A knowledge-enhanced data weaving method for forest and grass multi-source data

Abstract

This invention discloses a knowledge-enhanced data weaving method for multi-source forestry and grassland data, belonging to the interdisciplinary field of forestry and grassland data governance and artificial intelligence. The method includes: S1, data access; S2, constructing a forestry and grassland-specific metadata foundation; S3, building a unified semantic layer for forestry and grassland; S4, deploying a forestry and grassland-specific policy engine; S5, integrating forestry and grassland AI enhancement modules; and S6, testing and optimization. This invention achieves logical integration and on-demand flow of distributed data through metadata-driven approaches, semantic unification, and intelligent governance, providing a new method and approach to addressing the challenges of multi-source data management.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of forestry and grassland data governance and artificial intelligence, specifically involving a knowledge-enhanced data weaving method for multi-source forestry and grassland data. Background Technology

[0002] Forestry and grassland resource management involves complex data types and dispersed sources. Current industry data management faces the following core pain points:

[0003] 1. Severe data silos: Forestry and grassland data are scattered across multiple platforms and formats, including real-time data generated by field monitoring equipment (sensors, drones), image data provided by remote sensing satellite platforms (Gaofen satellites, Sentinel satellites), databases of locally deployed business systems (afforestation management, fire prevention command, pest and disease control), historical data after digitization of paper archives, and interface data from third-party meteorological / land planning data platforms. The data formats cover TIFF (remote sensing images), JSON (sensors), relational data tables, documents, etc. Cross-source data integration requires manual adaptation, which is extremely inefficient.

[0004] 2. High data access threshold: Forestry and grassland personnel (such as grassroots monitors, emergency command personnel, and planning analysts) need to master the access methods and data formats of different data sources. Cross-source queries require the intervention of professional technicians, resulting in low data utilization efficiency.

[0005] 3. Significant compliance and security risks: Forestry and grassland data include classified data such as core areas of national parks and ecologically sensitive areas, as well as sensitive information such as the coordinates of monitoring points and the distribution of rare species. The existing management methods lack refined access control, which can easily lead to data leakage or unauthorized access risks.

[0006] 4. Insufficient real-time decision support: Scenarios such as fire prevention early warning and emergency response to pest and disease outbreaks require real-time integration of multi-source data (such as real-time monitoring data + historical fire data + meteorological data). The traditional T+1 data integration model cannot meet the second-level response requirements, resulting in delayed emergency response.

[0007] Data fabric, as a composable data management architecture, enables the automatic discovery, integration, access, and governance of data in a distributed environment through technologies such as metadata, knowledge graphs, and AI / ML. Its core principles of logical unity and on-demand flow align perfectly with the data management needs of the forestry and grassland sector. While existing data fabric technologies have demonstrated data integration potential in fields such as finance and e-commerce, their direct application to forestry and grassland resource management scenarios still faces the following inherent technical obstacles:

[0008] 1. Deep semantic heterogeneity: Forestry and grassland data not only have diverse formats, but their core business concepts (such as forest health) also have fundamental differences in definition, calculation model and scale in different data sources (remote sensing interpretation, ground survey, pest and disease reports). The simple field mapping of existing data cannot achieve true semantic alignment.

[0009] 2. Strong spatiotemporal correlation: Forest and grassland ecological processes have strong spatiotemporal dependencies (such as phenology and succession). General data weaving techniques lack the ability to explicitly model such complex spatiotemporal correlations, resulting in the inability to effectively integrate time-series data and spatial data to form accurate decision support in scenarios such as fire prevention and emergency response, and pest and disease prediction.

[0010] 3. Multi-dimensional and complex security requirements: Forestry and grassland data security not only involves traditional user-role permissions, but is also deeply coupled with dynamic business scenarios such as ecologically sensitive areas, state secrets, and emergency response levels. Existing static, role-based access control models cannot flexibly and accurately meet these dynamic and complex security management requirements. Summary of the Invention

[0011] This invention aims to overcome the shortcomings of existing technologies, such as inefficient integration of multi-source forestry and grassland data, high access barriers, insufficient security control, and delayed real-time response. This invention provides a knowledge-enhanced data weaving method for multi-source forestry and grassland data, which realizes automatic discovery, unified semantic mapping, secure and controllable access, and intelligent fusion of multi-source forestry and grassland data, reduces the data usage threshold, and improves the efficiency and standardization of forestry and grassland resource management and emergency decision-making.

[0012] Technical Solution: To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0013] A knowledge-enhanced data weaving method for multi-source forestry and grassland data includes the following steps:

[0014] S1. Data Access: Through a configurable data interface adapter module, access is provided for multi-source forestry and grassland data, including field monitoring data, remote sensing image data, forestry and grassland business system data, historical archive data, and third-party meteorological and land data.

[0015] S2. Construct a dedicated metadata foundation for forestry and grassland: Collect metadata from multiple sources of forestry and grassland data, and automatically label it through a three-dimensional tag system to build a metadata management center that supports automatic metadata updates, version tracking, and multi-dimensional retrieval.

[0016] S3. Build a unified semantic layer for forestry and grassland: Construct a semantic model based on the ontology of the forestry and grassland domain. This model defines the core concepts of forestry and grassland and their hierarchical relationships, unifies the measurement standards of ambiguous indicators, maps fields in heterogeneous data sources to the unified semantic fields of this model, and deploys a virtualized query engine that supports unified SQL queries.

[0017] S4. Deploy a forestry and grassland-specific strategy engine: Formulate multi-dimensional data access strategies that are dynamically linked with sensitivity level labels, business roles, and emergency scenarios to achieve dynamic permission control and data desensitization based on data sensitivity level, user business role, and emergency response level.

[0018] S5. Integrated Forestry and Grassland AI Enhancement Module: Construct a dedicated recommendation model trained based on user behavior logs and metadata tags in the forestry and grassland field, as well as a forestry and grassland data quality detection model constructed based on a spatiotemporal constraint graph structure; deploy real-time data processing components to achieve rapid fusion of real-time and offline data;

[0019] S6. Testing and Optimization: For core forestry and grassland business scenarios, conduct joint testing and iterative optimization of data access, metadata management, semantic query, access control and AI enhancement modules.

[0020] As a preferred option, in S1, forestry and grassland multi-source data includes field monitoring equipment, real-time streaming data, image data, forestry and grassland business systems, historical archives, and meteorological and land data;

[0021] Field monitoring equipment: Based on multiple sensors, it collects temperature, humidity, and vegetation coverage data of the study area;

[0022] Image data: Image data of forest vegetation and fire clues collected by drones.

[0023] As a preferred option, the specific implementation process in S2 is as follows:

[0024] S201, Comprehensive collection of multi-source metadata;

[0025] S202. Forestry and Grassland Characteristic Metadata Labeling: Establish a forestry and grassland characteristic metadata labeling system, which includes business attribute labels, sensitivity level labels, and management attribute labels.

[0026] S203. Refined metadata management: Establish a metadata management center, configure an automatic update mechanism, and set differentiated update frequencies for different types of data.

[0027] As a preferred option, the specific implementation process in S3 is as follows:

[0028] S301. Semantic Model Construction in the Forestry and Grassland Field: Based on the core business scenarios of forestry and grassland, construct a semantic model covering resource monitoring, fire prevention and emergency response, and afforestation planning scenarios, and map heterogeneous data with different formats and different field definitions into standardized semantic fields;

[0029] S302, Virtualized Query Engine Deployment and Adaptation: Deploy a virtualized query engine and a dedicated SQL parser to support unified cross-source SQL queries.

[0030] As a preferred option, the specific implementation process in S4 is as follows:

[0031] S401, Multi-dimensional access policy formulation: including sensitivity level policy, role-based access policy, and emergency scenario policy;

[0032] S402, Policy Engine Deployment and Linkage: Construct a de-identification processing unit, support multiple de-identification methods, adapt to the management and control requirements of data with different sensitivity levels, record all data access behaviors, and generate audit logs.

[0033] As a preferred option, the specific implementation process in S5 is as follows:

[0034] S501, Data-driven intelligent recommendation: Train a recommendation model specifically for the forestry and grassland sector, and automatically recommend related data based on the query history of business personnel and the current business scenario;

[0035] S502, Abnormal Data Detection and Correction: Training a forestry and grassland data quality detection model based on the GNN deep learning method;

[0036] S503, Real-time Forestry and Grassland Data Fusion: Employs a real-time and offline data fusion interface algorithm, using time-series alignment and feature-weighted fusion algorithms to achieve rapid data integration.

[0037] As a preferred option, the specific implementation details in S501 are as follows:

[0038] The core recommendation scoring formula of the forestry and grassland-specific recommendation model is as follows:

[0039] ;

[0040] in, This represents the final recommendation priority score given by business personnel u to candidate forestry and grassland data i; This indicates a tag similarity score based on metadata tags; This indicates a collaborative filtering score based on user query behavior. This represents the adaptation score based on the business scenario; α and β both represent weighting coefficients.

[0041] Preferably, in S502, the forestry and grassland data quality detection model adopts a graph neural network architecture, and its construction and detection process includes:

[0042] Step 1: Graph structure construction and node feature initialization;

[0043] Step 2: GCN graph convolution operation: Aggregate the node's own features and neighborhood association features to capture the global correlation of forestry and grassland data;

[0044] Step 3: Anomaly score calculation for nodes: Based on the aggregated node features, anomaly scores are calculated using two dimensions: reconstruction error and neighborhood similarity.

[0045] Step 4: Differentiated Early Warning Threshold Determination: Set differentiated early warning thresholds and determine the anomaly scores;

[0046] Step 5: Abnormal Data Correction: After an anomaly is detected, the recommended value is corrected by combining historical normal data of the same region, time period and type using a weighted fusion method.

[0047] As a preferred option, the specific implementation process in S503 is as follows:

[0048] Step 1: Data preprocessing and time-series alignment: Preprocess real-time and offline data, and then perform time-series alignment based on timestamps;

[0049] Step 2: Dynamically enhance the fusion interface: Employ a three-dimensional dynamic weighted fusion based on data freshness, data quality, and business weight;

[0050] Step 3: Data quality verification: Set a quality verification threshold. Unqualified data will trigger re-fusion or an alarm.

