A natural resource investigation monitoring data visualization processing method and system

Abstract

The present application relates to the technical field of monitoring data analysis, and particularly relates to a natural resource investigation monitoring data visualization processing method and system. The present application obtains the measurement data of various natural resources in multiple target regions; performs difference characteristic analysis on the measurement content of the same type of natural resources in the multiple target regions to determine the universality index corresponding to each type of natural resource; performs resource characteristic analysis according to the universality index of each type of natural resource to obtain the grading result of the natural resources owned by each target region; corrects the weight of each type of natural resource according to the influence degree of a single natural resource on the grading under each grading result to obtain the weight change coefficient of each type of natural resource; and outputs the visualization evaluation result of the natural resource content performance of each target region according to the weight change coefficient of each type of natural resource. The technical scheme of the present application can improve the accuracy and comparability of the natural resource characteristic evaluation under the premise of fully considering the resource content difference between regions.

Description

Technical Field

[0001] This invention relates to the technical field of monitoring data analysis, specifically to a method and system for visualizing and processing natural resource survey and monitoring data. Background Technology

[0002] Transforming multi-source, complex natural resource data into intuitive graphics and maps can greatly improve management efficiency and decision-making quality. Its core benefits include: a clear view of spatial patterns, rapid identification of resource-rich areas and vulnerable zones; precise problem location and timely detection of data trends, supporting targeted governance and dynamic monitoring; breaking down data barriers, promoting cross-departmental collaboration and public understanding through a shared visual language, and ultimately achieving efficient transformation from data to insights, and from insights to scientific action.

[0003] In existing technologies, a unified comparison of the natural resource content of different regions is conducted. However, due to the diversity and differences in the natural resources of different regions, the resulting evaluation values ​​cannot effectively distinguish regions with similar natural resource content, leading to low accuracy in the evaluation of regional natural resource characteristics. Summary of the Invention

[0004] To address the technical challenge of accurately evaluating the characteristics of natural resources in different regions due to their diversity and variations, this invention aims to provide a method and system for visualizing and processing natural resource survey and monitoring data. The specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of the present invention provide a method for visualizing and processing natural resource survey and monitoring data, the method comprising:

[0006] Acquire measurement data of various natural resources in multiple target areas, where the target areas are the regions whose natural resource characteristics are to be investigated;

[0007] To determine the universal indicators corresponding to various types of natural resources, a differential characteristic analysis was conducted on the measured content of similar natural resources in multiple target areas.

[0008] Based on the universality indicators of various natural resources, resource characteristics analysis was conducted on multiple target areas, and the classification results of the natural resources possessed by each target area were obtained.

[0009] The weights of various natural resources are adjusted according to the degree of influence of individual natural resources on the formation of the classification under each classification result, so as to obtain the weight change coefficients of various natural resources.

[0010] Based on the weight change coefficients of various natural resources and the corresponding resource reference weights, the visualization evaluation results of the natural resources performance in each target area are output.

[0011] In one optional embodiment, measurement data of various natural resources in multiple target areas are acquired, including:

[0012] Satellite images that meet the target resolution are input into a preset land identification model to divide the satellite images into multiple target areas based on different land types.

[0013] Configure a solar radiation monitoring task of preset duration for each target area to obtain the solar irradiance of the corresponding target area;

[0014] The average wind power density of the corresponding target area is obtained based on the environmental data collected by the meteorological towers in each target area.

[0015] Based on the hydrological data collected from multiple locations within each target area during the exploration mission, the groundwater content of the corresponding target area is obtained.

[0016] Based on the solar irradiance, average wind power density, and groundwater content of each target area, measurement data of various natural resources in the corresponding target area are obtained.

[0017] In one optional embodiment, a differential characteristic analysis is performed on the measured content of similar natural resources in multiple target areas to determine universal indicators corresponding to various types of natural resources, including:

[0018] Based on the ratio of the measured content of the current category of natural resources in each target area to the maximum measured content of the corresponding category of natural resources in all target areas, the resource measurement ratio of the corresponding category of natural resources in each target area is obtained.

[0019] The number of target areas in all target areas where the resource measurement ratio of the same type of natural resource is greater than the first ratio threshold is counted to obtain the universality coefficient of the corresponding type of natural resource.

[0020] The universality index of the corresponding category of natural resources is obtained by summing the universality coefficient and the proportion of resource measurements that are greater than the preset proportion threshold.

[0021] In one optional embodiment, resource characteristic analysis is performed on multiple target areas based on the prevalence indicators of various natural resources, and the classification results of the natural resources possessed by each target area are obtained, including:

[0022] The number of natural resources in each target area whose universality index for all categories of natural resources is less than the index threshold and whose resource measurement ratio for the corresponding category of natural resources is greater than the second ratio threshold is counted to obtain the first resource quantity for the corresponding target area.

[0023] Based on the quantity of the first resource in each target area and the quantity of the second resource whose universality index for all categories of natural resources is less than the index threshold, the rarity coverage of the rare resources in the corresponding target area is obtained.

[0024] Based on the rarity coverage of each target area and the resource proximity of rare resources in all associated target areas, the resource characteristic value of the corresponding target area is obtained.

