Data updating method and device for water conservancy knowledge graph and medium

Abstract

The invention discloses a data updating method and device for a water conservancy knowledge graph and a medium, and relates to the field of water conservancy data management, and the method comprises the steps: constructing an initial water conservancy knowledge graph based on a plurality of data sources of water conservancy data; monitoring the plurality of data sources, and judging whether a data change event is triggered or not; when the data change event is triggered, generating a knowledge graph updating task through a preset synchronization rule; when the number of the knowledge graph updating tasks is greater than 1, determining the processing priority of the knowledge graph updating tasks according to the importance of updating graph nodes in the water conservancy knowledge graph, and generating an updating task queue; and executing the update task queue, updating the water conservancy knowledge graph, and recording an update version snapshot. Through unified monitoring of a structured data source, an unstructured data source and an interface data source, the island effect of water conservancy data is broken, and unified linkage updating of cross-modal data such as reservoir attributes, monitoring reports and real-time water levels is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of water conservancy data management, in particular to a water conservancy knowledge graph data updating method, device and medium. BACKGROUND

[0002] As the core field of national infrastructure, the digital transformation of the water conservancy industry is not only an inevitable trend of technological upgrading, but also a strategic choice to cope with climate change, ensure water safety and improve water resource utilization efficiency. In the process of digital transformation of the water conservancy industry, the real-time, accuracy and relevance of water conservancy data have become key elements determining the core competitiveness of the industry. Knowledge graph technology, with its strong semantic association ability and dynamic data management characteristics, has gradually become a core technical support for solving the problems of digital transformation of water conservancy.

[0003] When traditional technology uses knowledge graph to process water conservancy data, it mainly relies on time-batch synchronization or manual intervention, lacks efficient data conversion mechanism, has limited integration capability for structured, semi-structured and unstructured data, is difficult to map multi-source heterogeneous data to knowledge graph, and lacks real-time monitoring and automatic triggering mechanism, making it difficult to respond to dynamic changes in data in a timely manner, resulting in inaccurate data and affecting the normal development of water conservancy business. SUMMARY

[0004] To solve the above problems, the present application proposes a water conservancy knowledge graph data updating method, comprising:

[0005] Based on a plurality of data sources of water conservancy data, an initial water conservancy knowledge graph is constructed; the plurality of data sources include structured data sources, unstructured data sources and interface data sources;

[0006] Monitoring the plurality of data sources to determine whether a data change event is triggered;

[0007] When the data change event is triggered, a knowledge graph updating task is generated through a preset synchronization rule;

[0008] When the number of knowledge graph updating tasks is greater than 1, the processing priority of the knowledge graph updating task is determined according to the importance of the updated graph node in the water conservancy knowledge graph, and an updating task queue is generated;

[0009] The updating task queue is executed to update the water conservancy knowledge graph and record the update version snapshot.

[0010] On the other hand, the present application also proposes a water conservancy knowledge graph data updating device, comprising:

[0011] At least one processor; and,

[0012] a memory in communication with the at least one processor; wherein

[0013] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform a data updating method of a water conservancy knowledge graph as described in the above examples.

[0014] In another aspect, the present application also provides a non-volatile computer storage medium storing computer executable instructions, which are configured to perform the data updating method of a water conservancy knowledge graph as described in the above examples.

[0015] The data updating method of a water conservancy knowledge graph provided by the present application can bring the following beneficial effects:

[0016] Through unified monitoring of structured data sources, unstructured data sources and interface data sources, the water conservancy data island effect is broken, and unified linkage updating of cross-modal data such as reservoir attributes, monitoring reports and real-time water levels is realized, solving the graph fragmentation problem caused by data source fragmentation in traditional methods.

[0017] Based on data importance classification, a business scenario adaptive task scheduling mechanism is constructed to dynamically adjust system resources, ensure high-value data change events, trigger resource preemption mechanism, realize real-time synchronization of knowledge graph, and eliminate scheduling delay bottleneck under traditional polling mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings described herein are used to provide further understanding of the present application, and form a part of the present application. The illustrative embodiments of the present application and their descriptions serve to explain the present application, and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 A flowchart of the data updating method of a water conservancy knowledge graph in the embodiments of the present application is shown;

[0020] Figure 2 A schematic diagram of the data updating device of a water conservancy knowledge graph in the embodiments of the present application is shown. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described in detail below with reference to the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0022] The technical solutions provided by the embodiments of the present application are described in detail below with reference to the drawings.

[0023] As shown in the Figure 1 application, the application provides a water conservancy knowledge graph data updating method, which comprises the following steps.

