Management method and device for environmental rights and interests data based on knowledge graph

Abstract

The invention provides a knowledge graph-based environmental right and interest data management method and device, and the method comprises the steps: collecting various data related to environmental right and interest data, determining the incidence relation between all kinds of data, and constructing an environmental right and interest knowledge graph; for a data source, performing deep association on related data nodes in the knowledge graph through multi-dimensional event nodes so as to generate a multi-level knowledge graph; performing step-by-step tracing on target data to be queried, and positioning an initial source and a data tracing path corresponding to the target data; and performing automatic verification on the data in the multi-level knowledge graph, obtaining an external data source, and performing cross verification on the data in the multi-level knowledge graph and the external data source. According to the method, various management operations are performed on the environmental right data based on the knowledge graph, so that the transparency and traceability of the environmental right data are remarkably improved, and the efficiency and the intelligent level of data management are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and device for managing environmental rights and interests data based on a knowledge graph. Background Art

[0002] As people pay more and more attention to environmental protection and sustainable development, environmental rights and interests, as an important indicator for measuring environmental quality and sustainability, have attracted more and more attention. Since environmental rights and interests involve a wide range of fields, environmental rights and interests data has the characteristics of huge data volume and data type. In order to effectively manage and supervise environmental rights and interests, the collection, processing, storage and analysis of relevant environmental rights and interests data have become key links.

[0003] In the related technologies, database management technology is generally used to store and query environmental rights and interests data, and then data verification and data traceability are performed manually. However, the data management solutions in the above-mentioned related technologies can no longer meet the needs of modern environmental rights and interests data supervision. For example, traditional database management technology is only conducive to storing a large amount of environmental rights and interests data, and there are obvious deficiencies in the traceability of cross-domain environmental rights and interests data. In addition, manual data verification and traceability methods may result in large deviations in data processing results, and low management efficiency.

[0004] Therefore, how to accurately and efficiently integrate, trace and verify cross-border rights and interests data has become an urgent problem that needs to be solved. Summary of the invention

[0005] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0006] To this end, the first purpose of this application is to propose a management method for environmental rights and interests data based on knowledge graph. This method performs various management operations on environmental rights and interests data based on knowledge graph, significantly improves the transparency and traceability of environmental rights and interests data, improves the efficiency and intelligence level of data management, and solves the problems of unclear data traceability, difficult verification and low credibility existing in traditional environmental rights and interests data management.

[0007] The second purpose of this application is to propose a management device for environmental rights and interests data based on knowledge graph.

[0008] A third object of the present application is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above objectives, the first aspect of this application is to propose a method for managing environmental rights and interests data based on a knowledge graph, comprising the following steps:

[0010] Collecting various data related to environmental rights and interests data, determining the association relationship between the environmental rights and interests data and various types of data in the various data, and constructing an environmental rights and interests knowledge graph based on the collected data and the association relationship;

[0011] According to the data source, the relevant data nodes in the knowledge graph are deeply associated through multi-dimensional event nodes to generate a multi-level knowledge graph;

[0012] Based on the multi-level knowledge graph, the target data to be queried is traced level by level to locate the initial source and data traceability path corresponding to the target data;

[0013] Through the structured relationships in the multi-level knowledge graph, the data in the graph is automatically verified, and external data sources are obtained to cross-validate the data in the multi-level knowledge graph with the external data sources.

[0014] Optionally, in one embodiment of the present application, the dimensions of the event node include time, place and subject of behavior, and the relevant data nodes in the knowledge graph are deeply associated through multi-dimensional event nodes, including: in different dimensions, constructing an association chain between the data in the environmental rights and interests knowledge graph and the event nodes to generate multiple event chains; wherein each data node in the multi-level knowledge graph contains data sources, related events and processing procedures.

[0015] Optionally, in one embodiment of the present application, the data in the graph is automatically verified through the structured relationships in the multi-level knowledge graph, including: detecting whether there are any anomalies in the data of any data node, and if there are anomalies, determining the related nodes of any data node based on the structured relationships in the multi-level knowledge graph; comparing the abnormal data of any data node with the data of the related nodes to verify whether the abnormal data meets the rules of the related nodes.

[0016] Optionally, in one embodiment of the present application, the automated verification of the data in the graph through the structured relationships in the multi-level knowledge graph also includes: performing consistency detection on the data in any data node and all of the related nodes through a graph algorithm, wherein the graph algorithm includes a graph traversal algorithm.

