Natural resource image data analysis method and system in combination with spatial-temporal characteristics

Abstract

The invention discloses a natural resource image data analysis method and system in combination with spatial-temporal characteristics. The method comprises the following steps: acquiring regular image data of a natural resource enrichment area; acquiring a natural resource contour map, recording contour line functions, and summarizing the contour line functions of all time nodes to construct a natural resource function model; dividing the natural resource contour map into a plurality of areas, and obtaining a natural resource contour line function image change trend in each interval by comparing contour line function images of each time node in different intervals in each area; the change trend of the region is judged by integrating the change trend of the natural resource contour line function images of all the intervals in the same region; and analyzing and acquiring natural resource change conditions. The method has the advantages that a natural resource function model is constructed in combination with spatial-temporal characteristics, and resource changes are accurately quantified; the method analyzes and captures tiny trends in different regions, reveals the overall law, is efficient and reliable, and assists resource management and sustainable development.

Description

Technical Field

[0001] The present invention relates to function image processing, and in particular to a natural resource image data analysis method and system combining time and space characteristics. Background Art

[0002] The natural resource image data analysis method and system combined with spatiotemporal characteristics obtains regular image data of natural resource-rich areas, extracts the contour map of natural resources and records the contour line function, and constructs a natural resource function model that reflects the spatiotemporal changes of resources. The system divides the contour map into multiple areas, and analyzes the change trend of each interval by comparing the contour line function images at different time nodes in each area. Then, the changes in each interval in the same area are combined to determine the overall resource change trend of the area. Based on the summary of the change trends in each area, a comprehensive analysis of natural resource changes is finally achieved. This method can intuitively show the distribution and dynamic changes of natural resources, and provide a scientific basis for resource management and protection.

[0003] The current natural resource image data analysis methods on the market are mainly based on remote sensing technology, geographic information systems and big data analysis technology. These methods obtain multispectral or high-resolution image data of surface natural resources through satellites, drones or other remote sensing equipment to monitor and evaluate vegetation coverage, water resources, mineral resources, etc. Common methods include classification-based land use change analysis, model-based ecosystem assessment, and target identification and trend prediction achieved through machine learning technology. In addition, some methods combine spatiotemporal dynamic analysis technology and use multi-period image data to study the spatiotemporal evolution of resource changes. Data processing usually involves image preprocessing, feature extraction, change detection and visualization analysis. These methods are widely used in ecological protection, resource development and environmental governance, providing strong support for scientific decision-making. Summary of the invention

[0004] In order to improve the existing natural resource image data analysis method and system, a natural resource image data analysis method and system combining spatiotemporal characteristics is provided. This method regularly collects image data of natural resource-rich areas, extracts contour functions, and constructs a spatiotemporal change model to analyze the dynamic change trend of resources. Systematic zoning analysis and trend judgment provide a scientific basis for natural resource management and change monitoring.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: The natural resource image data analysis method combining temporal and spatial characteristics is characterized by comprising: Obtain regular imagery data of areas rich in natural resources; Based on the image data, obtain the natural resource contour map, record the contour line function, and summarize the contour line function of each time node to build a natural resource function model; The natural resource contour map is divided into several regions. Based on the natural resource function model, the contour function images of each time node in different intervals in each region are compared to obtain the change trend of the natural resource contour function image in each interval. By synthesizing the changing trends of the natural resource contour function images of each interval in the same region, the changing trend of the region can be determined; Analyze changes in natural resource acquisition based on changing trends in each region.

[0006] Preferably, the obtaining of the natural resource contour map based on the image data, calculating the contour line function, and summarizing the contour line functions at each time node to construct the natural resource function model specifically includes: Based on image data, important nodes of the natural resource-rich area outline are marked according to fixed pixels; Based on the important nodes of the contour, each node is connected into a continuous contour line through the spatial interpolation method. The formula is: 0,1], where and are the coordinates of adjacent nodes; For each time node t, the recorded contour function is ; Summarize the contour functions of all time nodes to form a time series {L( ),L( ),...,L( )}, construct a natural resource function model.

[0007] Preferably, the natural resource contour map is divided into several regions, and based on the natural resource function model, by comparing contour line function images of various time nodes in different intervals in each region, obtaining the change trend of the natural resource contour line function image in each interval specifically includes: Based on the natural resource contour map, it is divided into several grid areas of equal size; Contour function based on different time nodes in the same interval , , calculate the difference between the two and the overall change; Based on the interval with large overall variation , calculate the change in area, shape, and center of gravity; A time series is constructed based on the changes in each time node, and the changing trend of the natural resource contour function image in each interval is analyzed through the regression model.

