodes and preset data compliance rules, and obtains the preset first table association data.

[0098] The legal knowledge base contains laws and regulations related to power grid data protection, covering requirements for the collection, processing, storage, and transmission of power grid data. Table association refers to the process of linking data between multiple tables, such as sharing one or more common fields, which serve as the basis for association, thereby establishing a mapping relationship between different data.

[0099] First, each legal provision in the pre-defined legal knowledge base is broken down and categorized based on factors such as its legal hierarchy, corresponding data processing nodes, and the subjects and objects of legal relationships. For example, if a legal provision stipulates that the collection and use of personal data should follow a specific process, this provision can be broken down according to different data processing nodes. These data processing nodes may include, but are not limited to, data collection processes, data usage processes, and data protection requirements. The resulting provisions are then categorized, for example, into "Data Collection Process Provisions," "Data Usage Process Provisions," and "Data Protection Provisions," etc.

[0100] Furthermore, based on the split and categorized legal provisions, data compliance requirements are determined. For example, for legal provisions concerning data collection processes, the corresponding data compliance requirements could be: requiring explicit consent from users before collecting personal data; requiring users to be informed of the purpose and processing method of the data when collecting it; requiring only necessary personal data to be collected and anonymizing the data when it is not needed; etc. Finally, the historical classification and grading results, data compliance requirements, business process nodes, and preset data compliance rules determined in the above embodiments or steps are subjected to table association processing to obtain preset first table association data.

[0101] In this embodiment, by establishing the association between historical classification and grading results, data compliance requirements, business process nodes, and data compliance rules, and by embedding a legal knowledge base in the process of determining data compliance requirements, and by splitting and classifying legal provisions based on the data processing nodes corresponding to the legal provisions, compliance management is deeply embedded in the actual business process, which can effectively solve the problem of compliance management not being implemented.

[0102] In one embodiment, such as Figure 5 As shown, prior to S700, the method also included:

[0103] S620, based on a pre-set legal knowledge base and pre-set data compliance rules, identifies data compliance risk points. These data compliance risk points are used to characterize the legal risks arising from violations of the pre-set data compliance rules.

[0104] S640 performs table join processing on historical classification and grading results, preset data compliance rules, data compliance risk points, and preset data compliance control strategies to obtain preset second table join data.

[0105] Specifically, legal provisions related to data compliance can be extracted from a pre-defined legal knowledge base. Based on these provisions and pre-defined compliance rules, the potential legal risks arising from violations of these rules can be identified. For example, pre-defined data compliance rules may include, but are not limited to: power grid companies may only use personal information according to user authorization and purpose, and may not use it beyond the scope agreed upon by the user. In this case, data compliance risks include, but are not limited to: leakage or illegal acquisition of user personal information, or the use of user personal information for other purposes without user consent, thereby violating the laws and regulations in the pre-defined legal knowledge base. Further, historical classification and grading results are correlated with the pre-defined data compliance rules, data compliance risk points, and pre-defined data compliance control strategies to obtain a pre-defined second table of correlated data.

[0106] In this embodiment, data compliance risk points of power grid data are analyzed using a legal knowledge base and data compliance rules. Historical classification and grading results are then correlated with preset data compliance rules, data compliance risk points, and preset data compliance control strategies to obtain a preset second table of correlated data. When this second table of correlated data is used to determine data compliance strategies, the matched strategies can reduce the legal risks of power grid data, thereby improving the compliance of power grid data.

[0107] To provide a clearer explanation of the power grid data processing method provided in this application, a specific embodiment and accompanying drawings are described below. Figure 6 The specific embodiment includes the following steps:

[0108] S601, extract entities and relationships between entities from historical power grid data, identify entities as nodes in the knowledge graph, and identify relationships between entities as edges in the knowledge graph to obtain the initial knowledge graph.