[0025] The resource feature values ​​of all target regions are normalized, ordered, and clustered in sequence, and the clustering results are determined as the hierarchical results of each target region.

[0026] In an optional embodiment, before obtaining the resource feature values ​​of the corresponding target area, the method further includes:

[0027] The resource measurement proportions of the same type of natural resources in all target areas are cumulatively summed to obtain the proportion summation result;

[0028] Based on the summation of the proportions and the minimum proportion difference of the current target area, the resource proximity of the current target area is obtained. The minimum proportion difference is the difference between the maximum and minimum resource measurement proportions of the natural resources corresponding to the minimum universality index of the current target area.

[0029] In one optional embodiment, the weights of various natural resources are adjusted according to the degree of influence of individual natural resources on the formation of the classification under each classification result, so as to obtain the weight change coefficients of various natural resources, including:

[0030] The differences in the characteristics of similar natural resources in each target area within each level are analyzed to obtain the degree of influence of various types of natural resources on the level at each level;

[0031] Based on the degree of impact and the dissimilarity of similar natural resources in different target areas under the same level, the weight change coefficients of various natural resources at the corresponding level are obtained.

[0032] In one optional embodiment, the differences in the characteristics of similar natural resources in each target area within each grading are analyzed to obtain the degree of influence of various natural resources on the grading at each level, including:

[0033] The average content of the same type of natural resource in all target areas at the current level is calculated to obtain the average content of the same type of natural resource at the current level.

[0034] To analyze the differences in the average content of single-category natural resources at adjacent levels, we can obtain the degree of difference in the level of corresponding categories of natural resources at adjacent levels.

[0035] Based on the cumulative results of the degree of difference between levels and the degree of difference between the same type of natural resource levels in adjacent levels, the degree of influence of various types of natural resources on the classification under each level is obtained.

[0036] In an optional embodiment, based on the degree of influence and the dissimilarity characteristics of similar natural resources in target areas under the same level, the weight change coefficients of various natural resources at the corresponding level are obtained, including:

[0037] Based on the current impact level of each type of natural resource at the current level and the maximum impact level at the corresponding level, obtain the current impact ratio of the current type of natural resource at the corresponding level;

[0038] Based on the dissimilarity characteristics of natural resources of the current category in all target areas at the current level, obtain the cumulative dissimilarity results for the current level;

[0039] Based on the current impact ratio and cumulative dissimilarity results at the current level, obtain the weight change coefficient of the corresponding category of natural resources at the current level.

[0040] In one optional embodiment, based on the weight change coefficients of various natural resources and the corresponding resource reference weights, a visualized evaluation result of the natural resources performance of each target area is output, including:

[0041] Based on the weight change coefficient of each type of natural resource and the corresponding resource reference weight, the target reference weight of the corresponding type of natural resource is obtained.

[0042] The target regions are rendered hierarchically according to the target reference weight of each category of natural resources, and the rendering results are displayed based on the preset stereoscopic image.

[0043] Secondly, embodiments of the present invention also provide a natural resource survey and monitoring data visualization processing system, the system being the system corresponding to any method in the first aspect, the system comprising:

[0044] The acquisition module is used to acquire measurement data of various natural resources in multiple target areas, where the target areas are the regions whose natural resource characteristics are to be investigated.

[0045] The determination module is used to analyze the differences in the measured content of similar natural resources in multiple target areas in order to determine the universal indicators corresponding to various types of natural resources.

[0046] The first acquisition module is used to perform resource characteristic analysis on multiple target areas based on the universality indicators of various natural resources, and to obtain the classification results of the natural resources possessed by each target area.

[0047] The second acquisition module is used to adjust the weights of various natural resources according to the degree of influence of individual natural resources on the formation of the classification under each classification result, so as to obtain the weight change coefficients of various natural resources.

[0048] The output module is used to output a visual evaluation result of the natural resources performance of each target area based on the weight change coefficients of various natural resources and the corresponding resource reference weights.

[0049] The present invention has the following beneficial effects:

[0050] The technical solution of this invention acquires measurement data of various natural resources in multiple target areas. Due to natural differences in resource distribution and environmental conditions across different regions, to reduce the adverse effects of amplifying extreme value areas in horizontal comparisons of measured content across regions, the invention performs a difference characteristic analysis on the measured content of similar natural resources in multiple target areas to determine the universal indicators corresponding to each type of natural resource. Based on these universal indicators, the commonality or rarity of each type of natural resource is characterized globally. Resource characteristic analysis is then performed on multiple target areas based on the universal indicators of various natural resources, and the grading results of the natural resources possessed in each target area are obtained. This grading reduces the dominance of single resources in the evaluation, allowing comparisons between regions to be based on overall resource structure differences. Furthermore, the weights of various natural resources are adjusted according to the influence of individual natural resources on the grading results, resulting in weight change coefficients for each type of natural resource. This ensures that resource weights are no longer statically set but adaptively adjusted according to the regional resource distribution characteristics. Based on the weight change coefficients of various natural resources and the corresponding resource reference weights, a visualized evaluation result of the natural resource content performance in each target area is output. The technical solution of this invention can improve the accuracy and comparability of natural resource characteristic evaluation while fully considering the differences in resource content between regions, and provide reliable support for the intuitive expression of natural resource survey and monitoring results and scientific decision-making. Attached Figure Description