[0024] S101: Based on a plurality of data sources of water conservancy data, an initial water conservancy knowledge graph is constructed; the plurality of data sources comprise a structured data source, an unstructured data source and an interface data source.

[0025] Specifically, there are a plurality of data sources of water conservancy data, wherein the plurality of data sources comprise a structured data source, an unstructured data source and an interface data source.

[0026] The knowledge graph objects pre-constructed in the water conservancy system are acquired, the knowledge graph objects are divided into a plurality of categories according to water conservancy business scenarios, including engineering entities, monitoring data, business rules and the like, standardization graph labels are defined, the naming rule of “business object + attribute” can be used for definition, and the graph labels are added to the corresponding knowledge graph objects.

[0027] Further, the storage paths of each data in the data sources are acquired, the graph labels corresponding to the knowledge graph objects are associated with the storage paths respectively through the preset mapping rules corresponding to the data sources, so as to establish the mapping relationship between the data sources and the knowledge graph objects, each data in the data sources is synchronized to the corresponding knowledge graph object, the corresponding graph nodes are generated, and the water conservancy knowledge graph is constructed.

[0028] In the embodiments of the present application, for structured data, the mapping rule of the database table field to the graph label is defined through the R2RML language, and the physical storage path is bound; for unstructured data, the target label is selected or the label is automatically matched through semantic analysis of a large model when manually uploading, and the mapping of the label and the file storage directory is established; for interface data, the interface address is associated with the graph label, and a timing synchronization rule is set.

[0029] The graph nodes are generated based on the graph objects and stored in the associated directory in the graph database. Through the construction of the knowledge graph, the mapping relationship between the labels and the data directories is visualized in a tree structure, the synchronization state of each label directory is monitored in real time, and abnormal retry is supported.

[0030] S102: The plurality of data sources are monitored, and it is judged whether a data change event is triggered.

[0031] Specifically, the data change of the plurality of data sources of the water conservancy knowledge graph is captured through a multi-dimensional monitoring mechanism, the multi-dimensions include daily synchronization data flow, the latest label graph update situation, synchronization failure situation, failure error reason and the like, so as to realize the transparency of the knowledge base data management through the multi-dimensional index statistics analysis of the library graph.

[0032] For structured data sources, real-time monitoring of operation logs of structured databases is performed, the operation logs are parsed, and it is determined whether there is a data change operation, such as adding, deleting or modifying a record. For unstructured data sources, unstructured data files are scanned to obtain a current file list, a corresponding current file fingerprint set is generated through a hash algorithm, and it is determined whether it is consistent by comparing with the latest version fingerprint library. For interface data sources, the most recent two times of interface full response data adjacent to the current time are cached through double buffering, the double buffering respectively stores the Nth snapshot data and the N+1th snapshot data which are continuous in time sequence, the Nth snapshot data and the N+1th snapshot data are compared recursively based on the data primary key, and it is determined whether there is a difference.

[0033] It should be noted that each snapshot data includes a complete record set based on a primary key index. Taking the primary key as an anchor point, each record in the N+1th snapshot data is traversed, and the corresponding record in the Nth snapshot data is matched through the primary key. The fields of the matched record are hierarchically recursively checked, if the field is a basic type, the value difference is directly compared, and if the field is a composite type, the sub-fields are recursively disassembled until the basic type. According to the comparison result, a difference record set is generated, if the N+1th snapshot data exists and the Nth snapshot data does not exist, it is marked as “new record”; if the Nth snapshot data exists and the N+1th snapshot data does not exist, it is marked as “deleted record”; if the primary key exists but the field value changes, it is marked as “field modification”, and the specific change field path is attached.

[0034] In the embodiment of the application, for structured data sources, a change data capture technology is used in combination with database log analysis to real-time monitor the adding, deleting and modifying operations of MySQL and other relational databases. For example, by parsing the database binary log (Binlog) to obtain table structure change records, an ETL tool is used to implement incremental data extraction.

[0035] For unstructured data sources, a file system monitoring service is used to generate file content fingerprints through a SHA-256 hash algorithm, and the target directory is periodically scanned, such as a hydrological report folder and an engineering drawing folder. The current fingerprint is compared with the historical fingerprint library to identify file addition, modification or deletion events.

[0036] For interface data sources, a timing task or an event triggering mechanism is deployed to monitor the response data of external system interfaces, such as meteorological data APIs and sensor interfaces, and a R2RML mapping language is used to define the conversion rules of interface data to knowledge graph ontology.

[0037] S103: When a data change event is triggered, a knowledge graph update task is generated through a preset synchronization rule.

