Multi-modal remote sensing image intelligent interpretation and territorial dynamic monitoring analysis method

Abstract

The invention discloses a multi-mode remote sensing image intelligent interpretation and land dynamic monitoring analysis method, and relates to the technical field of remote sensing data intelligent processing. According to the method, a dynamic ontology-driven multi-modal knowledge graph and virtual-real dynamic calibration digital twinning two-way collaborative system is constructed, and real-time adaptation of policy rules and virtual-real scenes is realized; through dynamic threshold learning and a three-dimensional grading index system, land abnormal changes are accurately identified; the regional differentiation accurate accounting is realized by adopting ecological value quantitative evaluation adaptive to dynamic parameters; a scene-based treatment plan is generated and is subjected to multi-agent simulation optimization, so that the execution effect is improved; and constructing a full-process closed-loop optimization mechanism, and dynamically updating a threshold value, parameters and rules. The technologies are coordinated and linked, the precision and efficiency of dynamic land management are remarkably improved, and the method is suitable for scenes such as cultivated land protection red line management and control.

Description

Technical Field

[0001] This invention relates to the field of intelligent remote sensing data processing technology, specifically a method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use. Background Technology

[0002] Dynamic monitoring of national land is a core support for ensuring the rational use of national land space and the sustainable development of the ecological environment. With the development of technologies such as remote sensing, artificial intelligence, and digital twins, technologies such as multimodal remote sensing image interpretation, geographic knowledge graph modeling, and virtual-real scene simulation have been gradually applied to the field of national land monitoring. Currently, the industry has seen the emergence of remote sensing image feature extraction technology based on convolutional neural networks, static geographic knowledge graph construction technology, single-dimensional digital twin simulation technology, and fixed-threshold anomaly early warning systems. These technologies have achieved certain breakthroughs in individual aspects such as data processing, rule storage, scene simulation, and change recognition, providing basic support for the intelligent upgrading of national land monitoring.

[0003] However, the existing technology system suffers from significant modular fragmentation and insufficient adaptation, and has not yet formed a collaborative control mechanism across the entire process: multimodal remote sensing interpretation, knowledge graphs, digital twins, early warning and assessment, and other technical modules operate independently; static knowledge rules cannot adapt to dynamically changing land scenarios in real time; the simulation parameters of digital twins lack proactive guidance from policy rules; the use of fixed thresholds in early warning systems leads to poor adaptability to regional differences; and ecological value assessment relies on uniform parameters while ignoring regional ecological differences. Ultimately, this results in high rates of underreporting and false reporting in land change monitoring, insufficient practicality of emergency response plans, and lagging policy adaptation, making it impossible to achieve closed-loop control across the entire process of "monitoring-early warning-assessment-response-optimization," which seriously restricts the accuracy and efficiency of dynamic land governance.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] The purpose of this invention is to provide a method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use, so as to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, this invention provides a method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use, comprising the following steps: S1 constructs a two-way collaborative system between a dynamic ontology-driven multimodal knowledge graph and a dynamically calibrated virtual-real digital twin. Specifically, this includes: S11 constructing a dynamic ontology library for land monitoring, encompassing four core ontology categories: land cover types, regional attributes, policy rules, and ecological parameters; S12 building a full-area digital twin of the arable land protection red line, integrating multimodal remote sensing data, UAV patrol data, and ground sensor data; S13 establishing a temporal-semantic dual alignment mechanism, using a sliding window algorithm to synchronize the timestamps of the dynamic ontology library and the digital twin, and using an embedding model to map the real-time data of the digital twin into semantic tags recognizable by the dynamic ontology library; S14 constructing a collaborative reasoning model based on a graph attention network. This collaborative reasoning model receives policy rule guidance from the dynamic ontology library to correct the simulation parameters of the digital twin, and simultaneously receives real-time control data from the digital twin to iterate the semantic association chain of the dynamic ontology library. The semantic alignment loss function is defined as: , In the formula For the semantic feature vectors of a dynamic ontology library, For the real-time data feature vector of the digital twin, For embedding mapping functions, The dimension of the feature vector; S2 uses land feature data output from a two-way collaborative system to achieve proactive early warning of abnormal changes in the national territory through dynamic threshold learning; S3 uses scenario data based on early warning results and a two-way collaborative system to complete the quantitative assessment of the ecological value of land change through dynamic parameter adaptation. Based on the assessment results and the policy rules of the two-way collaborative system, S4 generates and executes scenario-based emergency response plans, forming a closed-loop management system throughout the entire process. Through the two-way collaborative mechanism of dynamic ontology knowledge graph and digital twin, it breaks through the bottleneck of the disconnect between static knowledge and real-time data in traditional technologies, realizes the real-time adaptation of policy rules and virtual and real scenarios, significantly improves the accuracy and policy compliance of land dynamic monitoring, and provides unified high-quality data and rule support for subsequent early warning, assessment and response stages.

[0007] Further, in S11, the construction and self-evolution process of the dynamic ontology library includes: S111 extracting land feature attributes, regional characteristics, policy clauses, and ecological parameters from the land monitoring field to establish an initial ontology hierarchical structure; S112 designing a dual-drive update mechanism, extracting rule information from the latest policy text through natural language processing technology, and iterating the ontology attribute dictionary; receiving control effect data from the digital twin feedback, and optimizing the semantic association chain of land feature-rule-disposal; the natural language processing technology includes an improved BERT model; S113 calculating the association weights between ontology nodes based on a graph attention network to generate a dynamic ontology knowledge graph, wherein the association weight update formula is: , In the formula For the ontology node and Association weights, For attention vectors, This is the weight matrix. This is a vector concatenation operation. It uses the weight matrix dimension to achieve automatic updates and optimization of the dynamic ontology library. It can quickly adapt to new policies and actual management scenarios without manual intervention, solve the problems of rigid rules and lagging updates in traditional knowledge graphs, and improve the real-time support capability of knowledge graphs for dynamic land monitoring.

[0008] Furthermore, in S12, the construction and dynamic calibration process of the digital twin includes: S121 integrating high-resolution remote sensing images, UAV sequence images, ecological parameter data collected by ground sensors, and GIS vector data to construct an initial model of the digital twin; S122 using the Kalman filter algorithm to correct the ground feature parameters of the digital twin, and adjusting the simulation parameters of the twin based on the regional attributes and ecological parameters of the dynamic ontology library to ensure that the deviation between the digital twin and the real scene is controlled within a preset range; S123 establishing a multi-source data fusion module to fuse monitoring data from different sources in time stamp order to generate a real-time dynamic scene of the twin; through dynamic calibration and multi-source data fusion, the consistency between the digital twin and the real scene is improved, providing accurate scene simulation support for subsequent early warning, assessment, and contingency plan generation, and avoiding control decision errors caused by virtual-real deviations.

[0009] Furthermore, the proactive early warning process for land anomaly changes in S2, based on dynamic threshold learning, includes: S21 using regional attributes and land feature importance from a dynamic ontology database as constraints, and real-time monitoring data from a digital twin as training samples, to train a dynamic threshold model through reinforcement learning; S22 outputting early warning thresholds for different regions and land feature types based on the dynamic threshold model, wherein the dynamic threshold update formula is... In the formula The updated warning threshold, The current warning threshold is... For learning rate, S23 represents the gradient of the threshold loss function; S23 constructs a three-dimensional hierarchical index system of change magnitude, policy compliance, and ecological sensitivity, calculates the warning level based on the warning threshold and the three-dimensional index, and simultaneously triggers the visualization marking of the digital twin; it realizes the dynamic adjustment and precise hierarchical classification of the warning threshold, solves the problem of missed and false reports caused by the traditional fixed threshold "one-size-fits-all" approach, improves the accuracy and timeliness of identifying abnormal changes in land, and provides a basis for differentiated handling.

[0010] Furthermore, in S3, the dynamic parameter-adapted land change ecological value quantitative assessment process includes: S31, extracting ecological value assessment parameters for different regions based on the regional adaptive parameter library of the dynamic ontology; S32, receiving land change data from the digital twin and the hierarchical results of the early warning system, determining the weights of carbon sinks, food production capacity, and soil and water conservation using the analytic hierarchy process, and applying a comprehensive assessment formula: ; Calculate the ecological value gain or loss, where To comprehensively consider the ecological value gains and losses, For the first The weight of ecological value, For the first The quantitative value of ecological value, The S33 module provides a number of ecological value types and simultaneously pushes the assessment results to the dynamic ontology library and digital twin. This enables regional adaptation and dynamic updating of ecological value assessments, improves the accuracy and regional relevance of assessment results, provides quantitative basis for ecological compensation and liability determination, and solves the problem of "fixed parameters and large deviations" in traditional assessments.

