CN-122017908-A - Beidou RTK verification point location selection method and system based on multi-source information fusion

CN122017908ACN 122017908 ACN122017908 ACN 122017908ACN-122017908-A

Abstract

The invention discloses a Beidou RTK verification point position selection method and system based on multi-source information fusion, and relates to the technical field of data processing. The method comprises the steps of carrying out environmental impact evaluation on a target area based on multi-source information to generate an area quality partition map, constructing a candidate verification point library comprising known control points and historical stable points, giving initial confidence to each candidate verification point, obtaining operation characteristic parameters in a historical measurement time window, generating a comprehensive strength compensation coefficient of verification requirement strength of current operation, adjusting the number of preset reference verification points to determine the total number of adaptation verification points, carrying out iterative optimization of a verification point distribution scheme based on a preset multi-target optimization function, the area quality partition map, the total number of adaptation verification points and the initial confidence of the candidate verification points by taking the candidate verification point library as an optimizing space, and generating an optimal verification point distribution scheme to carry out verification point selection. The invention effectively improves the accuracy of Beidou RTK verification point selection.

Inventors

ZHAO ZHONGHAI
XU LILI
GUAN CANGHAI
JIN LIHUA
SUN LIANGYU
CHEN DAYONG
Ma Chuanning
WANG BO

Assignees

自然资源部第二大地测量队(黑龙江第一测绘工程院)

Dates

Publication Date: 20260512
Application Date: 20260323

Claims (10)