[0017] Optionally, in one embodiment of the present application, locating the initial source and data traceability path corresponding to the target data includes: analyzing the target data and the multi-level knowledge graph through a graph traversal algorithm to determine the initial source corresponding to the target data; determining the shortest flow path between the initial source and the target data through a shortest path algorithm, and using the shortest flow path as the data traceability path corresponding to the target data.

[0018] Optionally, in one embodiment of the present application, after cross-verifying the data in the multi-level knowledge graph with the external data source, it also includes: obtaining a data source of a third-party verification system; and independently verifying the environmental data in the multi-level knowledge graph through the data source of the third-party verification system.

[0019] Optionally, in one embodiment of the present application, after locating the initial source and data traceability path corresponding to the target data, it also includes: displaying detailed information of the initial source and each node in the data traceability path through a multi-level graph structure; after cross-validating the data in the multi-level knowledge graph with the external data source, it also includes: obtaining the currently newly generated environmental rights and interests related data, and adding the newly generated environmental rights and interests related data to the multi-level knowledge graph.

[0020] To achieve the above purpose, the second aspect of the present application also proposes a management device for environmental rights and interests data based on a knowledge graph, comprising the following modules:

[0021] Data collection module, used to collect various data related to environmental rights and interests data;

[0022] A knowledge graph construction module, used to determine the association relationship between the environmental rights and interests data and various types of data in the multiple data, and to construct an environmental rights and interests knowledge graph based on the collected data and the association relationship;

[0023] The knowledge graph construction module is also used to deeply associate relevant data nodes in the knowledge graph through multi-dimensional event nodes according to the data source to generate a multi-level knowledge graph;

[0024] The data query and traceability module is used to trace the target data to be queried step by step based on the multi-level knowledge graph, and locate the initial source and data traceability path corresponding to the target data.

[0025] The data verification module is used to automatically verify the data in the multi-level knowledge graph through the structured relationships in the graph, obtain external data sources, and cross-verify the data in the multi-level knowledge graph with the external data sources.

[0026] In order to implement the above-mentioned embodiments, the third aspect of the present application also proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the management method of environmental rights and interests data based on the knowledge graph in the above-mentioned first aspect is implemented.

[0027] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects: The present application adopts environmental rights and interests data traceability and verification technology based on knowledge graph, and with its efficient data integration capability, traceability verification capability and intelligent analysis capability, it can ensure the authenticity, accuracy and traceability of the environmental rights and interests data after management. Among them, through the data traceability and verification method based on the knowledge graph, the transparency and traceability of environmental data can be greatly improved, avoiding the trust crisis caused by unclear data sources or cumbersome verification process. The present application can improve the management efficiency of environmental rights and interests data while ensuring data quality, and provide accurate and reliable environmental rights and interests data for all parties. Therefore, the environmental rights and interests data management method based on the knowledge graph of the present application can significantly improve the intelligence level of environmental rights and interests data management and the transparency and accuracy of processed data through integration, traceability and verification and other management operations, and the management operation is simple and easy to implement.

[0028] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0030] Figure 1 A flowchart of a method for managing environmental rights and interests data based on a knowledge graph proposed in an embodiment of the present application;

[0031] Figure 2 A schematic diagram of the structure of a device for managing environmental rights and interests data based on a knowledge graph proposed in an embodiment of the present application;

[0032] Figure 3 A schematic diagram of the structure of a specific knowledge graph-based environmental rights and interests data management device proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0033] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0034] It should be noted that since environmental rights and interests involve a wide range of areas, including but not limited to the right to use natural resources such as water, air, and land, pollution emission rights, and carbon emission quotas, there are many types and contents of environmental rights and interests data, involving different aspects. As a result, there are several major problems in the current management of environmental rights and interests data:

[0035] First, data sources are scattered and difficult to integrate. Environmental rights and interests data usually comes from different departments, institutions and systems, such as environmental monitoring stations and environmental protection project implementation units. Due to the diversity of data sources, different data formats and standards, it is difficult to unify and integrate data, information sharing is difficult, and it is difficult to form a complete and standardized environmental rights and interests data system.

[0036] Second, it is difficult to trace the source of environmental rights and interests data. In the traditional environmental data management system, data traceability is poor. On the one hand, the data collection and recording process usually lacks unified standards, resulting in unclear data sources and flow information; on the other hand, due to the lack of clear correlation between data, it is difficult to trace the specific source, processing process and responsible party of the data, causing the credibility of the data to be questioned.