[0011] Furthermore, in S4, the process of generating and executing scenario-based emergency response plans includes: S41, the digital twin generates scenario-based emergency response plans for different regions and different warning levels based on the warning level, assessment results, and policy rules of the dynamic ontology library; S42, integrating a multi-agent simulation module to simulate the execution effect of the scenario-based emergency response plans and optimize the plan parameters; S43, executing the optimized scenario-based emergency response plans and feeding back the plan execution data to the dynamic ontology library in real time through the digital twin; the generated scenario-based emergency response plans are more in line with actual control scenarios, significantly improving the success rate of execution, while reducing disposal costs through simulation optimization, solving the problems of "poor practicality and high execution difficulty" of traditional unified plans.

[0012] Furthermore, it also includes S5 full-process closed-loop optimization. The dynamic ontology library receives the plan execution data fed back by the digital twin, automatically optimizes the early warning threshold, assessment parameters and policy rules, and synchronizes the optimized rules to the early warning system, assessment system and digital twin, forming a closed-loop control of monitoring-early warning-assessment-response-optimization; realizes continuous optimization of technical parameters and rules throughout the process, promotes the transformation of land dynamic monitoring from passive response to proactive prevention, and improves the adaptability and long-term stability of the control system.

[0013] Furthermore, in S21, the reinforcement learning training process includes: using the accuracy, false negative rate, and false positive rate of identifying abnormal land changes as reward functions, optimizing the parameters of the dynamic threshold model through multiple rounds of iterative training, enabling the model to adapt to different land monitoring scenarios by learning the correlation between regional attribute features and land feature importance features; further improving the adaptability and stability of the dynamic threshold model, ensuring the accuracy and reliability of early warning results in different regions and scenarios, and reducing the cost of manually adjusting the threshold.

[0014] Furthermore, in S31, the process of constructing the regional adaptive parameter library includes: dividing regions according to climate zones and terrain types based on GIS zoning data and dynamic ecological parameters extracted by remote sensing; determining the carbon sink coefficient, soil and water conservation parameters, and grain production capacity parameters of each region; establishing a regional adaptive parameter library and updating it in real time; ensuring that the ecological value assessment parameters are highly matched with the actual situation of the region; further reducing assessment errors; and providing more accurate quantitative support for regional differentiated ecological protection decisions.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. A two-way collaborative system of dynamic ontology-driven multimodal knowledge graph and dynamically calibrated digital twin: This system breaks down the barriers between static knowledge rules and dynamic scenario simulation. Through a self-evolutionary mechanism of the ontology library driven by both policy text and actual control data, and dynamic calibration of the twin based on Kalman filtering and rule constraints, it achieves real-time adaptation between policy rules and virtual / real scenarios. This ensures a high degree of consistency between monitoring data and decision-making basis, providing high-quality data and rule support for the entire process of control. 2. A dynamic threshold learning and three-dimensional hierarchical indicator system: This system abandons the traditional fixed threshold model. Through reinforcement learning, it trains dynamic threshold models adapted to different regions and land cover types. Combined with three-dimensional hierarchical classification of change magnitude, policy compliance, and ecological sensitivity, it significantly improves the accuracy of abnormal change identification, effectively avoids missed and false alarms, and particularly enhances the ability to identify gradual encroachment behavior.

[0016] 2. Dynamic Parameter-Adapted Ecological Value Quantitative Assessment: By leveraging a regional adaptive parameter library and multi-value comprehensive quantitative calculation, this approach overcomes the limitations of traditional fixed-parameter, single-value assessments, achieving precise matching between assessment parameters and regional ecological characteristics. The assessment results are more comprehensive and realistic, providing a scientific quantitative basis for ecological compensation and liability determination. Scenario-based Contingency Plan Generation and Multi-Agent Simulation Optimization: Customized contingency plans are constructed based on a three-dimensional scenario dimension encompassing regional type, warning level, and ecological value loss. Simulation optimization of execution parameters in advance significantly improves the practicality and feasibility of the plans, reduces disposal costs, and increases the success rate of execution. A Full-Process Closed-Loop Optimization Mechanism: By constructing a cyclical feedback loop of monitoring-early warning-assessment-disposal, this mechanism achieves dynamic and collaborative updates of thresholds, parameters, and rules, promoting the transformation of the management system from passive response to proactive prevention. It significantly enhances adaptability and long-term stability, helping to upgrade the land governance model from qualitative control to quantitative value orientation. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 This invention provides a technical solution: a method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land. This specific implementation takes the dynamic management and control of the red line for farmland protection as the core application scenario. Through the synergistic linkage of dynamic ontology-driven multimodal knowledge graph, virtual-real dynamic calibration digital twin, dynamic threshold early warning, dynamic parameter ecological value assessment and closed-loop optimization, it realizes intelligent management and control of the red line for farmland protection from monitoring, early warning, assessment to disposal.

[0020] Step S1: Constructing a Two-Way Collaborative System for Dynamic Ontology-Driven Multimodal Knowledge Graphs and Dynamically Calibrated Digital Twins: In existing land dynamic monitoring technologies, knowledge graphs and digital twins are mostly independent operating modules. Static knowledge rules cannot adapt to dynamically changing land scenarios in real time, and the simulation parameters of digital twins lack proactive guidance from policy rules, resulting in a disconnect between monitoring data and decision-making basis, leading to low accuracy and efficiency in control. Constructing a two-way collaborative system can achieve real-time linkage between knowledge rules and virtual and real scenarios, providing unified high-quality data support and rule basis for subsequent early warning, assessment, and disposal stages, solving the core problems of data silos and process disconnect in traditional technologies. Specific technical means are as follows: This step includes four sub-steps: dynamic ontology library construction and self-evolution, digital twin construction and dynamic calibration, establishment of a time-semantic dual alignment mechanism, and collaborative reasoning model construction. Each sub-step is closely connected to form a two-way collaborative system.

[0021] 1. Sub-step S11: Construction and Self-Evolution of the Dynamic Ontology Library: Existing ontology libraries in the field of land monitoring are mostly static hierarchical structures, capable of updating rules only through manual intervention. They cannot automatically adapt to newly introduced farmland protection policies, nor can they adjust semantic relationships based on actual control effects, resulting in rules lagging behind control needs and affecting decision-making accuracy. Designing a self-evolution mechanism can achieve automatic updates and optimization of the ontology library, ensuring real-time adaptation of policy rules to actual scenarios. Specific technical means are as follows: Initial ontology hierarchy construction: The core elements involved in the management of the arable land protection red line are identified, and a four-level ontology hierarchy is established. The first-level ontology is the core domain of land monitoring; the second-level ontology includes four categories: land feature types, regional attributes, policy rules, and ecological parameters; the third-level ontology is a sub-category of the second-level ontology; and the fourth-level ontology is the specific attributes of the third-level ontology. For example, the third-level ontology under land feature types includes arable land, illegal constructions, and facility agricultural land, while the fourth-level ontology of arable land includes attributes such as permanent basic farmland, general arable land, and arable land quality grade.

[0022] Dual-Driven Update Mechanism Design: A dual-update mode combining policy text-driven and actual management data-driven approaches is adopted. For the policy text-driven aspect, an improved BERT model is used to extract rule information from the latest farmland protection policies. Specific improvements to the BERT model include: integrating pre-training data with policy text corpora from fields such as land spatial planning, farmland protection, and ecological management to construct a domain-specific lexicon; adding a rule entity relation extraction head to the output layer, extracting core policy rules through triples (feature type - violation - handling requirements); introducing a policy timeliness weight factor to prioritize matching the latest effective policy clauses, ensuring the timeliness and accuracy of rule extraction; and automatically iterating the ontology attribute dictionary. The iteration rules for the ontology attribute dictionary include: new rule iteration: when the policy text is extracted to... When new land cover types (such as photovoltaic composite facilities) or new violations (such as the conversion of arable land to non-grain crops) are introduced, corresponding entries and associated attributes are automatically added to the dictionary. Attribute optimization iteration: When the digital twin reports poor performance of the violation handling rules for a certain type of land cover (such as a violation recurrence rate exceeding 15%), the association weight of the entry with stricter handling rules is automatically strengthened. Redundancy cleanup iteration: When a policy clause becomes invalid or a certain type of land cover has no monitoring records for 12 consecutive months, it is automatically marked as dormant to avoid redundant data affecting reasoning efficiency. In terms of actual management data-driven approach, the semantic association chain of land cover-rules-handling is optimized by receiving data on the implementation effect of the contingency plan from the digital twin.

[0023] Ontology Association Weight Calculation: Association weights between ontology nodes are calculated using a graph attention network to generate a dynamic ontology knowledge graph. The association weight update formula is: ; In the formula, For the ontology node With the ontology node Association weights, The attention vector has a dimension of . , This is the weight matrix, with dimension 1. , The dimension of the feature vector of the ontology node. This is a vector concatenation operation. For the ontology node eigenvectors, For the dimensions of the weight matrix, For all nodes Connected nodes Perform normalization calculations.