[0038] Specifically, when a data change event is triggered, data change information is captured, the data change information is parsed, a change storage path and a data change operation in the data change information are extracted, a change graph label associated with the change storage path is obtained, an update graph node corresponding to the change graph label is determined, and a knowledge graph update task corresponding to the update graph node is generated based on the data change operation.

[0039] In the embodiments of the present application, before determining the trigger data change event, the corresponding data change information is processed for different change data sources.

[0040] When the change data source is a structured data source, data change information is extracted, the data change information includes a changed data field and a change operation, it is determined that the change data source is an unstructured data source, a data change event is identified based on a preset file change rule, and it is determined that the change data source is an interface data source, a difference field is determined, and a change type is marked.

[0041] The preset file change rule for the unstructured data source is: if the first file fingerprint exists in the current file fingerprint set and does not exist in the latest version fingerprint library, it is marked as a new file event; if the second file fingerprint exists in the latest version fingerprint library and does not exist in the current file fingerprint set, it is marked as a delete file event; if the third file fingerprint has the same file path in the current file fingerprint set and the latest version fingerprint library, and the fingerprint values are different, it is marked as a file modification event, and a modification timestamp is recorded.

[0042] When the change data source is a structured data source, the changed data field is associated to a corresponding first knowledge graph node through a predefined mapping rule, and a knowledge graph update task corresponding to the first knowledge graph node is generated based on the change operation; when the change data source is an unstructured data source, entity attribute information corresponding to the changed file is extracted, a corresponding second knowledge graph node is matched, and a knowledge graph update task corresponding to the second knowledge graph node is generated based on the data change event; when the change data source is an interface data source, a third knowledge graph node corresponding to the difference field is matched, and a knowledge graph update task corresponding to the third knowledge graph node is generated based on the change type.

[0043] It should be noted that when the corresponding knowledge graph node is not matched, a to-be-reviewed instruction is generated and sent to a manager for confirmation.

[0044] It should be noted that the specific change source is determined by monitoring the event log, such as change data capture trigger record, file fingerprint difference report, interface response status code, etc. For structured data, R2RML mapping rules are used to convert table fields into node attributes of the knowledge graph; for unstructured data, entity and relationship are extracted through natural language understanding technology of large model. Taking label as object, different knowledge synchronization mechanisms are established according to data type, and multiple synchronization mechanisms such as listening capture, minute level, hour level, day level and month level are set. For example, water conservancy basic data is synchronized every 15 days, business rule library is synchronized every day, and monitoring rule type knowledge is synchronized according to the state of trigger.

[0045] S104: When the number of knowledge graph update tasks is greater than 1, the processing priority of the knowledge graph update task is determined according to the importance of the updated graph node in the water conservancy knowledge graph, and an update task queue is generated.

[0046] Specifically, when the number of knowledge graph update tasks is greater than 1, the task processing priority is determined by quantifying the data importance index to ensure that high-value data is synchronized first. The blood relationship chain corresponding to the updated graph node in the water conservancy knowledge graph is determined, and the multi-dimensional key coefficient corresponding to each graph node in the blood relationship chain is calculated based on multiple dimensions. The multi-dimensional key coefficient includes business key coefficient, time efficiency key coefficient and risk key coefficient. The multi-dimensional key coefficient is weighted to obtain the data importance of the updated graph node. According to the data importance, the processing priority of the corresponding knowledge graph update task is determined, and an update task queue is generated.

[0047] It should be noted that the blood relationship chain records the whole process flow transfer relationship of data from the original source to the knowledge graph node, which is used to trace the generation, processing and change history of data. For the business key coefficient, the business key coefficient is obtained by quantitatively processing the flood control warning type data, engineering dispatching instruction, historical archive data and the like; for the time efficiency key coefficient, the real-time data priority is higher than the historical statistical data; for the risk key coefficient, the risk propagation tree is constructed based on the number of downstream dependent systems and the maximum error influence radius.

[0048] In the embodiments of the present application, the weighted scoring method is used, for example, priority = business weight x business key coefficient + time efficiency weight x business key coefficient + risk weight x risk key coefficient. The tasks are arranged in descending order of priority, supporting parallel processing and resource isolation to avoid blocking high-priority tasks.

[0049] S105: The update task queue is executed, the water conservancy knowledge graph is updated, and the update version snapshot is recorded.

[0050] Specifically, the update operation is performed in priority order, the knowledge graph update tasks in the update task queue are executed in turn, the water conservancy knowledge graph is updated, the incremental change record of the water conservancy knowledge graph and the difference change result after the update are obtained, the update version snapshot of the record knowledge graph is recorded, and the update version snapshot includes the change source, the target graph element, the task ID, the incremental change record, the difference change result and the version number.