[0008] Preferably, the contour line function based on different time nodes in the same interval , , the calculation of the difference and overall change between the two specifically includes: Contour function based on different time nodes , , compare the changes of their contour lines, and calculate the difference between the two contour lines. The formula is:

[0009] Among them, ||·|| represents the Euclidean distance; Based on the spatial integration of the difference, the overall change is obtained, and the formula is:

[0010] Preferably, the interval based on the larger overall change amount , calculate its area change, shape change, and center of gravity change specifically including: The area change formula is: , the area change rate is: ; The shape change formula is: ; The formula for the change in center of gravity is: .

[0011] Preferably, judging the change trend of the region by comprehensively analyzing the change trend of the natural resource contour line function images of each interval in the same region specifically includes: Statistically calculate the changing trend of the natural resource contour line function images of each interval in the region. If the changing trends are consistent, the changing trend of the region can be determined; Based on the inconsistency of the change trends, the change trends of the adjacent areas of the area are obtained, and weights are given as influencing factors to analyze and determine the change trends of the area.

[0012] Preferably, based on the inconsistency of the change trends, obtaining the change trends of the adjacent areas of the area, assigning weights as influencing factors to analyze and determine the change trends of the area specifically includes: Obtain the change trend of adjacent areas and assign different weight values ​​based on the area of ​​adjacent areas; Based on the distribution of change trends in the region and the weight change trends of adjacent regions, the change trend of the region is comprehensively judged. The calculation formula is:

[0013] in, The proportion of this area is expanded. is the weight ratio of the region, is the additional weight of each adjacent region, is the changing trend of adjacent regions, and n is the total number of adjacent regions.

[0014] Preferably, the analyzing and obtaining the change of natural resources based on the change trend of each region specifically includes: Based on the changing trends of each region, the changes in the area, shape, and resource center of gravity of each region in the natural resource-rich area at each future time node are given; Provide sustainability assessment results of natural resources; Based on the changing trends of natural resources, suggestions for optimizing resource protection and development are put forward.

[0015] Furthermore, a natural resource image data analysis system combining spatiotemporal characteristics is characterized by comprising: Image acquisition module: The image acquisition module is used to acquire image data of natural resource-rich areas through satellites or drones; A model building module, the model building module is used to obtain the contour line function of each time node through the natural resource contour map, and construct a natural resource function model accordingly; A comparison module, which is used to compare the changes in the natural resource contour function images at each time node, calculate the difference and the overall change amount, and obtain the change trend of each interval; Comprehensive module: The comprehensive module is used to integrate the change trends in each interval to obtain the change trends of each area in the natural resource-rich area; A storage module, the storage module is used to store the contour line function data of each time node and the change trend data between each interval; A processing module, which is used for information transmission between modules and calculation of models and change trends; A memory and a processor, wherein the memory and the processor are linearly connected, and the processor executes the method according to any one of claims 1 to 8 by executing computer instructions.

[0016] Compared with the prior art, the advantages of the present invention are: By constructing a natural resource function model, the complex resource change law is quantified into a mathematical expression, making the analysis more scientific and systematic. Secondly, the method takes the contour function as the core, and through the comparison of different regions and multiple time nodes, it can accurately capture the subtle change trends of natural resources in spatial and temporal dimensions, providing high-precision support for dynamic monitoring. At the same time, through regional division and comprehensive analysis, it can not only refine local changes, but also reveal the overall change law, and meet the needs of multi-scale research. This method is based on the automatic extraction and analysis of image data, which greatly improves the data processing efficiency and the reliability of the results. It is suitable for applications in many fields such as natural resource management, ecological protection and development planning, and helps to formulate policies scientifically, allocate resources rationally, and achieve sustainable development goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of image data analysis of the method and system proposed in the present invention; Figure 2 A schematic diagram of constructing a natural resource function model for the method and system proposed in the present invention; Figure 3 A schematic diagram of interval variation trend of the method and system proposed in the present invention; Figure 4 A schematic diagram of calculating the difference variation of the method and system proposed in the present invention; Figure 5 A schematic diagram of a variation formula of the method and system proposed in the present invention; Figure 6 A schematic diagram of regional variation trends of the method and system proposed in the present invention; Figure 7 A schematic diagram of regional change trend calculation of the method and system proposed in the present invention; Figure 8 This is a schematic diagram for analyzing changes in the method and system proposed in the present invention. DETAILED DESCRIPTION