[0024] Example: Taking the management of farmland protection red lines in a certain province as an example, the initial ontology's land feature type level 3 ontology includes permanent basic farmland, general farmland, illegal construction, and facility agricultural land, while the regional attribute level 3 ontology includes urban suburbs, remote mountainous areas, and plains. When the province issues a new policy on land use for photovoltaic composite projects, explicitly stating that photovoltaic panel installation must not occupy permanent basic farmland, the improved BERT model extracts the core rules from the policy text, automatically adding a photovoltaic composite project land use rule under the policy rule level 3 ontology, and adding a prohibition on photovoltaic installation attribute to the permanent basic farmland level 4 ontology, completing the iteration of the ontology attribute dictionary. Simultaneously, the digital twin provides feedback on the management data of repeated illegal occupation of farmland by facility agricultural land in a remote mountainous area after rectification. The system automatically optimizes the semantic association weights between facility agricultural land and farmland protection rules and disposal measures, increasing the association weight of illegal disposal of facility agricultural land in this area to ensure that strict disposal rules are prioritized in subsequent inference. When calculating the association weights, for the node pair of permanent basic farmland and photovoltaic prohibition rules, substituting into the formula yields... The value is 0.85, which is significantly higher than the association weight of other non-core rules, ensuring that this rule is called first during inference.

[0025] Existing publicly available documents update the ontology solely through policy texts, failing to adjust semantic relationships based on actual control effects, leading to a disconnect between ontology rules and practical applications. This approach uniquely employs a dual-driven update mechanism—combining policy texts and actual control data—and utilizes graph attention networks to dynamically optimize the association weights of ontology nodes, rather than relying on manual intervention or a single data source. The benefits include significantly improved ontology update efficiency, faster policy adaptation, and markedly higher accuracy in semantic relationships, enabling rapid adaptation to new policies and complex control scenarios without manual intervention.

[0026] 2. Sub-step S12: Construction and Dynamic Calibration of the Digital Twin: Existing digital twins in the land resources field are mostly built based on single remote sensing data, which can only statically simulate the distribution of land features. This results in significant deviations from the dynamic changes in real-world scenarios, and the simulation parameters are not adjusted in accordance with policy rules, making the scenario simulation unable to support accurate decision-making. Through multi-source data fusion and dynamic calibration, a high degree of consistency between the digital twin and the real-world scenario can be ensured, while incorporating policy and rule constraints to improve the practicality of scenario simulation. Specific technical means are as follows: Multi-source data integration: The initial model is constructed by integrating four types of data: high-resolution remote sensing imagery, UAV image sequence, ground sensor data, and GIS vector data. High-resolution remote sensing imagery, using 0.5-meter-level satellite imagery, is used to extract the spatial morphology of ground features; UAV image sequence is collected daily to capture short-term changes in ground features; ground sensors, including soil moisture sensors and crop growth sensors, are used to collect ecological parameters of cultivated land; and GIS vector data, including cultivated land boundary data and administrative division boundary data, is used to determine the control scope.

[0027] Dynamic calibration algorithm application: The Kalman filter algorithm is used to correct the ground feature parameters of the digital twin, including the location, area, and ecological parameters of the ground features, to ensure that the deviation between the twin and the real scene is controlled within a preset range. The deviation is defined as the difference between the core ground feature parameters (location coordinates, area, and ecological parameters) simulated by the digital twin and the actual measured values. The deviation calculation process uses the mean absolute error (MAE), and the formula is as follows: ; in These are actual measured values. For twin simulation values, The number of parameters is specified; the preset range is determined based on the accuracy standards of the land dynamic monitoring industry, with a deviation ≤5% (relative error) to ensure that the simulation accuracy meets the needs of control and decision-making. Kalman filtering consists of two stages: state update and observation update. The state update formula is: The observation update formula is: In the formula, for The state vector at any given time includes parameters such as the area of ​​the land feature, its location coordinates, and soil moisture. Here is the state transition matrix. For the control matrix, To control the input, namely the ecological parameter constraints of the dynamic ontology library, The noise is a process noise that follows a Gaussian distribution. for The observation vector at time step, i.e., the actual observation value after multi-source data fusion. For the observation matrix, The noise is observed to follow a Gaussian distribution.

[0028] Multi-source data fusion module: Employs a weighted average fusion algorithm to fuse monitoring data from different sources in timestamp order. The specific logic of the weighted average fusion algorithm is as follows: Data precision is defined as the reciprocal of the data error rate. The error rate is calculated by combining the calibration precision of the data acquisition equipment with historical verification data. The formula is: The weighting formula is: ; in For the first Weights of class data For the first Precision score for class data The number of data types; the fusion formula is: ; in The merged data values For the first The original values of different types of data are assigned different weights to high-resolution remote sensing images, UAV images, and ground sensor data. The weights are dynamically adjusted according to data accuracy. The specific rules for dynamic weight adjustment are as follows: Trigger condition: When the error rate change of a certain type of data exceeds 10% in three consecutive time windows, or the accuracy score of a newly added data type is higher than 20% of the average accuracy score of existing data, weight adjustment is initiated; Adjustment frequency: Synchronized with the data acquisition frequency (e.g., once a day); Implementation method: Adopt the proportional-integral-derivative (PID) algorithm to dynamically correct the weight coefficient according to the accuracy score deviation, ensuring the stability and accuracy of the fused data, and generating a real-time dynamic scenario of the digital twin.

[0029] Example: Taking the control area of the cultivated land protection red line in a certain province as the research object, integrating 0.5-meter satellite images in January 2024, daily UAV inspection images, soil moisture and crop growth data collected by 50 ground sensors in this area, and the GIS vector data of the cultivated land red line boundary in this province, to construct an initial model of the digital twin. Through Kalman filtering for dynamic calibration, the state vector contains four parameters: the area of a certain permanent basic farmland, the coordinates of the center point, soil moisture content, and crop coverage. The state transition matrix is set as a diagonal matrix according to the cultivated land change law, and the control input is 25% of the soil moisture standard value of the permanent basic farmland in this area in the dynamic ontology library. Combining the observation data of UAV images and ground sensors (soil moisture content of 23%), the state vector is corrected through the observation update formula, and it is calculated that the soil moisture content parameter in it is updated to 23.5%, ensuring that the parameters of this cultivated land in the digital twin are highly consistent with the real scenario. When the dynamic ontology library updates the attribute that the carbon sequestration coefficient of dry land in the north is lower than that of paddy fields in the south, the digital twin automatically adjusts the carbon sequestration simulation parameters of dry land cultivated land in the northern part of this province, from the original 2 tons / acre·year to 1.2 tons / acre·year, further improving the virtual-real consistency.

[0030] The public document only performs static calibration through remote sensing data, without combining policy rules and ecological parameters to adjust simulation parameters, resulting in a large dynamic deviation between the digital twin and the real scenario, and unable to support accurate decision-making. The unique technical means of this step is to combine Kalman filtering with the policy rules and ecological parameters of the dynamic ontology library to achieve the联动校准 of virtual and real data and rule constraints, rather than simply relying on remote sensing data for static correction. The beneficial effect is that the deviation between the digital twin and the real scenario is significantly reduced, the accuracy of scenario simulation is greatly improved, it can reflect the dynamic changes of ground objects and policy constraint requirements in real time, and provide high-quality scenario support for subsequent early warning and evaluation links.

[0031] 3. Sub-step S13: Establishment of a temporal semantic dual alignment mechanism: The policy rules of the dynamic ontology library have effective time nodes, while the monitoring data of the digital twin has timestamp characteristics. Existing technologies only achieve alignment in a single dimension, failing to simultaneously address the issues of inconsistent timestamps and heterogeneous data formats. This results in ineffective collaboration between the knowledge graph rules and the twin's monitoring data. Establishing a temporal semantic dual alignment mechanism ensures consistency between the two at both temporal and semantic levels, enabling efficient interaction between data and rules. Specific technical means are as follows: Timestamp synchronization: A sliding window algorithm is used to set the time window size, aligning the effective time of policies and rules in the dynamic ontology with the collection time of monitoring data from the digital twin. The time window size is set according to the collection frequency of the monitoring data; for example, if drones collect data once a day, the time window is set to 24 hours, ensuring accurate matching between the policy effective time and the monitoring data for the corresponding time period.

[0032] Semantic mapping transformation: Designing embedding mapping functions This process transforms the real-time monitoring data feature vectors of the digital twin into semantic feature vectors recognizable by the dynamic ontology library. The semantic alignment loss function is: ; In the formula, This represents the semantic alignment loss value. For the first in the dynamic ontology library Feature vectors of semantic labels For the first digital twin The feature vector of each monitoring data point To embed the mapping function, an improved BERT model is used. The dimension of the feature vector. Calculate the square of the L2 norm.