[0037] Third, data verification and compliance checks are cumbersome. The verification process of environmental rights and interests data in relevant embodiments usually relies on manual inspection and multi-party coordination. For example, when verifying whether a certain environmental protection project complies with relevant laws and regulations, it is necessary to check multiple information sources such as the project's application materials, environmental monitoring data, and environmental impact assessment reports. This process is not only time-consuming and laborious, but also prone to omissions or human errors, low verification efficiency, and poor accuracy.

[0038] Fourth, there is a lack of efficient data sharing and collaboration mechanisms. In practical applications, data management and supervision involving environmental rights and interests is not just a matter of one aspect, and cross-domain collaboration is particularly important. However, due to the lack of unified data standards and sharing mechanisms among the relevant objects involved, data interaction and collaboration are very difficult, which affects the efficient use of environmental rights and interests data by all parties.

[0039] Fifth, data privacy and security issues. Environmental rights and interests data involve a large amount of sensitive information, so data security and privacy protection issues are particularly prominent. At present, some data may face the risk of data leakage, tampering and abuse during transmission and storage, affecting the transparency and credibility of environmental data.

[0040] In the relevant embodiments, the environmental rights data management is carried out by combining traditional database management technology and manual verification. However, in practical applications, the data management scheme in the above relevant embodiments still has the following problems:

[0041] First, many current environmental data management systems are based on traditional database management technology. These databases usually store data in tabular form and lack modeling and reasoning of complex relationships between data. Although this method can store a large amount of environmental data, it cannot effectively display the multi-dimensional relationships between data, especially in cross-domain data query and traceability. The data query process usually relies on traditional SQL queries, but this is inefficient for the association and relationship derivation between complex environmental data, and it is difficult to perform dynamic reasoning.

[0042] Second, most existing environmental data verification methods rely on manual review and on-site inspection. This process is not only labor-intensive and time-consuming, but also due to human factors, the accuracy and consistency of verification cannot be guaranteed. In addition, manual inspection is difficult to cope with large-scale and complex environmental data, and cannot achieve real-time monitoring and rapid feedback.

[0043] Third, environmental monitoring and project data management are separated. In traditional environmental data management, environmental monitoring data and environmental project management data are usually separated and lack effective association. This results in the inability to form a closed loop between environmental monitoring data and records during project implementation, which in turn affects the effectiveness and timeliness of environmental data supervision.

[0044] Fourth, environmental rights data cannot be traced or verified. In the existing environmental data management, although the data collection system may be relatively complete, it is often impossible to fully record the entire process of data collection and the relevant information of the participating entities. As a result, the source and processing path of the data cannot be traced, which increases the credibility risk of the data and affects the basis of environmental protection decision-making.

[0045] That is, the management scheme of environmental rights and interests data in the current relevant embodiments still cannot solve the problems existing in the process of environmental rights and interests data management. For example, the current environmental rights and interests data management still faces problems such as data dispersion, difficulty in tracing and verification. Traditional database management methods and manual data verification methods can no longer meet the management needs of modern environmental data.

[0046] To this end, this application proposes a management method and device for environmental rights and interests data based on knowledge graph, which adopts environmental rights and interests data traceability technology and data verification technology based on knowledge graph. With its efficient data integration capability, traceability verification capability and intelligent analysis capability, it provides an effective implementation solution for solving the relevant problems in the above-mentioned environmental rights and interests data management.

[0047] In order to facilitate understanding of the implementation principle of the management method of environmental rights and interests data based on the knowledge graph of this application, the relevant functions of the knowledge graph adopted and implemented in this application are first described in detail below:

[0048] First, use the knowledge graph to integrate scattered data sources. The knowledge graph can connect data from different fields and sources through unified nodes and relationships to form a structured graph, helping all parties to quickly understand the inherent connections between the data. For example, the knowledge graph constructed in this application can effectively link environmental monitoring data with relevant regulations, policies, and project execution records, helping all parties to understand the background and application scenarios of environmental data.

[0049] Second, the traceability and verifiability of environmental data can be achieved. An important advantage of the knowledge graph is that it can clearly record the source and flow path of the data. By constructing a knowledge graph of environmental rights and interests data, this application can clarify information such as the source of data collection, processing process, and responsible parties, making the data traceable. Users can use the graph query system to view information such as the source of the data, related regulatory activities, historical events, etc., to ensure the transparency and credibility of the data.

