Control network constraint adjustment method and system based on RANSAC data selection algorithm

Abstract

The invention provides a control network constraint adjustment method and system based on an RANSAC data selection algorithm, and the method comprises the steps: obtaining known starting data of a control network after determining that free network adjustment is completed; performing constraint adjustment pre-operation based on the known starting data by using an RANSAC (Random Sample Consensus) algorithm, and selecting target starting data in combination with an operation result; and the actual constraint adjustment operation of the control network is implemented based on the selected target starting data. By adopting the scheme, the known starting data is utilized to carry out constraint adjustment on the GNSS control network, the starting data is obtained through calculation and is compared with the known data, gross error values can be efficiently and quickly eliminated, and the starting data is quickly and accurately determined preferentially while engineering requirements are met.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering survey optimization, and in particular to a control network constraint adjustment method and system based on a RANSAC data selection algorithm. Background Art

[0002] The survey control network is composed of a series of points on the ground (called survey control points). The control points are connected by observations such as side length, direction, height difference or GNSS baseline to form a network. The spatial position of the point can be calculated by a certain method through the coordinates of the known points and the connection between the points. The survey control network can be applied to the measurement of data of different scales or projects (such as tunnels, bridges, large factory areas and nuclear power plants).

[0003] GNSS control network is a measurement control network established by using GNSS (Global Navigation Satellite System) satellite positioning measurement technology and methods. It is one of the mainstream methods of control measurement. The service objects of GNSS control network can be divided into: national or regional high-precision GNSS control network and local small-scale GNSS control network. The former has a distance between adjacent points ranging from hundreds of kilometers to thousands of kilometers. Its main task is to establish a high-precision three-dimensional national geodetic control benchmark to determine the conversion parameters of national geodetic coordinates or study the deformation laws of the earth's regions; the latter has a distance between adjacent points ranging from thousands of kilometers to tens of kilometers. It mainly establishes control benchmarks for cities, mining areas or other engineering construction, and directly serves urban measurement, mining measurement, land survey, and engineering measurement.

[0004] In engineering applications, the quality of the GNSS control network's initial calculation data directly determines the quality of the control network's final calculation results. In the prior art, when performing GNSS control measurements, the existing initial calculation data is directly used for control measurements without analysis, which can easily lead to errors in the results of the entire control network, and then cause all subsequent topographic measurements or field data collection to be incorrect, leading to incorrect construction guidance and causing irreparable losses.

[0005] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0006] To solve all or part of the above problems, the present invention provides a control network constraint adjustment method based on the RANSAC data selection algorithm. In one embodiment, the method includes:

[0007] After step S1 and determining that the free network adjustment is completed, obtain the known initial calculation data of the control network and record them in a list form;

[0008] Step S2, using RANSAC algorithm to perform constrained adjustment pre-calculation based on each known initial calculation data, and selecting target initial calculation data in combination with the calculation results;

[0009] Step S3, implementing actual constrained adjustment calculation of the control network based on the selected target starting data, wherein the control network is a GNSS control network.

[0010] Preferably, in one embodiment, in step S2, each data object in the list of known starting data is used as pre-calculation starting data to perform constrained adjustment pre-calculation to obtain the calculated coordinates of other starting data points; the calculated coordinates of other starting data points obtained by the constrained adjustment pre-calculation and the known true coordinates are analyzed for matching; based on the analysis results, the known starting data list is updated and target starting data that meets the requirements are selected.

[0011] Furthermore, in an optional embodiment, step S2 is performed according to the following logic:

[0012] Step A1, randomly selecting an object data object from a known starting data list as pre-calculation starting data;

[0013] Step A2, based on the pre-calculation starting data, taking other starting data as unknown points to perform constrained adjustment pre-calculation, and calculating the coordinates of other starting data points in the network;

[0014] Step A3, analyzing the degree of conformity between the distribution of coordinates of other starting data points calculated from the current starting data and the distribution of corresponding real known coordinates;

[0015] Step A4: Determine the available attributes of the current starting data according to the analysis results;

[0016] Step A5, updating the list of known starting data according to the available attributes of the starting data;

[0017] Step A6: Based on the updated known starting data list, repeat the above steps until the available attributes of all data objects in the list are obtained;

[0018] Step A7: Select target starting data from the updated known starting data list according to available attributes and setting requirements.

