A land survey report updating method and device, electronic equipment and medium

Abstract

The application discloses a land survey report updating method and device, electronic equipment and medium, constructs a first target knowledge graph of land survey based on survey requirement information, survey purpose and survey data; according to the report generation request, at least one to be filled information in the report template is determined, and a traceability identifier is inserted for the to-be-filled information; based on the report generation request, the first target knowledge graph is searched to obtain an information query link of the to-be-filled information, and an association relationship between the traceability identifier and the information query link is established; for any to-be-filled information, the first target knowledge graph is queried according to the information query link associated with the traceability identifier of the to-be-filled information to obtain information content of the to-be-filled information, and an initial report is generated according to the information content; when the updating operation is triggered, the initial report is updated according to the association relationship between the traceability identifier and the information query link of the to-be-updated information, and automatic updating of the report template is realized.

Description

Technical Field

[0001] This application relates to the field of land survey, and in particular to a method, apparatus, electronic device and medium for updating land survey reports. Background Technology

[0002] In related technologies, land surveys and statistical analyses can be conducted to obtain corresponding analytical results, which can then be filled into a template for a land survey report to generate the report.

[0003] However, land survey statistical analysis often employs various applications to analyze land use for different survey purposes, resulting in different versions and batches of data. Due to the dynamic changes in this data, land analysis results are frequently updated, making the updating process of related analytical results in survey reports quite complex and difficult to achieve automatic updates of analytical results in report templates. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and medium for updating land survey reports to solve the problem of difficulty in automatically updating the analytical results of land surveys in report templates.

[0005] To address the aforementioned technical problems, this application provides a method for updating land survey reports, including: Based on the survey requirements, objectives, and data of the land survey, a first target knowledge graph of the land survey is constructed; the first target knowledge graph includes the report template of the land survey. Based on the report generation request of the land survey, at least one piece of information to be filled in the report template is determined, and a preset traceability identifier is inserted for the information to be filled. Based on the report generation request, the first target knowledge graph is retrieved to obtain the information query link of the information to be filled in the first target knowledge graph, and the association between the source identifier and the information query link is established. For any of the information to be filled, the first target knowledge graph is queried according to the information query link that is associated with the source identifier of the information to be filled, to obtain the information content of the information to be filled, and the initial report of the land survey is generated based on the information content; When an update operation is triggered, the initial report is updated based on the association between the traceability identifier of the information to be updated and the information query link; wherein, the update operation is triggered by a change in the first target knowledge graph due to a change in at least one of the survey requirement information, the survey purpose, and the survey data; the information to be updated is the information to be filled affected by the change in the first target knowledge graph.

[0006] In this embodiment, based on the report generation request, the information to be filled in the land survey report template is determined, and a source identifier is inserted for the information to be filled. Simultaneously, based on the report generation request, an information query link for the information to be filled is established in the first target knowledge graph. An association is established between the source identifier of the information to be filled and the information query link, and based on this association, the content of the information to be filled is retrieved in the first target knowledge graph to fill in the various pieces of information to be filled in the report template, thereby achieving automatic report generation. When the first target knowledge graph changes due to at least one of the land survey requirements, survey objectives, or survey data, the affected information query link can be identified, and based on the association between the source identifier and the information query link, the affected information to be filled in the report can be accurately located and updated, thereby avoiding a full regeneration of the report and achieving automatic updating of the report content.

[0007] Optionally, the construction of a first target knowledge graph of the land survey based on the survey requirements, survey objectives, and survey data includes: Extract at least two entity words and the relationships between them from the survey requirements and objectives of the land survey. Based on the stated survey purpose, a constraint rule is determined for any of the entity terms, and the constraint rule is used as the first attribute of the entity term; the survey requirement information includes the survey objects and survey rules of the land survey; the entity terms include object terms extracted from the survey objects; The survey data is used as a second attribute of the object words; Based on the first attribute of the entity words and the association relationship between the entity words, establish the semantic association relationship between the entity words; An initial knowledge graph for the land survey is constructed using the entity words, the semantic relationships between the entity words, the first attribute, and the second attribute. According to the survey rules, the initial knowledge graph is adjusted to obtain the first target knowledge graph.

[0008] In this embodiment, entity words and their relationships are extracted from the survey requirements and objectives. The survey objectives are transformed into constraint rules between entity words as the first attribute of the entity words, and the survey data is used as the second attribute of the object words. Based on the entity words and their attributes, the land survey information, which originally existed in unstructured forms such as text, is transformed into semantic constraints in a knowledge graph that can be understood and executed by computers. This enables subsequent report generation and updates to automatically follow complex statistical standards and business rules.

[0009] Optionally, the survey requirement information further includes survey indicators; the entity terms include indicator terms extracted from the survey indicators; the survey rules include a survey strategy of statistically analyzing the survey data of the survey subjects to obtain survey data for at least one of the survey indicators; the step of adjusting the initial knowledge graph according to the survey rules to obtain the first target knowledge graph includes: The survey rules are broken down to obtain a survey strategy for any of the survey indicators; For any of the aforementioned survey indicators, the survey strategy of the survey indicator is added as a third attribute to the indicator term and the initial knowledge graph is displayed on a preset visualization interface. In response to the user's association operation with the indicator icons of the indicator words on the visualization interface, the dependency relationship between the survey strategies of the survey indicators is established, so that the survey strategies of the survey indicators are transformed into composite survey strategies, and the initial knowledge graph is transformed into the first target knowledge graph.

[0010] In this embodiment, by decomposing the statistical logic implicit in the survey rules into independent survey strategies corresponding to each survey indicator, and attaching these strategies to the indicator terms as third attributes, the originally vague and scattered statistical rules become clear and accessible in the knowledge graph. By associating indicator icons on the visual interface, users can establish dependencies between survey strategies in an intuitive and graphical way, creating composite survey strategies without writing complex rule code. This allows complex statistical tasks to be identified and executed in the form of composite survey strategies.

[0011] Optionally, the first target knowledge graph further includes at least one statistical model related to the indicator terms and at least one data visualization form related to the statistical model; the entity terms include thematic concept terms extracted from the survey purpose; the entity terms in the first target knowledge graph are connected by edges; the step of retrieving the first target knowledge graph based on the report generation request to obtain the information query link of the information to be filled in the first target knowledge graph includes: The report generation request is converted into a query statement for the first target knowledge graph based on the information to be filled, and the first target knowledge graph is retrieved based on the query statement to obtain target topic concept words associated with the query statement; Based on the query statement, a search is conducted among the indicator words connected to the target topic concept words to obtain the target indicator words associated with the query statement; Based on the query statement, a search is performed in at least one statistical model related to the target indicator term to obtain the target statistical model that matches the query statement; Based on the query statement, a search is performed in at least one data visualization form related to the target statistical model to obtain the target data visualization form that matches the query statement. The information query link is constructed by combining the target topic concept words, the target indicator words, the composite survey strategy of the target indicator words, the target statistical model, the target data visualization form, and the survey data of object words connected with the target indicator words.

[0012] In this embodiment of the application, the topic concepts, indicator terms, statistical models, data visualization forms, composite survey strategies of indicator terms, and survey data of object terms related to the report generation request are integrated into a unified information query link. This enables the information query link to automatically query the first target knowledge graph and obtain the information content of the information to be filled in the report template for the report generation request.

[0013] Optionally, when an update operation is triggered, updating the initial report based on the association between the traceability identifier of the information to be updated and the information query link includes: When an update operation is triggered, query at least one of the following in the first target knowledge graph: the changed entity words, the attributes of the entity words, and the connection relationships between the entity words. Determine the information query link that has changed due to at least one change in the entity word, the attribute of the entity word, and the connection relationship between the entity words, and determine the source identifier to be processed that is associated with the changed information query link; The information to be filled in the traceability identifier to be processed is taken as the information to be updated, and the information content of the information to be updated is updated based on the modified first target knowledge graph to obtain the first target report.

[0014] In this embodiment of the application, when the first target knowledge graph changes due to at least one change in the survey requirements, survey purpose and survey data of the land survey, instead of simply and crudely regenerating the entire report, the specific elements in the knowledge graph that have changed are first located, then the affected information query links are identified, and finally only the information to be updated associated with the affected information query links is updated, which significantly reduces the computational overhead of the report update.

[0015] Optionally, the first target report is displayed on the visualization interface; the land survey report update method further includes: In response to the user's operation on the traceability identifier in the first target report on the visualization interface, an information query link that is associated with the traceability identifier is determined; Based on the information query link, determine the process of generating the information content of the traceability identifier to be filled; Based on the generation process, a source audit report on the information content of the information to be filled is generated.

[0016] In this embodiment, users do not need to consult the underlying code or database logs. They can intuitively obtain the complete generation path of information content from raw data to final presentation simply by operating the traceability identifier in the report. This greatly improves the transparency of the report generation process and provides direct and operable technical support for the compliance review, result verification and responsibility determination of land survey data.

[0017] Optionally, when an update operation is triggered, updating the initial report based on the association between the traceability identifier of the information to be updated and the information query link includes: When the survey requirement information and / or the survey data change, extract the changed entity words related to the entity words from the survey requirement information and / or the survey data, and determine the association between the changed entity words based on the survey requirement information and / or the survey data; Based on the relationships between the modified entity words, the modified entity words are added to the first target knowledge graph to obtain the second target knowledge graph; in the second target knowledge graph, the modified entity words and entity words related to the modified entity words are connected by edges; During the updating process of the information content to be updated, it is detected whether the information content to be updated is abnormal; If the information content of the information to be updated is abnormal, the source identifier of the information to be updated is used as the processing identifier, and the second target knowledge graph is queried according to the information query link corresponding to the processing identifier to obtain the target change entity words that caused the abnormal information content. Based on the target change entity words, generate the abnormal reason for the information content of the information to be updated, and add the abnormal reason to the first target report to obtain the second target report.

