Land resource management system and method based on multi-dimensional data analysis

Abstract

This invention relates to the field of land resource management system technology, and more specifically, to a land resource management system and method based on multi-dimensional data analysis. The system includes: a data acquisition module for acquiring multi-source heterogeneous land resource data; a data management and analysis module for fusing, quality assessment, feature extraction, spatiotemporal dynamic analysis, and prediction of the multi-source heterogeneous land resource data; a decision support module for providing land resource management decision suggestions based on the output of the data management and analysis module; and a visualization module for three-dimensionally visualizing the results of the data management and analysis module and the decision support module. This system provides a comprehensive, multi-dimensional solution for land resource management and can effectively address the current complex and ever-changing land resource management needs.

Description

Technical Field

[0001] This invention relates to the field of land resource management system technology, and more specifically, to a land resource management system and method based on multi-dimensional data analysis. Background Technology

[0002] With rapid socio-economic development and accelerated urbanization, land resource management faces unprecedented challenges. Traditional land resource management methods are no longer sufficient to meet the current complex and ever-changing needs. In recent years, with the continuous advancements in remote sensing technology, geographic information systems, and artificial intelligence, land resource management systems are also undergoing continuous reform, developing towards greater intelligence and precision.

[0003] Currently, the most advanced land resource management systems are typically based on Geographic Information System (GIS) platforms, combined with remote sensing image analysis and data mining techniques. These systems can perform basic land use classification, change detection, and simple decision support functions. However, these systems still have many shortcomings when facing the increasingly complex needs of land resource management.

[0004] First, existing systems have significant shortcomings in data fusion. They can typically only process a limited number of data types, such as satellite remote sensing imagery and ground survey data, making it difficult to effectively integrate multi-source heterogeneous data from different sensors and at different scales. This results in the system being unable to comprehensively and accurately reflect the actual state of land resources, affecting the accuracy of subsequent analysis and decision-making.

[0005] Secondly, in terms of data analysis and feature extraction, existing systems mostly employ traditional statistical methods or simple machine learning algorithms. These methods struggle to fully uncover the complex features and potential patterns in high-dimensional data, especially when dealing with large-scale, high-dimensional land resource data, where they often fall short.

[0006] Furthermore, existing systems are also inadequate in their spatiotemporal dynamic analysis and prediction capabilities. Most of them can only perform static or short-term analyses, making it difficult to accurately simulate and predict the complex processes of long-term land resource evolution. This limitation prevents the systems from providing strong support for long-term land resource planning and management.

[0007] Finally, in terms of decision support, existing systems typically only offer suggestions based on simple rules or statistical models, lacking a deep understanding and analytical capability for complex situations. Faced with multi-objective, multi-constraint land resource management decision-making problems, these systems often struggle to provide optimal solutions and are even less capable of coping with rapidly changing decision-making environments. Summary of the Invention

[0008] To address the aforementioned problems, this invention proposes a land resource management system and method based on multi-dimensional data analysis. This invention aims to overcome the shortcomings of existing land resource management systems in areas such as data fusion, feature extraction, spatiotemporal dynamic analysis, and intelligent decision support, providing a comprehensive, efficient, and intelligent land resource management solution.

[0009] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0010] A land resource management system based on multi-dimensional data analysis, the system comprising:

[0011] The data acquisition module is used to acquire multi-source heterogeneous land resource data;

[0012] The data management and analysis module is used to fuse, assess the quality of, extract features, perform spatiotemporal dynamic analysis and prediction of the multi-source heterogeneous land resource data;

[0013] The decision support module is used to provide land resource management decision suggestions based on the output of the data management and analysis module;

[0014] The visualization module is used to present the results of the data management and analysis module and the decision support module in a three-dimensional visualization.

[0015] Preferably, the data acquisition module includes:

[0016] Satellite remote sensing data acquisition unit, used to acquire remote sensing image data of land resources;

[0017] High-precision map data acquisition unit, used to acquire high-precision map data of land resources;

[0018] A three-dimensional laser scanning data acquisition unit is used to acquire three-dimensional laser scanning data of land resources;

[0019] The drone data acquisition unit is used to acquire oblique photogrammetric data of land resources.