[0051] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0052] (1) This invention efficiently breaks down data silos: without the need to migrate physical data, it achieves logical integration of multi-source heterogeneous forestry and grassland data through metadata-driven and semantic unification, improving cross-source data integration efficiency by more than 80%, and solving the inefficiency problem of traditional manual adaptation and integration;

[0053] (2) This invention can significantly reduce the threshold for data use: through unified semantic query and visualization operation, non-technical business personnel can directly complete cross-source data query and call without professional technical support, and the data use cycle is shortened from several days to several hours, improving business processing efficiency;

[0054] (3) This invention achieves refined security and compliance management: through multi-dimensional access strategies and dynamic desensitization processing, it accurately matches the security management needs of forestry and grassland data, reduces the risk of unauthorized access by more than 90%, and meets the compliance management requirements of classified and sensitive data;

[0055] (4) This invention can enhance real-time decision support capabilities: through real-time data processing and fusion, it can achieve second-level data response in emergency scenarios, improve emergency response efficiency by more than 50%, and effectively solve the problem of lag in traditional data processing modes;

[0056] (5) The present invention has strong adaptability and scalability: it supports gradual deployment. Small and medium-sized forestry and grassland management departments can build a lightweight version of the method through open source tools, while large departments can expand the semantic model and AI capabilities to meet the needs of different scales and different business scenarios, and it has strong versatility. Attached Figure Description

[0057] Figure 1 This is a framework diagram of the forestry and grassland data weaving and data processing method of the present invention;

[0058] Figure 2 This is a schematic diagram of the forestry and grassland-specific metadata base structure of the present invention;

[0059] Figure 3 This is a schematic diagram of the semantic model mapping in the forestry and grassland field of the present invention;

[0060] Figure 4 This is the logic diagram for forestry and grassland data access control in this invention;

[0061] Figure 5 This is a flowchart of the real-time forestry and grassland data fusion process of the present invention;

[0062] Figure 6 This is a schematic diagram of the interaction of the core modules of this invention. Detailed Implementation

[0063] The present invention will be further illustrated below with reference to specific embodiments. These embodiments are implemented based on the technical solutions of the present invention, and it should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0064] This embodiment provides a knowledge-enhanced data weaving method for multi-source forestry and grassland data, which is a data weaving method that deeply adapts to the heterogeneous characteristics of multi-source forestry and grassland data in multiple dimensions such as semantics, spatiotemporal, and security. Based on the core ideas of unified data weaving logic, physical decentralization, and gradual evolution, and combined with the data and business characteristics of the forestry and grassland field, it includes data access, a forestry and grassland-specific metadata foundation, a unified semantic layer, a strategy engine, AI enhancement, and data service output, such as... Figure 1 As shown, the specific implementation steps are as follows:

[0065] S1. Data Access: Connects multi-source forestry and grassland data to the data interface adapter module;

[0066] This embodiment uses Linhai Forest Farm in Zhejiang Province as the research area, covering three core businesses: fire prevention and emergency response, pine wilt disease control, and afforestation planning. It involves multiple heterogeneous data sources, including field monitoring equipment, real-time data, image data, forestry and grassland operational systems, historical archives, meteorological and land data, and other data. Specifically:

[0067] Field monitoring equipment: More than 30 sensors distributed throughout the forest area collect data on temperature, humidity, and vegetation coverage. This data is stored on edge nodes in JSON format and updated in real-time (once per minute), tagged as Level 2 sensitive data and resource monitoring-related business tags. The data format is shown in Table 1 below.

[0068] Table 1 Data Format Table

[0069]

[0070] Image data: 20 patrol drones collected images of forest vegetation and fire clues in the forest area. The images are in TIFF format, updated daily, stored on a local server, and labeled as Level 3 general data and emergency response business tags.

[0071] Remote sensing satellite data: High-resolution satellite and Sentinel satellite imagery stored in Alibaba Cloud OSS, in TIFF format, updated weekly, covering vegetation cover and terrain information, labeled as Level 3 general data and planning management business tags.

[0072] Real-time data includes patrol trajectory data, patrol reported event data, and video push early warning data.

[0073] Forestry and grassland business system data includes afforestation management data (relational table format, T+1 update) stored in a MySQL database and historical fire data (relational table format, permanent storage) stored in a PostgreSQL database, labeled as Level 3 general data and Level 2 sensitive data, respectively. The specific data sources and their corresponding systems are shown in the table below.

[0074] Table 2 Data from the Forestry and Grassland Business System

[0075]

[0076] Platform data access: Fire warning data provided by the national-level fire prevention command system API interface, in JSON format, synchronized in real time, and labeled as Level 2 sensitive data and emergency response business tags.

[0077] Third-party data: namely meteorological and land data, real-time temperature, humidity and wind data provided by meteorological department API, in JSON format, updated hourly, and labeled as Level 3 general data and meteorological related business tags.

[0078] Classified data: Precise coordinates of the core area of ​​the national park, encrypted in a relational table format, statically stored, and marked as Level 1 classified data and resource monitoring business tag.

[0079] Historical archive data: timber harvesting data, forest land approval data, historical fire information, forest tending maps, afforestation plot maps, forestry industry data, pest control data, etc.

[0080] S2. Construct a dedicated metadata foundation for forestry and grassland: Collect metadata from multiple sources of forestry and grassland data, annotate it through a forestry and grassland-specific metadata tagging system, build a metadata management center, and realize automatic updates, version tracking, and multi-dimensional retrieval of metadata;

[0081] This step is used to achieve unified collection, feature annotation, and refined management of metadata from multiple forestry and grassland data sources, providing basic support for subsequent data weaving, such as... Figures 1-2 As shown, it specifically includes:

[0082] S201, Comprehensive collection of multi-source metadata;

[0083] A customized metadata collection adapter is deployed, combined with open-source tools such as OpenMetadata and DataHub, to automatically collect metadata from all types of data sources in the forestry and grassland field. Data sources include field monitoring equipment (sensors, drones), remote sensing satellite platforms, forestry and grassland business systems (afforestation management, fire prevention command, pest and disease control), historical archive databases, and third-party data platforms (meteorology, land resources). The collection scope covers data source, storage location, format type, field definition, update frequency, data volume, data owner, and preliminary information on business associations. For confidential data sources, a physical isolation collection mode is adopted, synchronizing only non-confidential metadata fields to avoid leakage of confidential information.

[0084] S202, Forestry and Grassland Characteristic Metadata Labeling;

[0085] A dedicated metadata tagging system for the forestry and grassland sector has been established. The system's classification hierarchy and tag value ranges are set according to industry standards such as the "National Forestry and Grassland Administration Information Resource Catalog" and the "Guidelines for Classification and Grading of Forestry and Grassland Data (Trial)." A combination of manual annotation and semi-automatic AI recommendation is used to standardize the metadata annotation. The tagging system includes three core tag categories:

[0086] Business attribute tags: These tags are linked to core forestry and grassland business scenarios, including resource monitoring (such as pine wilt disease monitoring and vegetation coverage monitoring), emergency response (such as fire monitoring and pest and disease early warning), and planning and management (such as Chinese fir afforestation area and forest stand transformation progress), achieving precise binding of metadata to business scenarios. Based on the "Functional Specifications for Core Forestry and Grassland Business Systems," the system is divided into primary categories such as resource monitoring, ecological restoration, disaster prevention and control, and forestry and grassland industry, and further subdivided under these categories. For example, the disaster prevention and control category is further subdivided into secondary tags such as "forest fire" and "forestry pests," ensuring seamless integration with the forestry and grassland business system.

[0087] Sensitivity Level Labels: Strictly adhering to the "Regulations on the Scope of Secrets in Forestry and Grassland Work," data is divided into three levels: core classified, important sensitive, and generally public, with a mandatory binding relationship established with metadata. Level 1 (classified) corresponds to data such as precise coordinates of the core area of ​​national parks and accurate distribution of rare species; Level 2 (sensitive) corresponds to data such as the location of forest monitoring points and the location of pest and disease outbreaks; and Level 3 (general) corresponds to general afforestation technical parameters and publicly available vegetation type data, providing a basis for hierarchical access control.

[0088] Management attribute tags: In accordance with the "Full Life Cycle Management Specification for Forestry and Grassland Data", the data ownership unit (such as provincial forestry and grassland bureaus, county-level monitoring stations), data responsible person, update cycle, quality level, data validity period, and data quality level are clearly defined to support the full life cycle management of data.

[0089] S203, Refined management of metadata;

[0090] Establish a metadata management center, configure an automatic update mechanism, and set differentiated update frequencies for different types of data—sensor real-time data metadata is updated once per hour, business system data metadata is updated T+1, and remote sensing data metadata is updated once per week; support metadata version traceability, retain change records for the past 6 months to avoid the risk of data tampering; deploy a fuzzy search engine to support multi-dimensional retrieval by tags, field names, business scenarios, data owners, etc., to ensure that data can be found and traced.

[0091] S3. Establish a unified semantic layer for forestry and grassland;

[0092] This step is used to solve the semantic inconsistency problem of heterogeneous forestry and grassland data, and to achieve unified querying of cross-source data. Based on core business scenarios, three core semantic models are constructed for fire prevention and emergency response, pest and disease control, and afforestation planning. Figure 3 The system maps sensor temperature and meteorological data temp to ambient temperature, and remote sensing image vegetation coverage and business system vegetation coverage to forest stand vegetation coverage. It also deploys the Trino virtualized query engine, configures multi-source data connection channels, develops a dedicated SQL parser for forestry and grassland, completes automatic matching of semantic fields with underlying data sources, tests cross-source query functions, and ensures a query success rate of ≥95%.

[0093] like Figure 1 , Figure 3 As shown, it specifically includes:

[0094] S301, Construction of a semantic model for the forestry and grassland domain;

[0095] Based on core forestry and grassland business scenarios, an ontology-based approach is used to construct a semantic model for the forestry and grassland domain.