[0109] S602 classifies historical power grid data from the dimensions of legal attributes and organizational operation, and performs hierarchical processing of historical power grid data from the dimension of data security to obtain historical classification and grading results. Based on the historical classification and grading results, corresponding category labels and level labels are assigned to the nodes in the initial knowledge graph to obtain a data classification and grading knowledge graph.

[0110] S603, extract the legal attribute dimension features and organizational operation dimension features of the power grid data to be processed, as well as the first data feature value of the legal attribute dimension features and the second data feature value under the organizational operation dimension features. Based on the data classification knowledge graph, match the first category of the power grid data to be processed under the legal attribute dimension and the second category under the organizational operation dimension.

[0111] S604. Compare the first data feature value and the second data feature value to obtain the comparison result. Based on the comparison result, determine the first category or the second category as the category of the power grid data to be processed. Based on the data hierarchical knowledge graph, match the security level of the power grid data to be processed.

[0112] S605, for each legal provision in the preset legal knowledge base, based on the legal hierarchy to which the legal provision belongs, the data processing node corresponding to the legal provision, and the legal relationship subject and object corresponding to the legal provision, splits the legal provision and classifies the split legal provisions to obtain classified legal provisions, and determines the data compliance requirements based on the classified legal provisions.

[0113] S606 performs table association processing on historical classification and grading results, data compliance requirements, business process nodes, and preset data compliance rules to obtain preset first table association data. Based on the preset first table association data, it determines the target compliance requirements and target business process nodes that match the classification and grading results.

[0114] S607, based on the target data compliance requirements and target business process nodes, selects target data compliance rules from the preset data compliance rules that match the classification and grading results.

[0115] S608, based on a pre-set legal knowledge base and pre-set data compliance rules, identifies data compliance risk points. It then performs table join processing on historical classification and grading results, pre-set data compliance rules, data compliance risk points, and pre-set data compliance control strategies to obtain a pre-set second table of joined data.

[0116] S609, based on the target data compliance rules and the pre-set associated data in the second table, determine the target compliance risk points that match the classification and grading results, and select the data compliance control strategies that match the target compliance risk points from the pre-set data compliance control strategies.

[0117] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0118] Based on the same inventive concept, this application also provides a power grid data processing apparatus for implementing the power grid data processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more power grid data processing apparatus embodiments provided below can be found in the limitations of the power grid data processing method described above, and will not be repeated here.

[0119] In one embodiment, such as Figure 7 As shown, a power grid data processing device 700 is provided, including: a data classification and grading module 710, a first matching module 720, a data rule determination module 730, a second matching module 740, and a data compliance strategy determination module 750, wherein:

[0120] The data classification and grading module 710 is used to classify and grade the power grid data to be processed, and obtain the classification and grading results.

[0121] The first matching module 720 is used to determine the target compliance requirements and target business process nodes that match the classification and grading results based on the preset first table association data. The first table association data includes the association relationship between historical classification and grading results, preset data compliance requirements, business process nodes and preset data compliance rules.

[0122] The data rule determination module 730 is used to select target data compliance rules that match the classification and grading results from the preset data compliance rules based on the target data compliance requirements and target business process nodes.

[0123] The second matching module 740 is used to determine the target compliance risk points that match the classification and grading results based on the target data compliance rules and the preset second table associated data. The preset second table associated data includes the relationship between historical classification and grading results, preset data compliance rules, preset data compliance risk points, and preset data compliance control strategies.

[0124] The data compliance strategy determination module 750 is used to select data compliance control strategies that match the target compliance risk points from the preset data compliance control strategies.

[0125] In one embodiment, the data classification and grading module 710 is further used to classify the power grid data to be processed from the legal attribute dimension and the organizational operation dimension, and to grade the power grid data to be processed from the data security dimension, through a pre-built data classification and grading model, so as to obtain the classification and grading results.