[0018] In this embodiment, when survey requirements and / or survey data change, the system accurately extracts the changed entity words and their relationships, and incrementally updates the graph by adding nodes and edges to obtain a second target knowledge graph. During the report content update process, the system can proactively detect whether there are any anomalies in the information to be updated. Once an anomaly is detected, the system uses the relationship between the source identifier and the information query link to reverse locate the specific changed entity words in the second target knowledge graph that caused the anomaly, thereby generating the cause of the anomaly. This significantly reduces the time cost and technical threshold of manually investigating the cause of the anomaly.

[0019] This application also discloses a land survey report updating device, comprising: The knowledge graph construction module is used to construct a first target knowledge graph of the land survey based on the survey requirements, survey objectives, and survey data; the first target knowledge graph includes the report template of the land survey. The traceability identifier insertion module is used to determine at least one piece of information to be filled in the report template according to the report generation request of the land survey, and to insert a preset traceability identifier into the information to be filled. The information query link acquisition module is used to retrieve the first target knowledge graph based on the report generation request, obtain the information query link of the information to be filled in the first target knowledge graph, and establish the association between the source identifier and the information query link; The initial report generation module is used to query the first target knowledge graph for any of the information to be filled, based on the information query link that is associated with the traceability identifier of the information to be filled, to obtain the information content of the information to be filled, and to generate the initial report of the land survey based on the information content. The report update module is used to update the initial report based on the association between the traceability identifier of the information to be updated and the information query link when an update operation is triggered; wherein, the update operation is triggered by a change in the first target knowledge graph due to a change in at least one of the survey requirement information, the survey purpose, and the survey data; the information to be updated is the information to be filled affected by the change in the first target knowledge graph.

[0020] Optionally, the map construction module includes: An extraction submodule is used to extract at least two entity words and the relationships between the entity words from the survey requirements and survey objectives of the land survey. The constraint rule determination submodule is used to determine the constraint rule for any of the entity words based on the survey purpose, and to use the constraint rule as the first attribute of the entity word; the survey requirement information includes the survey objects and survey rules of the land survey; the entity words include object words extracted from the survey objects; The second attribute, as a submodule, is used to use the survey data as the second attribute of the object words; The semantic association establishment submodule is used to establish semantic associations between entity words based on the first attribute of the entity words and the association between the entity words; The knowledge graph construction submodule is used to construct the initial knowledge graph of the land survey using the entity words, the semantic relationships between the entity words, the first attribute, and the second attribute. The first target knowledge graph acquisition submodule is used to adjust the initial knowledge graph according to the survey rules to obtain the first target knowledge graph.

[0021] Optionally, the survey requirement information further includes survey indicators; the entity terms include indicator terms extracted from the survey indicators; the survey rules include a survey strategy of statistically analyzing the survey data of the survey subjects to obtain survey data for at least one of the survey indicators; the first target knowledge graph acquisition submodule includes: A splitting unit is used to split the survey rules to obtain a survey strategy for any of the survey indicators; The graph display unit is used to add the survey strategy of the survey indicator as a third attribute of the indicator term to the indicator term for any of the survey indicators, and display the initial knowledge graph on a preset visualization interface. The dependency establishment unit is used to establish dependencies between the survey strategies of the survey indicators in response to the user's association operation on the indicator icons of the indicator words on the visualization interface, so that the survey strategies of the survey indicators are transformed into composite survey strategies and the initial knowledge graph is transformed into the first target knowledge graph.

[0022] Optionally, the first target knowledge graph further includes at least one statistical model related to the indicator terms and at least one data visualization form related to the statistical model; the entity terms include thematic concept terms extracted from the survey objective; the entity terms in the first target knowledge graph are connected by edges; the information query link obtaining module includes: The first retrieval submodule is used to convert the report generation request into a query statement of the information to be filled for the first target knowledge graph, and to retrieve the first target knowledge graph based on the query statement to obtain target topic concept words associated with the query statement; The second retrieval submodule is used to retrieve target indicator words associated with the query statement by searching among the indicator words connected to the target topic concept words. The third retrieval submodule is used to search in at least one statistical model related to the target indicator words based on the query statement, and obtain the target statistical model that matches the query statement. The fourth retrieval submodule is used to retrieve, based on the query statement, at least one data visualization form related to the target statistical model, and obtain the target data visualization form that matches the query statement. The information query link construction submodule is used to construct the information query link by combining the target topic concept words, the target indicator words, the composite survey strategy of the target indicator words, the target statistical model, the target data visualization form, and the survey data of object words connected with the target indicator words.

[0023] Optionally, the report update module includes: The query submodule is used to query at least one of the following when an update operation is triggered: the entity words that have changed in the first target knowledge graph, the attributes of the entity words, and the connection relationships between the entity words. The pending traceability identifier determination submodule is used to determine the information query link that has changed due to at least one change in the entity word, the attribute of the entity word, and the connection relationship between the entity words, and to determine the pending traceability identifier that is associated with the changed information query link. The first target report acquisition submodule is used to take the information to be filled in the traceability identifier to be processed as the information to be updated, and update the information content of the information to be updated based on the modified first target knowledge graph to obtain the first target report.

[0024] Optionally, the first target report is displayed on the visualization interface; the land survey report updating device further includes: The information query link determination module is used to determine the information query link that is associated with the traceability identifier in the first target report on the visualization interface in response to the user's operation. The generation process determination module is used to determine the generation process of the information content of the traceability identifier to be filled based on the information query link. The report generation module is used to generate a source audit report of the information content to be filled in, based on the generation process.

[0025] Optionally, the report update module includes: The modified entity term determination submodule is used to extract modified entity terms related to the entity terms from the survey requirement information and / or the survey data when the survey requirement information and / or the survey data change, and to determine the association relationship between the modified entity terms based on the survey requirement information and / or the survey data. The second target knowledge graph acquisition submodule is used to add the changed entity words to the first target knowledge graph according to the association relationship between the changed entity words, thereby obtaining the second target knowledge graph; in the second target knowledge graph, the changed entity words and the entity words related to the changed entity words are connected by edges; An anomaly detection submodule is used to detect whether the information content of the information to be updated is abnormal during the updating process of the information content to be updated. The target change entity word acquisition submodule is used to, if the information content of the information to be updated is abnormal, take the source identifier of the information to be updated as the processing identifier, and query the second target knowledge graph according to the information query link corresponding to the processing identifier to obtain the target change entity words that caused the abnormal information content; The second target report submodule is used to change entity words according to the target, generate the abnormal reason of the information content of the information to be updated, and add the abnormal reason to the first target report to obtain the second target report.

[0026] This application also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method described in this application.

[0027] This application also discloses a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in this application. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the steps of a land survey report update method provided in this application embodiment; Figure 2 This is a flowchart illustrating the construction process of a dynamic knowledge graph provided in an embodiment of this application; Figure 3This is a flowchart illustrating the generation process of a report framework provided in an embodiment of this application; Figure 4 This is a flowchart illustrating the generation process of an updatable intelligent analysis report provided in an embodiment of this application; Figure 5 This is a schematic diagram illustrating a data trust closed loop and dynamic report maintenance capability provided in the embodiments of this application; Figure 6 This is a flowchart illustrating the generation of an analysis report with decision-making and auxiliary suggestions, as provided in an embodiment of this application. Figure 7 This is a structural block diagram of a land survey report updating device provided in the embodiments of this application. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0030] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0031] To facilitate understanding of the technical solutions and effects of the embodiments of this application, the relevant technologies of this application will be briefly described below.

[0032] In related technologies, land surveys and statistical analyses can be conducted to obtain land survey reports. However, the process of conducting land surveys and statistical analyses to obtain reports has at least the following problems: (1) Land survey and statistical analysis is geared towards natural resource management operations. It can be used to analyze land use in different analytical scenarios, involving different versions and batches of data. The results obtained from the analysis of these different versions and batches of data are also frequently updated, making it difficult to update the template of the land survey and statistical analysis results report, and making it difficult to achieve automatic updates of the results data in the template.

[0033] (2) Land survey statistical analysis involves multiple regions, each of which may have multiple versions and years of data. Therefore, its data statistics are complex and the data volume is large. In related technologies, when updating the data of different regions in the land survey statistical analysis results report, the "secondary data entry and switching" method is usually adopted. That is, the original data of different regions are first copied from their original locations and imported into a unified analysis database. When it is necessary to update the report of a certain region, the data source connected to the report template is manually changed to switch the original data of that region to the target version or target year data. Then the report generation process is rerun to obtain the updated results report. However, due to the complexity of the data statistics and the large data volume, this method is prone to errors, extremely slow, and cannot achieve automatic updates.

[0034] (3) The relevant technologies usually rely on manual operation to generate land survey statistical analysis results reports, which has at least the following problems: ① High dependence on manual operation, requiring manual extraction of data from multiple sources, verification and filling in of fixed templates, which is a tedious and repetitive process; ② Static templates are difficult to adapt to the differentiated report generation needs of different topics, levels or policy calibers, and the templates have poor adaptability; if the report generation needs change, the report generation template needs to be redeveloped, which is costly; ③ After the data or rules of the land survey are updated, the generated statistical analysis results reports cannot be automatically synchronized and need to be regenerated manually, resulting in a long report production cycle and difficulty in supporting real-time decision-making; ④ Difficult batch processing: Faced with a large number of report generation needs from different regions, the report generation speed is slow, the resource consumption is large, and it is difficult to complete the process efficiently and consistently.