[0051] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating a method for visualizing natural resource survey and monitoring data, provided in one embodiment of the present invention;

[0053] Figure 2 A flowchart illustrating the calculation of a universality index provided in one embodiment of the present invention;

[0054] Figure 3 A flowchart illustrating the natural resource classification of target areas according to an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the structure of a natural resource survey and monitoring data visualization processing system provided in one embodiment of the present invention. Detailed Implementation

[0056] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a natural resource survey and monitoring data visualization processing method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0058] In one embodiment of the present invention, the normalization process can specifically be, for example, maximum and minimum value normalization. Furthermore, subsequent normalization steps can all employ maximum and minimum value normalization. In other embodiments of the present invention, other normalization methods can be selected based on the specific range of values, which will not be elaborated further. The maximum and minimum value normalization in the embodiments of the present invention aims to perform standardization processing. The maximum and minimum values ​​of the normalization process can be set according to specific scenarios and data numerical characteristics. Adjusting, calibrating, or optimizing the maximum and minimum values ​​does not constitute a limitation of the present invention.

[0059] Existing technologies often use a uniform comparison method to compare the manifestations of natural resources in different target areas. However, due to the diversity and variations in the manifestations of natural resources across different regions, traditional methods fail to consider the potentially significant differences in their manifestations. To address these technical problems, this invention provides a method and system for visualizing natural resource survey and monitoring data. This method transforms multi-source, complex natural resource data into intuitive graphics and maps, greatly improving management efficiency and decision-making quality. Its core benefits include: a clear view of spatial patterns, rapid identification of resource-rich areas and vulnerable zones; precise identification of problems and changes, supporting targeted governance and dynamic monitoring; and breaking down data barriers, using a common visual language to promote cross-departmental collaboration and public understanding, achieving efficient transformation from data to insights, and from insights to scientific action. The specific technical solutions provided by this invention are described in detail below with reference to the accompanying drawings.

[0060] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for visualizing and processing natural resource survey and monitoring data, provided in one embodiment of the present invention. This method can be run on a natural resource survey processing terminal, which can be a server or a computer device capable of running the method; no specific limitations are imposed here. The processing method includes:

[0061] S11: Obtain measurement data of various natural resources in multiple target areas, where the target areas are the regions whose natural resource characteristics are to be investigated.

[0062] Specifically, target areas can be defined based on the needs of natural resource surveys. For example, for mountainous areas that need development, the region can be divided based on the topography of the mountain, resulting in multiple target areas. Each target area is labeled accordingly, and the label of the target area is denoted as j, where j is a natural number greater than 1. The measurement data of various natural resources can be obtained based on geological exploration results or natural environment data, as long as it can characterize the various natural resources in the area. The type number of the natural resource is denoted as a, where a is also a natural number greater than 1.

[0063] For example, step S11 includes sub-steps S11-1 to S11-5, which are described in detail below:

[0064] S11-1: Input satellite images that meet the target resolution (such as Landsat or Sentinel) into a preset land identification model to divide the land into multiple target regions based on different land types in the satellite images. The land identification model can be obtained through deep learning training, for example, configuring the land identification model as a convolutional neural network model and training it until the accuracy of dividing the target regions reaches a preset value. Input the satellite images into the trained land identification model, and derive multiple target regions based on the model's output.

[0065] S11-2: Configure a preset duration of sunshine monitoring task for each target area to obtain the solar irradiance of the corresponding target area. The preset duration can be configured based on actual needs, for example, configured as 10:00-16:00 daily, for a total of 6 hours. Sunshine data within 6 hours is monitored through satellite remote sensing to obtain the solar irradiance of each target area.

[0066] S11-3: Based on the environmental data collected by the meteorological tower within each target area, obtain the average wind power density for that target area. The meteorological tower has a multi-layered structure, configured with different collection heights such as 50m, 10m, and 0.5m (or hub height). Mechanical wind speed / direction sensors (such as wind cups) and temperature / humidity / barometric pressure sensors are installed to acquire raw time series data such as wind speed, wind direction (°), temperature (°C), barometric pressure (Pa), and air density (kg / m³) for each target area. This data is used as environmental data, and the wind power density P is calculated using a formula. ρ is the air density. Given the wind speed at time t, the wind power density at different times within the target area is obtained. This allows us to obtain the average wind power density at different times within the target area over a 6-hour period, i.e., the average wind power density.

[0067] S11-4: Based on the hydrological data collected from multiple locations within each target area during the exploration mission, the groundwater content of the corresponding target area is obtained. Through drilling, pumping tests, and other methods in hydrogeological exploration, multiple locations are tested in the target area to be monitored, thereby calculating and estimating the groundwater content within the area, and thus obtaining the groundwater content of each target area.

[0068] S11-5: Based on the solar irradiance, average wind power density, and groundwater content of each target area, obtain measurement data for various natural resources in that target area. Configure environmental variables for each type of natural resource, and store the data obtained in the above manner to derive the measurement data for various natural resources in each target area. It should be noted that each measurement data characterizes the natural resource characteristics of the corresponding target area. For example, a high solar irradiance in a target area indicates that the area is suitable for solar power generation, or as a natural crop drying area, or as a planting area for sun-loving plants; similarly, a high average wind power density in a target area indicates that the area is suitable for constructing a wind power station.

