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

Abstract

The invention relates to the technical field of land resource management systems, in particular to a land resource management system and method based on multi-dimensional data analysis, and the system comprises a data collection module which is used for obtaining multi-source heterogeneous land resource data; the data management and analysis module is used for performing fusion, quality evaluation, feature extraction, spatial-temporal dynamic analysis and prediction on the multi-source heterogeneous land resource data; the decision support module is used for providing land resource management decision suggestions based on the output of the data management analysis module; and the visualization module is used for performing three-dimensional visualization expression on results of the data management analysis module and the decision support module, and the system provides an omnibearing and multi-dimensional solution for land resource management, and can effectively meet current complex and changeable land resource management requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of land resource management systems, and more specifically, to a land resource management system and method based on multi-dimensional data analysis. Background Art

[0002] With the rapid development of social economy and the acceleration of urbanization, land resource management faces unprecedented challenges. Traditional land resource management methods can no longer meet the current complex and changing needs. In recent years, with the continuous progress in remote sensing technology, geographic information systems and artificial intelligence, land resource management systems are also constantly reforming and developing in a more intelligent and refined direction.

[0003] At present, the most advanced land resource management systems are usually based on the Geographic Information System (GIS) platform, combined with remote sensing image analysis and data mining technology. These systems can achieve basic land use classification, change detection and simple decision support functions. However, these systems still have many shortcomings in the face of the increasingly complex land resource management needs.

[0004] First, existing systems have obvious shortcomings in data fusion. They can usually only process a limited number of data types, such as satellite remote sensing images and ground survey data, and it is difficult to effectively integrate multi-source heterogeneous data from different sensors and different scales. This results in the system being unable to fully and accurately reflect the actual status 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 use traditional statistical methods or simple machine learning algorithms. These methods are difficult to fully explore the complex features and potential laws in high-dimensional data, especially when dealing with large-scale, high-dimensional land resource data.

[0006] Furthermore, existing systems are also limited in their ability to perform spatiotemporal dynamic analysis and prediction. Most of them can only perform static or short-term analysis, and it is difficult to accurately simulate and predict the complex process of long-term evolution of land resources. This limitation makes it impossible for the system to provide strong support for long-term land resource planning and management.

[0007] Finally, in terms of decision support, existing systems can usually only provide suggestions based on simple rules or statistical models, and lack the ability to deeply understand and analyze complex situations. Faced with multi-objective and multi-constrained land resource management decision-making problems, these systems often find it difficult to give the optimal solution, let alone cope with the rapidly changing decision-making environment. Summary of the invention

[0008] In view of the above problems, the present invention proposes a land resource management system and method based on multi-dimensional data analysis. The invention aims to solve the deficiencies of existing land resource management systems in data fusion, feature extraction, spatiotemporal dynamic analysis and intelligent decision support, and provide a comprehensive, efficient and intelligent land resource management solution.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions:

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

[0011] Data acquisition module, used to obtain multi-source heterogeneous land resource data;

[0012] A data management and analysis module, used for fusing, quality assessment, feature extraction, spatiotemporal dynamic analysis and prediction of the multi-source heterogeneous land resource data;

[0013] A decision support module, 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 perform three-dimensional visualization of the results of the data management and analysis module and the decision support module.

[0015] Preferably, the data acquisition module comprises:

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

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

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

[0019] UAV data collection unit is used to obtain oblique photography 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, and 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 step is implemented based on Lie group theory, specifically comprising: Treated as Lie group G i The point on which Xi is the original data of i data sources; data fusion is achieved through the following formula: Among them, Y is the fused data, exp is the exponential mapping, log is the logarithmic mapping, and w i is the weight.

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

[0023] Preferably, the multi-dimensional feature extraction step driven by deep learning 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 through the following formula: Among them, H (l) is the feature matrix of the first layer, q is the quality score, T k is a k-order Chebyshev polynomial, λ max is the maximum eigenvalue of L, is the weight matrix and σ is the activation function.

