Natural resource data management method and device, equipment and storage medium

Abstract

The invention discloses a natural resource data management method and device, equipment and a storage medium, and relates to the technical field of data management, and the method comprises the steps: collecting original natural resource data, and initializing a natural resource data set; performing structured processing operation on the natural resource data set, constructing a storage structure for storing and managing the data set, and forming a structured natural resource data set; performing element extraction on the structured natural resource data set, performing fusion processing on key knowledge elements obtained by extraction, and generating a target retrieval database according to a processing result; according to demand information input by a user, knowledge information matched with the demand information is retrieved and determined from the target retrieval database, and a corresponding visual scene is generated and displayed to the user based on the knowledge information. According to the method provided by the invention, the efficiency and convenience of acquiring and using the natural resource data by the user can be improved, and the requirements on visual display and analysis of the data in a natural resource management service are met.

Description

Technical Field

[0001] This application relates to the field of data management technology, and in particular to a method, apparatus, device and storage medium for natural resource data management. Background Technology

[0002] Scientific and rational management of natural resource data contributes to the sustainable use of natural resources and ecological protection. Because raw natural resource data is scattered and comes in diverse formats, making it difficult to use directly, a natural resource database can be used to integrate diverse natural resource data for easier unified management by users.

[0003] However, traditional natural resource databases lack intelligent combination and construction capabilities when building visualization scenarios, making it impossible to meet high timeliness requirements. Therefore, they require high levels of business skills and professional knowledge from users, making them difficult to learn and thus unable to meet the business needs of different users. Summary of the Invention

[0004] The main objective of this application is to provide a natural resource data management method, apparatus, equipment, and storage medium, which aims to solve the technical problem that existing natural resource databases are unable to meet the business needs of different users when managing natural resource data.

[0005] To achieve the above objectives, this application proposes a natural resource data management method, which includes: Collect raw natural resource data, and initialize a natural resource dataset based on the raw natural resource data. The natural resource dataset is subjected to structuring operations to construct a storage structure for storing and managing the natural resource dataset, thus forming a structured natural resource dataset; The structured natural resource dataset is subjected to element extraction, and the extracted key knowledge elements are fused and processed to generate a target retrieval database based on the processing results. Based on the user's input requirements, the system retrieves and determines knowledge information that matches the requirements from the target retrieval database, generates and displays a corresponding visualization scene to the user based on the knowledge information.

[0006] In one embodiment, the step of collecting raw natural resource data and initializing a natural resource dataset based on the raw natural resource data includes: Scattered raw natural resource data are obtained from different natural resource business systems. The raw natural resource data includes attribute data, spatial data, and related business record data of natural resources. The original natural resource data is preprocessed to remove noise and duplicate data, resulting in processed natural resource data. A natural resource dataset is then initialized based on the processed natural resource data.

[0007] In one embodiment, the step of performing structuring operations on the natural resource dataset to construct a storage structure for storing and managing the natural resource dataset, thereby forming a structured natural resource dataset, includes: Based on the data types in the natural resource dataset, determine the corresponding database table structure, and store the natural resource dataset according to each of the database table structures; An index structure is established in the stored natural resource dataset to form a structured natural resource dataset.

[0008] In one embodiment, the step of extracting elements from the structured natural resource dataset, fusing the extracted key knowledge elements, and generating a target retrieval database based on the processing results includes: The structured natural resource dataset is subjected to element extraction using data mining algorithms and knowledge extraction rules to obtain key knowledge elements, which include natural resource characteristic information, natural resource correlation information, and natural resource change patterns. The key knowledge elements are subjected to redundant information removal, conflict information processing, and multi-source integration fusion processing, and a target retrieval database is generated based on the processed key knowledge elements.

[0009] In one embodiment, after the step of generating the target retrieval database based on the processed key knowledge elements, the method further includes: The target retrieval database is subjected to data timeliness detection to obtain the detection results; The target retrieval database is updated and maintained based on the detection results.

[0010] In one embodiment, the step of retrieving and determining knowledge information matching the user-inputted demand information from the target retrieval database includes: Interpret the user-inputted requirement information to obtain the target keywords and key semantics within the requirement information; Based on the target keywords and target key semantics, a search operation is performed in the target retrieval database to determine a set of matching knowledge entries, which contains several pieces of knowledge information.

[0011] In one embodiment, the step of generating and displaying a corresponding visual scene to the user based on the knowledge information includes: The layout structure and element information of the visualization scene are determined based on the set of knowledge items, and the corresponding visualization chart type and map display format are determined when the user's personalized layout instruction is received. The visualization chart type and map display format are populated with information based on the knowledge information in the knowledge item set to obtain a visualization scene and display it to the user.

[0012] Furthermore, to achieve the above objectives, this application also proposes a natural resource data management device, the device comprising: The data acquisition module is used to collect raw natural resource data and initialize a natural resource dataset based on the raw natural resource data. The data storage module is used to perform structured processing operations on the natural resource dataset, construct a storage structure for storing and managing the natural resource dataset, and form a structured natural resource dataset. The knowledge extraction module is used to extract elements from the structured natural resource dataset, fuse the extracted key knowledge elements, and generate a target retrieval database based on the processing results. The intelligent question-answering module is used to retrieve and determine knowledge information that matches the user's input requirement information from the target retrieval database, generate and display the corresponding visualization scene to the user based on the knowledge information.

