Automatic precision method and equipment for fuzzy position of historical soil survey sample point

Abstract

The invention discloses an automatic precision method and device for a fuzzy position of a historical soil survey sample point. The method mainly comprises the following steps: performing structured processing on scene description texts of a target sample point and a reference sample point to obtain position information and geographical scene information; loading the geographic element vector layer, and generating a geographic scene unit layer; through data preliminary screening, scene units which are not adjacent to the sampling points are removed; taking the reference sampling point positioning data as an important reference standard, and utilizing an entropy method to obtain a geographic scene feature importance weight; and matching the target sample point with the scene units one by one, obtaining the similarity between the target sample point and each scene unit in the geographic scene unit layer according to the membership function and the feature importance weight, finding out the scene unit with the highest similarity, and assigning the accurate position of the target sample point as the center point position of the scene unit. The method is short in time consumption and high in accuracy.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and in particular to a method and device for automatically refining the fuzzy position of a historical soil survey sample point. Background Art

[0002] Although current field sampling data often have accurate spatial positioning information, many historical sample survey data often only have simple location descriptions of spatial locations without accurate spatial positioning information, which seriously affects the use value of these survey data. In the face of rough sample location description information, how to use the sample point's parent material, elevation and other geographic environmental information to perform spatial constraints and accurately determine these fuzzy locations to a relatively accurate location has important research significance and application value.

[0003] At present, the main methods for dealing with this problem are: first, standardize the place names, that is, rewrite the place names into the form of xx province xx city xx county (city / district) xx town xx village xx; then, use the network coding positioning function to obtain the longitude and latitude coordinates; finally, combine the topological relationship, azimuth relationship, etc. to optimize the location, and then perform manual judgment and adjustment. However, in this processing method, whether it is the standardization of place names or the subsequent manual judgment, there are defects such as long time consumption and large workload; in addition, due to manual judgment, it is difficult to guarantee the objectivity and reliability of the data after manual adjustment. Summary of the invention

[0004] In view of the problems existing in the prior art, the purpose of the present invention is to provide a method and device for automatically refining the fuzzy positions of historical soil survey sample points with short time consumption and high accuracy.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] A method for automatically refining the fuzzy position of historical soil survey sample points comprises the following steps:

[0007] (1) All soil survey sample points to be precisely located in the target area are taken as target sample points, and all soil survey sample points that have been precisely located in the target area are taken as reference sample points. The scene description texts of the target sample points and the reference sample points are structured to obtain the rough positioning position and geographic scene feature value of the target sample points, as well as the precise position and geographic scene feature value of the reference sample points;

[0008] (2) Generate a target sample point distribution layer P1 based on the rough positioning position, and generate a reference sample point distribution layer P2 based on the precise position of the reference sample point;

[0009] (3) Obtaining a scene unit distribution map of all geographic scene features of the target area, and superimposing the scene unit distribution maps of all geographic scene features of the target area to generate a geographic scene unit layer S1;

[0010] (4) In combination with the reference sample point distribution layer P2, the similarity between each reference sample point and the geographic scene feature value of the corresponding scene unit on the geographic scene unit layer S1 is calculated, and the feature importance weight of each geographic scene feature is calculated based on the similarity, wherein the lower the similarity corresponding to the geographic scene feature, the greater the feature importance weight;

[0011] (5) Based on the target sample point distribution layer P1, non-adjacent units in the geographic scene unit layer S1 are eliminated to generate a geographic scene unit layer S2;

[0012] (6) combining and analyzing the target sample point distribution layer P1 and the geographic scene unit layer S2 to obtain the geographic scene feature value of the scene unit corresponding to each target sample point on the geographic scene unit layer S2, as well as the geographic scene feature value of the adjacent scene unit;

[0013] (7) Calculate the similarity between each geographic scene feature value of each target sample point and the corresponding geographic scene feature value of the corresponding scene unit, and use the feature importance weight as the weight to calculate the weighted sum of the similarities between all geographic scene feature values ​​as the similarity sim0 between each target sample point and the corresponding scene unit;

[0014] (8) If the similarity sim0 is greater than the threshold δ, the precise position of the target sample point is assigned to the center point position of the corresponding scene unit. Otherwise, the similarity between the target sample point and all adjacent scene units is calculated according to step (7), and the maximum value is obtained. If the maximum value is greater than the threshold δ, the precise position of the target sample point is assigned to the center point position of the adjacent scene unit corresponding to the maximum value.

[0015] (9) If the similarity between the target sample point and the corresponding scene unit and the adjacent scene unit is less than the threshold δ, the similarity between the target sample point and all the scene units in the administrative area where the target sample point is located on the geographic scene unit layer S2 is calculated, and the precise position of the target sample point is assigned to the center point position of the scene unit with the greatest similarity.