[0069] At this point, measurement data of various natural resources in multiple target areas have been obtained based on the above method, and we proceed to step S12.

[0070] S12: Conduct differential characteristic analysis on the measured content of similar natural resources in multiple target areas to determine the universal indicators corresponding to various types of natural resources.

[0071] Specifically, different natural resources require different formation conditions, which determine their scarcity. For example, certain rare earth elements such as rhodium and iridium have extremely low abundance in the Earth's crust, are irreplaceable in their key uses, and are extremely rare compared to conventional resources. In contrast, renewable resources such as sand, solar energy, wind energy, and air have vast and widespread reserves, resulting in low scarcity. The scarcity of different resources, along with their varying uses, determines their value. Therefore, to evaluate the overall performance of natural resources in different target areas, priority should be given to assessing the scarcity of different natural resources. This necessitates analyzing the differences in the measured content of natural resources in each target area to derive universal indicators characterizing the scarcity or prevalence of various natural resources.

[0072] For example, please refer to Figure 2 , Figure 2 This is a flowchart of calculating a universality index provided in an embodiment of the present invention. Step S12 includes sub-steps S12-1 to S12-3, which are described in detail below:

[0073] S12-1: Based on the ratio of the measured content of the current category of natural resource in each target area to the maximum measured content of the corresponding category of natural resource in all target areas, obtain the resource measurement proportion of the corresponding category of natural resource in each target area. For a single natural resource 'a', obtain the natural resource content value of natural resource 'a' in different target areas, i.e., the measured content. , representing the measured content of natural resource a in target region j. The maximum measured content of natural resource a in different target regions is obtained by comparison, denoted as . This allows us to obtain information about the natural resource content 'a', which is then recorded as the resource measurement ratio. , Resource measurement ratio This determines the proportion of natural resource a in target area j for the current category. It should be noted that this step aims to perform resource measurement and analysis. When the value is 0, it indicates that this type of natural resource does not exist, and therefore it is not included in the statistics. Thus, the obtained... All are values ​​greater than 0.

[0074] S12-2: Statistically count the number of target areas where the resource measurement ratio of the same type of natural resource exceeds a first ratio threshold across all target areas to obtain the prevalence coefficient of the corresponding natural resource category. The first ratio threshold can be set based on actual conditions or the experience of technical personnel, and is not specifically limited here; for example, it can be set to 0.6 to statistically analyze the performance of natural resource 'a' across all areas. Number of regions >0.6 The total number of all target regions is The universality coefficient is the ratio of the two. .

[0075] S12-3: Based on the cumulative sum of the universality coefficient and the proportion of resources measured that are greater than a preset proportion threshold, the universality index of the corresponding category of natural resources is obtained. The universality index of natural resource a is defined as follows: , ,ratio The larger the value, and the better the performance of natural resources 'a' across all monitored areas. and The larger the value, the greater the content of natural resource a in each region, proving that the conditions for the generation of natural resource a are easier to achieve, and its rarity is smaller; conversely, it indicates that its rarity is greater.

[0076] In practical applications, the max-min normalization method can be used to... After normalization, we get Its value range is [0,1]. Universal indicators for different categories of natural resources are obtained using the above methods to serve as a reference for evaluating natural resources in different regions, thereby increasing the reference value of the resulting visualization maps.

[0077] At this point, the universal indicators corresponding to various natural resources have been determined, and we proceed to step S13.

[0078] S13: Based on the universality indicators of various natural resources, conduct resource characteristic analysis on multiple target areas and obtain the classification results of the natural resources possessed by each target area.

[0079] Specifically, the types of natural resources contained in different regions may vary significantly, making direct comparisons between target regions difficult. Understandably, to improve the rationality of comparisons, regions containing similar natural resources should be compared. Therefore, it is necessary to assess the natural resource performance of a region based on the abundance of rarer natural resources within each target region, and then classify the regions using clustering methods based on their natural resource performance. Since universality indicators reflect the prevalence of resources in each target region and can characterize the scarcity of natural resource types in the corresponding target region, differential analysis of resource measurement can be conducted based on universality indicators, thereby deriving the classification results for each target region.

[0080] For example, please refer to Figure 3 , Figure 3 A flowchart for classifying natural resources for each target area. Step S13 includes sub-steps S13-1 to S13-4, which are described in detail below:

[0081] S13-1: Count the number of natural resources in each target area whose universality index for all categories of natural resources is less than the index threshold, and whose resource measurement ratio for the corresponding category of natural resources is greater than the second ratio threshold, to obtain the first resource quantity for the corresponding target area. Both the index threshold and the second ratio threshold can be set based on actual needs or the experience of technical personnel; for example, the index threshold can be 0.5, and the second ratio threshold can be set to 0.6. For a single target area j, it is necessary to count the universality index in target area j. And the proportion of resource measurement of its resource performance. The number of natural resources greater than 0.6 is recorded as the first resource quantity. .

