Multi-source information fusion Beidou RTK verification point location selection method and system

Abstract

The invention discloses a multi-source information fusion Beidou RTK verification point location selection method and system, and relates to the technical field of data processing. Comprising the steps of performing environmental impact assessment on a target measurement area based on multi-source information, and generating a measurement area quality partition map; constructing a candidate verification point library containing known control points and historical stable points, and endowing each candidate verification point with an initial confidence coefficient; obtaining operation characteristic parameters in the historical measurement time window, and generating a comprehensive strength compensation coefficient of the verification demand strength of the current operation; adjusting the number of preset reference verification point locations, and determining the total number of adaptive verification point locations; and taking the candidate verification point library as an optimization space, performing iterative optimization of a verification point location distribution scheme based on a preset multi-objective optimization function, the test area quality partition map, the total number of adaptive verification point locations and the initial confidence of the candidate verification points, and generating an optimal verification point location distribution scheme to perform verification point location selection. According to the invention, the accuracy of Beidou RTK verification point location selection is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for selecting BeiDou RTK verification points based on multi-source information fusion. Background Technology

[0002] With the widespread application of the BeiDou Navigation Satellite System, real-time dynamic differential (RTK) measurement technology has become an important operational method in fields such as engineering surveying and environmental monitoring. In existing technologies, the selection of verification points for RTK measurement operations usually relies on the personal experience of the surveyors, who subjectively select a few known points or feature points for measurement quality verification.

[0003] However, the selection of verification points in existing BeiDou RTK measurements relies on experience and lacks scientific basis, resulting in uneven spatial distribution of verification points and difficulty in effectively covering high-risk areas such as areas with severe signal blockage and significant multipath effects. At the same time, the verification frequency and work intensity cannot be dynamically matched. When the work environment changes or the measurement intensity increases, the existing technology cannot adjust the verification strategy in a timely manner, resulting in insufficient accuracy in the selection of BeiDou RTK verification points, which affects the overall reliability of the measurement results and the effectiveness of quality control. Summary of the Invention

[0004] This invention provides a method and system for selecting BeiDou RTK verification points by multi-source information fusion, aiming to solve the technical problem of insufficient accuracy in the selection of BeiDou RTK verification points in the prior art.

[0005] In view of the above problems, the present invention provides a method and system for selecting BeiDou RTK verification points by multi-source information fusion.

[0006] In a first aspect, the present invention provides a method for selecting BeiDou RTK verification points based on multi-source information fusion, including:

[0007] An environmental impact assessment of the target survey area is conducted based on multi-source information of the target survey area, generating a survey area quality zoning map that reflects the advantages and disadvantages of measurement conditions at different locations. Based on the multi-source information, a candidate verification point library containing known control points and historical stable points is constructed, and an initial confidence level is assigned to each candidate verification point in the candidate verification point library. Obtain the operation characteristic parameters within the historical measurement time window, and generate a comprehensive intensity compensation coefficient for the verification requirement intensity of the current operation based on the operation characteristic parameters; Based on the comprehensive strength compensation coefficient, adjust the number of preset benchmark verification points to determine the total number of adaptable verification points required for the current operation. Using the candidate verification point library as the optimization space, the verification point distribution scheme is iteratively optimized based on the preset multi-objective optimization function, the quality zoning map of the test area, the total number of suitable verification points, and the initial confidence of the candidate verification points, so as to generate the optimal verification point distribution scheme for verification point selection.

[0008] Secondly, this invention provides a BeiDou RTK verification point selection system based on multi-source information fusion, comprising: The survey area environmental assessment module is used to conduct environmental impact assessment of the target survey area based on multi-source information of the target survey area, and generate a survey area quality zoning map that reflects the advantages and disadvantages of measurement conditions at different locations; The candidate point library construction module is used to construct a candidate verification point library containing known control points and historical stable points based on the multi-source information, and to assign an initial confidence level to each candidate verification point in the candidate verification point library. The compensation coefficient generation module is used to obtain the operation characteristic parameters within the historical measurement time window, and generate a comprehensive intensity compensation coefficient for the verification requirement intensity of the current operation based on the operation characteristic parameters. The adaptation point determination module is used to adjust the number of preset benchmark verification points based on the comprehensive strength compensation coefficient, and determine the total number of adaptation verification points required for the current operation. The point location optimization module is used to iteratively optimize the distribution scheme of verification points based on the candidate verification point library as the optimization space, the quality zoning map of the test area, the total number of suitable verification points, and the initial confidence of the candidate verification points, and generate the optimal verification point distribution scheme for verification point selection.

[0009] One or more technical solutions provided in this invention have at least the following technical effects or advantages: This invention provides a method and system for selecting BeiDou RTK verification points through multi-source information fusion. It assesses the environmental quality of the measurement area and generates a quality zoning map, enabling a quantitative characterization of the advantages and disadvantages of measurement conditions at different locations. A candidate verification point library with initial confidence levels is constructed to provide a reliable source of candidates for verification point selection. A comprehensive intensity compensation coefficient is generated by analyzing historical operation characteristics, dynamically adjusting the total number of verification points to ensure precise matching between verification frequency and current operation intensity. Based on this, using the candidate verification point library as the optimization space, and combining the quality zoning map, the total number of points, and the initial confidence level, a multi-objective optimization function is used for iterative optimization to generate the optimal verification point distribution scheme. This invention effectively improves the accuracy and scientific rigor of BeiDou RTK verification point selection, ensures a balanced spatial distribution of verification resources, prioritizes coverage of high-risk areas, achieves dynamic matching between verification resources and operation quality, and enhances the overall reliability of measurement results and the effectiveness of quality control. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the method for selecting BeiDou RTK verification points based on multi-source information fusion provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the BeiDou RTK verification point selection system based on multi-source information fusion provided in an embodiment of the present invention; The components represented by each number in the attached diagram are explained below: The module includes: 11. Environmental assessment of the test area; 12. Candidate point library construction; 13. Compensation coefficient generation; 14. Adaptive point location determination; and 15. Point location scheme optimization. Detailed Implementation

[0011] This invention provides a method and system for selecting BeiDou RTK verification points by multi-source information fusion, which is used to address the technical problem of insufficient accuracy in the selection of BeiDou RTK verification points in the prior art.

[0012] Example 1, as Figure 1 As shown, this invention provides a method for selecting BeiDou RTK verification points based on multi-source information fusion, the method comprising: S100: Based on multi-source information of the target survey area, conduct an environmental impact assessment of the target survey area and generate a survey area quality zoning map that reflects the advantages and disadvantages of measurement conditions at different locations.

[0013] In this embodiment of the invention, an environmental impact assessment of the target survey area is conducted based on multi-source information, generating a survey area quality zoning map reflecting the advantages and disadvantages of measurement conditions at different locations. In BeiDou RTK measurement operations, measurement conditions vary significantly at different locations within the survey area. Densely built-up areas may obstruct satellite signals, electromagnetic interference may exist near high-voltage lines, and differential signal attenuation may occur at the edge of the base station's coverage area. However, existing technologies often neglect environmental differences in the selection of verification points, resulting in selected verification points that fail to represent the true quality status of the survey area. Therefore, this invention first conducts an environmental impact assessment of the target survey area, and then generates a survey area quality zoning map through multi-source information fusion and spatial analysis techniques, providing spatial guidance for subsequent verification point selection.

[0014] Step S100 in the method provided in this embodiment of the invention includes: Acquire multi-source information of the target survey area, wherein the multi-source information includes at least basic geographic information data, reference station network service information, historical measurement results data, real-time observation status data, and operation process record data; Based on the aforementioned basic geographic information data, the topographic and landform features and land cover features of the survey area are extracted. According to satellite signal propagation theory, potential areas with signal blockage risk and multipath effect risk are interpreted. Based on the service information of the reference station network, the spatial distribution density of the reference stations and the coverage strength of the differential signal are analyzed. According to the signal attenuation model, potential areas with differential signal attenuation risk are assessed. Based on electromagnetic environment information, the spatial distribution of various electromagnetic interference sources is identified, and potential areas with electromagnetic interference risks are delineated according to the electromagnetic field propagation characteristics. Based on the spatial overlay analysis results of different risk types, areas with multiple risk couplings are classified as poor measurement conditions, areas with a single risk are classified as medium measurement conditions, and areas with no risk exposure are classified as excellent measurement conditions.

[0015] First, multi-source information for the target survey area is acquired. This multi-source information includes at least basic geographic information data, benchmark station network service information, historical measurement results data, real-time observation status data, and operational process record data. Multi-source information refers to various heterogeneous data sets used for environmental assessment of the survey area. It is the foundational data for environmental risk identification and includes at least basic geographic information data, benchmark station network service information, historical measurement results data, real-time observation status data, and operational process record data.

[0016] Among them, basic geographic information data refers to basic surveying and mapping data describing the geospatial characteristics of the survey area, such as topography, landforms, and land features; reference station network service information refers to service-related data such as the spatial distribution of reference stations and differential signal coverage intensity provided by the BeiDou satellite navigation and positioning reference station network; historical measurement result data refers to the observation data and calculation results collected during past measurement operations in the survey area; real-time observation status data refers to real-time observation data such as the current BeiDou satellite signal reception status and PDOP value in the survey area; and operation process record data refers to operation records, environmental records, and other data from historical BeiDou RTK measurement operations in the survey area.

[0017] Specifically, various multi-source information of the target survey area is collected through formal channels such as the surveying and mapping geographic information public service platform, the Beidou reference station network service center, surveying operation manuals, and professional surveying and mapping data management systems. The collected data is preprocessed by standardizing the format, matching coordinates, and removing redundant data to ensure the integrity, timeliness, and spatial consistency of the data, thus forming a standardized multi-source information dataset of the survey area.

[0018] For example, taking a municipal surveying target area as an example, the system obtains basic geographic information data at a scale of 1:2000 from the local surveying and mapping geographic information platform; it obtains reference station network service information such as spatial distribution coordinates of reference stations around the target area and differential signal coverage intensity raster data from the local Beidou satellite navigation and positioning reference station network service center; it retrieves historical measurement results data, real-time observation status data, and operation process record data of the survey area for the past 6 months from the professional data management system of the surveying and mapping team; it preprocesses all data, converts them into the CGCS2000 coordinate system, removes duplicate ground feature data and invalid observation records, and forms a multi-source information dataset of the target survey area.

[0019] Secondly, based on the aforementioned basic geographic information data, topographic and landform features and feature cover characteristics of the survey area are extracted. According to satellite signal propagation theory, potential areas with signal obstruction and multipath effect risks are interpreted. Satellite signal propagation theory is a physical theory describing the straight-line propagation of electromagnetic waves in space and their reflection / diffraction upon encountering obstacles. Signal obstruction risk refers to the risk that a location in the survey area may be blocked by terrain or features, preventing the normal reception of BeiDou satellite signals; this is one of the main factors affecting RTK initialization and positioning accuracy. Multipath effect risk refers to the risk that satellite signals, after being reflected by strong reflective surfaces such as land features or water surfaces, may be received by the receiver simultaneously with directly propagating satellite signals, causing observational deviations. Topographic features refer to the topographic relief and natural geographical features of the survey area. Feature cover characteristics refer to the distribution characteristics of artificial and natural features such as man-made structures and vegetation cover in the survey area.

[0020] Specifically, based on the preprocessed basic geographic information data, spatial analysis tools of Geographic Information System (GIS) are used to extract the topographic features and land cover features of the survey area. According to satellite signal propagation theory, the extracted features are interpreted for risk: areas with large topographic relief and land cover heights exceeding the satellite cutoff elevation angle are identified as potential areas for signal obstruction risk; areas with large areas of strong reflective surfaces are identified as potential areas for multipath effect risk. Finally, the two types of potential risk areas are spatially vectorized and labeled to form corresponding risk area layers.

[0021] For example, based on the basic geographic information data of the target survey area, it was found that the northeast side of the survey area is a contiguous area of ​​high-rise residential buildings with more than 30 floors, the southwest side is an open water surface along the river, and the central part is a plain area without tall buildings. According to the satellite signal propagation theory, the high-rise residential area in the northeast side has obvious satellite signal line propagation blockage above the conventional 15° cutoff elevation angle of Beidou RTK measurement, and this area is interpreted as a potential area of ​​signal blockage risk. The water surface along the river in the southwest side is a strong reflective surface. After the satellite signal is reflected by the water surface, it will form a multipath effect with the direct signal, and the area within 50m along the river is interpreted as a potential area of ​​multipath effect risk. The two types of risk areas are vectorized and labeled in GIS to generate corresponding risk layers.

[0022] Secondly, based on the service information of the aforementioned reference station network, the spatial distribution density of reference stations and the differential signal coverage strength are analyzed. According to the signal attenuation model, potential areas with differential signal attenuation risks are assessed. The spatial distribution density of reference stations refers to the number of BeiDou satellite navigation and positioning reference stations around the target measurement area and the spatial distance between adjacent reference stations, directly affecting the coverage effect of the differential signal. The differential signal coverage strength refers to the signal strength of the differential signal broadcast by the BeiDou reference station network at different locations in the measurement area, and is a key guarantee for obtaining a fixed solution in RTK measurements. The signal attenuation model adopts the free-space propagation attenuation model, a mathematical model describing the attenuation of signal strength as the propagation distance increases when electromagnetic waves propagate in unobstructed free space, which can quantify the degree of differential signal attenuation.