[0035] (4) The land survey statistical analysis results reports generated by related technologies have significant defects in data security and data traceability, including: ① lack of data lineage: the data in the report is difficult to trace back to the original source, processing process and rules used, which makes it difficult to verify and assign responsibility for the report; ② chaotic version management: there are multiple versions of data sources, rules and the report itself, and there is a lack of system correlation records, which leads to the problem of unclear versions when the report is historically traced and compared; ③ uncontrollable impact of changes: when the basic data or rules change, it is not possible to quickly assess the specific impact on existing reports, making it difficult to achieve accurate incremental updates and posing a risk of data inconsistency; ④ weak audit support: there is a lack of full-chain, visualized traceability vouchers, which makes it difficult to meet the strict requirements of reliable data sources, transparent processes and reproducible results in quality inspection, external audit and compliance verification, thus creating hidden dangers for data security and management.

[0036] Reference Figure 1 The diagram illustrates a flowchart of a land survey report update method provided in an embodiment of this application, which may specifically include the following steps: Step 101: Based on the survey requirements, survey objectives, and survey data of the land survey, construct a first target knowledge graph of the land survey; the first target knowledge graph includes the report template of the land survey; In this embodiment, the survey requirements information for land surveys can be obtained through multi-source heterogeneous data input. The survey requirements information for land surveys may include: core data of the land survey, indicator data for land statistical analysis, and policy documents and business rules for the land survey. Specifically, the core data of the land survey may include: spatial and attribute data such as land type attributes, ownership, land type area, slope classification, and change patches; the indicator data for land statistical analysis may include: total area, annual changes, spatial distribution, etc.; and the policy documents and business rules for the land survey may include: technical regulations of the Ministry of Natural Resources, local special policies, management procedures, and constraints, etc.

[0037] In this embodiment of the application, land survey data can be obtained by conducting a land survey. The land survey may have a specific purpose, which can also be referred to as the analysis scenario.

[0038] In this embodiment, a first objective knowledge graph for land surveys can be constructed based on the survey requirements, objectives, and data. Different first objective knowledge graphs can be constructed for different survey objectives.

[0039] In some embodiments of this application, constructing a first target knowledge graph of the land survey based on the survey requirements, survey objectives, and survey data includes: Extract at least two entity words and the relationships between them from the survey requirements and objectives of the land survey. Based on the stated survey purpose, a constraint rule is determined for any of the entity terms, and the constraint rule is used as the first attribute of the entity term; the survey requirement information includes the survey objects and survey rules of the land survey; the entity terms include object terms extracted from the survey objects; The survey data is used as a second attribute of the object words; Based on the first attribute of the entity words and the association relationship between the entity words, establish the semantic association relationship between the entity words; An initial knowledge graph for the land survey is constructed using the entity words, the semantic relationships between the entity words, the first attribute, and the second attribute. According to the survey rules, the initial knowledge graph is adjusted to obtain the first target knowledge graph.

[0040] In this embodiment of the application, at least two entity words can be extracted from the survey requirements and survey objectives of the land survey using entity recognition technology.

[0041] Specifically, the core data of the land survey in the survey requirements information may include the survey subjects, the indicator data of land statistical analysis may include survey indicators, and the policy documents and business rules of the land survey may include survey rules. Entity terms may include object terms extracted from the survey subjects, indicator terms extracted from the survey indicators, and rule terms extracted from the survey rules. Entity terms in the survey purpose may be subject concept terms, also known as subject concept entities.

[0042] For example, entity recognition technology can be used to extract object terms from core data of land surveys, including land category concepts and spatial entities; indicator terms can be extracted from indicator data of land statistical analysis, including statistical indicator entities; and rule terms can be extracted from policy documents and business rules of land surveys, including policy rule entities. Land category concepts can include: paddy fields, dry land, forest land, etc.; spatial entities can include districts / counties, plots, ecological red lines, etc.; statistical indicator entities can include: cultivated land area, construction land scale, etc.; and policy rule entities can include: statistical caliber, land category control requirements, etc.

[0043] In this embodiment of the application, relation extraction technology can be used to extract the association relationships between entity words. The association relationships may include: "arable land → includes → paddy field", "ecological red line → control → forest land".

[0044] In this embodiment of the application, based on the purpose of the survey, constraint rules for any entity term can be determined, and these constraint rules are used as the first attribute of the entity term. Specifically, the constraint rules for an entity term are the rules governing its relationship with other entity terms when conducting land survey statistics according to the survey purpose.

[0045] In a specific example, based on the survey objectives, constraint rules for each entity term can be defined as the first attribute of the entity term. For example, the constraint rule for the entity term "arable land" could be: "A reduction in arable land must be associated with a construction land approval map node." This means that during statistics, if the area of ​​arable land decreases, it must be associated with a map node related to "construction land approval." The constraint rule for the entity term "ecological land" could be: "Construction land must be excluded from the definition of ecological land." This means that when calculating ecological land, the statistical scope should not include the portion related to "construction land."

[0046] In this embodiment of the application, the survey data of the land survey can be targeted at the survey object, and the survey data can be used as the second attribute of the object term.

[0047] In this embodiment, semantic relationships between entity words can be established based on the constraint rules and associations between entity words. For example, a multi-dimensional semantic relationship can be constructed, such as "land category concept entity → spatial entity → statistical indicator entity → policy rule entity → thematic concept entity," to achieve semantic interoperability across entity types. If the land category concept entity is "arable land," its constraint rule is "a reduction in arable land requires association with a construction land approval map node." Based on this constraint rule, when the area of ​​arable land within the spatial entity "district / county" decreases, it must be associated with the corresponding "construction land approval" node of that county. This allows for the aggregation and verification of whether the statistical indicator entity "amount of arable land reduction" of that county complies with the policy rule entity of the "balance between occupation and compensation policy," ultimately serving the thematic concept entity in the "annual change analysis of arable land" analysis scenario.

[0048] In this embodiment of the application, entity words, semantic relationships between entity words, first attributes, and second attributes are formally expressed and stored in a graph database to form an initial knowledge graph for the land survey.

[0049] Reference Figure 2 This document illustrates a flowchart of a dynamic knowledge graph construction process provided in an embodiment of this application. It involves acquiring multi-source heterogeneous data input, extracting data and knowledge from this data to obtain land change survey data, statistical analysis indicators, and policy documents and business rules. The land change survey data includes: land type, ownership, and spatial entities; the statistical analysis indicators include: area, slope, and change range; and the policy documents and business rules include: management procedures and constraints. Semantic modeling and association are then performed, including entity identification and extraction, relationship and attribute definition, and formalization of rule constraints. Next, the knowledge graph is constructed and stored, creating a multi-dimensional graph structure of "land type concept - spatial entity - statistical indicator - policy rule," which is stored in a graph database. Finally, dynamic semantic fusion is performed, including semantic retrieval and reasoning, dynamic binding of business rules, and synchronization with real-time data and policies to form a dynamic knowledge graph.

[0050] In this embodiment, semantic retrieval can be configured for the initial knowledge graph. Through multi-hop reasoning, entity association queries and semantic reasoning can be performed in the graph based on user-reported needs.

[0051] In this embodiment, the initial knowledge graph can be adjusted according to the survey rules to obtain the first target knowledge graph. In this embodiment, a dynamic link interface can also be established between the first target knowledge graph and real-time updated land survey data and policy documents. When the underlying data or policies are updated, the graph automatically updates entities and relationships synchronously, ensuring that the knowledge system is synchronized with the current state of land survey operations in real time.

[0052] In this embodiment, entity words and their relationships are extracted from the survey requirements and objectives. The survey objectives are transformed into constraint rules between entity words as the first attribute of the entity words, and the survey data is used as the second attribute of the object words. Based on the entity words and their attributes, the land survey information, which originally existed in unstructured forms such as text, is transformed into semantic constraints in a knowledge graph that can be understood and executed by computers. This enables subsequent report generation and updates to automatically follow complex statistical standards and business rules.

[0053] In this embodiment of the application, by constructing a knowledge graph, the custom analysis report template format in related technologies is broken. The relevant policy requirements and business rules of land survey are associated with data entities and statistical indicators such as "land category attributes", "ownership", "land category area", "slope classification", and "change patch" of land use. A multi-dimensional land survey business knowledge graph containing "land category concept - spatial entity - statistical indicator - policy rule - analysis scenario" is constructed, and its constraint rules are recorded. It can be dynamically linked and semantically fused with real-time updated land survey data and policy documents to ensure that the knowledge system is synchronized with the current business situation.

[0054] In some embodiments of this application, the survey requirement information further includes survey indicators; the entity terms include indicator terms extracted from the survey indicators; the survey rules include a survey strategy of statistically analyzing the survey data of the survey subjects to obtain survey data for at least one of the survey indicators; and adjusting the initial knowledge graph according to the survey rules to obtain the first target knowledge graph includes: The survey rules are broken down to obtain a survey strategy for any of the survey indicators; For any of the aforementioned survey indicators, the survey strategy of the survey indicator is added as a third attribute to the indicator term and the initial knowledge graph is displayed on a preset visualization interface. In response to the user's association operation with the indicator icons of the indicator words on the visualization interface, the dependency relationship between the survey strategies of the survey indicators is established, so that the survey strategies of the survey indicators are transformed into composite survey strategies, and the initial knowledge graph is transformed into the first target knowledge graph.

[0055] In this embodiment of the application, the survey rules in the survey requirement information include: a survey strategy that statistically analyzes the survey data of the survey subjects to obtain survey data of at least one survey indicator.