[0082] S13-2: Based on the quantity of the first resource in each target area and the quantity of the second resource (where the prevalence index of all types of natural resources is less than the index threshold), the rarity coverage of the rare resources in the corresponding target area is obtained. The quantity of the second resource is denoted as... This value represents a universality indicator. The number of all natural resources less than 0.5. Rarity coverage is the ratio of the quantity of the first resource to the quantity of the second resource, i.e. Among them, in When the value is 0, the ratio calculation is meaningless. In this case, the rare coverage can be directly set to the preset minimum value, which is the minimum rare coverage value determined based on historical experience, such as 0, without any restrictions.

[0083] S13-3: Based on the rarity coverage of each target region and the resource proximity of all associated rare resources in target regions, obtain the resource characteristic value of the corresponding target region. Rarity coverage quantifies the proportion of rare resources in target region j, while resource proximity quantifies the difference between the rare resources in target region j and those in abundant regions. The product of these two values ​​yields the resource characteristic value of the corresponding target region. The resource characteristic value is denoted as... This is used to characterize the natural resource situation of target region j. It should be noted that the resource proximity can be quantified based on the following methods:

[0084] The first step is to sum the resource measurement proportions of similar natural resources across all target areas to obtain a summed proportion result. The summed proportion result is... , The number of target areas.

[0085] The second step involves obtaining the resource proximity of the current target area based on the sum of the proportions and the minimum proportion difference. The minimum proportion difference is the difference between the maximum and minimum resource measurement proportions of the natural resources corresponding to the minimum universality index of the current target area. The resource proximity is the ratio of the sum of the proportions to the minimum proportion difference, i.e. Set a coefficient of 1 to avoid calculation errors caused by a denominator of 0. First, identify the resource type with the lowest general index in the current region and label it as a critical resource. This represents the maximum proportion of this key resource measured across all regions.

[0086] Furthermore, this can be achieved through the formula: The resource characteristic values ​​of target region j are calculated. ,ratio The larger the value, the better the performance of all natural resources a in the target region j. and The larger and more universal the indicator Performance of natural resources corresponding to the minimum value The maximum value of natural resource performance corresponding to the minimum degree of general ownership obtained in all target areas. The difference The smaller the value, the greater the content of rare natural resources in the target area j, indicating a greater variety of natural resources, a higher degree of resource similarity in the calculation, and better performance of natural resources; conversely, the smaller the value, the lower the degree of resource similarity in the calculation, and the worse performance of natural resources.

[0087] S13-4: Normalize, sort, and cluster the resource characteristic values ​​of all target regions sequentially, and determine the clustering results as the hierarchical results for each target region. After calculating the resource characteristic values ​​for each target region, the max-min normalization method can also be used to further refine the resource characteristic values. After normalization, we get Its value range is [0,1]. The natural resource situation obtained from the analysis of different regions... The values ​​are arranged from largest to smallest to obtain the corresponding one-dimensional sequence. The DBSCAN density clustering algorithm is used to cluster the one-dimensional data sequence, resulting in multiple cluster segments. The construction status of each individual cluster segment is then analyzed. The corresponding region is divided into a natural resource level, resulting in multiple natural resource status levels (or grades). The reference weights of the natural resources are then adjusted based on the performance of each grade to make the final natural resource assessments for different regions more meaningful.

[0088] At this point, the classification results of the natural resources possessed by each target area have been completed, and we proceed to step S14.

[0089] S14: Adjust the weights of various natural resources according to the degree of influence of individual natural resources on the formation of the classification under each classification result, so as to obtain the weight change coefficients of various natural resources.

[0090] Specifically, the rarity of natural resources varies across different natural resource classifications. When conducting a more detailed evaluation of the natural resources in different target areas within a single classification, resources with greater rarity are generally more valuable and better reflect the resource content of each area within that classification. Therefore, resources with greater rarity in each area should be given greater weight in the evaluation. Thus, the influence of individual natural resources on the classification process can be analyzed, quantifying the magnitude of weight adjustments for various natural resources, and ultimately deriving the weight change coefficients for each type of natural resource.

[0091] For example, step S14 includes sub-steps S14-1 to S14-2, which are described in detail below:

[0092] S14-1: Analyze the differences in the characteristics of similar natural resources in each target area within each grade to obtain the degree of influence of various natural resources on the grade at each level. Within each grade, statistical analysis can be performed on the measured content of similar natural resources in multiple target areas within the same grade. By calculating the mean, dispersion, or the range of change between adjacent grades, the differences in the characteristics of this type of natural resource within that grade are extracted. The statistical characteristics of similar natural resources in this grade are compared with the statistical characteristics of corresponding natural resources in all other grades except the current grade k to determine the distinguishing ability of this type of natural resource between different grades. If a certain type of natural resource shows significant content differences among all other grades except the current grade k, and this difference accounts for a large proportion in the current grade, it indicates that this natural resource has a strong distinguishing effect on the formation of the grade boundary, and its corresponding degree of influence is determined to be high; conversely, if this type of natural resource does not change significantly between different grades, its degree of influence on the formation of the grade is relatively low. Through the above analysis, the degree of influence of various natural resources on the formation of the grade at each level can be obtained without relying on human experience settings.