[0019] Specifically, in one embodiment, step S2 includes:

[0020] The process of analyzing the degree of conformity between the distribution of coordinates of other starting data points calculated from the current starting data and the distribution of corresponding real known coordinates includes:

[0021] The calculated starting data point coordinates and the real known coordinates are represented by the same applicable coordinates, and the plane difference and elevation difference between the calculated coordinates of each starting data and the real coordinates are calculated;

[0022] Taking the distance between the calculation starting data point and the pre-calculation starting data point as the constraint item, determine whether the plane difference and elevation difference of each calculation starting data point meet the requirements;

[0023] When the plane gap meets the requirements or both meet the requirements, it indicates that the degree of conformity of the current calculated data points is qualified.

[0024] Furthermore, in one embodiment, the process of determining whether the plane difference and elevation difference of each calculation starting data point meet the requirements includes:

[0025] If the plane difference S between the starting data point and the actual known coordinates is calculated hi If the following formula is satisfied, it means that the plane gap meets the requirements, otherwise it does not meet the requirements;

[0026] S hi -(a*S oi +b)≤d h

[0027] in, S hi = is the plane difference between the i-th calculation starting data point and the corresponding real known coordinates, x i is the horizontal coordinate of the i-th calculation starting data point, y i is the ordinate of the i-th calculation starting point, x' i is the horizontal coordinate corresponding to the real known coordinate, y' i is the ordinate corresponding to the real known coordinate, S oi is the straight-line distance between the i-th calculation starting data point and the pre-calculation starting data point, a and b are linear parameters, d h is the plane error constraint threshold set;

[0028] If the height difference S between the starting data point and the actual known coordinates is calculated vi If the following formula is satisfied, it means that the elevation difference meets the requirement, otherwise it does not meet the requirement;

[0029] S vi- (a*S oi +b)≤d v

[0030] Among them, S vi =|z i -z' i |, z i is the vertical coordinate of the i-th calculation starting data point, z' iis the vertical coordinate corresponding to the real known coordinate, S vi is the elevation difference between the i-th calculation starting data point and the real known coordinates, S oi is the straight-line distance between the i-th calculation starting data point and the pre-calculation starting data point, d h is the set elevation error constraint threshold. Preferably, in one embodiment, in step S2, it includes:

[0031] The method according to claim 3, characterized in that, in step A5, it includes:

[0032] After the pre-calculation, the degree of conformity of each starting data point corresponding to the current pre-calculation starting data is analyzed, and the points with unqualified conformity are marked. If the number of marked points exceeds the set threshold, the pre-calculation starting data is cleared from the starting data list and the starting data list is updated.

[0033] In one embodiment, step A7 includes:

[0034] Based on each starting data in the updated list, the plane gap and elevation gap corresponding to the coordinate points of the multiple starting data corresponding to the calculation are calculated respectively, and the comprehensive conformity index is determined by combining the weights. According to the needs, one or more starting data with comprehensive conformity indexes that meet the set requirements are selected as the target starting data.

[0035] In an optional embodiment, the comprehensive compliance index is determined according to the following formula:

[0036]

[0037] In the formula, P is the comprehensive conformity index, S hi is the plane difference between the i-th calculation starting data point and the real known coordinates, n is the number of calculation data points obtained by pre-calculation, C h is the weight value corresponding to the plane gap, S vi is the elevation difference between the i-th calculation starting data point and the real known coordinates, C v is the weight value corresponding to the elevation difference.

[0038] Based on other aspects of the method described in any one or more of the above embodiments, the present invention further provides a storage medium storing program codes that can implement the method described in any one or more of the above embodiments.

[0039] Based on the application aspects of the method described in any one or more of the above embodiments, the present invention also provides a control network constrained adjustment system based on a RANSAC data selection algorithm, which executes the method described in any one or more of the above embodiments.

[0040] Compared with the closest prior art, the present invention also has the following beneficial effects:

[0041] The present invention provides a control network constraint adjustment method and system based on the RANSAC data selection algorithm. After determining that the free network adjustment is completed, the RANSAC algorithm is first used to perform constraint adjustment pre-calculation based on each known starting data, and the target starting data is selected in combination with the calculation results; and then the actual constraint adjustment calculation of the control network is implemented based on the selected target starting data. With this scheme, the known starting data is used to constrain the GNSS control network, and the starting data is calculated and compared with the known data, so that the gross error value can be eliminated efficiently and quickly, and high-quality starting data can be determined quickly and accurately while meeting the engineering needs.

[0042] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0044] Figure 1 It is a flow chart of a control network constraint adjustment method based on a RANSAC data selection algorithm provided by an embodiment of the present invention;

[0045] Figure 2 It is a schematic diagram of the initial data selection process of the control network constraint adjustment method based on the RANSAC data selection algorithm provided by an embodiment of the present invention;

[0046] Figure 3 It is a structural diagram of a control network constraint adjustment system based on a RANSAC data selection algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will describe the implementation methods of the present invention in detail in conjunction with the accompanying drawings and embodiments, so that the implementers of the present invention can fully understand how the present invention applies technical means to solve technical problems and achieve the implementation process of technical effects and implement the present invention specifically according to the above implementation process. It should be noted that as long as there is no conflict, the various embodiments and various features of the embodiments in the present invention can be combined with each other, and the technical solutions formed are all within the protection scope of the present invention.

