A slope positioning method for geological surveying and mapping

Abstract

The present invention discloses a slope positioning method for geological surveying and mapping, which relates to the technical field of geological surveying and mapping. The method comprises utilizing surveying equipment and satellite remote sensing to obtain the precision of the surveying equipment, the actual measured coordinates of slope feature points relative to a reference point, the image resolution, and the theoretical coordinates of the slope feature points, performing correction processing on the theoretical coordinates and the actual measured coordinates, obtaining the corrected feature point coordinates, performing horizontal boundary optimization processing on the corrected feature point coordinates, obtaining the horizontal boundary optimized feature point coordinates, performing precision fusion processing on the horizontal boundary optimized feature point coordinates, obtaining the precision fused feature point coordinates, and stopping iteration until the difference between the precision fused feature point coordinates and the corrected feature point coordinates meets an iterative change threshold. The present invention improves the precision of slope positioning, the accuracy of boundary fitting, and the reliability of comprehensive positioning in measurement and mapping, thereby improving the overall efficiency of the slope positioning method for geological surveying and mapping.

Description

Technical Field

[0001] The present invention relates to the technical field of geological surveying and mapping, in particular to a slope positioning method for geological surveying and mapping. Background Art

[0002] Topographic surveying is an important part of engineering surveying. However, there are many challenges in the slope positioning work of geological surveying.

[0003] Based on the many challenges mentioned above, first, most existing methods rely solely on remote sensing images or ground measurement equipment, and fail to fully combine the advantages of both, resulting in low positioning accuracy, causing deviations in the coordinates of the feature points initially located, and unable to accurately reflect the true position of the slope. Secondly, when fitting the slope boundary, existing technologies do not fully consider the horizontal distribution of feature points and the degree of deviation from the fitted boundary, resulting in low boundary fitting accuracy and inability to accurately depict the slope boundary. In addition, existing methods often lack a comprehensive assessment of the comprehensive accuracy of slope positioning, do not consider the elevation distribution of the slope in the vertical direction, and have not established an effective iterative optimization mechanism, making it difficult to continuously improve positioning accuracy under different measurement conditions. Summary of the Invention

[0004] The purpose of the present invention is to provide a slope positioning method for geological surveying and mapping, which solves the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution, which specifically includes the following implementation steps:

[0006] S1. Using surveying equipment and satellite remote sensing, obtain the accuracy of the surveying equipment, the actual measured coordinates of the slope feature points relative to the reference points, the image resolution, and the theoretical coordinates of the slope feature points;

[0007] S2. Based on the accuracy of the measuring device and the image resolution, the theoretical coordinates and the actual measured coordinates are corrected to obtain corrected feature point coordinates;

[0008] S3.1. Based on the corrected feature point coordinates, determining the horizontal center point of the slope, the vertical distance between any feature point and the ideal boundary, and the fitting accuracy, obtaining an adjustment coefficient for adjusting the slope boundary;

[0009] S3.2. Based on the adjustment coefficient, the theoretical coordinates, and the corrected feature point coordinates, performing horizontal boundary optimization processing on the corrected feature point coordinates to obtain horizontal boundary optimized feature point coordinates;

[0010] S4.1. Based on the coordinates of the feature points after horizontal boundary optimization and the preset ideal accuracy value, determine the average vertical height of the slope surface and the accuracy index results related to the fitting accuracy described in S3.1, and obtain an iteration coefficient;

[0011] S4.2. Based on the iteration coefficient, the corrected feature point coordinates, and the horizontal boundary optimized feature point coordinates, perform precision fusion processing on the horizontal boundary optimized feature point coordinates to obtain precision fused feature point coordinates;

[0012] S5. The feature point coordinates after the precision fusion are introduced into S2-S4.2, replacing the theoretical coordinates for iteration;

[0013] An iterative change threshold is selected, and iteration is stopped until the difference between the coordinates of the feature point after the precision fusion and the coordinates of the corrected feature point meets the iterative change threshold.

[0014] Optionally, the theoretical coordinates, the actual measured coordinates, the corrected feature point coordinates, the horizontal boundary optimized feature point coordinates, and the precision fused feature point coordinates are all three-dimensional coordinates including X, Y, and Z axes;

[0015] The iterative change threshold is set to 0.01m;

[0016] The iterative change threshold is met specifically when the three-dimensional coordinates of the feature point coordinates after the precision fusion minus the corrected feature point coordinates are both smaller than the iterative change threshold.