[0056] In this embodiment, survey rules can be broken down to obtain survey strategies for any survey indicator. In one example, the technical procedures for summarizing and statistically analyzing land survey data are decomposed into atomic rules of scope, which are then stored in the form of "formula + constraint". For example, the atomic rule of scope for cultivated land survey indicator is: "Cultivated land scope = paddy field + irrigated land + dry land"; the atomic rule of scope for scope for construction land survey indicator is: "Construction land scope = urban construction land + rural construction land + industrial and mining land"; and the atomic rule of scope for scope for scope for ecological land survey indicator is: "Ecological land scope = forest land + grassland + wetland - illegally occupied portion".

[0057] In this embodiment, for any survey indicator, the survey strategy of the survey indicator can be added as a third attribute of the indicator term and displayed as an initial knowledge graph on a preset visualization interface. Users can select, stitch, connect, and set parameters for the indicator icons of the indicator terms on the visualization interface to establish dependencies between the survey strategies of different survey indicators, transforming the survey strategies of the survey indicators into composite survey strategies, and transforming the initial knowledge graph into a first target knowledge graph. In a specific example, users can manipulate the indicator icons of the two survey indicators "newly added construction land" and "cultivated land" to combine the survey strategies of these two indicators and construct a composite survey strategy. For example, the composite survey strategy could be: "Special statistical caliber of newly added construction land occupying cultivated land = survey strategy of newly added construction land → occupation → survey strategy of cultivated land (paddy field + irrigated land + dry land)". This composite strategy indicates that when calculating "newly added construction land occupying cultivated land", it is necessary to first calculate "newly added construction land" and then associate it with the "cultivated land" it occupies, and the statistical caliber of cultivated land follows the sum of "paddy field, irrigated land, and dry land".

[0058] In this embodiment of the application, a binding relationship can be established between the survey strategy of the indicator words in the first target knowledge graph and the survey rules in the survey requirement information. When the survey rules are updated, the corresponding survey strategy is automatically updated synchronously.

[0059] In this embodiment, the first target knowledge graph supports a dual-track parallel mode of "general survey strategy + district / county-customized survey strategy". In the knowledge graph, regional attributes (such as ecological protection zone, development zone, and ordinary district / county) are marked for each district / county. When a report for a certain region is generated, the regional attributes are automatically identified and the corresponding general survey strategy or district / county-customized survey strategy is loaded to achieve regionally differentiated land statistics.

[0060] In this embodiment, by decomposing the statistical logic implicit in the survey rules into independent survey strategies corresponding to each survey indicator, and attaching these strategies to the indicator terms as third attributes, the originally vague and scattered statistical rules become clear and accessible in the knowledge graph. By associating indicator icons on the visual interface, users can establish dependencies between survey strategies in an intuitive and graphical way. This eliminates the need to write complex rule code, enabling the creation of composite survey strategies. This significantly reduces the configuration threshold and operational complexity of composite survey strategies, improves strategy building efficiency and reusability, and allows complex statistical tasks to be identified and executed in the form of composite survey strategies.

[0061] Step 102: Based on the report generation request of the land survey, determine at least one piece of information to be filled in the report template, and insert a preset traceability identifier into the information to be filled; In this embodiment of the application, users can submit report generation requests through natural language or structured forms, such as "Special Analysis Report on Cultivated Land Protection in a Certain Province's Land Use Change Survey in a Certain Year" or "Analysis Report on Land Use Structure in a Certain District or County".

[0062] In this embodiment of the application, the first target knowledge graph may include a land survey report template. Based on the land survey report generation request, at least one piece of information to be filled in the report template can be determined.

[0063] In one example, based on the report generation request, the target indicator terms, target statistical models, and target data visualization formats related to the report generation request in the first target knowledge graph can be determined. Then, based on the target indicator terms, target statistical models, and target data visualization formats, the report structure in the land survey report template is intelligently assembled to generate a logical outline of the report, including the core chapters, the logical relationships between chapters, the core content of each chapter, and the layout of visualization charts. The core chapters may include: current situation analysis, change analysis, problem identification, and data appendices. The steps of intelligently assembling the report structure may include: (1) Framework template matching: Based on the report topic type, user level, and output format, the standard report framework template is called from the knowledge graph. The template predefines the chapter level, title paradigm, logical order, and content constraints.

[0064] (2) Automatic arrangement of core chapters: Based on the core indicator set, statistical model type, and spatiotemporal analysis dimension, the core chapters are automatically arranged according to the standard business logic of land survey business specifications: current situation analysis → change analysis → structural analysis → spatial overlay analysis → problem identification → compliance evaluation → countermeasures and suggestions → data appendix.

[0065] (3) Chapter content allocation: The core indicators, charts, and analysis conclusions are allocated to the corresponding chapters according to the principle of "one chapter with multiple sections and one section with multiple points", forming a chapter-level content list.

[0066] (4) Logical relationship verification: Check the causal and sequential relationships between chapters according to the constraints of the logic diagram to ensure the closed loop of business logic of "current situation → change → problem → suggestion" and automatically correct sequence conflicts.

[0067] (5) Structured output of the outline: Generate a standardized logical outline with hierarchical numbering, chapter titles, key points and chart placeholders as the skeleton for report generation.

[0068] In this embodiment, the output outline can be broken down into atomic units, namely, indicator data, charts, tables, and textual conclusions—the four smallest traceable units. At least one piece of information to be filled in the report template may include: indicator data to be filled, charts to be filled, tables to be filled, and textual conclusions to be filled.

[0069] Step 103: Based on the report generation request, retrieve the first target knowledge graph to obtain the information query link of the information to be filled in the first target knowledge graph, and establish the association between the source identifier and the information query link; In this embodiment of the application, based on the report generation request submitted by the user, multi-hop reasoning can be performed on the first target knowledge graph to obtain the information query link of the information to be filled in the first target knowledge graph.

[0070] In this embodiment of the application, an association can be established between the traceability identifier of the information to be filled and the information query link of the information to be filled.

[0071] In this embodiment of the application, the steps of inserting a traceability identifier into the information to be filled and establishing the association between the traceability identifier of the information to be filled and the information query link of the information to be filled may include: (1) Generate a unique traceability ID (Identifier) ​​for each piece of information to be filled.

[0072] (2) Accurately bind the source identifier of the information to be filled with the information query link in the first target knowledge graph to establish a relationship.

[0073] (3) Source point anchoring embedding: Insert source markers after the main text of the indicator data to be filled, below the chart to be filled, in the upper right corner of the table to be filled, and at the end of the conclusion paragraph of the text to be filled, and establish index association in the background.

[0074] (4) Source storage and indexing: Store “Source ID - Location of information to be filled - Information query link” in the graph database.

[0075] In some embodiments of this application, the first target knowledge graph further includes at least one statistical model related to the indicator terms and at least one data visualization form related to the statistical model; the entity terms include thematic concept terms extracted from the survey purpose; the entity terms in the first target knowledge graph are connected by edges; the step of retrieving the first target knowledge graph based on the report generation request to obtain the information query link of the information to be filled in the first target knowledge graph includes: The report generation request is converted into a query statement for the first target knowledge graph based on the information to be filled, and the first target knowledge graph is retrieved based on the query statement to obtain target topic concept words associated with the query statement; Based on the query statement, a search is conducted among the indicator words connected to the target topic concept words to obtain the target indicator words associated with the query statement; Based on the query statement, a search is performed in at least one statistical model related to the target indicator term to obtain the target statistical model that matches the query statement; Based on the query statement, a search is performed in at least one data visualization form related to the target statistical model to obtain the target data visualization form that matches the query statement. The information query link is constructed by combining the target topic concept words, the target indicator words, the composite survey strategy of the target indicator words, the target statistical model, the target data visualization form, and the survey data of object words connected with the target indicator words.

[0076] In this embodiment, after receiving a user's report generation request, natural language processing technology is used to parse information such as the core topic, analysis dimensions, and user role in the request. User roles can be, for example, provincial-level management departments or district / county-level implementing departments. Next, the parsed information is transformed into a query statement for the first target knowledge graph.

[0077] Based on the first target knowledge graph, users' report generation requests cannot directly obtain all the necessary entity terms through a single-step query. Therefore, it is necessary to perform multi-hop queries and semantic expansion based on the hierarchical semantic relationships of the first target knowledge graph, through multi-level node jumps, relationship propagation, semantic expansion, and constraint filtering. This identifies and associates target topic concept terms, target indicator terms, target statistical models, survey data of object terms connected to target indicator terms, composite survey strategies for target indicator terms, and target data visualization forms that match the query statement. Among these, target indicator terms can be: cultivated land area, cultivated land reduction, and cultivated land replenishment, etc.; survey data of object terms connected to target indicator terms can be: land use change survey data for a certain year, and land parcel data for a certain district or county; target data visualization forms can be: bar charts, pie charts, spatial distribution maps, and trend charts.

[0078] In this embodiment, an information query link can be constructed by combining target topic concept terms, target indicator terms, composite survey strategies for target indicator terms, target statistical models, target data visualization formats, and survey data of object terms connected to the target indicator terms. The information query link can be: survey data → composite survey strategy for target indicator terms → target statistical model → indicator calculation results → target data visualization formats. This information query link can also be called a lineage link.

[0079] In this embodiment, the first target knowledge graph includes at least one statistical model related to indicator terms and at least one data visualization form related to the statistical model. The statistical model can be: a trend analysis model, a spatial distribution model, or a proportion analysis model.