[0048] Although the flowcharts describe the operations as sequential processes, many of the operations may be performed in parallel, concurrently, or simultaneously. The order of the operations may be rearranged. A process may be terminated when its operations are completed, but may also have additional steps not included in the accompanying drawings. A process may correspond to a method, function, procedure, subroutine, subprogram, etc.

[0049] Computer devices include user devices and network devices. Among them, user devices or clients include but are not limited to computers, smart phones, PDAs, etc.; network devices include but are not limited to a single network server, a server group consisting of multiple network servers, or a cloud composed of a large number of computers or network servers based on cloud computing. Computer devices can be operated alone to implement the present invention, or they can be connected to the network and implement the present invention through interactive operations with other computer devices in the network. The network where the computer device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, VPN network, etc.

[0050] The terms "first", "second", etc. may be used herein to describe various units, but these units should not be limited by these terms, and these terms are used only to distinguish one unit from another unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items. When a unit is referred to as being "connected" or "coupled" to another unit, it can be directly connected or coupled to the other unit, or there can be intermediate units.

[0051] The terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms "one", "one" and "item" used herein are also intended to include plural numbers. It should also be understood that the terms "include" and / or "comprise" used herein specify the existence of stated features, integers, steps, operations, units and / or components, without excluding the existence or addition of one or more other features, integers, steps, operations, units, components and / or combinations thereof.

[0052] GNSS control network is a measurement control network established by using GNSS (Global Navigation Satellite System) satellite positioning measurement technology and methods. It is one of the mainstream methods of control measurement. The service objects of GNSS control network can be divided into: national or regional high-precision GNSS control network and local small-scale GNSS control network. The former has a distance between adjacent points ranging from hundreds of kilometers to thousands of kilometers. Its main task is to establish a high-precision three-dimensional national geodetic control benchmark to determine the conversion parameters of national geodetic coordinates or study the deformation laws of the earth's regions; the latter has a distance between adjacent points ranging from thousands of kilometers to tens of kilometers. It mainly establishes control benchmarks for cities, mining areas or other engineering construction, and directly serves urban measurement, mining measurement, land survey, and engineering measurement.

[0053] In engineering applications, the quality of the GNSS control network's initial calculation data directly determines the quality of the control network's final calculation results. In the prior art, when GNSS control measurement is performed, if the existing initial calculation data is used without analysis, such as the initial calculation value containing erroneous information, the results of the entire control network will be incorrect, and will directly affect the correctness of subsequent topographic measurement or field data collection results, thereby causing errors in the measurement data of design, construction and management.

[0054] The researchers of the present invention consider using a reasonable algorithm to select the starting data before the adjustment operation. The RANSAC algorithm first uses the minimum necessary observation value set obtained by random sampling to establish a target model, and then compares the observation value set obtained by this model with the known data. If the calculated observation values ​​are consistent with the known data, it can be considered that these consistent observation values ​​are valid and can be used for the final constrained adjustment.

[0055] In order to solve the above problems, the present invention provides a control network constrained adjustment method and system based on the RANSAC data selection algorithm, which uses the RANSAC algorithm to perform constrained adjustment pre-calculation based on each known starting data, and selects the target starting data in combination with the calculation results. Through the RANSAC algorithm, the starting data are randomly selected for constrained adjustment multiple times, and the comparison results of the existing starting data and the known data can be obtained. The result is a quantitative result that can intuitively display the difference, thereby making an evaluation for accuracy and reliability judgment; using the known starting data, the free network is constrained. The degree of conformity between different starting data can be judged, and the starting data can be selected preferentially while meeting the engineering needs.

[0056] Next, the detailed process of the method of the embodiment of the present invention is described in detail based on the accompanying drawings. The steps shown in the flowchart of the accompanying drawings can be executed in a computer system including a set of computer executable instructions. Although the logical order of each step is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0057] Embodiment 1

[0058] The processing of GNSS control network data generally includes data collection, data preprocessing, baseline solution, unconstrained adjustment, constrained adjustment, and output. Before free network adjustment, the main purpose is to obtain observation data and determine the observation quality of the data, while constrained adjustment is to obtain the desired data results and provide a basis for the next step. The basis of constrained adjustment is the starting data.

