Natural resource comprehensive survey management optimization method based on pattern spot grid sampling

Abstract

The invention discloses a natural resource comprehensive survey management optimization method based on pattern spot grid sampling, and relates to the technical field of pattern spot grid sampling. The method comprises the following steps: obtaining related information of natural resources in a target area, screening out large-area distribution type natural resources, carrying out overall sample distribution on the part of natural resources by adopting an area weighted random sampling mode, and calculating a sample area of the part of natural resources by applying a pre-constructed minimum sample plot calculation model; outputting a minimum sample plot standard value corresponding to the target area; according to the technical key points, the method comprises the following steps: distinguishing primary, secondary and calibrated natural resource types in each survey cell, operating a rule engine, and dynamically allocating the number of sample plots according to the conditions such as proportions of different types of natural resources and plaque density; especially for calibrating natural resources, a mechanism for dynamically adjusting a measurement starting ratio threshold value is introduced, so that the comprehensiveness and the accuracy of investigation are ensured, and the investigation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of spot grid sampling, and in particular to a natural resource comprehensive survey and management optimization method based on spot grid sampling. Background Art

[0002] Patch grid sampling is a commonly used sampling method in geographic information systems (GIS) and remote sensing technology. It is mainly used to select representative sample points or areas from a large amount of geographic spatial data for analysis and research. The following is a detailed explanation of patch grid sampling: The concept of patches, patches refer to areas on the map where geographical units (plots) with similar or identical characteristics (such as landforms, land use types, soil types, etc.) are divided into a category and outlined with boundary lines; these areas are represented on the map with different colors or patterns for easy identification and distinction; The concept of grid sampling, grid sampling is a method of dividing the study area into several uniform grids, and then selecting sample points or areas in each grid for sampling; this method is suitable for situations where the sample area is relatively uniform and has no obvious directionality or aggregation.

[0003] However, in the process of using the patch grid sampling technology to conduct natural resource survey and management, the main technical problems include: inaccurate calculation of the number of sample plots: in traditional methods, the calculation of the number of sample plots often does not fully consider the differences in the areas occupied by different types of natural resources in the target area, resulting in the calculated number of sample plots not meeting actual needs, affecting the accuracy and efficiency of the survey; uneven sample plot layout and unreasonable density: traditional sample plot layout methods lack scientificity, and are prone to problems such as uneven layout and unreasonable sample plot density, resulting in limited accuracy and efficiency of survey results; fixed thresholds cannot adapt to complex distributions: traditional methods use fixed thresholds for sample plot allocation, which cannot adapt to the distribution of complex natural resources, especially for surveys of calibrated natural resources, which may lead to incomplete or inaccurate surveys; the number of sample plots is difficult to flexibly adjust: the number of sample plots in sample plot layout is often difficult to flexibly adjust according to actual conditions, and cannot meet the needs of natural resource surveys in different regions, affecting the accuracy and effectiveness of the survey; For example, when conducting a forest resource survey in a certain area, traditional methods may lead to inaccurate calculation of the number of sample plots because the differences in the areas of different forest types are not taken into account; or the sample plots may be unevenly distributed due to unscientific layout methods; or important forest species may be missed due to unreasonable fixed threshold settings; or the number of sample plots cannot be adjusted to meet the survey needs of a specific area. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a natural resource comprehensive survey and management optimization method based on patch grid sampling, which solves the problems raised in the background technology.

[0005] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: A natural resource comprehensive survey management optimization method based on spot grid sampling, the method comprising the steps of: obtaining relevant information of natural resources in a target area, screening large-area distributed natural resources, and using a pre-built minimum sample plot calculation model to output the minimum sample plot standard value corresponding to the target area; According to the minimum sample plot standard value and the actual area under the target area, a square grid is used for sample plot layout; Take each square grid as a survey cell, distinguish the primary natural resource type, secondary natural resource type and calibrated natural resource type in each survey cell, and run the constructed rule engine to summarize the total number of survey plots in the target area; Determine whether the total number of surveyed sample plots meets the sample plot standard requirements. If not, perform calibration actions in the identification and adjustment action set based on the judgment results to meet the sample plot standard requirements.

[0006] Furthermore, relevant information on natural resources includes at least: The types of natural resources, the actual area and proportion of each type of natural resource in the target area; The process of screening out large-area distributed natural resources is as follows: when the actual area occupied by the corresponding type of natural resources exceeds the set value, it means that this type of natural resources is large-area distributed natural resources.

[0007] Furthermore, for large-area distributed natural resources, the process of running the minimum sample plot calculation model is as follows: Using the improved minimum sample number determination method, calculate the sample size n: ; Where P is the broadcast contact rate, is the probability degree, D is the allowable error; Use the area coefficient for weighted processing and calculate the minimum number of sample plots Q: ; In the formula, B is the total number of natural resource types, S is the area of ​​the most widely distributed natural resources, and S i is the area of ​​all kinds of natural resources except the most widely distributed natural resources, and i is the number of the natural resource type.