[0051] Further, the historical version is retrieved by the version control engine according to a timestamp or a task ID, and the graph change difference is visually displayed, when the version rollback is performed, the update operation is reversely performed based on the incremental record after the target snapshot fingerprint consistency is verified, and a new snapshot of the rollback operation is generated. In the embodiment of the application, the influence range of the rollback is also identified through the knowledge blood relationship, and a risk assessment report is generated.

[0052] In the embodiment of the application, the transaction interface of the Neo4j graph database is called, the node creation, attribute update, relationship establishment and the like are performed in the order of the task queue, and the parallel processing technology is used to improve the batch update efficiency. After each update task is executed, a version snapshot containing the following information is generated: timestamp, update task list, number of nodes or relationships affected by changes. A hash algorithm is used to generate a unique fingerprint of the updated graph state, which is stored in the version control table, associated with the knowledge blood relationship, and records the full-link information such as data source, extraction task and conversion rule, supporting the visualization of the blood relationship through the graph. The historical version rollback is supported, and the specified version can be restored based on the snapshot when the update is abnormal.

[0053] The application realizes the consistency synchronization of unstructured, semi-structured and structured data and graph data, can more intelligently manage various key data in the water conservancy industry, and provides high-precision reference for early warning and decision-making. According to different knowledge levels, an automatic knowledge synchronization mechanism is established, the intelligent application of water conservancy industry data is effectively improved, and the data more accurately reflects the factual data.

[0054] The application breaks the water conservancy data island effect through unified monitoring of structured data sources, unstructured data sources and interface data sources, realizes unified linkage update of cross-modal data such as reservoir attributes, monitoring reports and real-time water levels, and solves the graph fragmentation problem caused by data source fragmentation in traditional methods.

[0055] Based on the importance classification of data, a task scheduling mechanism adaptive to business scenarios is constructed, system resources are dynamically adjusted, high-value data change events are ensured, a resource preemption mechanism is triggered, real-time synchronization of the knowledge graph is realized, and the scheduling delay bottleneck under the traditional polling mechanism is eliminated.

[0056] As shown in Figure 2 The embodiment of the application also provides a data update device for a water conservancy knowledge graph.

[0057] at least one processor; and

[0058] a memory communicatively connected with the at least one processor; wherein

[0059] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the data updating method of the water conservancy knowledge graph as described in any of the above embodiments.

[0060] The embodiments of the present application also provide a non-volatile computer storage medium, which stores computer executable instructions, and the computer executable instructions are configured to implement the data updating method of the water conservancy knowledge graph as described in any of the above embodiments.

[0061] Each of the embodiments of the present application is described in a progressive manner, and the same or similar parts of each of the embodiments can be referred to each other. Each of the embodiments mainly describes the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, and thus the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0062] The device and medium provided by the embodiments of the present application are one-to-one corresponding to the method, and thus the device and medium also have the similar beneficial technical effects as the method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device and medium will not be described here.

[0063] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions described in the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 an apparatus with the functionality to achieve the specified processes or functions in the block or blocks.

[0065] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a Figure 1 one or more processes and / or blocks Figure 1 an apparatus with the functionality to achieve the specified processes or functions in the block or blocks.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the processes specified in the flowchart Figure 1 one or more processes and / or blocks Figure 1 an apparatus with the functionality to achieve the specified processes or functions in the block or blocks.

[0067] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0068] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) about which the computer stores information about an operating system, application software, and / or the like. Memory is an example of computer readable media.

[0069] Computer readable media includes permanent and non-permanent, moveable and non- moveable media that can be implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that is accessible to a computing device. According to the definition provided herein, computer readable media excludes transitory media, such as modulated data signals and carrier waves.

[0070] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0071] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A data updating method for a water conservancy knowledge graph, characterized in that: include: Build an initial water conservancy knowledge graph based on multiple data sources of water conservancy data; The multiple data sources include structured data sources, unstructured data sources and interface data sources; Monitor the multiple data sources to determine whether a data change event is triggered; When a data change event is triggered, a knowledge graph update task is generated through preset synchronization rules; When the number of the knowledge graph update tasks is greater than 1, determining the processing priority of the knowledge graph update tasks according to the importance of the updated graph nodes in the water conservancy knowledge graph, and generating an update task queue; Execute the update task queue, update the water conservancy knowledge graph, and record the updated version snapshot.