[0033] Alignment result verification: Calculate the semantic alignment loss value. If the loss value is greater than the preset threshold, adjust the parameters of the embedding mapping function and remap. The preset threshold is determined according to the semantic alignment accuracy requirements and is set to 0.05. Specific techniques for adjusting the parameters of the embedding mapping function include: adjusting the number of attention heads in the BERT model (range 8-16) to strengthen the semantic mapping of key features; optimizing the embedding layer weight matrix and minimizing the semantic alignment loss function through the stochastic gradient descent (SGD) algorithm; adjusting the dropout rate (range 0.1-0.3) to avoid mapping deviation caused by model overfitting, until the loss value meets the requirements to ensure the accuracy of semantic mapping.

[0034] Example: In the management of farmland protection red lines in a certain province, a new policy prohibiting the non-agricultural use of farmland, effective March 1, 20XX, was added to the dynamic ontology. A digital twin collected drone monitoring data from March 1st to March 2nd of that year. A sliding window algorithm was used to set a 24-hour time window, aligning the policy's effective date with the monitoring data from March 1st. The feature vector of the monitoring data for a specific plot of farmland in the digital twin is shown. Including parameters such as area change of 0.5 mu, location coordinates (118.5°E, 32.1°N), and change in land cover type (from cultivated land to illegal construction), the data is processed through an embedded mapping function. Convert it into a semantic feature vector that can be recognized by the dynamic ontology library. The semantic tags correspond to cultivated land, permanent basic farmland, area change, and illegal occupation. The semantic alignment loss value is calculated. If the value is less than the preset threshold of 0.05, the alignment is confirmed to be valid; if a set of data is calculated to be... If the attention parameter of the mapping function is adjusted, the loss value is reduced to 0.04 after the transformation, ensuring that the policy rules and monitoring data are accurately matched at the semantic level.

[0035] Publicly available documents only achieve alignment along the time dimension, failing to address the issue of semantic format heterogeneity, resulting in ineffective interaction between rules and data. The unique technical approach of this step lies in simultaneously achieving timestamp synchronization and semantic mapping transformation. A dual alignment mechanism is constructed through a sliding window algorithm and embedded mapping functions, rather than single-dimensional alignment. The beneficial effects include significantly improved collaboration efficiency between the dynamic ontology library and the digital twin, significantly enhanced accuracy of data interaction, and avoidance of decision-making errors caused by time or semantic discrepancies.

[0036] 4. Sub-step S14: Construction of Collaborative Reasoning Model: Existing reasoning models only use the rules of the knowledge graph to guide the simulation of the digital twin in a one-way manner, or only update the knowledge graph through the twin's data. This fails to achieve bidirectional driven optimization by rules and data, resulting in a lack of dynamic adaptability in the reasoning results. Constructing a collaborative reasoning model can achieve bidirectional linkage between rule-guided calibration and data feedback iteration, improving the accuracy of the reasoning results. Specific technical means are as follows: Inference Model Architecture: A bidirectional inference model is constructed based on a graph attention network, consisting of a rule guidance layer and a data feedback layer. The rule guidance layer receives policy rules and semantic association weights from a dynamic ontology library and generates twin parameter calibration instructions; the data feedback layer receives real-time monitoring data and treatment effect data from the digital twin and generates ontology library rule optimization instructions.

[0037] Two-way reasoning process: The rule guidance layer calibrates the simulation parameters of the twin based on the feature-rule association weights in the ontology. For example, based on the protection rules for permanent basic farmland, it calibrates the simulated values ​​of ecological parameters of this type of farmland in the twin. The data feedback layer adjusts the association weights of the corresponding features in the ontology based on the illegal occupation monitoring data of the twin. For example, if illegal construction occupies farmland frequently in a certain area, it increases the association weight between illegal construction and strict disposal rules.

[0038] Inference output: The model outputs calibrated twin parameters and optimized ontology rules, achieving dynamic collaboration between the two.

[0039] Example: In the protection and management of farmland in the suburbs of a city in a certain province, the rule guidance layer of the collaborative reasoning model receives rules and associated weights from a dynamic ontology library regarding the prohibition of illegal construction on permanent basic farmland in the suburbs of the city. The system generates calibration instructions for illegal construction identification parameters in the twin, adjusting the spectral threshold for illegal construction identification from 0.6 to 0.5 to improve the sensitivity of illegal construction identification. The data feedback layer receives data from the twin monitoring three small illegal constructions occupying farmland in the area, automatically adjusting the association weights between permanent basic farmland in the urban suburbs and illegal construction disposal rules in the ontology database, and adjusting the original... The accuracy was increased to 0.95 to ensure that subsequent inference prioritizes matching the disposal rules for demolition within a specified period. Through bidirectional inference, dynamic optimization of rules and data is achieved, simultaneously improving the accuracy of illegal construction identification by the twin and the rule adaptability of the ontology.

[0040] Publicly available documents rely solely on knowledge graphs for unidirectional reasoning, failing to leverage actual monitoring data to optimize rules and resulting in poor adaptability of the inference results. The unique approach of this step lies in constructing a bidirectional collaborative reasoning model that integrates rule guidance and data feedback. This model utilizes graph attention networks to achieve dynamic interaction and optimization between rules and data, rather than relying on unidirectional reasoning. The beneficial effects include significantly improved accuracy of the inference results and a markedly enhanced adaptability of the model to complex control scenarios, enabling it to dynamically adapt to the control needs of different regions.

[0041] This step overcomes the bottleneck of static knowledge being disconnected from real-time data in traditional technologies through the two-way collaboration between a dynamic ontology library and a digital twin, achieving real-time adaptation of policies and rules to virtual and real scenarios. Compared with existing technologies, the update efficiency of the knowledge graph is significantly improved, the simulation accuracy of the digital twin is significantly enhanced, and the collaborative interaction efficiency of the two is significantly strengthened. This provides unified, high-quality data and rule support for subsequent early warning, assessment, and response stages, greatly improving the overall accuracy and policy compliance of land dynamic monitoring.

[0042] II. Step S2: Based on the land feature data output by the two-way collaborative system, proactive early warning of abnormal land changes is achieved through dynamic threshold learning. Existing land anomaly early warning systems mostly use fixed thresholds, failing to consider the differences in the importance of arable land in different regions and the gradual nature of illegal changes. This leads to underreporting of small-scale illegal occupation of arable land in urban suburbs and delayed early warning of large-scale illegal occupation in remote mountainous areas. Furthermore, it cannot identify gradual, piecemeal encroachment. Through dynamic threshold learning, the early warning threshold can be dynamically adjusted based on regional attributes, land feature importance, and historical data, achieving accurate identification and tiered early warning of abnormal changes, solving the problem of the one-size-fits-all approach of traditional fixed thresholds. Specific technical means are as follows: This step includes three sub-steps: dynamic threshold model training, early warning threshold generation, and construction of a three-dimensional tiered indicator system. Based on the land feature data output by the two-way collaborative system, accurate early warning of abnormal changes is achieved.

[0043] 1. Sub-step S21: Dynamic Threshold Model Training: Existing threshold models are mostly trained based on single datasets, without incorporating regional attributes and policy constraints, resulting in poor model adaptability and an inability to meet the control needs of different regions. By training a dynamic threshold model through reinforcement learning, regional attributes and the importance of land features can be used as constraints, improving the model's adaptability to different scenarios. Specific technical methods are as follows: Training sample construction: The ground feature data output by the two-way collaborative system is used as the training samples. The samples include four types of features: regional attributes, ground feature type, historical change area, and historical treatment effect. For example, the sample features are urban suburbs, permanent basic farmland, 0.5 mu, and demolition within a time limit, and the corresponding label is red alert.

[0044] Reinforcement learning model architecture: A dynamic threshold model is constructed using an improved Q-learning algorithm. The model includes a state layer, an action layer, and a reward layer. The state layer represents the sample feature vector, the action layer represents the adjustment amount of the warning threshold, and the reward layer represents the evaluation index of the warning effect.

[0045] Reward function design: A reward function is constructed using the accuracy rate, false negative rate, and false positive rate of identifying land anomalies as the core indicators. In the formula, As a reward value, , , These are the weighting coefficients. To improve recognition accuracy, The false negative rate, This represents the false alarm rate.

[0046] Model training process: Initialize model parameters, input training samples, adjust the warning threshold through the action layer, calculate the reward value, update the model parameters based on the reward value, iterate training until the reward value converges, and obtain a stable dynamic threshold model.

[0047] Example: Taking a training sample of farmland protection red line management in a certain province as an example, the sample covers two types of regional attributes: urban suburbs and remote mountainous areas; two types of land cover: permanent basic farmland and general farmland; historical change area ranging from 0.1 mu to 5 mu; and historical treatment effects including demolition within a time limit and rectification and restoration. Initialize the dynamic threshold model parameters and set... , , Given sample data of permanent basic farmland in the suburbs of the city, the action layer outputs a warning threshold adjustment amount of -0.2 (the adjustment amount is defined as the threshold offset, and a negative value indicates a reduction in the threshold), adjusting the initial threshold of 0.8 mu to 0.6 mu. The calculation logic of the adjustment amount is as follows: the adjustment direction is determined based on the gradient descent direction of the reward function, the adjustment magnitude is determined based on the deviation between the sample and the optimal threshold, ensuring that the threshold iteration converges to the optimal solution, and the reward value is calculated. When the reward value is positive, the model parameters are updated. After multiple rounds of iterative training, the model converges when the reward value stabilizes above 0.8, revealing dynamic threshold adjustment rules for different regions and land cover types: the threshold for permanent basic farmland in urban suburbs is 0.5-0.6 mu, and the threshold for general cultivated land in remote mountainous areas is 4-5 mu.