[0008] Furthermore, when a square grid is used for sample plot layout, the specifications of the unit grid are selected from a specification library, and the specification library includes at least 3×3km, 4×4km, and 5×5km; Among them, the process selected from the specification library is: Basic parameter input: the total area Sz of the known target area and the minimum number of sample plots Q; Grid density calculation: Generate candidate grids covering the entire area through GIS, that is, the specification library, and calculate the total number of grids Ns and sample density Md under each specification; where Ns=Sz / area occupied by the corresponding specification; Md=Q / Ns; Standard setting: meet the setting of at least one sample plot per grid; Standard comparison selection: Select the candidate grid with a plot density Md greater than 1 and the smallest difference with 1 from the specification library, and use it as the required specification of the square grid.

[0009] Furthermore, the main natural resource types are: natural resources distributed over large areas; Secondary natural resource types are: natural resource types other than primary natural resources and demarcated natural resources in the target area; The types of demarcated natural resources are: natural resources of special concern, that is, natural resources that are demarcated independently.

[0010] Furthermore, the content of running the rule engine is: Sample plots are allocated cell by cell according to the conditions, and the following judgment process is performed for each survey cell: Condition 1: When any of the main natural resource types accounts for more than 80%, only one corresponding sample plot will be set; Condition 2: If condition 1 is not met, check the proportion of all natural resources; for each type of resource, if its proportion exceeds 20% but does not exceed 80%, allocate 1 corresponding sample plot; Condition 3: If the calibrated natural resources meet any of the following conditions, the starting measurement ratio will be adjusted to the set value; Among them, condition a is: the proportion of calibrated natural resources does not exceed 20%; condition b is: the patch is fragmented, that is, the patch density is ≥ 0.5 / km²; sample plot allocation: in each grid that meets condition a or b, if the proportion of calibrated natural resources exceeds the set value, 1 corresponding sample plot will be allocated; Condition 4: Use remote sensing images of the target area to locate linear spots; if there are historical survey points in the survey cell, re-survey and allocate a corresponding sample plot; for each new linear spot, allocate a corresponding sample plot; Summarize the total number of survey plots in the target area: cumulatively sum the number of corresponding plots set in each survey cell, and the total number obtained is the total number of survey plots in the target area.

[0011] Furthermore, the process of adjusting the starting measurement ratio to the set value is as follows: The actual area proportion Zq of the calibrated natural resources and the excess value of the patch density Ms are converted and adjusted, and the set value is calculated by linear combination. The calculation method is: ; Where T_new and T_0 are the set value and the starting measurement ratio respectively, and T_0=20%. α and β are weight coefficients, and their value range is [0, 1].

[0012] Furthermore, the process of judging whether the total number of surveyed plots meets the plot standard requirements is as follows: Compare the total number of survey plots corresponding to the target area with the minimum plot standard value; When the total number of surveyed plots does not exceed the minimum plot standard value, it means that the plot standard requirements are not met; When the total number of surveyed plots exceeds the minimum plot standard value, but the difference between the total number of surveyed plots and the minimum plot standard value exceeds the allowable value, it means that the plot standard requirements are not met; When the total number of surveyed sample plots exceeds the minimum sample plot standard value, and the difference between the total number of surveyed sample plots and the minimum sample plot standard value does not exceed the allowable value, it means that the sample plot standard requirements are met; Among them, the allowable value is: minimum sample site standard value*10%.

[0013] Furthermore, when the judgment result is: the total number of surveyed sample plots does not exceed the minimum sample plot standard value; The calibration action content in the identification and adjustment action set is: calculate the total amount difference of natural resource types between two adjacent survey cells in the target area, compare the total amount difference with the standard threshold, extract the adjacent survey cells with a total amount difference higher than the standard threshold, and mark them as special attention areas, and increase the total number of each survey plot in the special attention area by at least 1 plot; When the judgment result is: the total number of surveyed sample plots exceeds the minimum sample plot standard value, and the difference between the total number of surveyed sample plots and the minimum sample plot standard value exceeds the allowable value; Then the calibration action content in the identification adjustment action set is: An iterative optimization strategy is adopted to simultaneously execute the plans in the preset plan library, gradually approaching and reaching the requirement that the total number of surveyed sample plots exceeds the minimum sample plot standard value, and the difference between the total number of surveyed sample plots and the minimum sample plot standard value does not exceed the allowable value.

[0014] Furthermore, the solutions in the solution library include: Solution 1: Increase the threshold of condition 2 in the rule engine from 20% in stages; Solution 2: Increase the setting value of condition 3 given in the specification engine in stages according to the ladder value; Solution 3: quantitatively reduce the historical survey points of condition 4 given in the specification engine in accordance with the requirements of the screening criteria; the screening criteria are: the total number of historical survey points in the same survey cell exceeds 3; the quantitative reduction process is: randomly select H survey cells that meet the screening criteria, and reduce 1 historical survey point in each survey cell, where H is a positive integer greater than 0.