[0050] Third, use knowledge graphs to improve data verification efficiency and accuracy. The environmental rights and interests data verification method based on the knowledge graph in this application can automatically check data consistency and compliance through the relationship between nodes and edges in the graph. By setting verification rules, it is possible to automatically determine whether a certain data complies with relevant environmental laws and regulations and whether it is consistent with other data, greatly improving the efficiency and accuracy of data verification.

[0051] Fourth, promote cross-departmental and cross-field collaboration and sharing. Knowledge graphs can promote data sharing and collaboration among different departments and institutions through unified data models and standardized node relationships. For example, environmental monitoring agencies, enterprises, and regulatory authorities can jointly analyze data through shared knowledge graphs to improve decision-making efficiency.

[0052] Fifth, realize intelligent decision-making and in-depth analysis. The knowledge graph is not only a storage and query tool, it can also support intelligent decision-making analysis through graph reasoning technology. For example, this application combines historical data and real-time monitoring data, and can automatically identify potential environmental risks through the knowledge graph, provide decision support, and provide a more scientific basis for environmental supervision.

[0053] The following describes a method and device for managing environmental rights and interests data based on a knowledge graph proposed in an embodiment of the present application with reference to the accompanying drawings.

[0054] Figure 1 A flowchart of a method for managing environmental rights and interests data based on a knowledge graph proposed in an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0055] Step S101, collect various data related to environmental rights and interests data, determine the association relationship between the environmental rights and interests data and various types of data in the various data, and construct an environmental rights and interests knowledge graph based on the collected data and the association relationship.

[0056] Specifically, this application first constructs an environmental rights and interests knowledge graph, and in order to construct the knowledge graph, it is necessary to first collect a variety of data related to environmental rights and interests data.

[0057] For example, environmental rights and interests data can be environmental rights and interests items, including rights and interests data such as the right to use and quotas of various environmental resources. The various data related to environmental rights and interests data include: environmental protection rules and system data, environmental protection policy restriction data, environmental monitoring data, various environmental protection events and environmental protection project execution data, and other data related to environmental rights and interests.

[0058] Then, the collected data is standardized and various types of data information are represented by standardized data models, so as to convert the collected data into a unified knowledge graph structure.

[0059] As a possible implementation method, this application collects and processes data through an automated processing system. First, machine learning and natural language processing technologies are used to extract key information related to environmental rights and interests from unstructured data in different fields, and then the extracted unstructured data is converted into a structured language that can be recognized by a computer. Then, based on the structured language, modeling is performed through preset open source tools to facilitate the subsequent generation of a knowledge graph based on the environmental rights and interests data ontology.

[0060] Furthermore, the associations between the various types of data collected are determined. For example, the associations between data can be various relationships such as "included in" and "conform to". Then, the entities in the various types of data collected are used as nodes, and the "edges" between the nodes are determined according to the associations between the various types of data, thereby generating an environmental rights and interests knowledge graph.

[0061] For example, environmental rights and interests data is associated with specific events, behaviors, responsible entities and other entity information to form basic nodes and edges in the knowledge graph. The nodes in the knowledge graph include but are not limited to: "environmental rights and interests items", "regulatory policies", "monitoring events", "project actions", "responsible entities" and "monitoring data of a certain object". The edges formed represent the relationship between these nodes, and the types of edges include but are not limited to: conform to, depend on, and belong to.

[0062] Among them, each node in the environmental rights and interests knowledge graph of this application can be further subdivided according to actual needs. For example, project actions can be subdivided into actions of different environmental protection projects, and monitoring events can be subdivided into monitoring times for different environmental resources.

[0063] Therefore, this application constructs an initial environmental rights knowledge graph.

[0064] Step S102, based on the data source, the relevant data nodes in the knowledge graph are deeply associated through multi-dimensional event nodes to generate a multi-level knowledge graph.

[0065] Specifically, in order to facilitate subsequent data tracing, this application uses multi-dimensional event nodes to further multi-dimensionally associate the relevant data nodes in the knowledge graph initially generated in the previous step, and converts the initially generated knowledge graph into a multi-level network graph by constructing association chains between each node in the knowledge graph and the event nodes in different dimensions.