[0059] In the unconstrained adjustment link, the observations used in the adjustment are all baseline vectors. The adjustment that only introduces the coordinates of a starting point that provides position reference information is called the minimum constrained adjustment. For example, the three-dimensional coordinates of a known national high-level geodetic control point can be introduced as the starting point of the minimum constrained adjustment. If no external starting data is introduced, it is called a free network adjustment, that is, the coordinates of a certain point in the observation value are used.

[0060] A commonality between them is that they do not introduce initial data that will change the scale and orientation of the GNSS network, and these initial data will affect the geometric shape of the network. Therefore, the two types of adjustments, the minimum constraint adjustment and the free network adjustment, are sometimes collectively referred to as unconstrained adjustment. Since in unconstrained adjustment, the geometric shape of the network is completely determined by the baseline vector and has nothing to do with the external initial data, the unconstrained adjustment result actually depends entirely on the baseline vector. Therefore, the quality of the unconstrained adjustment result and the size of the geometric inconsistency between the observations reflected in the adjustment process are the true reflection of the quality of the observations themselves. Due to this feature of the unconstrained adjustment of the GNSS network, on the one hand, the network accuracy index obtained by the unconstrained adjustment is used as an indicator to measure its internal compliance accuracy. On the other hand, the quality of the observations reflected by the unconstrained adjustment is used as a basis for judging the gross error value of the observation data and making corresponding processing.

[0061] After the unconstrained adjustment is qualified, the constrained adjustment step is further executed, which will introduce known starting data that will change the scale and orientation of the network for adjustment.

[0062] Usually, the datum and coordinate system of the adjustment are selected first, and then the coordinates of the known points used for starting calculation are introduced, and the known points are used as fixed starting points for constrained adjustment calculations. The constrained adjustment results can be quality checked later. The main indicators include baseline vector correction number, the difference between the constrained adjustment baseline vector correction number and the unconstrained baseline vector correction number of the same name, as well as point position mean error, adjacent point mean error and relative mean error, baseline edge direction mean error, weakest edge relative mean error, etc.

[0063] Among them, in the process of using known points as fixed starting points for constrained adjustment calculations, if the existing starting data is used without analysis, such as bringing in starting values ​​containing erroneous information, it will cause errors in the results of the entire control network and directly affect the correctness of subsequent topographic measurements or field data collection results, thereby leading to errors in the measurement data of design, construction and management, causing irreparable losses.

[0064] Based on this, the embodiment of the present invention provides a control network constraint adjustment method based on the RANSAC data selection algorithm. Figure 1The flowchart of the control network constraint adjustment method based on the RANSAC data selection algorithm provided in the first embodiment of the present invention is shown. Figure 1 It can be seen that the method includes the following steps.

[0065] After step S1 and determining that the free network adjustment is completed, obtain the known initial calculation data of the control network and record them in a list form;

[0066] Step S2, using RANSAC algorithm to perform constrained adjustment pre-calculation based on each known initial calculation data, and selecting target initial calculation data in combination with the calculation results;

[0067] Step S3, implementing actual constrained adjustment calculation of the control network based on the selected target starting data, wherein the control network is a GNSS control network.

[0068] In an optional embodiment, in the step S2, the following operations are included:

[0069] Use each data object in the list of known starting data as pre-calculation starting data to perform constrained adjustment pre-calculation to obtain the calculated coordinates of other starting data points;

[0070] Perform matching analysis on the calculated coordinates of other starting data points obtained by constraint adjustment pre-calculation and the known true coordinates;

[0071] Based on the analysis results, the list of known starting data is updated and target starting data that meets the requirements is selected.

[0072] Further, Figure 2 FIG. 4 is a schematic diagram showing a starting data selection process of a control network constraint adjustment method based on a RANSAC data selection algorithm provided in an embodiment of the present invention. Figure 2 As shown, in an optional embodiment, it is set to perform step S2 according to the following logic:

[0073] Step A1, randomly selecting an object data object from a known starting data list as pre-calculation starting data;

[0074] Step A2, based on the pre-calculation starting data, taking other starting data as unknown points to perform constrained adjustment pre-calculation, and calculating the coordinates of other starting data points in the network;

[0075] Step A3, analyzing the degree of conformity between the distribution of coordinates of other starting data points calculated from the current starting data and the distribution of corresponding real known coordinates;

[0076] Step A4: Determine the available attributes of the current starting data according to the analysis results;

[0077] Step A5, updating the list of known starting data according to the available attributes of the starting data;

[0078] Step A6: Based on the updated known starting data list, repeat the above steps until the available attributes of all data objects in the list are obtained;

[0079] Step A7: Select the target starting data from the updated known starting data list according to the available attributes and setting requirements.