[0024] Preferably, the spatiotemporal dynamic analysis and prediction step is implemented based on the theory of dynamical systems and differential equations, and specifically includes: constructing a nonlinear dynamical system:

[0025] Where χ is the state tensor, F is the nonlinear dynamical system function, and θ is the parameter; the Neural Ordinary Differential Equation approximation is used Among them, MLP θ is a multi-layer perceptron; the prediction result is obtained by solving the differential equation: in, is the prediction result at time T.

[0026] Preferably, the intelligent decision support step based on quantum computing is implemented based on a variational quantum circuit, specifically comprising: constructing a quantum state: Where U(θ) is a parameterized quantum circuit; calculate the action value function: Among them, H ais the Hamiltonian associated with action a; get the strategy function:

[0027] The land resource management method based on multi-dimensional data analysis based on the system comprises:

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

[0029] Perform integration, quality assessment, feature extraction, spatiotemporal dynamic analysis and prediction on the multi-source heterogeneous land resource data;

[0030] Providing land resource management decision-making recommendations based on the results of the spatiotemporal dynamic analysis and prediction;

[0031] The analysis, prediction and decision-making recommendation results are expressed in 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 embodied in the following aspects:

[0034] The land resource management system of the present invention has achieved significant technological breakthroughs and improved application effects at multiple levels through its unique design and innovative algorithms. From a macro perspective, the system provides an all-round, multi-dimensional solution for land resource management, which can effectively cope with the current complex and changing land resource management needs.

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

[0036] At the data level, the multi-source heterogeneous data fusion and standardization algorithm of the present invention solves the problem that traditional systems are difficult to effectively integrate multi-source data. The fusion method based on Lie group theory can process data of different types and scales, such as high-resolution satellite images, lidar point clouds, drone oblique photography, etc., and achieves 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 multi-dimensional feature extraction algorithm of the present invention greatly improves the data analysis capability of the system. The method based on spectral graph theory and manifold learning can effectively capture the complex features in high-dimensional data and realize the accurate extraction of multi-dimensional information such as land use type, terrain characteristics, and ecological environment. This deep analysis capability provides a solid data foundation for subsequent decision support.

[0038] At the prediction level, the spatiotemporal dynamic analysis and prediction algorithm of the present invention breaks through the limitation that traditional systems can only perform static or short-term analysis. Based on the theory of dynamic systems and the method of Neural Ordinary Differential Equations, it can accurately simulate the complex dynamic process of the land resource system 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, the present invention introduces an intelligent decision support algorithm based on quantum computing, which is a major innovation. The introduction of quantum computing enables the system to consider a large number of decision possibilities in a very short time, greatly improving the quality and efficiency of decision making. Especially when faced with complex decision problems with multiple objectives and multiple constraints, the algorithm can quickly find the optimal solution and provide managers with scientific and reasonable decision suggestions.

[0040] It is worth noting that these modules and algorithms are not simply linearly related, but form an organic whole that cooperates and promotes each other. For example, multi-source data fusion improves the accuracy of feature extraction, and deep feature extraction provides more valuable input for spatiotemporal dynamic analysis. The results of spatiotemporal prediction have become an important basis for quantum decision support. This synergistic effect not only solves the possible contradictions between various modules, such as the contradiction between data volume and processing efficiency, but also produces the effect of 1+1>2.

[0041] In practical applications, the system of the present invention has shown 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 shortages and increased environmental pressure. The system's high-precision change detection capability helps to promptly discover problems such as illegal land use, and its scientific decision-making support function can optimize land resource allocation and improve resource utilization efficiency. The superposition and synergy of these effects will ultimately promote land resource management to develop in a more refined, intelligent and sustainable direction.

[0042] In summary, the present invention has achieved technical breakthroughs in data fusion, feature extraction, dynamic analysis, and intelligent decision-making through its innovative system architecture and algorithms, providing a comprehensive, efficient, and intelligent solution for land resource management. This not only strongly supports the current land resource management needs, but also reserves space for new challenges that may arise in the future. It has important theoretical value and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flowchart of the overall system of the present invention.

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

[0045] Figure 3 It is a workflow diagram of the data management and analysis module of the present invention.

[0046] Figure 4 It is a workflow diagram of the decision support module and the visualization module of the present invention. DETAILED DESCRIPTION

[0047] The scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. The embodiments in the present invention and all other embodiments obtained by ordinary persons in the art without making creative work are within the scope of protection of the present invention.