[0017] Optionally, the steps for obtaining the correction of S2 are as follows:

[0018] S2.1. Divide the image resolution by the sum of the image resolution and the accuracy of the measuring device to obtain a correction coefficient for correcting the theoretical coordinates and the actual measured coordinates;

[0019] The calculation formula of the correction coefficient is as follows:

[0020] ;

[0021] in:

[0022] k is the correction factor, f is the image resolution, and j is the accuracy of the measurement equipment;

[0023] S2.2. Based on the correction coefficient k, perform coordinate deviation processing on the three-dimensional coordinates of the theoretical coordinates and the actual measured coordinates to obtain a first three-dimensional deviation feature group;

[0024] S2.3. Multiply the first three-dimensional difference feature group by the correction coefficient k to obtain correction features for correcting the theoretical coordinates and the actual measured coordinates;

[0025] S2.4. Correct the correction feature using the theoretical coordinates to obtain the corrected feature point coordinates.

[0026] Optionally, the steps of obtaining the adjustment coefficient in S3.1 are as follows:

[0027] S3.1.1. Obtain the number of feature points in all X-axes and the three-dimensional coordinates of the corrected feature point coordinates, and obtain an average X-coordinate value using the arithmetic mean;

[0028] S3.1.2. Using analytical geometry methods, combine the corrected feature point coordinates and the ideal boundary coordinates to obtain the vertical distance from any feature point to the ideal curve;

[0029] S3.1.3. Accumulate the product of the vertical distance from any feature point to the ideal curve and the center point, and divide the product by the number of feature points to obtain a boundary fitting accuracy evaluation index;

[0030] Wherein, the ideal boundary is a fitted slope boundary curve obtained by least square curve fitting, and the ideal boundary coordinates are the coordinates of the fitted slope boundary curve;

[0031] S3.1.4. Determine an accuracy threshold based on the fitting accuracy, and divide the accuracy threshold by the sum of the accuracy threshold and the boundary fitting accuracy evaluation index to obtain the adjustment coefficient;

[0032] The calculation formula of the adjustment coefficient is as follows:

[0033] ;

[0034] in:

[0035] t is the adjustment coefficient, B is the boundary fitting accuracy evaluation index, and JH is the accuracy threshold.

[0036] Optionally, the step of obtaining the coordinates of the feature points after the horizontal boundary optimization in S3.2 is as follows:

[0037] S3.2.1. Based on the adjustment coefficient t, perform coordinate deviation processing on the three-dimensional coordinates of the corrected feature point coordinates and the theoretical coordinates to obtain a second three-dimensional deviation feature group;

[0038] S3.2.2. Multiply the second three-dimensional difference feature group by the adjustment coefficient t to obtain a horizontal boundary optimization feature for performing horizontal boundary optimization processing on the corrected feature point coordinates;

[0039] S3.2.3. The corrected feature point coordinates are used to correct the horizontal boundary optimization features to obtain the horizontal boundary optimization feature point coordinates.

[0040] Optionally, the steps of obtaining the iteration coefficient in S4.1 are as follows:

[0041] S4.1.1. The steps in S3.1.1 are the same as those in S3.1.1, except that the X axis is replaced by the Z axis, and the average Z coordinate value is obtained.

[0042] S4.1.2. Multiply the boundary fitting accuracy evaluation index by the average Z coordinate value to obtain a slope positioning comprehensive accuracy index;

[0043] S4.1.3. Predetermine an ideal accuracy value, and divide the ideal accuracy value by the sum of the ideal accuracy value and the slope positioning comprehensive accuracy index to obtain an iteration coefficient;

[0044] The boundary fitting accuracy evaluation index is the accuracy index result related to the fitting accuracy described in S3.1;

[0045] The calculation formula of the iteration coefficient is as follows:

[0046] ;

[0047] in:

[0048] PD is the comprehensive accuracy index of slope positioning, F is the iteration coefficient, and L is the ideal accuracy value.