[0080] The specific process for matching and binding the target statistical model with the target data visualization format can be as follows: Based on the indicator characteristics and constraint rules of the target indicator terms, the optimal statistical model is automatically matched as the target statistical model from at least one statistical model related to the target indicator terms through a rule-based reasoning algorithm; then, a weighted selection is performed based on user roles and report usage scenarios, and finally, the target data visualization format is determined from at least one data visualization format related to the target statistical model. The target data visualization format can be: a macro-spatial distribution map for provincial reports and a detailed plot list for district / county reports.

[0081] The matching and binding function between the target statistical model and the target data visualization form is based on the relationship between the indicator words built into the first target knowledge graph and the statistical model, and between the statistical model and the data visualization form. It uses rule reasoning as the automatic matching method and role scenarios as weighted constraints to achieve intelligent, compliant, and differentiated binding between the statistical model and the visualization form. It ensures that the matching results are authoritative and stable, and has dynamic adaptation capabilities. It is a deterministic matching mechanism that is rule-driven, auditable, and traceable.

[0082] In this embodiment of the application, the topic concepts, indicator terms, statistical models, data visualization forms, composite survey strategies of indicator terms, and survey data of object terms related to the report generation request are integrated into a unified information query link. This enables the information query link to automatically query the first target knowledge graph and obtain the information content of the information to be filled in the report template for the report generation request.

[0083] Step 104: For any of the information to be filled, query the first target knowledge graph according to the information query link that is associated with the source identifier of the information to be filled, obtain the information content of the information to be filled, and generate the initial report of the land survey based on the information content; In this embodiment of the application, for any information to be filled, the first target knowledge graph is queried and calculated based on the information query link that is associated with the traceability identifier of the information to be filled, so as to obtain the information content of the information to be filled and fill it into the corresponding position of the report frame to form the initial report of the land survey.

[0084] In one example, a preliminary report from a land survey may include: (1) Core chapters: Current situation analysis, change analysis, spatial analysis, problem identification, data appendix, etc., are automatically generated by knowledge graph reasoning, indicator calculation and logical reasoning, and constitute the main content of the report.

[0085] (2) Non-core chapters: cover, title page, table of contents, compilation instructions, introduction, terminology definition, supporting documents, signature, appendix, etc. are automatically filled using templates and read fixed information (such as project name, compiling unit, time, scope, policy basis) from knowledge graph or configuration library, without the need for intelligent reasoning.

[0086] (3) Unified assembly and output: The core chapters generated by the intelligent system and the templated non-core chapters are merged according to the standard format to form a formal report with complete structure, unified logic and conformity to the specifications of land survey results.

[0087] Reference Figure 3This document illustrates a flowchart of a report framework generation process provided in an embodiment of this application. The process begins by obtaining the user's report generation request. Based on the request, it performs requirement language parsing and intent understanding, including receiving thematic requirements and parsing core themes, analysis dimensions, and user roles to obtain a query statement for the first target knowledge graph. The query statement is then used to perform a structured query on the first target knowledge graph, conducting graph semantic retrieval and reasoning. Graph semantic retrieval and reasoning include multi-hop queries and semantic expansion within the land survey business knowledge graph, identifying and associating "core indicator entities," identifying and associating "data source entities," matching and binding statistical models, and identifying and associating "visual entities." Finally, the report structure is intelligently assembled, including generating a report logic outline, filling in core indicators and data, binding visual charts, matching and binding statistical models, embedding data source anchors, and outputting the report framework.

[0088] Step 105: When an update operation is triggered, the initial report is updated according to the association between the traceability identifier of the information to be updated and the information query link; wherein, the update operation is triggered by a change in the first target knowledge graph due to a change in at least one of the survey requirement information, the survey purpose, and the survey data; the information to be updated is the information to be filled affected by the change in the first target knowledge graph.

[0089] In this embodiment, when the first target knowledge graph changes due to at least one change in the survey requirement information, survey purpose, and survey data, the information to be filled affected by the change in the first target knowledge graph can be used as the information to be updated. The initial report is then updated based on the association between the source identifier of the information to be updated and the information query link. The source identifier of the information to be updated can also be referred to as a growth point.

[0090] In this embodiment of the application, if there is no source identifier for the information to be filled in the initial report, and no information query link is associated with the source identifier of the information to be filled, and if it is necessary to update the initial report, the initial report can be analyzed to decompose it into at least one report atomic unit, and a source identifier can be assigned to the report atomic unit; the first target knowledge graph can be analyzed to obtain the information query link of the report atomic unit in the first target knowledge graph. The report atomic unit is the information content of the information to be filled. In one example, the steps of analyzing the initial report and the first target knowledge graph, assigning a source identifier to the report atomic unit, and obtaining the information query link are as follows: (1) Report Atomic Unit Deconstruction and Unique Identifier: The initial report framework is deconstructed into the smallest report atomic units, including data items, charts, tables and conclusion paragraphs. A unique traceability identifier is assigned to each atomic unit to achieve refined management of the units.

[0091] (2) Data lineage anchoring and link construction: Starting from each report atomic unit, trace back along the first target knowledge graph, construct and record the complete information query link from raw data → data cleaning rules → statistical model → composite survey strategy → report atomic unit, bind the information query link with the source identifier, store it in the database, and generate a visual information query graph.

[0092] In some embodiments of this application, when an update operation is triggered, updating the initial report based on the association between the traceability identifier of the information to be updated and the information query link includes: When an update operation is triggered, query at least one of the following in the first target knowledge graph: the changed entity words, the attributes of the entity words, and the connection relationships between the entity words. Determine the information query link that has changed due to at least one change in the entity word, the attribute of the entity word, and the connection relationship between the entity words, and determine the source identifier to be processed that is associated with the changed information query link; The information to be filled in the traceability identifier to be processed is taken as the information to be updated, and the information content of the information to be updated is updated based on the modified first target knowledge graph to obtain the first target report.

[0093] In this application embodiment, a real-time monitoring mechanism is established for the survey requirements information, survey purpose and survey data of the land survey. When a change event is detected in at least one of the survey requirements information, survey purpose and survey data, the event is automatically captured and the change content, change time and change subject are recorded.

[0094] Then, based on the entity association analysis of the first target knowledge graph, at least one of the following is queried in the first target knowledge graph: changed entity words, entity word attributes, and connection relationships between entity words. The information query links that have changed due to changes in at least one of the entity words, entity word attributes, and connection relationships between entity words are identified, and the source identifiers to be processed that are associated with the changed information query links are identified.

[0095] In this embodiment, a change impact analysis report for a change event can be generated based on the pending traceability identifier that is associated with the changed information query link. The report includes the change content, the affected reporting units, the degree of impact, and recommended update operations. Establishing update operations can include incremental updates or full updates.

[0096] In this embodiment of the application, the information to be filled in the traceability identifier to be processed is taken as the information to be updated, and multi-hop reasoning and semantic expansion are performed based on the modified first target knowledge graph to update the information content of the information to be updated, while other information content remains unchanged, thus obtaining the first target report.

[0097] Reference Figure 4 This paper generates a flowchart of an updatable intelligent analysis report as provided in this embodiment. Based on the generated initial report framework, report elements are deconstructed and their lineage anchored, including deconstructing the report into atomic units (data items, charts, conclusions, etc.) and anchoring each unit with a unique traceability identifier. Then, reverse tracing and lineage chain construction are performed, including: identifying the graph and tracing back from the report unit, recording the complete data lineage path (raw data → cleaning rules → statistical model → caliber version), and constructing a visual lineage relationship graph. Next, the report unit is bound to the lineage chain, establishing the association between the traceability identifier of the report unit and the information query link. Then, change monitoring and impact analysis are performed, including continuously monitoring the upstream of the lineage chain (data updates, rule revisions), automatically capturing change events, and assessing the scope and degree of change impact based on graph correlation analysis. Next, "growth point" intelligent identification is performed, including accurately locating the specific report unit traceability identifier affected by the change, marking it as a "growth point," and generating an impact analysis report (change content, impact scope, and recommended actions). Finally, an updatable intelligent analysis report marked with "growth points" is output.

[0098] In this embodiment of the application, when the first target knowledge graph changes due to at least one change in the survey requirements, survey purpose and survey data of the land survey, instead of simply and crudely regenerating the entire report, the specific elements in the knowledge graph that have changed are first located, then the affected information query links are identified, and finally only the information to be updated associated with the affected information query links is updated, which significantly reduces the computational overhead of the report update.

[0099] In this embodiment, based on the report generation request, the information to be filled in the land survey report template is determined, and a source identifier is inserted for the information to be filled. Simultaneously, based on the report generation request, an information query link for the information to be filled is established in the first target knowledge graph. An association is established between the source identifier of the information to be filled and the information query link, and based on this association, the content of the information to be filled is retrieved in the first target knowledge graph to fill in the various pieces of information to be filled in the report template, thereby achieving automatic report generation. When the first target knowledge graph changes due to at least one of the land survey requirements, survey objectives, or survey data, the affected information query link can be identified, and based on the association between the source identifier and the information query link, the affected information to be filled in the report can be accurately located and updated, thereby avoiding a full regeneration of the report and achieving automatic updating of the report content.

[0100] In some embodiments of this application, the first target report is displayed on the visualization interface; the land survey report update method further includes: In response to the user's operation on the traceability identifier in the first target report on the visualization interface, an information query link that is associated with the traceability identifier is determined; Based on the information query link, determine the process of generating the information content of the traceability identifier to be filled; Based on the generation process, a source audit report on the information content of the information to be filled is generated.

[0101] In this embodiment of the application, the first target report can be displayed on a visualization interface.