[0093] Furthermore, sub-step S14-1 includes:

[0094] The first step is to calculate the average content of the same type of natural resource in all target areas within the current level to obtain the average content of the same type of natural resource in the current level. The current level is denoted as k, where k is a natural number greater than 1. The average content of natural resource a in all target areas j included in a single level k is... The average content of natural resource a obtained in the k-1 grading is .

[0095] The second step is to analyze the differences in the average content of single-type natural resources at adjacent levels to obtain the degree of difference in the content of corresponding natural resources at adjacent levels. The difference in the average content of single-type natural resources at adjacent levels is: This allows us to obtain the degree of difference in the levels of natural resource a in the classification k and k-1. , Adding 1 to the denominator is to prevent errors caused by the calculation from occurring within an acceptable range, as this is a safety measure.

[0096] The third step involves calculating the degree of influence of various natural resources on the classification at each level based on the cumulative results of the degree of difference in levels and the degree of difference in the same type of natural resource levels within adjacent levels. Taking natural resource 'a' as an example, the degree of difference obtained for natural resource 'a' under all other levels except the current level k is calculated. The sum of the differences represents the cumulative result of the degree of difference, which can be expressed by the formula: Calculations show that This refers to the number of levels (or grades).

[0097] Through the formula: When evaluating the performance of natural resources in different regions within the tier k, the degree of influence of natural resource a is calculated as follows: ; To obtain the maximum degree of difference among all natural resources in grades k and k-1 by comparison; To determine the degree of difference in natural resource a in grades k and k-1. With the greatest degree of difference The absolute value of the difference; 0.01 is a preset coefficient to avoid calculation errors where the denominator is 0 during the calculation process.

[0098] It's understandable, when The larger the difference, the greater the value. The smaller the value of natural resource 'a', the greater the change in its content relative to level k-1, and the more significant it is for evaluating the natural resource performance of different regions within level k; therefore, its weight should be greater. Conversely, the larger the value of natural resource 'a', the smaller its weight should be. The degree of influence serves as one of the criteria for determining the reference weight of natural resource 'a' when evaluating the natural resource performance of different regions within level k, thus making the subsequent visualization results more valuable.

[0099] S14-2: Based on the degree of impact and the dissimilarity of similar natural resources in target areas under the same classification, obtain the weight change coefficients for various natural resources at the corresponding level. For the same classification, if the numerical difference of a certain natural resource among target areas in the classification is small, assigning it a large reference weight may lead to difficulty in distinguishing areas with different natural resource performances within the same classification. Therefore, when the degree of difference of a certain natural resource among target areas in the same classification is small, its reference weight for obtaining accurate regional infrastructure evaluation values ​​should be appropriately reduced.

[0100] For multiple target regions within the same tier, it is necessary to statistically analyze the distribution differences or dispersion of similar natural resources among regions to characterize the stability of such resources within the tier. When a certain type of natural resource not only has a high influence on the formation of the tier in the current tier but also exhibits significant dissimilarity between different regions within the same tier, it indicates that this type of natural resource has a stronger expressive ability to characterize the differences within the target region. Therefore, a higher weight change coefficient should be assigned during the weight adjustment process. Conversely, if a certain type of natural resource is relatively evenly distributed within the current tier, i.e., its dissimilarity characteristics are weak, its role in distinguishing regions within the same tier is limited, and its weight change coefficient should be correspondingly reduced. By comprehensively considering the degree of influence and dissimilarity characteristics, the tiered adaptive adjustment of natural resource weights is achieved.

[0101] Furthermore, sub-step S14-2 includes:

[0102] The first step is to obtain the current impact ratio of each type of natural resource at the corresponding level based on its current impact level and maximum impact level at the current level. Through comparative analysis, the impact levels of all natural resources in level k can be obtained. maximum value The current impact ratio is the degree of influence of natural resource a when evaluating the natural resource performance of the target area in level k. With the maximum value The ratio, i.e. It should be noted that the maximum impact of resource a is... In objective scenarios, it is not zero; if If a value of 0 results in a meaningless calculation, the value obtained from the ratio will be directly set to a preset parameter, such as a value of 1, to enable subsequent analysis.

[0103] The second step is to obtain the cumulative dissimilarity result for the current level based on the dissimilarity characteristics of natural resources of the current category in all target areas at the current level. This yields the dissimilarity of natural resource 'a' in level k, which is recorded as the cumulative dissimilarity result. , Let k be the number of target regions in level k. This represents the average proportion of this type of resource within this level.

[0104] The third step is to obtain the weight change coefficient of the corresponding category of natural resources at the current level based on the current impact ratio and cumulative dissimilarity results. According to the formula: The weighting coefficient of natural resource 'a' as the natural resource in the grade k is calculated to reflect the change in its performance evaluation. .when The larger, The larger the value, the greater the difference in natural resource a across different target regions in level k, and the greater the variation in level k. This makes it more suitable to assign a larger weight to evaluate the natural resource content of target regions in level k.

[0105] Similarly, the min-max normalization method can be used to... After normalization, the value range is [0, 1]. Then, the normalized value is reduced by 0.5 to obtain... Its range is [-0.5, 0.5]. When The larger the value of natural resource a, the greater its reference weight should be as an accurate indicator of the performance of natural resources in the region within the classification k; conversely, the reference weight should be reduced.