[0126] In one embodiment, such as Figure 8 As shown, the power grid data processing device 700 also includes a model building module 760, which is used to extract entities and relationships between entities from historical power grid data, determine entities as nodes of the knowledge graph, determine the relationships between entities as edges of the knowledge graph, obtain an initial knowledge graph, classify historical power grid data from the dimensions of legal attributes and organizational operation, and perform hierarchical processing of historical power grid data from the dimension of data security, obtain historical classification and grading results, and assign corresponding category labels and level labels to the nodes in the initial knowledge graph based on the historical classification and grading results, obtain a data classification and grading knowledge graph, and the pre-built data classification and grading model includes the classification and grading knowledge graph.

[0127] In one embodiment, the data classification and grading knowledge graph includes a data classification knowledge graph and a data grading knowledge graph. The data classification and grading module 710 is further used to extract the legal attribute dimension features and organizational operation dimension features of the power grid data to be processed, as well as the first data feature value of the legal attribute dimension features and the second data feature value under the organizational operation dimension features. Based on the data classification knowledge graph, the first category of the power grid data to be processed under the legal attribute dimension and the second category under the organizational operation dimension are matched. The first data feature value corresponds to the first category, and the second data feature value corresponds to the second category. The first data feature value and the second data feature value are compared to obtain a comparison result. Based on the comparison result, the first category or the second category is determined as the category of the power grid data to be processed. Based on the data grading knowledge graph, the security level of the power grid data to be processed is matched. The classification and grading result includes the category and the security level.

[0128] In one embodiment, such as Figure 8As shown, the power grid data processing device 700 also includes a first table associated data determination module 770, which is used to split each legal provision in the preset legal knowledge base based on the legal hierarchy to which the legal provision belongs, the data processing node corresponding to the legal provision, and the legal relationship subject and object corresponding to the legal provision, and classify the split legal provisions to obtain classified legal provisions. Based on the classified legal provisions, data compliance requirements are determined, and table association processing is performed on the historical classification and grading results, data compliance requirements, business process nodes and preset data compliance rules to obtain the preset first table associated data.

[0129] In one embodiment, such as Figure 8 As shown, the power grid data processing device 700 also includes a second table associated data determination module 780, which is used to determine data compliance risk points based on a preset legal knowledge base and preset data compliance rules. The data compliance risk points are used to characterize the legal risks that arise when the preset data compliance rules are violated. The module performs table association processing on the historical classification and grading results, preset data compliance rules, data compliance risk points and preset data compliance control strategies to obtain the preset second table associated data.

[0130] Each module in the aforementioned power grid data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0131] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data such as power grid data to be processed. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a power grid data processing method.

[0132] Those skilled in the art will understand that Figure 9The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0133] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described power grid data processing method embodiment.

[0134] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described power grid data processing method embodiment.

[0135] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described power grid data processing method embodiment.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0137] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0138] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A power grid data processing method, characterized in that, The method includes: The power grid data to be processed is classified and graded to obtain the classification and grading results; Based on the preset first table of associated data, the target data compliance requirements and target business process nodes that match the classification and grading results are determined. The first table of associated data includes the relationship between historical classification and grading results, preset data compliance requirements, business process nodes and preset data compliance rules. Based on the target data compliance requirements and the target business process nodes, target data compliance rules that match the classification and grading results are selected from the preset data compliance rules; Based on the target data compliance rules and the preset second table association data, target compliance risk points that match the classification and grading results are determined. The preset second table association data includes the relationship between the historical classification and grading results, the preset data compliance rules, the preset data compliance risk points, and the preset data compliance control strategies. Select data compliance control strategies that match the target compliance risk points from the preset data compliance control strategies.

2. The method according to claim 1, characterized in that, The process of classifying and grading the power grid data to be processed to obtain classification and grading results includes: By using a pre-built data classification and grading model, the power grid data to be processed is classified from the dimensions of legal attributes and organizational operation, and graded from the dimension of data security, thus obtaining the classification and grading results.