[0023] Specifically, based on the reference station network service information, the number of BeiDou reference stations within a preset range around the target survey area is counted, and the spatial distribution density of the reference stations is calculated; the raw data of the differential signal coverage intensity of the survey area is extracted, and combined with the free space propagation attenuation model, parameters such as the straight-line distance between the reference station and different locations in the survey area are substituted to quantify the differential signal attenuation value at each location; a differential signal attenuation threshold is set, for example, 20%, and areas with attenuation values ​​exceeding the threshold are assessed as potential areas of differential signal attenuation risk, and corresponding risk area layers are generated in GIS.

[0024] For example, based on the reference station network service information of the target survey area, it was found that there are 3 BeiDou reference stations within a 50km radius of the survey area, with a spatial distribution density of 0.02 stations per 100km. 2 The distribution is relatively sparse. The original data of differential signal coverage intensity of the target survey area were extracted. Combined with the free space propagation attenuation model, the differential signal attenuation value at each location in the survey area was calculated. It was found that the distance between the northwest of the survey area and the nearest reference station is 48km. The differential signal attenuation value in this area is 30%, which exceeds the set threshold of 20%. This area is assessed as a potential risk area for differential signal attenuation. The vectorized labeling of this risk area was completed in GIS, and the corresponding risk layer was generated.

[0025] Furthermore, based on electromagnetic environment information, the spatial distribution of various electromagnetic interference sources is identified, and potential areas with electromagnetic interference risks are delineated according to the propagation characteristics of electromagnetic fields. Electromagnetic environment information refers to information such as the electromagnetic field strength, types of electromagnetic interference sources, and their spatial distribution in and around the target measurement area. Electromagnetic interference sources refer to facilities / areas that emit electromagnetic waves and interfere with BeiDou satellite signals, such as high-power radio transmission towers, microwave stations, high-voltage transmission lines, and substations. Electromagnetic field propagation characteristics describe the physical properties of electromagnetic waves as they propagate through space, such as the attenuation of intensity with distance and the attenuation upon encountering obstacles; these characteristics serve as the theoretical basis for delineating the scope of electromagnetic interference risks.

[0026] Specifically, electromagnetic environment information is extracted from multi-source information, and combined with the electromagnetic interference source registration data of the local radio management department, the types, spatial coordinates and power levels of various electromagnetic interference sources around the target survey area are identified; based on the electromagnetic field propagation characteristics and the interference thresholds of different types of electromagnetic interference sources, the electromagnetic interference impact range of each interference source is calculated; the area within the interference impact range in the survey area is designated as a potential electromagnetic interference risk area, and a corresponding risk area layer is generated in GIS.

[0027] For example, based on the electromagnetic environment information of the target survey area and the filing data of the local radio management bureau, a high-power radio and television transmission tower was identified 2km south of the survey area, and there were no large electromagnetic interference sources to the east. According to the electromagnetic field propagation characteristics, the electromagnetic interference influence range of the radio and television transmission tower was calculated to be 1km around the surrounding area. Therefore, the area within 0-1km south of the target survey area was designated as a potential electromagnetic interference risk area, and the vectorization of the risk area was completed in the GIS to generate the corresponding risk layer.

[0028] Finally, based on the spatial overlay analysis results of different risk types, areas with multiple coupled risks are classified as poor measurement condition areas, areas with a single risk are classified as medium measurement condition areas, and areas with no risk exposure are classified as excellent measurement condition areas. Spatial overlay analysis refers to a spatial analysis method that overlays multiple spatial layers with different themes in a GIS, analyzes the intersection, union, and other relationships of spatial elements between layers, and thus identifies the overlay of different risks. Risk coupling refers to the simultaneous presence of multiple environmental risks in a certain area of ​​the survey area, which is the basis for judging poor measurement conditions. Excellent measurement condition areas are areas with no environmental risk exposure, good satellite signal and differential signal reception, and no electromagnetic interference, making them the optimal areas for BeiDou RTK measurement. Medium measurement condition areas are areas with only a single environmental risk, with average measurement conditions, requiring certain measures to ensure measurement accuracy. Poor measurement condition areas are areas with two or more coupled environmental risks, with poor measurement conditions, and are high-risk areas where the accuracy of BeiDou RTK measurement is most easily affected.

[0029] Specifically, the four risk area layers (signal obstruction, multipath effect, differential signal attenuation, and electromagnetic interference) are imported into GIS for full-domain spatial overlay analysis. The risk overlay situation in each area is identified, distinguishing between single-risk areas and areas with multiple coupled risks. The target survey area is spatially partitioned according to rules: areas with coupled risks are classified as inferior areas, areas with single risks as medium areas, and areas with no risks as superior areas. Finally, each partition is labeled with color and attributes to generate a survey area quality zoning map that reflects the quality of measurement conditions at different locations.

[0030] For example, the four risk area layers of the target survey area are spatially overlaid and analyzed in GIS to identify the risk status of each area: the northwest of the survey area has both differential signal attenuation risk and a small amount of signal obstruction risk, containing two types of risk coupled; the 0-1km south side of the survey area has a single electromagnetic interference risk; the high-rise residential area in the northeast has a single signal obstruction risk; the 50m area along the river in the southwest has a single multipath effect risk; and the plain area in the central part of the survey area has no risk exposure. According to the zoning rules, the northwest is designated as the poor measurement condition area, the 0-1km south side, the high-rise residential area in the northeast, and the 50m area along the river in the southwest are designated as the medium measurement condition area, and the plain area in the central part is designated as the excellent measurement condition area. The poor, medium, and excellent areas are marked with red, yellow, and green colors respectively, and the risk attributes are described to generate a Beidou RTK measurement quality zoning map of the target survey area.

[0031] In this embodiment of the invention, an environmental impact assessment of the survey area is conducted through multi-source information fusion. This achieves comprehensive and accurate identification of four major environmental risks in BeiDou RTK measurements: satellite signal obstruction, multipath effects, differential signal attenuation, and electromagnetic interference. Spatial overlay analysis clarifies the risk type, quantity, and coupling of each location in the survey area. The survey area is divided into three levels—excellent, medium, and poor—based on measurement conditions, and a visualized quality zoning map is generated. This clearly defines the quality and risk level of measurement conditions in each area of ​​the survey area, solving the problems of single-information support, incomplete risk identification, and ambiguous determination of regional measurement conditions in traditional survey area environmental assessments. This provides a scientific, accurate, and visualized spatial environmental basis for the subsequent construction of a candidate verification point library, the allocation of verification point numbers, and the optimization of the optimal verification point distribution scheme, ensuring that the selection of subsequent verification points matches the actual measurement conditions of the survey area.

[0032] S200: Construct a candidate verification point library containing known control points and historical stable points based on the multi-source information, and assign an initial confidence level to each candidate verification point in the candidate verification point library.

[0033] In this embodiment of the invention, a candidate verification point library containing known control points and historically stable points is constructed based on the multi-source information, and an initial confidence level is assigned to each candidate verification point in the library. In BeiDou RTK measurement operations, the selection of verification points relies on a reliable source base. Existing technologies typically only use known control points as candidates, resulting in a single source and insufficient consideration of the historical performance and field adaptability of the points. In actual measurements, some control points may become unstable due to environmental changes or human-caused damage, while some temporary points that have been repeatedly used and performed stably in historical measurements are not included in the candidate library, leading to a waste of resources. Furthermore, even within the same candidate library, the confidence levels of different points vary, but a systematic quantitative evaluation method is lacking, making subsequent selection difficult and hindering scientific decision-making. Therefore, it is necessary to construct a scientific candidate verification point library based on multi-source information and assign a reasonable initial confidence level to each candidate point through multi-dimensional feature calculations, providing reliable data support for the optimized selection of subsequent verification points.

[0034] Step S200 in the method provided in this embodiment of the invention includes: Data on known control points within the target survey area are extracted from the multi-source information, wherein the data on known control points includes the coordinate results, grade attributes, and positional stability records of each known control point. Historical measurement results data are extracted from the multi-source information, and historical measurement points that meet the preset stability index are selected as historical stable points. The known control points and the historical stable points are integrated, and duplicate and conflicting points are eliminated to construct a candidate verification point library covering the target measurement area. Collect observation data from multiple periods of historical measurements for each candidate verification point, calculate the dispersion of observation results in each period, and determine the point stability characteristic value based on the dispersion. Extract the measurement area quality zoning map attributes of the area where each candidate verification point is located, and determine the environmental risk impact coefficient based on the quality level of the measurement conditions in the area. Obtain the historical observation condition records of each candidate verification point in the historical measurement, and determine the observation quality characteristic value based on the observation condition records, wherein the observation condition records include the average position accuracy attenuation factor and the average signal-to-noise ratio; Collect on-site survey information for each candidate verification point, and determine the recoverability feature value of the point based on the on-site survey information. The on-site survey information includes the integrity of the point markers, accessibility, and identifiability. The baseline weight of each candidate verification point is determined based on its type attribute, wherein the type attribute includes high-level known control points, same-level known control points, and historically stable points; The location stability characteristic value, environmental risk impact coefficient, observation quality characteristic value, and location recoverability characteristic value are normalized respectively. The normalized characteristic values ​​are then weighted and fused to generate the confidence correction coefficient for each candidate verification point. The baseline weight of each candidate verification point is compensated and corrected to generate the initial confidence level for each candidate verification point.

[0035] First, known control point data within the target survey area is extracted from the multi-source information. This known control point data includes the coordinate results, grade attributes, and positional stability records of each known control point. Known control points refer to existing survey control points within the survey area with precise coordinates and elevations, typically categorized by accuracy into different grades. Coordinate results refer to the final positioning data of known control points, such as plane coordinates and elevation coordinates, obtained through standardized measurements; these serve as direct references for verifying RTK survey results. Grade attributes refer to the control point grades classified according to national surveying and mapping standards, such as Level 1, Level 2, and Level 3 control points; higher grades generally indicate higher positional accuracy and stability. Positional stability records refer to records of the long-term status of known control points from authoritative departments or historical operations, including whether displacement, damage, or settlement has occurred, directly reflecting the reliability of the position.

[0036] Specifically, from the multi-source information of the target survey area, spatial retrieval and attribute filtering are used to extract data on all known control points covering the target survey area; the extracted data is then verified for completeness to ensure that it includes the coordinate results, grade attributes, and positional stability records of each known control point, and invalid control point data with missing key information is removed to form a standardized dataset of known control points.

[0037] For example, 28 known control points covering the target survey area were retrieved from historical survey data and authoritative local surveying and mapping databases. The CGCS2000 coordinate system coordinates, control point level, and stability records of each control point were checked one by one. Two control points with missing stability records were removed, and a standardized dataset of 26 valid known control points was finally obtained.

[0038] Secondly, historical measurement results data are extracted from the multi-source information, and historical measurement points that meet the preset stability index are selected as historical stable points. The known control points and the historical stable points are integrated, and duplicate and conflicting points are eliminated to construct a candidate verification point library covering the target measurement area.

[0039] The preset stability index is used to determine whether historical measurement points have verification value, including the internal and external conformity accuracy of multi-period measurement results. Internal conformity accuracy refers to the accuracy index between multiple periods of measurement results for the same point, reflecting the consistency of the point's own measurements; external conformity accuracy refers to the deviation accuracy between the coordinates of historical measurement points and the coordinates of known control points, reflecting the degree of fit between the point and the benchmark. Historical stable points are historical measurement points that meet the preset stability index, have stable long-term measurement status, and reliable coordinate results, serving as an important supplement to the candidate verification point database. Conflicting points are points where known control points and historical stable points coincide in spatial coordinates or are separated by a preset threshold, and where there is a significant deviation in coordinate results. Duplicate points are points where known control points and historical stable points coincide in spatial coordinates or are separated by a preset threshold, and where the coordinate results are basically consistent.

[0040] Specifically, historical measurement data of the target survey area are extracted from multi-source information, and historical measurement points with multiple periods of measurement results are selected. The internal and external conformity accuracy of each historical measurement point is calculated and compared with the preset stability index threshold. Historical measurement points that meet the index are selected as historical stable points. Known control points and historical stable points are spatially integrated, and duplicate points are eliminated through coordinate comparison. For suspected conflict points that have not been eliminated, they are reviewed in conjunction with point stability records and historical operation records. Points with coordinate result conflicts are eliminated. The spatial distribution of the integrated effective points is sorted out to ensure coverage of different areas of the target survey area, and a comprehensive and reliable candidate verification point library is constructed.

[0041] For example, the preset stability index thresholds are: internal conformity accuracy ≤ 2cm and external conformity accuracy ≤ 3cm. From the historical measurement data of the target survey area over the past year, 42 historical measurement points with 3 or more measurement results are selected; the internal conformity accuracy and external conformity accuracy of each point are calculated, and after comparison, 35 points have an internal conformity accuracy ≤ 2cm and an external conformity accuracy ≤ 3cm, which meet the preset stability index and are identified as historical stable points; the 35 historical stable points are spatially integrated with 26 known control points, and 8 duplicate points and 2 conflict points are found through coordinate comparison. After removing duplicate and conflict points, 51 valid points remain, forming a candidate verification point library containing 51 candidate verification points.