[0049] Optionally, the steps of S4.2 for obtaining the coordinates of the feature points after precision fusion are as follows:

[0050] S4.2.1. Based on the iteration coefficient F, perform coordinate deviation processing on the three-dimensional coordinates of the feature point coordinates after the horizontal boundary optimization and the feature point coordinates after the correction to obtain a third three-dimensional deviation feature group;

[0051] S4.2.2. Multiply the third three-dimensional difference feature group by the iteration coefficient F to obtain a precision fusion feature for precision fusion of the feature point coordinates after the horizontal boundary optimization and the feature point coordinates after the correction;

[0052] S4.2.3. The coordinates of the feature points after the horizontal boundary optimization are corrected with the precision fusion features to obtain the coordinates of the feature points after the precision fusion.

[0053] Optionally, the surveying equipment includes an ordinary handheld GPS device and a high-precision total station surveying equipment;

[0054] The measurement device accuracy j of the ordinary handheld GPS device is between 1-5m;

[0055] The measurement equipment accuracy j of the high-precision total station measurement equipment has a value interval of 0.01-0.1m;

[0056] The accuracy threshold JH is set between 0.5 and 2 m in projects with low slope positioning accuracy;

[0057] The accuracy threshold JH is set between 0.01-0.1m in projects with high slope positioning accuracy;

[0058] The ideal accuracy value L is set to be ≤0.1m.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. In S2, the present invention comprehensively considers the image resolution and the accuracy of the measuring equipment, and then obtains the correction coefficient k. The initially identified theoretical coordinates and the actual measured coordinates are combined for correction to obtain more accurate corrected feature point coordinates. In this way, the advantages of different data sources are fully utilized, and the shortcomings of a single data source are compensated. Then, the positioning deviation caused by image data errors and measurement equipment accuracy problems is effectively reduced, and the accuracy and reliability of slope feature point positioning are improved.

[0061] 2. The present invention determines the horizontal center point of the slope, the vertical distance between any feature point and the ideal boundary, and the fitting accuracy, obtains the adjustment coefficient t for adjusting the corrected feature point coordinates, and obtains the adjusted horizontal boundary optimized feature point coordinates. This method comprehensively considers the horizontal distribution of feature points and the degree of deviation from the fitted boundary, and can more accurately evaluate the accuracy of slope boundary fitting. The feature point coordinates can be reasonably adjusted according to the evaluation results, thereby improving the accuracy of slope boundary fitting.

[0062] 3. The present invention determines the average height of the slope in the vertical direction, the precision index results related to the fitting accuracy, and the preset ideal precision value, obtains the iteration coefficient F, and then uses the iteration coefficient F to perform feedback iterative adjustment on the feature point coordinates after the horizontal boundary optimization, obtains the feature point coordinates after the adjustment again, and performs cyclic iteration. In this way, while comprehensively considering the slope boundary fitting accuracy and the slope elevation distribution in the vertical direction, an effective comprehensive precision evaluation and iterative optimization mechanism is established. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of the overall method of slope positioning method for local geological mapping. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0065] About the slope positioning method for local geological mapping;

[0066] Different from the existing slope positioning methods used in geological surveying, which have the problems of limited positioning accuracy, inaccurate boundary fitting, and lack of comprehensive accuracy evaluation and iterative optimization;

[0067] The existing geological surveying and mapping slope positioning method, while this algorithm unit improves the positioning accuracy of feature points, optimizes the slope boundary fitting and realizes comprehensive accuracy feedback and iterative optimization.

[0068] For example 1, please refer to Figure 1 This embodiment provides a slope positioning method for geological surveying and mapping, which specifically includes the following implementation steps:

[0069] S1. Using surveying equipment and satellite remote sensing, obtain the accuracy of the surveying equipment, the actual measured coordinates of the slope feature points relative to the reference points, the image resolution, and the theoretical coordinates of the slope feature points;

[0070] Surveying equipment includes common handheld GPS devices and high-precision total station surveying equipment;

[0071] S2. Based on the accuracy of the measuring equipment and the image resolution, the theoretical coordinates and the actual measured coordinates are corrected to obtain the corrected feature point coordinates;

[0072] S3.1. Based on the corrected feature point coordinates, determine the horizontal center point of the slope, the vertical distance between any feature point and the ideal boundary, and the fitting accuracy, and obtain an adjustment coefficient for adjusting the slope boundary.