[0048] like Figure 1-4 As shown, the present 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, drone aerial photography, etc. The data management and analysis module 2 is the core of the system, which performs fusion, quality assessment, feature extraction, spatiotemporal dynamic analysis and prediction on the collected data. The decision support module 3 provides decision 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 manner, which is convenient for managers to intuitively understand and use.

[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 three-dimensional laser scanning data acquisition unit 13 and an unmanned aerial vehicle data acquisition unit 14. This multi-source data acquisition method can comprehensively obtain various information on land resources. For example, the satellite remote sensing data acquisition unit 11 can obtain a wide range of land use conditions, the high-precision map data acquisition unit 12 can provide detailed topographic information, the three-dimensional laser scanning data acquisition unit 13 can obtain accurate surface elevation data, and the unmanned aerial vehicle data acquisition unit 14 can obtain 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, multi-dimensional feature extraction driven by deep learning, spatiotemporal dynamic analysis and prediction, and intelligent decision support based on quantum computing. This progressive processing method can fully mine the useful information in the data and provide a reliable basis for subsequent decision support.

[0051] In one embodiment of the present invention, the multi-source heterogeneous data fusion and standardization step is implemented based on Lie group theory. Treated as Lie group G i The point on which X i is the original data of the ith data source. Data fusion is achieved through the following formula:

[0052]

[0053] Among them, Y is the fused data, exp is the exponential mapping, log is the logarithmic mapping, and w i This fusion method based on Lie group theory can effectively handle the nonlinear relationship 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, a higher weight (such as w1=0.7) can be given to satellite data, while a lower weight (such as w2=0.3) can be given to ground data. This ensures the comprehensiveness of the data while making full use of the large coverage advantage of satellite data.

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

[0055]

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

[0057]

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

[0059] Through the synergy of the above five steps, the present invention realizes comprehensive analysis and scientific decision-making of land resource data. For example, when conducting urban land use planning, the system can comprehensively consider multiple factors such as terrain, vegetation coverage, and transportation network, predict future urban expansion trends through spatiotemporal dynamic analysis, and give the optimal land use plan based on the intelligent decision support system of quantum computing. This multi-dimensional and comprehensive analysis method greatly improves the scientificity and accuracy of land resource management and provides strong support for sustainable development.

[0060] In another embodiment of the present invention, the deep learning driven multi-dimensional 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, which is defined as: where I is the unit 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 (such as 0.9) in A, and a lower weight (such as 0.1) to units that are farther away. Next, feature extraction is achieved through the following formula:

[0063]

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

[0065] In practical applications, we can choose K = 3, that is, use the third-order Chebyshev polynomial, which can usually capture enough local structural information. For the activation function σ, we can choose the ReLU function because it can effectively introduce nonlinearity and has high computational efficiency. This feature extraction method based on spectral graph theory is particularly suitable for processing land resource data with complex spatial relationships, and can effectively extract key information such as land use and terrain characteristics.

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

[0067]

[0068] Among them, 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 in multiple dimensions such as land use type, vegetation coverage, and urbanization degree.

[0069] In order to approximate the complex nonlinear function F, the present invention uses the Neural Ordinary Differential Equation

[0070]

[0071] Among them, MLP θ is a multi-layer perceptron. The advantage of this method is that it can adaptively learn complex nonlinear dynamics and is very suitable for simulating the complex changes in land resource systems. By solving the differential equation, we can get the prediction results:

[0072]

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

[0074] In another embodiment of the present invention, the intelligent decision support step based on quantum computing is implemented based on variational 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 In the application of land resource management, we can encode different land use plans into different quantum states and use the characteristics of quantum superposition to consider multiple possibilities at the same time.

[0077] Next, calculate the action-value function:

[0078]

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

[0080] Finally, we get the policy function:

[0081]

[0082] This policy function gives the given land resource status Under the condition of , the probability of choosing various decisions a. This decision support method based on quantum computing can consider a large number of possibilities in a very short time and provide the best decision suggestions for land resource management.