[0020] Preferably, the data management and analysis module processes the multi-source heterogeneous land resource data based on the multi-dimensional land resource data collaborative analysis algorithm MD-LRDCA. The MD-LRDCA algorithm includes the following steps: multi-source heterogeneous data fusion and standardization; multi-dimensional data quality assessment; deep learning-driven multi-dimensional feature extraction; spatiotemporal dynamic analysis and prediction; and intelligent decision support based on quantum computing.

[0021] Preferably, the multi-source heterogeneous data fusion and standardization steps are implemented based on Lie group theory, specifically including: […]. Considered as Li group G i Points on, where Xi Given the original data from i data sources, data fusion is achieved using the following formula: Where Y represents the fused data, exp is the exponential mapping, log is the logarithmic mapping, and w i As weight.

[0022] Preferably, the multi-dimensional data quality assessment step is implemented based on algebraic topology and persistent cohomology theory, specifically including: calculating the persistent cohomology characteristics of the fused data Y; and calculating the quality score using the following formula: Where q is the quality score, d is the highest dimension, and w k For the weight, β k (Y) is the k-th dimension Betti number, defined as: in, Let be the i-th filtered k-dimensional homology group.

[0023] Preferably, the deep learning-driven multidimensional feature extraction step is implemented based on spectral graph theory and manifold learning, specifically including: constructing a normalized graph Laplacian matrix L = ID. -1 / 2 AD -1 / 2 Where A is the adjacency matrix and D is the degree matrix; feature extraction is achieved using the following formula: Among them, H (l) Let T be the feature matrix of the first layer, q be the quality score, and T be the feature matrix of the first layer. k It is a k-th order Chebyshev polynomial. λ max Let L be the largest eigenvalue. Let σ be the weight matrix and σ be the activation function.

[0024] Preferably, the spatiotemporal dynamic analysis and prediction steps are based on dynamical system theory and differential equations, specifically including: constructing a nonlinear dynamical system:

[0025] Where χ is the state tensor, F is the nonlinear dynamical system function, and θ is the parameter; approximation is performed using a neural ordinary differential equation. Among them, MLP θ It is a multilayer perceptron; the prediction results are obtained by solving differential equations: in, This represents the prediction result at time T.

[0026] Preferably, the quantum computing-based intelligent decision support step is implemented using variable quantum circuits, specifically including: constructing quantum states: Where U(θ) is a parameterized quantum circuit; calculate the action value function: Where H aGiven the Hamiltonian associated with action a; the policy function is obtained:

[0027] A land resource management method based on multi-dimensional data analysis of the aforementioned system, the method comprising:

[0028] Acquire multi-source heterogeneous land resource data;

[0029] The multi-source heterogeneous land resource data is fused, quality assessed, feature extracted, and subjected to spatiotemporal dynamic analysis and prediction.

[0030] Based on the results of the aforementioned spatiotemporal dynamic analysis and prediction, suggestions are provided for land resource management decisions.

[0031] The analysis, predictions, and decision recommendations are presented in a three-dimensional visualization.

[0032] Preferably, the steps of fusing, quality assessment, feature extraction, spatiotemporal dynamic analysis and prediction of multi-source heterogeneous land resource data are implemented using the multi-dimensional land resource data collaborative analysis algorithm MD-LRDCA.

[0033] Compared with the prior art, the beneficial effects of the present invention are reflected in the following aspects:

[0034] The land resource management system of this invention achieves significant technological breakthroughs and improved application effects on multiple levels through its unique design and innovative algorithms. From a macro perspective, this system provides a comprehensive and multi-dimensional solution for land resource management, effectively addressing the complex and ever-changing needs of current land resource management.

[0035] First, the system architecture of this invention achieves seamless integration of data acquisition, analysis, and decision support. The multi-source heterogeneous data acquisition capability of the data acquisition module provides the system with rich and comprehensive raw data; the data management and analysis module, as the core of the system, achieves in-depth data mining and analysis through advanced algorithms; the decision support module transforms the analysis results into specific management suggestions, and finally presents them intuitively to the user through the visualization module. This modular design not only improves the system's flexibility and scalability but also realizes intelligent management of the entire process from data to decision.

[0036] At the data level, the multi-source heterogeneous data fusion and standardization algorithm of this invention solves the problem that traditional systems struggle to effectively integrate multi-source data. The fusion method based on Lie group theory can handle different types and scales of data, such as high-resolution satellite imagery, LiDAR point clouds, and UAV oblique photography, achieving high-precision data fusion. This not only improves the accuracy of subsequent analysis but also provides the possibility of discovering potential correlations between data.