[0073] S3.2. Based on the adjustment coefficient, the theoretical coordinates, and the corrected feature point coordinates, the corrected feature point coordinates are subjected to horizontal boundary optimization processing to obtain the horizontal boundary optimized feature point coordinates.

[0074] S4.1. Based on the coordinates of the feature points after horizontal boundary optimization and the preset ideal accuracy value, determine the average vertical height of the slope surface and the accuracy index results related to the fitting accuracy in S3.1, and obtain the iteration coefficient;

[0075] S4.2. Based on the iteration coefficient, the corrected feature point coordinates, and the horizontal boundary optimized feature point coordinates, perform precision fusion processing on the horizontal boundary optimized feature point coordinates to obtain the precision fused feature point coordinates;

[0076] S5. After precision fusion, the feature point coordinates are introduced into S2-S4.2 to replace the theoretical coordinates for iteration;

[0077] The iteration change threshold is selected, and the iteration is stopped until the difference between the coordinates of the feature point after precision fusion and the corrected feature point meets the iteration change threshold.

[0078] In this embodiment, S1 uses surveying equipment and satellite remote sensing to obtain the accuracy of the surveying equipment, the actual measured coordinates of slope feature points relative to the reference point, the image resolution, and the theoretical coordinates of the slope feature points. After processing by the calculation unit of S2, the accuracy of the feature point positioning is improved, laying a precise foundation for subsequent surveying and mapping. S3.1 to S3.2, based on the results of S2, comprehensively consider the distribution of feature points and boundary deviations to optimize the slope boundary fitting. S4.1 to S4.2, combined with the results of S3.2, comprehensively evaluate the comprehensive accuracy, and optimize the positioning results through iterative adjustment. The results are fed back to S2 for cyclic optimization.

[0079] The cyclic coordination of S1 to S5 improves the accuracy of slope positioning, the accuracy of boundary fitting and the reliability of comprehensive positioning in measurement and mapping, and enhances the overall effectiveness of the slope positioning method for geological mapping.

[0080] See also Figure 1 , the theoretical coordinates, actual measured coordinates, corrected feature point coordinates, feature point coordinates after horizontal boundary optimization, and feature points after precision fusion are all three-dimensional coordinates including X, Y, and Z axes;

[0081] The iterative change threshold is set to 0.01m;

[0082] Meeting the iterative change threshold specifically means that the three-dimensional coordinates of the feature point coordinates after precision fusion minus the feature point coordinates after correction are all less than the iterative change threshold.

[0083] In this embodiment, the calculation formula that satisfies the reference is as follows:

[0084] <0.01;

[0085] <0.01;

[0086] <0.01;

[0087] in:

[0088] x 1i is the corrected X axis, y 1i is the corrected Y axis, z 1i is the corrected Z axis, x 3i X axis after precision fusion, y 3iY axis after precision fusion, z 3i For precision fusion of the Z axis;

[0089] The cyclic influence of the feature point coordinates after precision fusion on the corrected feature point coordinates forms an iterative optimization process. In each iteration, the feature point coordinates after precision fusion are substituted into the calculation of S2 as new theoretical coordinates. In this way, S2 can be corrected based on more accurate initial data in the new round of calculation, further reducing positioning deviation.

[0090] As the iteration proceeds, when the change in the slope feature point coordinates obtained between two adjacent iterations is less than the iteration change threshold, it is considered that the iteration has converged and achieved the required accuracy requirement, and the loop can be ended.

[0091] For example 2, please refer to Figure 1 , the correction acquisition steps of S2 are as follows:

[0092] S2.1. Divide the image resolution by the sum of the image resolution and the accuracy of the measuring device to obtain the correction coefficient for correcting the theoretical coordinates and the actual measured coordinates;

[0093] The calculation formula of the correction coefficient is as follows:

[0094] ;

[0095] in:

[0096] k is the correction factor, f is the image resolution, and j is the accuracy of the measurement equipment;

[0097] The measurement device accuracy j of ordinary handheld GPS devices ranges from 1 to 5 m;

[0098] The measurement equipment accuracy j of the high-precision total station measurement equipment is in the range of 0.01-0.1m;

[0099] S2.2. Based on the correction coefficient k, the three-dimensional coordinates of the theoretical coordinates and the actual measured coordinates are processed to obtain a first three-dimensional deviation feature group;

[0100] S2.3. Multiply the first three-dimensional difference feature group by the correction coefficient k to obtain correction features for correcting the theoretical coordinates and the actual measured coordinates;

[0101] S2.4. Correct the feature points using the theoretical coordinates to obtain the corrected feature point coordinates.