[0066] In one embodiment of the present application, the dimensions of event nodes include time, place and subject of behavior, and the relevant data nodes in the knowledge graph are deeply associated through multi-dimensional event nodes, including: in different dimensions, constructing an association chain of data and event nodes in the environmental rights and interests knowledge graph to generate multiple event chains; wherein each data node in the multi-level knowledge graph contains data sources, related events and processing procedures.

[0067] Specifically, in this embodiment, each event can be associated with the relevant data of environmental rights and interests through multiple dimensions such as the time, location and subject of the event. For example, each event node (such as resource monitoring events, environmental pollution events and project progress verification events, etc.) is associated with environmental rights and interests items, and a timeline of environmental rights and interests data and an associated chain of events are constructed. Based on the associated chain, the associated events of a certain environmental rights and interests data at different time nodes, the processing process of the event, and the changes of the data over time can be determined.

[0068] For example, a pollution emission event may be directly related to an environmental protection project. The pollution emission event leads to the generation of an environmental protection project, and the environmental protection project includes a monitoring event for a certain environmental resource. These determined associations will be established in the knowledge graph, and based on the event chain, it can be determined that the source of the monitoring event for a certain environmental resource is the pollution emission event. Similarly, multiple event chains can be formed according to the principle of the above example.

[0069] Furthermore, this application conducts in-depth mining of each node in the knowledge graph through the correlation relationship of events, supplements the knowledge graph after the event chain, and can determine the source of each node based on the correlation relationship. Each data node in the generated multi-level network graph will contain information such as its source, related events and processing process.

[0070] Step S103, based on the multi-level knowledge graph, trace the target data to be queried step by step to locate the initial source and data traceability path corresponding to the target data.

[0071] Specifically, this step traces the data in the environmental rights knowledge graph. Through the multi-level knowledge graph obtained in the previous step, the target data to be queried can be traced back to the original source and complete processing process of the target data, thereby ensuring the authenticity, integrity and transparency of the environmental data.

[0072] For example, users can enter keywords such as an environmental rights item or a monitoring event through the knowledge graph query system, use the keywords as target data, automatically generate a data traceability path in the multi-level knowledge graph, and determine the initial source of the target data.

[0073] In one embodiment of the present application, the initial source and data traceability path corresponding to the target data are located, including: analyzing the target data and the multi-level knowledge graph through a graph traversal algorithm to determine the initial source corresponding to the target data; determining the shortest flow path between the initial source and the target data through a shortest path algorithm, and using the shortest flow path as the data traceability path corresponding to the target data.

[0074] Specifically, in this embodiment, a variety of graph algorithms are used, such as the shortest path algorithm and the graph traversal algorithm, etc., which can quickly locate the source of the target data in the knowledge graph and its flow path. Among them, by analyzing the source information of the node where the target data is located in the multi-level network graph, and traversing the entire knowledge graph through the graph traversal algorithm, the source node corresponding to the target data is determined, thereby obtaining the initial source corresponding to the target data. Because there may be multiple associations between different nodes, this embodiment also determines the shortest flow path between the source node and the node where the target data is located through the shortest path algorithm, and uses the obtained path as the data tracing path corresponding to the target data.

[0075] Therefore, this embodiment can quickly and accurately locate the source of the data to be queried and its flow path.

[0076] Furthermore, after locating the initial source and data traceability path corresponding to the target data in this embodiment, it also includes: displaying detailed information of the initial source and each node in the data traceability path through a multi-level graph structure.

[0077] Specifically, this embodiment visualizes the data traceability query results. The query system displays the source of the target data, historical events, and related policies, etc., through a graph structure, and detailed information of each node in the data traceability path, so that users can clearly see the source of the data, the flow trajectory, and the detailed information of each node in the flow trajectory.

[0078] For example, when a user queries the air quality data of a certain area, the query system can display detailed information such as the monitoring source, monitoring time, monitoring equipment and responsible entity of the data.

[0079] Step S104, automatically verify the data in the graph through the structured relationship in the multi-level knowledge graph, obtain the external data source, and cross-verify the data in the multi-level knowledge graph with the external data source.

[0080] Specifically, this step verifies and manages the data in the environmental rights knowledge graph. On the one hand, the verification process includes the use of structured relationships in the knowledge graph to automatically verify the data. For example, by comparing relevant laws, policies, monitoring records, environmental events and other information, the compliance and accuracy of the data can be verified. On the other hand, for each data node, the corresponding relationship in the knowledge graph can be queried for cross-verification with external data sources to ensure data consistency and compliance.