[0015] (III) Beneficial effects The present invention provides a natural resource comprehensive survey and management optimization method based on spot grid sampling, which has the following beneficial effects: (1) The scheme first screened out large-area distributed natural resources in the target area, and then used the improved minimum sample number determination method in statistics, combined with the area-weighted random sampling method, to calculate the minimum number of sample plots under given confidence and allowable error conditions; it fully considered the differences in the areas occupied by different types of natural resources in the target area, making the calculated minimum number of sample plots more in line with the actual situation, solving the problem of inaccurate sample plot number calculation in traditional methods, and providing a scientific and reasonable basis for sample plot layout for comprehensive natural resource surveys, thereby improving the efficiency and accuracy of surveys; (2) This scheme adopts square grids to lay out sample plots based on the minimum sample plot standard value and the actual area of ​​the target area, and generates a candidate grid specification library covering the entire area through GIS, taking into account the grid density, sample plot density, and the standard of at least one sample plot per grid. By comparing and selecting candidate grids with a sample plot density exceeding 1 and the smallest difference between the sample plot density and 1 as the layout specification, the technical effect of scientifically and reasonably laying out the sample plots for natural resource surveys is achieved, solving the problems of uneven layout and unreasonable sample plot density existing in traditional sample plot layout methods, and improving the accuracy and efficiency of natural resource surveys. (3) The scheme distinguishes the primary, secondary and calibrated natural resource types in each survey cell and runs a rule engine to dynamically allocate the number of sample plots based on the proportion of different types of natural resources, patch density and other conditions. In particular, for calibrated natural resources, a mechanism for dynamically adjusting the threshold of the starting measurement (area) proportion is introduced to solve the problem that the traditional fixed threshold cannot adapt to the complex distribution of natural resources. At the same time, for linearly distributed natural resources, the map patch and typical location survey are used to ensure the comprehensiveness and accuracy of the survey. The automated judgment process of the rule engine improves the survey efficiency and reduces the subjectivity of human intervention. (4) The scheme first determines whether the total number of survey plots meets the preset standards. If not, the scheme performs corresponding adjustments according to the specific situation. In the case where the total number of plots is insufficient, the scheme calculates the difference in the total amount of natural resource types between adjacent survey cells to identify areas of special concern and increase the number of plots to improve the survey accuracy of these areas. In the case where the total number of plots is too large and exceeds the allowable value, the scheme adopts an iterative optimization strategy and comprehensively uses multiple schemes such as increasing the condition threshold, adjusting the set value, and reducing the number of historical survey points to gradually adjust the number of plots to ensure that it does not exceed the allowable value while meeting the minimum plot standard. The scheme solves the problem that the number of plots in traditional plot layout is difficult to flexibly adjust and cannot meet the natural resource survey needs of different regions, ensures the accuracy and effectiveness of natural resource surveys, and achieves the technical effect of flexibly adjusting the number of survey plots to meet the plot standard requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the overall method steps in the present invention; Figure 2 It is a schematic diagram of laying out a survey grid for a target area in an embodiment of the present invention; Figure 3 A schematic diagram of the distribution of natural resource types in the survey cells that meet condition one in the present invention; Figure 4 A schematic diagram of the distribution of natural resource types in the survey cells that meet condition 2 in the present invention; Figure 5 A schematic diagram of the distribution of natural resource types in the survey cells that meet condition three in the present invention; Figure 6 This is a schematic diagram of the distribution of natural resource types that meet condition four in the present invention; Figure 7 It is a schematic diagram of the total number of survey plots arranged in the target area in the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example: See also Figures 1 to 7 This embodiment provides a natural resource comprehensive survey management optimization method based on spot grid sampling, the method comprising the following steps: S1. Obtain relevant information about natural resources in the target area, screen out large-area distributed natural resources, and use area-weighted random sampling to distribute the natural resources as a whole. Use the pre-built minimum sample plot calculation model to output the minimum sample plot standard value corresponding to the target area. The target area refers to a pre-selected region or county; the relevant information on natural resources includes at least: the type of natural resources, the area occupied by each type of natural resources in the target area (which can be an approximate area), and the proportion of each type of natural resources in the target area (i.e., the percentage occupied); Methods for screening out large-area distributed natural resources include: Method 1 (the method used in this embodiment): when the area occupied by the natural resources of the corresponding type exceeds the set value, it means that the natural resources of this type are large-area distributed natural resources; in addition, it also includes method 2: through spatial distribution density analysis, the resource-intensive area is determined, and then the large-area distributed natural resources are identified; this method is suitable for areas with uneven resource distribution (the specific screening principle will not be elaborated here, and its main purpose is to screen the large-area distributed natural resources that are conventionally recognized); Example 1: Region A (i.e. the target area) covers six types of natural resources: land, minerals, forests, grasslands, water, and wetlands. After screening, the areas occupied by land (arable land), forests, and grasslands are all over 200km 2 , so it can be used as a large-area distributed natural resource on the surface. If the grassland type natural resource has the largest area, it is the most widely distributed natural resource; It should be noted that this plan adopts area-weighted random sampling for overall sample layout; water and wetland resources adopt the method of map spots and typical location surveys for sample layout; mineral resources adopt the method of data collection for investigation; For large-area distributed natural resources, the process of running the minimum sample calculation model is as follows: S101. Use the improved minimum sample number determination method in statistics to calculate the sample size n: ; Where P is the broadcast contact rate, is the probability degree, D is the allowable error; Table 1 below gives the minimum number of samples required for different allowable error levels:

[0019] According to the minimum sample number determination method, taking into account the survey accuracy and cost to a certain extent, under the preset conditions of 90% confidence level and 0.05 level of allowable error, the minimum sample size is 271; S102. Use the area coefficient (i.e., the area occupied by each type of natural resource in the target area) for weighted processing to calculate the minimum number of sample plots Q: ; Where B is the total number of natural resource types (the total number of types in the target area), n is the sample size (n=271 when the confidence level is 90% and the allowable error is 0.05), S is the area of ​​the most widely distributed natural resources, and S i is the area of ​​all kinds of natural resources except the most widely distributed natural resources, and i is the number of the natural resource type; Combined with Example 1 given above, Example 2 is given to illustrate: The grassland in region A S = 2919 km 2 , forest S1 = 1066km 2 , cultivated land S2 = 208km 2 , the rest S3 = 271km 2 , the calculation process of the minimum number of sample plots Q corresponding to area A is: Q=271+1066*(271 / 2919)+208*(271 / 2919)+271*(271 / 2919)=414; Therefore, the minimum number of sample plots for large-area distributed natural resources in the comprehensive natural resources survey in Region A is 414, including 271 grasslands, 99 forests, 19 cultivated lands, and 25 others.

[0020] By adopting the above scheme, the technical effect of accurately calculating the minimum number of sample plots required for large-area distributed natural resources in the target area is achieved; the scheme first screens out the large-area distributed natural resources in the target area, and then uses the improved minimum sample number determination method in statistics, combined with the area-weighted random sampling method, to calculate the minimum number of sample plots under given confidence and allowable error conditions; In the above process, weighted processing is performed through area coefficient, which fully considers the differences in the areas occupied by different types of natural resources in the target area, so that the calculated minimum number of sample plots is more in line with the actual situation. This technical solution solves the problems of inaccurate calculation of sample plot numbers and difficulty in balancing survey accuracy and cost in traditional methods. It provides a scientific and reasonable basis for sample plot layout for comprehensive natural resource surveys, improves the efficiency and accuracy of surveys, and also provides strong support for the protection and management of natural resources.

[0021] S2. Based on the minimum sample plot standard value and the actual area under the target area, a square grid is used to lay out the sample plots; Among them, when square grids are used for sample plot layout, the unit grid specification is selected from the specification library, and the specification library includes at least 3×3km, 4×4km and 5×5km, and also includes less than 2×2km and more than 6×6km, but the specifications usually laid out are between 3×3km and 5×5km. Specifications that are too low or too high are not considered or ignored. For the specification library, it is a candidate grid covering the entire domain generated by GIS; The sub-steps included in S2 are: S201. Basic parameter input: the total area Sz and the minimum number of sample plots Q of the known target area; S202, grid density calculation: Generate candidate grids covering the entire area (e.g., 3×3 km to 5×5 km) through GIS, i.e., the specification library, and calculate the total number of grids Ns and the sample density Md under each specification or size; Where Ns = Sz / area occupied by the corresponding specification; for example: for a 4×4km grid, the total area of ​​region A is Sz = 4464km 2 , so the corresponding specification occupies an area of ​​16km 2 , Ns=4464 / 16≈279; Md=Q / Ns; for example, combined with Example 2, we can know that: Md=414 / 279≈1.48; S203, standard setting: satisfying the condition or standard of at least one sample plot per grid; S204, standard comparison selection: select the candidate grid whose sample plot density Md exceeds 1 and whose difference with 1 is the smallest from the specification library, and use it as the required specification of the square grid, and then lay out the sample plots; For example, if the candidate grid is 3×3 km (Ns≈496), the plot density Md=414 / 496≈0.83, which cannot meet the standard of at least one plot per grid; while for 5×5 km (N≈179), the plot density Md=2.31, which is too redundant; 4×4 km corresponds to Md=414 / 279≈1.48, compared with 2.31 corresponding to 5×5 km, it is obvious that: 4×4km is the required specification for a square grid; Reference Figure 2 It can be seen that according to the minimum number of sample plots of 414, considering the area of ​​Region A, a 4×4km grid was used for sample plot layout, and a total of 338 survey grids were laid out; In addition, it should be noted that the alternative solutions for subsequent S203 to S204 are: Spatial uniformity optimization: Use Thiessen polygons (Voronoi diagrams) to analyze the distribution of existing plots and identify coverage gaps; combine resource distribution hotspots (such as dense grassland areas) and iteratively adjust the grid size to make the grid density of the blank area ≥ 1; Cost-accuracy trade-off: For a 4×4 km grid, N=4464 / 16≈279, but the hotspot grids need to be encrypted to 338 (+21%) to ensure that the number of sample plots in each grid is ≥1 and the total number of samples is ≤414. Verification shows that 4×4 km balances coverage uniformity and cost and was ultimately adopted.