[0018] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0019] The natural resource image data analysis system combining temporal and spatial characteristics includes: Image acquisition module: The image acquisition module is used to acquire image data of natural resource-rich areas through satellites or drones; A model building module, the model building module is used to obtain the contour line function of each time node through the natural resource contour map, and construct a natural resource function model accordingly; A comparison module, which is used to compare the changes in the natural resource contour function images at each time node, calculate the difference and the overall change amount, and obtain the change trend of each interval; Comprehensive module: The comprehensive module is used to integrate the change trends in each interval to obtain the change trends of each area in the natural resource-rich area; A storage module, the storage module is used to store the contour line function data of each time node and the change trend data between each interval; A processing module, which is used for information transmission between modules and calculation of models and change trends; A memory and a processor, wherein the memory and the processor are linearly connected, and the processor controls the operation of a natural resource image data analysis system combining temporal and spatial characteristics by executing computer instructions.

[0020] See also Figure 1 As shown in the figure, this scheme also proposes a natural resource image data analysis method combining temporal and spatial characteristics, including: Step 1: Obtain regular image data of natural resource-rich areas; Step 2: Based on the image data, obtain the natural resource contour map, record the contour line function, and summarize the contour line function of each time node to construct a natural resource function model; Step 3: Divide the natural resource contour map into several areas, and based on the natural resource function model, obtain the change trend of the natural resource contour function image in each interval by comparing the contour function images of each time node in different intervals in each area; Step 4: Determine the changing trend of the region by synthesizing the changing trend of the natural resource contour line function images of each interval in the same region; Step 5: Based on the changing trends in each region, analyze and obtain changes in natural resources.

[0021] See also Figure 2 As shown in the figure, based on the image data, the natural resource contour map is obtained, the contour line function is calculated, and the contour line function of each time node is summarized to construct a natural resource function model, which specifically includes: Based on image data, important nodes of the natural resource-rich area outline are marked according to fixed pixels; Based on the important nodes of the contour, each node is connected into a continuous contour line through the spatial interpolation method. The formula is: 0,1], where and are the coordinates of adjacent nodes; For each time node t, the recorded contour function is ; Summarize the contour functions of all time nodes to form a time series {L( ),L( ),...,L( )}, construct a natural resource function model.

[0022] Specifically, the important nodes of the outline of the natural resource-rich area based on the image data and marked according to fixed pixels include turning points, that is, positions with the largest curvature, intersection points, that is, points where multiple outlines intersect, and feature points, that is, starting points and ending points of the outline. The fixed pixel marking converts the natural resource features in the image data (such as forests, water bodies, and minerals) into fixed pixel points to mark important nodes.

[0023] See also Figure 3 As shown in the figure, the natural resource contour map is divided into several areas. Based on the natural resource function model, by comparing the contour function images of each time node in different intervals in each area, the change trend of the natural resource contour function image in each interval is obtained, which specifically includes: Based on the natural resource contour map, it is divided into several grid areas of equal size; Contour function based on different time nodes in the same interval , , calculate the difference between the two and the overall change; Based on the interval with large overall variation , calculate the change in area, shape, and center of gravity; A time series is constructed based on the changes in each time node, and the changing trend of the natural resource contour function image in each interval is analyzed through the regression model.

[0024] It is understandable that the shape of natural resource contours may be very complex, and simple shape description indicators cannot accurately describe the changes. This problem can be solved by using geometric indicators (such as compactness and aspect ratio) to describe the overall shape changes and introducing shape overlap indicators (such as Jaccard coefficient) to analyze the shape similarity at different time nodes.

[0025] The trend of natural resource changes may be nonlinear, and simple linear regression may not accurately capture the law of change. Exploratory analysis can be performed on the change amount to determine whether there are periodic or nonlinear characteristics. Select a suitable model based on the characteristics of the data: Periodic changes: Use Fourier transform or ARIMA model. Nonlinear trends: Use machine learning models (such as random forest regression, support vector regression, or neural network).

[0026] See also Figure 4 As shown, the contour function based on different time nodes in the same interval , , the calculation of the difference and overall change between the two specifically includes: Contour function based on different time nodes , , compare the changes of their contour lines, and calculate the difference between the two contour lines. The formula is:

[0027] Among them, ||·|| represents the Euclidean distance; Based on the spatial integration of the difference, the overall change is obtained, and the formula is:

[0028] See also Figure 5 As shown, based on the interval with large overall change , calculate its area change, shape change, and center of gravity change specifically including: The area change formula is: , the area change rate is: ; The shape change formula is: ; The formula for the change in center of gravity is: .