[0016] Furthermore, step (1) specifically includes:

[0017] (1-1) Obtain scene description texts of all target samples and reference samples;

[0018] (1-2) Recognizing the scene description text by using a preset regular expression to obtain the location description text and geographic scene feature value of the target sample point, as well as the precise location and geographic scene feature value of the reference sample point;

[0019] (1-3) The location description text of the target sample point is processed according to the administrative address format to obtain the administrative location of the target sample point, and a rough positioning is performed based on the administrative location to obtain a rough positioning location.

[0020] Furthermore, step (2) specifically includes:

[0021] (2-1) Generate a target sample point distribution layer P1 based on the rough positioning positions of all target sample points;

[0022] (2-2) Generate a reference sample point distribution layer P2 based on the precise positions of all reference sample points.

[0023] Furthermore, step (4) specifically includes:

[0024] (4-1) According to the reference sample point distribution layer P2, obtain the scene unit corresponding to the reference sample point on the geographic scene unit layer S1;

[0025] (4-2) For the numerical type geographic scene feature value of the reference sample point, a membership function suitable for the change characteristics of the corresponding geographic scene feature is used to calculate the similarity between the geographic scene feature value and the corresponding geographic scene feature value of the scene unit corresponding to the reference sample point as the membership value of the geographic scene feature value;

[0026] (4-3) for the enumerated type geographic scene feature value of the reference sample point, according to a preset mapping rule, obtaining a similarity between the geographic scene feature value and a corresponding geographic scene feature value of a scene unit corresponding to the reference sample point as a membership value of the geographic scene feature value, wherein the preset mapping rule is used to indicate a mapping relationship between the geographic scene feature value and the similarity;

[0027] (4-4) According to the membership values ​​of all geographic scene feature values ​​of the reference sample points, the feature importance weight of each geographic scene feature is calculated, wherein the lower the membership value of the geographic scene feature, the greater the feature importance weight.

[0028] Furthermore, step (4-2) specifically includes:

[0029] (4-2-1) For geographic scene features of numerical type whose data variation range does not exceed a preset range, a triangular membership function or a trapezoidal membership function is used to calculate the similarity between the corresponding geographic scene feature values ​​as the membership value of the corresponding geographic scene feature value;

[0030] (4-2-2) For geographic scene features of numerical type whose data variation exceeds a preset range, any function among the polynomial membership function, Gaussian membership function, and root membership function is used to calculate the similarity between the corresponding geographic scene feature values ​​as the membership value of the corresponding geographic scene feature value.

[0031] Furthermore, the specific formula for calculating similarity using the triangular membership function is:

[0032] μ=1-|xp-xs| / (xs-a)

[0033] Where, μ is the similarity, xp is the geographic scene feature value of the reference sample point, xs is the geographic scene feature value of the scene unit corresponding to the reference sample point on the geographic scene unit layer S1, and a is a variable parameter;

[0034] The specific formula for calculating similarity using the trapezoidal membership function is:

[0035]

[0036] In the formula, x′=xs±b, b is the neighborhood radius of the trapezoidal maximum value, and c is a variable parameter;

[0037] The specific formula for calculating similarity using the Gaussian membership function is:

[0038]

[0039] Where σ is the standard deviation of the membership function;

[0040] The specific formula for calculating similarity using the polynomial membership function is:

[0041] μ=a0+a1|xp-xs|+a2x 2 |xp-xs| 2 +...+at|xp-xs| t

[0042] Where a0, a1, ..., at are the coefficients of the polynomial;

[0043] The specific formula for calculating similarity using the root membership function is:

[0044]

[0045] In the formula, d is a variable parameter.

[0046] Furthermore, step (5) specifically includes:

[0047] (5-1) Screen out the township-level administrative units where each soil survey sample point is located to generate a township layer C;

[0048] (5-2) On the target sample point distribution layer P1, a number of circular adjacent areas are divided with each soil survey sample point as the center and a preset distance as the radius, and the area outside the circular adjacent area on the target sample point distribution layer P1 is deleted, thereby generating a buffer layer B;

[0049] (5-3) Perform an intersection operation on the geographic scene unit layer S1, the township layer C, and the buffer layer B to generate a geographic scene unit layer S2.

[0050] Furthermore, step (6) specifically includes:

[0051] (6-1) Solving the topological relationship between the target sample point distribution layer P1 and the geographic scene unit layer S2 to obtain the corresponding scene unit of each target sample point on the geographic scene unit layer S2;

[0052] (6-2) extracting all geographic scene feature values ​​of the corresponding scene unit of each target sample point on the geographic scene unit layer S2;

[0053] (6-3) By solving the topological relationship, all scene units adjacent to the scene unit corresponding to each target sample point are obtained, and all geographic scene feature values ​​of these adjacent scene units are extracted.

[0054] Furthermore, step (7) specifically includes:

[0055] (7-1) For different geographic scene feature values, select a membership function consistent with step (4), calculate the similarity between each geographic scene feature value of each target sample point and the corresponding geographic scene feature value of the corresponding scene unit on the geographic scene unit layer S2, and use it as the membership value of the geographic scene feature corresponding to the target sample point;

[0056] (7-2) Taking the feature importance weight of the geographic scene feature as the corresponding weight, calculate the weighted sum of the membership value of each geographic scene feature of the target sample point as the similarity sim0 between each target sample point and the corresponding scene unit.