[0048] Publicly available documents train static threshold models based solely on single datasets, failing to incorporate regional attributes and policy constraints, resulting in poor model adaptability. This approach uniquely incorporates regional attributes and the importance of land features as constraints, designing a reward function that includes recognition accuracy, false negative rate, and false positive rate. A dynamic threshold model is trained through reinforcement learning, rather than a static model driven by a single dataset. The beneficial effects include significantly improved model adaptability to different regions and land feature types, allowing for adjustments to warning thresholds that better align with actual control needs, thus laying the foundation for accurate early warning.

[0049] 2. Sub-step S22: Early Warning Threshold Generation: Existing early warning thresholds are fixed values ​​and cannot be dynamically adjusted based on regional attributes and the importance of ground features, resulting in low early warning accuracy. Based on a trained dynamic threshold model and combined with real-time data from a two-way collaborative system, targeted early warning thresholds are generated, enabling differentiated early warnings for different scenarios. Specific technical methods are as follows: Real-time data input: Input real-time ground feature data output by the two-way collaborative system, including current area attributes, ground feature type, real-time change area, etc.

[0050] Dynamic threshold calculation: Using a trained dynamic threshold model, the warning threshold for the current scene is output. The threshold update formula is: In the formula, The updated warning threshold, The current warning threshold is... For learning rate, Let be the gradient of the threshold loss function, where the loss function is the deviation between the predicted threshold and the actual optimal threshold.

[0051] Threshold verification and adjustment: The generated warning threshold is applied to the real-time monitoring data. If the missed detection rate or false alarm rate exceeds the preset value, the learning rate is adjusted and recalculated until the requirements are met.

[0052] Example: In the management of permanent basic farmland in the suburbs of a city in a certain province, the input is real-time data from a two-way collaborative system. The region attribute is suburban, the land cover type is permanent basic farmland, and the real-time changing area is 0.5 mu. The current warning threshold is calculated using a dynamic threshold model. For 0.6 acres, the gradient of the loss function The learning rate is -0.1. The value is 0.05. Substituting this value into the formula yields the updated warning threshold. The threshold value is 0.595 mu (approximately 0.067 hectares). Applying this threshold to real-time data for early warning achieves a 95% accuracy rate, meeting the preset requirements. Therefore, the early warning threshold for this scenario is determined to be 0.595 mu. In the management of general cultivated land in remote mountainous areas, after inputting real-time data, the calculated early warning threshold is 4.8 mu. After application, the false negative rate is less than 5%, confirming the effectiveness of this threshold and enabling differentiated threshold settings for different scenarios.

[0053] Existing technologies use fixed thresholds, which cannot be dynamically adjusted according to the scenario. The unique approach of this method lies in using a model trained through reinforcement learning to dynamically calculate the warning threshold based on real-time ground cover data. Continuous optimization is achieved through a threshold update formula, rather than a fixed threshold setting. The beneficial effects are a significantly improved adaptability of the warning threshold to the actual scenario, and a substantial increase in the accuracy of warnings for different regions and ground cover types, providing an accurate basis for tiered warnings.

[0054] 3. Sub-step S23: Construction of a three-dimensional hierarchical indicator system and determination of early warning levels: Existing early warning hierarchies are based solely on the single indicator of changed area, failing to consider policy compliance and ecological sensitivity, leading to inaccurate hierarchies. Minor violations in important areas are overlooked, while over-response occurs for violations in general areas. Constructing a three-dimensional hierarchical indicator system can achieve precise classification of early warning levels, providing a basis for differentiated handling. Specific technical means are as follows: Three-dimensional indicator selection: A grading system is constructed using three indicators: magnitude of change, policy compliance, and ecological sensitivity. The magnitude of change is the ratio of the actual area of ​​change to the warning threshold; policy compliance is the severity of the violation of policy rules; and ecological sensitivity is the ecological importance level of the area where the violation occurred.

[0055] Indicator weight determination: The weights of the three indicators were determined using the analytic hierarchy process (AHP). The weight of the magnitude of change was 0.4, the weight of policy compliance was 0.3, and the weight of ecological sensitivity was 0.3.

[0056] Warning Level Calculation: A weighted score is calculated for each of the three-dimensional indicators, ranging from 0 to 100 points. Warning levels are determined based on the score: 90-100 points = Red Warning, 70-89 points = Orange Warning, and 0-69 points = Blue Warning. The weighted score formula is as follows: In the formula, For weighted scores, , , The weights for these factors are the magnitude of change, policy compliance, and ecological sensitivity, respectively. , , The scores are for the three indicators.

[0057] Warning Triggering and Visualization: The warning level is determined based on the weighted score, and the visual markers of the digital twin are triggered simultaneously. The red warning marker flashes red, the orange warning marker is orange, and the blue warning marker is blue.

[0058] Example: In the management of permanent basic farmland in the suburbs of a city in a certain province, an illegal construction occupies 0.5 mu of cultivated land. The warning threshold is 0.595 mu. The score for the change range is... Score: This behavior violates the regulations on the protection of permanent basic farmland; policy compliance score. The area is close to a water source, and its ecological sensitivity score is [score missing]. Substitute the values ​​into the formula to calculate the weighted score. A score corresponding to an orange alert is displayed in the digital twin, indicating the area is marked orange. In the management of general farmland in remote mountainous areas, if someone illegally occupies 3 mu (approximately 0.2 hectares), the alert threshold is 4.8 mu (approximately 0.22 hectares), and the score reflects this change. Score, policy compliance score Score, Ecological Sensitivity Score Score, weighted score The score corresponds to a blue alert and is marked in blue.

[0059] Existing technologies rely solely on single-indicator grading. This approach uniquely constructs a three-dimensional grading indicator system encompassing the magnitude of change, policy compliance, and ecological sensitivity. Weights are determined using the analytic hierarchy process (AHP), enabling precise classification of warning levels rather than simple grading based on a single indicator. The significant benefit is a substantial improvement in the accuracy of warning grading, allowing for accurate differentiation of the severity of different violations, providing a scientific basis for differentiated handling, and preventing resource misallocation.

[0060] This step, through dynamic threshold learning and a three-dimensional hierarchical indicator system, overcomes the limitations of traditional fixed thresholds and single-indicator grading, achieving accurate early warning of abnormal land changes. Compared with existing technologies, the false alarm rate of abnormal changes is significantly reduced, the false alarm rate is greatly decreased, the accuracy of early warning grading is significantly improved, and the ability to identify ant-like encroachment in urban suburbs is significantly enhanced. This provides accurate early warning information for subsequent assessment and disposal, and greatly improves the timeliness of response to abnormal land changes.

[0061] III. Step S3: Based on the early warning results and scenario data from the two-way collaborative system, complete the quantitative assessment of the ecological value of land change through dynamic parameter adaptation: Existing land change ecological value assessments mostly use fixed parameters, failing to consider the ecological differences and dynamic changes in land features across different regions, resulting in large deviations in assessment results and an inability to accurately reflect the ecological losses caused by changes in cultivated land in different regions. Through dynamic parameter adaptation, a high degree of matching between assessment parameters and the actual situation in the region can be achieved, improving the accuracy of ecological value assessment and providing a quantitative basis for ecological compensation and liability determination. The specific technical means are as follows: This step includes three sub-steps: construction of a regional adaptive parameter library, quantitative calculation of ecological value, and feedback of assessment results. Based on the early warning results and scenario data, an accurate assessment of ecological value is achieved.

[0062] 1. Sub-step S31: Construction of a Regional Adaptive Parameter Library: The existing assessment parameter library uses uniform, fixed values ​​and does not differentiate based on regional climate, topography, and other characteristics. This results in the same carbon sink coefficient for paddy fields in the south and dry land in the north, leading to inconsistencies between the assessment results and reality. Constructing a regional adaptive parameter library can achieve regional differentiation and dynamic updating of assessment parameters, improving the accuracy of the assessment. Specific technical methods are as follows: Regional division: Based on GIS zoning data, the controlled area is divided into different sub-regions according to climate zone and terrain type, such as the southern humid plain and the northern arid mountainous area.

[0063] Parameter selection and determination: Three core assessment parameters were selected: carbon sequestration coefficient, soil and water conservation parameters, and grain production capacity parameters. Dynamic ecological parameters extracted through remote sensing and historical observation data were used to determine the parameter values ​​for each sub-region. For example, the carbon sequestration coefficient of cultivated land in the humid plains of southern China is higher than that in the arid mountainous regions of northern China.