[0106] At this point, the weight change coefficients of various natural resources have been obtained, and we proceed to step S15.

[0107] S15: Based on the weight change coefficients of various natural resources and the corresponding resource reference weights, output the visualized evaluation results of the natural resources performance of each target area.

[0108] Specifically, for each type of natural resource, the weight change coefficient obtained under the corresponding level needs to be integrated with the pre-set resource reference weight to obtain the target reference weight that reflects the actual contribution of the current resource in the regional evaluation. The target reference weight comprehensively considers the influence of natural resources in the process of level formation, as well as their ability to express differences within the same level region, thereby avoiding the bias caused by evaluating regional resources based solely on fixed weights or a single indicator.

[0109] For example, step S15 includes sub-steps S15-1 to S15-2, which are described in detail below:

[0110] S15-1: Based on the weight change coefficient of each type of natural resource and the corresponding resource reference weight, obtain the target reference weight of the corresponding category of natural resource. The target reference weight of natural resource a in level k is denoted as... Through the formula: Calculate the target reference weight ; The resource reference weight for natural resource a can be preset based on the importance of natural resource a. It can be set to 1 / N by default (N is the total number of resource types) or manually set by the user according to their attention preferences, objective scenarios and monitoring accuracy requirements.

[0111] S15-2: Based on the target reference weights for each category of natural resources, the target regions are rendered in a hierarchical manner, and the rendering results are displayed based on a pre-set 3D image. Different target regions are classified using the above method, and the target reference weights of the regions included in each classification are obtained. In the 3D models of multiple regions, the comprehensive level of regional natural resources is intuitively represented through color rendering (e.g., dark green to red represents excellent to poor). Simultaneously, specific 3D icons (e.g., water droplets, trees, minerals, etc.) are used to represent the characteristics of various resources, and their relative abundance is encoded by icon size, quantity, or height, making the types and abundance readily apparent. An interactive design can also be adopted, allowing clicking on any region to pop up a detailed information panel displaying the region's classification and specific score, and may also include detailed data on various natural resources. This integrates the classification results, endowment structure, and detailed evaluation, supporting the entire process from macro-level situational awareness to micro-level decision analysis.

[0112] Based on the same technical concept as the processing method, this embodiment of the invention also provides a natural resource survey and monitoring data visualization processing system, which is the system corresponding to any of the above-described methods. Please refer to... Figure 4 , Figure 4 The diagram shows the structure of the processing system, which includes an acquisition module 401, a determination module 402, a first acquisition module 403, a second acquisition module 404, and an output module 405.

[0113] The acquisition module 401 is used to acquire measurement data of various natural resources in multiple target areas, where the target areas are the areas where the characteristics of the natural resources to be investigated are located.

[0114] The determination module 402 is used to perform differential characteristic analysis on the measured content of similar natural resources in multiple target areas in order to determine the universal indicators corresponding to various types of natural resources.

[0115] The first acquisition module 403 is used to perform resource characteristic analysis on multiple target areas based on the universality indicators of various natural resources, and to obtain the classification results of the natural resources possessed by each target area.

[0116] The second acquisition module 404 is used to adjust the weights of various natural resources according to the degree of influence of individual natural resources on the formation of the classification under each classification result, so as to obtain the weight change coefficients of various natural resources.

[0117] The output module 405 is used to output a visual evaluation result of the natural resources performance of each target area based on the weight change coefficients of various natural resources and the corresponding resource reference weights.

[0118] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for visualizing and processing natural resource survey and monitoring data, characterized in that, The method includes: Acquire measurement data of various natural resources in multiple target areas, where the target areas are the regions whose natural resource characteristics are to be investigated; A differential characteristic analysis was conducted on the measured content of similar natural resources in the multiple target areas to determine the universal indicators corresponding to various types of natural resources; Based on the universality indicators of various natural resources, the resource characteristics of the multiple target areas are analyzed, and the classification results of the natural resources possessed by each target area are obtained. The weights of various natural resources are adjusted according to the degree of influence of individual natural resources on the formation of the classification under each classification result, so as to obtain the weight change coefficients of various natural resources. Based on the weight change coefficients of various natural resources and the corresponding resource reference weights, the visualized evaluation results of the natural resources performance of each target area are output. Methods for acquiring measurement data include: Satellite images that meet the target resolution are input into a preset land identification model to divide the satellite images into multiple target areas according to different land types. Configure a solar radiation monitoring task of preset duration for each target area to obtain the solar irradiance of the corresponding target area; The average wind power density of the corresponding target area is obtained based on the environmental data collected by the meteorological towers in each target area. Based on the hydrological data collected from multiple locations within each target area during the exploration mission, the groundwater content of the corresponding target area is obtained. Based on the solar irradiance, average wind power density, and groundwater content of each target area, measurement data of various natural resources in the corresponding target area are obtained; Methods for determining universal indicators corresponding to various natural resources include: The resource measurement ratio of the corresponding category of natural resources in each target area is obtained by comparing the measured content of the current category of natural resources in each target area with the maximum measured content of the corresponding category of natural resources in all target areas. The number of target areas in all target areas where the resource measurement ratio of the same type of natural resource is greater than the first ratio threshold is counted to obtain the universality coefficient of the corresponding type of natural resource. The universality index of the corresponding category of natural resources is obtained by summing the universality coefficient and the proportion of resource measurements that are greater than the preset proportion threshold.