[0057] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above method.

[0058] Compared with the prior art, the present invention has the following beneficial effects: the present invention realizes a method for automatically refining the fuzzy position of historical soil survey sample points through data structured processing, coarse positioning based on location description information, acquisition of geographic environment information based on geographic scene units, geographic scene similarity calculation, acquisition of the best matching geographic scene unit, sample point positioning and other processing steps. The present invention can greatly reduce the cost of manual intervention and effectively improve the processing efficiency while achieving a positioning result with an accuracy similar to or even better than that obtained by manual processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flow chart of a method for automatically refining the fuzzy positions of historical soil survey sample points provided in this embodiment;

[0060] Figure 2 The distribution diagram of the PR sample points, PA sample points and PT sample points used in this embodiment;

[0061] Figure 3 A distribution layer of subclasses required for overlay analysis in this embodiment;

[0062] Figure 4 A distribution layer of the mapped parent material required for the overlay analysis in this embodiment;

[0063] Figure 5 The distribution layer of topography required for the overlay analysis in this embodiment;

[0064] Figure 6 The scene unit distribution layer S1 is formed by overlay analysis in this embodiment;

[0065] Figure 7 The township distribution layer C is obtained based on the sample point PR in this embodiment;

[0066] Figure 8 This is a buffer distribution layer B with a radius of 5 km set based on the sample point PR in this embodiment;

[0067] Fig. 9 The search range distribution layer BC is generated based on the intersection operation of layers C and B in this embodiment;

[0068] Fig.10 The experimental scene unit distribution layer S2 is selected based on the intersection operation of the layer S and BC in this embodiment;

[0069] Fig.11 : is a distribution diagram of the coordinates PRC obtained by solving the PR sample points in this embodiment and the coordinates PR of the rough positioning;

[0070] Fig.12This is a distribution diagram of the coordinates PTC obtained by solving the PT sample points in this embodiment, the coordinates PT of the rough positioning, and the precise position coordinates RPT. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0072] The experimental data of this embodiment are derived from the second national soil survey data of Jiangxi Province and the soil system survey data of Jiangxi Province. The geographical scene features required for this embodiment are six attributes: soil parent material, soil subtype, topography, temperature, precipitation and altitude. Among them, soil parent material, soil subtype and topography data are vector data, and the remaining three are raster data. The purpose of the experiment is to obtain the matching of scene feature information between the target sample point and the scene unit, and to comprehensively analyze and determine the coordinates of the fuzzy position to improve the spatial positioning accuracy and data usage value of the historical field sample survey data, so as to facilitate the comparison of the ancient and modern soil data in the subsequent period.

[0073] The method for automatically refining the fuzzy position of historical soil survey sample points provided by the embodiment of the present invention is as follows: Figure 1 As shown, the specific steps include:

[0074] (1) All soil survey sample points to be precisely located in the target area are taken as target sample points, and all soil survey sample points that have been precisely located in the target area are taken as reference sample points. The scene description texts of the target sample points and the reference sample points are structured to obtain the coarse positioning position and geographic scene feature values ​​of the target sample points, as well as the precise position and geographic scene feature values ​​of the reference sample points.

[0075] This step specifically includes:

[0076] (1-1) Obtain scene description texts of all target samples and reference samples;

[0077] (1-2) The scene description text is recognized by using a preset regular expression to obtain the location description text and geographic scene feature values ​​of the target sample point, as well as the precise location and geographic scene feature values ​​of the reference sample point; wherein the regular expression can be set according to the text format, and the location description text and each geographic scene feature value of the target sample point can be recognized from the scene description text by using the set regular expression, wherein the geographic scene features specifically include parent material, soil subtype, topography, temperature, precipitation and altitude.

[0078] (1-3) The location description text of the target sample point is processed according to the administrative address format to obtain the administrative location of the target sample point, and the coarse positioning is performed according to the administrative location to obtain the coarse positioning location. The administrative location is specifically in the form of xx village xx town xx county (city / district) xx province xx city. The administrative location is coarsely positioned based on the spatial coding to obtain a roughly estimated latitude and longitude coordinate.

[0079] In this embodiment, the target soil survey sample points to be accurately located are derived from the second national soil census data of Jiangxi Province, a total of 77, and the target soil survey sample points to be accurately located are defined as target sample points PR. The soil survey sample points that have been accurately located mainly come from the soil system survey data of Jiangxi Province, a total of 69, and the soil survey sample points that have been accurately located are defined as reference sample points PA. This type of sample point has precise geographic coordinates and is mainly used to obtain weights. The sample points used to verify the accuracy PT mainly come from the soil system survey data of Jiangxi Province, a total of 50. This type of sample point has precisely located coordinates and can be used for later inspection. In the verification process, the sample point is first roughly located and then brought into this method for inspection. The rough positioning position and geographic scene feature values ​​of the target sample point PR are shown in Table 1, the precise position and geographic scene feature values ​​of the reference sample point PA are shown in Table 2, and the precise position and geographic scene feature values ​​of the verification accuracy sample point PT are shown in Table 3.