[0096] First, extract the core concepts such as forest land, forest trees, monitoring points, fire conditions, etc. and their hierarchical relationships;

[0097] Secondly, for the indicators that are prone to ambiguity such as vegetation coverage and canopy density, define their unified calculation logic and dimension based on the forestry and grass industry standards (for example, uniformly use percentage representation and clarify the algorithm basis for remote sensing inversion or field investigation);

[0098] Finally, construct the semantic relationships between concepts, such as the location of the fire is associated with the forest land, and the forest land contains forest trees, to form a semantic knowledge graph that covers scenarios such as resource monitoring, fire prevention emergency, and afforestation planning and is capable of reasoning, thereby mapping heterogeneous data with different formats and different field definitions into standardized semantic fields.

[0099] (1) Example of the core concept hierarchical structure;

[0100] Table 3 Example table of the core concept hierarchical structure

[0101]

[0102] (2) Example of solving the problem of different meanings with the same name;

[0103] There are multiple definitions and calculation methods for vegetation coverage in different data sources, and this model solves it uniformly by establishing a semantic mapping rule table.

[0104] Table 4 Semantic mapping rule table for different meanings with the same name

[0105]

[0106] (3) Example of the semantic relationship between concepts;

[0107] Use the Web Ontology Language (OWL) syntax to describe the semantic relationships between core concepts and form a knowledge structure capable of reasoning, as follows:

[0108] <!-- Define classes and hierarchical relationships -->

[0109] <owl:class rdf:ID="林地" / >

[0110] <owl:class rdf:id="生态公益林">

[0111] <rdfs:subclassof rdf:resource="#林地" / >

[0112] < / owl:class>

[0113] <owl:class rdf:ID="林木" / >

[0114] <owl:class rdf:ID="监测点" / >

[0115] <owl:class rdf:ID="火情" / >

[0116] <!-- Define attribute relationships -->

[0117] <owl:objectproperty rdf:id="包含林木">

[0118] <rdfs:domain rdf:resource="#林地" / >

[0119] <rdfs:range rdf:resource="#林木" / >

[0120] < / owl:objectproperty>

[0121] <owl:objectproperty rdf:id="发生地点">

[0122] <rdfs:domain rdf:resource="#火情" / >

[0123] <rdfs:range rdf:resource="#林地" / >

[0124] < / owl:objectproperty>

[0125] <owl:objectproperty rdf:id="布设监测点">

[0126] <rdfs:domain rdf:resource="#林地" / >

[0127] <rdfs:range rdf:resource="#监测点" / >

[0128] < / owl:objectproperty>

[0129] <!-- Example of defining data attributes -->

[0130] <owl:datatypeproperty rdf:id="植被覆盖度">

[0131] <rdfs:domain rdf:resource="#林地" / >

[0132] <rdfs:range rdf:resource="&xsd;float" / >

[0133] <rdfs:comment> Values ​​range from 0 to 100, unit: %, calculated based on forestry and grassland industry standard LY / T 1234-2023.< / rdfs:comment>

[0134] < / owl:datatypeproperty>

[0135] (4)Semantic mapping and reasoning example;

[0136] Based on the above ontology, the fields in heterogeneous data sources are automatically mapped through the following rules, as shown in Table 5 below.

[0137] Table 5 Heterogeneous data source field mapping table

[0138]

[0139] Through the above ontology construction method, the unified expression and intelligent reasoning of multi-source data of forest and grass in the semantic level are realized, providing a standardized semantic basis for unified semantic query, cross-source data fusion and intelligent recommendation.

[0140] S302. Deployment and adaptation of virtualized query engine: Deploy a virtualized query engine and a dedicated SQL parser to support unified SQL cross-source query;

[0141] Use Trino or Dremio as the core virtualized query engine, build a cross-source query layer, configure multi-source data connection channels, and achieve seamless docking with edge nodes, local databases, cloud storage, and SaaS platform APIs; Develop a dedicated SQL parser for forest and grass to realize the automatic matching of semantic fields and underlying data source fields, support business personnel to complete cross-source data query through unified SQL statements, and do not need to pay attention to the differences in data physical storage location and format; At the same time, optimize the query performance, set query cache for real-time data, and set partition query mechanism for offline large data volume to ensure query response efficiency.

[0142] Example SQL: Query the monitoring data of pine wilt disease and corresponding meteorological data in a certain area in the past 3 months, written based on unified semantic fields:

[0143] SQL

[0144] SELECT m. Monitoring time, m. Infection rate, w. Ambient temperature, w. Ambient humidity

[0145] FROM Semantic model. Pest and disease monitoring. Pine wilt disease m

[0146] JOIN Semantic model. Meteorological data. Real-time meteorology w

[0147] ON m.region code = w.region code

[0148] AND m. Monitoring time BETWEEN w. Data date AND DATE_ADD(w. Data date, INTERVAL 1DAY)

[0149] WHERE m.region_code='province_01'

[0150] AND m. Monitoring time >= DATE_SUB(CURRENT_DATE(), INTERVAL 3 MONTH);

[0151] In this context, SELECT specifies the columns to be returned in the query results; FROM specifies the table from which the data is sourced, and is typically used in conjunction with JOIN; ON specifies the join condition for the JOIN operation, i.e., how the two tables are related. DATE_ADD is a SQL function used to add a specified time interval to a date or datetime value; INTERVAL is a fixed keyword, DAY is for days, and MONTH is for months; CURRENT_DATE() is a built-in SQL function used to retrieve the current date of the database server; BETWEEN is an operator for range queries, primarily selecting all records where a field value lies between two specified values, including the boundary values ​​themselves; WHERE specifies filtering conditions to select data rows that meet specific criteria.

[0152] S4, Deploy a forestry and grassland-specific strategy engine;

[0153] This step is used to implement hierarchical security management of forestry and grassland data, ensuring compliant data access. It employs the Apache Ranger deployment strategy engine and configures multi-dimensional access policies: provincial administrators can access all data (Level 1 data anonymized), county administrators can only access non-confidential data within their county, and grassroots monitors can only access Level 3 data and authorized Level 2 data within their jurisdiction. It develops processing units for coordinate anonymization and field masking, and interfaces with metadata sensitivity level tags to achieve automatic anonymization during data access. An audit log module is deployed to record all access activities. Specifically, this includes:

[0154] S401, Multi-dimensional access strategy formulation;

[0155] Based on the sensitivity level of forestry and grassland data and the permissions of business roles, three core access policies were formulated to achieve dynamic permission control:

[0156] Sensitivity Level Strategy: Level 1 classified data is only accessible to core personnel of provincial-level or higher forestry and grassland authorities. Access requires secondary identity verification, and classified fields are anonymized (e.g., precise coordinates are converted to a 5km to -5km range description). Level 2 sensitive data requires an online application-approval process. Access is granted for a specified validity period (maximum 7 days) after approval by the relevant department head. Level 3 general data is open to all authorized forestry and grassland personnel. Specific procedures are as follows... Figure 4 As shown.

[0157] 1. Basic pre-strategy process: Sensitive level tag binding and dynamic updating;

[0158] All control actions are based on metadata sensitivity level tags, which are deeply bound to the entire data lifecycle. The preliminary process is as follows:

[0159] Tag initialization and binding: After the collection of multi-source metadata is completed, the sensitivity level is initially judged based on the data content. Through dual review by business and confidentiality departments, a unique sensitivity level tag (Level 1 confidential / Level 2 sensitive / Level 3 ordinary) is bound to each piece of metadata and each core field. The tag is deeply bound to the physical storage location of the data and field-level information, and cannot be tampered with without authorization. Tag changes must go through an online approval process, and metadata version change records are retained simultaneously.

[0160] Dynamic and synchronized tag updates: When the metadata management center updates data according to differentiated cycles, a sensitivity level review mechanism is triggered synchronously. For example, when rare species distribution information is added to ordinary afforestation data, or when routine monitoring data is upgraded to fire-related confidential early warning data, a tag upgrade review is automatically triggered. After review by the business and confidentiality departments, the tag update is completed, ensuring that the tag and data sensitivity level always match. The tag change results are synchronized to the strategy engine in real time, and the control rules are automatically adapted.

[0161] The highest level of control rule: When a single data query request involves data of multiple sensitivity levels, the policy engine automatically matches the corresponding control process according to the highest sensitivity level (e.g., if a query involves both Level 1 classified and Level 2 sensitive data, the entire process is controlled according to the Level 1 classified process), preventing lower-level processes from accessing higher-level data without authorization.

[0162] 2. Detailed procedures for tiered management and control;

[0163] 1) Level 1 Classified Data Control Process (Highest Control Level);

[0164] Corresponding data range:

[0165] Metadata tags are bound to Level 1 classified information, including: precise coordinates of the core area of ​​national parks, accurate distribution points of rare and endangered species, unpublished core classified forestry and grassland plans, and unreleased core survey data of national forestry and grassland resources, etc., strictly in accordance with national confidentiality management requirements.

[0166] Full process execution steps:

[0167] Access request interception and access threshold verification;

[0168] Upon detecting a Level 1 classified data query request, the policy engine immediately blocks the ordinary query channel and only opens the dedicated classified access channel, first performing dual access verification:

[0169] Access control verification: Only personnel in core confidential positions of provincial and above forestry and grassland authorities can initiate access requests. Requests from personnel at other levels / positions will be rejected directly, and unauthorized access will be recorded in the audit log.

[0170] Identity verification: Applicants must complete two-factor authentication (account password + hardware key / face liveness detection). Verification is required for each visit. If the verification fails, the process will be terminated and a full verification log will be retained.

[0171] Visit application and dual departmental approval;

[0172] After the access verification is passed, the applicant must submit a formal written application, clearly specifying the purpose of access, the scope of data use, the duration of use, the confidentiality commitment, and the declassification management plan. The application materials are simultaneously sent to the provincial forestry and grassland confidentiality committee and the data ownership department for dual approval.

[0173] The approval period shall not exceed 3 working days. Urgent and classified matters may be expedited, with a time limit not exceeding 4 working hours.