[0083] In one embodiment of the present invention, the present invention also provides a land resource management method based on multi-dimensional data analysis. The method includes obtaining multi-source heterogeneous land resource data, fusing and analyzing the data, providing decision 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, socio-economic conditions, etc., so as to make more scientific and reasonable 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 enables the land resource management method to have powerful data processing and analysis capabilities, and can extract valuable information from massive multi-source heterogeneous data and make accurate predictions and decisions.

[0085] For example, when conducting urban planning, the system can simultaneously analyze satellite images, topographic maps, population distribution data, economic development data and other multi-source information. Through the MD-LRDCA algorithm, the system can predict urban expansion trends in the next 10 years, identify potential environmental risk areas, and give optimal land use planning recommendations. This scientific decision-making method based on data and algorithms has greatly improved the efficiency and accuracy of land resource management.

[0086] In general, the land resource management system and method based on multi-dimensional data analysis provided by the present invention realizes refined management of land resources and scientific decision-making through advanced data analysis technology and algorithms. It can not only improve the efficiency of land resource utilization, but also provide strong support for sustainable development, which is of great significance for solving the current problems of land resource shortage and increasing environmental pressure.

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

[0088] Example 1 uses the MD-LRDCA algorithm of the present invention, integrating multi-source data such as satellite remote sensing, high-precision maps, 3D laser scanning, and drone oblique photography. Comparative Example 1 uses a traditional GIS system, which mainly relies on single 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 fully reflect the performance of the land resource management system in terms of data processing, analysis capabilities, and actual application effects.

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

[0091] 1. Data fusion accuracy: Root mean square error (RMSE) is used for evaluation, and the unit is meter. It is calculated by comparing the deviation between the fused data and the high-precision reference data.

[0092] 2. Land use classification accuracy: The Kappa coefficient is used for evaluation, with a value range of 0-1. The accuracy is calculated by randomly selecting sample points and comparing the classification results with the field survey results.

[0093] 3. Change detection sensitivity: The minimum detectable change area (MDA) is used for evaluation, and the unit is square meters. By gradually reducing the artificially set change area, the minimum change area that the test system can detect is tested.

[0094] 4. Decision support response time: in seconds. Measures the time from inputting a decision problem to the system giving a recommendation.

[0095] 5. Resource utilization efficiency: It is evaluated by annual economic output per unit area, with the unit being 10,000 yuan / hectare / year. It is calculated by comparing the economic benefits of land before and after system planning.

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

[0097]

[0098]

[0099] It can be clearly seen from the test results that the land resource management system of the present invention is significantly better than 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 of the present invention reaches 0.15 meters, which is much better than the 0.8 meters of the traditional system. This is due 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 basis for subsequent analysis and decision-making, and can more accurately reflect the actual land resource status.

[0101] 2. Land use classification accuracy: The Kappa coefficient of the system of the present invention reaches 0.92, while the traditional system is only 0.78. This significant improvement is attributed to the multi-dimensional feature extraction step driven by deep learning, especially the method based on spectral graph theory and manifold learning. Higher classification accuracy means that the current land use situation can be grasped more accurately, providing strong support for scientific decision-making.

[0102] 3. Change detection sensitivity: The minimum change area that the system of the present invention can detect is 25 square meters, while the traditional system can only detect changes of 100 square meters. This high sensitivity is due to the nonlinear dynamic system model in the spatiotemporal dynamic analysis and prediction steps. The higher detection sensitivity enables the system to detect small-scale land use changes in a timely manner, which is conducive to early intervention and management.

[0103] 4. Decision support response time: The system of the present invention can give decision suggestions in just 5 seconds, while the traditional system takes 60 seconds. This significant performance improvement is mainly due to the intelligent decision support step based on quantum computing. The fast response time enables managers to make decisions quickly in emergency situations, improving the efficiency of land resource management.

[0104] 5. Resource utilization efficiency: After adopting the system of the present invention, the annual economic output per unit area has increased to 850,000 yuan / hectare·year, while the traditional system is only 650,000 yuan / hectare·year. This improvement fully proves the excellent ability of the system of the present invention in optimizing land resource allocation, which not only improves economic benefits, but also provides strong support for sustainable development.