[0013] In addition, to achieve the above objectives, this application also proposes a natural resource data management device, the device comprising: a memory, a processor, and a natural resource data management program stored in the memory and executable on the processor, the natural resource data management program being configured to implement the steps of the natural resource data management method as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium storing a natural resource data management program, which, when executed by a processor, implements the steps of the natural resource data management method as described above.

[0015] This application discloses a natural resource data management method, comprising: collecting raw natural resource data and initializing a natural resource dataset based on the raw natural resource data; performing structured processing on the natural resource dataset to construct a storage structure for storing and managing the data set, forming a structured natural resource dataset; extracting elements from the structured natural resource dataset and fusing the extracted key knowledge elements to generate a target retrieval database based on the processing results; retrieving and determining knowledge information matching the user's input requirements from the target retrieval database, generating and displaying a corresponding visualization scene to the user based on the knowledge information.

[0016] This application enables the collection and initialization of raw natural resource data to form a standardized natural resource dataset. Through structured processing, it constructs an effective storage structure, facilitating data storage and management. Furthermore, it extracts key knowledge elements from the structured dataset and performs fusion processing to generate a target retrieval database, thereby enabling in-depth data value mining. Further, based on user-input requirements, it can quickly retrieve matching knowledge information from the target retrieval database and generate intuitive visualizations for users, improving the efficiency and convenience of users acquiring and using natural resource data, and meeting the needs for intuitive data display and analysis in natural resource management operations. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the natural resource data management method of this application; Figure 2 This is a schematic diagram illustrating the data composition of raw natural resource data; Figure 3 This is a flowchart illustrating the second embodiment of the natural resource data management method of this application; Figure 4 A schematic diagram illustrating the construction process of a structured natural resource dataset; Figure 5 This is an example diagram illustrating how key knowledge elements are integrated and processed. Figure 6This is a flowchart illustrating the third embodiment of the natural resource data management method of this application; Figure 7 This is a schematic diagram of the knowledge retrieval process based on the target retrieval database; Figure 8 This is a schematic diagram illustrating the entire process of the natural resource data management method in this application; Figure 9 This is a schematic diagram of the module structure of the first embodiment of the natural resource data management device of this application; Figure 10 This is a schematic diagram of the structure of the natural resources data management equipment for this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] This application provides a natural resource data management method, referencing... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the natural resource data management method of this application. In this embodiment, the method includes steps S10 to S40: Step S10: Collect raw natural resource data and initialize the natural resource dataset based on the raw natural resource data.

[0024] It should be noted that the executing entity in this embodiment can be a computing electronic device with data processing, network communication, and program execution functions, such as a mobile phone, laptop computer, tablet computer, and data management server, or other electronic devices capable of accessing the natural resources data management system. This embodiment and the following embodiments will be specifically described using a natural resources data management device (hereinafter referred to as "management device") as an example.

[0025] It is understandable that the raw natural resource data may be collected from multiple sources, and the collected raw natural resource data may contain problems such as noise, data duplication, and inconsistent formats. Therefore, the raw natural resource data can be preprocessed first, and then an initial natural resource dataset can be formed based on the preprocessed natural resource data.

[0026] In addition, you can refer to this place. Figure 2 A detailed explanation of the data composition of the original natural resource data is provided. Figure 2 This is a schematic diagram illustrating the data composition of raw natural resource data.

[0027] Depend on Figure 2 It is known that raw natural resource data may include natural resource business rules, natural resource relationships, natural resource expert experience, natural resource policies and regulations, etc.

[0028] Among them, natural resource business rules refer to the norms and guidelines formulated to ensure the rational use of resources, sustainable development, and compliant operation in the process of natural resource management, development, and protection; natural resource relationships refer to the interrelationships and influences between different natural resources and between them and ecosystems and socio-economic systems; natural resource expert experience refers to the knowledge, skills, and insights accumulated by experts in the field of natural resources through long-term practice and research; and natural resource policies and regulations refer to a series of laws, regulations, policies, and standards formulated to regulate the management, development, utilization, and protection of natural resources.

[0029] It should be understood that transforming scattered, multi-source, and heterogeneous raw natural resource data into a structured knowledge system to build a natural resource database is beneficial for providing intuitive and scientific visualization support for the construction of visualization scenarios.

[0030] To illustrate in detail how to initialize the natural resource dataset, step S10 specifically includes: steps S101~S102: Step S101: Obtain scattered raw natural resource data from different natural resource business systems.

[0031] It should be understood that natural resource operations can involve multiple aspects, such as land management, mineral development, forest protection, and water resource utilization. These operations are typically managed and recorded by different business systems. For example, the land management system's database stores information such as land ownership and land use types; the mineral development system records data such as mineral reserves and mining licenses. To gain a comprehensive understanding of natural resources, raw natural resource data can be obtained from these dispersed business systems.