[0080] Table 1 Target sample point information

[0081]

[0082] Table 2 Reference sample point information

[0083]

[0084]

[0085] Table 3 Verification accuracy sample point information

[0086]

[0087] (2) Generate a target sample point distribution layer P1 based on the rough positioning position, and generate a reference sample point distribution layer P2 based on the precise position of the reference sample point.

[0088] This step specifically includes:

[0089] (2-1) Generate a target sample point distribution layer P1 based on the rough positioning positions of all target sample points;

[0090] (2-2) Generate a reference sample point distribution layer P2 based on the precise positions of all reference sample points.

[0091] In addition, for verification, the coarse positioning latitude and longitude coordinates of the verification sample point PT are obtained based on the network space coding service to generate the verification sample point distribution layer P3. In this embodiment, for simplicity, the target sample point distribution layer P1, the reference sample point distribution layer P2 and the verification sample point distribution layer P3 are displayed in one figure, as shown in the following figure. Figure 2 shown.

[0092] (3) Obtain a scene unit distribution map of all geographic scene features in the target area, and overlay the scene unit distribution maps of all geographic scene features in the target area to generate a geographic scene unit layer S1.

[0093] In this embodiment, the vector scene unit distribution maps of the soil parent material, soil subtype, topography and other geographical scene features are superimposed based on GIS software, and then the raster data such as precipitation, temperature, altitude, etc. are calculated and assigned to the corresponding scene units to generate the geographical scene unit layer S1. Among them, the scene unit distribution maps of the soil parent material, soil subtype and topography of this embodiment are respectively as follows: Figure 3 , Figure 4 , Figure 5 As shown, the generated geographic scene unit layer S1 is as follows Figure 6 shown.

[0094] (4) In combination with the reference sample point distribution layer P2, the similarity between each reference sample point and the geographic scene feature value of its corresponding scene unit on the geographic scene unit layer S1 is calculated, and the feature importance weight of each geographic scene feature is calculated based on the similarity.

[0095] This step specifically includes:

[0096] (4-1) According to the reference sample point distribution layer P2, obtain the scene unit corresponding to the reference sample point on the geographic scene unit layer S1;

[0097] (4-2) For the numerical type of geographic scene feature values ​​of the reference sample point, such as temperature, precipitation, altitude, etc., a membership function suitable for the change characteristics of the corresponding geographic scene feature is used to calculate the similarity between the geographic scene feature value and the corresponding geographic scene feature value of the scene unit corresponding to the reference sample point as the membership value of the geographic scene feature value;

[0098] (4-3) for the enumerated type of geographic scene feature value of the reference sample point, such as soil parent material, subclass, land use type, etc., according to the preset mapping rule, obtaining the similarity between the geographic scene feature value and the corresponding geographic scene feature value of the scene unit corresponding to the reference sample point as the membership value of the geographic scene feature value, wherein the preset mapping rule is used to indicate the mapping relationship between the geographic scene feature value and the similarity;

[0099] (4-4) According to the membership values ​​of all geographic scene feature values ​​of the reference sample points, the feature importance weight of each geographic scene feature is calculated, wherein the lower the membership value of the geographic scene feature, the greater the feature importance weight. For example, the feature importance weight λ of each geographic scene feature can be calculated by the entropy method i In this embodiment, the feature importance weights of sub-class, topography, parent material, temperature, precipitation, and altitude are 0.566607, 0.058110, 0.297511, 0.031473, 0, 0.013905, and 0.032395, respectively.

[0100] The geographic scene feature information of the scene units corresponding to all reference sample points on the geographic scene unit layer S1 is shown in Table 4 below.

[0101] Table 4 Geographic scene feature information of scene units corresponding to reference sample points

[0102] ID Soil system name Subclass Topography Soil parent material Temperature precipitation altitude 1 Nanshengli Group Paddy soil 101 River-lake erosion / sediment 17.925 1546.921 17.27175 2 Later Zhou Dynasty Paddy soil 101 River-lake erosion / sediment 18.20219 1644.6 18.49621 3 Street Paddy soil 101 River-lake erosion / sediment 17.78078 1530.416 16.103 4 Moshan System Paddy soil 101 Quaternary red sandy soil 17.94481 1571.232 24.66764 5 Under the cedar tree Red soil 211 Quartzite 18.6883 1384.282 266.7122 6 Xiejiatan system Paddy soil 101 River-lake erosion / sediment 17.675 1530.262 21.87786 7 Yunqian Department Paddy soil 101 Mudstone 17.91416 1567.116 26.81573 8 Yang Jian system Red soil 211 other 17.42955 1432 214.9281 9 Rentian system Lime purple soil 101 Purple rock 19.33748 1465.311 224.6062 10 Shaocun system Red soil 101 Mudstone 17.675 1586 37.91094 … …… …… …… …… …… …… …… 69 Zuoxi system Red soil 211 Quartzite 17.61543 1358.428 381.3419