[0174] If the application is rejected, the reason will be given in writing and the entire process will be recorded and archived. If the application is approved, only a specific temporary access right will be granted for a specified data range and a specified period (not exceeding 15 days). The authorization cannot exceed the scope.

[0175] Field-level mandatory data masking;

[0176] After access permissions take effect, the policy engine's customized data masking unit automatically performs mandatory data masking that cannot be turned off on Level 1 classified data. Specific rules are as follows:

[0177] Spatial coordinate data: Precise latitude and longitude coordinates are converted into a fuzzy description within a range of ±5km, retaining only the spatial information of the county-level area and completely removing precise point information;

[0178] Species distribution data: Only publicly available information such as species name and provincial distribution range is retained, while confidential information such as precise location, population size, and core habitat parameters are removed;

[0179] Other classified fields: All fields are masked with asterisks, retaining only non-classified metadata information such as data ownership and update time.

[0180] Data access and real-time monitoring throughout the process;

[0181] The de-identified classified data can only be accessed on designated classified terminals. Downloading, copying, screenshotting, sending, and printing are strictly prohibited. All mouse clicks, page stays, and query switching operations are fully recorded.

[0182] During the access process, the system monitors the operation behavior in real time. If abnormal behavior such as querying beyond the scope, taking screenshots without authorization, or logging in from multiple terminals occurs, the access permission will be terminated immediately, the account will be locked, and a real-time alarm will be triggered simultaneously by the provincial forestry and grassland confidentiality department.

[0183] Automatic permission revocation and archiving;

[0184] After the access period expires, the system will automatically revoke all access permissions, immediately close the classified access channel, and clear the terminal cache data.

[0185] The entire process of application, approval, access, and operation is recorded in an unalterable manner and simultaneously archived in the classified audit log for permanent retention, meeting national confidentiality compliance traceability requirements.

[0186] 2) Level 2 Sensitive Data Control Process (Medium-Level Control);

[0187] Corresponding data range;

[0188] Metadata tags are bound to Level 2 sensitive data, including: precise location of forest monitoring points, location of pest and disease outbreaks, unpublished fire warning information, unpublished approval data for the use of forest land in construction projects, core patrol routes of forest rangers, and unreleased municipal forest and grassland resource survey data, etc.

[0189] Full process execution steps:

[0190] The access request was triggered based on the jurisdiction.

[0191] After the policy engine identifies a Level 2 sensitive data request, it first performs an access verification: only personnel from the corresponding business departments of forestry and grassland at the municipal level and above, and grassroots forest rangers / grid members in the data application jurisdiction can initiate an application. Requests from personnel outside the corresponding area / position are directly rejected, and the access behavior is recorded.

[0192] Online application and tiered approval;

[0193] After the access verification is passed, the applicant submits an access application online through the system, specifying the purpose of access, the scope of data use, and the duration of use, without the need for offline written materials:

[0194] The approval process is simplified to Level 1 approval by the head of the corresponding business department. In urgent business scenarios, approval can be authorized to the deputy head of the department, and the approval time limit shall not exceed 4 working hours.

[0195] If approved, specific access permissions will be granted to a designated area for a specified period (not exceeding 7 days); if not approved, the application will be rejected and the reasons will be explained, and the entire process will be recorded and retained.

[0196] Differentiated desensitization treatment;

[0197] The policy engine performs adaptive data masking based on the applicant's jurisdiction. Rules cannot be adjusted arbitrarily; only provincial administrators can modify the configuration.

[0198] The applicant is an on-duty personnel in the jurisdiction corresponding to the data: they can view the complete point coordinates and attribute information, and only perform ±1km range fuzzing processing on the associated data outside the jurisdiction.

[0199] For applicants who are municipal-level or above managers in areas other than the corresponding region: perform ±1km range blurring on the precise location coordinates, retain core business attribute information, and remove irrelevant sensitive fields.

[0200] Data access and permission control;

[0201] Data within the authorized scope can be viewed online and downloaded with authorization. Download operations must be logged separately, clearly indicating the purpose of the download, and must not be forwarded to unauthorized personnel or unauthorized systems.

[0202] If abnormal behavior such as out-of-range queries, multiple batch downloads in a short period of time, or logins from IP addresses in different locations occurs, a system alarm will be triggered immediately, access permissions will be suspended, and will be restored after a security review by the business department.

[0203] Access revocation and audit archiving;

[0204] Once the access period expires, the system will automatically revoke access permissions and close the access channel for the corresponding data.

[0205] The entire process of application, approval, access, download, and export records is synchronously stored in the audit log and retained for no less than 3 years to meet industry compliance audit requirements.

[0206] 3) Level 3 General Data Management Process (Lower Level Management);

[0207] Corresponding data range;

[0208] Metadata tags are bound to three levels of general data, including: publicly released forestry and grassland resource statistics, general afforestation technical parameters, publicly available vegetation type data, publicly available statistics on public welfare forests, publicly available meteorological data from third parties, and publicly available forestry and grassland policy documents.

[0209] Full process execution steps:

[0210] Access requests are automatically allowed;

[0211] After the strategy engine identifies a Level 3 ordinary data request, it only verifies that the applicant is an authorized person who has been verified by real name within the forestry and grassland system. No additional application or approval process is required, and the access request is automatically granted without any access time limit.

[0212] No mandatory desensitization process;

[0213] The data is fully accessible and supports online querying, multi-dimensional statistical analysis, and batch export. There are no mandatory field-level anonymization requirements. Only a second pop-up confirmation is required for batch export operations exceeding 100,000 records at a time, and logs are recorded synchronously.

[0214] Regularly record access behavior;

[0215] The system automatically records basic information such as visitor, visit time, query content, export operation, and terminal IP, and stores it in the audit log for a period of no less than one year.

[0216] If behaviors such as high-frequency batch export, access from overseas IPs, or abnormal batch queries outside of working hours occur within a short period of time, the system security alarm will be automatically triggered, and the account permissions will be temporarily locked until the security department reviews and restores them.

[0217] 3. Special control procedures for sensitive strategies in emergency scenarios;

[0218] Deeply integrated with the emergency scenario strategies of the original method, when provincial-level or above forestry and grassland emergency responses (fire prevention emergency, major pest and disease outbreak, major natural disaster emergency, etc.) are initiated, the strategy engine automatically triggers the emergency control mode, dynamically adjusting the strategies for sensitive levels to balance emergency response efficiency and data security.

[0219] Rapid authorization: Emergency command team members can initiate a full-level data access application with one click through the emergency command platform. The approval process is simplified to final review by the emergency commander-in-chief, with an approval time of no more than 10 minutes. In extreme emergency fire / disaster scenarios, temporary access can be granted first, and the approval process can be completed within 24 hours.

[0220] Dynamic adaptation of desensitization rules: In emergency scenarios, complete and accurate location information is temporarily opened for the first and second level data of the core emergency response area, and forced blurring desensitization is turned off to ensure the accuracy of emergency response; confidential / sensitive data in non-response areas still strictly follow the original desensitization control rules.

[0221] Automatic closed-loop revocation of permissions: After the emergency response ends (either manually closed or automatically closed 72 hours after emergency activation), the system immediately revokes all temporary emergency authorizations and restores the original hierarchical control rules; at the same time, all data access, operation and export records during the emergency are archived separately to the confidential audit log and permanently retained.

[0222] 4. A closed-loop guarantee mechanism throughout the entire process;

[0223] Tag-Policy Linkage Mechanism: Sensitive level policies are deeply linked with sensitive level tags in the metadata base throughout the entire process. Tag changes are synchronized to the policy engine in real time, and control rules are automatically adapted to prevent tags from becoming disconnected from control.

[0224] Unalterable audit mechanism: The entire process of access application, approval, operation, and permission change for all levels of data generates encrypted and unalterable audit logs, supporting multi-dimensional traceability by personnel, time, data type, and operation behavior, meeting national confidentiality and industry compliance audit requirements.

[0225] Violation handling mechanism: For behaviors such as unauthorized access, unauthorized applications, violations, and data leaks, the system automatically executes a handling process of account locking, real-time alarm push, violation record archiving, and synchronization with the human resources department. In serious cases, the information will be simultaneously pushed to the local confidentiality management department and public security department for handling.

[0226] Rule iteration and optimization mechanism: Every six months, in conjunction with new national confidentiality regulations, adjustments to forestry and grassland operations, and reviews of violations, we optimize the sensitivity classification standards, approval processes, and desensitization rules to ensure that the control processes continuously adapt to compliance requirements and actual business needs.

[0227] Role-based access control strategy: Permissions are assigned according to management level and business division of labor. Provincial administrators can access all data (including anonymized first-level data), municipal administrators can access non-confidential data in their city, and grassroots monitors can only access third-level data and authorized second-level data within their jurisdiction, ensuring that rights and responsibilities are matched.

[0228] Emergency Scenario Strategy: Establish a dynamic emergency access control mechanism that links with fire prevention command and pest and disease emergency response platforms. Once an emergency scenario is activated, emergency teams are automatically authorized to access all levels of data in the corresponding area (Level 1 data is anonymized). After the emergency response is completed (either manually or automatically after 24 hours), all temporary authorizations are automatically revoked, balancing emergency efficiency and data security.

[0229] S402, Strategy Engine Deployment and Linkage;

[0230] Use Apache Ranger or OpenPolicyAgent (OPA) as the policy engine (e.g.) Figure 4 As shown, it deeply integrates with the unified semantic layer to build a fully automated management and control chain for query triggering, permission verification, data masking, and data return; it develops a customized data masking unit that supports various data masking methods such as coordinate fuzzing, field masking, and data sampling to adapt to the management and control needs of data with different sensitivity levels; at the same time, it records all data access behaviors and generates audit logs for compliance traceability.

[0231] S5 integrates a forestry and grassland AI enhancement module to achieve intelligent data weaving and efficient reuse;

[0232] Deploy the TensorFlow framework to build a recommendation model and a data quality detection model specific to the forestry and grassland field; develop a real-time-offline data fusion interface ( Figure 5 The system deploys a Flink real-time processing engine and a Kafka message queue to connect with real-time data from sensors and drones, enabling real-time data to be accessed to the semantic layer within 5 seconds and completing the connection with the fire command APP.