[0032] It should be noted that the raw natural resource data can be further divided into natural resource attribute data, spatial data, and related business record data.

[0033] Among them, attribute data consists of various attribute information describing the characteristics of natural resources. For example, land attribute data includes land use (such as arable land, construction land, etc.), soil type, fertility level, etc.; mineral resource attribute data includes mineral type, ore grade, mining method, etc. These attribute data can reflect the basic state and characteristics of natural resources.

[0034] Spatial data refers to information such as the location, extent, and distribution of natural resources in geographic space. For example, spatial data for land includes the boundary coordinates and area of ​​land parcels. This data can be used to accurately locate and display the location and shape of land in a Geographic Information System (GIS). Spatial data for mineral resources includes the geographic coordinate range of the mining area and the burial depth of the ore body, which is used to show the geographic distribution of mineral resources.

[0035] Relevant business record data covers a variety of records related to natural resource business activities. For example, in land management, land registration records and land approval documents are business record data, which record information on business processes such as changes in land ownership and land use approval; in forest resource management, forest logging records and afforestation records are also important business record data, which can reflect the development, utilization and protection of forest resources.

[0036] Step S102: Perform preprocessing operations on the original natural resource data to remove noise and duplicate data, obtain processed natural resource data, and initialize a natural resource dataset based on the processed natural resource data.

[0037] It should be understood that raw natural resource data may be subject to interference from various factors during generation, collection, and transmission, resulting in noisy data. This noisy data can be erroneous, unreasonable, or incomplete. For example, in land survey data, instrument malfunctions or human error may cause significant deviations from actual land area data; or in mineral resource data, ore grade testing results may contain unreliable data due to equipment errors. Data cleaning and other preprocessing techniques can identify and remove this noisy data, preserving accurate and reliable natural resource data.

[0038] Secondly, due to potential data sharing or duplicate records between different business systems, a large number of duplicate data records exist in the collected raw natural resource data. For example, information about the same piece of land may be recorded in both the land registration system and the land use planning system. Without processing, this results in data redundancy. Removing duplicate data can reduce the amount of data and improve data processing and storage efficiency.

[0039] It should be noted that after the above preprocessing operations, relatively clean and well-organized natural resource data can be obtained. Based on this processed data, an initial natural resource dataset is established according to a certain data structure and organization. This initial dataset provides a standardized and unified data foundation for subsequent data processing and analysis, ensuring the accuracy and reliability of subsequent operations.

[0040] Step S20: Perform structuring operations on the natural resource dataset to construct a storage structure for storing and managing the natural resource dataset, thus forming a structured natural resource dataset.

[0041] It should be understood that in order to transform the initially disorganized natural resource dataset into a data set with logical relationships and a hierarchical structure, data processing techniques and tools can be used to thoroughly organize the initialized natural resource dataset. For example, this includes classifying the data, defining relationships between data points, and setting data attributes, giving it a clear structure and organizational form.

[0042] Next, based on the type and characteristics of the data in the dataset and user needs, a suitable database table structure, file system structure, or other storage organization form can be designed and created. Then, the structured data is stored in the corresponding storage structure, and an indexing and querying mechanism is established to enable efficient data retrieval, access, and management.

[0043] It should be noted that after the above processing, the resulting natural resource dataset has good structured characteristics, the relationships between data are clear, and it is convenient for subsequent data query, analysis and application.

[0044] Step S30: Extract elements from the structured natural resource dataset, fuse the extracted key knowledge elements, and generate a target retrieval database based on the processing results.

[0045] It should be understood that data mining, knowledge discovery, and other techniques can be used to identify and extract key knowledge elements of significant value and representativeness from structured natural resource datasets. For example, key elements such as the distribution and area information of different land types can be extracted from land use data; key elements such as mineral types, reserves, and mining methods can be extracted from mineral resource data.

[0046] Next, the extracted key knowledge elements can be comprehensively analyzed and integrated to resolve redundancy, conflicts, and inconsistencies among the elements. For example, key elements from different data sources that describe the same natural resource object can be merged and coordinated to form a unified, complete, and accurate set of knowledge elements.

[0047] Finally, the fused key knowledge elements can be organized and stored according to certain rules and indexing methods to construct a dedicated retrieval database, namely the target retrieval database. This database can quickly respond to user query requests and provide efficient data support for subsequent knowledge retrieval and application scenarios.

[0048] Step S40: Based on the user's input requirement information, retrieve and determine the knowledge information that matches the requirement information from the target retrieval database, generate and display the corresponding visualization scene to the user based on the knowledge information.

[0049] It should be understood that users can input specific natural resource-related needs through the interactive interface of the management device, such as querying the current land use status of a certain region or the distribution of a certain mineral resource. The management device can then analyze and interpret the user's request information, extracting key information and semantic points. Based on this key information, it can then quickly search the target retrieval database to find matching knowledge information.

[0050] In practice, the management device evaluates and filters the search results to determine the knowledge information that best meets the user's needs. Then, using the determined knowledge information as basic data and combining it with visualization technology, it generates intuitive and vivid visualization scenarios.