1. The Beidou RTK verification point position selection method for multi-source information fusion is characterized by comprising the following steps of: Performing environmental impact evaluation on a target area based on multi-source information of the target area, and generating an area quality partition map reflecting the advantages and disadvantages of measurement conditions of different positions; constructing a candidate verification point library containing known control points and historical stable points based on the multi-source information, and giving an initial confidence degree to each candidate verification point in the candidate verification point library; acquiring operation characteristic parameters in a historical measurement time window, and generating a comprehensive strength compensation coefficient of the verification demand strength of the current operation based on the operation characteristic parameters; Adjusting the number of preset reference verification points based on the comprehensive strength compensation coefficient, and determining the total number of the adaptation verification points required by the current operation; And taking the candidate verification point library as an optimizing space, performing iterative optimization of a verification point location distribution scheme based on a preset multi-objective optimizing function, the quality partition map of the measuring area, the total number of the adaptive verification points and the initial confidence of the candidate verification points, generating an optimal verification point location distribution scheme, and performing verification point location selection.
2. The method for selecting the Beidou RTK verification point position by multi-source information fusion according to claim 1, wherein the method is characterized in that the method for evaluating the environmental influence of the target area based on the multi-source information of the target area to generate the area quality partition map reflecting the advantages and disadvantages of different position measurement conditions, and comprises the following steps: The method comprises the steps of obtaining multi-source information of a target area, wherein the multi-source information at least comprises basic geographic information data, reference station network service information, historical measurement result data, real-time observation state data and operation process record data; extracting the geographic and geomorphic features and the ground object coverage features of the area based on the basic geographic information data, and interpreting potential areas with signal shielding risks and multipath effect risks according to a satellite signal propagation theory; analyzing the spatial distribution density of the reference station and the coverage intensity of the differential signal based on the service information of the reference station network, and evaluating a potential area with the risk of differential signal attenuation according to a signal attenuation model; Identifying the spatial distribution of various electromagnetic interference sources based on electromagnetic environment information, and defining a potential area with electromagnetic interference risk according to electromagnetic field propagation characteristics; Based on the spatial superposition analysis results of different risk types, dividing the area with multiple risk couplings into measurement condition inferior areas, dividing the area with single risk into measurement condition middle areas, and dividing the area without any risk exposure into measurement condition superior areas.
3. The method for selecting the beidou RTK verification point position based on the multi-source information fusion according to claim 1, wherein constructing a candidate verification point library containing known control points and history stable points based on the multi-source information, and assigning an initial confidence degree to each candidate verification point in the candidate verification point library, comprises: Extracting known control point data in a target area from the multi-source information, wherein the known control point data comprises coordinate achievements, grade attributes and point position stability records of all known control points; Extracting historical measurement result data from the multi-source information, screening historical measurement points meeting a preset stability index as historical stable points, integrating the known control points with the historical stable points, eliminating repeated points and conflict points, and constructing a candidate verification point library covering a target area; collecting multi-period observation data of each candidate verification point in history measurement, calculating the discrete degree of observation results in each period, and determining a point position stability characteristic value according to the discrete degree; Extracting the quality partition map attribute of the area where each candidate verification point is located, and determining an environmental risk influence coefficient according to the measurement condition quality level of the area; Acquiring a historical observation condition record of each candidate verification point in history measurement, and determining an observation quality characteristic value according to the observation condition record, wherein the observation condition record comprises a position accuracy attenuation factor mean value and a signal-to-noise ratio mean value; Collecting field investigation information of each candidate verification point, and determining a point location restorability characteristic value according to the field investigation information, wherein the field investigation information comprises the point location mark integrity degree, accessibility and identifiability; determining reference weight of each candidate verification point according to the type attribute of the candidate verification point, wherein the type attribute comprises a high-level known control point, a level known control point and a history stable point; And respectively carrying out normalization processing on the point position stability characteristic value, the environment risk influence coefficient, the observation quality characteristic value and the point position restorability characteristic value, carrying out weighted fusion on the normalized characteristic values to generate a trusted correction coefficient of each candidate verification point, carrying out compensation correction on the reference weight of each candidate verification point, and generating the initial confidence coefficient of each candidate verification point.
4. The method for selecting the beidou RTK verification point position based on multi-source information fusion according to claim 1, wherein the step of obtaining the operation characteristic parameters in the history measurement time window comprises the following steps: Calculating to obtain a measurement accuracy index according to the plane coordinate deviation of each measurement point in the historical measurement time window; Obtaining the proportion of the fixed solution according to each measurement point in the historical measurement time window, and calculating to obtain the fixed solution acquisition rate; calculating to obtain a duration average value of continuous operation according to the duration of each continuous operation in the historical measurement time window; calculating to obtain an average position accuracy attenuation factor according to the position accuracy attenuation factors of all measurement points in the historical measurement time window; And taking the measurement accuracy index, the fixed solution acquisition rate, the average value of the duration and the average position accuracy attenuation factor as operation characteristic parameters.
5. The multiple source information fusion Beidou RTK verification point location selection method of claim 4, wherein generating a comprehensive strength compensation coefficient of verification demand strength of a current operation based on the operation characteristic parameters comprises: taking the ratio of a preset reference measurement accuracy index to the measurement accuracy index as a first required intensity compensation coefficient; Taking the ratio of a preset reference fixed solution acquisition rate to the fixed solution acquisition rate as a second required intensity compensation coefficient; Taking the ratio of the continuous operation duration mean value to a preset reference continuous operation duration mean value as a third required intensity compensation coefficient; taking the ratio of the average position accuracy attenuation factor to a preset reference average position accuracy attenuation factor as a fourth required intensity compensation coefficient; And carrying out weighted fusion on the first required intensity compensation coefficient, the second required intensity compensation coefficient, the third required intensity compensation coefficient and the fourth required intensity compensation coefficient to obtain a comprehensive intensity compensation coefficient for verifying the required intensity.
6. The method for selecting the beidou RTK verification point positions based on the multi-source information fusion of claim 5, wherein adjusting the number of preset reference verification point positions based on the comprehensive intensity compensation coefficient, and determining the total number of the adaptation verification point positions required by the current operation comprises: determining the number of preset reference verification points according to the operation type, multiplying the comprehensive strength compensation coefficient by the number of the reference verification points and rounding up to obtain the total number of theoretical verification points of the current operation; And adjusting the total number of the theoretical verification points by using a preset quantity constraint condition to obtain the total number of the adaptive verification points required by the current operation, wherein the quantity constraint condition comprises a minimum verification point quantity guarantee threshold value and a maximum verification point quantity limit threshold value.