[0233] Specifically, it includes:

[0234] S501, Data-driven intelligent recommendation: Train a recommendation model specifically for the forestry and grassland sector, and automatically recommend related data based on the query history of business personnel and the current business scenario;

[0235] For example, when querying monitoring data on pine wilt disease, the system automatically recommends remote sensing data on abnormal vegetation, historical disease incidence data, and meteorological temperature and humidity data for the corresponding area, thereby improving data reuse efficiency.

[0236] The core recommendation scoring formula (final recommendation priority calculation) of the forestry and grassland-specific recommendation model is as follows:

[0237]

[0238] in, This represents the final recommendation priority score given by business personnel u to candidate forestry and grassland data i; This indicates a tag similarity score based on metadata tags; This indicates a collaborative filtering score based on user query behavior. This represents the adaptation score based on the business scenario; α and β both represent weight coefficients, which are obtained by grid search optimization on the forestry and grassland dataset.

[0239] (1) Tag similarity score (Core sub-item, accounting for α=0.62):

[0240] Based on the three key characteristics of forestry and grassland metadata (business attributes, sensitivity levels, and management attributes), the similarity between the query preference tags of business personnel and the candidate data tags is calculated using the following formula:

[0241]

[0242] in, This indicates the tag type index (1=business attribute tag, 2=sensitivity level tag, 3=management attribute tag). Indicates tag weight (adapting to forestry and grassland business priority). Business attribute tags have the highest weight; Sensitive level tags; (Manage attribute tags); This represents the preference vector of business personnel u on the k-th type of label (based on historical query data statistics, such as when querying data on pine wilt disease, the weight of the pest and disease monitoring business label is increased). This represents the feature vector of candidate data i on the k-th class label (based on metadata annotation results, for example, the business attribute label vector of remote sensing vegetation anomaly data is [0.8, 0, 0.2], corresponding to the weights of resource monitoring, emergency response, and planning management). This represents cosine similarity, which calculates the similarity between the preference vector and the feature vector. The value ranges from [0,1], and the higher the value, the higher the similarity.

[0243] (2) Collaborative filtering score (Auxiliary item, accounting for 1-α=0.38):

[0244] Based on the similarity of query behavior among business personnel, the query preferences of similar business personnel are supplemented, as shown in the following formula:

[0245]

[0246]

[0247] in, Indicates to business personnel The top 20 groups of similar individuals with similar query behaviors (e.g., grassroots monitors in the same region and position). Indicates business personnel and The similarity of query behaviors was calculated using the Pearson correlation coefficient; Indicates people of the same type Candidate data The number of historical queries (normalized values ​​are [0,1]). Indicates business personnel With business personnel The covariance of the query behavior preference vector; Indicates business personnel Historical query behavior preference vector; Indicates business personnel Historical query behavior preference vector; Indicates business personnel Historical query behavior preference vector Standard deviation; Indicates business personnel Historical query behavior preference vector The standard deviation.

[0248] (3) Business scenario adaptation score (Corrected sub-term, β=0.1):

[0249] To adapt to core forestry and grassland business scenarios (fire prevention and emergency response, pest and disease control, afforestation planning), and correct recommendation biases, the formula is as follows:

[0250]

[0251] in, This indicates the matching indicator function (also called the indicator function) for forestry and grassland business scenarios. Indicates the current business scenario. This indicates a scenario where candidate data are associated; it is set to 1 if a match is found and 0 if no match is found. Indicates the current query time. Indicates the update time of the candidate data; This represents the time decay threshold (adapting to the forest and grassland data update frequency, set to 7 days, meaning that if the data update time exceeds 7 days, the scene adaptation score decays).

[0252] Model parameter calibration:

[0253] Weight parameters: α=0.62 (dominated by tag similarity), β=0.1 (scenario adaptation correction) to ensure that the recommendation results fit the characteristics of forestry and grassland metadata tags and business needs.

[0254] Threshold parameters: When the recommendation score R(u,i)≥0.7, candidate data is automatically pushed; when 0.5≤R(u,i)<0.7, it is displayed as optional recommendation data; when R(u,i)<0.5, it is not recommended.

[0255] Training parameters: Stochastic gradient descent (SGD) was used for training, with a learning rate η=0.01, 100 iterations, and a regularization parameter λ=0.001 to avoid overfitting; the training dataset consisted of query logs and metadata tag data from forestry and grassland business personnel over the past year.

[0256] S502, Abnormal Data Detection and Correction: Training a forestry and grassland data quality detection model based on the GNN deep learning method;

[0257] Real-time monitoring is performed to address issues such as sensor data drift, remote sensing image interpretation errors, data loss, and format abnormalities. Differentiated early warning thresholds are set (e.g., temperature data drift ±5℃, humidity drift ±10% triggers an alarm). When an anomaly is detected, alarm information is automatically pushed, and correction schemes are recommended based on historical data to ensure the accuracy of the woven data.

[0258] The core framework of the forestry and grassland data quality detection model (GNN infrastructure):

[0259] The model uses Graph Convolutional Network (GCN) in Graph Neural Network (GNN) as its basic architecture, abstracting multi-source forestry and grassland data into a graph structure of data nodes and related edges: data nodes are single forestry and grassland data (such as a temperature and humidity record from a sensor or a pixel interpretation value from a remote sensing image), and related edges are the intrinsic relationships between data (such as the temporal relationship between sensor data in the same area, remote sensing and operational data in the same time period, and historical and real-time data). The model captures the global correlation features of data nodes through graph convolution operations, thereby achieving high accuracy in anomaly detection.

[0260] Core algorithm of the model:

[0261] (1) Graph structure construction and node feature initialization;

[0262] First, the multi-source forestry and grassland data are mapped into a graph structure. It also initializes node feature vectors to adapt to forestry and grassland data types (numerical: sensor data; image interpretation: remote sensing data; structured: business data).

[0263] Where V represents a data node; This represents the nth data node; n is the total number of data nodes (the number of nodes detected in a single batch, adapted to the real-time data throughput of forestry and grassland, and taken as 1000-5000).

[0264] Where E represents the associated edge; This indicates the associated value between i and j; Represents a node and There is a correlation (same region / same time period / same business). This indicates no connection.

[0265] Node feature vector initialization: ,in, This represents the feature matrix of all data nodes in the graph structure; Indicates the first Each node's feature vector; T represents the update frequency; Indicates the total number of data nodes; Represents the set of real numbers; Indicates feature dimensions (adapted to forestry and grassland data, (Including data values, collection time, region code, data type, update frequency, and tag features), specific initialization rules:

[0266]

[0267] in, Indicates the first Data nodes eigenvectors; This represents the original data values ​​(after normalization). (e.g., the sensor temperature of 25℃ is normalized to 0.5). This indicates the time-series encoding of the data collection (time-series encoding, converting the timestamp into a value in the range [0,1]). This represents the region code (one-hot encoding, such as mapping a provincial region code to a 0-1 vector of the corresponding dimension). Indicates the data type (one-hot encoding, sensor data = 1, remote sensing data = 2, operational data = 3, after normalization). This indicates the update frequency (normalized, e.g., 1 update / minute = 1, T+1 update = 0.1). This represents the metadata tag characteristics (extracting the encoded values ​​of sensitivity level and business attribute tags). T represents the update frequency.

[0268] (2) GCN graph convolution operation (node ​​feature aggregation);

[0269] By using two layers of graph convolution operations, the features of a node itself and its neighborhood association features are aggregated to capture the global correlation of forestry and grassland data (such as the collaborative features of multiple sensor data in the same area, and the matching features between remote sensing and operational data). The formula is as follows:

[0270] First layer graph convolution (initial aggregation of neighborhood features) Specifically:

[0271]

[0272] The second layer of graph convolution (global feature deep aggregation) is as follows:

[0273]

[0274] in, This represents the node feature matrix output after the first layer graph convolution, with dimension 1. This is used to initially aggregate the node's own features and neighborhood features; This represents the global feature matrix of the nodes output after the second layer graph convolution, with dimension 1. This is the core feature ultimately used for anomaly scoring calculation; The activation function is represented by the ReLU function. This is used to capture the nonlinear correlation features of forest and grassland data and avoid gradient vanishing. This represents the normalized adjacency matrix. This is used to normalize the node association weights, ensuring the stability of graph convolution operations; This represents the adjacency matrix with added self-loops. This is the original adjacency matrix. It is an identity matrix used to preserve the feature associations of nodes themselves and avoid feature loss; express The degree matrix, with elements on the diagonal as... The sum of the elements in the corresponding row is used to normalize the adjacency matrix and balance the association weights of different nodes. This represents the first layer convolution weight matrix. (Feature Dimensions), 32 is the output feature dimension, obtained through model training, adapted to forestry and grassland data features; This represents the second-layer convolutional weight matrix, with 32 for the input feature dimension and 16 for the output feature dimension. It is obtained through model training and is used for deep aggregation of global features. , represents the bias vectors for the first and second layer graph convolutions, respectively, used to adjust the feature output and improve the model's fitting ability. X represents the feature matrix of all data nodes in the graph structure.

[0275] (3) Calculation of node anomaly scores (core anomaly detection formula);

[0276] Based on the aggregated node features, anomaly scores are calculated using two dimensions: reconstruction error and neighborhood similarity. This approach takes into account both single-node anomalies (such as sensor drift) and associated anomalies (such as mismatch between remote sensing and operational data). The formula is as follows:

[0277]

[0278]

[0279]

[0280]

[0281] in, Indicates abnormal rating; This indicates the weighting parameters (adapted to the characteristics of forestry and grassland data; single-node anomalies have higher weights than associated anomalies, as sensor drift and format anomalies are the main anomaly types). This indicates reconstruction error (single node anomaly detection, such as data drift or format anomaly). Represents the node's feature value; Reconstruct values ​​for node features. , The weights and biases are obtained through training to reconstruct them; This represents the output feature matrix of the second-layer graph convolution operation at the i-th node; As the L2 norm is used, the larger the reconstruction error, the higher the probability of single-node anomalies.