[0037] At the analytical level, the deep learning-driven multidimensional feature extraction algorithm of this invention significantly enhances the system's data analysis capabilities. Based on spectral theory and manifold learning, the method effectively captures complex features in high-dimensional data, achieving accurate extraction of multidimensional information such as land use type, terrain features, and ecological environment. This deep analytical capability provides a solid data foundation for subsequent decision support.

[0038] At the predictive level, the spatiotemporal dynamic analysis and prediction algorithm of this invention overcomes the limitations of traditional systems that can only perform static or short-term analyses. Based on dynamical system theory and the method of neural ordinary differential equations, it can accurately simulate the complex dynamic processes of land resource systems and achieve accurate prediction of long-term trends. This provides important support for the long-term planning and sustainable management of land resources.

[0039] At the decision support level, this invention introduces a quantum computing-based intelligent decision support algorithm, a significant innovation. The introduction of quantum computing enables the system to consider a massive number of decision possibilities in an extremely short time, greatly improving the quality and efficiency of decision-making. Especially when facing complex decision problems with multiple objectives and constraints, this algorithm can quickly find the optimal solution, providing managers with scientific and reasonable decision-making suggestions.

[0040] It is worth noting that these modules and algorithms do not have a simple linear relationship, but rather form a synergistic and mutually reinforcing organic whole. For example, multi-source data fusion improves the accuracy of feature extraction, while deep feature extraction provides more valuable input for spatiotemporal dynamic analysis. The results of spatiotemporal prediction then become an important basis for quantum decision support. This synergistic effect not only resolves potential contradictions between modules, such as the contradiction between data volume and processing efficiency, but also produces a synergistic effect greater than the sum of its parts.

[0041] In practical applications, the system of this invention has demonstrated significant comprehensive benefits. It not only greatly improves the efficiency and accuracy of land resource management but also provides new ideas and methods for solving complex problems such as land resource scarcity and increasing environmental pressure. The system's high-precision change detection capability helps to promptly identify problems such as illegal land use, while its scientific decision support function can optimize land resource allocation and improve resource utilization efficiency. The synergy and combination of these effects will ultimately drive land resource management towards a more refined, intelligent, and sustainable direction.

[0042] In summary, this invention, through its innovative system architecture and algorithms, achieves technological breakthroughs in multiple aspects such as data fusion, feature extraction, dynamic analysis, and intelligent decision-making, providing a comprehensive, efficient, and intelligent solution for land resource management. This not only strongly supports current land resource management needs but also reserves space to address potential future challenges, possessing significant theoretical value and broad application prospects. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the overall system flow of the present invention.

[0044] Figure 2 This is a structural diagram of the data acquisition module of the present invention.

[0045] Figure 3 This is a flowchart of the data management and analysis module of the present invention.

[0046] Figure 4 This is a flowchart of the decision support module and visualization module of the present invention. Detailed Implementation

[0047] The solutions in 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. All other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0048] like Figure 1-4 As shown, this invention provides a land resource management system based on multi-dimensional data analysis, including a data acquisition module 1, a data management and analysis module 2, a decision support module 3, and a visualization module 4. The data acquisition module 1 is responsible for acquiring multi-source heterogeneous land resource data, which may come from different sources, such as satellite remote sensing and drone aerial photography. The data management and analysis module 2 is the core of the system; it performs data fusion, quality assessment, feature extraction, spatiotemporal dynamic analysis, and prediction on the acquired data. The decision support module 3 provides decision-making suggestions for land resource management based on the output of the data management and analysis module 2. Finally, the visualization module 4 presents the analysis results and decision suggestions in a three-dimensional visualization format, facilitating intuitive understanding and use by management personnel.

[0049] Preferably, the data acquisition module 1 further includes a satellite remote sensing data acquisition unit 11, a high-precision map data acquisition unit 12, a 3D laser scanning data acquisition unit 13, and a UAV data acquisition unit 14. This multi-source data acquisition method can comprehensively acquire various information about land resources. For example, the satellite remote sensing data acquisition unit 11 can acquire land use information over a large area, the high-precision map data acquisition unit 12 can provide detailed topographic information, the 3D laser scanning data acquisition unit 13 can acquire accurate surface elevation data, and the UAV data acquisition unit 14 can acquire high-resolution oblique photography data. These data together constitute a comprehensive description of land resources.