[0105] In summary, the land resource management system of the present invention is significantly superior to the traditional system in terms of data processing accuracy, analysis ability and practical application effect. This comprehensive performance improvement not only improves the efficiency and accuracy of land resource management, but also provides strong technical support for scientific decision-making and sustainable development. Especially in the current context of increasingly tight land resources and increasing environmental pressure, the application of the system of the present invention will provide 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 gives full play to the advantages of the MD-LRDCA algorithm, and realizes 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 the leading level in technology, but also achieves significant economic and social benefits in practical applications, which fully reflects the innovation and practical value of the present invention.

[0107] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto; any person familiar with the art who, within the scope disclosed by the present invention, makes substitutions or changes based on the scheme and improved concepts of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A land resource management system based on multi-dimensional data analysis, characterized in that: The system comprises: Data acquisition module, used to obtain multi-source heterogeneous land resource data; A data management and analysis module, used for fusing, quality assessment, feature extraction, spatiotemporal dynamic analysis and prediction of the multi-source heterogeneous land resource data; A decision support module, used to provide land resource management decision suggestions based on the output of the data management and analysis module; The visualization module is used to perform three-dimensional visualization of the results of the data management and analysis module and the decision support module.

2. The system according to claim 1, characterized in that The data acquisition module comprises: Satellite remote sensing data acquisition unit, used to obtain remote sensing image data of land resources; A high-precision map data acquisition unit, used to obtain high-precision map data of land resources; A three-dimensional laser scanning data acquisition unit, used to obtain three-dimensional laser scanning data of land resources; UAV data collection unit is used to obtain oblique photography data of land resources.

3. The system according to claim 1, characterized in that 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; multi-dimensional feature extraction driven by deep learning; spatiotemporal dynamic analysis and prediction; and intelligent decision support based on quantum computing.

4. The system according to claim 3, characterized in that The multi-source heterogeneous data fusion and standardization step is implemented based on Lie group theory and specifically includes: Treated as Lie group G i The point on which X i is the original data of i data sources; data fusion is achieved through the following formula: Among them, Y is the fused data, exp is the exponential mapping, log is the logarithmic mapping, and w i is the weight.

5. The system according to claim 4, characterized in that The multi-dimensional data quality assessment step is implemented based on algebraic topology and continuous homology theory, and specifically includes: calculating the continuous homology features of the fused data Y; and calculating the quality score by the following formula: Among them, q is the quality score, d is the highest dimension, and w k is the weight, β k (Y) is the k-th dimension Betti number, defined as: in, is the i-th filtered k-dimensional homology group.

6. The system according to claim 5, characterized in that The deep learning driven multi-dimensional feature extraction step is based on spectral graph theory and manifold learning and specifically includes: 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 through the following formula: Among them, H (l) is the feature matrix of the first layer, q is the quality score, T k is a k-order Chebyshev polynomial, is the maximum eigenvalue of L, is the weight matrix and σ is the activation function.

7. The system according to claim 6, characterized in that The spatiotemporal dynamic analysis and prediction steps are implemented based on dynamic system theory and differential equations, and specifically include: constructing a nonlinear dynamic system: in, is the state tensor, F is the nonlinear dynamic system function, and θ is the parameter; the Neural Ordinary Differential Equation is used to approximate Among them, MLP θ is a multi-layer perceptron; the prediction result is obtained by solving the differential equation: in, is the prediction result at time T.

8. The system according to claim 7, characterized in that The intelligent decision support step based on quantum computing is implemented based on a variational quantum circuit, and specifically includes: constructing a quantum state: Where U(θ) is a parameterized quantum circuit; calculate the action value function: Among them, H a is the Hamiltonian associated with action a; get the strategy function:

9. A land resource management method based on multi-dimensional data analysis based on the system according to any one of claims 1 to 8, characterized in that: The method comprises: Acquire multi-source heterogeneous land resource data; Perform integration, quality assessment, feature extraction, spatiotemporal dynamic analysis and prediction on the multi-source heterogeneous land resource data; Providing land resource management decision-making recommendations based on the results of the spatiotemporal dynamic analysis and prediction; The analysis, prediction and decision-making recommendation results are expressed in three-dimensional visualization.

10. The method according to claim 9, 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 multi-dimensional land resource data collaborative analysis algorithm MD-LRDCA.

Images