7. The method for selecting the beidou RTK verification point positions by multi-source information fusion according to claim 1, wherein the iterative optimization of the verification point position distribution scheme is performed by taking the candidate verification point library as an optimization space based on a preset multi-objective optimization function, the quality partition map of the area, the total number of the adaptation verification point positions and the initial confidence of the candidate verification points, and comprises the following steps: Determining the quantity proportion of verification points selected from a measurement condition priority zone, a measurement condition middle zone and a measurement condition inferior zone according to the total number of the adaptation verification points, the area proportion of each quality class zone in the quality zone diagram of the measuring zone and the risk weight, wherein the measurement condition inferior zone is given with the risk priority weight so that the quantity of the verification points is not lower than a preset lowest-level zone verification point proportion threshold; selecting a corresponding number of candidate verification points from each quality class area according to the verification point number proportion and the initial confidence coefficient of the candidate verification points, and generating a plurality of initial verification point distribution schemes as optimized initial populations; Using the candidate verification point library as an optimizing space, adopting an intelligent optimization algorithm to carry out iterative optimization on verification point position distribution schemes in the initial population, calculating a scheme quality coefficient of each verification point position distribution scheme according to a preset multi-objective optimization function, and selecting a good scheme according to the scheme quality coefficient to carry out cross operation and mutation operation to generate a new verification point position distribution scheme; repeatedly executing iterative optimization until the iterative termination condition is met, and taking the verification point location distribution scheme with the highest scheme quality coefficient when the iteration is terminated as an optimal verification point location distribution scheme; Selecting a corresponding number of candidate verification points from each quality class area respectively, and generating a plurality of initial verification point distribution schemes, wherein the initial verification point distribution schemes comprise: From the candidate verification point set corresponding to the measurement condition inferior region, a required number of candidate verification points are forcedly selected according to the order from high to low of initial confidence level, so that the reliability of the verification points in the high risk region is ensured; From the candidate verification point set corresponding to the measurement condition middle region, taking the initial confidence coefficient of each candidate verification point as weight to carry out weighted random extraction, and selecting a required number of candidate verification points, wherein the probability that the point with higher confidence coefficient is selected is higher; dividing the measurement condition optimal region into a plurality of subareas from a candidate verification point set corresponding to the measurement condition optimal region by adopting a space grid dividing method, and uniformly and randomly extracting in each subarea until a required number of candidate verification points are selected, so that the space distribution uniformity of the verification points of the low-risk region is ensured; And merging the candidate verification points selected from the quality level areas to form a complete candidate verification point distribution scheme, and repeatedly executing the selection process for a plurality of times to generate a plurality of different candidate verification point distribution schemes.
8. The Beidou RTK verification point selection method based on multi-source information fusion of claim 7, wherein the preset multi-objective optimization function comprises a spatial distribution uniformity target, a region coverage integrity target, a point location reliability degree target, a time distribution balance target and a risk region priority target, wherein the spatial distribution uniformity target is used for evaluating the distribution uniformity degree of each verification point on a space of a detection zone, and the Thiessen polygon division is carried out on the target detection zone to calculate the variation coefficient of each Thiessen polygon area, and the reciprocal of the variation coefficient is used as a spatial distribution uniformity quantization value; the regional coverage integrity target is used for evaluating coverage degree of each verification point to regions with different measurement conditions, calculating ratios of actual number of verification points to target number in regions with different quality grades respectively, and taking the minimum value in the ratios as a regional coverage integrity quantized value; The point location reliability degree target is used for evaluating the sum of the positions of the selected verification points, and adding the initial positions of the verification points in the scheme to obtain a point location reliability degree quantized value; The time distribution balance target is used for evaluating the distribution balance degree of each verification point on an operation time axis, dividing the operation time into a plurality of time intervals, counting the distribution frequency of the verification points in each time interval, calculating the information entropy of the distribution frequency, and taking the information entropy as a time distribution balance quantization value; The risk area priority target is used for evaluating the ratio of the high risk area in the verification point positions, calculating the proportion of the verification point number in the poor area of the measurement condition to the total verification point number of the scheme, and taking the proportion as a risk area priority quantification value; And respectively carrying out normalization processing on each target quantized value, and carrying out weighted summation on each normalized quantized value to obtain a scheme quality coefficient of each verification point location distribution scheme.
9. The method for selecting the verification point positions of the Beidou RTK with the multi-source information fusion according to claim 7, wherein the iterative optimization of the verification point position distribution scheme in the initial population by adopting an intelligent optimization algorithm comprises the following steps: Coding each candidate verification point location distribution scheme into individuals, initializing a population containing a preset number of individuals, calculating a scheme quality coefficient of each individual in the population according to a preset multi-objective optimization function, and selecting a preset number of excellent individuals from the current population to enter a next generation population according to the sequence of the scheme quality coefficients from high to low; Performing pairwise crossover operation on the selected excellent individuals, and generating new child individuals according to preset crossover probability; Performing mutation operation on the generated offspring individuals, and adjusting the individuals according to preset mutation probability; Combining the offspring individuals and the father individuals to form a new generation population, and recalculating the scheme quality coefficient of each individual in the population; And repeatedly executing the steps until an iteration termination condition is met, decoding an individual with the highest scheme quality coefficient in the population during iteration termination to obtain a corresponding verification point number list as an optimal verification point distribution scheme, wherein the iteration termination condition comprises convergence of the scheme quality coefficient or reaching a preset maximum iteration number.
10. The Beidou RTK verification point selection system for multi-source information fusion is characterized by being used for implementing the Beidou RTK verification point selection method for multi-source information fusion according to any one of claims 1-9, and comprises the following steps: The detection zone environment evaluation module is used for evaluating the environmental influence of a target detection zone based on multi-source information of the target detection zone and generating a detection zone quality partition map reflecting the advantages and disadvantages of measurement conditions at different positions; The candidate point library construction module is used for constructing a candidate verification point library containing known control points and history stable points based on the multi-source information, and giving initial confidence to each candidate verification point in the candidate verification point library; The compensation coefficient generation module is used for acquiring the operation characteristic parameters in the history measurement time window and generating a comprehensive strength compensation coefficient of the verification demand strength of the current operation based on the operation characteristic parameters; the adaptation point position determining module is used for adjusting the number of preset reference verification point positions based on the comprehensive strength compensation coefficient and determining the total number of the adaptation verification point positions required by the current operation; and the point position scheme optimizing module is used for carrying out iterative optimization on the verification point position distribution scheme based on a preset multi-objective optimizing function, the quality partition map of the area, the total number of the adaptive verification points and the initial confidence of the candidate verification points by taking the candidate verification point library as an optimizing space, generating an optimal verification point position distribution scheme and carrying out verification point position selection.