[0102] In this embodiment, based on the calculation of the correction coefficient k, the theoretical coordinate P is obtained. i (x i ,y i ,z i) and the actual measured coordinates O o (x o ,y o ,z o ) is calculated using the following formula for preliminary correction:

[0103] ;

[0104] in:

[0105] x 1i is the corrected X axis, y 1i is the corrected Y axis, z 1i is the corrected Z axis, thus forming the corrected feature point coordinate P 1i (x 1i ,y 1i ,z 1i );

[0106] 、 and The calculation results are all modified characteristics;

[0107] The first three-dimensional deviation feature set includes 、 and .

[0108] The calculation formula for image resolution is: image resolution = map distance / pixel. At the same time, it is known that 1 inch (in) = 96 pixels (pixel), 1 inch (in) = 0.0254m, so 1 pixel = 0.0254 / 96 = 2.65×10 -4 m, and the image resolution f is calculated through these formulas and conversion relationships, as well as based on map distance, pixels, and scale data to ensure the consistency of the calculation unit quantity.

[0109] See also Figure 1 , S3.1 The specific steps for obtaining the adjustment coefficient are as follows:

[0110] S3.1.1. Obtain the number of all X-axis and three-dimensional feature points in the corrected feature point coordinates, and use the arithmetic mean to obtain the average X-coordinate value of the slope surface in the horizontal direction;

[0111] S3.1.2. Using analytical geometry methods, combine the corrected feature point coordinates with the ideal boundary coordinates to obtain the vertical distance from any feature point to the ideal curve;

[0112] S3.1.3. Accumulate the product of the vertical distance from any feature point to the ideal curve and the center point, and then divide it by the number of feature points to obtain the boundary fitting accuracy evaluation index;

[0113] Wherein, the ideal boundary is the fitted slope boundary curve using the least square curve fitting, and the ideal boundary coordinates are the coordinates of the fitted slope boundary curve;

[0114] S3.1.4. Determine an accuracy threshold based on the fitting accuracy, and divide the accuracy threshold by the sum of the accuracy threshold and the boundary fitting accuracy evaluation index to obtain an adjustment coefficient;

[0115] The calculation formula of the adjustment coefficient is as follows:

[0116] ;

[0117] in:

[0118] t is the adjustment coefficient, B is the boundary fitting accuracy evaluation index, and JH is the accuracy threshold;

[0119] The accuracy threshold JH is set between 0.5 and 2 m in projects with low slope positioning accuracy;

[0120] The accuracy threshold JH is between 0.01 and 0.1 m in projects with high slope positioning accuracy;

[0121] Furthermore, the steps of S3.2 for obtaining the coordinates of the feature points after horizontal boundary optimization are as follows:

[0122] S3.2.1. Based on the adjustment coefficient t, perform coordinate deviation processing on the three-dimensional coordinates of the corrected feature point coordinates and the theoretical coordinates to obtain a second three-dimensional deviation feature group;

[0123] S3.2.2. Multiply the second three-dimensional difference feature group by the adjustment coefficient t to obtain horizontal boundary optimization features for performing horizontal boundary optimization processing on the corrected feature point coordinates;

[0124] S3.2.3. The corrected feature point coordinates are used to correct the horizontal boundary optimization features to obtain the coordinates of the feature points after horizontal boundary optimization.

[0125] In this embodiment, the calculation formulas for the average X coordinate value in S3.1.1 and the boundary fitting accuracy evaluation index in S3.1.3 are as follows:

[0126] ;

[0127] ;

[0128] X is the average X coordinate value, n is the number of feature points, l j is the vertical distance of the j-th feature point.

[0129] Based on the calculation of the average X coordinate value X, the boundary fitting accuracy evaluation index B and the adjustment coefficient t, the adjustment coefficient t is introduced to optimize the feature point coordinates P after the horizontal boundary is optimized.1i (x 1i ,y 1i ,z 1i ) is calculated using the following formula for preliminary correction:

[0130] ;

[0131] in:

[0132] x 2i X coordinate and y coordinate after optimization of horizontal boundary 2i Y coordinate after optimization of the horizontal boundary, z 2i is the X coordinate after horizontal boundary optimization, thus forming the feature point coordinate P after horizontal boundary optimization 2i (x 2i ,y 2i ,z 2i );

[0133] 、 and The calculation results of are all horizontal boundary optimization features;

[0134] The second three-dimensional difference feature group includes 、 and .