[0022] The scheme adopts square grids to lay out sample plots based on the minimum sample plot standard value and the actual area of ​​the target area, and generates a candidate grid specification library covering the entire area through GIS, taking into account the grid density, sample plot density, and the standard of at least one sample plot per grid. By comparing and selecting candidate grids with a sample plot density exceeding 1 and the smallest difference with 1 as the layout specification, the uniformity and rationality of the sample plot layout are ensured. The application of this technical solution has achieved the technical effect of scientifically and reasonably arranging sample plots for natural resource surveys, solved the problems of uneven layout and unreasonable sample plot density existing in traditional sample plot layout methods, and improved the accuracy and efficiency of natural resource surveys. By scientifically and reasonably arranging sample plots, it provides more accurate and reliable data support for the protection and management of natural resources, and also provides a scientific basis for subsequent natural resource planning and utilization.

[0023] S3. Take each grid as a survey cell, distinguish the primary natural resource type, secondary natural resource type and calibrated natural resource type in each survey cell, and run the established rule engine to summarize the total number of survey plots in the target area; Among them, the main natural resource types are: large-area distributed natural resources mentioned in S1; Secondary natural resource types are: natural resource types other than primary natural resources and demarcated natural resources in the target area; The types of calibrated natural resources are: natural resources of special concern, that is, natural resources that are calibrated independently and require special attention; for example, if most of the target area is a mining area, the calibrated natural resources can be mineral resources; if most of the target area is a forest, the calibrated natural resources can be arbor forests, because arbor forests are of certain importance to forest resources; The content of running the rule engine is: Sample plots are allocated cell by cell according to the conditions, and the following judgment process is performed for each survey cell: Condition 1: The proportion of major natural resources is greater than 80% (the proportions mentioned below are all area proportions); Judgment logic: When any of the main natural resource types accounts for more than 80%, it will be used as a single dominant grid and only one corresponding plot will be set (e.g. grassland > 80% → 1 grassland plot); Priority: Condition 1 takes precedence over condition 2. If condition 1 is met, condition 2 is skipped.

[0024] Example: See attached Figure 3 It can be seen that in a certain survey cell, grassland accounts for 97.2%, shrub forest 2.5%, and water body 0.3%, so only one grassland resource survey plot is set up (the main natural resource type accounts for more than 80%, and the main natural resources are surveyed); Condition 2: 80% ≥ any natural resource ratio > 20%; Judgment logic: If condition 1 is not met, check the proportion of all natural resources (including major and minor ones); for each type of resource, if its proportion exceeds 20% but does not exceed 80%, allocate 1 corresponding plot (multiple plots are allowed: if multiple types of resources exceed 20% but do not exceed 80%, allocate 1 plot for each type); Example: See attached Figure 4 It is known that in Figure 4 In the given survey cells, grassland accounts for 67.4%, shrub forest 28.1%, water body 0.7%, road 0.8%, bare land 3%, so set up 1 grassland resource survey plot and 1 shrub forest survey plot (multiple natural resource types account for more than 20%, and all natural resource types above 20% are surveyed); Condition 3: The proportion of calibrated natural resources > the set value; Adjustment of thresholds for calibrated natural resource types (i.e. special resources, such as arbor forests); Judgment logic: Fragmentation judgment: If the calibrated natural resources meet any of the following conditions, reduce the starting measurement (area) ratio to the set value (refer to Figure 5 shown); Among them, condition a is: the proportion of calibrated natural resources < 20%; bCondition: fragmentation of patches (i.e., patch density ≥ 0.5 / km²); Sample plot allocation: In each grid that meets the conditions, if the proportion of calibrated natural resources exceeds the set value, one corresponding sample plot will be allocated; Example: In a grid, arbor forest (i.e., the calibrated natural resource type) accounts for 8.3%, and the patch density is 0.6 / km² → set one arbor forest sample plot; The process of reducing the starting measurement (area) ratio to the set value is as follows: The actual area ratio of the calibrated natural resources and the excess value of the patch density are converted and adjusted, and the set value is calculated by linear combination. The calculation method is: ; Where, T_new and T_0 are the set value and the starting area ratio, respectively, and T_0=20%, Zq and Ms are the actual area ratio and patch density of the calibrated natural resources, respectively, and α and β are weight coefficients with a value range of [0, 1]; The adjustment principle is: when the calibrated natural resources meet conditions A or B, the threshold of the starting area ratio needs to be dynamically reduced; the adjustment range should increase as the actual area ratio of the calibrated natural resources approaches 5% or the patch density increases, and the final threshold lower limit is 5%. The total adjustment amount shall not exceed the difference between the initial threshold (20%) and the lower threshold (5%) (i.e. 15%); Example description: Input parameters: Zq=8.3%, Ms=0.6 / km², T_0=20%; The value of α is 0.64, and the value of β is 0.75; Calculation process: T_new = max (5%, 20% - (0.64 * 0.117 + 0.75 * 0.1)) = max (5%, 20% - (7.5% + 7.5%)) = max (5%, 5%) = 5%; therefore, the set value is 5%; this formula dynamically calculates the threshold of the starting (area) ratio by linearly combining the area ratio and the excess value of the plaque density; in the example scenario, the output is 5%, while retaining adaptability to complex situations, solving the shortcomings of the traditional fixed threshold; Condition 4: Conduct a survey of the spots and typical locations of linearly distributed natural resources; Judgment logic: Spot recognition: Use remote sensing images under the target area to locate linear (or small area) spots; Re-survey rules: If there are historical survey points in the survey cell, re-survey and allocate 1 sample plot; For each new spot, allocate 1 corresponding sample plot; Example: 2 wetland spots are found in a grid → set 2 wetland sample plots; The specific content is as follows: For linear and small natural resources such as water resources and wetlands, use map spots and typical location surveys to re-survey the survey points of previous years (see the specific Figure 6 can be seen); Summarize the total number of survey plots in the target area: cumulatively sum the number of corresponding plots set in each survey cell, and the total number obtained is the total number of survey plots in the target area; Combined with the example content given under each condition in the rule engine built by running, it can be concluded that a total of 571 survey plots were set up in area A, including 24 arbor forests, 125 shrub forests, 25 cultivated land, 271 grasslands, 84 groundwater, 11 surface water, and 31 wetlands, and the attached Figure 7 Auxiliary reference.