Description

Beidou RTK verification point location selection method and system based on multi-source information fusion Technical Field The invention relates to the technical field of data processing, in particular to a Beidou RTK verification point location selection method and system for multi-source information fusion. Background With the wide application of Beidou satellite navigation systems, real-time dynamic differential (RTK) measurement technology has become an important operation means in the fields of engineering mapping, environment monitoring and the like. In the prior art, the verification point selection of the RTK measurement operation generally depends on personal experience of a measurer, and a few known points or characteristic points are selected through subjective judgment to perform measurement quality verification. Meanwhile, the verification frequency and the operation intensity cannot be dynamically matched, when the working environment changes or the measurement intensity is increased, the verification strategy is difficult to adjust in time in the prior art, so that the accuracy of Beidou RTK verification point position selection is insufficient, and the overall reliability of measurement results and the effectiveness of quality control are affected. Disclosure of Invention The invention provides a Beidou RTK verification point position selection method and system based on multi-source information fusion, and aims to solve the technical problem that in the prior art, the Beidou RTK verification point position selection accuracy is insufficient. In view of the above problems, the invention provides a Beidou RTK verification point selection method and system for multi-source information fusion. In a first aspect, the invention provides a Beidou RTK verification point location selection method for multi-source information fusion, which comprises the following steps: Performing environmental impact evaluation on a target area based on multi-source information of the target area, and generating an area quality partition map reflecting the advantages and disadvantages of measurement conditions of different positions; constructing a candidate verification point library containing known control points and historical stable points based on the multi-source information, and giving an initial confidence degree to each candidate verification point in the candidate verification point library; acquiring operation characteristic parameters in a historical measurement time window, and generating a comprehensive strength compensation coefficient of the verification demand strength of the current operation based on the operation characteristic parameters; Adjusting the number of preset reference verification points based on the comprehensive strength compensation coefficient, and determining the total number of the adaptation verification points required by the current operation; And taking the candidate verification point library as an optimizing space, performing iterative optimization of a verification point location distribution scheme based on a preset multi-objective optimizing function, the quality partition map of the measuring area, the total number of the adaptive verification points and the initial confidence of the candidate verification points, generating an optimal verification point location distribution scheme, and performing verification point location selection. In a second aspect, the present invention provides a beidou RTK verification point location selection system for multi-source information fusion, including: The detection zone environment evaluation module is used for evaluating the environmental influence of a target detection zone based on multi-source information of the target detection zone and generating a detection zone quality partition map reflecting the advantages and disadvantages of measurement conditions at different positions; The candidate point library construction module is used for constructing a candidate verification point library containing known control points and history stable points based on the multi-source information, and giving initial confidence to each candidate verification point in the candidate verification point library; The compensation coefficient generation module is used for acquiring the operation characteristic parameters in the history measurement time window and generating a comprehensive strength compensation coefficient of the verification demand strength of the current operation based on the operation characteristic parameters; the adaptation point position determining module is used for adjusting the number of preset reference verification point positions based on the comprehensive strength compensation coefficient and determining the total number of the adaptation verification point positions required by the current operation; and the point position scheme optimizing module is used for carrying out iterative optimization on the verificati