[0135] It is worth noting that in the slope positioning of geological surveying, the X coordinate direction is usually related to the horizontal extension direction of the slope. The average X coordinate value X of all the corrected coordinates of the feature points can represent the center position or average position information of the slope in the horizontal direction. In actual application, if the corrected feature point coordinate P 1i (x 1i ,y 1i ,z 1i ) If the deviation from the average X coordinate value X in the X direction is too large, it means that there is a deviation in the slope boundary fitting and adjustment is required;

[0136] The boundary fitting accuracy evaluation index B is calculated by multiplying the vertical distance of the jth feature point by the average X coordinate value X. This is to comprehensively consider the distance from the feature point to the boundary and the distribution of the slope in the horizontal direction.

[0137] The adjustment coefficient t is calculated in combination with the accuracy threshold JH and the boundary fitting accuracy evaluation index B in order to reasonably adjust the feature point coordinates according to the actual fitting accuracy to achieve the surveying and mapping effect.

[0138] See also Figure 1 , S4.1 The specific steps for obtaining the iteration coefficient are as follows:

[0139] S4.1.1. Follow the same steps as S3.1.1, except that the X-axis is replaced by the Z-axis, and the average Z-coordinate value is obtained.

[0140] S4.1.2. Multiply the boundary fitting accuracy evaluation index by the average Z coordinate value to obtain the slope positioning comprehensive accuracy index;

[0141] S4.1.3. Determine the ideal accuracy value in advance, and divide the ideal accuracy value by the sum of the ideal accuracy value and the slope positioning comprehensive accuracy index to obtain the iteration coefficient;

[0142] Among them, the boundary fitting accuracy evaluation index is the accuracy index result related to the fitting accuracy in S3.1;

[0143] The calculation formula of the iteration coefficient is as follows:

[0144] ;

[0145] in:

[0146] PD is the comprehensive accuracy index of slope positioning, F is the iteration coefficient, and L is the ideal accuracy value;

[0147] The ideal accuracy value L is set to ≤0.1m.

[0148] Furthermore, the steps in S4.2 to obtain the coordinates of the feature points after precision fusion are as follows:

[0149] S4.2.1. Based on the iteration coefficient F, perform coordinate deviation processing on the three-dimensional coordinates of the feature point coordinates after horizontal boundary optimization and the feature point coordinates after correction to obtain a third three-dimensional deviation feature group;

[0150] S4.2.2. Multiply the third three-dimensional difference feature group by the iteration coefficient F to obtain a precision fusion feature for precision fusion of the feature point coordinates after horizontal boundary optimization and the feature point coordinates after correction;

[0151] S4.2.3. After the horizontal boundary is optimized, the feature point coordinates are corrected for the precision fusion feature to obtain the feature point coordinates after precision fusion.

[0152] In this embodiment, first, the calculation formulas for the average Z coordinate value in S4.1.1 and the slope positioning comprehensive accuracy index in S4.1.2 are as follows:

[0153] ;

[0154] ;

[0155] in:

[0156] Z is the average Z coordinate value, and PD is the comprehensive accuracy index of slope positioning.

[0157] Based on the calculation of the average Z coordinate value Z, the slope positioning comprehensive accuracy index PD and the iteration coefficient F, the iteration coefficient F is introduced to optimize the coordinates of the feature point P after the horizontal boundary is optimized. 2i (x 2i ,y 2i ,z 2i ) is calculated using the following formula for preliminary correction:

[0158] ;

[0159] in:

[0160] x 3i X axis after precision fusion, y 3i Y axis after precision fusion, z 3i The Z axis after precision fusion, thus forming the feature point coordinates P after precision fusion 3i (x 3i ,y 3i ,z 3i );

[0161] 、 and The calculation results are all precision fusion features;

[0162] The third three-dimensional difference feature group includes 、 and .