[0064] Dynamic parameter updates: Receives real-time ecological parameter data from the digital twin in the two-way collaborative system, and regularly updates the values ​​in the parameter library to ensure that the parameters are consistent with the actual ecological conditions of the region.

[0065] Example: Taking the farmland protection red line control area of ​​a certain province as an example, it is divided into four sub-regions according to climate zone (subtropical and temperate) and topography (plain and mountainous). Using vegetation cover and soil moisture data extracted through remote sensing, the carbon sequestration coefficient of farmland in the subtropical plain is determined to be 2 tons per mu per year, and in the temperate mountainous area, it is 1.2 tons per mu per year. The soil and water conservation parameters for the subtropical plain are 300 cubic meters per mu per year, and for the temperate mountainous area, they are 500 cubic meters per mu per year. The grain production capacity parameters for the subtropical plain are 1000 jin per mu per year, and for the temperate mountainous area, they are 800 jin per mu per year. When the digital twin detects that the vegetation cover of a certain plot of farmland in the subtropical plain decreases from 90% to 80%, the carbon sequestration coefficient of that area is automatically updated from 2 tons / mu / year to 1.8 tons / mu / year, ensuring the dynamic accuracy of the parameters.

[0066] Publicly available documents use uniform and fixed assessment parameters, failing to consider regional differences, resulting in significant biases in the assessment results. The unique technical approach of this step lies in dividing regions according to climate zones and topographic types, and constructing a regional adaptive parameter library by combining remote sensing dynamic ecological parameters. This achieves regional differentiation and dynamic updating of parameters, rather than using uniform and fixed parameters. The beneficial effect is a high degree of matching between the assessment parameters and the actual regional conditions, significantly improving the accuracy of the assessment results and greatly enhancing regional adaptability.

[0067] 2. Sub-step S32: Quantitative Calculation of Ecological Value: Existing ecological value assessments often focus on single values, failing to achieve comprehensive evaluation of carbon sequestration, food production capacity, and soil and water conservation. This results in biased assessments that cannot support comprehensive decision-making. Comprehensive quantitative calculation can fully reflect the ecological value gains and losses of land changes, providing a complete quantitative basis for decision-making. Specific technical methods are as follows: Value weight determination: The weights of the three ecological values ​​of carbon sink, food production capacity and soil and water conservation were determined by the analytic hierarchy process. The weight of carbon sink was 0.4, the weight of food production capacity was 0.3, and the weight of soil and water conservation was 0.3.

[0068] Single value quantification: The market value method is used to calculate the value of carbon sinks and the value of grain production capacity. The value of carbon sinks is the amount of carbon sinks multiplied by the carbon trading price, and the value of grain production capacity is the amount of grain produced multiplied by the market price of grain. The substitution cost method is used to calculate the value of soil and water conservation, which is the amount of soil and water conservation multiplied by the construction cost of water conservancy projects.

[0069] Comprehensive value calculation: The comprehensive ecological value gain or loss is calculated by weighted summation, using the following formula: ; In the formula, To comprehensively consider the ecological value gains and losses, , , The weights for carbon sequestration, food production capacity, and soil and water conservation are respectively. , , These are the quantitative values ​​for three categories of ecological value.

[0070] Example: In the management of permanent basic farmland in the suburbs of a city in a certain province, an illegal construction occupies 1 mu (approximately 0.16 acres) of arable land, and the warning level is orange. According to the regional adaptive parameter library, this area belongs to the subtropical plain region, with an arable land carbon sequestration coefficient of 1.8 tons per mu per year, a carbon trading price of 60 yuan per ton, and a carbon sequestration value. Yuan; Grain production capacity is 1000 jin per mu per year, grain market price is 1.5 yuan per jin, and the value of grain production capacity is... Yuan; the soil and water conservation volume is 300 cubic meters per mu per year, the construction cost of water conservancy projects is 2 yuan per cubic meter, and the value of soil and water conservation... Yuan. Substitute into the formula to calculate the comprehensive ecological value gain or loss. This means that the violation results in an ecological value loss of 663.2 yuan per year.

[0071] Existing technologies only assess a single ecological value. The unique approach of this method lies in using the analytic hierarchy process (AHP) to determine the weights of multiple ecological values, combining market valuation and substitution cost methods to achieve comprehensive quantitative calculations, rather than a single value assessment. The beneficial effects are more comprehensive and accurate assessment results, fully reflecting the ecological value gains and losses of land changes, providing a scientific quantitative basis for ecological compensation and liability determination, and avoiding the problem of emphasizing protection while neglecting accounting.

[0072] 3. Sub-step S33: Evaluation Result Feedback: Current evaluation results are only used for individual calculations and are not fed back to the knowledge graph and digital twin, thus failing to support subsequent contingency plan generation and rule optimization. Feeding the evaluation results back to the two-way collaborative system can achieve coordinated linkage between evaluation, early warning, and response, forming a closed-loop management system. Specific technical means are as follows: Result format conversion: Convert the comprehensive ecological value gains and losses results into a data format that can be recognized by the two-way collaborative system, including information such as the assessment object, assessment value, and assessment time.

[0073] Real-time push feedback: The assessment results are synchronously pushed to the dynamic ontology library and digital twin through the data interface. The dynamic ontology library records the matching of the assessment results with policy rules, and the digital twin visualizes the distribution of ecological value loss.

[0074] Application of feedback results: If the evaluation results exceed the ecological red line threshold set by the dynamic ontology library, the digital twin contingency plan generation process will be automatically triggered.

[0075] Example: In the management and control of cultivated land in a certain province, the comprehensive ecological value loss of illegally occupied cultivated land is 1,000 yuan per year, while the ecological red line threshold set by the dynamic ontology is 800 yuan. The assessment result exceeds the threshold. After the assessment result is converted into a standard format, it is pushed to the dynamic ontology and digital twin. The dynamic ontology records that the assessment result violates the ecological protection rules and upgrades the policy compliance level of the violation by one level. The digital twin visualizes the ecological loss amount of 1,000 yuan / year in the area and automatically triggers the scenario-based contingency plan generation process to initiate the violation handling procedure.

[0076] Existing technology assessment results are disconnected from other stages. The unique technical approach of this step lies in feeding the assessment results back to a dynamic ontology library and digital twin in real time, enabling coordinated linkage between assessment, early warning, and response, rather than isolated calculations. The beneficial effect is a significant increase in the application value of the assessment results, providing precise value basis for subsequent plan generation and rule optimization, and promoting closed-loop linkage of control processes.

[0077] This step, through a regional adaptive parameter library and comprehensive quantitative assessment, overcomes the limitations of traditional fixed parameters and single-value assessments, achieving accurate evaluation of the ecological value of land change. Compared with existing technologies, the assessment error is significantly reduced, regional adaptability is greatly improved, and the assessment results are more comprehensive and accurate. This provides a scientific quantitative basis for ecological compensation and liability determination, and through result feedback, it enables coordinated linkage with early warning and response processes, promoting the shift of land management from qualitative to quantitative approaches.

[0078] IV. Step S4: Based on the assessment results and policy rules of the two-way collaborative system, generate and implement scenario-based emergency response plans: Existing land violation response plans are mostly uniform templates, failing to consider regional differences, warning levels, and ecological value losses, resulting in poor practicality. Violation response plans for urban suburbs are not applicable to remote mountainous areas, and the same measures are used for minor and serious violations, leading to high implementation difficulty and poor results. Generating scenario-based emergency response plans can achieve precise matching of response measures with actual scenarios, improve the success rate of plan implementation, and reduce response costs. Specific technical means are as follows: This step includes three sub-steps: scenario-based plan generation, plan simulation optimization, and plan execution and data feedback. Based on the assessment results and policy rules, it achieves accurate generation and efficient execution of emergency response plans.

[0079] 1. Sub-step S41: Scenario-based contingency plan generation: Existing contingency plans are generated only based on a single violation type, without considering regional attributes, warning levels, and ecological value loss, resulting in poor plan targeting. Generating contingency plans by combining multi-dimensional scenario information can ensure that the response measures are highly matched with the actual scenario, improving the practicality of the contingency plans. Specific technical means are as follows: Scenarios are categorized into three dimensions: regional type, warning level, and ecological value loss. Regional types include urban suburbs and remote mountainous areas; warning levels include red, orange, and blue; and ecological value loss includes high, medium, and low levels.

[0080] Contingency Plan Rule Base Construction: Based on the regulations on handling land violations and the policy rules of the dynamic ontology database, a scenario-contingency plan mapping rule base is constructed. For example, a red alert for high ecological damage in the suburbs of the city corresponds to demolition within a time limit + revegetation + fines, while an orange alert for ecological damage in remote mountainous areas corresponds to rectification + ecological restoration + accountability talks.

[0081] Contingency plan generation process: Input the region type, warning level, and ecological value loss information, query the contingency plan rule base, and generate a targeted scenario-based contingency plan, which includes disposal measures, execution time limits, responsible parties, etc.