[0025] The scheme distinguishes the primary, secondary and demarcated natural resource types in each survey cell and runs a rule engine to dynamically allocate the number of sample plots based on the proportion of different types of natural resources, patch density and other conditions; Especially for the calibration of natural resources, a mechanism for dynamically adjusting the threshold of the starting measurement (area) ratio is introduced to solve the problem that the traditional fixed threshold cannot adapt to the complex distribution of natural resources; at the same time, the use of map patches and typical location surveys for linearly distributed natural resources ensures the comprehensiveness and accuracy of the survey; through the automated judgment process of the rule engine, the scientific and reasonable allocation of survey plots is achieved, the survey efficiency is improved, and the subjectivity of human intervention is reduced; finally, the total number of survey plots obtained provides reliable data support for the management and protection of natural resources in the target area, and provides a basis for the formulation of scientific and reasonable natural resource plans and policies, effectively solving technical problems such as unreasonable layout of natural resource survey plots and low survey efficiency, and achieving the technical effect of fine and dynamic layout of natural resource survey plots.

[0026] S4, judging whether the total number of surveyed sample plots meets the sample plot standard requirements, and if not, executing the calibration action in the identification adjustment action set according to the judgment result to meet the sample plot standard requirements; The process of judging whether the total number of surveyed sample plots meets the sample plot standard requirements is as follows: Compare the total number of survey plots corresponding to the target area with the minimum plot standard value. When the total number of survey plots does not exceed the minimum plot standard value, it means that the plot standard requirement is not met; when the total number of survey plots exceeds the minimum plot standard value, but the difference between the total number of survey plots and the minimum plot standard value exceeds the allowable value, it also means that the plot standard requirement is not met; only when the total number of survey plots exceeds the minimum plot standard value and the difference between the total number of survey plots and the minimum plot standard value does not exceed the allowable value, it means that the plot standard requirement is met; Allowable value: minimum sample plot standard value*10%; When the judgment result is: the total number of surveyed sample plots does not exceed the minimum sample plot standard value; The calibration action content of the identification and adjustment action set is: calculate the total amount difference of natural resource types between two adjacent survey cells in the target area, and compare the total amount difference with the standard threshold, extract the adjacent survey cells with a total amount difference higher than the standard threshold, and mark them as special attention areas, and increase the total number of each survey sample plot in the special attention area by at least 1 sample plot (add sample plots of the corresponding type according to needs, such as the sample plot with the smallest area ratio as the corresponding sample plot to be added); (the action concept and principle are: identify those areas with a total amount difference higher than the standard threshold, which may be places where the distribution of natural resources changes greatly, or areas where the survey accuracy needs special attention; for the identified areas with high total amount difference, consider increasing the number of survey sample plots to ensure that the natural resources in these areas are fully investigated and recorded); When the judgment result is: the total number of surveyed sample plots exceeds the minimum sample plot standard value, and the difference between the total number of surveyed sample plots and the minimum sample plot standard value exceeds the allowable value; Then the calibration action content in the identification adjustment action set is: Adopt an iterative optimization strategy and simultaneously execute the solutions in the preset solution library, gradually approaching and eventually reaching the requirement that the total number of surveyed sample plots exceeds the minimum sample plot standard value, and the difference between the total number of surveyed sample plots and the minimum sample plot standard value does not exceed the allowable value (for example: the difference between the total number of sample plots and 414 is less than 10% of 414); The solutions in the solution library include: Solution 1: Increase the threshold of condition 2 given in the rule engine from 20% in steps (e.g. 1%), but never exceed 80%, so that the total number of survey plots is reduced after each step increase; Solution 2: Increase the set value of condition 3 in the specification engine in steps (e.g. 1%), so that the total number of survey plots is reduced after each step increase. Solution 3: quantitatively reduce the historical survey points of condition 4 given in the specification engine according to the requirements of meeting the screening criteria; wherein the screening criteria is: the total number of historical survey points in the same survey cell exceeds 3; the quantitative reduction process is: randomly select H survey cells that meet the screening criteria, and reduce 1 historical survey point in each survey cell, where H is a positive integer greater than 0, and in this embodiment, the value is the side length specification of the square grid used in S2, for example: 4×4km, that is, H=4; In actual operation, it is necessary to comprehensively consider various solutions in the solution library; For example, while increasing the threshold of condition 2 and optimizing the starting measurement (area) ratio of arbor forests, the number of water resources and wetland patches and typical location surveys is reduced, and the combination of natural resources within the grid is comprehensively considered; by continuously adjusting and optimizing these plans, we can gradually approach and eventually reach the requirement that the difference between the total number of sample plots and 414 is less than 10% of 414; In order to synchronously run the three schemes in the scheme library, an iterative optimization strategy can be adopted; first, set the initial conditions and calculate the difference between the current total number of sample plots and the standard value; then, gradually adjust the thresholds of conditions two and three according to the preset step values, and reduce the historical survey points according to the screening standards; after each adjustment, recalculate the total number of sample plots and evaluate the degree of closeness to the target value; if the difference does not reach the allowable value, continue to iterate and optimize, comprehensively consider the impact of each scheme, and dynamically adjust the strategy; through continuous iteration, gradually approach and finally meet the total number of survey sample plots, and ensure that the difference is within the allowable range; Assume that after comprehensive consideration and multiple adjustments, we finally get a total number of sample plots that meets the requirements; For example, we raised the threshold of condition two to 28%, increased the starting point (area) of tree forests to 8%, and appropriately reduced the number of water resources and wetland patches and typical location surveys; after these adjustments, we recalculated the total number of sample plots and obtained a result that met the requirements and was relatively reasonable.