[0163] It is worth noting that in slope positioning, the Z coordinate represents elevation information. Calculating the average Z coordinate value of the feature point coordinates after all horizontal boundary optimization can reflect the average elevation of the slope in the vertical direction, so as to more comprehensively reflect the spatial characteristics of the slope.

[0164] The slope positioning comprehensive accuracy index PD is calculated by multiplying the boundary fitting accuracy evaluation index B by the average Z coordinate value Z. This is to comprehensively consider the slope boundary fitting accuracy and the elevation distribution of the slope in the vertical direction. In actual geological mapping, a slope with high boundary fitting accuracy in the horizontal direction but large differences in elevation distribution in the vertical direction, or vice versa, will affect the accuracy of the entire slope positioning. By calculating the slope positioning comprehensive accuracy index PD, these two important aspects of information can be combined to provide a more comprehensive basis for the final iterative adjustment.

[0165] The iteration coefficient F is calculated based on the slope positioning comprehensive accuracy index PD and the ideal accuracy value L. It is used to provide feedback and adjust the feature point coordinates according to the actual comprehensive accuracy. In this way, the slope positioning results can be continuously iterated and optimized, gradually approaching the ideal accuracy requirements, thereby improving the accuracy of surveying and mapping.

[0166] In addition, Before calculation, the boundary fitting accuracy evaluation index B and the average Z coordinate value Z must be normalized:

[0167] ;

[0168] ;

[0169] Among them, B norm and Z norm are the normalized boundary fitting accuracy evaluation index and the average Z coordinate value, B ref and Z ref All are reference benchmark values.

[0170] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A slope positioning method for geological surveying and mapping, characterized in that: The specific implementation steps are as follows: S1. Using surveying equipment and satellite remote sensing, obtain the accuracy of the surveying equipment, the actual measured coordinates of the slope feature points relative to the reference points, the image resolution, and the theoretical coordinates of the slope feature points; S2. Based on the accuracy of the measuring device and the image resolution, the theoretical coordinates and the actual measured coordinates are corrected to obtain corrected feature point coordinates; S3.

1. Based on the corrected feature point coordinates, determining the horizontal center point of the slope, the vertical distance between any feature point and the ideal boundary, and the fitting accuracy, obtaining an adjustment coefficient for adjusting the slope boundary; S3.

2. Based on the adjustment coefficient, the theoretical coordinates, and the corrected feature point coordinates, performing horizontal boundary optimization processing on the corrected feature point coordinates to obtain horizontal boundary optimized feature point coordinates; S4.

1. Based on the coordinates of the feature points after horizontal boundary optimization and the preset ideal accuracy value, determine the average vertical height of the slope surface and the accuracy index results related to the fitting accuracy described in S3.1, and obtain an iteration coefficient; S4.

2. Based on the iteration coefficient, the corrected feature point coordinates, and the horizontal boundary optimized feature point coordinates, perform precision fusion processing on the horizontal boundary optimized feature point coordinates to obtain precision fused feature point coordinates; S5. The feature point coordinates after the precision fusion are introduced into S2-S4.2, replacing the theoretical coordinates for iteration; Selecting an iterative change threshold, and stopping iteration until the difference between the coordinates of the feature point after the precision fusion and the feature point after the correction meets the iterative change threshold; The steps for obtaining the correction of S2 are as follows: S2.

1. Divide the image resolution by the sum of the image resolution and the accuracy of the measuring device to obtain a correction coefficient for correcting the theoretical coordinates and the actual measured coordinates; The calculation formula of the correction coefficient is as follows: ； in: k is the correction factor, f is the image resolution, and j is the accuracy of the measurement equipment; S2.

2. Based on the correction coefficient k, perform coordinate deviation processing on the three-dimensional coordinates of the theoretical coordinates and the actual measured coordinates to obtain a first three-dimensional deviation feature group; S2.

3. Multiply the first three-dimensional deviation feature group by the correction coefficient k to obtain correction features for correcting the theoretical coordinates and the actual measured coordinates; S2.

4. Correct the correction feature using the theoretical coordinates to obtain the corrected feature point coordinates; The specific steps of obtaining the adjustment coefficient in S3.1 are as follows: S3.1.

1. Obtain the number of feature points in all X-axes and the three-dimensional coordinates of the corrected feature point coordinates, and obtain an average X-coordinate value using the arithmetic mean; S3.1.