[0103] Wherein, step (4-2) specifically includes:

[0104] (4-2-1) For geographic scene features of numerical type whose data variation range does not exceed the preset range, such as temperature, precipitation, etc., the upper and lower limits of the variation within a certain area are not large. A triangular membership function or a trapezoidal membership function is used to calculate the similarity between the corresponding geographic scene feature values ​​as the membership value of the corresponding geographic scene feature value. In this embodiment, the triangular membership function is used for processing;

[0105] (4-2-2) For geographic scene features of numerical type whose data change amplitude exceeds the preset range, such as altitude, mutations may occur within a certain area. However, for the case where the change in the properties of soil samples does not change linearly with increasing altitude, any function selected from the polynomial membership function, Gaussian membership function, and root membership function is used to calculate the similarity between the corresponding geographic scene feature values ​​as the membership value of the corresponding geographic scene feature value. In this embodiment, the root membership function is used for processing.

[0106] Among them, the specific formula for calculating similarity using the triangular membership function is:

[0107] μ=1-|xp-xs| / (xs-a)

[0108] Where, μ is the similarity, xp is the geographic scene feature value of the reference sample point, xs is the geographic scene feature value of the scene unit corresponding to the reference sample point on the geographic scene unit layer S1, and a is a variable parameter;

[0109] The specific formula for calculating similarity using the trapezoidal membership function is:

[0110]

[0111] In the formula, x′=xs±b, b is the neighborhood radius of the trapezoidal maximum value, and c is a variable parameter;

[0112] The specific formula for calculating similarity using the Gaussian membership function is:

[0113]

[0114] Where σ is the standard deviation of the membership function;

[0115] The specific formula for calculating similarity using the polynomial membership function is:

[0116] μ=a0+a1|xp-xs|+a2x 2 |xp-xs| 2 +...+at|xp-xs| t

[0117] Where a0, a1, ..., at are the coefficients of the polynomial;

[0118] The specific formula for calculating similarity using the root membership function is:

[0119]

[0120] In the formula, d is a variable parameter.

[0121] In step (4-3), the preset mapping rule is the matching mapping relationship between each scene feature and the data. The relationship between different values ​​needs to be considered and the membership value is set according to the conformity of the data. For example, the preset mapping rule can be:

[0122] A: If the geographic scene feature value is completely consistent with the corresponding geographic scene feature value of the scene unit corresponding to the reference sample point, the membership value is set to 1;

[0123] B: If the relationship between the geographic scene feature value and the corresponding geographic scene feature value of the scene unit corresponding to the reference sample point is close, the membership value is set to 0.3-0.8. In this embodiment, in the soil parent material, the Quaternary red clay, Quaternary sub-red sandy soil and red sandstone are set to 0.5, and other sediments with river and lake impact are also set to 0.5; in the subclass, the paddy soil, the ponding paddy soil, the latent paddy soil, and the infiltration paddy soil are set to 0.6, the red soil, the red yellow soil and the red soil are set to 0.6, and the red soil and various paddy soils are set to 0.4; in the topography, the low-altitude plains and low-altitude hills, low-altitude hills and small undulating low mountains, small undulating low mountains and medium undulating low mountains, medium undulating low mountains and medium undulating middle mountains, medium undulating middle mountains and large undulating middle mountains are set to 0.5.

[0124] C: If similar values ​​such as "other" appear, consider the matching value between it and other values ​​and set the membership value to about 0.5;

[0125] D: When the other eigenvalues ​​are irrelevant, the membership value is set to 0.

[0126] (5) Based on the target sample point distribution layer P1, non-adjacent cells in the geographic scene unit layer S1 are eliminated to generate the geographic scene unit layer S2.

[0127] This step specifically includes:

[0128] (5-1) The township-level administrative units where each soil survey sample point is located are selected to generate the township layer C, such as Figure 7 As shown;

[0129] (5-2) On the target sample point distribution layer P1, a number of circular adjacent areas are divided with each soil survey sample point as the center and a preset distance as the radius. The area outside the circular adjacent area on the target sample point distribution layer P1 is deleted to generate a buffer layer B, such as Figure 8 As shown; in this embodiment, the preset distance is 5km;

[0130] (5-3) Perform an intersection operation on the geographic scene unit layer S1, the township layer C and the buffer layer B to generate a geographic scene unit layer S2. In this embodiment, first perform an intersection operation on layers C and B to generate a search layer BC ( Fig. 9 ), and then based on the intersection operation of layer S and BC, generate scene unit layer S2 ( Fig.10 ).

[0131] (6) The target sample point distribution layer P1 and the geographic scene unit layer S2 are combined for analysis to obtain the geographic scene feature value of the scene unit corresponding to each target sample point on the geographic scene unit layer S2, as well as the geographic scene feature value of the adjacent scene unit.