[0029] Specifically, compare the calculation results with the results of historical documents and geological survey data to verify the rationality of the analysis. Use cross-validation to compare the results of different data sources. Explain the drivers of change, including natural factors: such as the impact of climate change, hydrological conditions, geological activities, etc. on natural resource changes. Human factors: such as land use changes, mining activities, agricultural development, etc. Evaluate the impact of different parameter selections (such as threshold settings, shape indicators) on the results. Check whether there are abnormal results due to algorithm or data bias.

[0030] See also Figure 6 As shown in the figure, by integrating the changing trends of the natural resource contour function images of each interval in the same region, the changing trends of the region are judged to include: Statistically calculate the changing trend of the natural resource contour line function images of each interval in the region. If the changing trends are consistent, the changing trend of the region can be determined; Based on the inconsistency of the change trends, the change trends of the adjacent areas of the area are obtained, and weights are given as influencing factors to analyze and determine the change trends of the area.

[0031] See also Figure 7 As shown, based on the inconsistent change trends, the change trends of the adjacent areas of the area are obtained, and the change trends of the area are determined by weighting as influencing factors. Specifically, the following are included: Obtain the change trend of adjacent areas and assign different weight values ​​based on the area of ​​adjacent areas; Based on the distribution of change trends in the region and the weight change trends of adjacent regions, the change trend of the region is comprehensively judged. The calculation formula is:

[0032] in, The proportion of this area is expanded. is the weight ratio of the region, is the additional weight of each adjacent region, is the changing trend of adjacent regions, and n is the total number of adjacent regions.

[0033] It is understandable that the trend of change in adjacent areas may have its own prediction errors, which in turn affects the comprehensive judgment of the target area. Changes in some adjacent areas may be affected by short-term factors (such as human development) and do not reflect long-term trends. By quantifying the error range of the prediction of the trend of change in adjacent areas, and evaluating the impact of the error on the comprehensive results through Monte Carlo simulation. Continuously optimize the prediction model to reduce the impact of the error in the trend prediction of a single area. Smooth short-term fluctuations (such as moving average, exponential smoothing), extract long-term change trends, mark abnormal change areas, and reduce their weights.

[0034] The changing trends of some adjacent areas may have an excessive impact on the comprehensive judgment of the target area, causing the results to deviate from reality. For some special terrains (such as mountains, rivers) or isolated areas, relying solely on adjacent areas may not be scientific enough. Therefore, the weights of adjacent areas can be normalized to avoid excessive influence of a single area on the results or to model the boundary areas and isolated areas separately, or to combine expert experience to empower weights, and for complex terrain, combine DEM (digital elevation model) for spatial analysis.

[0035] See also Figure 8 As shown in the figure, based on the changing trends in each region, the analysis of changes in natural resource acquisition specifically includes: Based on the changing trends of each region, the changes in the area, shape, and resource center of gravity of each region in the natural resource-rich area at each future time node are given; Provide sustainability assessment results of natural resources; Based on the changing trends of natural resources, suggestions for optimizing resource protection and development are put forward.

[0036] In summary, the advantages of the present invention are: by regularly acquiring image data of natural resource-rich areas, dynamic monitoring of resource distribution and changes can be achieved, and data acquisition has high frequency and timeliness. Secondly, the method constructs contour function and natural resource function model based on image data, which can accurately depict the boundaries and change trends of resources, and improve the accuracy and scientificity of analysis. Thirdly, through the regional division and zoning analysis of the natural resource contour map, combined with the contour function image comparison of each time node, the spatial differences and trends of resource changes can be refined, and the spatial resolution and operability of the analysis can be improved. In addition, by integrating the change trends of each region, the system can comprehensively reflect the resource dynamics of the entire region, providing a global perspective and decision-making support for resource management. Finally, the method is both intuitive and quantifiable, and improves the efficiency of resource monitoring and analysis in a data-driven manner, providing a scientific basis for resource protection, rational development and sustainable utilization, and is an important tool for modern resource management.

[0037] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A natural resource image data analysis method combining temporal and spatial characteristics, characterized in that: include: Obtain regular imagery data of areas rich in natural resources; Based on the image data, obtain the natural resource contour map, record the contour line function, and summarize the contour line function of each time node to build a natural resource function model; The natural resource contour map is divided into several regions. Based on the natural resource function model, the contour function images of each time node in different intervals in each region are compared to obtain the change trend of the natural resource contour function image in each interval. By synthesizing the changing trends of the natural resource contour function images of each interval in the same region, the changing trend of the region can be determined; Analyze changes in natural resource acquisition based on changing trends in each region.