2. The data updating method of the water conservancy knowledge graph according to claim 1 is characterized in that: The water conservancy knowledge graph is constructed based on multiple data sources of water conservancy data, specifically including: Based on the water conservancy business category to which the knowledge graph object belongs, define the corresponding graph label and add it to the knowledge graph object; Obtain the storage path of each data in the data source, and associate the graph label corresponding to the knowledge graph object with the storage path through the preset mapping rules corresponding to the data source, so as to establish a mapping relationship between the data source and the knowledge graph object; Synchronize the data in the data source to the corresponding knowledge graph object, generate corresponding graph nodes, and construct a water conservancy knowledge graph.

3. The data updating method of the water conservancy knowledge graph according to claim 2 is characterized in that: The obtaining of the storage path of each data in the data source and associating the graph label corresponding to the knowledge graph object with the storage path through the preset mapping rule corresponding to the data source specifically includes: Obtaining a first storage path corresponding to each field in a structured database, determining a corresponding first atlas label based on a water conservancy business category corresponding to the structured database, and establishing a mapping relationship with the physical storage path; Obtain unstructured data files; Based on the user's selection, or by matching the key entity, a corresponding second graph label is obtained, and a mapping relationship is established with the second storage path of the unstructured data file; wherein the key entity is obtained by extracting the unstructured data file through a semantic recognition model; The interface source data is obtained, and the corresponding third atlas label is obtained through pattern matching, and a mapping relationship is established with the interface address of the interface source data.

4. The data updating method of the water conservancy knowledge graph according to claim 3 is characterized in that: The monitoring of multiple data sources of the water conservancy knowledge graph to determine whether a data change event is triggered specifically includes: Monitor the operation log of the structured database in real time, parse the operation log, and determine whether there is a data modification operation; Scan the unstructured data file to obtain the current file list, generate the corresponding current file fingerprint set through the hash algorithm, and compare it with the latest version of the fingerprint library to determine whether it is consistent; Cache the two most recent full-response data of the interface adjacent to the current time by using a double buffer; the double buffer stores the Nth snapshot data and the N+1th snapshot data that are consecutive in time sequence respectively; Based on the data primary key, the Nth snapshot data and the N+1th snapshot data are recursively compared to determine whether there is a difference.

5. The data updating method of the water conservancy knowledge graph according to claim 4 is characterized in that: The knowledge graph update task is generated by presetting synchronization rules, specifically including: Capturing data change information; the data change information includes a changed storage path and a data change operation; Obtaining a change graph tag associated with the change storage path, and determining an update graph node corresponding to the change graph tag; Based on the data change operation, a knowledge graph update task corresponding to the changed graph node is generated.

6. The data updating method of a water conservancy knowledge graph according to claim 1 is characterized in that: Determining the processing priority of the knowledge graph update task according to the importance of the graph nodes in the water conservancy knowledge graph specifically includes: Determine the lineage chain corresponding to the updated graph node in the water conservancy knowledge graph, and calculate the multi-dimensional criticality coefficient corresponding to the lineage chain based on multiple dimensions; the multi-dimensional criticality coefficient includes a business criticality coefficient, a timeliness criticality coefficient, and a risk criticality coefficient; Weighting the multi-dimensional criticality coefficients to obtain the data importance corresponding to the updated graph node; According to the importance of the data, the processing priority of the corresponding knowledge graph update task is determined.

7. The data updating method of the water conservancy knowledge graph according to claim 1 is characterized in that: The executing the update task queue, updating the water conservancy knowledge graph, and recording the updated version snapshot of the knowledge graph specifically includes: Execute the knowledge graph update tasks in the update task queue in sequence, update the water conservancy knowledge graph, and obtain the incremental change record of the water conservancy knowledge graph and the updated difference change results; Record the updated version snapshot of the water conservancy knowledge graph, store it in the graph database and establish a version index corresponding to the updated version snapshot; the version snapshot includes the change source, target graph element, task ID, the incremental change record, the difference change result and version number.

8. The data updating method of the water conservancy knowledge graph according to claim 7 is characterized in that: After recording the updated version snapshot of the water conservancy knowledge graph, storing it in a graph database, and establishing a version index corresponding to the updated version snapshot, the method further includes: Through the version control engine, historical versions of the knowledge graph are retrieved based on the version index, and the version snapshots corresponding to the historical versions are visually displayed; When performing a version rollback, the update operation is performed in reverse based on the incremental record, and a new snapshot of the rollback operation is generated.

9. A data updating device for a water conservancy knowledge graph, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data updating method of the water conservancy knowledge graph as described in any one of claims 1 to 8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to execute a data updating method for a water conservancy knowledge graph as described in any one of claims 1 to 8.

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