[0042] Next, multi-period observation data of each candidate verification point in historical measurements are collected, and the dispersion of the observation results in each period is calculated. The location stability characteristic value is determined based on the dispersion. Multi-period observation data refers to multiple sets of coordinate data obtained from the same candidate verification point at different times and under the same measurement conditions, used to evaluate the long-term stability of the location. The dispersion reflects the degree of dispersion among the multi-period observation results, quantified using standard deviation; the smaller the dispersion, the higher the location stability. The location stability characteristic value is a numerical value calculated based on the dispersion of the multi-period observation data, quantifying the stability of the candidate verification point. The value ranges from [0,1], with a larger value indicating higher location stability.

[0043] Specifically, from the historical measurement data of multi-source information, multiple observation data of the point that meet the accuracy requirements are retrieved, and gross errors and outliers are removed according to the specifications; according to the accuracy calculation method of the BeiDou RTK measurement specifications, the comprehensive standard deviation of the plane coordinates or the standard deviation of the geodetic height of the multiple observation results of the point is calculated and recorded as the dispersion quantification value σ; a preset upper limit threshold for dispersion σ is set. max If σ≥σ max If the stability of a point does not meet the verification requirements, it is directly removed from the candidate verification point pool. The point stability eigenvalue is calculated using a conventional positive vectorization formula in the surveying and mapping field: Point stability eigenvalue = 1 - σ / σ max .

[0044] For example, this operation is a BeiDou RTK level 2 dynamic control measurement, with a preset σ max =5cm, select two typical points from the candidate pool for calculation: Point A is a high-level known control point: retrieve the effective plane observation data of the past 5 years and 5 periods, remove the gross error data of 1 period, calculate the comprehensive standard deviation of plane coordinates σ=0.8cm, σ<5cm, point stability characteristic value=1-0.8 / 5=0.84; Point B is a historically stable point: retrieve the effective plane observation data of the past 1 year and 4 periods, without gross error data, calculate the comprehensive standard deviation of plane coordinates σ=1.5cm, σ<5cm, point stability characteristic value=1-1.5 / 5=0.70.

[0045] Furthermore, the survey area quality zoning map attributes of each candidate verification point's location are extracted, and the environmental risk impact coefficient is determined based on the measurement condition level of the area. The survey area quality zoning map attributes refer to the measurement condition level attribute corresponding to the area where each candidate verification point is located in the survey area quality zoning map, including areas with excellent, medium, and poor measurement conditions. The environmental risk impact coefficient is a numerical value that quantitatively characterizes the degree of impact of environmental risk in the candidate verification point's location on the verification work. Its value ranges from [0,1], with a larger value indicating a greater impact of environmental risk on the site verification and lower site suitability.

[0046] Specifically, the spatial coordinates of each candidate verification point are extracted and matched to the measurement area quality zoning map generated by S100 to determine its measurement condition level. According to the preset level-coefficient mapping rule, the candidate verification points in different level areas are assigned corresponding environmental risk impact coefficients: 0.2 for areas with excellent measurement conditions, 0.5 for areas with medium measurement conditions, and 0.8 for areas with poor measurement conditions, thus completing the determination of the environmental risk impact coefficient of each candidate verification point.

[0047] For example, point A is located in the area with excellent measurement conditions and is assigned an environmental risk impact coefficient of 0.2; point B is located in the area with moderate measurement conditions and is assigned an environmental risk impact coefficient of 0.5.

[0048] In addition, the historical observation condition records of each candidate verification point are obtained, and the observation quality characteristic value is determined based on these records. These records include the average position accuracy attenuation factor (PDOP) and the average signal-to-noise ratio (SNR). The position accuracy attenuation factor (PDOP) characterizes the influence of the spatial distribution of BeiDou satellites on positioning accuracy; a smaller value indicates a more reasonable satellite distribution and higher positioning accuracy. The signal-to-noise ratio (SNR) reflects the quality of signal reception; a larger value indicates stronger signal anti-interference capability and better observation quality. The observation quality characteristic value is a numerical value that quantifies the historical observation quality of the candidate verification point, calculated based on the average PDOP and the average SNR. The value ranges from [0,1], with a larger value indicating higher observation quality.

[0049] Specifically, the mean value of the position accuracy attenuation factor and the mean value of the signal-to-noise ratio are extracted from the historical measurement data of each candidate verification point. After the two indicators are normalized by Min-Max, the observation quality characteristic value of each candidate verification point is calculated by weighted summation.

[0050] For example, the PDOP mean weight is 0.4, and the SNR mean weight is 0.6. For point A: the PDOP mean of 10 observations is 1.2, normalized to 0.85; the SNR mean is 45 dBHz, normalized to 0.85; the observation quality characteristic value = 0.85 × 0.4 + 0.85 × 0.6 = 0.85. For point B: the PDOP mean of 8 observations is 1.5, normalized to 0.75; the SNR mean is 40 dBHz, normalized to 0.75; the observation quality characteristic value = 0.75 × 0.4 + 0.75 × 0.6 = 0.75.

[0051] Simultaneously, on-site reconnaissance information for each candidate verification point is collected. Based on this information, the recoverability characteristic value of each point is determined. The on-site reconnaissance information includes the integrity of the point markers, accessibility, and identifiability. On-site reconnaissance information refers to the information obtained by personnel visiting the candidate verification point site to verify its actual condition. The integrity of the point markers refers to the completeness and clarity of the candidate verification point's markings, directly affecting point identification in subsequent verification operations. Accessibility refers to the ease of access to the area where the candidate verification point is located, influenced by factors such as terrain and obstacles, and is fundamental to ensuring the feasibility of verification operations. Identifiability refers to the ease with which the candidate verification point can be distinguished from surrounding features, avoiding misjudgment during verification operations. The recoverability characteristic value is a numerical value calculated based on the on-site reconnaissance information, quantifying the ease of recovery after damage to the candidate verification point. The value ranges from [0,1], with a larger value indicating easier recovery and higher maintenance value.

[0052] Specifically, professional personnel conduct on-site surveys of each candidate verification point, recording the integrity, accessibility, and identifiability of the location markers; based on the preset information scoring rules, each of the three information items is assigned a basic score of 0-1; the three basic scores are weighted and summed, with each item having a weight of 1 / 3, to obtain the location recoverability characteristic value of each candidate verification point.

[0053] For example, at point A: the sign is intact (1 point), the accessibility of the plain road is perfect (1 point), and the sign is clear and identifiable (1 point), so the recoverability characteristic value of the point is (1+1+1) / 3=1.0; at point B: the sign is slightly worn but intact (0.9 points), the accessibility of the country road is good (0.9 points), and the sign is identifiable (0.9 points), so the recoverability characteristic value of the point is (0.9+0.9+0.9) / 3=0.9.

[0054] Subsequently, the baseline weight is determined based on the type attribute of each candidate verification point. This type attribute includes high-level known control points, known control points of the same level, and historically stable points. The type attribute refers to the classification of candidate verification points, including three categories: high-level known control points, known control points of the same level, and historically stable points. The baseline accuracy and reliability differ among different types of points. The baseline weight is a basic weight assigned based on the candidate verification point's type attribute; the higher the type level, the greater the baseline weight.

[0055] Among them, high-level known control points refer to national / local statutory measurement control points with a level higher than that of this BeiDou RTK measurement operation. Their accuracy and stability have been verified by authoritative departments, and their authority and reliability are the highest. Known control points of the same level refer to national / local statutory measurement control points with a level consistent with that of this BeiDou RTK measurement operation. Their accuracy and stability match the requirements of this operation and serve as an important benchmark for result verification. Historically stable points refer to non-statutory measurement points selected from historical measurement results that meet the preset stability indicators. They have no standardized level attributes and serve as a supplement to candidate verification points.

[0056] Specifically, based on the type attribute of each candidate verification point, its baseline weight is determined according to the preset weight allocation rules: 0.8 is assigned to high-level known control points, 0.6 is assigned to known control points of the same level, and 0.4 is assigned to historically stable points, thus completing the determination of the baseline weight of each candidate verification point.

[0057] For example, point A: its level is higher than the level of this operation, and it is assigned a baseline weight of 0.8; point B: it is a non-statutory level control point, and it is assigned a baseline weight of 0.4.

[0058] Finally, the location stability characteristic value, environmental risk impact coefficient, observation quality characteristic value, and location recoverability characteristic value are normalized respectively. The normalized characteristic values ​​are then weighted and fused to generate a confidence correction coefficient for each candidate verification point. The baseline weights of each candidate verification point are then compensated and corrected to generate an initial confidence level for each candidate verification point. Normalization refers to the standardization process that uniformly converts the location stability characteristic value, environmental risk impact coefficient, observation quality characteristic value, and location recoverability characteristic value to the [0,1] interval, ensuring that characteristic values ​​with different dimensions can be weighted and fused. The initial confidence level is a quantitative representation of the verification priority of the candidate verification point obtained after compensating and correcting the baseline weights; its value ranges from [0,1], with a larger value indicating a higher verification priority.

[0059] Specifically, the location stability characteristic value, environmental risk impact coefficient, observation quality characteristic value, and location recoverability characteristic value are normalized to eliminate the influence of dimensions. Four normalized characteristic values ​​are weighted: stability characteristic value weight 0.35, environmental risk impact coefficient weight 0.25, observation quality characteristic value weight 0.2, and recoverability characteristic value weight 0.2. These are then weighted and fused to generate a confidence correction coefficient for each candidate verification point. Finally, the baseline weight of each candidate verification point is multiplied by its corresponding confidence correction coefficient to compensate for the baseline weight, resulting in the initial confidence level for each candidate verification point.

[0060] For example, at point A: the point stability eigenvalue is 0.84, the environmental risk impact coefficient is 0.2, the observation quality eigenvalue is 0.85, and the point recoverability eigenvalue is 1.0. The confidence correction coefficient is 0.84×0.35+0.2×0.25+0.85×0.2+1.0×0.2=0.714; the baseline weight is 0.8, and the initial confidence level is 0.8×0.714≈0.571. At point B: the point stability eigenvalue is 0.70, the environmental risk impact coefficient is 0.5, the observation quality eigenvalue is 0.75, and the point recoverability eigenvalue is 0.9. The confidence correction coefficient is 0.70×0.35+0.5×0.25+0.75×0.2+0.9×0.2=0.70; the baseline weight is 0.4, and the initial confidence level is 0.4×0.70=0.28.

[0061] In this embodiment of the invention, multi-source information fusion was used to scientifically screen known control points and historically stable points, eliminating duplicate and conflicting points and constructing a comprehensive and reliable candidate verification point library. Confidential correction coefficients were calculated based on four dimensions: point stability, environmental risk, observation quality, and recoverability. These coefficients, combined with type attributes, determined the baseline weights and completed compensation corrections, enabling the quantitative assignment of initial confidence levels for each candidate verification point and accurately distinguishing the verification priorities of different points. This step provides scientific, quantitative, and differentiated candidate data support for the subsequent optimized selection of verification points, ensuring the reliability and relevance of verification point selection from the outset.

[0062] S300: Obtain the operation characteristic parameters within the historical measurement time window, and generate a comprehensive strength compensation coefficient for the verification requirement intensity of the current operation based on the operation characteristic parameters.

[0063] In this embodiment of the invention, operational characteristic parameters within a historical measurement time window are obtained, and a comprehensive intensity compensation coefficient for the verification requirement intensity of the current operation is generated based on these parameters. The verification requirement intensity of BeiDou RTK measurements directly determines the scale of verification resource allocation. Existing technologies often use fixed verification requirement intensity settings, leading to a disconnect between verification resource allocation and actual operational quality requirements: in scenarios with high measurement accuracy, reliable calculation, low operational intensity, and good satellite conditions, verification resources are redundantly wasted; while in scenarios with poor accuracy, unstable calculation, high operational fatigue, and poor satellite geometry, verification resources are insufficient, making it difficult to effectively guarantee the quality of measurement results. Therefore, it is necessary to extract operational characteristic parameters from historical measurement time windows, quantify the impact of each parameter on verification requirements, and generate a comprehensive intensity compensation coefficient to provide a scientific basis for subsequent dynamic adjustment of the number of verification points.

[0064] Step S300 in the method provided in this embodiment of the invention includes: Among them, the acquisition of operation characteristic parameters within the historical measurement time window includes: The measurement accuracy index is calculated based on the plane coordinate deviation of each measurement point within the historical measurement time window; The fixed solution acquisition rate is calculated based on the proportion of fixed solutions obtained at each measurement point within the historical measurement time window. The average duration of continuous operations is calculated based on the duration of each continuous operation within the historical measurement time window; The average position accuracy attenuation factor is calculated based on the position accuracy attenuation factor of each measurement point within the historical measurement time window. The measurement accuracy index, fixed solution acquisition rate, average continuous operation time, and average position accuracy attenuation factor are used as operation characteristic parameters.