[0081] For example, the external trusted data sources obtained can be environmental protection policies and data released by various environmental protection departments or institutions. This application combines the data relationships in the knowledge graph with external data sources for cross-verification to ensure that the environmental data in the knowledge graph is consistent with the actual situation. For example, when verifying the compliance of an environmental project, the system will check whether the implementation records of the project meet the requirements of relevant laws and regulations and whether it is carried out in accordance with the prescribed procedures.

[0082] In one embodiment of the present application, the data in the graph is automatically verified through the structured relationships in the multi-level knowledge graph, including: detecting whether there are anomalies in the data in any data node, and if there are anomalies, determining the related nodes of any data node based on the structured relationships in the multi-level knowledge graph; comparing the abnormal data of any data node with the data of the related nodes to verify whether the abnormal data meets the rules of the related nodes.

[0083] Specifically, this embodiment performs management operations of automatic rule verification, and the system automatically checks the compliance of the data in a certain node based on the set rules and policies. For example, if the environmental monitoring data in a certain node shows that the pollution exceeds the standard, the data is determined to be abnormal. Then, according to the structured relationship in the knowledge graph, the relevant node of the environmental monitoring data node is determined to be a certain environmental protection law and regulation node. The system will automatically check whether the data conflicts with the environmental protection laws and regulations in the relevant nodes, and compare whether the pollution exceeding the standard value is within the conditions permitted by the environmental protection laws and regulations. If the environmental protection laws and regulations in the relevant nodes are not met, the relevant responsible parties can be reminded.

[0084] Therefore, this embodiment reduces errors and omissions in manual verification and improves verification efficiency by performing automatic verification management operations.

[0085] In one embodiment of the present application, the data in the graph is automatically verified through the structured relationships in the multi-level knowledge graph, and also includes: consistency detection of the data in any data node and all related nodes through a graph algorithm, wherein the graph algorithm includes a graph traversal algorithm.

[0086] Specifically, this embodiment performs a management operation of data consistency verification, first combining the structured relationship in the above-mentioned knowledge graph and the graph traversal algorithm to determine all related nodes of a certain node in the knowledge graph. Then, the graph traversal algorithm is used to detect the consistency of the environmental data in the node and all related nodes to ensure that there are no contradictions or errors between the related data in all related nodes.

[0087] For example, the system can check whether a project's environmental impact assessment report matches the actual environmental monitoring data to ensure the consistency of environmental data.

[0088] In one embodiment of the present application, after cross-verifying the data in the multi-level knowledge graph with the external data source, it also includes: obtaining the data source of the third-party verification system; and independently verifying the environmental data in the multi-level knowledge graph through the data source of the third-party verification system.

[0089] Specifically, this embodiment performs management operations for third-party verification and report generation. The multi-level knowledge graph is connected to the third-party verification agency to obtain the third-party data source. The third-party data source is then used to independently verify the environmental data in the multi-level knowledge graph to increase the credibility of the data. Among them, the independent verification may not consider the association relationship between the data in the knowledge graph of this application, and only judge whether the environmental data in the knowledge graph matches the third-party data source.

[0090] Furthermore, for each verification management operation in the above embodiment, a detailed verification report can be generated according to each verification result, and the report will list all verification items, verification rules, verification results, and abnormal data that need to be further processed. Then, the verification report is displayed to the user using the visual interface provided by the query system, so that the user can query and understand the verification status. This embodiment can also support users at different levels to query, analyze, generate reports, and other operations on data by setting permissions.

[0091] Based on the above embodiment, in the process of managing the data in the environmental rights and interests knowledge graph, the multi-level knowledge graph can also be updated according to the real-time newly generated environmental rights and interests related data. In one embodiment of the present application, after cross-validating the data in the multi-level knowledge graph with the external data source, it also includes: obtaining the currently newly generated environmental rights and interests related data, and adding the newly generated environmental rights and interests related data to the multi-level knowledge graph.

[0092] Specifically, the environmental rights and interests related data are the environmental rights and interests data collected in step S101 and various related data. For the newly generated data among the various data types collected in S101, for example, the environmental monitoring data in the current period, the above steps S101 to S104 can be repeatedly executed to add the newly generated data to the multi-level knowledge graph and manage the newly generated data, thereby realizing the update function of the environmental rights and interests knowledge graph.