2. The natural resource image data analysis method combining temporal and spatial characteristics according to claim 1 is characterized in that: The method of obtaining a natural resource contour map based on image data, calculating contour line functions, and summarizing contour line functions at various time nodes to construct a natural resource function model specifically includes: Based on image data, important nodes of the natural resource-rich area outline are marked according to fixed pixels; Based on the important nodes of the contour, each node is connected into a continuous contour line through the spatial interpolation method. The formula is: 0,1], where and are the coordinates of adjacent nodes; For each time node t, the recorded contour function is ; Summarize the contour functions of all time nodes to form a time series {L( ),L( ),...,L( )}, construct a natural resource function model.

3. The natural resource image data analysis method combining temporal and spatial characteristics according to claim 2 is characterized in that: The natural resource contour map is divided into several regions, and based on the natural resource function model, by comparing the contour function images of each time node in different intervals in each region, obtaining the change trend of the natural resource contour function image in each interval specifically includes: Based on the natural resource contour map, it is divided into several grid areas of equal size; Contour function based on different time nodes in the same interval , , calculate the difference between the two and the overall change; Based on the interval with large overall variation , calculate the change in area, shape, and center of gravity; A time series is constructed based on the changes in each time node, and the changing trend of the natural resource contour function image in each interval is analyzed through the regression model.

4. The natural resource image data analysis method combining temporal and spatial characteristics according to claim 3 is characterized in that: The contour line function based on different time nodes in the same interval , , the calculation of the difference and overall change between the two specifically includes: Contour function based on different time nodes , , compare the changes of their contour lines, and calculate the difference between the two contour lines. The formula is: ； Among them, ||·|| represents the Euclidean distance; Based on the spatial integration of the difference, the overall change is obtained, and the formula is:

5. The natural resource image data analysis method combining temporal and spatial characteristics according to claim 4 is characterized in that: The interval based on the larger overall change , calculate its area change, shape change, and center of gravity change specifically including: The area change formula is: , the area change rate is: ; The shape change formula is: ; The formula for the change in center of gravity is: .

6. The natural resource image data analysis method combining temporal and spatial characteristics according to claim 5 is characterized in that: The determination of the change trend of the region by synthesizing the change trend of the natural resource contour line function image of each interval in the same region specifically includes: Statistically calculate the changing trend of the natural resource contour line function images of each interval in the region. If the changing trends are consistent, the changing trend of the region can be determined; Based on the inconsistency of the change trends, the change trends of the adjacent areas of the area are obtained, and weights are given as influencing factors to analyze and determine the change trends of the area.

7. The natural resource image data analysis method combining temporal and spatial characteristics according to claim 6 is characterized in that: The obtaining of the change trend of the adjacent regions of the region based on the inconsistency of the change trend and assigning weights as influencing factors to analyze and determine the change trend of the region specifically includes: Obtain the change trend of adjacent areas and assign different weight values ​​based on the area of ​​adjacent areas; Based on the distribution of change trends in the region and the weight change trends of adjacent regions, the change trend of the region is comprehensively judged. The calculation formula is: ； in, The proportion of this area is expanded. is the weight ratio of the region, is the additional weight of each adjacent region, is the changing trend of adjacent regions, and n is the total number of adjacent regions.

8. The natural resource image data analysis method combining temporal and spatial characteristics according to claim 7 is characterized in that: The analysis of changes in natural resources based on the change trends in each region specifically includes: Based on the changing trends of each region, the changes in the area, shape, and resource center of gravity of each region in the natural resource-rich area at each future time node are given; Provide sustainability assessment results of natural resources; Based on the changing trends of natural resources, suggestions for optimizing resource protection and development are put forward.

9. A natural resource image data analysis system combining spatiotemporal characteristics, used to implement a natural resource image data analysis method combining spatiotemporal characteristics as claimed in any one of claims 1 to 8, characterized in that: include: Image acquisition module: The image acquisition module is used to acquire image data of natural resource-rich areas through satellites or drones; A model building module, the model building module is used to obtain the contour line function of each time node through the natural resource contour map, and construct a natural resource function model accordingly; A comparison module, which is used to compare the changes in the natural resource contour function images at each time node, calculate the difference and the overall change amount, and obtain the change trend of each interval; Comprehensive module: The comprehensive module is used to integrate the change trends in each interval to obtain the change trends of each area in the natural resource-rich area; A storage module, the storage module is used to store the contour line function data of each time node and the change trend data between each interval; A processing module, which is used for information transmission between modules and calculation of models and change trends; A memory and a processor, wherein the memory and the processor are linearly connected, and the processor executes the method according to any one of claims 1 to 8 by executing computer instructions.

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