[0050] In one embodiment of the present invention, the data management and analysis module 2 processes multi-source heterogeneous land resource data based on the Multi-Dimensional Land Resource Data Collaborative Analysis Algorithm (MD-LRDCA). The MD-LRDCA algorithm includes five main steps: multi-source heterogeneous data fusion and standardization, multi-dimensional data quality assessment, deep learning-driven multi-dimensional feature extraction, spatiotemporal dynamic analysis and prediction, and intelligent decision support based on quantum computing. This progressive processing approach can fully extract useful information from the data, providing a reliable foundation for subsequent decision support.

[0051] In one embodiment of the invention, the multi-source heterogeneous data fusion and standardization steps are implemented based on Lie group theory. This step involves input data... Considered as Li group G i Points on, where X i Let be the original data from the i-th data source. Data fusion is achieved using the following formula:

[0052]

[0053] Where Y represents the fused data, exp is the exponential mapping, log is the logarithmic mapping, and w i The weights are assigned accordingly. This fusion method based on Lie group theory can effectively handle nonlinear relationships between different types of data, and is particularly suitable for processing multi-source heterogeneous data involved in land resource management. For example, when fusing satellite remote sensing data and ground measurement data, satellite data can be assigned a higher weight (e.g., w1 = 0.7), while ground data can be assigned a lower weight (e.g., w2 = 0.3). This ensures data comprehensiveness while fully utilizing the wide coverage advantage of satellite data.

[0054] Preferably, the multi-dimensional data quality assessment step is implemented based on algebraic topology and persistent cohomology theory. This step first calculates the persistent cohomology characteristics of the fused data Y, and then calculates the quality score using the following formula:

[0055]

[0056] Where q is the quality score, d is the highest dimension, and w k For the weight, β k (Y) is the k-th dimension Betti number, defined as:

[0057]

[0058] in, Let w0 be the k-dimensional homology group filtered out. In land resource management applications, we can set weights based on the importance of different dimensions. For example, for terrain data, we might focus more on 0-dimensional and 1-dimensional topological features, so we can set w0 = 0.4, w1 = 0.4, and w2 = 0.2. This quality assessment method based on topological features can capture the essential structure of the data and is particularly effective in discovering anomalies or errors in the data.

[0059] This invention, through the synergistic effect of the above five steps, achieves comprehensive analysis and scientific decision-making regarding land resource data. For example, in urban land use planning, the system can comprehensively consider multiple factors such as topography, vegetation cover, and transportation networks, predict future urban expansion trends through spatiotemporal dynamic analysis, and provide the optimal land use plan based on a quantum computing-based intelligent decision support system. This multi-dimensional and comprehensive analysis method greatly improves the scientific nature and accuracy of land resource management, providing strong support for sustainable development.

[0060] In another embodiment of the present invention, the deep learning-driven multidimensional feature extraction step is implemented based on spectral graph theory and manifold learning. This step first constructs a normalized graph Laplacian matrix L, which is defined as:

[0061] L = ID -1 / 2 AD -1 / 2

[0062] In another embodiment of the present invention, the deep learning-driven multidimensional feature extraction step is implemented based on spectral graph theory and manifold learning. This step first constructs a normalized graph Laplacian matrix L, defined as: where I is the identity matrix, A is the adjacency matrix, and D is the degree matrix. In the context of land resource management, the adjacency matrix A can represent the spatial relationship or similarity between different land units. For example, for two adjacent land units, we can assign a higher weight (e.g., 0.9) to A, while assigning a lower weight (e.g., 0.1) to units that are farther apart. Next, feature extraction is achieved using the following formula:

[0063]

[0064] Among them, H (l) Let T be the feature matrix of layer 1, q be the quality score calculated in the previous step, and T be the feature matrix of layer 1. k It is a k-th order Chebyshev polynomial. λ max Let L be the largest eigenvalue. Let σ be the weight matrix and σ be the activation function.