2. The method for visualizing natural resource survey and monitoring data according to claim 1, characterized in that, The process of analyzing the resource characteristics of the multiple target areas based on the universality indicators of various natural resources, and obtaining the classification results of the natural resources possessed by each target area, includes: The number of natural resources in each target area whose universality index for all categories of natural resources is less than the index threshold and whose resource measurement ratio for the corresponding category of natural resources is greater than the second ratio threshold is counted to obtain the first resource quantity for the corresponding target area. Based on the quantity of the first resource in each target area and the quantity of the second resource whose universality index for all categories of natural resources is less than the index threshold, the rarity coverage of the rare resources in the corresponding target area is obtained. Based on the rarity coverage of each target area and the resource proximity of rare resources in all associated target areas, the resource characteristic value of the corresponding target area is obtained. The resource feature values ​​of all target regions are normalized, ordered, and clustered in sequence, and the clustering results are determined as the hierarchical results of each target region.

3. The method for visualizing natural resource survey and monitoring data according to claim 2, characterized in that, Before obtaining the resource feature values ​​of the corresponding target area, the method further includes: The resource measurement proportions of the same type of natural resources in all target areas are cumulatively summed to obtain the proportion summation result; Based on the summation of the ratios and the minimum ratio difference of the current target area, the resource proximity of the current target area is obtained. The minimum ratio difference is the difference between the maximum and minimum resource measurement ratios of the natural resources corresponding to the minimum universality index of the current target area.

4. The method for visualizing natural resource survey and monitoring data according to claim 1, characterized in that, The process of adjusting the weights of various natural resources based on the degree of influence of individual natural resources on the formation of the classification under each classification result, to obtain the weight change coefficients of various natural resources, includes: The differences in the characteristics of similar natural resources in each target area within each level are analyzed to obtain the degree of influence of various types of natural resources on the level at each level; Based on the degree of influence and the dissimilarity of similar natural resources in different target areas under the same level, the weight change coefficients of various natural resources at the corresponding level are obtained.

5. The method for visualizing and processing natural resource survey and monitoring data according to claim 4, characterized in that, The analysis of the differences in similar natural resources in each target area within each grading is conducted to obtain the degree of influence of various natural resources on the grading at each level, including: The average content of the same type of natural resource in all target areas at the current level is calculated to obtain the average content of the same type of natural resource at the current level. To analyze the differences in the average content of single-category natural resources at adjacent levels, we can obtain the degree of difference in the level of corresponding categories of natural resources at adjacent levels. Based on the cumulative results of the degree of difference between the levels and the degree of difference between the same type of natural resource levels in each adjacent level, the degree of influence of each type of natural resource on the classification is obtained.

6. The method for visualizing and processing natural resource survey and monitoring data according to claim 4, characterized in that, The step of obtaining the weight change coefficients of various natural resources at the corresponding level based on the degree of influence and the dissimilarity characteristics of similar natural resources in each target area under the same level includes: Based on the current impact level of each type of natural resource at the current level and the maximum impact level at the corresponding level, obtain the current impact ratio of the current type of natural resource at the corresponding level; Based on the dissimilarity characteristics of natural resources of the current category in all target areas at the current level, obtain the cumulative dissimilarity results for the current level; Based on the current impact ratio and cumulative dissimilarity results at the current level, obtain the weight change coefficient of the corresponding category of natural resources at the current level.

7. The method for visualizing natural resource survey and monitoring data according to claim 1, characterized in that, The process involves outputting a visualized evaluation result of the natural resources performance in each target area based on the weight change coefficients of various natural resources and their corresponding resource reference weights. This includes: Based on the weight change coefficient of each type of natural resource and the corresponding resource reference weight, the target reference weight of the corresponding type of natural resource is obtained. The target regions are rendered hierarchically according to the target reference weight of each category of natural resources, and the rendering results are displayed based on the preset stereoscopic image.

8. A data visualization and processing system for natural resource surveys and monitoring, characterized in that, The system is the system corresponding to any one of the methods described in claims 1-7, and the system includes: The acquisition module is used to acquire measurement data of various natural resources in multiple target areas, where the target areas are the regions whose natural resource characteristics are to be investigated. The determination module is used to perform differential characteristic analysis on the measured content of similar natural resources in the multiple target areas in order to determine the universal indicators corresponding to various types of natural resources; The first acquisition module is used to perform resource characteristic analysis on the multiple target areas based on the universality indicators of various natural resources, and to obtain the classification results of the natural resources possessed by each target area. The second acquisition module is used to adjust the weights of various natural resources according to the degree of influence of individual natural resources on the formation of the classification under each classification result, so as to obtain the weight change coefficients of various natural resources. The output module is used to output a visual evaluation result of the natural resources performance of each target area based on the weight change coefficients of various natural resources and the corresponding resource reference weights.

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