[0065] First, the measurement accuracy index is calculated based on the plane coordinate deviation of each measurement point within the historical measurement time window. Plane coordinate deviation refers to the difference in plane distance between the coordinates of a historical measurement point and the true coordinates of its corresponding known control point / historical stable point. The measurement accuracy index is a quantitative indicator calculated based on plane coordinate deviation; a larger parameter indicates higher measurement accuracy. It is negatively correlated with the verification requirement level, meaning higher accuracy corresponds to lower verification requirements.

[0066] Specifically, the planar coordinate results and corresponding true coordinates of each measurement point within the historical measurement time window are extracted, and the planar coordinate deviation of each point is calculated. The mean of the deviations of all points is calculated, and the measurement accuracy index is calculated using the benchmark deviation normalization method: Measurement accuracy index = preset benchmark planar coordinate deviation / actual average planar coordinate deviation. The preset benchmark planar coordinate deviation is the allowable planar point position error for BeiDou RTK control measurement, for example, 5cm.

[0067] For example, this operation is a Beidou RTK level 2 dynamic control measurement. The historical measurement time window is taken from the past month, with a total of 80 measurement points. The calculated average deviation of the plane coordinates of each point is 4cm, and the preset benchmark deviation is 5cm. The measurement accuracy index = 5 / 4 = 1.25. This index is greater than 1, indicating that the historical measurement accuracy is better than the benchmark level, and the verification requirement is low.

[0068] Secondly, the fixed solution acquisition rate is calculated based on the proportion of measurement points that obtain fixed solutions within the historical measurement time window. A fixed solution refers to a solution in BeiDou RTK calculation where the carrier phase ambiguity is fixed as an integer, resulting in the highest positioning accuracy and reliability. The fixed solution acquisition rate is the proportion of points that obtain fixed solutions in historical measurements to the total number of measurement points. A higher parameter indicates higher calculation reliability and is negatively correlated with the verification requirement level; that is, the more reliable the calculation, the lower the verification requirement. The fixed solution acquisition rate is calculated as: Total number of measurement points within the historical measurement time window, and the number of points that obtain fixed solutions. The fixed solution acquisition rate is calculated as: Fixed solution acquisition rate = Number of fixed solution points / Total number of measurement points.

[0069] For example, there are 80 measurement points within the historical measurement time window, of which 74 points obtained fixed solutions; the fixed solution acquisition rate = 74 / 80 = 0.925, which is relatively high, indicating that the historical solution has good reliability and the verification requirement is low.

[0070] Next, the average duration of continuous operations is calculated based on the duration of each continuous operation within the historical measurement time window. A continuous operation refers to a single complete measurement process from operation initiation to completion, including data collection at multiple measurement points. The average duration of continuous operations is the arithmetic mean of the duration of each single operation in history. A larger parameter indicates a greater risk of operation fatigue and is positively correlated with the verification requirement level; that is, the longer the operation duration, the higher the verification requirement. The start and end times of each continuous operation within the historical measurement time window are extracted, and the duration of each single operation is calculated. The arithmetic mean of all single operation durations is then used to obtain the average duration of continuous operations.

[0071] For example, there were 6 consecutive operations within the historical measurement time window, with single operation durations of 4.0h, 4.2h, 4.5h, 4.0h, 4.3h, and 4.4h respectively; the average duration of continuous operation was (4.0+4.2+4.5+4.0+4.3+4.4) / 6≈4.23h. This duration is slightly higher than the baseline operation duration, indicating a slightly higher risk of operator fatigue and an increased need for verification.

[0072] Furthermore, based on the position accuracy attenuation factor of each measurement point within the historical measurement time window, the average position accuracy attenuation factor (PDOP) is calculated. The position accuracy attenuation factor (PDOP) characterizes the degree of influence of the spatial distribution of BeiDou satellites on positioning accuracy. A larger parameter indicates worse satellite geometric conditions and is positively correlated with the verification requirement level; that is, the larger the PDOP, the more easily positioning accuracy is affected, and the higher the verification requirement. The average position accuracy attenuation factor is the arithmetic mean of the PDOP values ​​of all measurement points within the historical measurement time window, and is a quantitative indicator of overall satellite observation conditions.

[0073] Specifically, the PDOP observation values ​​of each measurement point within the historical measurement time window are extracted, and the arithmetic mean of all PDOP values ​​is calculated to obtain the average position accuracy attenuation factor. For example, the PDOP values ​​of 80 measurement points within the historical measurement time window range from 1.1 to 1.5, and the calculated arithmetic mean is 1.30, which is slightly higher than the baseline PDOP value, indicating that the satellite geometry is slightly worse and the verification requirements have increased.

[0074] Finally, the measurement accuracy index, fixed solution acquisition rate, average continuous operation duration, and average position accuracy attenuation factor are used as operation characteristic parameters. Operation characteristic parameters refer to the set of measurement accuracy index, fixed solution acquisition rate, average continuous operation duration, and average position accuracy attenuation factor, which serve as the data foundation for quantifying historical operation quality and verifying requirements. The four parameters calculated above are standardized and organized to form a unified operation characteristic parameter dataset, which serves as the input for subsequent calculation of the requirement intensity compensation coefficient.

[0075] For example, the integrated operation feature parameter dataset is as follows: measurement accuracy index: 1.25, fixed solution acquisition rate: 0.925, average continuous operation duration: 4.23h, average position accuracy decay factor: 1.30.

[0076] The process of generating a comprehensive intensity compensation coefficient for the verification requirement intensity of the current task based on the task characteristic parameters includes: The ratio of the preset benchmark measurement accuracy index to the measurement accuracy index is used as the first demand intensity compensation coefficient. The ratio of the preset benchmark fixed solution acquisition rate to the fixed solution acquisition rate is used as the second demand intensity compensation coefficient. The ratio of the average continuous working time to the average continuous working time of the preset benchmark is used as the third demand intensity compensation coefficient. The ratio of the average position accuracy attenuation factor to the preset benchmark average position accuracy attenuation factor is used as the fourth demand intensity compensation coefficient. The first demand intensity compensation coefficient, the second demand intensity compensation coefficient, the third demand intensity compensation coefficient, and the fourth demand intensity compensation coefficient are weighted and fused to obtain the comprehensive intensity compensation coefficient for verifying the demand intensity.

[0077] First, the ratio of the preset benchmark measurement accuracy index to the measurement accuracy index is used as the first demand intensity compensation coefficient.

[0078] Secondly, the ratio of the preset benchmark fixed solution acquisition rate to the fixed solution acquisition rate is used as the second demand intensity compensation coefficient.

[0079] Furthermore, the ratio of the average continuous working time to the preset benchmark average continuous working time is used as the third demand intensity compensation coefficient.

[0080] Furthermore, the ratio of the average position accuracy attenuation factor to the preset benchmark average position accuracy attenuation factor is used as the fourth demand intensity compensation coefficient.

[0081] The demand intensity compensation coefficient is the ratio of a single operational characteristic parameter to a baseline parameter. It quantifies the impact of this parameter on the verification demand intensity and is divided into four demand intensity compensation coefficients, corresponding to the measurement accuracy index, fixed solution acquisition rate, average continuous operation time, and average position accuracy attenuation factor, respectively. The preset baseline parameters represent the parameter levels under normal operational scenarios. For example, the preset baselines are: measurement accuracy index 1.0, fixed solution acquisition rate 0.9, average continuous operation time 4.0h, and average position accuracy attenuation factor 1.2.

[0082] Specifically, the demand intensity compensation coefficients are calculated according to the following rules: First demand intensity compensation coefficient = preset benchmark measurement accuracy index / actual measurement accuracy index; Second demand intensity compensation coefficient = preset benchmark fixed solution acquisition rate / actual fixed solution acquisition rate; Third demand intensity compensation coefficient = actual average continuous operation time / preset benchmark average continuous operation time; Fourth demand intensity compensation coefficient = actual average position accuracy attenuation factor / preset benchmark average position accuracy attenuation factor.

[0083] For example, based on the task feature parameter dataset and preset benchmark parameters, the following are calculated: First demand intensity compensation coefficient = 1.0 / 1.25 = 0.80; Second demand intensity compensation coefficient = 0.9 / 0.925 ≈ 0.973; Third demand intensity compensation coefficient = 4.23 / 4.0 ≈ 1.058; Fourth demand intensity compensation coefficient = 1.30 / 1.2 ≈ 1.083.

[0084] Finally, the first, second, third, and fourth demand intensity compensation coefficients are weighted and fused to obtain the comprehensive intensity compensation coefficient for verification demand. The comprehensive intensity compensation coefficient is the final quantitative value obtained by weighting and fusing the four demand intensity compensation coefficients. It represents the overall compensation level of the current operation's verification demand intensity; a larger coefficient indicates a higher verification demand and requires more verification resources. Weighted fusion refers to setting fixed weights based on the influence of each parameter on the verification demand, and then summing the four compensation coefficients to eliminate the impact of fluctuations in a single parameter.

[0085] Specifically, the weights are set as follows: the first demand intensity compensation coefficient has a weight of 0.3, the second demand intensity compensation coefficient has a weight of 0.3, the third demand intensity compensation coefficient has a weight of 0.2, and the fourth demand intensity compensation coefficient has a weight of 0.2. The comprehensive intensity compensation coefficient is calculated by weighted summation: Comprehensive intensity compensation coefficient = first demand intensity compensation coefficient × 0.3 + second demand intensity compensation coefficient × 0.3 + third demand intensity compensation coefficient × 0.2 + fourth demand intensity compensation coefficient × 0.2.

[0086] For example, substituting the compensation coefficients, the comprehensive intensity compensation coefficient is calculated as follows: 0.80×0.3+0.937×0.3+1.058×0.2+1.083×0.2=0.9493. This comprehensive intensity compensation coefficient is slightly less than 1, indicating that the current operational verification demand intensity is slightly lower than the benchmark level, and the verification resource allocation can be appropriately adjusted.

[0087] In this embodiment of the invention, by extracting the operational characteristic parameters within the historical measurement time window, the impact of measurement accuracy, solution reliability, operational intensity, and satellite observation conditions on verification requirements is precisely quantified. Combining the positive and negative correlation between parameters and verification requirements, the intensity compensation coefficients for each requirement are calculated. Then, a comprehensive intensity compensation coefficient is generated through weighted fusion, realizing a dynamic and scientific assessment of the intensity of verification requirements. This solves the problem of mismatch between traditional fixed verification requirement settings and actual operational quality, and provides a quantitative basis for subsequent dynamic adjustment of the number of verification points. It avoids redundant waste of verification resources, improves the verification coverage of high-risk operational scenarios, and effectively enhances the pertinence and effectiveness of quality control of BeiDou RTK measurement results.

[0088] S400: Adjust the number of preset benchmark verification points based on the comprehensive strength compensation coefficient to determine the total number of adaptable verification points required for the current operation.

[0089] In this embodiment of the invention, the number of preset benchmark verification points is adjusted based on the comprehensive strength compensation coefficient to determine the total number of adaptable verification points required for the current operation. The reasonable allocation of the total number of verification points is a key aspect of BeiDou RTK measurement quality control. In existing technologies, the number of verification points is often fixed based on the operation type, without dynamically adjusting according to the actual verification demand intensity reflected in historical operations. This easily leads to insufficient points when demand is high and redundant points when demand is low, failing to guarantee verification coverage for high-risk operation scenarios and resulting in ineffective consumption of verification resources. Therefore, this invention dynamically adjusts parameters based on the comprehensive strength compensation coefficient generated by S300, combined with the benchmark number for the operation type. Simultaneously, it defines the value range through quantity constraints, ensuring that the determined total number of adaptable verification points matches both the actual verification needs of the current operation and the feasibility of on-site operations, thus establishing a scientific quantitative benchmark for the subsequent optimized selection of verification points.

[0090] Step S400 in the method provided in this embodiment of the invention includes: The number of preset benchmark verification points is determined according to the type of operation. The comprehensive strength compensation coefficient is multiplied by the number of benchmark verification points and rounded up to obtain the total number of theoretical verification points for the current operation. The total number of theoretical verification points is adjusted by applying preset quantity constraints to obtain the total number of suitable verification points required for the current operation. The quantity constraints include a minimum verification point quantity guarantee threshold and a maximum verification point quantity limit threshold.

[0091] First, the number of preset benchmark verification points is determined based on the operation type. The comprehensive strength compensation coefficient is multiplied by the number of benchmark verification points and rounded up to obtain the total theoretical verification points for the current operation. The preset number of benchmark verification points refers to the basic number of verification points preset according to different BeiDou RTK operation types, combined with industry standards and field operation experience. It serves as a benchmark reference value for verification point configuration, with a unique benchmark number corresponding to each operation type. The total theoretical verification points are the value obtained by multiplying the comprehensive strength compensation coefficient by the number of benchmark verification points and rounding up, reflecting the theoretical verification point configuration scale corresponding to the current operation's verification demand intensity.

[0092] Specifically, based on the type of BeiDou RTK operation, and in accordance with the BeiDou network real-time dynamic measurement (RTK) technical specifications and operational requirements, the number of preset benchmark verification points is determined; the comprehensive strength compensation coefficient calculated in S300 is retrieved and multiplied by the number of benchmark verification points to obtain the calculated value of the theoretical number of points; the calculated value of the theoretical number of points is rounded up to obtain the total number of theoretical verification points for the current operation.