[0065] In practical applications, we can choose K=3, i.e., use a 3rd-order Chebyshev polynomial, which can usually capture sufficient local structure information. For the activation function σ, we can choose the ReLU function because it can effectively introduce nonlinearity while being computationally efficient. This feature extraction method based on spectral theory is particularly suitable for processing land resource data with complex spatial relationships, and can effectively extract key information such as land use and topographic features.

[0066] Preferably, the spatiotemporal dynamic analysis and prediction steps are based on dynamical system theory and differential equations. This step first constructs a nonlinear dynamical system:

[0067]

[0068] Here, x is the state tensor, representing the changes of various indicators of land resources over time; F is the nonlinear dynamic system function; and θ is the parameter. In land resource management, χ may contain information from multiple dimensions such as land use type, vegetation cover, and urbanization level.

[0069] To approximate the complex nonlinear function F, this invention uses a neural ordinary differential equation.

[0070]

[0071] Among them, MLP θ This is a multilayer perceptron. The advantage of this method is its ability to adaptively learn complex nonlinear dynamics, making it highly suitable for simulating the complex changing processes of land resource systems. By solving the differential equations, we can obtain the prediction results:

[0072]

[0073] Where, χ T The result is shown at time T, and χ0 represents the initial state. In practical applications, we can choose time spans such as T = 5 (years) or T = 10 (years) to predict short- to medium-term land resource change trends. This prediction method based on dynamic systems can capture the nonlinear characteristics of land resource changes, providing a scientific basis for long-term planning.

[0074] In another embodiment of the present invention, the quantum computing-based intelligent decision support step is implemented using variable quantum circuits. This innovative step first constructs a quantum state:

[0075] |ψ(θ)>=U(θ)|χ T >

[0076] Where U(θ) is a parameterized quantum circuit, To predict the results Encoded quantum states. In land resource management applications, we can encode different land use schemes into different quantum states, utilizing the property of quantum superposition to consider multiple possibilities simultaneously.

[0077] Next, calculate the action value function:

[0078]

[0079] Among them, H a Let be the Hamiltonian associated with action 'a'. In land resource management, action 'a' may represent different land use decisions, such as "increasing farmland area" or "expanding urban construction land".

[0080] Finally, we obtain the policy function:

[0081]

[0082] This strategy function provides a given land resource state. The probability of choosing various decisions 'a' is considered. This quantum computing-based decision support method can consider a massive number of possibilities in a very short time, providing optimal decision recommendations for land resource management.

[0083] In one embodiment of the present invention, a land resource management method based on multi-dimensional data analysis is also provided. This method includes acquiring multi-source heterogeneous land resource data, fusing and analyzing the data, providing decision-making suggestions, and visualizing the results in three dimensions. The advantage of this method is that it can comprehensively consider various factors, such as topography, climate, and socio-economic conditions, thereby making more scientific and rational land resource management decisions.

[0084] Preferably, the data analysis step in the above method is implemented using the Multi-Dimensional Land Resource Data Collaborative Analysis Algorithm (MD-LRDCA). The application of this algorithm endows land resource management methods with powerful data processing and analysis capabilities, enabling the extraction of valuable information from massive amounts of multi-source heterogeneous data and the making of accurate predictions and decisions.

[0085] For example, in urban planning, the system can simultaneously analyze multi-source information such as satellite imagery, topographic maps, population distribution data, and economic development data. Using the MD-LRDCA algorithm, the system can predict urban expansion trends over the next 10 years, identify potential environmental risk areas, and provide optimal land use planning recommendations. This data- and algorithm-based scientific decision-making method significantly improves the efficiency and accuracy of land resource management.

[0086] In summary, the land resource management system and method based on multi-dimensional data analysis provided by this invention achieves refined management and scientific decision-making for land resources through advanced data analysis technologies and algorithms. It not only improves land resource utilization efficiency but also provides strong support for sustainable development, and is of great significance in addressing current challenges such as land resource scarcity and increasing environmental pressure.

[0087] To verify the superiority of the land resource management system based on multi-dimensional data analysis of the present invention, we selected a provincial-level administrative region as the research object and compared the performance of the system of the present invention (Example 1) with that of the traditional land resource management system (Comparative Example 1) on several key indicators.

[0088] Example 1 employs the MD-LRDCA algorithm of this invention, integrating multi-source data such as satellite remote sensing, high-precision maps, 3D laser scanning, and UAV oblique photography. Comparative Example 1 uses a traditional GIS system, primarily relying on single-source satellite remote sensing data and ground survey data.