[0282] k represents the number of neighboring nodes, with a value of 10, which is adapted to the association density of forestry and grassland data in the same region / time period to ensure that the association characteristics between nodes can be effectively captured. Indicates neighborhood similarity (related to anomaly detection, such as missing data or remote sensing interpretation errors); For nodes Top-k neighbor nodes (k=10, adapted to the data association density of forest and grassland in the same region / time period). This represents the global feature cosine similarity between the i-th node and the j-th neighboring node, with a value range of [0,1]. The higher the similarity, the more consistent the features of the two nodes are and the closer their association. This represents the output feature matrix of the second-layer graph convolution operation at the j-th node.

[0283] Abnormal scoring The higher the value, the higher the probability of an anomaly.

[0284] (4) Differentiated early warning threshold determination;

[0285] Differentiated early warning thresholds are set based on the abnormal characteristics of different types of forestry and grassland data. To avoid false alarms and false negatives caused by a single threshold, the determination formula is as follows:

[0286]

[0287] in, Indicates abnormal rating; The differential threshold is used to adapt to the core data types of forestry and grassland after training and calibration.

[0288] Sensor data (numerical type): Synchronously superimposed physical threshold verification (such as temperature drift) Humidity drift (This directly triggers an alarm). Represents the original data value; For nodes The historical average data value for the same period (average value for the same period in the last 30 days, adapted to the time series characteristics of forestry and grassland data).

[0289] Remote sensing image data (interpreted): Combined with the interpretation accuracy threshold (e.g., interpretation error ≥15%, trigger an alarm).

[0290] Business data (structured): To address data missing or formatting issues, an additional missing value detection mechanism is added (e.g., ...). (And there was no reasonable labeling, triggering an alarm).

[0291] (5) Correction of abnormal data;

[0292] Upon detecting an anomaly, the recommended value is adjusted by combining historical normal data from the same region, time period, and type, using a weighted fusion method, as shown in the following formula:

[0293]

[0294] in, Indicates abnormal nodes The revised recommended value; Represents a node Normal node set in the neighborhood ; Represents a node A single normal data node within the neighborhood is a set. (Abnormal node) Elements in the set of normal neighboring nodes; Indicates a normal node The weighting coefficient is used to measure the effect of the normal node on the abnormal node. The greater the weight of the correction value, the stronger its impact on the correction result. Indicates a normal node The first feature dimension value corresponds to a normal node. Feature vector The first element, with the abnormal node of Dimensions are consistent.

[0295] Model training and validation:

[0296] (1) Training of a forestry and grassland-specific recommendation model:

[0297] Dataset: The training set was constructed using query logs from business personnel of Linhai Forest Farm in Zhejiang Province over the past two years (a total of 100,000 query records) and a forestry and grassland metadata tag library. Query click records were used as positive samples, and records without clicks or exposures without clicks were used as negative samples, with a positive-to-negative sample ratio of 1:3.

[0298] Parameter optimization: The weight coefficients α and β in claim 7 are not fixed values. Using a grid search method, with the normalized depreciation cumulative gain (NDCG@10) as the optimization objective on the validation set, traversing α∈[0.5, 0.8] (step size 0.05) and β∈[0,0.2] (step size 0.02), the optimal parameter combination α=0.62 and β=0.09 was finally determined, making the model optimal on the validation set with NDCG@10. The time decay threshold T was determined to be 7 days based on statistical analysis of the timeliness requirements of forestry and grassland data.

[0299] Training process: The Stochastic Gradient Descent (SGD) optimizer was used with a learning rate of 0.005 and an L2 regularization coefficient of 0.001. The training was iterated for 100 rounds. Training was stopped when the validation set loss no longer decreased for 5 consecutive rounds to prevent overfitting.

[0300] (2) Training of GCN anomaly detection model:

[0301] Graph Structure Construction: Nodes in the graph structure are single data records. The edge construction rules are based on the spatiotemporal correlation constraints of the forestry and grassland business logic. The specific design is as follows:

[0302] 1. Basis for determining the time threshold;

[0303] In this scheme, the time correlation threshold is set to 30 minutes, and its determination is based on the following Table 6.

[0304] Table 6. Basis for Determining Time-Related Thresholds

[0305]

[0306] 2. Differentiated association rules for different business scenarios;

[0307] For different core forestry and grassland business scenarios, this solution adopts differentiated spatiotemporal association rules, as shown in Table 7 below.

[0308] Table 7. Business Scenario Data Differentiation Association Rules Table

[0309]

[0310] The aforementioned differentiation rules automatically identify the business scenario to which the current data belongs (based on the business attribute tags in the metadata) through the business scenario awareness module, and dynamically call the corresponding graph construction rules to ensure that the graph structure can accurately reflect the data association characteristics under different business scenarios.

[0311] 3. Mechanism for handling isolated nodes;

[0312] When a data node does not meet any preset association rules (i.e., it cannot establish an association edge with any other node), the data node processing strategy is shown in Table 8 below.

[0313] Table 8 Node Data Processing Strategy Table

[0314]

[0315] Training data: Samples that have been manually verified as normal in historical data are used as positive samples, and abnormal samples (such as those with sensor drift, missing data, logical contradictions, etc.) are artificially constructed as negative samples. The ratio of positive to negative samples is 5:1.

[0316] Model Structure and Training: A two-layer Graph Convolutional Network (GCN) is used, with hidden layer dimensions of 32 and 16, respectively. The Adam optimizer is employed with an initial learning rate of 0.001, and the loss function is a weighted sum of binary cross-entropy loss and reconstruction error. During training, an early stopping strategy is used, stopping training when the F1-Score on the validation set fails to improve for 10 consecutive rounds. Finally, the weight parameter γ is determined to be 0.65 during training to balance the importance of single-node anomalies and associated anomalies.

[0317] S503, real-time data fusion of forestry and grassland;

[0318] like Figure 5 As shown, the system integrates the Flink real-time processing engine and Kafka message queue, connecting to real-time data sources such as drones and sensors to achieve rapid real-time data collection, preprocessing, and semantic layer access, with a total latency of ≤5 seconds. A real-time-offline data fusion interface has been developed, employing a time-series alignment + feature-weighted fusion algorithm. This supports one-click access to real-time monitoring data and offline historical data in emergency scenarios, enabling rapid data integration and supporting second-level emergency decision-making. The real-time and offline data fusion interface algorithm, including its formula system, parameter definitions, and adaptation logic, is as follows, tailored to forestry and grassland emergency scenarios and data characteristics.

[0319] Real-time and offline data fusion interface algorithm: It solves the problems of temporal misalignment, format heterogeneity, and feature differences between real-time forestry and grassland data (sensors, drones) and offline data (historical fire data, afforestation data, remote sensing historical images). Through temporal alignment, feature normalization, and dynamic weighted fusion, it outputs standardized and consistent fused data, which is suitable for second-level decision-making scenarios such as fire prevention emergency and pest outbreaks. The interface supports synchronous / asynchronous calls, with synchronous call response time ≤ 5 seconds and asynchronous call latency ≤ 10 seconds.

[0320] Specifically, it includes:

[0321] 1. Data preprocessing and time series alignment;

[0322] First, both real-time and offline data are preprocessed (formatting and anomaly filtering). Then, time-series alignment is performed based on timestamps to ensure the temporal consistency of the fused data and adapt to the time-series characteristics of forestry and grassland data (high frequency for real-time data and low frequency for offline data).

[0323] (1) Format normalization (unified to JSON standardized format, adapted to interface output):

[0324]

[0325] in, This represents the raw value of a single real-time / offline data point (such as sensor temperature, vegetation cover). , Indicates the minimum and maximum values ​​of the reasonable range of values ​​for the corresponding data type (based on forestry and grassland business specifications, such as temperature 0-40℃ and vegetation coverage 0-100%). This represents the normalized data value, ranging from [0,1], eliminating dimensional differences (such as the numerical differences between temperature and humidity).

[0326] (2) Time alignment (based on timestamp interpolation to resolve the difference in update frequency between real-time and offline data);

[0327] Let the real-time data time series be (Update frequency: 1 time / minute), offline data time series is (Update frequency T+1 / week), the aligned time series is (Using real-time data timestamps as a benchmark), the offline data interpolation formula is:

[0328]

[0329] in, , Indicates the aligned target timestamp; , , Indicates packages in offline data Two adjacent timestamps ( ); This indicates that offline data is aligned with timestamps. The interpolation results are synchronized with the real-time data sequence. , These represent the timestamps of offline data. , The original values ​​at that location provide the basic data for interpolation calculations; , This represents the interpolation weights, where the sum of the two weights is 1, and the timestamp. The closer , The greater the weight, the lower the weight. The greater the weight, the more reasonable the interpolation result is.

[0330] 2. Dynamically enhance the fusion interface algorithm;

[0331] A three-dimensional dynamic weighted fusion of data freshness, data quality, and business weight is adopted to prioritize the timeliness of real-time data (a core requirement in emergency scenarios) while also considering the stability of offline data. The formula is as follows:

[0332]

[0333] in, This indicates alignment time. The standardized forestry and grassland data values, which combine real-time performance and stability, solve the respective defects of real-time data (high frequency but may have instantaneous anomalies) and offline data (stable but with delayed updates), providing accurate and consistent data support for second-level emergency decision-making. Indicates the weight of real-time data (calculated based on freshness and quality); This indicates the weight of offline data (calculated based on quality and business adaptability). It represents the standardized processing results of real-time forestry and grassland data (such as sensor temperature and humidity, and drone vegetation monitoring values), which solves the problem of inconsistent units of the original real-time data values ​​(such as temperature in °C and humidity in %), and adapts to the calculation needs of weighted fusion. This indicates that offline data is aligned with timestamps. The interpolation result at the location.

[0334] Weight constraints: , , (Real-time data has higher weight and is more suitable for emergency scenarios).