[0093] In summary, the management method of environmental rights and interests data based on the knowledge graph of the embodiment of the present application adopts the environmental rights and interests data traceability and verification technology based on the knowledge graph. With its efficient data integration capabilities, traceability verification capabilities and intelligent analysis capabilities, it can ensure the authenticity, accuracy and traceability of the environmental rights and interests data after management. Among them, through the data traceability and verification method based on the knowledge graph, the transparency and traceability of environmental data can be greatly improved, avoiding the trust crisis caused by unclear data sources or cumbersome verification processes. This method can improve the management efficiency of environmental rights and interests data while ensuring data quality, and provide accurate and reliable environmental rights and interests data for all parties. Therefore, this method is based on the environmental rights and interests data management method of the knowledge graph. Through integration, traceability and verification and other management operations, it can significantly improve the intelligence level of environmental rights and interests data management and the transparency and accuracy of processed data, and the management operation is simple and easy to implement.

[0094] In order to implement the above embodiment, the present application also proposes a management device for environmental rights and interests data based on knowledge graph. Figure 2 A schematic diagram of the structure of a management device for environmental rights and interests data based on a knowledge graph proposed in an embodiment of the present application is shown in FIG. Figure 2 As shown, the system includes: a data collection module 100, a knowledge graph construction module 200, a data query and traceability module 300 and a data verification module 400.

[0095] The data collection module 100 is used to collect various data related to environmental rights and interests data.

[0096] The knowledge graph construction module 200 is used to determine the association relationship between the environmental rights and interests data and various types of data in the multiple data, and to construct an environmental rights and interests knowledge graph based on the collected data and the association relationship.

[0097] The knowledge graph construction module 200 is also used to deeply associate relevant data nodes in the knowledge graph through multi-dimensional event nodes according to the data source to generate a multi-level knowledge graph.

[0098] The data query and traceability module 300 is used to trace the target data to be queried step by step based on a multi-level knowledge graph, and locate the initial source and data traceability path corresponding to the target data.

[0099] The data verification module 400 is used to automatically verify the data in the multi-level knowledge graph through the structured relationships in the graph, obtain external data sources, and cross-verify the data in the multi-level knowledge graph with the external data sources.

[0100] Optionally, in one embodiment of the present application, Figure 3As shown, the device also includes a visualization and report generation module 500, and the visualization and report generation module 500 is specifically used to: visualize the data verification results and generate various data verification reports.

[0101] Optionally, in one embodiment of the present application, the knowledge graph construction module 200 is specifically used to: construct association chains of data and event nodes in the environmental rights and interests knowledge graph in different dimensions to generate multiple event chains; wherein each data node in the multi-level knowledge graph includes data sources, related events and processing procedures.

[0102] Optionally, in one embodiment of the present application, the data verification module 400 is specifically used to: detect whether there are any anomalies in the data of any data node, and if there are anomalies, determine the related nodes of any data node based on the structured relationship in the multi-level knowledge graph; compare the abnormal data of any data node with the data of the related nodes to verify whether the abnormal data meets the rules of the related nodes.

[0103] Optionally, in one embodiment of the present application, the data verification module 400 is specifically used to: perform consistency detection on the data in any data node and all related nodes through a graph algorithm, wherein the graph algorithm includes a graph traversal algorithm.

[0104] Optionally, in one embodiment of the present application, the data query and traceability module 300 is specifically used to: analyze the target data and the multi-level knowledge graph through a graph traversal algorithm to determine the initial source corresponding to the target data; determine the shortest flow path between the initial source and the target data through a shortest path algorithm, and use the shortest flow path as the data traceability path corresponding to the target data.

[0105] Optionally, in one embodiment of the present application, the data verification module 400 is specifically used to: obtain a data source of a third-party verification system; and independently verify environmental data in a multi-level knowledge graph through the data source of the third-party verification system.

[0106] Optionally, in one embodiment of the present application, the visualization and report generation module 500 is specifically used to: display the initial source and detailed information of each node in the data traceability path through a multi-level graph structure; the knowledge graph construction module 200 is specifically used to: obtain the newly generated environmental rights and interests related data, and add the newly generated environmental rights and interests related data to the multi-level knowledge graph.

[0107] It should be noted that the above explanation of the embodiment of the method for managing environmental rights and interests data based on the knowledge graph is also applicable to the device of this embodiment and will not be repeated here.