[0051] For example, various charts (such as pie charts showing the area ratio of different types of land, bar charts showing the changes in land use in different regions) and maps (such as land type distribution maps, land use change comparison maps, etc.) can be generated from the knowledge information on the current status of land use. These visualizations can then be presented to users, enabling them to understand and analyze the information contained in the natural resource data more conveniently and quickly.

[0052] This embodiment can collect and initialize raw natural resource data to form a standardized natural resource dataset, and construct an effective storage structure through structured processing to facilitate data storage and management. Simultaneously, this embodiment can extract key knowledge elements from the structured dataset and perform fusion processing to generate a target retrieval database, thereby achieving in-depth data value mining. Furthermore, based on user-input requirements, it can quickly retrieve matching knowledge information from the target retrieval database and generate intuitive visualizations for users, improving the efficiency and convenience of users acquiring and using natural resource data, and meeting the needs for intuitive data display and analysis in natural resource management operations.

[0053] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the natural resource data management method of this application.

[0054] In this embodiment, to specifically illustrate how to implement the storage and management of natural resource data, step S20 further includes: steps S201~S202: Step S201: Determine the corresponding database table structure according to the data type in the natural resource dataset, and store the natural resource dataset according to each of the database table structures.

[0055] It's important to note that the data in the natural resource dataset can first be analyzed by type, with different data types corresponding to different database table structures. For example, land use status data can be divided into fields such as land category code, land category name, area, and ownership unit, and the corresponding database table structure will include these fields. For mineral resource reserve data, the table structure might include fields such as mineral type, deposit type, reserves, and grade. By designing a reasonable table structure, the standardization and logic of data storage can be ensured, facilitating subsequent data operations and management.

[0056] Next, the data collected in the natural resource dataset can be stored in corresponding tables according to the defined database table structure. For example, various attribute data of land use status (such as land type, area, etc.) can be stored in the land use status table, and spatial data of mineral resources (such as mining area coordinates, ore body morphology, etc.) can be stored in the mineral resource spatial data table. This gives the data a clear organizational form and storage location, improving the efficiency and accuracy of data storage, and also facilitating subsequent data query, update, and statistical operations.

[0057] Step S202: Establish an index structure in the stored natural resource dataset to form a structured natural resource dataset.

[0058] It should be understood that to improve the efficiency of data retrieval, an index structure can be built on the stored natural resource dataset. Based on the index structure, the specific location of data within the database can be quickly determined. Common index structures include B-tree indexes and hash indexes. Choosing the appropriate index structure based on the characteristics of the data and query requirements can significantly improve the speed of data retrieval.

[0059] In addition, you can refer to this place. Figure 4 The process of constructing a structured natural resource dataset is explained. Figure 4 This is a schematic diagram illustrating the process of constructing a structured natural resource dataset.

[0060] Depend on Figure 4It is understood that after acquiring raw natural resource data and initializing a natural resource dataset based on this data, the following steps can be taken: knowledge modeling to systematize and structure knowledge; knowledge storage to manage knowledge in a unified manner; knowledge aggregation to collect knowledge into a central natural resource think tank; knowledge coding to identify and mark each piece of knowledge; knowledge classification to categorize and organize knowledge according to standards or rules; real-time knowledge updates to maintain the timeliness of knowledge; knowledge maintenance to ensure the accuracy of knowledge; and knowledge security management to ensure data security and reliability. Through unified storage and management of knowledge, and by clarifying the hierarchical classification catalog, accurate knowledge retrieval, rapid use, and efficient management are facilitated.

[0061] Furthermore, to illustrate how to extract more granular and valuable natural resource knowledge points from the existing structured natural resource dataset to meet different user needs, step S30 specifically includes: steps S301~S302: Step S301: Extract key knowledge elements from the structured natural resource data set using data mining algorithms and knowledge extraction rules. The key knowledge elements include natural resource characteristic information, natural resource association information, and natural resource change patterns.

[0062] It should be understood that data mining algorithms such as association rule mining, cluster analysis, and decision trees, as well as knowledge extraction rules based on expert experience and domain knowledge, can be used to conduct in-depth analysis of structured natural resource datasets.

[0063] For example, clustering analysis algorithms can group land use type data with similar characteristics into one category and uncover potential land use patterns; decision tree algorithms can be used to classify mineral resource data and extract characteristic information of different mineral types.

[0064] It should be noted that by applying the above algorithms and rules, key knowledge elements such as natural resource characteristic information, natural resource correlation information, and natural resource change patterns can be extracted.

[0065] Natural resource characteristic information, such as: land use status data, land type, ownership, and area attributes of land parcels; mineral resource data, such as mining methods, production scale, main mining minerals, and mining area; natural resource change patterns, such as: annual data of land use change surveys and monthly data of satellite and aerial remote sensing images; natural resource correlation information, i.e., the relationship between different natural resources; in addition, natural resource data content can be extracted, that is, a summary description of the data.

[0066] Step S302: Perform redundant information removal, conflict information processing, and multi-source integration processing on the key knowledge elements, and generate a target retrieval database based on the processed key knowledge elements.