[0027] The scheme first determines whether the total number of survey plots meets the preset standards. If not, corresponding adjustments are made according to the specific situation. In the case of insufficient total number of plots, by calculating the difference in the total amount of natural resource types between adjacent survey cells, special attention areas are identified and the number of plots is increased to improve the survey accuracy of these areas. In the case of too many plots and exceeding the allowable value, an iterative optimization strategy is adopted to gradually adjust the number of plots by comprehensively using multiple schemes such as increasing condition thresholds, adjusting set values, and reducing historical survey points to ensure that it does not exceed the allowable value while meeting the minimum plot standard. This technical solution solves the problem that the number of sample plots in traditional sample plot layout is difficult to flexibly adjust and cannot meet the needs of natural resource surveys in different regions. It improves the rationality and scientificity of sample plot layout, ensures the accuracy and effectiveness of natural resource surveys, and achieves the technical effect of flexibly adjusting the number of survey sample plots to meet the standard requirements of sample plots.

[0028] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0029] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0030] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A natural resources comprehensive survey and management optimization method based on spot grid sampling, characterized in that: The method comprises the steps of: obtaining relevant information of natural resources in the target area, screening large-area distributed natural resources, and using a pre-built minimum sample plot calculation model to output the minimum sample plot standard value corresponding to the target area; According to the minimum sample plot standard value and the actual area under the target area, a square grid is used for sample plot layout; Take each square grid as a survey cell, distinguish the primary natural resource type, secondary natural resource type and calibrated natural resource type in each survey cell, and run the constructed rule engine to summarize the total number of survey plots in the target area; Determine whether the total number of surveyed sample plots meets the sample plot standard requirements. If not, perform calibration actions in the identification and adjustment action set based on the judgment results to meet the sample plot standard requirements.

2. According to claim 1, a natural resources comprehensive survey and management optimization method based on spot grid sampling is characterized by: The relevant information on natural resources shall include at least: The types of natural resources, the actual area and proportion of each type of natural resource in the target area; The process of screening out large-area distributed natural resources is as follows: when the actual area occupied by the corresponding type of natural resources exceeds the set value, it means that this type of natural resources is large-area distributed natural resources.

3. The method for optimizing the comprehensive survey and management of natural resources based on spot grid sampling according to claim 1 is characterized by: For large-area distributed natural resources, the process of running the minimum sample calculation model is as follows: Using the improved minimum sample number determination method, calculate the sample size n: ； Where P is the broadcast contact rate, is the probability degree, D is the allowable error; Use the area coefficient for weighted processing and calculate the minimum number of sample plots Q: ； In the formula, B is the total number of natural resource types, S is the area of ​​the most widely distributed natural resources, and S i is the area of ​​all kinds of natural resources except the most widely distributed natural resources, and i is the number of the natural resource type.

4. The method for optimizing the comprehensive survey and management of natural resources based on spot grid sampling according to claim 3 is characterized by: When using square grids for plot layout, the unit grid specifications are selected from the specification library, and the specification library includes at least 3×3km, 4×4km and 5×5km; Among them, the process selected from the specification library is: Basic parameter input: the total area Sz of the known target area and the minimum number of sample plots Q; Grid density calculation: Generate candidate grids covering the entire area through GIS, that is, the specification library, and calculate the total number of grids Ns and sample density Md under each specification; where Ns=Sz / area occupied by the corresponding specification; Md=Q / Ns; Standard setting: meet the setting of at least one sample plot per grid; Standard comparison selection: Select the candidate grid with a sample density Md greater than 1 and the smallest difference with 1 from the specification library, and use it as the required specification of the square grid.