[0080] The accuracy and reliability evaluation of the initial data are important determining operations for the quality of the constrained adjustment. In the above embodiment of the present invention, the specific application idea of ​​using the RANSAC algorithm to select the initial data in the GNSS control network processing is:

[0081] In the control network obtained by free network adjustment, a known starting data is randomly selected.

[0082] Through pre-calculation, the free network is constrained and adjusted, the coordinates of all remaining unknown points in the network are calculated, and the coordinates of the calculated points are analyzed to see if they match the corresponding known points; that is, the calculated known starting data coordinates are compared with their known true coordinates to check the distribution of each point coordinates. Based on the match, it can be analyzed whether the current starting data used as the basis for pre-calculation meets the accuracy requirements of the starting data or whether it needs to be eliminated.

[0083] Through such calculations for each starting data, we can determine which points have better accuracy and are highly consistent with other starting data, and select the starting data with good accuracy and high consistency as the target (official) starting data for the project.

[0084] Furthermore, in step S2, the process of analyzing the degree of conformity between the distribution of coordinates of other starting data points calculated from the current starting data and the distribution of corresponding real known coordinates includes:

[0085] The calculated starting data point coordinates and the real known coordinates are represented by the same applicable coordinates, and the plane difference and elevation difference between the calculated coordinates of each starting data and the real coordinates are calculated;

[0086] Taking the distance between the calculation starting data point and the pre-calculation starting data point as the constraint item, determine whether the plane difference and elevation difference of each calculation starting data point meet the requirements;

[0087] When the plane difference meets the requirements or both meet the requirements, it indicates that the degree of conformity of the current calculated data points is qualified. The method of this embodiment can accurately characterize the objective error degree between the calculated point coordinates and the real point coordinates.

[0088] In a preferred embodiment, the process of determining whether the plane difference and elevation difference of each calculation starting data point meet the requirements includes:

[0089] If the plane difference S between the starting data point and the actual known coordinates is calculated hi If the following formula is satisfied, it means that the plane gap meets the requirements, otherwise it does not meet the requirements;

[0090] S hi -(a*S oi +b)≤d h

[0091] in, S hi = is the plane difference between the i-th calculation starting data point and the corresponding real known coordinates, x i is the horizontal coordinate of the i-th calculation starting data point, y i is the ordinate of the i-th calculation starting point, x' i is the horizontal coordinate corresponding to the real known coordinate, y' i is the ordinate corresponding to the real known coordinate, S oi is the straight-line distance between the i-th calculation starting data point and the pre-calculation starting data point, a and b are linear parameters, d h is the plane error constraint threshold set;

[0092] If the height difference S between the starting data point and the actual known coordinates is calculated vi If the following formula is satisfied, it means that the elevation difference meets the requirement, otherwise it does not meet the requirement;

[0093] S vi- (a*S oi +b)≤d v

[0094] Among them, S vi =|z i -z' i |, z i is the vertical coordinate of the i-th calculation starting data point, z' i is the vertical coordinate corresponding to the real known coordinate, S vi is the elevation difference between the i-th calculation starting data point and the real known coordinates, S oi is the straight-line distance between the i-th calculation starting data point and the pre-calculation starting data point, d h It is the set elevation error constraint threshold.

[0095] In an optional embodiment, the S oi The plane straight line distance between the i-th calculation starting data point and the pre-calculation starting data point can be obtained according to the following formula:

[0096]

[0097] In the formula, x iis the horizontal coordinate of the i-th calculation starting data point, y i is the ordinate of the i-th calculation starting point, x o is the horizontal coordinate of the pre-calculation starting data point, y o It is the vertical coordinate of the data point where the pre-calculation starts.

[0098] Optionally, in one embodiment, after a single round of pre-calculation, the degree of conformity of each starting data point corresponding to the current pre-calculation starting data is further analyzed, and points with unqualified degrees of conformity are marked. If the number of marked points exceeds a set threshold, the pre-calculation starting data is cleared from the starting data list and the starting data list is updated.

[0099] Preferably, in one embodiment, step A7 includes: based on each starting data in the updated list, respectively calculating the average of the plane difference and the elevation difference corresponding to the coordinate points of the multiple calculated starting data corresponding to the operation, determining the comprehensive consistency index in combination with the weight, and selecting one or more starting data whose comprehensive consistency index meets the set requirements as the target starting data according to the needs.

[0100] In a preferred embodiment, the comprehensive compliance index can be determined according to the following formula:

[0101]

[0102] In the formula, P is the comprehensive conformity index, S hi is the plane difference between the i-th calculation starting data point and the real known coordinates, n is the number of calculation data points obtained by pre-calculation, C h is the weight value corresponding to the plane gap, S vi is the elevation difference between the i-th calculation starting data point and the real known coordinates, C v is the weight value corresponding to the elevation difference.