[0089] We selected the following five key indicators for comparison: data fusion accuracy, land use classification accuracy, change detection sensitivity, decision support response time, and resource utilization efficiency. These indicators comprehensively reflect the performance of the land resource management system in terms of data processing, analysis capabilities, and practical application effectiveness.

[0090] The testing standards and methods are as follows:

[0091] 1. Data fusion accuracy: Evaluated using root mean square error (RMSE), measured in meters. It is calculated by comparing the deviation of the fused data with the high-precision reference data.

[0092] 2. Land use classification accuracy: Evaluated using the Kappa coefficient, with a value ranging from 0 to 1. It is calculated by randomly selecting sample points and comparing the classification results with the field survey results.

[0093] 3. Change Detection Sensitivity: Evaluated using the Minimum Detectable Area of ​​Change (MDA), measured in square meters. This is the minimum area of ​​change that the test system can detect by progressively reducing the manually set area of ​​change.

[0094] 4. Decision Support Response Time: Measured in seconds. This measures the time from when a decision problem is input to when the system provides a suggestion.

[0095] 5. Resource utilization efficiency: This is assessed using annual economic output per unit area, expressed in RMB 10,000 per hectare per year. The result is calculated by comparing the land economic benefits before and after the system planning.

[0096] The table below shows the detection results of Example 1 and Comparative Example 1 on the above five indicators:

[0097]

[0098]

[0099] The test results clearly show that the land resource management system of this invention is significantly superior to the traditional system in all indicators. Let us analyze these results and their significance one by one:

[0100] 1. Data Fusion Accuracy: The fusion accuracy of the system in this invention reaches 0.15 meters, far superior to the 0.8 meters of traditional systems. This is attributed to the multi-source heterogeneous data fusion and standardization steps in the MD-LRDCA algorithm, especially the fusion method based on Lie group theory. High-precision data fusion provides a reliable foundation for subsequent analysis and decision-making, and can more accurately reflect the actual land resource situation.

[0101] 2. Land Use Classification Accuracy: The Kappa coefficient of this invention's system reaches 0.92, while the traditional system only achieves 0.78. This significant improvement is attributed to the deep learning-driven multidimensional feature extraction steps, particularly the methods based on spectral theory and manifold learning. Higher classification accuracy means a more precise understanding of the current land use situation, providing strong support for scientific decision-making.

[0102] 3. Change Detection Sensitivity: The system of this invention can detect a minimum change area of ​​25 square meters, while traditional systems can only detect changes of 100 square meters. This high sensitivity is attributed to the nonlinear dynamic system model in the spatiotemporal dynamic analysis and prediction steps. Higher detection sensitivity enables the system to promptly detect small-scale land use changes, facilitating early intervention and management.

[0103] 4. Decision Support Response Time: The system of this invention can provide decision suggestions in just 5 seconds, while traditional systems require 60 seconds. This significant performance improvement is mainly due to the intelligent decision support steps based on quantum computing. The rapid response time enables managers to make quick decisions in emergency situations, improving the efficiency of land resource management.

[0104] 5. Resource Utilization Efficiency: After adopting the system of this invention, the annual economic output per unit area increased to 850,000 yuan / hectare·year, while the traditional system only reached 650,000 yuan / hectare·year. This improvement fully demonstrates the superior ability of the system of this invention in optimizing land resource allocation, not only improving economic efficiency but also providing strong support for sustainable development.

[0105] In summary, the land resource management system of this invention significantly outperforms traditional systems in terms of data processing accuracy, analytical capabilities, and practical application effectiveness. This comprehensive performance improvement not only enhances the efficiency and accuracy of land resource management but also provides strong technical support for scientific decision-making and sustainable development. Especially given the current context of increasingly scarce land resources and mounting environmental pressures, the application of this system will offer new ideas and methods for solving these complex problems.

[0106] Based on the above test results and analysis, we can determine that Example 1 is the best embodiment of the present invention. In this embodiment, the system fully leverages the advantages of the MD-LRDCA algorithm, achieving high-precision fusion of multi-source data, accurate land use classification, sensitive change detection, rapid decision support, and efficient resource utilization. This embodiment not only reaches a leading level in technology but also achieves significant economic and social benefits in practical applications, fully demonstrating the innovation and practical value of the present invention.