[0082] Example: In the protection and management of farmland in the suburbs of a city in a certain province, an illegal construction occupies permanent basic farmland, with a red warning level and a high ecological value loss (1200 yuan per year). Inputting three scenario dimensions, the system queries the contingency plan rule base to generate a scenario-based contingency plan: demolish the illegal construction within 7 days, complete revegetation within 15 days, plant local dominant crop wheat, and impose a fine of 50,000 yuan per mu. The responsible entity is the local natural resources department, and the supervising entity is the municipal natural resources bureau. In the management of general farmland in remote mountainous areas, an illegal occupation of farmland has an orange warning level and a medium ecological value loss (800 yuan per year). The generated contingency plan: rectify the illegal facilities within 15 days, complete ecological restoration within 30 days, plant native cypress, conduct accountability talks with the responsible party, and the responsible entity is the local township government, with the supervising entity being the county-level natural resources bureau.

[0083] Publicly available documents failed to consider ecological value loss and regional differences when generating contingency plans, resulting in poor targeting. The unique technical approach of this step lies in classifying contingency plans into three scenario dimensions: regional type, warning level, and ecological value loss. This allows for the construction of a scenario-plan mapping rule base, generating targeted, scenario-based contingency plans rather than uniform templates. The beneficial effect is a significantly improved match between the contingency plans and actual scenarios, greatly enhancing their practicality and providing a scientific basis for efficient response.

[0084] 2. Sub-step S42: Contingency Plan Simulation Optimization: Currently, contingency plans are executed directly after generation without prior simulation of their effects. This results in some plans failing to execute effectively due to limitations such as terrain and resources, leading to high handling costs. By simulating the execution effects of contingency plans through multi-agent simulation, the parameters can be optimized, improving the feasibility and cost-effectiveness of the plans. Specific technical methods are as follows: Multi-agent model construction: Construct three types of agents: disposal subject, execution object, and supervision subject. The disposal subject is the natural resources authority, the execution object is the person responsible for the violation, and the supervision subject is the discipline inspection and supervision department.

[0085] Simulation parameter settings: Input the scenario data of the digital twin, including terrain conditions, traffic conditions, resource distribution, etc., and set the simulation time step. For example, one day is one time step.

[0086] Simulation process and optimization: Run the multi-agent simulation model to simulate the execution process of the plan, and output the evaluation indicators of the execution effect, including completion rate, cost, ecological restoration cycle, etc. If the execution effect does not meet the expectation, adjust the plan parameters. For example, extend the execution time limit, adjust the type of vegetation for revegetation, and resimulate until the effect meets the standard.

[0087] Example: In the control of cultivated land protection in a remote mountainous area of a certain province, the generated plan is to rectify illegal facilities within 15 days and complete ecological restoration within 30 days, and plant Platycladus orientalis. Through the simulation execution of the multi-agent simulation model, input the scenario data of the digital twin: This area is mountainous, with inconvenient transportation and large equipment cannot enter, and the supply distance of Platycladus orientalis seedlings is far. The simulation results show that the rectification completion rate is only 60%, the ecological restoration cycle is 6 months, and the cost exceeds the budget by 30%. Adjust the plan parameters, extend the rectification time limit to 20 days, use small portable equipment for construction, and change to local and easily surviving wild jujube trees. Resimulate, the rectification completion rate is increased to 90%, the ecological restoration cycle is shortened to 4 months, the cost is controlled within the budget, and the execution effect meets the standard.

[0088] The public document did not optimize the plan through simulation and directly executed it, resulting in poor effects. The unique technical means of this step is to construct a multi-agent simulation model, combine the scenario data of the digital twin to simulate the execution effect of the plan, optimize the plan parameters, rather than directly execute the original plan. The beneficial effects are that the feasibility and economy of the plan are greatly improved, the execution success rate is significantly increased, and the disposal cost is significantly reduced.

[0089] 3. Sub-step S43: Plan execution and data feedback: After the existing plan is executed, the effect data is not timely feedback, which cannot provide a basis for subsequent rule optimization, resulting in the recurrence of the same type of violations. Feeding back the execution effect data to the two-way collaborative system can realize the linkage between plan execution and rule optimization, and improve the long-term effectiveness of control. The specific technical means are as follows: Execution data collection: Collect key data during the execution of the plan, including completion rate, execution cost, ecological restoration effect, recurrence rate of violations, etc.

[0090] Data feedback push: Real-time feedback the execution data to the digital twin and the dynamic ontology library through the data interface. The digital twin updates the scenario state, and the dynamic ontology library records the matching situation between the execution effect and the rules.

[0091] Application of feedback data: If the execution data shows a high recurrence rate of violations, the dynamic ontology library automatically triggers the rule optimization process to adjust the warning threshold or disposal rules.

[0092] Example: In the protection and management of farmland in the suburbs of a city in a certain province, after implementing the plan of demolition within a time limit + revegetation + fines, the following execution data was collected: demolition completion rate 100%, execution cost 30,000 yuan per mu, vegetation coverage restored to 90% after revegetation, and recurrence rate of violations 5%. The data was fed back to a digital twin and a dynamic ontology library. The digital twin updated the scene status of the area to "revegetation completed," and the dynamic ontology library recorded that the plan was effective in handling the red alert in the suburbs of the city, requiring no rule adjustments. If, after implementing the plan in another remote mountainous area, the recurrence rate of violations reached 20%, the dynamic ontology library automatically triggered rule optimization, lowering the warning threshold for that area from 4.8 mu to 4 mu, and simultaneously changing the accountability interview in the handling measures to administrative penalties, strengthening the control efforts.

[0093] The existing technical plan implementation is disconnected from rule optimization. The unique technical approach of this step lies in feeding back plan implementation data to a two-way collaborative system in real time, achieving linkage between implementation effectiveness and rule optimization, rather than implementing the plan in isolation. The beneficial effect is a significant improvement in the long-term effectiveness of plan implementation, enabling timely identification and resolution of problems during handling, and promoting continuous optimization of management rules.

[0094] This step, through scenario-based contingency plan generation and simulation optimization, overcomes the limitations of traditional unified template contingency plans and direct execution, achieving accurate generation and efficient execution of contingency plans. Compared with existing technologies, the practicality and feasibility of the contingency plans are significantly improved, the success rate of execution is significantly increased, and the disposal cost is significantly reduced. At the same time, through data feedback, it achieves linkage with rule optimization, promoting the transformation of land violation disposal from passive response to proactive prevention.

[0095] V. Step S5: Full-Process Closed-Loop Optimization: The existing land dynamic monitoring process is executed linearly, with data from each stage not forming a closed-loop feedback mechanism. This prevents continuous optimization of technical parameters and rules based on actual control effects, resulting in poor adaptability and insufficient long-term stability of the control system. Constructing a full-process closed-loop optimization mechanism can achieve cyclical feedback of data from monitoring, early warning, assessment, and response stages, continuously optimizing thresholds, parameters, and rules, and improving the long-term stability and adaptability of the control system. Specific technical means are as follows: Closed-loop feedback link construction: Construct a closed-loop feedback link of monitoring data - early warning threshold - evaluation parameters - handling rules, with data from each link feeding back to the preceding link in sequence, driving parameter and rule optimization.

[0096] Optimize trigger condition settings: Set optimization trigger conditions. When the early warning false alarm rate, assessment error, contingency plan execution failure rate exceeds the preset threshold, or the violation recurrence rate is higher than the set value, the corresponding optimization process will be triggered.

[0097] Multi-stage collaborative optimization: the early warning threshold is optimized based on monitoring data and the recurrence rate of violations; the assessment parameters are optimized based on the ecological value data observed in the field; the disposal rules are optimized based on the implementation effect of the plan; all optimized parameters and rules are synchronized to the two-way collaborative system to achieve full-process optimization.

[0098] Example: In the management of farmland protection red lines in a certain province, monitoring data for three consecutive months showed that the underreporting rate of violations on general farmland in a remote mountainous area exceeded 10%, triggering the early warning threshold optimization process. Based on the monitoring data of the area (an average monthly change in the area of ​​violations of 3.5 mu) and the recurrence rate of violations (18%), the early warning threshold was lowered from 4 mu to 3.5 mu, the dynamic threshold model parameters were updated, and after retraining, the model's underreporting rate dropped to below 5%. Simultaneously, field observations revealed that the actual carbon sink coefficient of the area was 1.1 tons / mu·year, lower than the 1.2 tons / mu·year in the assessment parameter library, triggering assessment parameter optimization. The carbon sink coefficient of the area was adjusted to 1.1 tons / mu·year, reducing the assessment error from 15% to 8%. If the failure rate of a scenario-based contingency plan exceeds 15%, the disposal rule optimization is triggered, extending the disposal time limit for the scenario from 15 days to 20 days, and adjusting the responsible entity to the county-level natural resources bureau. After optimization, the failure rate was reduced to 8%, ensuring the effectiveness of subsequent implementation. All optimized thresholds, parameters, and rules are synchronized to the dynamic ontology library and digital twin, achieving full-process collaborative optimization.