[0093] For example, the number of benchmark verification points is preset to 10 according to the type of work; the comprehensive strength compensation coefficient after S300 correction is retrieved as 0.9493; the product is calculated as: 10 × 0.9493 = 9.493; round up: take the smallest integer greater than or equal to 9.493, and the total number of theoretical verification points is 10.

[0094] Secondly, the total number of theoretical verification points is adjusted using preset quantity constraints to obtain the total number of suitable verification points required for the current operation. These quantity constraints include a minimum verification point quantity guarantee threshold and a maximum verification point quantity limit threshold. Quantity constraints refer to the rules for determining the number of verification points to ensure the basic effectiveness and on-site feasibility of the verification work, including a minimum verification point quantity guarantee threshold and a maximum verification point quantity limit threshold.

[0095] The minimum verification point quantity guarantee threshold refers to the lower limit of the number of points set to achieve basic verification coverage of the survey area and ensure the quality of measurement results. Below this threshold, verification of key areas of the survey area cannot be completed. Different operation types correspond to fixed lower limit values. The maximum verification point quantity limit threshold refers to the upper limit of the number of points set in combination with on-site operation efficiency and manpower and material costs. Above this threshold, verification resources will be excessively consumed and operation efficiency will be reduced. Different operation types correspond to fixed upper limit values. The total number of suitable verification points refers to the total number of verification points finally determined after adjusting the quantity constraints, which matches the current operation verification needs and meets the practical requirements. It serves as the quantitative basis for optimizing the subsequent verification point distribution scheme.

[0096] Specifically, based on the type of this operation, a minimum guaranteed threshold and a maximum limit threshold for the number of verification points are preset. The total number of theoretical verification points is compared with the minimum threshold: if the total number of theoretical verification points is less than the minimum guaranteed threshold, the minimum guaranteed threshold is directly used as the total number of verification points; if the total number of theoretical verification points is greater than or equal to the minimum guaranteed threshold, it is further compared with the maximum limit threshold: if the total number of theoretical verification points is greater than the maximum limit threshold, the maximum limit threshold is used as the total number of verification points; if the total number of theoretical verification points is between the minimum guaranteed threshold and the maximum limit threshold, the total number of theoretical verification points is directly used as the total number of verification points.

[0097] For example, the preset quantity constraints are: minimum verification point quantity guarantee threshold = 5, maximum verification point quantity limit threshold = 20; theoretical total number of verification points = 10; 10 > 5, no lower limit adjustment is triggered; 10 < 20, and is between 5 and 20, therefore the total number of compatible verification points for the current operation is directly determined to be 10. If the comprehensive strength compensation coefficient is 0.3, the theoretical total number = 10 × 0.3 = 3, rounded up = 3, 3 < 5, the total number of compatible points is 5; if the comprehensive strength compensation coefficient is 2.1, the theoretical total number = 10 × 2.1 = 21, rounded up = 21, 21 > 20, the total number of compatible points is 20.

[0098] In this embodiment of the invention, the number of benchmark verification points is determined based on the type of operation. Combined with the actual verification demand intensity reflected in historical operations, a comprehensive intensity compensation coefficient is used to dynamically adjust the number of verification points, breaking the limitations of traditional fixed-number configurations. Simultaneously, by pre-setting minimum and maximum quantity constraint thresholds, a scientific range of values ​​is defined for the number of points, ensuring both the basic effectiveness of the verification work and the feasibility and resource rationality of on-site operations. The final determined total number of suitable verification points accurately matches the actual verification demand intensity of current BeiDou RTK operations and complies with industry standards and practical requirements. This establishes a scientific, reasonable, and feasible quantitative benchmark for subsequent optimization of verification point distribution schemes based on multi-objective optimization, ensuring the adaptability of subsequent verification point selection in terms of quantity.

[0099] S500: Using the candidate verification point library as the optimization space, iteratively optimize the verification point distribution scheme based on the preset multi-objective optimization function, the test area quality zoning map, the total number of suitable verification points, and the initial confidence of the candidate verification points, and generate the optimal verification point distribution scheme for verification point selection.

[0100] In this embodiment of the invention, the candidate verification point library is used as the optimization space. Based on a preset multi-objective optimization function, the measurement area quality zoning map, the total number of suitable verification points, and the initial confidence level of the candidate verification points, the verification point distribution scheme is iteratively optimized to generate the optimal verification point distribution scheme for verification point selection. The spatial distribution of verification points directly determines the effectiveness of BeiDou RTK measurement quality verification. In the prior art, the selection of verification points is mostly based on empirical random or uniform distribution, without comprehensive optimization in conjunction with multi-dimensional objectives such as measurement area quality level, point location confidence, and operation time. Iterative optimization of the scheme is also not achieved through scientific algorithms, which easily leads to problems such as insufficient verification coverage in high-risk areas, uneven distribution in low-risk areas, inconsistent point reliability, and unbalanced time allocation. This results in the verification work failing to fully and accurately match the actual measurement quality control requirements of the measurement area. Therefore, this step uses the candidate verification point library as the optimization space, combines the quality zoning of the test area, the total number of suitable points, and the initial confidence level of the points, and generates an initial scheme through scientific allocation of the number ratios. Then, iterative optimization is carried out based on the preset multi-objective optimization function and intelligent optimization algorithm to finally generate the optimal verification point distribution scheme, so as to achieve accurate and scientific selection of verification points.

[0101] Step S500 in the method provided in this embodiment of the invention includes: Based on the total number of adaptive verification points and the area proportion and risk weight of each quality level region in the measurement area quality zoning map, the proportion of verification points selected from the measurement condition good area, measurement condition medium area and measurement condition poor area are determined respectively. Among them, the measurement condition poor area is given a risk priority weight so that the number of its verification points is not less than the preset minimum poor area verification point proportion threshold. Based on the ratio of the number of verification points and the initial confidence level of the candidate verification points, a corresponding number of candidate verification points are selected from each quality level region to generate several initial verification point distribution schemes as the initial population for optimization. Using the candidate verification point library as the optimization space, an intelligent optimization algorithm is used to iteratively optimize the distribution scheme of verification points in the initial population. The scheme quality coefficient of each verification point distribution scheme is calculated according to the preset multi-objective optimization function. The superior schemes are selected according to the high and low scheme quality coefficients and crossover and mutation operations are performed to generate new verification point distribution schemes. Repeatedly perform iterative optimization until the iteration termination condition is met, and take the verification point distribution scheme with the highest scheme quality coefficient at the time of iteration termination as the optimal verification point distribution scheme. Specifically, a corresponding number of candidate verification points are selected from each quality level region to generate several initial verification point distribution schemes, including: From the set of candidate verification points corresponding to the poor measurement conditions, the required number of candidate verification points are forcibly selected in descending order of initial confidence level to ensure the reliability of verification points in high-risk areas. From the set of candidate verification points corresponding to the measurement conditions, a weighted random sampling is performed using the initial confidence level of each candidate verification point as the weight, and the required number of candidate verification points are selected. Among them, the higher the confidence level, the greater the probability of the point being selected. From the set of candidate verification points corresponding to the optimal measurement conditions area, the optimal measurement conditions area is divided into several sub-regions using a spatial grid partitioning method. Uniform random sampling is performed in each sub-region until the required number of candidate verification points are selected, ensuring the spatial distribution uniformity of verification points in low-risk areas. The candidate verification points selected from each quality level region are merged to form a complete candidate verification point distribution scheme. The above selection process is repeated several times to generate several different candidate verification point distribution schemes.

[0102] First, based on the total number of compatible verification points and the area proportion and risk weight of each quality level region in the survey area quality zoning map, the proportion of verification points selected from areas with excellent measurement conditions, areas with moderate measurement conditions, and areas with poor measurement conditions is determined. Specifically, areas with poor measurement conditions are assigned a risk priority weight to ensure that the number of verification points in these areas is not lower than a preset minimum threshold for the proportion of verification points in poorly measured areas. The area proportion refers to the ratio of the area of ​​the excellent, moderate, and poorly measured areas to the total area of ​​the target survey area, serving as the basis for the initial allocation of points. A risk weight greater than 1 is assigned to areas with poor measurement conditions to increase the number of verification points in these areas, reflecting the risk priority principle. The minimum threshold for the proportion of verification points in poorly measured areas is the lower limit for the number of points in poorly measured areas set to ensure verification coverage of high-risk areas; it is a fixed proportion of the total number of compatible verification points. The proportion of verification points is the ratio of the final number of verification points to be selected in each quality level region to the total number of compatible verification points, serving as the basis for subsequent point selection.

[0103] Specifically, firstly, based on the area proportion of each quality level region, a preliminary number of verification points is allocated to the superior, medium, and inferior regions; then, a risk weight coefficient is assigned to the inferior regions to adjust the number of points, ensuring that the proportion of inferior region points is not lower than the preset minimum inferior region verification point proportion threshold; next, a portion of the number of points is differentially deducted from the superior and medium regions and added to the inferior regions, ensuring that the sum of the number of points in each region equals the total number of suitable verification points; finally, the final number of points in each region is divided by the total number of suitable verification points to obtain the verification point proportion of each region.

[0104] For example, the total number of verification points adapted for this operation is 10. After the above method of allocation and calculation, the final ratio of the number of verification points in each area is determined as follows: 40% for areas with excellent measurement conditions, 30% for areas with medium measurement conditions, and 30% for areas with poor measurement conditions.

[0105] Secondly, based on the proportion of verification points and the initial confidence level of the candidate verification points, a corresponding number of candidate verification points are selected from each quality level region to generate several initial verification point distribution schemes as the initial population for optimization. The initial population is a set of several independent verification point distribution schemes and serves as the initial input for the intelligent optimization algorithm's iterative optimization.

[0106] Specifically, a corresponding number of candidate verification points are selected from each quality level region to generate several initial verification point distribution schemes, including: From the set of candidate verification points corresponding to the poor measurement conditions, the required number of candidate verification points are forcibly selected in descending order of initial confidence level to ensure the reliability of verification points in high-risk areas. From the set of candidate verification points corresponding to the measurement conditions, a weighted random sampling is performed using the initial confidence level of each candidate verification point as the weight, and the required number of candidate verification points are selected. Among them, the higher the confidence level, the greater the probability of the point being selected. From the set of candidate verification points corresponding to the optimal measurement conditions area, the optimal measurement conditions area is divided into several sub-regions using a spatial grid partitioning method. Uniform random sampling is performed in each sub-region until the required number of candidate verification points are selected, ensuring the spatial distribution uniformity of verification points in low-risk areas. The candidate verification points selected from each quality level region are merged to form a complete candidate verification point distribution scheme. The above selection process is repeated several times to generate several different candidate verification point distribution schemes.

[0107] First, from the candidate verification point set corresponding to the area with poor measurement conditions, the required number of candidate verification points are forcibly selected in descending order of initial confidence level to ensure the reliability of verification points in high-risk areas. Forced selection means directly selecting points based on initial confidence level from high to low to ensure the reliability of points in high-risk areas. The candidate verification point set corresponding to the area with poor measurement conditions is extracted, and all candidate verification points in the set are sorted in descending order of initial confidence level. Selection is then performed sequentially starting from the first point in the sorted list until the required number of points for the area is reached, thus completing the selection of verification points for the area with poor measurement conditions.

[0108] For example, in this operation, three verification points need to be selected in the inferior area. The candidate verification points in the inferior area are sorted according to the initial confidence level as B1 (0.62), B2 (0.58), B3 (0.55), B4 (0.49)... and the first three points are directly selected: B1, B2, B3.

[0109] Next, from the set of candidate verification points corresponding to the central area of ​​the measurement conditions, a weighted random sampling is performed using the initial confidence level of each candidate verification point as a weight. The required number of candidate verification points are selected, with higher confidence levels indicating a greater probability of selection. Weighted random sampling means calculating the sampling probability using the initial confidence level of the point as a weight; higher confidence levels result in a greater probability of selection. The set of candidate verification points corresponding to the central area of ​​the measurement conditions is extracted, and the sampling probability of each candidate verification point within the set is calculated using its initial confidence level as a weight. Higher confidence levels result in a greater sampling probability. Based on this probability, points are randomly sampled from the set until the required number of points for the central area is reached, thus completing the selection of verification points for the central area.

[0110] For example, in this task, three verification points need to be selected in the central area. After calculating the sampling probability using the initial confidence of the 15 candidate verification points in the central area as weights, three points are randomly selected: Z2, Z5, and Z9.

[0111] Next, from the set of candidate verification points corresponding to the optimal measurement conditions area, the optimal measurement conditions area is divided into several sub-regions using a spatial grid partitioning method. Uniform random sampling is performed within each sub-region until the required number of candidate verification points are selected, ensuring the spatial uniformity of verification point distribution in low-risk areas. Spatial grid partitioning refers to dividing the area into several equal-area sub-regions, with points randomly selected from each sub-region to ensure uniform spatial distribution. The set of candidate verification points corresponding to the optimal measurement conditions area is extracted, and the geographical scope of the optimal area is divided into equal-area sub-regions equal to the number of points required for selection within the optimal area using a spatial grid partitioning method. One point is randomly selected from the candidate verification points within each sub-region until all sub-regions have been selected, thus completing the selection of verification points for the optimal area.