[0102] In response to a user's click on the source identifier of the information to be filled in the first target report on the visualization interface, the system treats the information to be filled as the target element and triggers a source query request for the target element, capturing its source identifier. Based on the source identifier of the target element, the system determines the information query chain corresponding to the source identifier, and based on this information query chain, determines the generation process of the target element's information content, which is the source information of the target element's information content.

[0103] In this embodiment, the traceability information can be displayed in a visual form, including data sources, calculation rules, and the basis for the composite survey strategy. Data sources may include: original database tables, data versions, and collection times. Calculation rules can be: a visualized rule chain diagram showing the combination process of atomic rules. The basis for the composite survey strategy may be: clauses of the Ministry of Natural Resources' technical regulations and original texts of local policies.

[0104] In this embodiment of the application, a standardized traceability audit report can be automatically generated based on the traceability information, which includes full-chain traceability credentials for use in quality inspection, external audit and compliance verification.

[0105] In this embodiment, a first target knowledge graph is displayed on the visual interface. The user modifies the first target knowledge graph on the visual interface. In response to the user's modification of the first target knowledge graph, the change event can be automatically captured, and the initial report can be updated. The updated content in the initial report can be highlighted. The steps for updating the initial report are the same as those for updating the initial report caused by changes in at least one of the first target knowledge graph, such as changes in survey requirements, survey objectives, and survey data; therefore, they will not be described again here.

[0106] Reference Figure 5 This illustration shows a schematic diagram of a data trust loop and dynamic reporting maintenance capability provided in an embodiment of this application. Starting from user-initiated source tracing queries or upstream rule and data changes, a complete data trust loop and dynamic reporting maintenance capability is ultimately formed.

[0107] User-initiated source tracing queries include querying any data item or chart in the report and capturing the target element ID. Next, a forward tracing is performed to the "data lineage" link in the knowledge graph, tracing the complete path upwards and displaying the source information. Source information includes: data source (database, table, version), calculation rules (visualized rule link diagram), and the basis for the interpretation (original policy clauses). Finally, a visual source tracing report is output for auditing and verification purposes.

[0108] Upstream rule and data changes include modifying statistical rules or switching statistical versions, capturing change events and their impact. Then, a reverse linkage is established based on the "report element-rule" relationship in the knowledge graph, locating all affected report data items and charts, triggering automatic recalculation and refresh, re-rendering charts, and highlighting the changed parts. Finally, a real-time updated intelligent report is output (with highlighted changes).

[0109] In this embodiment, users do not need to consult the underlying code or database logs. They can intuitively obtain the complete generation path of information content from raw data to final presentation simply by operating the traceability identifier in the report. This greatly improves the transparency of the report generation process and provides direct and operable technical support for the compliance review, result verification and responsibility determination of land survey data.

[0110] In some embodiments of this application, when an update operation is triggered, updating the initial report based on the association between the traceability identifier of the information to be updated and the information query link includes: When the survey requirement information and / or the survey data change, extract the changed entity words related to the entity words from the survey requirement information and / or the survey data, and determine the association between the changed entity words based on the survey requirement information and / or the survey data; Based on the relationships between the modified entity words, the modified entity words are added to the first target knowledge graph to obtain the second target knowledge graph; in the second target knowledge graph, the modified entity words and entity words related to the modified entity words are connected by edges; During the updating process of the information content to be updated, it is detected whether the information content to be updated is abnormal; If the information content of the information to be updated is abnormal, the source identifier of the information to be updated is used as the processing identifier, and the second target knowledge graph is queried according to the information query link corresponding to the processing identifier to obtain the target change entity words that caused the abnormal information content. Based on the target change entity words, generate the abnormal reason for the information content of the information to be updated, and add the abnormal reason to the first target report to obtain the second target report.

[0111] In this embodiment of the application, when the survey requirements information and / or survey data change, dynamic event entities can be extracted from land survey data, policy documents, natural resource management work ledgers, etc., including data anomaly events (such as abnormal reduction of cultivated land, illegal increase of construction land), policy adjustment events (such as update of statistical caliber, adjustment of land category control policy), and management operation events (such as construction land approval, land consolidation, and investigation and handling of illegal land use).

[0112] In this embodiment, dynamic event entities can be used as change entity terms, and these change entity terms can be fused with the first target knowledge graph to obtain a second target knowledge graph. Specifically, attributes of each change entity term can be defined based on survey requirements and / or survey data, and logical relationships between change entity terms can be defined. The attributes of change entity terms can include: event time, occurrence area, event scale, associated subject, etc. The logical relationships between change entity terms can include: causal relationships (e.g., "illegal approval of construction land → abnormal reduction of arable land"), sequential relationships (e.g., "land consolidation project approval → land consolidation implementation → increase in supplementary arable land"), and associative relationships (e.g., "ecological red line adjustment → change in forest land control requirements"). Based on this, through named entity recognition, syntactic analysis, temporal judgment, and business rule reasoning, automated extraction of event attributes and automated annotation of logical relationships between events are achieved. Causal, sequential, and associative relationships are automatically identified, and compliance verification and graph entry are completed without manual processing of each item.

[0113] The specific steps for defining the attributes of each modified entity term and defining the logical relationships between modified entity terms may include: (1) Predefined basic rules and relationship types: event attribute specifications in the field of land survey (event time, occurrence area, event scale, associated subjects, land type, threshold conditions, etc.), event relationship type set (causal relationship, sequential relationship, related relationship, conditional relationship, constraint relationship), manually constructed relationship trigger word library (cause, trigger, cause, therefore, subsequently, first, later, simultaneously, related, impact, etc.), manually set business rule constraints (such as only judging an abnormal event when the reduction of cultivated land exceeds the threshold, and construction land approval must be associated with the balance of land occupation and compensation, etc.). On this basis, the entire process of attribute extraction, relationship identification, and label injection into the library is automated, without the need for manual sentence-by-sentence operation.

[0114] (2) Automatic extraction of event entity attributes: Automatic extraction of event entity attributes is carried out through rule matching, including: event time (extracting year, quarter, monitoring period, update time), occurrence area (extracting spatial units such as province, city, county, township, development zone, ecological protection zone, etc.), event scale (extracting area, change, proportion, number of map patches, intensity level), associated entities (extracting natural resources department, park, township, land use unit, approval agency), and event type (automatically labeled as abnormal, approval, rectification, adjustment, investigation, etc.).

[0115] (3) Automatic identification of logical relationships between events: Identify conjunctions, time-sequence words, causal words, and sequential words in sentences, match them with manually preset trigger word libraries, and automatically label sequential relationships according to time sequence (i.e., first occurrence → later occurrence → result). Combine with the land survey knowledge graph to perform business rule reasoning and determine causal relationships according to business constraints (e.g., if the time of illegal approval is earlier than the reduction of cultivated land, then label it as "illegal approval of construction land → abnormal reduction of cultivated land").

[0116] (4) Automated assignment and verification of attributes and relationships: Automatically fill attribute values ​​for event entities, assign relationship tags to event pairs, and perform business compliance verification, including: whether the relationship complies with the land regulations, whether the time sequence is contradictory, whether the region matches, and whether the land category is consistent.

[0117] In this embodiment, modified entity words can be added to the first target knowledge graph based on the relationships between them, resulting in a second target knowledge graph. In the second target knowledge graph, modified entity words and related entity words are connected by edges.

[0118] In this embodiment of the application, during the updating process of the information content to be updated, it is detected whether the information content to be updated is abnormal. An abnormal event in the information content to be updated could be: the reduction in cultivated land in a certain district or county exceeds a threshold.

[0119] If the information content to be updated is abnormal, there is no need to wait for the complete report to be generated. The source identifier of the information to be updated is used as the processing identifier, and multi-hop reasoning is performed on the second target knowledge graph based on the information query link corresponding to the processing identifier to obtain the target change entity words that caused the abnormal information content. In the embodiments of this application, at least one target change entity word can be concatenated to obtain the causal chain behind the abnormal event. The causal chain can be: "abnormal reduction of cultivated land in a certain district / county → approval of illegal construction land → failure to implement cultivated land occupation and compensation balance → inadequate implementation of land use control".

[0120] The specific process for multi-hop reasoning on the second target knowledge graph can be as follows: (1) Starting point location: Abnormal event triggering. If an abnormality is detected in the indicator calculation process (such as the reduction of cultivated land in a certain district or county exceeding the policy threshold), the abnormal event is used as the starting point node for reasoning.

[0121] (2) First hop: Direct cause retrieval (single hop). Query upstream along the "result → direct cause" causal relationship edge in the second target knowledge graph to find the directly triggering event. For example: Abnormal reduction of arable land → retrieval reveals illegal occupation of newly added construction land.

[0122] (3) Second jump: Tracing intermediate causes (second jump). Taking the result of the first jump as a new starting point, continue to query upstream along the causal / conditional relationship to locate the intermediate links. For example: illegal occupation of newly added construction land → retrieval of approval of illegal construction land.

[0123] (4) Third jump: Finding missing deep constraints (third jump). Continue jumping upstream to find missing conditions at the system / rule / management level. For example: approval of illegal construction land → search reveals unfulfilled farmland compensation balance.

[0124] (5) Fourth Jump: Root Cause Identification (Fourth Jump). The final jump identifies the root cause at the management and policy implementation level. For example: Failure to implement the balance between cultivated land occupation and compensation → Search results show that land use control is not being properly implemented.

[0125] (6) Verification and closure of the causal chain: 1) Verify the entire chain based on time consistency, regional consistency, and business compliance.

[0126] 2) Eliminate invalid branches and retain the only most reasonable causal chain.