[0132] This step specifically includes:

[0133] (6-1) Solving the topological relationship between the target sample point distribution layer P1 and the geographic scene unit layer S2 to obtain the corresponding scene unit of each target sample point on the geographic scene unit layer S2;

[0134] (6-2) extracting all geographic scene feature values ​​of the corresponding scene unit of each target sample point on the geographic scene unit layer S2;

[0135] (6-3) By solving the topological relationship, all units adjacent to the scene unit corresponding to each target sample point are obtained, and all geographic scene feature values ​​of these adjacent scene units are extracted.

[0136] (7) Calculate the similarity between each geographic scene feature value of each target sample point and the corresponding geographic scene feature value of the corresponding scene unit, and use the feature importance weight as the weight to calculate the weighted sum of the similarities between all geographic scene feature values ​​as the similarity sim0 between each target sample point and the corresponding scene unit.

[0137] This step specifically includes:

[0138] (7-1) For different geographic scene feature values, select the membership function consistent with step (4), calculate the similarity between each geographic scene feature value of each target sample point and the corresponding geographic scene feature value of the corresponding scene unit on the geographic scene unit layer S2, and use it as the membership value of the geographic scene feature corresponding to the target sample point; in this embodiment, a triangular membership function is used for temperature and precipitation, a root membership function is used for altitude, and an enumeration membership function is used for the remaining three geographic scene features (soil parent material, soil subtype and topography);

[0139] (7-2) Taking the feature importance weight of the geographic scene feature as the corresponding weight, the weighted sum of the membership values ​​of each geographic scene feature of the target sample point is calculated according to the following formula as the similarity sim0 between each target sample point and the corresponding scene unit.

[0140]

[0141] Where μi is the membership value of the i-th geographic scene feature of the target sample point.

[0142] (8) If the similarity sim0 is greater than the threshold δ, the precise position of the target sample point is assigned to the center point position of the corresponding scene unit. Otherwise, the similarity between the target sample point and all adjacent scene units is calculated according to step (7), and the maximum value is obtained. If the maximum value is greater than the threshold δ, the precise position of the target sample point is assigned to the center point position of the adjacent scene unit corresponding to the maximum value. In this embodiment, the threshold δ is 0.7.

[0143] (9) If the similarity between the target sample point and the corresponding scene unit and the adjacent scene unit is less than the threshold δ, the similarity between the target sample point and all the scene units in the administrative area where the target sample point is located on the geographic scene unit layer S2 is calculated, and the precise position of the target sample point is assigned to the center point position of the scene unit with the greatest similarity.

[0144] In this embodiment, after the precise coordinate processing, the PRC and the PR distribution map of the target sample points in the original rough positioning are obtained ( Fig.11 ), the coordinate data are shown in Table 5.

[0145] Table 5 Position coordinates of target sample points before and after precise calibration

[0146] ID Soil Coarse positioning longitude Coarse positioning latitude Calibrated longitude Calibrated dimensions 1 Thick layer of black sandy red soil 117.05 27.44 116.9916292 27.36545012 2 Thick layer of sandy red soil 115.21 26.31 115.2104215 26.27451851 3 Thick gray sandy soil 116.58 27.97 116.5374917 27.98822916 4 Thick layer of dark yellow sandy mud red soil 114.17 27.29 114.119533 27.30222102 5 Thin layer of gray-yellow sandy mud red soil 115.37 24.95 115.3456234 24.97460872 6 Thick gray-yellow sandy soil 114.17 27.29 114.119533 27.30222102 7 Thick layer of black eel mud red soil 115.26 26.42 115.2145165 26.53554198 8 Thick gray eel mud red soil 117.93 29.54 117.6758397 29.25254943 9 Thin layer of black eel mud red soil 116.46 28.29 116.5291958 28.3087536 10 Thick gray eel soil 116.71 28.35 116.7089346 28.39133726 … … … … … … 77 Fully submerged ash tide sandy mud field 116.66 28.76 116.6844226 28.71287956

[0147] After inspection and verification, the data of the second soil survey in Jiangxi processed by the method of the present invention has a good precision optimization effect, which is close to the manual positioning accuracy and even better than manual calibration. Specifically including:

[0148] a) Comparative evaluation based on the environmental similarity between the target sample point PR and the scene unit in which it is located. By comparing the similarity between the calibrated unit and the initial scene unit, there is a significant improvement, and the environmental similarity between 60% of the adjusted sampling points and the unit in which they are located is greater than the set threshold, and the remaining sample points are only slightly below the threshold, with only 1-2 points having relatively low values;

[0149] b) Based on the comparative evaluation of the verification sample PT, it can be found that more than 70% of the points have a similarity greater than the threshold, and nearly 90% of the points are greater than 0.6. At the same time, by comparing with the rough positioning coordinates and the actual positioning coordinates, 72% of the sample points meet at least one of the following conditions, that is, they meet the inspection standards: 1) The obtained coordinates are within 3km of the actual coordinates; 2) The corrected coordinates are closer to the actual coordinates than the rough positioning coordinates; 3) The corrected coordinates and the actual coordinates are in the same scene unit. Data such as Fig.12 As shown in Table 6. Taking into account the accuracy of the vector information used and other objective factors, it can better reflect the local geographical scene characteristics and perform more accurate positioning in a smaller range. The error is within the allowable range and basically meets the inspection requirements.