[0067] It should be understood that, due to the large number and wide scope of natural resource objects involved in knowledge extraction, redundancy and duplication exist among the knowledge. Therefore, knowledge fusion processing can be performed. This fusion processing can include: redundant information removal, conflict information handling, and multi-source integration. Redundant information removal involves analyzing the extracted key knowledge elements to identify and remove duplicate or meaningless information; conflict information handling involves employing strategies to resolve inconsistencies when data from different sources or at different times provides inconsistent information; multi-source integration can integrate key knowledge elements from different business systems and data sources to form a unified knowledge system.

[0068] Furthermore, you can also refer to this section. Figure 5 The fusion process in this embodiment will be described in detail. Figure 5 This is an example diagram illustrating how key knowledge elements are integrated and processed.

[0069] Depend on Figure 5 It can be seen that this fusion process can be specifically divided into categories such as: removal of temporary knowledge scenarios, removal of duplicate knowledge scenarios, removal of erroneous knowledge scenarios, fusion of multi-source knowledge scenarios, integration of similar knowledge scenarios, and association of business application scenarios.

[0070] Among them, temporary knowledge scenarios refer to drafts used when building visualization scenarios with knowledge; these are process versions and are ultimately discarded. Duplicate knowledge scenarios refer to duplicate knowledge that appears when different users build visualization scenarios. Erroneous knowledge scenarios refer to knowledge that does not match the required scenario with the actual presentation effect. Multi-source knowledge scenarios refer to scenarios that need to be merged when building different business requirement scenarios such as land change surveys and farmland protection. Similar knowledge scenarios refer to scenarios where all current status analysis elements need to be integrated in the analysis of the current status of mountain, water, forest, field, lake, grassland, and sand resources. Business application scenario association refers to associating relevant business knowledge when building a certain type of scenario; for example, when building a farmland early warning scenario, it is associated with farmland protection target knowledge.

[0071] It should also be noted that, based on the key knowledge elements after fusion processing, a target retrieval database is constructed according to a certain data organization and indexing method. This target retrieval database efficiently organizes knowledge elements so that users can quickly retrieve the knowledge information they need.

[0072] In addition, a data timeliness detection mechanism can be established to periodically or in real-time check the timeliness of data in the target retrieval database. Based on the timeliness detection results, the target retrieval database can be updated and maintained accordingly: outdated data can be updated in a timely manner and replaced with the latest knowledge element information; the structure of the target retrieval database can be optimized and adjusted to adapt to the storage and query needs of new data, ensuring that the accuracy and usability of the target retrieval database can be maintained continuously.

[0073] This embodiment determines the corresponding database table structure based on the data types in the natural resource dataset and stores the natural resource dataset according to each database table structure. An index structure is established in the stored natural resource dataset to form a structured natural resource dataset. This not only facilitates data storage and management but also supports complex query and data analysis operations, providing a solid data foundation for subsequent applications such as natural resource knowledge extraction, fusion, and visualization. Furthermore, data mining algorithms and knowledge extraction rules are used to extract elements from the structured natural resource dataset to obtain key knowledge elements, including natural resource characteristic information, natural resource association information, and natural resource change patterns. Redundant information is removed, conflicting information is processed, and multi-source integration is performed on the key knowledge elements. Based on the processed key knowledge elements, a target retrieval database is generated, achieving efficient organization of knowledge elements so that users can quickly retrieve the knowledge information they need.

[0074] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 6 , Figure 6 This is a flowchart illustrating the third embodiment of the natural resource data management method of this application.

[0075] In this embodiment, after step S40, the method further includes: steps S401~S402: Step S401: Interpret the user-inputted demand information to obtain the target keywords and target key semantics in the demand information.

[0076] It should be understood that users can input relevant descriptive information through the input interface (such as text boxes, voice input, etc.) provided by the management device based on their own needs for querying, analyzing, or making decisions about natural resources. For example, users can input information such as "querying the changes in the cultivated land area of ​​a certain region and the main reasons" or "analyzing the relationship between the distribution of a certain mineral resource and the surrounding ecological environment".

[0077] Next, the management system can parse the natural language requirements input by the user: on the one hand, it uses lexical analysis and syntactic analysis in natural language processing technology to identify keywords in the requirements. For example, in the requirement of "querying the changes in cultivated land area in a certain region and the main reasons", keywords may include "certain region", "cultivated land area", "changes", "main reasons", etc.

[0078] On the other hand, management equipment can grasp the core semantics of user needs by understanding and analyzing semantics, and clarify the specific content and key issues of natural resources that users want to know, that is, determine the key semantics of the target. For example, in the above example, the user's intention is to obtain information on the dynamic changes in the cultivated land area of ​​a specific region and the analysis of the reasons behind it.

[0079] Step S402: Perform a search operation in the target retrieval database based on the target keywords and target key semantics to determine a set of matching knowledge entries, wherein the set of knowledge entries contains several pieces of knowledge information.

[0080] It should be noted that the management device can extract target keywords and target key semantics as a basis to carry out retrieval work in the target retrieval database: matching keywords with knowledge entries in the target retrieval database, and at the same time, combining key semantics to perform semantic comparison and screening of the content contained in the knowledge entries.