[0103] This operation can timely clear the starting data points that obviously do not meet the requirements. For the subsequent pre-calculation constrained adjustment process, calculations will no longer be performed on the cleared points, which will improve the calculation efficiency to a certain extent. In addition, if the number of updated starting data objects is small, the calculation pressure in the subsequent analysis of the degree of conformity and calculation of the comprehensive degree of conformity index will be relatively reduced, further improving the timeliness of the selection of target starting data.

[0104] Among them, in a preferred embodiment, the corresponding distances of multiple coordinate points obtained by calculation are first summed and then divided by the number of coordinate points to obtain the distance mean as a comprehensive matching degree indicator.

[0105] The present invention ensures data quality by using the RANSAC algorithm to select the accuracy and reliability of the GNSS control network initial calculation data; after selection, the existing initial calculation data can be used to the greatest extent, so that the data quality is consistent with the existing information, providing a basis for engineering services; after verification by engineering practice, the GNSS control network initial calculation data selection technology based on the RANSAC algorithm has reached the domestic leading level.

[0106] By adopting the technical means in the embodiments of the present invention, quantitative data for comparing the degree of consistency between the initial calculation data can be provided, providing a basis for evaluating the accuracy and reliability of the initial calculation data; the present invention uses known initial calculation data to constrain the GNSS control network, calculates the initial calculation data and compares it with the known data, which can efficiently and quickly eliminate gross errors; the present invention uses known initial calculation data to constrain the free network, which can determine the degree of consistency between different initial calculation data, and select the initial calculation data on a preferential basis while meeting the engineering needs.

[0107] For the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the order of the actions described, because according to the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0108] It should be pointed out that, in other embodiments of the present invention, the method can also obtain a new control network constraint adjustment method by combining one or several of the above embodiments to achieve high-quality optimization of the control network adjustment operation.

[0109] It should be noted that, based on the method in any one or more of the above-mentioned embodiments of the present invention, the present invention also provides a storage medium, on which is stored a program code that can implement the method as described in any one or more of the above-mentioned embodiments, and when the code is executed by the operating system, the control network constraint adjustment method based on the RANSAC data selection algorithm as described above can be implemented.

[0110] Embodiment 2

[0111] The method is described in detail in the above-mentioned embodiments disclosed by the present invention. The method of the present invention can be realized by various devices or systems. Therefore, based on other aspects of the method described in any one or more of the above-mentioned embodiments, the present invention also provides a control network constraint adjustment system based on the RANSAC data selection algorithm, which is used to execute the control network constraint adjustment method based on the RANSAC data selection algorithm described in any one or more of the above-mentioned embodiments. Specific embodiments are given below for detailed description.

[0112] Specifically, Figure 3 FIG. 4 shows a schematic diagram of the structure of a control network constraint adjustment system based on a RANSAC data selection algorithm provided in an embodiment of the present invention. Figure 3 As shown, the system includes:

[0113] A starting data recording module, which is configured to obtain known starting data of the control network and record them in a list form after the free network adjustment is completed;

[0114] The initial calculation data selection module is configured to use the RANSAC algorithm to perform constrained adjustment pre-calculation based on each known initial calculation data, and select the target initial calculation data based on the calculation results;

[0115] The adjustment implementation module is configured to implement actual constrained adjustment operations of the control network based on selected target starting data, wherein the control network is a GNSS control network.

[0116] In a preferred embodiment, the starting data selection module is configured as follows: using each data object in the list of known starting data as pre-calculation starting data to perform constrained adjustment pre-calculation to obtain the calculated coordinates of other starting data points; performing a matching analysis on the calculated coordinates of other starting data points obtained by the constrained adjustment pre-calculation and the known true coordinates; updating the known starting data list based on the analysis results and selecting target starting data that meets the requirements.

[0117] Furthermore, in an optional embodiment, the starting data selection module is configured to perform the following operations:

[0118] Step A1, randomly selecting an object data object from a known starting data list as pre-calculation starting data;

[0119] Step A2, based on the pre-calculation starting data, taking other starting data as unknown points to perform constrained adjustment pre-calculation, and calculating the coordinates of other starting data points in the network;

[0120] Step A3, analyzing the degree of conformity between the distribution of coordinates of other starting data points calculated from the current starting data and the distribution of corresponding real known coordinates;

[0121] Step A4: Determine the available attributes of the current starting data according to the analysis results;

[0122] Step A5, updating the list of known starting data according to the available attributes of the starting data;

[0123] Step A6: Based on the updated known starting data list, repeat the above steps until the available attributes of all data objects in the list are obtained;

[0124] Step A7: Select target starting data from the updated known starting data list according to available attributes and setting requirements.