[0335] The specific calculation formula is as follows:

[0336]

[0337]

[0338]

[0339] in, Indicates freshness; This indicates data quality (calculated based on a score output by the anomaly detection model; higher quality results in a higher weight). Indicates the current time; The timeliness of real-time data (such as temperature, humidity, and smoke concentration) directly affects the accuracy of decision-making. The longer the data collection time, the lower the reference value (e.g., temperature and humidity data from 30 minutes ago cannot reflect the real-time environment of the current forest fire spread). Indicates the aligned target timestamp; This indicates an abnormal score. Minutes (the threshold for forestry and grassland emergency data attenuation; real-time performance deteriorates after 30 minutes).

[0340] The specific calculation formula is as follows:

[0341]

[0342] in, Indicates data quality; offline data quality level mapping values ​​(Excellent = 1.0, Good = 0.8, Satisfactory = 0.6, Unsatisfactory = 0.2, based on data annotations from the metadata management center). This indicates business adaptability; the matching degree between the current business scenario and offline data (emergency scenario = 0.4, planning scenario = 0.8, monitoring scenario = 0.6), adapting to the core business needs of forestry and grassland.

[0343] 3. Integrate data quality verification algorithms;

[0344] To ensure the accuracy of the fused data, a quality verification threshold is set. Unqualified data triggers re-fusion or an alarm. The formula is as follows:

[0345]

[0346] in, Represents timestamp The quality score of the fused data, with a value range of [0,1], comprehensively reflects the impact of real-time and offline data quality on the fusion result and is used to determine whether the fused data is qualified. This represents the weighted contribution value of real-time data quality. The larger the weight, the higher the data quality, and the greater the contribution to the quality of the fused data. This represents the weighted contribution value of offline data quality. The higher the weight and the higher the data quality, the greater the contribution to the quality of the fused data.

[0347] Judgment rules:

[0348] like The fused data is qualified, and the interface outputs normally.

[0349] like The merged data is available, and the interface outputs and pushes quality alerts.

[0350] like If the fused data is unqualified, a refusion is triggered (weights and interpolation are recalculated). If the data is still unqualified after refusion, an alarm is pushed and real-time data is output (prioritizing emergency use).

[0351] S6. Testing and Optimization: For core forestry and grassland business scenarios, conduct joint testing and iterative optimization of data access, metadata management, semantic query, access control and AI enhancement modules.

[0352] Targeting three core business scenarios ( Figure 6 We conducted cross-source query, access control, real-time fusion, and anomaly detection tests to fix issues such as query delays and permission matching errors; we organized practical tests for grassroots business personnel to optimize SQL parsing adaptability and visual query experience, ensuring that business personnel could get started within one day.

[0353] like Figure 6 As shown, this system achieves the full-process weaving and on-demand retrieval of multi-source forestry and grassland data through the collaborative work of five core modules: metadata collection and management, unified semantic layer, strategy engine, AI enhancement, and data service interface. The specific implementation process is as follows:

[0354] I. Metadata Collection and Management Phase;

[0355] 1. Metadata Collection: The metadata collector connects to various data sources in forestry and grassland (sensors, remote sensing platforms, business systems, historical archives, etc.) to automatically collect metadata information such as data source, storage location, field definition, update frequency, and business association. For confidential data sources, only non-confidential metadata fields are synchronized to ensure data security.

[0356] 2. Metadata storage and tagging: The collected metadata is stored in the metadata repository and labeled based on the forestry and grassland-specific tagging system (business attributes, sensitivity level, management attributes) to form structured metadata assets; at the same time, the tag information is synchronized to the unified semantic layer module to provide a foundation for subsequent semantic mapping and access control.

[0357] II. The unified semantic layer construction phase;

[0358] 1. Data Model Construction: The data model builder uses metadata tags to map heterogeneous forestry and grassland data (JSON, TIFF, relational tables, etc.) into a unified semantic model, achieving semantic alignment of cross-source data (such as mapping sensor temperature and meteorological temp to ambient temperature).

[0359] 2. Semantic Tag Mapping: The semantic tag mapper binds metadata tags to unified semantic fields, forming a tag-semantic-data source relationship, providing semantic basis for the strategy engine to generate permission rules and for the AI ​​module to push recommendation data.

[0360] 3. Information Synchronization: Synchronize the tag association information of the semantic layer to the strategy engine module and the AI ​​enhancement module to ensure that each module operates based on unified semantic logic.

[0361] III. Policy Engine Permission Control Phase;

[0362] 1. Permission rule generation: The permission rule generator dynamically generates hierarchical access permission rules based on metadata sensitivity level tags, business role information, and semantic layer relationships (e.g., Level 1 classified data is only authorized to provincial core personnel).

[0363] 2. Policy Execution: The policy executor receives query requests forwarded by the data service interface, and performs control operations such as permission verification and data anonymization based on user roles, data sensitivity levels, and business scenarios. After verification, the compliant data is pushed to the data service interface or AI enhancement module.

[0364] 3. Audit log retention: All permission verification and data access behaviors generate tamper-proof audit logs for compliance traceability.

[0365] IV. AI Enhancement Module Intelligent Processing Stage;

[0366] 1. Data mining analysis: The data mining engine performs operations such as anomaly detection, quality assessment, and correlation analysis based on unified semantic layer data (e.g., sensor data drift detection and remote sensing data interpretation error identification).

[0367] 2. Intelligent Recommendation Calculation: The recommendation algorithm model combines business personnel's query history, current business scenarios, and metadata tags to calculate a recommendation score and generate a list of related data recommendations (e.g., when querying pine wilt disease data, it automatically recommends remote sensing vegetation anomaly data in the same area).

[0368] 3. Results Push: Push anomaly detection results, data quality scores, and recommended data to the data service interface module and business systems to provide intelligent support for business decisions.

[0369] V. The interaction phase between data service interfaces and business systems;

[0370] 1. Query Request Reception: The business system initiates a data query request through the data service interface module (RESTful API or SDK toolkit). The request includes information such as business scenario, data range, and user identity.

[0371] 2. Request processing: After receiving a request, the data query engine will work with the strategy engine to perform permission verification. If the verification is successful, the query statement will be parsed based on the unified semantic layer and automatically routed to the corresponding data source to obtain the data. If intelligent recommendation is required, the recommendation results of the AI ​​enhancement module will be called.

[0372] 3. Result Return: The compliant data after permission verification, AI recommendation data, or fused standardized data are returned to the business system through the data service interface for further processing by the data mining engine and recommendation algorithm model, and finally presented to forestry and grassland business personnel.

[0373] VI. Closed-loop process and iterative optimization;

[0374] Data flow between modules is achieved through standardized interfaces, forming a closed loop of the entire process: metadata collection → semantic unification → access control → intelligent processing → on-demand invocation.

[0375] Based on feedback from business systems and audit logs, we continuously optimize metadata tags, semantic mapping rules, permission policies, and AI models to ensure that the system adapts to the dynamic changes in forestry and grassland business scenarios.

[0376] Based on the technical solution of this invention, further experimental tests were conducted, and the results are as follows:

[0377] 1) Experimental background and data environment;

[0378] This experiment was conducted in Linhai Forest Farm, Zhejiang Province, covering three core areas: fire prevention and emergency response, pine wilt disease control, and afforestation planning. It involved multiple heterogeneous data sources. The experimental data environment is shown in Table 9 below.

[0379] Table 9 Experimental Data Environment

[0380]

[0381] 2) Cross-source data integration efficiency comparison experiment:

[0382] Experimental objective: To verify the advantages of the metadata-driven data weaving method of this invention over the traditional manual ETL adaptation method in terms of cross-source data integration efficiency.

[0383] Experimental methods: The traditional ETL method and the method of this invention were used to integrate multiple heterogeneous data sources, and the data access time, metadata annotation time and total integration time were measured.

[0384] Table 10 Comparison of Cross-Source Data Integration Efficiency

[0385]

[0386] Experimental conclusion: The metadata-driven data weaving method of the present invention reduces the total integration time of 8 source data from 96 hours to 16 hours, improving efficiency by 83.33%, which verifies the high efficiency of the technical solution of this application.

[0387] 3) Performance comparison experiment of unified semantic query;

[0388] Experimental objective: To verify the advantages of the unified semantic layer query of the present invention in terms of performance and ease of use compared with traditional multi-system separate queries.

[0389] Experimental method: Ten typical forestry and grassland business query scenarios were selected, and queries were performed using both traditional methods and the method of this invention. The query time, SQL complexity, user learning cost and other indicators were compared.

[0390] Table 11 Performance Comparison of Unified Semantic Query

[0391]

[0392] Experimental conclusion: Unified semantic query reduced the average query time from 4.5 hours to 15 minutes, improving efficiency by 94.4%, and shortened the training time for business personnel from 2 weeks to 1 day, effectively lowering the threshold for data use.

[0393] 4) Comparison experiment on the effectiveness of access control;

[0394] Experimental objective: To verify the advantages of the multi-dimensional policy engine of this invention in terms of security and compliance compared to traditional permission control methods.

[0395] Experimental method: Simulate 1000 data access requests of different levels and compare the performance of traditional methods and the method of this invention in terms of illegal access interception rate, accuracy of de-identification processing, and audit traceability.

[0396] Table 12 Comparison of Access Control Effectiveness

[0397]

[0398] Experimental conclusion: The multi-dimensional policy engine increased the illegal access interception rate to 98.5% and reduced the risk of sensitive data leakage from 15% to 0.8%, a reduction of 94.67%, verifying the security and compliance advantages of this application.

[0399] 5) Real-time data fusion performance comparison experiment;

[0400] Experimental objective: To verify the advantages of the real-time data fusion of the present invention over the traditional T+1 offline integration mode in terms of emergency response capabilities.

[0401] Experimental method: Simulate fire emergency scenarios and compare key indicators such as real-time data access latency, fusion processing time, and decision response time.

[0402] Table 13 Real-time data fusion performance comparison

[0403]

[0404] Experimental conclusion: Real-time data fusion reduces emergency decision-making response time from 48 hours to 30 seconds, an improvement of 99.94%, and increases emergency response efficiency by 58%, verifying the real-time decision support advantages of the technical solution proposed in this application.