[0081] For example, in response to the aforementioned need for information on changes in arable land area, the management device can search for knowledge entries containing keywords such as "arable land area" and "changes" in the target retrieval database, and filter out the set of knowledge entries that semantically match "querying the changes in arable land area in a certain region and the main reasons", such as historical arable land area statistics for a certain region and analysis of the driving factors of changes in arable land area.

[0082] After the retrieval process, the management device can collect and organize a set of knowledge entries that match the user's needs, forming a knowledge entry set. Each piece of knowledge in this set is related to the user's input needs to some extent, providing the user with more comprehensive answers and references from different angles and levels.

[0083] Furthermore, in order to achieve real-time adjustment of the visualized scene based on user demand information, step S40 also includes: steps S403~S404: Step S403: Determine the layout structure and element information of the visualization scene based on the knowledge item set, and determine the corresponding visualization chart type and map display format when receiving the user's personalized layout instruction.

[0084] It should be understood that management equipment can initially determine the layout structure and element information of the visualization scene based on the scope of knowledge content covered by the knowledge item set, the data types, and the user's common display needs. The chart area is used to display the proportional relationship of various land use types (such as pie charts) and the area change trend (such as line charts or bar charts); the map area is used to intuitively present the spatial distribution of land use; and the text description area is used to explain key information such as data sources and reasons for changes.

[0085] For example, if the knowledge item set mainly contains data on the current status and changes of land use, the layout of the visualization scene may include chart areas, map areas, and text description areas.

[0086] The management device can also provide users with clear and easy-to-understand options for visualization chart types and map display formats on the interactive interface. Users can select or customize personalized visualization chart types and map display formats based on their own preferences, understanding of data, and specific analysis objectives, within the range of options, through the visualization interface (such as drop-down menus, icon selection areas, etc.) or by inputting commands (such as directly entering the desired chart type name, map display format description, etc.).

[0087] For example, users might prefer to use heat maps to visually demonstrate the enrichment of a mineral resource in a specific area, or they might want to present the underground distribution and mining status of mineral resources in the form of 3D topographic maps combined with ore body models. For land use change data, users might prefer to use comparative bar charts to visually compare the area changes of various land use types in different years. Furthermore, each chart can be matched with appropriate colors based on the style of the management equipment to ensure aesthetic appeal.

[0088] Step S404: Fill the visualization chart type and map display format with information according to the knowledge information in the knowledge item set to obtain a visualization scene and display it to the user.

[0089] It should be noted that the management equipment uses the specific knowledge information in the knowledge item set to fill in detailed information for the selected visualization chart type and map display format. For the chart part, the data content such as data values, proportions, and trends from the knowledge information are filled into the corresponding chart elements according to the chart drawing rules and data mapping relationships. For the map display part, spatial data such as geographic coordinates, ranges, and attributes from the knowledge information, as well as related non-spatial attribute data (such as land use type names, mineral reserves, etc.), can be combined with map layers through Geographic Information System (GIS) technology to perform symbolization, labeling, and other operations to generate intuitive map visualization content.

[0090] Next, the visualized charts and maps filled with information are integrated according to the previously determined visualization scene layout structure to form a complete visualization scene. The generated visualization scene can then be presented to users through an interactive interface, allowing users to directly view, analyze, and interpret the visualization content on the interactive interface, thereby gaining a deeper understanding of relevant knowledge and information about natural resources.

[0091] In addition, users can freely adjust the visualization layout by dragging and dropping according to their personalized requirements, and fine-tune the position, color and content of the charts to achieve the best display effect.

[0092] Based on the above interaction process, users only need to describe their business needs to obtain knowledge information matching those needs. This allows for the construction of faster, more vivid, and more user-friendly personalized visualization scenarios, saving users time in selecting, matching, and laying out components, and reducing user learning costs. For example, when a user wants to build a visualization scenario of the current status of land resources, the system automatically generates pie charts of primary land categories, bar charts of land categories by league / city, timelines of data for each year, and matching historical land use status maps. Simultaneously, each chart is matched with appropriate colors according to the system style to ensure the system's aesthetic appeal. After the visualization scenario recommendation is completed, users can further adjust the visualization layout by dragging and dropping according to their personalized requirements, fine-tuning the chart positions, colors, and content to achieve the best display effect.

[0093] Furthermore, this can be referenced here. Figure 7 This section explains the knowledge retrieval process for users of the target search database. Figure 7 This is a schematic diagram of the knowledge retrieval process based on a target retrieval database.

[0094] Depend on Figure 7 It can be seen that when a user makes a request, the associated device can retrieve knowledge from the target retrieval database and infer the user's expectations, providing matching knowledge information. The knowledge reasoning process can be divided into three types: reasoning based on natural resource spatiotemporal scenarios, reasoning based on natural resource case scenarios, and reasoning based on natural resource business rules.