[0125] Optionally, in one embodiment, the starting data selection module is specifically configured to analyze the degree of conformity between the distribution of coordinates of other starting data points calculated by the current starting data and the distribution of corresponding real known coordinates according to the following operations:

[0126] The calculated starting data point coordinates and the real known coordinates are represented by the same applicable coordinates, and the plane difference and elevation difference between the calculated coordinates of each starting data and the real coordinates are calculated;

[0127] Taking the distance between the calculation starting data point and the pre-calculation starting data point as the constraint item, determine whether the plane difference and elevation difference of each calculation starting data point meet the requirements;

[0128] When the plane gap meets the requirements or both meet the requirements, it indicates that the degree of conformity of the current calculated data points is qualified.

[0129] Furthermore, in one embodiment, the starting data selection module is configured to determine whether the plane difference and elevation difference of each calculation starting data point meet the requirements in the following manner:

[0130] If the plane difference S between the starting data point and the actual known coordinates is calculated hi If the following formula is satisfied, it means that the plane gap meets the requirements, otherwise it does not meet the requirements;

[0131] S hi -(a*S oi +b)≤d h

[0132] in, S hi = is the plane difference between the i-th calculation starting data point and the corresponding real known coordinates, x i is the horizontal coordinate of the i-th calculation starting data point, y i is the ordinate of the i-th calculation starting point, x' i is the horizontal coordinate corresponding to the real known coordinate, y' i is the ordinate corresponding to the real known coordinate, S oi is the straight-line distance between the i-th calculation starting data point and the pre-calculation starting data point, a and b are linear parameters, d h is the plane error constraint threshold set;

[0133] If the height difference S between the starting data point and the actual known coordinates is calculated vi If the following formula is satisfied, it means that the elevation difference meets the requirement, otherwise it does not meet the requirement;

[0134] S vi- (a*S oi +b)≤d v

[0135] Among them, S vi =|z i -z' i |, z i is the vertical coordinate of the i-th calculation starting data point, z' i is the vertical coordinate corresponding to the real known coordinate, S vi is the elevation difference between the i-th calculation starting data point and the real known coordinates, S oi is the straight-line distance between the i-th calculation starting data point and the pre-calculation starting data point, d h It is the set elevation error constraint threshold.

[0136] After the pre-calculation, further, in one embodiment, the starting data selection module analyzes the degree of conformity of each starting data point corresponding to the current pre-calculation starting data, marks the points with unqualified degrees of conformity, and if the number of marked points exceeds the set threshold, the pre-calculation starting data is cleared from the starting data list, and the starting data list is updated.

[0137] In an optional embodiment, the starting data selection module is set to calculate the average of the plane difference and elevation difference corresponding to the coordinate points of the multiple calculated starting data corresponding to the respective starting data in the updated list, and determine the comprehensive consistency index based on the weight, and select one or more starting data whose comprehensive consistency index meets the set requirements as the target starting data according to the needs.

[0138] Among them, in a preferred embodiment, the starting data selection module is set to determine the comprehensive compliance index according to the following logic:

[0139]

[0140] In the formula, P is the comprehensive conformity index, S hi is the plane difference between the i-th calculation starting data point and the real known coordinates, n is the number of calculation data points obtained by pre-calculation, C h is the weight value corresponding to the plane gap, S vi is the elevation difference between the i-th calculation starting data point and the real known coordinates, C v is the weight value corresponding to the elevation difference.

[0141] In the control network constrained adjustment system based on the RANSAC data selection algorithm provided by the embodiment of the present invention, each module or unit structure can be operated independently or in combination according to actual data processing requirements and matching operation requirements to achieve corresponding technical effects.

[0142] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should be extended to equivalent substitutions of these features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0143] The "one embodiment" mentioned in the specification means that a particular feature, structure or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present invention. Therefore, the phrase "one embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0144] Although the embodiments disclosed in the present invention are as above, the above contents are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A control network constraint adjustment method based on RANSAC data selection algorithm, characterized in that: The method comprises: After step S1 and determining that the free network adjustment is completed, obtain the known initial calculation data of the control network and record them in a list form; Step S2, using RANSAC algorithm to perform constrained adjustment pre-calculation based on each known initial calculation data, and selecting target initial calculation data in combination with the calculation results; Step S3, implementing actual constrained adjustment calculation of the control network based on the selected target starting data, wherein the control network is a GNSS control network.