[0099] Publicly available documents only optimize single aspects, failing to create a complete closed-loop process and exhibiting poor adaptability. The unique technical approach of this step lies in constructing a closed-loop feedback chain encompassing monitoring, early warning, assessment, and response. It sets optimization trigger conditions for multiple stages, achieving coordinated optimization of thresholds, parameters, and rules, rather than optimizing a single stage. The beneficial effects include a significantly improved adaptability and enhanced long-term stability of the control system, enabling it to dynamically adapt to the control needs of different regions and periods, and promoting the continuous upgrading of the dynamic land monitoring and governance model.

[0100] This step, through closed-loop optimization of the entire process, breaks through the limitations of traditional linear control processes, achieving dynamic and collaborative optimization of parameters and rules at each stage. Compared with existing technologies, the adaptive capability of the control system is significantly improved, the rate of false alarms, assessment errors, and failure rate of contingency plan execution are significantly reduced, and the recurrence rate of violations is significantly decreased. This promotes the transformation of land dynamic monitoring from passive response to proactive prevention, and from static control to dynamic adaptation, providing long-term and stable technical support for the management of arable land protection red lines.

[0101] In summary, this technical solution constructs a complete technical system for the dynamic management and control of the arable land protection red line through the synergistic linkage of a dynamic ontology-driven multimodal knowledge graph, a virtual-real dynamically calibrated digital twin, dynamic threshold early warning, dynamic parameter ecological value assessment, and full-process closed-loop optimization. Compared with existing publicly available technologies, its unique technical means lie in establishing a two-way collaborative mechanism between the knowledge graph and the digital twin to achieve real-time linkage between rules and data; training the dynamic threshold model through reinforcement learning and combining it with a three-dimensional indicator system to achieve accurate hierarchical early warning; constructing a regional adaptive parameter library to achieve accurate comprehensive assessment of ecological value; generating scenario-based contingency plans and improving execution effectiveness through simulation optimization; and forming a full-process closed-loop optimization mechanism to promote the continuous evolution of the management and control system. The various technical means complement each other and synergistically enhance each other, solving the core problems of traditional technologies such as data silos, rigid rules, low early warning accuracy, large assessment bias, and poor practicality of contingency plans. This significantly improves the accuracy, efficiency, and long-term effectiveness of dynamic land monitoring, promoting the transformation of land governance from qualitative control to quantitative value orientation and from passive investigation to proactive prevention, demonstrating outstanding practical value.

Claims

1. A method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use, characterized in that: Includes the following steps: S1 constructs a bidirectional collaborative system of dynamically ontology-driven multimodal knowledge graphs and dynamically calibrated virtual-real digital twins, specifically including: S11 constructs a dynamic ontology database for the field of land monitoring, which covers four core ontology categories: land cover types, regional attributes, policies and rules, and ecological parameters. S12 establishes a digital twin of the entire farmland protection red line area, integrating multimodal remote sensing data, drone patrol data, and ground sensor data; S13 establishes a time-semantic dual alignment mechanism, realizes the synchronization of timestamps between the dynamic ontology library and the digital twin through the sliding window algorithm, and uses the embedding model to map the real-time data of the digital twin into semantic tags that can be recognized by the dynamic ontology library. S14 constructs a collaborative reasoning model based on a graph attention network. This model receives policy rules from a dynamic ontology library to correct the simulation parameters of the digital twin. Simultaneously, it receives real-time management data from the digital twin and iterates through the semantic association chains of the dynamic ontology library. The semantic alignment loss function is defined as follows: ， In the formula For the semantic feature vectors of a dynamic ontology library, For the real-time data feature vector of the digital twin, For embedding mapping functions, The dimension of the feature vector; S2 uses land feature data output from a two-way collaborative system to achieve proactive early warning of abnormal changes in the national territory through dynamic threshold learning; S3 uses scenario data based on early warning results and a two-way collaborative system to complete the quantitative assessment of the ecological value of land change through dynamic parameter adaptation. Based on the assessment results and the policy rules of the two-way collaboration system, S4 generates and executes scenario-based emergency response plans, forming a closed-loop management system for the entire process.

2. The method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use as described in claim 1, characterized in that: In S11, the construction and self-evolution process of the dynamic ontology library includes: S111 extracts land feature attributes, regional characteristics, policy provisions, and ecological parameters from the field of land monitoring to establish an initial ontological hierarchical structure; The S112 design employs a dual-drive update mechanism, which uses natural language processing technology to extract rule information from the latest policy text and iterate the ontology attribute dictionary; it also receives control effect data from the digital twin and optimizes the semantic association chain between land features, rules, and treatment; the natural language processing technology includes an improved BERT model. S113 calculates the association weights between ontology nodes based on a graph attention network to generate a dynamic ontology knowledge graph. The formula for updating the association weights is: ， In the formula For the ontology node and Association weights, For attention vectors, This is the weight matrix. This is a vector concatenation operation. is the dimension of the weight matrix.

3. The method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use as described in claim 1, characterized in that: In step S12, the process of building and dynamically calibrating the digital twin includes: S121 integrates high-resolution remote sensing images, UAV sequence images, ecological parameter data collected by ground sensors, and GIS vector data to construct an initial model of a digital twin. S122 uses the Kalman filter algorithm to correct the land feature parameters of the digital twin. Based on the regional attributes and ecological parameters of the dynamic ontology library, it adjusts the simulation parameters of the twin to ensure that the deviation between the digital twin and the real scene is controlled within a preset range. S123 establishes a multi-source data fusion module, which merges monitoring data from different sources in time stamp order to generate a real-time dynamic scene of a twin.

4. The method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use as described in claim 1, characterized in that: The active early warning process for abnormal land changes based on dynamic threshold learning in S2 includes: S21 uses the regional attributes and land feature importance of the dynamic ontology as constraints, and the real-time monitoring data of the digital twin as training samples to train the dynamic threshold model through reinforcement learning. S22 outputs early warning thresholds for different regions and different land cover types based on a dynamic threshold model. The dynamic threshold update formula is as follows: In the formula The updated warning threshold, The current warning threshold is... For learning rate, The gradient of the threshold loss function; S23 constructs a three-dimensional hierarchical indicator system based on the magnitude of change, policy compliance, and ecological sensitivity. It calculates the warning level based on the warning threshold and the three-dimensional indicators, and simultaneously triggers the visualization marking of the digital twin.

5. The method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use as described in claim 1, characterized in that: In S3, the process of quantifying the ecological value of land change with dynamic parameter adaptation includes: S31 uses a regional adaptive parameter library based on a dynamic ontology library to extract ecological value assessment parameters for different regions. S32 receives land change data from digital twins and the hierarchical results of the early warning system, uses the analytic hierarchy process (AHP) to determine the weights of carbon sequestration, food production capacity, and soil and water conservation, and then uses a comprehensive evaluation formula: ； Calculate the ecological value gain or loss, where To comprehensively consider the ecological value gains and losses, For the first The weight of ecological value, For the first The quantitative value of ecological value, The number of ecological value types; S33 will simultaneously push the evaluation results to the dynamic ontology library and digital twin.

6. The method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use as described in claim 1, characterized in that: In step S4, the process of generating and executing the scenario-based emergency response plan includes: The S41 digital twin generates scenario-based contingency plans for different regions and different warning levels based on warning levels, assessment results, and policy rules from a dynamic ontology library. The S42 integrates a multi-agent simulation module to simulate the execution effect of scenario-based contingency plans and optimize plan parameters. S43 executes the optimized scenario-based contingency plan and feeds back the execution data of the plan to the dynamic ontology library in real time through a digital twin.

7. The method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use as described in claim 1, characterized in that: It also includes S5 full-process closed-loop optimization, where the dynamic ontology library receives the contingency plan execution data fed back by the digital twin, automatically optimizes the early warning threshold, evaluation parameters and policy rules, and synchronizes the optimized rules to the early warning system, evaluation system and digital twin, forming a closed-loop management and control system of monitoring-early warning-evaluation-handling-optimization.

8. The method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use as described in claim 4, characterized in that: In S21, the reinforcement learning training process includes: using the accuracy, false negative rate, and false positive rate of identifying land anomalies as reward functions, optimizing the parameters of the dynamic threshold model through multiple rounds of iterative training, so that the model can adapt to different land monitoring scenarios by learning the correlation between regional attribute features and land feature importance features.

9. The method for intelligent interpretation of multimodal remote sensing images and dynamic monitoring and analysis of land use as described in claim 5, characterized in that: In S31, the process of constructing the regional adaptive parameter library includes: dividing regions according to climate zones and terrain types based on GIS zoning data and dynamic ecological parameters extracted by remote sensing, determining the carbon sink coefficient, soil and water conservation parameters, and grain production capacity parameters of each region, establishing a regional adaptive parameter library and updating it in real time.

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