[0095] For example, the reasoning process based on the spatiotemporal scenario of natural resources can be as follows: When a user builds a scenario of the third national land survey, considering that the technical requirements of the national land survey vary greatly from year to year, it can be divided into the first, second and third surveys. By obtaining the keyword "third survey", a scenario building suggestion for this time period is formed. The process of reasoning based on natural resource case scenarios can be summarized as follows: Cases are built by reasoning through historical scenarios; the overlap between user needs and historical cases is assessed; and when a need matches a historical case, corresponding scenario building suggestions are provided. For example, to build an aerial imagery resource analysis scenario, a satellite imagery resource analysis scenario can be recommended based on knowledge.

[0096] The process of reasoning based on natural resource business rules can be summarized as follows: Based on the relationship between the demand scenario and related business operations, suggestions are provided. For example, a land use status analysis scenario is built. Because land use is closely related to spatial planning and control in natural resource management, knowledge suggestions are provided by matching the current land use status with relevant indicators from the "three zones and three lines" overlay analysis.

[0097] In addition, you can refer to this place. Figure 8 This application provides a complete description of the natural resource data management method. Figure 8 This is a schematic diagram of the entire process of the natural resource data management method in this application.

[0098] Depend on Figure 8 As can be seen, this application, through the processes of knowledge acquisition, natural resource knowledge storage and management, natural resource knowledge extraction, natural resource knowledge fusion, and natural resource knowledge reasoning based on original natural resource data, ultimately constitutes a complete natural resource think tank, namely a target retrieval database, which can provide users with a standardized and usable data foundation for knowledge services and meet users' needs for visualization of knowledge information.

[0099] First, information such as business rules, relationships, expert experience, and policies and regulations related to natural resources can be collected. Next, operations such as knowledge modeling, storage, aggregation, classification, updating, maintenance, and security management are performed; natural resource element attributes, data temporality, object relationships, and content are extracted. Then, temporary, repetitive, and erroneous scenarios are removed from the extracted knowledge, and multi-source and similar scenarios are integrated and associated with business scenarios. Finally, reasoning is performed using spatiotemporal factors, case studies, and business rules.

[0100] The above processes collectively construct a natural resource think tank encompassing natural resource objects, application scenario templates, business models, visualization components, and statistical analysis indicators, providing users with a meticulously crafted, comprehensive, and practical foundation for knowledge services. Ultimately, it enables the visualization of various natural resource knowledge services, covering land, forests, grasslands, wetlands, minerals, water resources, and specific scenarios such as farmland protection, change zone prediction, key area monitoring, and the "three zones and three lines" (referring to pollution control, ecological protection, and environmental protection), thus meeting diverse user needs.

[0101] Specifically, a low-code development approach can be adopted, utilizing structured data organized and processed by the think tank's backend. This data is then visualized on the interactive interface to represent various think tank scenarios, including natural resource inventory scenarios, special resource scenarios, "three zones and three lines" scenarios, geographical pattern scenarios, economic zoning scenarios, cultural and economic scenarios, nature reserve scenarios, change survey parcel prediction scenarios, change survey data early warning scenarios, annual change survey scenarios, arable land protection scenarios, and border line analysis scenarios. Charts are displayed in pie charts, bar charts, line charts, Sankey diagrams, etc., while maps are displayed in color block distribution maps, land type distribution maps, etc. The statistical data in the charts and maps are linked, and clicking on administrative regions allows for drill-down and deeper exploration, thus meeting users' personalized visualization needs.

[0102] This application method integrates target retrieval databases related to natural resource management, development and utilization, and ecological protection. As the number of users and the demand increase, the target retrieval database can be continuously enriched and improved, making it easier to build visualization scenarios. It can quickly and automatically build corresponding scenarios in real time according to user needs, making the construction time shorter and more flexible. It helps users discover and diagnose business problems, and instantly presents business insights hidden behind rapidly changing and complex data, becoming an indispensable part of natural resource management.

[0103] In addition, this application also provides a natural resource data management device, referring to Figure 9 , Figure 9 This is a schematic diagram of the module structure of the first embodiment of the natural resource data management device of this application; as shown below. Figure 9 As shown, the device includes: The data acquisition module 901 is used to collect raw natural resource data and initialize a natural resource dataset based on the raw natural resource data. The data storage module 902 is used to perform structured processing operations on the natural resource dataset, construct a storage structure for storing and managing the natural resource dataset, and form a structured natural resource dataset. The knowledge extraction module 903 is used to extract elements from the structured natural resource dataset, fuse the extracted key knowledge elements, and generate a target retrieval database based on the processing results. The intelligent question-answering module 904 is used to retrieve and determine knowledge information that matches the user's input requirement information from the target retrieval database, generate and display the corresponding visualization scene to the user based on the knowledge information.

[0104] This embodiment can collect and initialize raw natural resource data to form a standardized natural resource dataset, and construct an effective storage structure through structured processing to facilitate data storage and management. Simultaneously, this embodiment can extract key knowledge elements from the structured dataset and perform fusion processing to generate a target retrieval database, thereby achieving in-depth data value mining. Furthermore, based on user-input requirements, it can quickly retrieve matching knowledge information from the target retrieval database and generate intuitive visualizations for users, improving the efficiency and convenience of users acquiring and using natural resource data, and meeting the needs for intuitive data display and analysis in natural resource management operations.

[0105] This application also provides a natural resource data management device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the natural resource data management method in the first embodiment described above.