[0405] 6) Model performance testing;

[0406] 1. Performance testing of a recommendation model specifically for the forestry and grassland sector;

[0407] Experimental objective: To verify the advantages of forestry and grassland-specific recommendation models over general recommendation algorithms in terms of recommendation accuracy and recall.

[0408] Experimental method: The query logs and metadata tag data of forestry and grassland business personnel over the past year were used as the training set to compare the performance of the recommendation model of this invention with collaborative filtering algorithm and tag similarity algorithm.

[0409] Table 14 Model Performance Comparison

[0410]

[0411] Experimental conclusions: The forestry and grassland domain-specific recommendation model achieves a Precision@10 score of 0.83 and an F1-Score of 0.80, significantly outperforming general recommendation algorithms. This fully verifies the effectiveness of the composite recommendation strategy that integrates tag similarity, collaborative filtering, and business scenario adaptation.

[0412] 2. Performance testing of the forestry and grassland data quality detection model;

[0413] Experimental objective: To verify the advantages of the GCN-based forestry and grassland data quality detection model over traditional statistical methods in terms of anomaly detection accuracy and recall.

[0414] Experimental method: Historical sensor data from Linhai Forest Farm in Zhejiang Province were used, and known anomalies (data drift, format anomalies, missing data, etc.) were injected. The performance of the GCN model and traditional anomaly detection methods were compared.

[0415] Table 15 Performance Comparison of Forestry and Grassland Data Quality Detection Models

[0416]

[0417] Experimental conclusions: The anomaly detection accuracy of the GCN-based forestry and grassland data quality detection model reached 0.94, the F1-Score reached 0.92, and the false alarm rate was only 5%, which is significantly better than traditional methods, fully verifying the effectiveness of graph neural networks in capturing global correlation features of forestry and grassland data.

[0418] 7) Comprehensive analysis and conclusions;

[0419] 1. Summary of experimental results;

[0420] Table 16 Summary of Experimental Results

[0421]

[0422] 2. Analysis of the advantages of the technical solution;

[0423] The above experiments have verified that the technical solution of this application has the following advantages:

[0424] (1) Significantly improved efficiency of cross-source data integration: The metadata-driven data weaving method avoids the tedious manual adaptation work in the traditional ETL method. Through automated metadata collection, annotation and management, the data integration efficiency is improved by 83.33%.

[0425] (2) The threshold for data use has been greatly reduced: the unified semantic layer simplifies complex multi-system queries into unified SQL statements, shortens the training time for business personnel from 2 weeks to 1 day, and reduces the query time by 94.4%, enabling non-technical personnel to directly access multi-source data.

[0426] (3) Outstanding security and compliance management: The multi-dimensional strategy engine has achieved refined access control, with an illegal access interception rate of 98.5% and a reduction of sensitive data leakage risk by 94.67%.

[0427] (4) Strong real-time decision support capability: Real-time data fusion reduces emergency decision response time from 48 hours to 30 seconds, improving by 99.94% and emergency response efficiency by 58%.

[0428] (5) Excellent AI model performance: The precision@10 of the forestry and grassland domain-specific recommendation model reached 0.83 and the F1-Score reached 0.80; the accuracy of the anomaly detection model based on GCN reached 0.94 and the F1-Score reached 0.92, which are significantly better than the general algorithm, verifying the effectiveness of the domain-specific AI strategy.

[0429] In summary, this application achieves logical integration and on-demand flow of distributed data through metadata-driven approaches, semantic unification, and intelligent governance. All experimental indicators meet or exceed existing technologies, fully demonstrating the effectiveness and advancement of this application. This application fully aligns with the characteristics of forestry and grassland data—multi-source heterogeneity, high sensitivity, and strong real-time requirements—and achieves secure data management, intelligent fusion, and efficient reuse through modular design, providing reliable data support for core forestry and grassland operations such as fire prevention and emergency response, pest and disease control, and afforestation planning.

[0430] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A knowledge-enhanced data weaving method for forest and grass multi-source data, characterized in that, Includes the following steps: S1. Data Access: Through a configurable data interface adapter module, access is provided for multi-source forestry and grassland data, including field monitoring data, remote sensing image data, forestry and grassland business system data, historical archive data, and third-party meteorological and land data. S2. Construct a dedicated metadata foundation for forestry and grassland: Collect metadata from multiple sources of forestry and grassland data, and automatically label it through a three-dimensional tag system to build a metadata management center that supports automatic metadata updates, version tracking, and multi-dimensional retrieval. S3. Build a unified semantic layer for forestry and grassland: Construct a semantic model based on the ontology of the forestry and grassland domain. This model defines the core concepts of forestry and grassland and their hierarchical relationships, unifies the measurement standards of ambiguous indicators, maps fields in heterogeneous data sources to the unified semantic fields of this model, and deploys a virtualized query engine that supports unified SQL queries. S4. Deploy a forestry and grassland-specific strategy engine: Formulate multi-dimensional data access strategies that are dynamically linked with sensitivity level labels, business roles, and emergency scenarios to achieve dynamic permission control and data desensitization based on data sensitivity level, user business role, and emergency response level. S5. Integrated Forestry and Grassland AI Enhancement Module: Construct a dedicated recommendation model trained based on user behavior logs and metadata tags in the forestry and grassland field, as well as a forestry and grassland data quality detection model constructed based on a spatiotemporal constraint graph structure; deploy real-time data processing components to achieve rapid fusion of real-time and offline data; S6. Testing and Optimization: For core forestry and grassland business scenarios, conduct joint testing and iterative optimization of data access, metadata management, semantic query, access control and AI enhancement modules.

2. The knowledge-enhanced data weaving method for forest and grass multi-source data according to claim 1, characterized in that: In S1, forestry and grassland multi-source data includes field monitoring equipment, real-time streaming data, image data, forestry and grassland business systems, historical archives, and meteorological and land data; Field monitoring equipment: Based on multiple sensors, it collects temperature, humidity, and vegetation coverage data of the study area; Image data: Image data of forest vegetation and fire clues collected by drones.

3. The knowledge-enhanced data weaving method for forest and grass multi-source data according to claim 1, characterized in that: In S2, the specific implementation process is as follows: S201, Comprehensive collection of multi-source metadata; S202. Forestry and Grassland Characteristic Metadata Labeling: Establish a forestry and grassland characteristic metadata labeling system, which includes business attribute labels, sensitivity level labels, and management attribute labels. S203. Refined metadata management: Build a metadata management center, configure an automatic update mechanism, and set differentiated update frequencies for different types of data.

4. The knowledge-enhanced data weaving method for forest and grass multi-source data according to claim 1, characterized in that: In S3, the specific implementation process is as follows: S301. Semantic Model Construction in the Forestry and Grassland Field: Based on the core business scenarios of forestry and grassland, construct a semantic model covering resource monitoring, fire prevention and emergency response, and afforestation planning scenarios, and map heterogeneous data with different formats and different field definitions into standardized semantic fields; S302, Virtualized Query Engine Deployment and Adaptation: Deploy a virtualized query engine and a dedicated SQL parser to support unified cross-source SQL queries.

5. The knowledge-enhanced data weaving method for forest and grass multi-source data according to claim 1, characterized in that: In S4, the specific implementation process is as follows: S401, Multi-dimensional access policy formulation: including sensitivity level policy, role-based access policy, and emergency scenario policy; S402, Policy Engine Deployment and Linkage: Construct a de-identification processing unit, support multiple de-identification methods, adapt to the management and control requirements of data with different sensitivity levels, record all data access behaviors, and generate audit logs.

6. The knowledge-enhanced data weaving method for forest and grass multi-source data according to claim 1, characterized in that: In S5, the specific implementation process is as follows: S501, Data-driven intelligent recommendation: Train a recommendation model specifically for the forestry and grassland sector, and automatically recommend related data based on the query history of business personnel and the current business scenario; S502, Abnormal Data Detection and Correction: Training a forestry and grassland data quality detection model based on the GNN deep learning method; S503, Real-time Forestry and Grassland Data Fusion: Employs a real-time and offline data fusion interface algorithm, using time-series alignment and feature-weighted fusion algorithms to achieve rapid data integration.

7. The knowledge-enhanced data weaving method for forest and grass multi-source data according to claim 6, characterized in that: In S501, the specific implementation details are as follows: The core recommendation scoring formula of the forestry and grassland-specific recommendation model is as follows: ； in, This represents the final recommendation priority score given by business personnel u to candidate forestry and grassland data i. This indicates a tag similarity score based on metadata tags; This indicates a collaborative filtering score based on user query behavior. This represents the adaptation score based on the business scenario; α and β both represent weighting coefficients.

8. The knowledge-enhanced data weaving method for forestry and grassland multi-source data according to claim 6, characterized in that: In S502, the forestry and grassland data quality detection model adopts a graph neural network architecture, and its construction and detection process includes: Step 1: Graph structure construction and node feature initialization; Step 2: GCN graph convolution operation: Aggregate the node's own features and neighborhood association features to capture the global correlation of forestry and grassland data; Step 3: Anomaly score calculation for nodes: Based on the aggregated node features, anomaly scores are calculated using two dimensions: reconstruction error and neighborhood similarity. Step 4: Differentiated Early Warning Threshold Determination: Set differentiated early warning thresholds and determine the anomaly scores; Step 5: Abnormal Data Correction: After an anomaly is detected, the recommended value is corrected by combining historical normal data of the same region, time period and type using a weighted fusion method.

9. The knowledge-enhanced data weaving method for forest and grass multi-source data according to claim 6, characterized in that: In S503, the specific implementation process is as follows: Step 1: Data preprocessing and time-series alignment: Preprocess real-time and offline data, and then perform time-series alignment based on timestamps; Step 2: Dynamically enhance the fusion interface: Employ a three-dimensional dynamic weighted fusion based on data freshness, data quality, and business weight; Step 3: Data quality verification: Set a quality verification threshold. Unqualified data will trigger re-fusion or an alarm.

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