3. The method according to claim 2, characterized in that, Before obtaining the classification and grading results by classifying the power grid data to be processed from the legal attribute dimension and the organizational and operational dimension using a pre-built data classification and grading model, and by grading the power grid data to be processed from the data security dimension, the method further includes: Extract entities and relationships between entities from historical power grid data; The entities are identified as nodes in the knowledge graph, and the relationships between the entities are identified as edges in the knowledge graph, thus obtaining an initial knowledge graph. The historical power grid data is classified from the dimensions of legal attributes and organizational operation, and graded from the dimension of data security to obtain historical classification and grading results; Based on the historical classification and grading results, corresponding category labels and level labels are assigned to the nodes in the initial knowledge graph to obtain a data classification and grading knowledge graph. The pre-built data classification and grading model includes the classification and grading knowledge graph.

4. The method according to claim 3, characterized in that, The data classification and grading knowledge graph includes a data classification knowledge graph and a data grading knowledge graph. The process involves classifying the power grid data to be processed from legal attribute and organizational operation dimensions using a pre-built data classification and grading model, and grading the data from a data security dimension to obtain classification and grading results, including: Extract the legal attribute dimension features and organizational operation dimension features of the power grid data to be processed, as well as the first data feature value of the legal attribute dimension features and the second data feature value under the organizational operation dimension features; Based on the data classification knowledge graph, the first category of the power grid data to be processed under the legal attribute dimension and the second category under the organizational operation dimension are matched. The first data feature value corresponds to the first category and the second data feature value corresponds to the second category. The first data feature value and the second data feature value are compared to obtain a comparison result. Based on the comparison result, the first category or the second category is determined as the category of the power grid data to be processed. Based on the data hierarchical knowledge graph, the security level of the power grid data to be processed is matched, and the classification and hierarchical results include the category and the security level.

5. The method according to any one of claims 1 to 4, characterized in that, Before determining the target data compliance requirements and target business process nodes that match the classification and grading results based on the preset first table association data, the method further includes: For each legal provision in the preset legal knowledge base, based on the legal hierarchy to which the legal provision belongs, the data processing node corresponding to the legal provision, and the legal relationship subject and object corresponding to the legal provision, the legal provision is split and the split legal provisions are classified to obtain classified legal provisions; Based on the categorized legal provisions, determine the data compliance requirements; The historical classification and grading results, the data compliance requirements, business process nodes, and preset data compliance rules are subjected to table association processing to obtain the preset first table association data.

6. The method according to any one of claims 1 to 4, characterized in that, Before determining the target compliance risk points that match the classification and grading results based on the target data compliance rules and the preset associated data in the second table, the method further includes: Based on a pre-defined legal knowledge base and pre-defined data compliance rules, data compliance risk points are identified. These data compliance risk points are used to characterize the legal risks arising from violations of the pre-defined data compliance rules. The historical classification and grading results, the preset data compliance rules, the data compliance risk points, and the preset data compliance control strategies are subjected to table association processing to obtain the preset second table association data.

7. A power grid data processing device, characterized in that, The device includes: The data classification and grading module is used to classify and grade the power grid data to be processed, and obtain the classification and grading results. The first matching module is used to determine the target data compliance requirements and target business process nodes that match the classification and grading results based on the preset first table association data. The first table association data includes the association relationship between historical classification and grading results, preset data compliance requirements, business process nodes and preset data compliance rules. The data rule determination module is used to select target data compliance rules that match the classification and grading results from the preset data compliance rules based on the target data compliance requirements and the target business process nodes. The second matching module is used to determine the target compliance risk points that match the classification and grading results based on the target data compliance rules and the preset second table association data. The preset second table association data includes the association relationship between the historical classification and grading results, the preset data compliance rules, the preset data compliance risk points, and the preset data compliance control strategies. The data compliance strategy determination module is used to select data compliance control strategies that match the target compliance risk points from the preset data compliance control strategies.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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