[0106] The following is for reference. Figure 10 , Figure 10 This is a schematic diagram of the structure of the natural resource data management device of this application. The natural resource data management device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., as well as fixed terminals such as digital TVs, desktop computers, etc. Figure 10 The natural resource data management device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0107] like Figure 10As shown, the natural resource data management device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the natural resource data management device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the natural resource data management equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a natural resource data management equipment with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0108] The natural resource data management device provided in this application, employing the natural resource data management method described in the above embodiments, can solve the technical problems of natural resource data management. Compared with the prior art, the beneficial effects of the natural resource data management device provided in this application are the same as those of the natural resource data management method provided in the above embodiments, and other technical features of the natural resource data management device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0109] This application also provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the natural resource data management method in the above embodiments.

[0110] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0111] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described natural resource data management method, and is capable of solving the technical problems of natural resource data management. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the natural resource data management method provided in the above embodiments, and will not be repeated here.

[0112] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other elements in the process, method, article, or system that includes that element.

[0113] The sequence numbers of the above embodiments of the present invention are merely for description and do not represent the superiority or inferiority of the embodiments. They are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A method for managing natural resource data, characterized in that, The method includes: Collect raw natural resource data and initialize a natural resource dataset based on the raw natural resource data; The natural resource dataset is subjected to structuring operations to construct a storage structure for storing and managing the natural resource dataset, thus forming a structured natural resource dataset; The structured natural resource dataset is subjected to element extraction, and the extracted key knowledge elements are fused and processed to generate a target retrieval database based on the processing results. Based on the user's input requirements, the system retrieves and determines knowledge information that matches the requirements from the target retrieval database, generates and displays a corresponding visualization scene to the user based on the knowledge information.

2. The method as described in claim 1, characterized in that, The steps of collecting raw natural resource data and initializing the natural resource dataset based on the raw natural resource data include: Scattered raw natural resource data are obtained from different natural resource business systems. The raw natural resource data includes attribute data, spatial data, and related business record data of natural resources. The original natural resource data is preprocessed to remove noise and duplicate data, resulting in processed natural resource data. A natural resource dataset is then initialized based on the processed natural resource data.

3. The method as described in claim 1, characterized in that, The step of performing structuring processing on the natural resource dataset to construct a storage structure for storing and managing the natural resource dataset, thereby forming a structured natural resource dataset, includes: Based on the data types in the natural resource dataset, determine the corresponding database table structure, and store the natural resource dataset according to each of the database table structures; An index structure is established in the stored natural resource dataset to form a structured natural resource dataset.

4. The method as described in claim 1, characterized in that, The steps of extracting elements from the structured natural resource dataset, fusing the extracted key knowledge elements, and generating a target retrieval database based on the processing results include: The structured natural resource dataset is subjected to element extraction using data mining algorithms and knowledge extraction rules to obtain key knowledge elements, which include natural resource characteristic information, natural resource correlation information, and natural resource change patterns. The key knowledge elements are subjected to redundant information removal, conflict information processing, and multi-source integration fusion processing, and a target retrieval database is generated based on the processed key knowledge elements.

5. The method as described in claim 4, characterized in that, After the step of generating the target retrieval database based on the processed key knowledge elements, the method further includes: The target retrieval database is subjected to data timeliness detection to obtain the detection results; The target retrieval database is updated and maintained based on the detection results.

6. The method as described in claim 1, characterized in that, The step of retrieving and determining knowledge information matching the user's input requirement from the target retrieval database includes: Interpret the user-inputted requirement information to obtain the target keywords and key semantics within the requirement information; Based on the target keywords and target key semantics, a search operation is performed in the target retrieval database to determine a set of matching knowledge entries, which contains several pieces of knowledge information.

7. The method as described in claim 6, characterized in that, The step of generating and displaying the corresponding visual scene to the user based on the knowledge information includes: The layout structure and element information of the visualization scene are determined based on the set of knowledge items, and the corresponding visualization chart type and map display format are determined when the user's personalized layout instruction is received. The visualization chart type and map display format are populated with information based on the knowledge information in the knowledge item set to obtain a visualization scene and display it to the user.

8. A natural resource data management device, characterized in that, The device includes: The data acquisition module is used to collect raw natural resource data and initialize a natural resource dataset based on the raw natural resource data. The data storage module is used to perform structured processing operations on the natural resource dataset, construct a storage structure for storing and managing the natural resource dataset, and form a structured natural resource dataset. The knowledge extraction module is used to extract elements from the structured natural resource dataset, fuse the extracted key knowledge elements, and generate a target retrieval database based on the processing results. The intelligent question-answering module is used to retrieve and determine knowledge information that matches the user's input requirement information from the target retrieval database, generate and display the corresponding visualization scene to the user based on the knowledge information.

9. A natural resource data management device, characterized in that, The device includes a memory, a processor, and a natural resource data management program stored in the memory and executable on the processor. When executed by the processor, the natural resource data management program implements the steps of the natural resource data management method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a natural resource data management program, which, when executed by a processor, implements the steps of the natural resource data management method as described in any one of claims 1 to 7.

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