[0107] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto; any substitutions or modifications made by those skilled in the art within the scope disclosed in the present invention, based on the scheme and improved concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A land resource management system based on multi-dimensional data analysis, characterized in that, The system includes: The data acquisition module is used to acquire multi-source heterogeneous land resource data; The data management and analysis module is used to fuse, assess the quality of, extract features, perform spatiotemporal dynamic analysis and prediction of the multi-source heterogeneous land resource data; The decision support module is used to provide land resource management decision suggestions based on the output of the data management and analysis module; A visualization module is used to present the results of the data management and analysis module and the decision support module in a three-dimensional visualization. The data management and analysis module processes the multi-source heterogeneous land resource data based on the Multi-Dimensional Land Resource Data Collaborative Analysis Algorithm (MD-LRDCA). The MD-LRDCA algorithm includes the following steps: multi-source heterogeneous data fusion and standardization; multi-dimensional data quality assessment; deep learning-driven multi-dimensional feature extraction; spatiotemporal dynamic analysis and prediction; and intelligent decision support based on quantum computing. The multi-source heterogeneous data fusion and standardization steps are implemented based on Lie group theory, specifically including: […]. Considered as Li group G i Points on, where X i Given the original data from i data sources, data fusion is achieved using the following formula: Where Y represents the fused data, exp is the exponential mapping, log is the logarithmic mapping, and w i As weight; The multi-dimensional data quality assessment steps are based on algebraic topology and persistent cohomology theory, specifically including: calculating the persistent cohomology characteristics of the fused data Y; and calculating the quality score using the following formula: Where q is the quality score, d is the highest dimension, and w k Let βk(Y) be the weight, and βk(Y) be the Betti number of the k-th dimension, defined as: in, Let i be the k-dimensional homology group that has been filtered. The deep learning-driven multidimensional feature extraction step is implemented based on spectral graph theory and manifold learning, specifically including: constructing a normalized graph Laplacian matrix L = ID -1 / 2 AD -1 / 2 Where A is the adjacency matrix and D is the degree matrix; feature extraction is achieved using the following formula: Among them, H (l) Let T be the feature matrix of the first layer, q be the quality score, and T be the feature matrix of the first layer. k It is a k-th order Chebyshev polynomial. Let L be the largest eigenvalue. Let σ be the weight matrix and σ be the activation function. The spatiotemporal dynamic analysis and prediction steps are based on dynamical system theory and differential equations, specifically including: constructing a nonlinear dynamical system: in, Let F be the state tensor, F be the function of the nonlinear dynamical system, and θ be the parameters; F is approximated using the constant differential equation: Among them, MLP θ It is a multilayer perceptron; the prediction results are obtained by solving differential equations: in, The prediction result at time T; The quantum computing-based intelligent decision support steps are implemented using variable quantum circuits, and specifically include: constructing quantum states: Where U(θ) is a parameterized quantum circuit; calculate the action value function: Where H a Given the Hamiltonian associated with action a; the policy function is obtained:

2. The system according to claim 1, characterized in that, The data acquisition module includes: Satellite remote sensing data acquisition unit, used to acquire remote sensing image data of land resources; High-precision map data acquisition unit, used to acquire high-precision map data of land resources; A three-dimensional laser scanning data acquisition unit is used to acquire three-dimensional laser scanning data of land resources; The drone data acquisition unit is used to acquire oblique photogrammetric data of land resources.

3. A land resource management method based on multi-dimensional data analysis according to the system described in any one of claims 1-2, characterized in that, The method includes: Acquire multi-source heterogeneous land resource data; The multi-source heterogeneous land resource data is fused, quality assessed, feature extracted, and subjected to spatiotemporal dynamic analysis and prediction. Based on the results of the aforementioned spatiotemporal dynamic analysis and prediction, suggestions are provided for land resource management decisions. The analysis, prediction, and decision-making recommendations are presented in a three-dimensional visualization.

4. The method according to claim 3, characterized in that, The steps of fusing, quality assessment, feature extraction, spatiotemporal dynamic analysis and prediction of multi-source heterogeneous land resource data are implemented using the aforementioned multi-dimensional land resource data collaborative analysis algorithm MD-LRDCA.

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