[0150] Table 6 Verification of inspection points

[0151]

[0152]

[0153] Different test methods have been proven to be able to better meet people's experimental needs. They can, to a certain extent, refer to the sampling points selected for the soil survey at that time and meet the current needs for comparative analysis of ancient and modern soil data.

[0154] In the embodiment of the present invention, only the fuzzy position is refined for the second soil census data of Jiangxi Province, and the soil system survey data of Jiangxi Province is used as an important reference standard. The method of the present invention is also applicable to the position precision method under the constraints of geographical scene characteristics in other fields.

[0155] The embodiment of the present invention also provides a computer device to provide services for the implementation of the above method. The device may include: a memory storing a computer executable program; a processor coupled to the memory; the processor calls the computer executable program stored in the memory 301 to execute the steps in the method described in the first embodiment. The memory may include a computer system readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory 301 may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). A program / utility having a set (at least one) of program modules may be stored in, for example, a memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include the implementation of a network environment. The computer executable program of the program module generally performs the functions and / or methods in the embodiments described in the present invention. The processor executes various functional applications and data processing by running the program stored in the memory, such as implementing the method provided in the first embodiment of the present invention. The code of the computer executable program can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, Python, and conventional procedural programming languages ​​such as "C" language or similar programming languages.

[0156] It should be understood that the above embodiments and descriptions only describe the principles, main features and advantages of the present invention. Without departing from the spirit and scope of the present invention, the present invention may be subject to various changes and improvements, and these changes and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for automatically refining the fuzzy position of historical soil survey sample points, characterized in that: The steps include: (1) All soil survey sample points to be precisely located in the target area are taken as target sample points, and all soil survey sample points that have been precisely located in the target area are taken as reference sample points. The scene description texts of the target sample points and the reference sample points are structured to obtain the rough positioning position and geographic scene feature value of the target sample points, as well as the precise position and geographic scene feature value of the reference sample points; (2) Generate a target sample point distribution layer P1 based on the rough positioning position, and generate a reference sample point distribution layer P2 based on the precise position of the reference sample point; (3) Obtaining a scene unit distribution map of all geographic scene features of the target area, and superimposing the scene unit distribution maps of all geographic scene features of the target area to generate a geographic scene unit layer S1; (4) In combination with the reference sample point distribution layer P2, the similarity between each reference sample point and the geographic scene feature value of the corresponding scene unit on the geographic scene unit layer S1 is calculated, and the feature importance weight of each geographic scene feature is calculated based on the similarity, wherein the lower the similarity corresponding to the geographic scene feature, the greater the feature importance weight; (5) Based on the target sample point distribution layer P1, non-adjacent units in the geographic scene unit layer S1 are eliminated to generate a geographic scene unit layer S2; (6) combining and analyzing the target sample point distribution layer P1 and the geographic scene unit layer S2 to obtain the geographic scene feature value of the scene unit corresponding to each target sample point on the geographic scene unit layer S2, as well as the geographic scene feature value of the adjacent scene unit; (7) Calculate the similarity between each geographic scene feature value of each target sample point and the corresponding geographic scene feature value of the corresponding scene unit, and use the feature importance weight as the weight to calculate the weighted sum of the similarities between all geographic scene feature values ​​as the similarity sim0 between each target sample point and the corresponding scene unit; (8) If the similarity sim0 is greater than the threshold δ, the precise position of the target sample point is assigned to the center point position of the corresponding scene unit. Otherwise, the similarity between the target sample point and all adjacent scene units is calculated according to step (7), and the maximum value is obtained. If the maximum value is greater than the threshold δ, the precise position of the target sample point is assigned to the center point position of the adjacent scene unit corresponding to the maximum value. (9) If the similarity between the target sample point and the corresponding scene unit and the adjacent scene unit is less than the threshold δ, the similarity between the target sample point and all the scene units in the administrative area where the target sample point is located on the geographic scene unit layer S2 is calculated, and the precise position of the target sample point is assigned to the center point position of the scene unit with the greatest similarity.

2. The method for automatically refining the fuzzy position of historical soil survey sample points according to claim 1 is characterized in that: Step (1) specifically includes: (1-1) Obtain scene description texts of all target samples and reference samples; (1-2) Recognizing the scene description text by using a preset regular expression to obtain the location description text and geographic scene feature value of the target sample point, as well as the precise location and geographic scene feature value of the reference sample point; (1-3) The location description text of the target sample point is processed according to the administrative address format to obtain the administrative location of the target sample point, and a rough positioning is performed based on the administrative location to obtain a rough positioning location.