[0112] For example, in this assignment, four verification points need to be selected in the optimal area. The optimal area is divided into four square sub-regions with equal area. One point is randomly selected from each sub-region, resulting in a total of four points: Y1, Y7, Y12, and Y18.

[0113] Finally, the candidate verification points selected from each quality level region are merged to form a complete candidate verification point distribution scheme. This selection process is repeated several times to generate several different candidate verification point distribution schemes. All verification points selected from the inferior, medium, and superior regions are integrated to form a complete candidate verification point distribution scheme. This selection process is repeated a preset number of times to generate several different candidate verification point distribution schemes. All schemes together form the initial population for the intelligent optimization algorithm to find the best solution.

[0114] For example, the inferior region {B1, B2, B3}, the middle region {Z2, Z5, Z9}, and the superior region {Y1, Y7, Y12, Y18} are merged to form a complete initial scheme; the point selection process is repeated 20 times to generate 20 different initial schemes, forming an initial population containing 20 individuals.

[0115] Furthermore, using the candidate verification point library as the optimization space, an intelligent optimization algorithm is used to iteratively optimize the distribution scheme of verification points in the initial population. The scheme quality coefficient of each verification point distribution scheme is calculated according to the preset multi-objective optimization function. Based on the scheme quality coefficient, the superior schemes are selected for crossover and mutation operations to generate new verification point distribution schemes.

[0116] The preset multi-objective optimization function includes spatial distribution uniformity objective, regional coverage integrity objective, point reliability objective, temporal distribution balance objective, and risk area priority objective. The spatial distribution uniformity objective is used to evaluate the uniformity of the distribution of each verification point in the test area. By dividing the target test area into Thiessen polygons, the coefficient of variation of the area of ​​each Thiessen polygon is calculated, and the reciprocal of the coefficient of variation is used as the quantification value of spatial distribution uniformity. The regional coverage integrity target is used to evaluate the coverage of each verification point in areas with different measurement conditions. The ratio of the actual number of verification points to the target number in areas with different quality levels is calculated, and the minimum value of the ratio is taken as the quantitative value of regional coverage integrity. The reliability target of the selected verification points is used to evaluate the sum of the confidence levels of the selected verification points. The initial confidence levels of each verification point in the scheme are added together to obtain the quantitative value of the reliability of the points. The time distribution balance target is used to evaluate the degree of distribution balance of each verification point on the operation time axis. The operation time is divided into several time intervals, the distribution frequency of the verification points in each time interval is counted, the information entropy of the distribution frequency is calculated, and the information entropy is used as the quantitative value of time distribution balance. The risk area priority target is used to assess the proportion of high-risk areas in the verification points, calculate the proportion of verification points in areas with poor measurement conditions to the total number of verification points in the plan, and use the proportion as the quantitative value of risk area priority. Each target quantification value is normalized, and the normalized quantification values ​​are weighted and summed to obtain the scheme quality coefficient of each verification point distribution scheme.

[0117] First, the spatial distribution uniformity target is used to evaluate the degree of uniformity of the distribution of each verification point in the survey area. This is achieved by dividing the target survey area into Thiessen polygons and calculating the coefficient of variation (CV) of the area of ​​each Thiessen polygon. The reciprocal of this CV is used as the quantification value of spatial distribution uniformity. The target survey area is divided into Thiessen polygons according to the coordinates of all points in the current verification point distribution scheme. The CV of the area of ​​each Thiessen polygon after division is calculated, and the reciprocal of this CV is used as the quantification value of spatial distribution uniformity. The smaller the CV, the larger the quantification value, indicating a more uniform spatial distribution of the points. For example, if Thiessen polygons are used to divide 10 verification points in an initial scheme, the calculated CV of the area of ​​each polygon is 0.3, and the quantification value of spatial distribution uniformity is approximately 1 / 0.3 ≈ 3.33.

[0118] Secondly, the regional coverage integrity target is used to evaluate the coverage degree of each verification point in areas with different measurement conditions. The ratio of the actual number of verification points to the target number is calculated for each area with different quality levels. The minimum of these ratios is taken as the quantified value of regional coverage integrity. The actual number of verification points selected in the current scheme's excellent, medium, and poor measurement conditions is counted separately. The ratio of the actual number to the target number of points in each area is calculated. The minimum of the three ratios is taken as the quantified value of regional coverage integrity. The closer the ratio is to 1, the larger the quantified value, representing more complete regional coverage. For example, in an initial scheme, the actual number of selected points in the excellent, medium, and poor areas is 4, 3, and 3 respectively, exactly matching the target number. The ratio for each area is 1, and the quantified value of regional coverage integrity is min(1,1,1) = 1.

[0119] Secondly, the reliability target of the selected verification points is used to evaluate the sum of the confidence levels of the selected verification points. The initial confidence levels of each verification point in the scheme are summed to obtain the quantitative value of the point reliability. The initial confidence levels of all verification points in the current scheme are extracted, and all initial confidence levels are summed. The summation result is the quantitative value of the point reliability; the larger the value, the higher the overall reliability of the points within the scheme. For example, if the initial confidence levels of 10 verification points in an initial scheme are 0.62, 0.58, 0.55, 0.72, 0.68, 0.65, 0.85, 0.82, 0.78, and 0.77, the summation yields a quantitative value of 7.02 for the point reliability.

[0120] Furthermore, the time distribution balance target is used to evaluate the degree of distribution balance of each verification point along the operation time axis. The operation time is divided into several time intervals, and the distribution frequency of the verification points within each time interval is statistically analyzed. The information entropy of the distribution frequency is calculated, and this information entropy is used as the quantification value of time distribution balance. Based on the overall operation time of this BeiDou RTK measurement, the operation time axis is divided into several equal-length time intervals. The distribution frequency of each verification point in the current scheme within each time interval is statistically analyzed, and the information entropy of this distribution frequency is calculated. The information entropy result is the quantification value of time distribution balance; the larger the information entropy, the more balanced the distribution of points along the operation time. For example, if the 4.5-hour operation time is divided into nine 0.5-hour time intervals, and the distribution frequency of 10 points in an initial scheme is statistically analyzed, the calculated information entropy is 1.85, and the quantification value of time distribution balance is 1.85.

[0121] Furthermore, the risk area priority target is used to assess the proportion of high-risk areas among the verification sites. It calculates the ratio of verification sites in areas with poor measurement conditions to the total number of verification sites in the plan, and uses this ratio as the risk area priority quantification value. The number of verification sites in areas with poor measurement conditions in the current plan is counted, and this number is divided by the total number of verification sites in the plan. The result is the risk area priority quantification value; the larger the value, the higher the proportion of verification sites in high-risk areas, and the more comprehensive the risk coverage. For example, in an initial plan with 10 verification sites, including 3 in areas with poor conditions, the risk area priority quantification value = 3 / 10 = 0.3.

[0122] Finally, each target quantified value is normalized, and the normalized quantified values ​​are then weighted and summed to obtain the scheme quality coefficient for each verification point distribution scheme. The five quantified values ​​obtained above are transformed to the [0,1] interval using Min-Max normalization to eliminate the dimensional differences between the quantified values; then, preset weights are assigned to the five normalized quantified values, and a weighted sum is performed according to the weights. The result is the scheme quality coefficient of the verification point distribution scheme, with the coefficient ranging from [0,1]. The larger the value, the better the overall quality of the scheme.

[0123] For example, the weights of the five preset quantitative values ​​are as follows: spatial distribution uniformity weight 0.25, regional coverage integrity weight 0.2, point reliability weight 0.25, temporal distribution balance weight 0.15, and risk area priority weight 0.15. After the five quantitative values ​​in the example above are normalized using Min-Max, they are 0.92, 1.0, 0.88, 0.90, and 1.0 respectively. The scheme quality coefficient = 0.25×0.92 + 0.2×1.0 + 0.25×0.88 + 0.15×0.90 + 0.15×1.0 = 0.935.

[0124] Finally, the iterative optimization is repeated until the iteration termination condition is met, and the verification point distribution scheme with the highest scheme quality coefficient at the time of iteration termination is taken as the optimal verification point distribution scheme.

[0125] The method involves iteratively optimizing the distribution scheme of verification points in the initial population using an intelligent optimization algorithm, including: Each candidate validation point distribution scheme is encoded as an individual, a population containing a preset number of individuals is initialized, the scheme quality coefficient of each individual in the population is calculated according to the preset multi-objective optimization function, and a preset number of excellent individuals are selected from the current population in descending order of scheme quality coefficient to enter the next generation population. Selected superior individuals are subjected to pairwise crossover operations to generate new offspring individuals with a preset crossover probability; The generated offspring individuals are subjected to mutation operations, and the individuals are adjusted according to a preset mutation probability; The offspring individuals are merged with the parent individuals to form a new generation of population, and the scheme quality coefficient of each individual in the population is recalculated. Repeat the above steps until the iteration termination condition is met. Decode the individual with the highest scheme quality coefficient in the population at the time of iteration termination to obtain the corresponding list of verification point numbers, which is used as the optimal verification point distribution scheme. The iteration termination condition includes the convergence of the scheme quality coefficient or reaching the preset maximum number of iterations.

[0126] First, each candidate validation site distribution scheme is encoded as an individual. A population containing a preset number of individuals is initialized. The scheme quality coefficient of each individual in the population is calculated according to a preset multi-objective optimization function. Then, a preset number of superior individuals are selected from the current population in descending order of their scheme quality coefficients to enter the next generation population. The population is a set of several validation site distribution schemes, with an initial population size of a preset fixed value. Superior individuals are those in the current population with high scheme quality coefficients and optimization value.

[0127] Specifically, each candidate verification point distribution scheme is encoded as an individual in the form of a point number sequence, and an initial population containing a preset number of individuals is initialized; based on a multi-objective optimization function, the scheme quality coefficient of each individual in the population is calculated; all individuals are sorted from high to low according to the scheme quality coefficient, and a preset number of excellent individuals are selected as parent individuals to enter the next generation iteration process.

[0128] For example, the initial population size is preset to 20 individuals, and the distribution schemes of the 20 initial verification points are encoded into a sequence of 10 location numbers. After calculating the scheme quality coefficients of all individuals, they are sorted from high to low coefficients, and the top 10 individuals are selected as superior parent individuals.

[0129] Secondly, the selected superior individuals undergo pairwise crossover operations to generate new offspring individuals with a preset crossover probability. Crossover operation refers to the algorithmic operation of exchanging segments of the encoding sequences of two parent individuals to generate new offspring individuals. The crossover probability is a preset, fixed parameter that indicates the probability of triggering the crossover operation. The selected superior parent individuals are paired up, and the encoding sequence segment exchange operation is performed with the preset crossover probability to generate corresponding new offspring individuals; pairs of individuals that do not reach the crossover probability threshold directly retain their original parent encoding sequences as offspring individuals.

[0130] For example, with a preset crossover probability of 0.8, 10 superior parent individuals are paired into 5 groups; 4 of these groups meet the crossover probability condition and perform crossover of the encoded sequence segments to generate 4 new offspring individuals; the remaining 1 group does not trigger crossover and directly retains the original sequence as the offspring individual, generating a total of 5 offspring individuals.

[0131] Next, the generated offspring individuals undergo mutation operations, adjusting them with a preset mutation probability. Mutation refers to the algorithmic operation of randomly replacing some position numbers in the encoding sequence of offspring individuals to improve population diversity. The mutation probability is a preset value that triggers the mutation operation, and is a fixed parameter.

[0132] Specifically, all generated offspring individuals are traversed, and offspring individuals are randomly selected with a preset mutation probability. The part numbering of some points in their coding sequence is replaced and adjusted; offspring individuals that do not reach the mutation probability threshold retain their original coding sequence.

[0133] For example, the preset mutation probability is 0.05. The mutation operation is performed on each of the five offspring individuals. One offspring individual triggers the mutation operation, randomly replacing one position number in its coding sequence. The other four offspring individuals retain their original coding, and the mutation operation is completed.

[0134] Next, the offspring individuals are merged with the parent individuals to form a new generation population, and the scheme quality coefficient of each individual in the population is recalculated. All offspring individuals that have completed the mutation operation are merged with superior parent individuals to form a new generation population; based on a multi-objective optimization function, the scheme quality coefficient of each individual in the new generation population is recalculated. For example, merging 5 offspring individuals with 10 superior parent individuals forms a new generation population containing 15 individuals; the scheme quality coefficient of all individuals in this population is recalculated, completing the population update.

[0135] Finally, the above steps are repeated until the iteration termination condition is met. The individual with the highest scheme quality coefficient in the population at the time of iteration termination is decoded to obtain the corresponding list of verification point numbers, which serves as the optimal verification point distribution scheme. The iteration termination condition includes either scheme quality coefficient convergence or reaching a preset maximum number of iterations. The iteration stops when either condition is met. Specifically, the scheme quality coefficient convergence condition is: the difference between the optimal coefficients of two adjacent generations ≤ a preset convergence threshold. Individual decoding refers to restoring the numerical encoding sequence of the optimal individual to a specific list of verification point numbers.

[0136] Specifically, the above iterative process is repeated, and it is determined in real time whether the iteration termination condition is met. After the iteration terminates, the individual with the highest scheme quality coefficient in the population is extracted, and the individual is decoded to obtain the list of verification point numbers, which is the optimal verification point distribution scheme.