2. Using analytical geometry methods, combine the corrected feature point coordinates and the ideal boundary coordinates to obtain the vertical distance from any feature point to the ideal curve; S3.1.

3. Accumulate the product of the vertical distance from any feature point to the ideal curve and the center point, and divide the product by the number of feature points to obtain a boundary fitting accuracy evaluation index; Wherein, the ideal boundary is a fitted slope boundary curve obtained by least square curve fitting, and the ideal boundary coordinates are the coordinates of the fitted slope boundary curve; S3.1.

4. Determine an accuracy threshold based on the fitting accuracy, and divide the accuracy threshold by the sum of the accuracy threshold and the boundary fitting accuracy evaluation index to obtain the adjustment coefficient; The calculation formula of the adjustment coefficient is as follows: ； in: t is the adjustment coefficient, B is the boundary fitting accuracy evaluation index, and JH is the accuracy threshold; The steps of S3.2 for obtaining the coordinates of the feature points after the horizontal boundary optimization are as follows: S3.2.

1. Based on the adjustment coefficient t, perform coordinate deviation processing on the three-dimensional coordinates of the corrected feature point coordinates and the theoretical coordinates to obtain a second three-dimensional deviation feature group; S3.2.

2. Multiply the second three-dimensional deviation feature group by the adjustment coefficient t to obtain a horizontal boundary optimization feature for performing horizontal boundary optimization processing on the corrected feature point coordinates; S3.2.

3. Correct the horizontal boundary optimization feature using the corrected feature point coordinates to obtain the horizontal boundary optimized feature point coordinates; The specific steps of obtaining the iteration coefficient in S4.1 are as follows: S4.1.

1. The steps in S3.1.1 are the same as those in S3.1.1, except that the X axis is replaced by the Z axis, and the average Z coordinate value is obtained. S4.1.

2. Multiply the boundary fitting accuracy evaluation index by the average Z coordinate value to obtain a comprehensive slope positioning accuracy index; S4.1.

3. Predetermine an ideal accuracy value, and divide the ideal accuracy value by the sum of the ideal accuracy value and the slope positioning comprehensive accuracy index to obtain an iteration coefficient; The boundary fitting accuracy evaluation index is the accuracy index result related to the fitting accuracy described in S3.1; The calculation formula of the iteration coefficient is as follows: ； in: PD is the comprehensive accuracy index of slope positioning, F is the iteration coefficient, and L is the ideal accuracy value; The steps of S4.2 for obtaining the coordinates of the feature points after precision fusion are as follows: S4.2.

1. Based on the iteration coefficient F, perform coordinate deviation processing on the three-dimensional coordinates of the feature point coordinates after the horizontal boundary optimization and the feature point coordinates after the correction to obtain a third three-dimensional deviation feature group; S4.2.

2. Multiply the third three-dimensional deviation feature group by the iteration coefficient F to obtain a precision fusion feature for precision fusion of the feature point coordinates after the horizontal boundary optimization and the feature point coordinates after the correction; S4.2.

3. The coordinates of the feature points after the horizontal boundary optimization are corrected with the precision fusion features to obtain the coordinates of the feature points after the precision fusion.

2. A slope positioning method for geological surveying and mapping according to claim 1, characterized in that: The theoretical coordinates, the actual measured coordinates, the corrected feature point coordinates, the horizontal boundary optimized feature point coordinates, and the precision fused feature point coordinates are all three-dimensional coordinates including X, Y, and Z axes; The iterative change threshold is set to 0.01m; The iterative change threshold is met specifically when the three-dimensional coordinates of the feature point coordinates after the precision fusion minus the corrected feature point coordinates are both smaller than the iterative change threshold.

3. A slope positioning method for geological surveying and mapping according to claim 2, characterized in that: The surveying equipment includes ordinary handheld GPS equipment and high-precision total station surveying equipment; The measurement device accuracy j of the ordinary handheld GPS device is between 1-5m; The measurement equipment accuracy j of the high-precision total station measurement equipment has a value interval of 0.01-0.1m; The accuracy threshold JH is set between 0.5 and 2 m in projects with low slope positioning accuracy; The accuracy threshold JH is set between 0.01 and 0.1 m in projects with high slope positioning accuracy; The ideal accuracy value L is set to be ≤0.1m.

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