2. The method according to claim 1, characterized in that: In the step S2, it includes: using each data object in the list of known starting data as pre-calculation starting data to perform constrained adjustment pre-calculation to obtain the calculated coordinates of other starting data points; performing matching analysis on the calculated coordinates of other starting data points obtained by the constrained adjustment pre-calculation and the known real coordinates; updating the known starting data list based on the analysis results and selecting target starting data that meets the requirements.

3. The method according to claim 1 or 2, characterized in that: Step S2 is performed according to the following logic: Step A1, randomly selecting an object data object from a known starting data list as pre-calculation starting data; Step A2, based on the pre-calculation starting data, taking other starting data as unknown points to perform constrained adjustment pre-calculation, and calculating the coordinates of other starting data points in the network; Step A3, analyzing the degree of conformity between the distribution of coordinates of other starting data points calculated from the current starting data and the distribution of corresponding real known coordinates; Step A4: Determine the available attributes of the current starting data according to the analysis results; Step A5, updating the list of known starting data according to the available attributes of the starting data; Step A6: Based on the updated known starting data list, repeat the above steps until the available attributes of all data objects in the list are obtained; Step A7: Select target starting data from the updated known starting data list according to available attributes and setting requirements.

4. The method according to claim 1, characterized in that: In step S2, the process of analyzing the degree of conformity between the distribution of coordinates of other starting data points calculated from the current starting data and the distribution of corresponding real known coordinates includes: The calculated starting data point coordinates and the real known coordinates are represented by the same applicable coordinates, and the plane difference and elevation difference between the calculated coordinates of each starting data and the real coordinates are calculated; Taking the distance between the calculation starting data point and the pre-calculation starting data point as the constraint item, determine whether the plane difference and elevation difference of each calculation starting data point meet the requirements; When the plane gap meets the requirements or both meet the requirements, it indicates that the degree of conformity of the current calculated data points is qualified.

5. The method according to claim 4, characterized in that The process of judging whether the plane difference and elevation difference of each calculation starting data point meet the requirements includes: If the plane difference S between the starting data point and the actual known coordinates is calculated hi If the following formula is satisfied, it means that the plane gap meets the requirements, otherwise it does not meet the requirements; S hi -(a*S oi +b)≤d h in, S hi = is the plane difference between the i-th calculation starting data point and the corresponding real known coordinates, x i is the horizontal coordinate of the i-th calculation starting data point, y i is the ordinate of the i-th calculation starting point, x' i is the horizontal coordinate corresponding to the real known coordinate, y' i is the ordinate corresponding to the real known coordinate, S oi is the straight-line distance between the i-th calculation starting data point and the pre-calculation starting data point, a and b are linear parameters, d h is the plane error constraint threshold set; If the height difference S between the starting data point and the actual known coordinates is calculated vi If the following formula is satisfied, it means that the elevation difference meets the requirement, otherwise it does not meet the requirement; S vi- (a*S oi +b)≤d v Among them, S vi =|z i -z' i |, z i is the calculated vertical coordinate of the i-th calculation starting data point, z' i is the vertical coordinate corresponding to the real known coordinate, S vi is the elevation difference between the i-th calculation starting data point and the real known coordinates, S oi is the straight-line distance between the i-th calculation starting data point and the pre-calculation starting data point, d h It is the set elevation error constraint threshold.

6. The method according to claim 3, characterized in that In step S2, it includes: After the pre-calculation, the degree of conformity of each starting data point corresponding to the current pre-calculation starting data is analyzed, and the points with unqualified conformity are marked. If the number of marked points exceeds the set threshold, the pre-calculation starting data is cleared from the starting data list and the starting data list is updated.

7. The method according to claim 3, characterized in that In step S2, it includes: Based on each starting data in the updated list, the average values ​​are calculated respectively according to the plane difference and elevation difference corresponding to the multiple calculated starting data coordinate points corresponding to the operation, and the comprehensive consistency index is determined based on the weight. According to the needs, one or more starting data with comprehensive consistency index that meets the set requirements are selected as the target starting data.

8. The method according to claim 6, characterized in that Determine the comprehensive compliance index according to the following formula: In the formula, P is the comprehensive conformity index, S hi is the plane difference between the i-th calculation starting data point and the real known coordinates, n is the number of calculation data points obtained by pre-calculation, C h is the weight value corresponding to the plane gap, S vi is the elevation difference between the i-th calculation starting data point and the real known coordinates, C v is the weight value corresponding to the elevation difference.

9. A storage medium, characterized in that: The storage medium stores program codes that can implement the method as claimed in any one of claims 1 to 8.

10. A control network constraint adjustment system based on RANSAC data selection algorithm, characterized in that: The system executes the method according to any one of claims 1 to 8.

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