[0137] For example, the maximum number of iterations is preset to 100, and the convergence threshold is 0.001. When iterating to the 28th iteration, the difference in the scheme quality coefficient between the best individuals in two adjacent generations is 0.0008, which is less than the preset convergence threshold of 0.001, thus satisfying the termination condition. The best individual in the current population is extracted, and after decoding, a list of verification point numbers is obtained, which is determined as the optimal verification point distribution scheme.

[0138] In this embodiment of the invention, the number of verification points in each quality level area is scientifically allocated by combining area proportion with risk priority principle. This matches the spatial scale of the measurement area while ensuring verification coverage in high-risk and poor-quality areas. A differentiated point selection strategy is adopted for different areas to generate an initial population, taking into account the reliability of poor-quality areas, the randomness of medium-quality areas, and the uniformity of high-quality areas, laying a high-quality foundation for iterative optimization. A multi-objective optimization function based on five objectives achieves comprehensive evaluation of the scheme, avoiding the one-sidedness of single-objective optimization. Through iterative optimization using a genetic algorithm, the optimal verification point distribution scheme is continuously screened, ultimately generating an optimal distribution scheme for verification points. This achieves comprehensive optimization of verification points in terms of spatial distribution, risk coverage, point reliability, and time allocation. This invention breaks through the limitations of traditional empirical point selection, ensuring that verification points accurately match the actual measurement quality control requirements of the measurement area, improving the pertinence, comprehensiveness, and effectiveness of BeiDou RTK measurement verification work, and providing a scientific and solid guarantee for the quality of measurement results.

[0139] Through the specific implementation methods described above, the embodiments of the present invention achieve the following technical effects: This invention provides a method and system for selecting BeiDou RTK verification points based on multi-source information fusion. Through survey area quality zoning assessment, candidate verification point library construction and confidence assignment, operational feature analysis and comprehensive intensity compensation coefficient calculation, dynamic adaptation of the total number of verification points, and multi-objective iterative optimization, it constructs a comprehensive BeiDou RTK verification point selection system from all dimensions: spatial risk assessment, basic point selection, verification requirement quantification, dynamic quantity adjustment, and scheme optimization. This effectively solves the technical defects of traditional technologies, such as reliance on experience in verification point selection, fixed and rigid quantity configuration, insufficient coverage of high-risk areas, uneven spatial distribution of points, and difficulty in guaranteeing verification reliability. It achieves precise matching between the number of verification points and the intensity of actual operational needs, and deep adaptation between the distribution of verification points and the risk level of the survey area. While ensuring priority coverage of high-risk areas such as areas with poor measurement conditions, it also considers the uniformity of spatial distribution, the balance of temporal distribution, and the reliability of the points themselves. This improves the scientific rigor, relevance, and effectiveness of BeiDou RTK measurement verification work, and effectively ensures the quality reliability of measurement results and the efficiency of operational implementation based on the reasonable allocation of verification resources.

[0140] Example 2, as Figure 2As shown, this invention provides a BeiDou RTK verification point selection system based on multi-source information fusion, the system comprising: The environmental assessment module 11 is used to conduct an environmental impact assessment of the target survey area based on multi-source information of the target survey area, and generate a survey area quality zoning map that reflects the advantages and disadvantages of measurement conditions at different locations. The candidate point library construction module 12 is used to construct a candidate verification point library containing known control points and historical stable points based on the multi-source information, and to assign an initial confidence level to each candidate verification point in the candidate verification point library. The compensation coefficient generation module 13 is used to obtain the operation characteristic parameters within the historical measurement time window, and generate a comprehensive intensity compensation coefficient for the verification requirement intensity of the current operation based on the operation characteristic parameters. The adaptation point determination module 14 is used to adjust the number of preset benchmark verification points based on the comprehensive strength compensation coefficient, and determine the total number of adaptation verification points required for the current operation. The point location optimization module 15 is used to iteratively optimize the distribution scheme of verification points based on the candidate verification point library as the optimization space, the preset multi-objective optimization function, the quality zoning map of the test area, the total number of suitable verification points and the initial confidence of the candidate verification points, and generate the optimal verification point distribution scheme for verification point selection.

[0141] In one embodiment, the survey area environmental assessment module 11 is further configured to: Acquire multi-source information of the target survey area, wherein the multi-source information includes at least basic geographic information data, reference station network service information, historical measurement results data, real-time observation status data, and operation process record data; Based on the aforementioned basic geographic information data, the topographic and landform features and land cover features of the survey area are extracted. According to satellite signal propagation theory, potential areas with signal blockage risk and multipath effect risk are interpreted. Based on the service information of the reference station network, the spatial distribution density of the reference stations and the coverage strength of the differential signal are analyzed. According to the signal attenuation model, potential areas with differential signal attenuation risk are assessed. Based on electromagnetic environment information, the spatial distribution of various electromagnetic interference sources is identified, and potential areas with electromagnetic interference risks are delineated according to the electromagnetic field propagation characteristics. Based on the spatial overlay analysis results of different risk types, areas with multiple risk couplings are classified as poor measurement conditions, areas with a single risk are classified as medium measurement conditions, and areas with no risk exposure are classified as excellent measurement conditions.

[0142] In one embodiment, the candidate point library construction module 12 is further configured to: Data on known control points within the target survey area are extracted from the multi-source information, wherein the data on known control points includes the coordinate results, grade attributes, and positional stability records of each known control point. Historical measurement results data are extracted from the multi-source information, and historical measurement points that meet the preset stability index are selected as historical stable points. The known control points and the historical stable points are integrated, and duplicate and conflicting points are eliminated to construct a candidate verification point library covering the target measurement area. Collect observation data from multiple periods of historical measurements for each candidate verification point, calculate the dispersion of observation results in each period, and determine the point stability characteristic value based on the dispersion. Extract the measurement area quality zoning map attributes of the area where each candidate verification point is located, and determine the environmental risk impact coefficient based on the quality level of the measurement conditions in the area. Obtain the historical observation condition records of each candidate verification point in the historical measurement, and determine the observation quality characteristic value based on the observation condition records, wherein the observation condition records include the average position accuracy attenuation factor and the average signal-to-noise ratio; Collect on-site survey information for each candidate verification point, and determine the recoverability feature value of the point based on the on-site survey information. The on-site survey information includes the integrity of the point markers, accessibility, and identifiability. The baseline weight of each candidate verification point is determined based on its type attribute, wherein the type attribute includes high-level known control points, same-level known control points, and historically stable points; The location stability characteristic value, environmental risk impact coefficient, observation quality characteristic value, and location recoverability characteristic value are normalized respectively. The normalized characteristic values ​​are then weighted and fused to generate the confidence correction coefficient for each candidate verification point. The baseline weight of each candidate verification point is compensated and corrected to generate the initial confidence level for each candidate verification point.

[0143] In one embodiment, the compensation coefficient generation module 13 is further configured to: Among them, the acquisition of operation characteristic parameters within the historical measurement time window includes: The measurement accuracy index is calculated based on the plane coordinate deviation of each measurement point within the historical measurement time window; The fixed solution acquisition rate is calculated based on the proportion of fixed solutions obtained at each measurement point within the historical measurement time window. The average duration of continuous operations is calculated based on the duration of each continuous operation within the historical measurement time window; The average position accuracy attenuation factor is calculated based on the position accuracy attenuation factor of each measurement point within the historical measurement time window. The measurement accuracy index, fixed solution acquisition rate, average continuous operation time, and average position accuracy attenuation factor are used as operation characteristic parameters.

[0144] The process of generating a comprehensive intensity compensation coefficient for the verification requirement intensity of the current task based on the task characteristic parameters includes: The ratio of the preset benchmark measurement accuracy index to the measurement accuracy index is used as the first demand intensity compensation coefficient. The ratio of the preset benchmark fixed solution acquisition rate to the fixed solution acquisition rate is used as the second demand intensity compensation coefficient. The ratio of the average continuous working time to the average continuous working time of the preset benchmark is used as the third demand intensity compensation coefficient. The ratio of the average position accuracy attenuation factor to the preset benchmark average position accuracy attenuation factor is used as the fourth demand intensity compensation coefficient. The first demand intensity compensation coefficient, the second demand intensity compensation coefficient, the third demand intensity compensation coefficient, and the fourth demand intensity compensation coefficient are weighted and fused to obtain the comprehensive intensity compensation coefficient for verifying the demand intensity.

[0145] In one embodiment, the adaptation point determination module 14 is further configured to: The number of preset benchmark verification points is determined according to the type of operation. The comprehensive strength compensation coefficient is multiplied by the number of benchmark verification points and rounded up to obtain the total number of theoretical verification points for the current operation. The total number of theoretical verification points is adjusted by applying preset quantity constraints to obtain the total number of suitable verification points required for the current operation. The quantity constraints include a minimum verification point quantity guarantee threshold and a maximum verification point quantity limit threshold.

[0146] In one embodiment, the location optimization module 15 is further configured to: Based on the total number of adaptive verification points and the area proportion and risk weight of each quality level region in the measurement area quality zoning map, the proportion of verification points selected from the measurement condition good area, measurement condition medium area and measurement condition poor area are determined respectively. Among them, the measurement condition poor area is given a risk priority weight so that the number of its verification points is not less than the preset minimum poor area verification point proportion threshold. Based on the ratio of the number of verification points and the initial confidence level of the candidate verification points, a corresponding number of candidate verification points are selected from each quality level region to generate several initial verification point distribution schemes as the initial population for optimization. Using the candidate verification point library as the optimization space, an intelligent optimization algorithm is used to iteratively optimize the distribution scheme of verification points in the initial population. The scheme quality coefficient of each verification point distribution scheme is calculated according to the preset multi-objective optimization function. The superior schemes are selected according to the high and low scheme quality coefficients and crossover and mutation operations are performed to generate new verification point distribution schemes. Repeatedly perform iterative optimization until the iteration termination condition is met, and take the verification point distribution scheme with the highest scheme quality coefficient at the time of iteration termination as the optimal verification point distribution scheme. Specifically, a corresponding number of candidate verification points are selected from each quality level region to generate several initial verification point distribution schemes, including: From the set of candidate verification points corresponding to the poor measurement conditions, the required number of candidate verification points are forcibly selected in descending order of initial confidence level to ensure the reliability of verification points in high-risk areas. From the set of candidate verification points corresponding to the measurement conditions, a weighted random sampling is performed using the initial confidence level of each candidate verification point as the weight, and the required number of candidate verification points are selected. Among them, the higher the confidence level, the greater the probability of the point being selected. From the set of candidate verification points corresponding to the optimal measurement conditions area, the optimal measurement conditions area is divided into several sub-regions using a spatial grid partitioning method. Uniform random sampling is performed in each sub-region until the required number of candidate verification points are selected, ensuring the spatial distribution uniformity of verification points in low-risk areas. The candidate verification points selected from each quality level region are merged to form a complete candidate verification point distribution scheme. The above selection process is repeated several times to generate several different candidate verification point distribution schemes.

[0147] The preset multi-objective optimization function includes spatial distribution uniformity objective, regional coverage integrity objective, point reliability objective, temporal distribution balance objective, and risk area priority objective. The spatial distribution uniformity objective is used to evaluate the uniformity of the distribution of each verification point in the test area. By dividing the target test area into Thiessen polygons, the coefficient of variation of the area of ​​each Thiessen polygon is calculated, and the reciprocal of the coefficient of variation is used as the quantification value of spatial distribution uniformity. The regional coverage integrity target is used to evaluate the coverage of each verification point in areas with different measurement conditions. The ratio of the actual number of verification points to the target number in areas with different quality levels is calculated, and the minimum value of the ratio is taken as the quantitative value of regional coverage integrity. The reliability target of the selected verification points is used to evaluate the sum of the confidence levels of the selected verification points. The initial confidence levels of each verification point in the scheme are added together to obtain the quantitative value of the reliability of the points. The time distribution balance target is used to evaluate the degree of distribution balance of each verification point on the operation time axis. The operation time is divided into several time intervals, the distribution frequency of the verification points in each time interval is counted, the information entropy of the distribution frequency is calculated, and the information entropy is used as the quantitative value of time distribution balance. The risk area priority target is used to assess the proportion of high-risk areas in the verification points, calculate the proportion of verification points in areas with poor measurement conditions to the total number of verification points in the plan, and use the proportion as the quantitative value of risk area priority. Each target quantification value is normalized, and the normalized quantification values ​​are weighted and summed to obtain the scheme quality coefficient of each verification point distribution scheme.

[0148] The method involves iteratively optimizing the distribution scheme of verification points in the initial population using an intelligent optimization algorithm, including: Each candidate validation point distribution scheme is encoded as an individual, a population containing a preset number of individuals is initialized, the scheme quality coefficient of each individual in the population is calculated according to the preset multi-objective optimization function, and a preset number of excellent individuals are selected from the current population in descending order of scheme quality coefficient to enter the next generation population. Selected superior individuals are subjected to pairwise crossover operations to generate new offspring individuals with a preset crossover probability; The generated offspring individuals are subjected to mutation operations, and the individuals are adjusted according to a preset mutation probability; The offspring individuals are merged with the parent individuals to form a new generation of population, and the scheme quality coefficient of each individual in the population is recalculated. Repeat the above steps until the iteration termination condition is met. Decode the individual with the highest scheme quality coefficient in the population at the time of iteration termination to obtain the corresponding list of verification point numbers, which is used as the optimal verification point distribution scheme. The iteration termination condition includes the convergence of the scheme quality coefficient or reaching the preset maximum number of iterations.