[0108] In summary, the management device for environmental rights and interests data based on the knowledge graph in the embodiment of the present application can achieve effective traceability and verification of environmental rights and interests data, helping all parties to improve their trust and management level of environmental data. Through the traceability and verification method of environmental rights and interests data based on the knowledge graph, the intelligence and transparency of environmental rights and interests data management can be greatly improved, and the trust problems and operational complexity existing in traditional data management methods can be effectively solved.

[0109] In order to implement the above embodiments, the present application also proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the management method of environmental rights and interests data based on the knowledge graph as described in any of the above-mentioned first aspect embodiments.

[0110] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0111] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0112] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0113] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0114] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0115] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0116] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0117] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for managing environmental rights and interests data based on knowledge graph, characterized in that: The following steps are involved: Collecting various data related to environmental rights and interests data, determining the association relationship between the environmental rights and interests data and various types of data in the various data, and constructing an environmental rights and interests knowledge graph based on the collected data and the association relationship; According to the data source, the relevant data nodes in the knowledge graph are deeply associated through multi-dimensional event nodes to generate a multi-level knowledge graph; Based on the multi-level knowledge graph, the target data to be queried is traced level by level to locate the initial source and data traceability path corresponding to the target data; Through the structured relationships in the multi-level knowledge graph, the data in the graph is automatically verified, and an external data source is obtained to cross-verify the data in the multi-level knowledge graph with the external data source.

2. The method according to claim 1, characterized in that The dimensions of the event node include time, location and subject of behavior. The multi-dimensional event nodes are used to deeply associate the relevant data nodes in the knowledge graph, including: In different dimensions, an association chain between the data in the environmental rights and interests knowledge graph and the event nodes is constructed to generate multiple event chains; wherein each data node in the multi-level knowledge graph includes data sources, associated events and processing procedures.

3. The method according to claim 1, characterized in that The method of automatically verifying the data in the graph through the structured relationships in the multi-level knowledge graph includes: Detecting whether data in any data node has an anomaly, and if an anomaly exists, determining a related node of any data node based on the structured relationship in the multi-level knowledge graph; The abnormal data of any data node is compared with the data of the related node to verify whether the abnormal data meets the rules of the related node.

4. The method according to claim 3, characterized in that The method of automatically verifying the data in the graph through the structured relationships in the multi-level knowledge graph further includes: A consistency check is performed on the data in any data node and all the related nodes through a graph algorithm, wherein the graph algorithm includes a graph traversal algorithm.

5. The method according to claim 1, characterized in that The locating the initial source and data tracing path corresponding to the target data includes: Analyze the target data and the multi-level knowledge graph by using a graph traversal algorithm to determine the initial source corresponding to the target data; The shortest flow path between the initial source and the target data is determined by a shortest path algorithm, and the shortest flow path is used as the data tracing path corresponding to the target data.

6. The method according to claim 1, characterized in that After cross-validating the data in the multi-level knowledge graph with the external data source, the method further includes: Obtain data sources from third-party verification systems; The environmental data in the multi-level knowledge graph is independently verified through the data source of the third-party verification system.

7. The method according to claim 1, characterized in that After locating the initial source and data tracing path corresponding to the target data, the method further includes: Display the initial source and detailed information of each node in the data traceability path through a multi-level graph structure; After cross-validating the data in the multi-level knowledge graph with the external data source, the method further includes: Acquire the currently newly generated environmental rights and interests related data, and add the newly generated environmental rights and interests related data to the multi-level knowledge graph.

8. A management device for environmental rights and interests data based on knowledge graph, characterized in that: Includes the following modules: Data collection module, used to collect various data related to environmental rights and interests data; A knowledge graph construction module, used to determine the association relationship between the environmental rights and interests data and various types of data in the multiple data, and to construct an environmental rights and interests knowledge graph based on the collected data and the association relationship; The knowledge graph construction module is also used to deeply associate relevant data nodes in the knowledge graph through multi-dimensional event nodes according to the data source to generate a multi-level knowledge graph; A data query and traceability module is used to trace the target data to be queried step by step based on the multi-level knowledge graph, and locate the initial source and data traceability path corresponding to the target data; The data verification module is used to automatically verify the data in the multi-level knowledge graph through the structured relationships in the graph, obtain external data sources, and cross-verify the data in the multi-level knowledge graph with the external data sources.

9. The device according to claim 8, characterized in that It also includes a visualization and report generation module, which is specifically used to: Visualize the data validation results and generate various data validation reports.

10. A non-transitory 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 method for managing environmental rights and interests data based on a knowledge graph as described in any one of claims 1 to 7.

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