[0127] 3) Form a structured event chain, such as: abnormal reduction of arable land → approval of illegal construction land → failure to implement the balance of arable land occupation and compensation → inadequate implementation of land use control.

[0128] 4) Output causal chain for narrative generation: Output the complete causal chain to the "Narrative Logic Generation" module to automatically write problem analysis, cause analysis and countermeasure suggestions.

[0129] In this embodiment, the causal chain behind an abnormal event can be used as the cause of the abnormality, and a standard narrative framework of "phenomenon-cause-impact-recommendation" can be automatically generated, filled with specific event chains and evidence. In this embodiment, the standard narrative framework and its internal event chains and evidence can be added to the first target report to obtain the second target report.

[0130] Reference Figure 6 This document illustrates a flowchart of the generation process for an analytical report with decision-making and supportive suggestions, as provided in an embodiment of this application. First, it extracts and integrates knowledge from changing events such as policies and regulations, natural resource management, and data updates. Then, it constructs a causal graph, including defining event entities, attributes, and time sequences; defining causal and sequential logical relationships; and building a directed graph of "event-relationship-event." Combining the report's preliminary conclusions or anomaly indicators, it performs causal reasoning and logical discovery, including graph matching and multi-hop reasoning; discovering and connecting causal chains; and supplementing the context. Finally, it generates narrative logic and outputs a structured output, including generating a standard narrative framework of "appearance-cause-impact-suggestion," filling in the inferred specific event chains and evidence, and outputting structured text paragraphs, key points, and decision-making warnings. Ultimately, an analytical report with decision-making support suggestions is obtained.

[0131] In this application embodiment, the changes in survey requirements and / or survey data are used as the driving force. A knowledge graph is used to describe the causal, sequential, and other logical relationships and evolution patterns between the changes. This not only displays data and describes apparent facts, but also automatically associates recent planning implementation compliance, land use control, and potential illegal land use clues with the region when an anomaly is detected in the report. This analyzes the deep-seated, causal, and related internal driving factors, providing auxiliary support for major decisions.

[0132] In this embodiment, when survey requirements and / or survey data change, the system accurately extracts the changed entity words and their relationships, and incrementally updates the graph by adding nodes and edges to obtain a second target knowledge graph. During the report content update process, the system can proactively detect whether there are any anomalies in the information to be updated. Once an anomaly is detected, the system uses the relationship between the source identifier and the information query link to reverse locate the specific changed entity words in the second target knowledge graph that caused the anomaly, thereby generating the cause of the anomaly. This significantly reduces the time cost and technical threshold of manually investigating the cause of the anomaly.

[0133] In this embodiment of the application, the thematic requirements of the statistical analysis report, such as statistical theme, analysis dimension, indicator scope, time range, spatial range, and output format, can be obtained by extracting business objectives, core indicators, constraint rules, analysis scenarios, and display requirements from the land survey statistical analysis report through knowledge extraction and semantic parsing, thereby obtaining standardized thematic requirements.

[0134] Using the first target knowledge graph for semantic retrieval and reasoning, the system understands the key thematic indicators required for the report, such as total area, spatial distribution, hierarchical composition, annual changes, and overlap analysis with ecological red lines. It automatically associates the corresponding data sources, statistical models, and visualization forms from the graph, and automatically generates the logical structure, core chapters, and key indicators of the report.

[0135] In this application embodiment, the linear generation mode of "data → template → report" for land surveys in the prior art is transformed into an intelligent and evolvable generation mode of "polymorphic data → knowledge graph → semantic reasoning → dynamic report" by using technologies such as business knowledge graph construction, dynamic assembly of statistical analysis rules, reasoning of event graphs, report "growth point" marking and version traceability updates. The report is no longer just a static display of data, but a dynamic, interactive, traceable and evolvable intelligent information entity based on business knowledge graphs, data semantic association, and user intent understanding.

[0136] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of this application.

[0137] Reference Figure 7 This application also discloses a structural block diagram of a land survey report updating device, including: The knowledge graph construction module 701 is used to construct a first target knowledge graph of the land survey based on the survey requirements, survey objectives, and survey data of the land survey; the first target knowledge graph includes the report template of the land survey; The traceability identifier insertion module 702 is used to determine at least one piece of information to be filled in the report template according to the report generation request of the land survey, and insert a preset traceability identifier into the information to be filled. The information query link acquisition module 703 is used to retrieve the first target knowledge graph based on the report generation request, obtain the information query link of the information to be filled in the first target knowledge graph, and establish the association between the traceability identifier and the information query link; The initial report generation module 704 is used to query the first target knowledge graph for any of the information to be filled, based on the information query link that is associated with the traceability identifier of the information to be filled, to obtain the information content of the information to be filled, and to generate the initial report of the land survey based on the information content. The report update module 705 is used to update the initial report based on the association between the traceability identifier of the information to be updated and the information query link when an update operation is triggered; wherein, the update operation is triggered by a change in the first target knowledge graph due to a change in at least one of the survey requirement information, the survey purpose, and the survey data; the information to be updated is the information to be filled affected by the change in the first target knowledge graph.

[0138] In one optional embodiment of this application, the atlas construction module includes: An extraction submodule is used to extract at least two entity words and the relationships between the entity words from the survey requirements and survey objectives of the land survey. The constraint rule determination submodule is used to determine the constraint rule for any of the entity words based on the survey purpose, and to use the constraint rule as the first attribute of the entity word; the survey requirement information includes the survey objects and survey rules of the land survey; the entity words include object words extracted from the survey objects; The second attribute, as a submodule, is used to use the survey data as the second attribute of the object words; The semantic association establishment submodule is used to establish semantic associations between entity words based on the first attribute of the entity words and the association between the entity words; The knowledge graph construction submodule is used to construct the initial knowledge graph of the land survey using the entity words, the semantic relationships between the entity words, the first attribute, and the second attribute. The first target knowledge graph acquisition submodule is used to adjust the initial knowledge graph according to the survey rules to obtain the first target knowledge graph.

[0139] In one optional embodiment of this application, the survey requirement information further includes survey indicators; the entity terms include indicator terms extracted from the survey indicators; the survey rules include a survey strategy of statistically analyzing the survey data of the survey subjects to obtain survey data for at least one of the survey indicators; the first target knowledge graph obtaining submodule includes: A splitting unit is used to split the survey rules to obtain a survey strategy for any of the survey indicators; The graph display unit is used to add the survey strategy of the survey indicator as a third attribute of the indicator term to the indicator term for any of the survey indicators, and display the initial knowledge graph on a preset visualization interface. The dependency establishment unit is used to establish dependencies between the survey strategies of the survey indicators in response to the user's association operation on the indicator icons of the indicator words on the visualization interface, so that the survey strategies of the survey indicators are transformed into composite survey strategies and the initial knowledge graph is transformed into the first target knowledge graph.

[0140] In one optional embodiment of this application, the first target knowledge graph further includes at least one statistical model related to the indicator terms and at least one data visualization form related to the statistical model; the entity terms include thematic concept terms extracted from the survey objective; the entity terms in the first target knowledge graph are connected by edges; the information query link obtaining module includes: The first retrieval submodule is used to convert the report generation request into a query statement of the information to be filled for the first target knowledge graph, and to retrieve the first target knowledge graph based on the query statement to obtain target topic concept words associated with the query statement; The second retrieval submodule is used to retrieve target indicator words associated with the query statement by searching among the indicator words connected to the target topic concept words. The third retrieval submodule is used to search in at least one statistical model related to the target indicator words based on the query statement, and obtain the target statistical model that matches the query statement. The fourth retrieval submodule is used to retrieve, based on the query statement, at least one data visualization form related to the target statistical model, and obtain the target data visualization form that matches the query statement. The information query link construction submodule is used to construct the information query link by combining the target topic concept words, the target indicator words, the composite survey strategy of the target indicator words, the target statistical model, the target data visualization form, and the survey data of object words connected with the target indicator words.

[0141] In one optional embodiment of this application, the report update module includes: The query submodule is used to query at least one of the following when an update operation is triggered: the entity words that have changed in the first target knowledge graph, the attributes of the entity words, and the connection relationships between the entity words. The pending traceability identifier determination submodule is used to determine the information query link that has changed due to at least one change in the entity word, the attribute of the entity word, and the connection relationship between the entity words, and to determine the pending traceability identifier that is associated with the changed information query link. The first target report acquisition submodule is used to take the information to be filled in the traceability identifier to be processed as the information to be updated, and update the information content of the information to be updated based on the modified first target knowledge graph to obtain the first target report.

[0142] In one optional embodiment of this application, the first target report is displayed on the visualization interface; the land survey report updating device further includes: The information query link determination module is used to determine the information query link that is associated with the traceability identifier in the first target report on the visualization interface in response to the user's operation. The generation process determination module is used to determine the generation process of the information content of the traceability identifier to be filled based on the information query link. The report generation module is used to generate a source audit report of the information content to be filled in, based on the generation process.

[0143] In one optional embodiment of this application, the report update module includes: The modified entity term determination submodule is used to extract modified entity terms related to the entity terms from the survey requirement information and / or the survey data when the survey requirement information and / or the survey data change, and to determine the association relationship between the modified entity terms based on the survey requirement information and / or the survey data. The second target knowledge graph acquisition submodule is used to add the changed entity words to the first target knowledge graph according to the association relationship between the changed entity words, thereby obtaining the second target knowledge graph; in the second target knowledge graph, the changed entity words and the entity words related to the changed entity words are connected by edges; An anomaly detection submodule is used to detect whether the information content of the information to be updated is abnormal during the updating process of the information content to be updated. The target change entity word acquisition submodule is used to, if the information content of the information to be updated is abnormal, take the source identifier of the information to be updated as the processing identifier, and query the second target knowledge graph according to the information query link corresponding to the processing identifier to obtain the target change entity words that caused the abnormal information content; The second target report submodule is used to change entity words according to the target, generate the abnormal reason of the information content of the information to be updated, and add the abnormal reason to the first target report to obtain the second target report.