5. The method for optimizing the comprehensive survey and management of natural resources based on spot grid sampling according to claim 1 is characterized by: The main natural resource types are: large-area distributed natural resources; Secondary natural resource types are: natural resource types other than primary natural resources and demarcated natural resources in the target area; The types of demarcated natural resources are: natural resources of special concern, that is, natural resources that are demarcated independently.

6. The method for optimizing the comprehensive survey and management of natural resources based on spot grid sampling according to claim 5 is characterized by: The content of running the rule engine is: Sample plots are allocated cell by cell according to the conditions, and the following judgment process is performed for each survey cell: Condition 1: When any of the main natural resource types accounts for more than 80%, only one corresponding sample plot will be set; Condition 2: If condition 1 is not met, check the proportion of all natural resources; for each type of resource, if its proportion exceeds 20% but does not exceed 80%, allocate 1 corresponding sample plot; Condition 3: If the calibrated natural resources meet any of the following conditions, the starting measurement ratio will be adjusted to the set value; Among them, condition a is: the proportion of calibrated natural resources does not exceed 20%; condition b is: the patch is fragmented, that is, the patch density is ≥ 0.5 / km²; sample plot allocation: in each grid that meets condition a or b, if the proportion of calibrated natural resources exceeds the set value, 1 corresponding sample plot will be allocated; Condition 4: Use remote sensing images of the target area to locate linear spots; if there are historical survey points in the survey cell, re-survey and allocate a corresponding sample plot; for each new linear spot, allocate a corresponding sample plot; Summarize the total number of survey plots in the target area: cumulatively sum the number of corresponding plots set in each survey cell, and the total number obtained is the total number of survey plots in the target area.

7. The method for optimizing the comprehensive survey and management of natural resources based on spot grid sampling according to claim 6 is characterized by: The process of adjusting the starting measurement ratio to the set value is as follows: The actual area proportion Zq of the calibrated natural resources and the excess value of the patch density Ms are converted and adjusted, and the set value is calculated by linear combination. The calculation method is: ； Where T_new and T_0 are the set value and the starting measurement ratio respectively, and T_0=20%. α and β are weight coefficients, and their value range is [0, 1].

8. The method for optimizing the comprehensive survey and management of natural resources based on spot grid sampling according to claim 6 is characterized by: The process of judging whether the total number of survey plots meets the plot standard requirements is as follows: Compare the total number of survey plots corresponding to the target area with the minimum plot standard value; When the total number of surveyed plots does not exceed the minimum plot standard value, it means that the plot standard requirements are not met; When the total number of surveyed plots exceeds the minimum plot standard value, but the difference between the total number of surveyed plots and the minimum plot standard value exceeds the allowable value, it means that the plot standard requirements are not met; When the total number of surveyed sample plots exceeds the minimum sample plot standard value, and the difference between the total number of surveyed sample plots and the minimum sample plot standard value does not exceed the allowable value, it means that the sample plot standard requirements are met; Among them, the allowable value is: minimum sample site standard value*10%.

9. The method for optimizing the comprehensive survey and management of natural resources based on spot grid sampling according to claim 8 is characterized by: When the judgment result is: the total number of surveyed sample plots does not exceed the minimum sample plot standard value; The calibration action content in the identification and adjustment action set is: calculate the total amount difference of natural resource types between two adjacent survey cells in the target area, compare the total amount difference with the standard threshold, extract the adjacent survey cells with a total amount difference higher than the standard threshold, and mark them as special attention areas, and increase the total number of each survey plot in the special attention area by at least 1 plot; When the judgment result is: the total number of surveyed sample plots exceeds the minimum sample plot standard value, and the difference between the total number of surveyed sample plots and the minimum sample plot standard value exceeds the allowable value; Then the calibration action content in the identification adjustment action set is: An iterative optimization strategy is adopted to simultaneously execute the plans in the preset plan library, gradually approaching and reaching the requirement that the total number of surveyed sample plots exceeds the minimum sample plot standard value, and the difference between the total number of surveyed sample plots and the minimum sample plot standard value does not exceed the allowable value.

10. The natural resources comprehensive survey and management optimization method based on spot grid sampling according to claim 9 is characterized by: The solutions in the solution library include: Solution 1: Increase the threshold of condition 2 in the rule engine from 20% in stages; Solution 2: Increase the setting value of condition 3 given in the specification engine in stages according to the ladder value; Solution 3: quantitatively reduce the historical survey points of condition 4 given in the specification engine in accordance with the requirements of the screening criteria; the screening criteria are: the total number of historical survey points in the same survey cell exceeds 3; the quantitative reduction process is: randomly select H survey cells that meet the screening criteria, and reduce 1 historical survey point in each survey cell, where H is a positive integer greater than 0.

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