3. The method for automatically refining the fuzzy position of historical soil survey sample points according to claim 1 is characterized in that: Step (2) specifically includes: (2-1) Generate a target sample point distribution layer P1 based on the rough positioning positions of all target sample points; (2-2) Generate a reference sample point distribution layer P2 based on the precise positions of all reference sample points.

4. The method for automatically refining the fuzzy position of historical soil survey sample points according to claim 1 is characterized in that: Step (4) specifically includes: (4-1) According to the reference sample point distribution layer P2, obtain the scene unit corresponding to the reference sample point on the geographic scene unit layer S1; (4-2) For the numerical type geographic scene feature value of the reference sample point, a membership function suitable for the change characteristics of the corresponding geographic scene feature is used to calculate the similarity between the geographic scene feature value and the corresponding geographic scene feature value of the scene unit corresponding to the reference sample point as the membership value of the geographic scene feature value; (4-3) for the enumerated type geographic scene feature value of the reference sample point, according to a preset mapping rule, obtaining a similarity between the geographic scene feature value and a corresponding geographic scene feature value of a scene unit corresponding to the reference sample point as a membership value of the geographic scene feature value, wherein the preset mapping rule is used to indicate a mapping relationship between the geographic scene feature value and the similarity; (4-4) According to the membership values ​​of all geographic scene feature values ​​of the reference sample points, the feature importance weight of each geographic scene feature is calculated using the entropy method, wherein the lower the membership value of the geographic scene feature, the greater the feature importance weight.

5. The method for automatically refining the fuzzy position of historical soil survey sample points according to claim 4 is characterized in that: Step (4-2) specifically includes: (4-2-1) For geographic scene features of numerical type whose data variation range does not exceed a preset range, a triangular membership function or a trapezoidal membership function is used to calculate the similarity between the corresponding geographic scene feature values ​​as the membership value of the corresponding geographic scene feature value; (4-2-2) For geographic scene features of numerical type whose data variation exceeds a preset range, any function among the polynomial membership function, Gaussian membership function, and root membership function is used to calculate the similarity between the corresponding geographic scene feature values ​​as the membership value of the corresponding geographic scene feature value.

6. The method for automatically refining the fuzzy position of historical soil survey sample points according to claim 5, characterized in that: The specific formula for calculating similarity using the triangular membership function is: μ=1-|xp-xs| / (xs-a) Where, μ is the similarity, xp is the geographic scene feature value of the reference sample point, xs is the geographic scene feature value of the scene unit corresponding to the reference sample point on the geographic scene unit layer S1, and a is a variable parameter; The specific formula for calculating similarity using the trapezoidal membership function is: In the formula, x′=xs±b, b is the neighborhood radius of the trapezoidal maximum value, and c is a variable parameter; The specific formula for calculating similarity using the Gaussian membership function is: Where σ is the standard deviation of the membership function; The specific formula for calculating similarity using the polynomial membership function is: μ=a0+a1|xp-xs|+a2x 2 |xp-xs| 2 +...+at|xp-xs| t Where a0, a1, ..., at are the coefficients of the polynomial; The specific formula for calculating similarity using the root membership function is: In the formula, d is a variable parameter.

7. The method for automatically refining the fuzzy position of historical soil survey sample points according to claim 1, characterized in that: Step (5) specifically includes: (5-1) Screen out the township-level administrative units where each soil survey sample point is located to generate a township layer C; (5-2) On the target sample point distribution layer P1, a number of circular adjacent areas are divided with each soil survey sample point as the center and a preset distance as the radius, and the area outside the circular adjacent area on the target sample point distribution layer P1 is deleted, thereby generating a buffer layer B; (5-3) Perform an intersection operation on the geographic scene unit layer S1, the township layer C, and the buffer layer B to generate a geographic scene unit layer S2.

8. The method for automatically refining the fuzzy position of historical soil survey sample points according to claim 1, characterized in that: Step (6) specifically includes: (6-1) Solving the topological relationship between the target sample point distribution layer P1 and the geographic scene unit layer S2 to obtain the corresponding scene unit of each target sample point on the geographic scene unit layer S2; (6-2) extracting all geographic scene feature values ​​of the corresponding scene unit of each target sample point on the geographic scene unit layer S2; (6-3) By solving the topological relationship, all scene units adjacent to the scene unit corresponding to each target sample point are obtained, and all geographic scene feature values ​​of these adjacent scene units are extracted.

9. The method for automatically refining the fuzzy position of historical soil survey sample points according to claim 4, characterized in that: Step (7) specifically includes: (7-1) For different geographic scene feature values, select a membership function consistent with step (4), calculate the similarity between each geographic scene feature value of each target sample point and the corresponding geographic scene feature value of the corresponding scene unit on the geographic scene unit layer S2, and use it as the membership value of the geographic scene feature corresponding to the target sample point; (7-2) Taking the feature importance weight of the geographic scene feature as the corresponding weight, calculate the weighted sum of the membership value of each geographic scene feature of the target sample point as the similarity sim0 between each target sample point and the corresponding scene unit.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: The processor executes the computer program to implement the method according to any one of claims 1 to 9.

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