[0144] It is understood that the above-described device embodiments correspond to the method embodiments of this application, and can implement the method provided by any of the above-described method embodiments of this application.

[0145] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0146] Based on the embodiments of the above methods, another embodiment of this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method described in any embodiment of this application.

[0147] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0148] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0149] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0150] Based on the above-described method embodiments, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the method described in any of the above-described method embodiments of this application.

[0151] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0152] Based on the above-described method embodiments, another embodiment of this application provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements the method described in any of the above-described method embodiments of this application.

[0153] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, or improvements made by those skilled in the art within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for reporting updates to a cadastral survey, characterized by, include: Based on the survey requirements, objectives, and data of the land survey, a first objective knowledge graph of the land survey is constructed. The first target knowledge graph includes the report template of the land survey; Based on the report generation request of the land survey, at least one piece of information to be filled in the report template is determined, and a preset traceability identifier is inserted for the information to be filled. Based on the report generation request, the first target knowledge graph is retrieved to obtain the information query link of the information to be filled in the first target knowledge graph, and the association between the source identifier and the information query link is established. For any of the information to be filled, the first target knowledge graph is queried according to the information query link that is associated with the source identifier of the information to be filled, to obtain the information content of the information to be filled, and the initial report of the land survey is generated based on the information content; When an update operation is triggered, the initial report is updated based on the association between the source identifier of the information to be updated and the information query link; wherein, the update operation is triggered by a change in the first target knowledge graph due to a change in at least one of the survey requirement information, the survey purpose, and the survey data; the information to be updated is the information to be filled affected by the change in the first target knowledge graph; The construction of the first objective knowledge graph of the land survey based on the survey requirements, survey objectives, and survey data includes: Extract at least two entity words and the relationships between them from the survey requirements and objectives of the land survey. Based on the stated survey purpose, a constraint rule is determined for any of the entity terms, and the constraint rule is used as the first attribute of the entity term; the survey requirement information includes the survey objects and survey rules of the land survey; the entity terms include object terms extracted from the survey objects; The survey data is used as a second attribute of the object words; Based on the first attribute of the entity words and the association relationship between the entity words, establish the semantic association relationship between the entity words; An initial knowledge graph for the land survey is constructed using the entity words, the semantic relationships between the entity words, the first attribute, and the second attribute. According to the survey rules, the initial knowledge graph is adjusted to obtain the first target knowledge graph; The survey requirements information further includes survey indicators; the entity terms include indicator terms extracted from the survey indicators; the survey rules include a survey strategy of statistically analyzing the survey data of the survey subjects to obtain survey data for at least one of the survey indicators; and the step of adjusting the initial knowledge graph according to the survey rules to obtain the first target knowledge graph includes: The survey rules are broken down to obtain a survey strategy for any of the survey indicators; For any of the aforementioned survey indicators, the survey strategy of the survey indicator is added as a third attribute to the indicator term and the initial knowledge graph is displayed on a preset visualization interface. In response to the user's association operation with the indicator icons of the indicator words on the visualization interface, the dependency relationship between the survey strategies of the survey indicators is established, so that the survey strategies of the survey indicators are transformed into composite survey strategies, and the initial knowledge graph is transformed into the first target knowledge graph.

2. The method for updating a report of a homeland investigation according to claim 1, wherein, The first target knowledge graph also includes at least one statistical model associated with the indicator terms and at least one data visualization form associated with the statistical model; the entity terms include thematic concept terms extracted from the survey objective; the entity terms in the first target knowledge graph are connected by edges; the step of retrieving the first target knowledge graph based on the report generation request to obtain the information query link of the information to be filled in the first target knowledge graph includes: The report generation request is converted into a query statement for the first target knowledge graph based on the information to be filled, and the first target knowledge graph is retrieved based on the query statement to obtain target topic concept words associated with the query statement; Based on the query statement, a search is conducted among the indicator words connected to the target topic concept words to obtain the target indicator words associated with the query statement; Based on the query statement, a search is performed in at least one statistical model associated with the target indicator term to obtain the target statistical model that matches the query statement; Based on the query statement, a search is performed in at least one data visualization form associated with the target statistical model to obtain the target data visualization form that matches the query statement; The information query link is constructed by combining the target topic concept words, the target indicator words, the composite survey strategy of the target indicator words, the target statistical model, the target data visualization form, and the survey data of object words connected with the target indicator words.

3. The method for updating a report of a homeland investigation according to claim 2, wherein, When an update operation is triggered, the initial report is updated based on the association between the traceability identifier of the information to be updated and the information query link, including: When an update operation is triggered, query at least one of the following in the first target knowledge graph: the changed entity words, the attributes of the entity words, and the connection relationships between the entity words. Determine the information query link that has changed due to at least one change in the entity word, the attribute of the entity word, and the connection relationship between the entity words, and determine the source identifier to be processed that is associated with the changed information query link; The information to be filled in the traceability identifier to be processed is taken as the information to be updated, and the information content of the information to be updated is updated based on the modified first target knowledge graph to obtain the first target report.

4. The method for updating a report of a land survey according to claim 3, wherein The first target report is displayed on the visualization interface; The method for updating land survey reports also includes: In response to the user's operation on the traceability identifier in the first target report on the visualization interface, an information query link that is associated with the traceability identifier is determined; Based on the information query link, determine the process of generating the information content of the traceability identifier to be filled; Based on the generation process, a source audit report on the information content of the information to be filled is generated.

5. The method for updating land survey reports according to claim 3, characterized in that, When an update operation is triggered, the initial report is updated based on the association between the traceability identifier of the information to be updated and the information query link, including: When the survey requirement information and / or the survey data change, extract the changed entity words associated with the entity words from the survey requirement information and / or the survey data, and determine the association relationship between the changed entity words based on the survey requirement information and / or the survey data; Based on the association between the modified entity words, the modified entity words are added to the first target knowledge graph to obtain the second target knowledge graph; in the second target knowledge graph, the modified entity words and the entity words associated with the modified entity words are connected by edges; During the updating process of the information content to be updated, it is detected whether the information content to be updated is abnormal; If the information content of the information to be updated is abnormal, the source identifier of the information to be updated is used as the processing identifier, and the second target knowledge graph is queried according to the information query link corresponding to the processing identifier to obtain the target change entity words that caused the abnormal information content. Based on the target change entity words, generate the abnormal reason for the information content of the information to be updated, and add the abnormal reason to the first target report to obtain the second target report.

6. A land survey report updating device, characterized in that, include: The knowledge graph construction module is used to construct the first target knowledge graph of the land survey based on the survey requirements, survey objectives, and survey data. The first target knowledge graph includes the report template of the land survey; The traceability identifier insertion module is used to determine at least one piece of information to be filled in the report template according to the report generation request of the land survey, and to insert a preset traceability identifier into the information to be filled. The information query link acquisition module is used to retrieve the first target knowledge graph based on the report generation request, obtain the information query link of the information to be filled in the first target knowledge graph, and establish the association between the source identifier and the information query link; The initial report generation module is used to query the first target knowledge graph for any of the information to be filled, based on the information query link that is associated with the traceability identifier of the information to be filled, to obtain the information content of the information to be filled, and to generate the initial report of the land survey based on the information content. The report update module is used to update the initial report based on the association between the traceability identifier of the information to be updated and the information query link when an update operation is triggered; wherein, the update operation is triggered by a change in the first target knowledge graph due to a change in at least one of the survey requirement information, the survey purpose, and the survey data; the information to be updated is the information to be filled affected by the change in the first target knowledge graph; The map construction module includes: An extraction submodule is used to extract at least two entity words and the relationships between the entity words from the survey requirements and survey objectives of the land survey. The constraint rule determination submodule is used to determine the constraint rule for any of the entity words based on the survey purpose, and to use the constraint rule as the first attribute of the entity word; the survey requirement information includes the survey objects and survey rules of the land survey; the entity words include object words extracted from the survey objects; The second attribute, as a submodule, is used to use the survey data as the second attribute of the object words; The semantic association establishment submodule is used to establish semantic associations between entity words based on the first attribute of the entity words and the association between the entity words; The knowledge graph construction submodule is used to construct the initial knowledge graph of the land survey using the entity words, the semantic relationships between the entity words, the first attribute, and the second attribute. The first target knowledge graph acquisition submodule is used to adjust the initial knowledge graph according to the survey rules to obtain the first target knowledge graph; The survey requirement information further includes survey indicators; the entity terms include indicator terms extracted from the survey indicators; the survey rules include a survey strategy of statistically analyzing the survey data of the survey subjects to obtain survey data for at least one of the survey indicators; the first target knowledge graph acquisition submodule includes: A splitting unit is used to split the survey rules to obtain a survey strategy for any of the survey indicators; The graph display unit is used to add the survey strategy of the survey indicator as a third attribute of the indicator term to the indicator term for any of the survey indicators, and display the initial knowledge graph on a preset visualization interface. The dependency establishment unit is used to establish dependencies between the survey strategies of the survey indicators in response to the user's association operation on the indicator icons of the indicator words on the visualization interface, so that the survey strategies of the survey indicators are transformed into composite survey strategies and the initial knowledge graph is transformed into the first target knowledge graph.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 5.

Images