[0149] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for selecting BeiDou RTK verification points based on multi-source information fusion, characterized in that, The method includes: An environmental impact assessment of the target survey area is conducted based on multi-source information of the target survey area, generating a survey area quality zoning map that reflects the advantages and disadvantages of measurement conditions at different locations. Based on the multi-source information, a candidate verification point library containing known control points and historical stable points is constructed, and an initial confidence level is assigned to each candidate verification point in the candidate verification point library. Obtain the operation characteristic parameters within the historical measurement time window, and generate a comprehensive intensity compensation coefficient for the verification requirement intensity of the current operation based on the operation characteristic parameters; Based on the comprehensive strength compensation coefficient, adjust the number of preset benchmark verification points to determine the total number of adaptable verification points required for the current operation. Using the candidate verification point library as the optimization space, the verification point distribution scheme is iteratively optimized based on the preset multi-objective optimization function, the quality zoning map of the test area, the total number of suitable verification points, and the initial confidence of the candidate verification points, so as to generate the optimal verification point distribution scheme for verification point selection.

2. The method for selecting BeiDou RTK verification points based on multi-source information fusion according to claim 1, characterized in that, An environmental impact assessment of the target survey area is conducted based on multi-source information, generating a survey area quality zoning map reflecting the advantages and disadvantages of measurement conditions at different locations, including: Acquire multi-source information of the target survey area, wherein the multi-source information includes at least basic geographic information data, reference station network service information, historical measurement results data, real-time observation status data, and operation process record data; Based on the aforementioned basic geographic information data, the topographic and landform features and land cover features of the survey area are extracted. According to satellite signal propagation theory, potential areas with signal blockage risk and multipath effect risk are interpreted. Based on the service information of the reference station network, the spatial distribution density of the reference stations and the coverage strength of the differential signal are analyzed. According to the signal attenuation model, potential areas with differential signal attenuation risk are assessed. Based on electromagnetic environment information, the spatial distribution of various electromagnetic interference sources is identified, and potential areas with electromagnetic interference risks are delineated according to the electromagnetic field propagation characteristics. Based on the spatial overlay analysis results of different risk types, areas with multiple risk couplings are classified as poor measurement conditions, areas with a single risk are classified as medium measurement conditions, and areas with no risk exposure are classified as excellent measurement conditions.

3. The method for selecting BeiDou RTK verification points based on multi-source information fusion according to claim 1, characterized in that, Based on the multi-source information, a candidate verification point library containing known control points and historical stable points is constructed, and an initial confidence level is assigned to each candidate verification point in the candidate verification point library, including: Data on known control points within the target survey area are extracted from the multi-source information, wherein the data on known control points includes the coordinate results, grade attributes, and positional stability records of each known control point. Historical measurement results data are extracted from the multi-source information, and historical measurement points that meet the preset stability index are selected as historical stable points. The known control points and the historical stable points are integrated, and duplicate and conflicting points are eliminated to construct a candidate verification point library covering the target measurement area. Collect observation data from multiple periods of historical measurements for each candidate verification point, calculate the dispersion of observation results in each period, and determine the point stability characteristic value based on the dispersion. Extract the measurement area quality zoning map attributes of the area where each candidate verification point is located, and determine the environmental risk impact coefficient based on the quality level of the measurement conditions in the area. Obtain the historical observation condition records of each candidate verification point in the historical measurement, and determine the observation quality characteristic value based on the observation condition records, wherein the observation condition records include the average position accuracy attenuation factor and the average signal-to-noise ratio; Collect on-site survey information for each candidate verification point, and determine the recoverability feature value of the point based on the on-site survey information. The on-site survey information includes the integrity of the point markers, accessibility, and identifiability. The baseline weight of each candidate verification point is determined based on its type attribute, wherein the type attribute includes high-level known control points, same-level known control points, and historically stable points; The location stability characteristic value, environmental risk impact coefficient, observation quality characteristic value, and location recoverability characteristic value are normalized respectively. The normalized characteristic values ​​are then weighted and fused to generate the confidence correction coefficient for each candidate verification point. The baseline weight of each candidate verification point is compensated and corrected to generate the initial confidence level for each candidate verification point.

4. The method for selecting BeiDou RTK verification points based on multi-source information fusion according to claim 1, characterized in that, Obtain the job characteristic parameters within the historical measurement time window, including: The measurement accuracy index is calculated based on the plane coordinate deviation of each measurement point within the historical measurement time window; The fixed solution acquisition rate is calculated based on the proportion of fixed solutions obtained at each measurement point within the historical measurement time window. The average duration of continuous operations is calculated based on the duration of each continuous operation within the historical measurement time window; The average position accuracy attenuation factor is calculated based on the position accuracy attenuation factor of each measurement point within the historical measurement time window. The measurement accuracy index, fixed solution acquisition rate, average continuous operation time, and average position accuracy attenuation factor are used as operation characteristic parameters.

5. The method for selecting BeiDou RTK verification points based on multi-source information fusion according to claim 4, characterized in that, Based on the aforementioned job characteristic parameters, a comprehensive intensity compensation coefficient for the verification requirement intensity of the current job is generated, including: The ratio of the preset benchmark measurement accuracy index to the measurement accuracy index is used as the first demand intensity compensation coefficient. The ratio of the preset benchmark fixed solution acquisition rate to the fixed solution acquisition rate is used as the second demand intensity compensation coefficient. The ratio of the average continuous working time to the average continuous working time of the preset benchmark is used as the third demand intensity compensation coefficient. The ratio of the average position accuracy attenuation factor to the preset benchmark average position accuracy attenuation factor is used as the fourth demand intensity compensation coefficient. The first demand intensity compensation coefficient, the second demand intensity compensation coefficient, the third demand intensity compensation coefficient, and the fourth demand intensity compensation coefficient are weighted and fused to obtain the comprehensive intensity compensation coefficient for verifying the demand intensity.

6. The method for selecting BeiDou RTK verification points based on multi-source information fusion according to claim 5, characterized in that, Based on the comprehensive strength compensation coefficient, the number of preset benchmark verification points is adjusted to determine the total number of adaptable verification points required for the current operation, including: The number of preset benchmark verification points is determined according to the type of operation. The comprehensive strength compensation coefficient is multiplied by the number of benchmark verification points and rounded up to obtain the total number of theoretical verification points for the current operation. The total number of theoretical verification points is adjusted by applying preset quantity constraints to obtain the total number of suitable verification points required for the current operation. The quantity constraints include a minimum verification point quantity guarantee threshold and a maximum verification point quantity limit threshold.

7. The method for selecting BeiDou RTK verification points based on multi-source information fusion according to claim 1, characterized in that, Using the candidate verification point library as the optimization space, the iterative optimization of the verification point distribution scheme is performed based on a preset multi-objective optimization function, the survey area quality zoning map, the total number of suitable verification points, and the initial confidence of the candidate verification points, including: Based on the total number of adaptive verification points and the area proportion and risk weight of each quality level region in the measurement area quality zoning map, the proportion of verification points selected from the measurement condition good area, measurement condition medium area and measurement condition poor area are determined respectively. Among them, the measurement condition poor area is given a risk priority weight so that the number of its verification points is not less than the preset minimum poor area verification point proportion threshold. Based on the ratio of the number of verification points and the initial confidence level of the candidate verification points, a corresponding number of candidate verification points are selected from each quality level region to generate several initial verification point distribution schemes as the initial population for optimization. Using the candidate verification point library as the optimization space, an intelligent optimization algorithm is used to iteratively optimize the distribution scheme of verification points in the initial population. The scheme quality coefficient of each verification point distribution scheme is calculated according to the preset multi-objective optimization function. The superior schemes are selected according to the high and low scheme quality coefficients and crossover and mutation operations are performed to generate new verification point distribution schemes. Repeatedly perform iterative optimization until the iteration termination condition is met, and take the verification point distribution scheme with the highest scheme quality coefficient at the time of iteration termination as the optimal verification point distribution scheme. Specifically, a corresponding number of candidate verification points are selected from each quality level region to generate several initial verification point distribution schemes, including: From the set of candidate verification points corresponding to the poor measurement conditions, the required number of candidate verification points are forcibly selected in descending order of initial confidence level to ensure the reliability of verification points in high-risk areas. From the set of candidate verification points corresponding to the measurement conditions, a weighted random sampling is performed using the initial confidence level of each candidate verification point as the weight, and the required number of candidate verification points are selected. Among them, the higher the confidence level, the greater the probability of the point being selected. From the set of candidate verification points corresponding to the optimal measurement conditions area, the optimal measurement conditions area is divided into several sub-regions using a spatial grid partitioning method. Uniform random sampling is performed in each sub-region until the required number of candidate verification points are selected, ensuring the spatial distribution uniformity of verification points in low-risk areas. The candidate verification points selected from each quality level region are merged to form a complete candidate verification point distribution scheme. The above selection process is repeated several times to generate several different candidate verification point distribution schemes.

8. The method for selecting BeiDou RTK verification points based on multi-source information fusion according to claim 7, characterized in that, The preset multi-objective optimization function includes spatial distribution uniformity objective, regional coverage integrity objective, point reliability objective, temporal distribution balance objective, and risk area priority objective. Among them, the spatial distribution uniformity objective is used to evaluate the degree of distribution uniformity of each verification point in the test area. By dividing the target test area into Thiessen polygons, the coefficient of variation of the area of ​​each Thiessen polygon is calculated, and the reciprocal of the coefficient of variation is used as the quantification value of spatial distribution uniformity. The regional coverage integrity target is used to evaluate the coverage of each verification point in areas with different measurement conditions. The ratio of the actual number of verification points to the target number in areas with different quality levels is calculated, and the minimum value of the ratio is taken as the quantitative value of regional coverage integrity. The reliability target of the selected verification points is used to evaluate the sum of the confidence levels of the selected verification points. The initial confidence levels of each verification point in the scheme are added together to obtain the quantitative value of the reliability of the points. The time distribution balance target is used to evaluate the degree of distribution balance of each verification point on the operation time axis. The operation time is divided into several time intervals, the distribution frequency of the verification points in each time interval is counted, the information entropy of the distribution frequency is calculated, and the information entropy is used as the quantitative value of time distribution balance. The risk area priority target is used to assess the proportion of high-risk areas in the verification points, calculate the proportion of verification points in areas with poor measurement conditions to the total number of verification points in the plan, and use the proportion as the quantitative value of risk area priority. Each target quantification value is normalized, and the normalized quantification values ​​are weighted and summed to obtain the scheme quality coefficient of each verification point distribution scheme.

9. The method for selecting BeiDou RTK verification points based on multi-source information fusion according to claim 7, characterized in that, The distribution scheme of validation points in the initial population is iteratively optimized using an intelligent optimization algorithm, including: Each candidate validation point distribution scheme is encoded as an individual, a population containing a preset number of individuals is initialized, the scheme quality coefficient of each individual in the population is calculated according to the preset multi-objective optimization function, and a preset number of excellent individuals are selected from the current population in descending order of scheme quality coefficient to enter the next generation population. Selected superior individuals are subjected to pairwise crossover operations to generate new offspring individuals with a preset crossover probability; The generated offspring individuals are subjected to mutation operations, and the individuals are adjusted according to a preset mutation probability; The offspring individuals are merged with the parent individuals to form a new generation of population, and the scheme quality coefficient of each individual in the population is recalculated. Repeat the above steps until the iteration termination condition is met. Decode the individual with the highest scheme quality coefficient in the population at the time of iteration termination to obtain the corresponding list of verification point numbers, which is used as the optimal verification point distribution scheme. The iteration termination condition includes the convergence of the scheme quality coefficient or reaching the preset maximum number of iterations.

10. A BeiDou RTK verification point selection system based on multi-source information fusion, characterized in that, The system is used to implement the BeiDou RTK verification point selection method for multi-source information fusion as described in any one of claims 1-9, the system comprising: The survey area environmental assessment module is used to conduct environmental impact assessment of the target survey area based on multi-source information of the target survey area, and generate a survey area quality zoning map that reflects the advantages and disadvantages of measurement conditions at different locations; The candidate point library construction module is used to construct a candidate verification point library containing known control points and historical stable points based on the multi-source information, and to assign an initial confidence level to each candidate verification point in the candidate verification point library. The compensation coefficient generation module is used to obtain the operation characteristic parameters within the historical measurement time window, and generate a comprehensive intensity compensation coefficient for the verification requirement intensity of the current operation based on the operation characteristic parameters. The adaptation point determination module is used to adjust the number of preset benchmark verification points based on the comprehensive strength compensation coefficient, and determine the total number of adaptation verification points required for the current operation. The point location optimization module is used to iteratively optimize the distribution scheme of verification points based on the candidate verification point library as the optimization space, the quality zoning map of the test area, the total number of suitable verification points, and the initial confidence of the candidate verification points, and generate the optimal verification point distribution scheme for verification point selection.

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