Network RTK reference station coordinate precision detection method and system

Abstract

The application provides a network RTK reference station coordinate precision detection method and system, comprising: reference station networking based on a Delaunay triangulation algorithm, determining a baseline list; obtaining satellite precise orbit information, selecting a to-be-solved baseline vector from the baseline list, solving the to-be-solved baseline vector by using a fast static positioning algorithm, and obtaining a preset high-precision relative coordinate result; converting the preset high-precision relative coordinate result into reference station coordinate values, comparing the reference station coordinate values with preset known coordinate values in the system, calculating coordinate difference values of each reference station on each baseline, performing outlier elimination based on the coordinate difference values, obtaining processed coordinate difference values; comprehensively processing the processed coordinate difference values of each reference station on multiple baselines, performing consistency analysis and statistical judgment, determining a coordinate abnormal reference station site in combination with a preset threshold, and outputting a diagnosis alarm result. The application effectively guarantees the reliability and precision of network RTK service.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and positioning technology, and in particular to a method and system for detecting the coordinate accuracy of a network RTK reference station. Background Technology

[0002] In network real-time kinematic (RTK) technology, the accuracy and stability of the base station coordinates are the cornerstone of the system's high-precision service. These coordinates are typically obtained from long-term observation data using high-precision post-processing software (such as Panda, Bernese, GAMIT / GLOBK), and are treated as fixed, known values ​​during system operation, directly used for ambiguity resolution and the establishment of regional error models.

[0003] However, the coordinates of reference stations can change due to various reasons (such as accidental antenna movement, geological subsidence, calculation errors, or human input errors). If a deviation in the coordinates of a reference station goes undetected, the resulting error correction model will contain systematic errors, which will then be broadcast to all users via the network, leading to a significant decrease in positioning accuracy or even calculation failure across a large area, severely impacting service reliability and integrity. Currently, the industry commonly uses a method of periodically (e.g., every six months or year) re-collecting long-term observation data from all reference stations, using high-precision post-processing software to calculate the entire framework network, and then comparing the newly calculated coordinates with the original coordinates in the system. While this method offers high accuracy, it suffers from significant lag, high workload, low automation, and consumes substantial human resources. It cannot achieve routine real-time or near-real-time monitoring, resulting in a long window between the occurrence and detection of coordinate anomalies, posing a significant potential risk.

[0004] Therefore, there is an urgent need for a method that can automatically, efficiently, and in near real-time monitor the stability of reference station coordinates. Summary of the Invention

[0005] This invention provides a method and system for detecting the coordinate accuracy of network RTK reference stations, which solves the shortcomings of existing technologies that rely on periodic high-precision post-processing software for network-wide calculation and comparison, resulting in a large workload and inability to detect problems in a timely manner. This invention enables automatic calibration of RTK system reference stations, significantly improving detection efficiency and timeliness, and effectively ensuring the reliability and accuracy of network RTK services.

[0006] In a first aspect, the present invention provides a method for detecting the coordinate accuracy of a network RTK reference station, comprising:

[0007] Base station networking was performed based on the Delaunay triangulation algorithm to determine the baseline list;

[0008] Acquire precise satellite orbit information, select a baseline vector to be solved from the baseline list based on the precise satellite orbit information, and use a fast static positioning algorithm to solve the baseline vector to be solved to obtain a preset high-precision relative coordinate result;

[0009] The preset high-precision relative coordinate results are converted into coordinate values ​​of each reference station. The coordinate values ​​of each reference station are compared with the preset known coordinate values ​​in the system. The coordinate difference of each reference station on each baseline is calculated. Gross errors are removed based on the coordinate difference to obtain the processed coordinate difference.

[0010] By combining the processed coordinate differences of each reference station on multiple baselines, consistency analysis and statistical judgment are performed. Based on the preset threshold, reference stations with abnormal coordinates are identified, and diagnostic alarm results are output.

[0011] According to the present invention, a method for detecting the coordinate accuracy of network RTK reference stations is provided, which is based on the Delaunay triangulation algorithm for reference station networking and determining a baseline list, including:

[0012] The receivers or data centers connected to each reference station by the satellite system collect GNSS multi-frequency pseudorange and observation information, and obtain the preset known coordinate values ​​in the system. The observation information includes data integrity rate, number of satellites and PDOP value. The coordinates and observation information set of all reference stations are obtained from the observation information. The list of valid reference stations is obtained by using the satellite number threshold, satellite geometric precision factor threshold and PDOP threshold.

[0013] Construct a Delaunay triangulation based on a list of valid reference stations;

[0014] Traverse each triangle in the Delaunay triangulation and extract three edges from the triangle to the initial baseline list;

[0015] For the first and second base stations corresponding to the two vertices on each edge, a unique identifier is determined by the maximum and minimum values ​​between the two base stations. The unique identifiers are arranged and combined to identify and merge duplicate baselines. An excessively long baseline threshold is used to remove excessively long baselines, and a baseline list is output.

[0016] According to the present invention, a method for detecting the coordinate accuracy of a network RTK reference station involves acquiring precise satellite orbit information, selecting a baseline vector to be solved from a baseline list based on the precise satellite orbit information, and using a fast static positioning algorithm to solve the baseline vector to obtain a preset high-precision relative coordinate result. The method includes:

[0017] By receiving real-time precise orbit information from B2B signals, decoding the real-time precise orbit information, and correcting the broadcast ephemeris, B2B decoding correction information is obtained.

[0018] By utilizing B2B decoding correction information, a fast static positioning algorithm is employed to perform long-distance ionosphere-free combined filtering on the baseline vector to be solved, calculate the baseline solution, and obtain the preset high-precision relative coordinate results.

[0019] According to the present invention, a method for detecting the coordinate accuracy of a network RTK reference station involves receiving real-time precise orbit information from a B2B signal, decoding the real-time precise orbit information, and correcting the broadcast ephemeris to obtain B2B decoding correction information, including:

[0020] Receive B2B messages, identify the start bit of the B2B message for frame synchronization, use forward error correction coding for error correction decoding, and extract the precision track correction number and precision clock error correction number from the decoded binary data according to the predefined message structure.

[0021] The B2B decoding correction information includes satellite orbit corrections and satellite clock corrections.

[0022] According to the present invention, a method for detecting the coordinate accuracy of a network RTK base station utilizes B2B decoding correction information, employs a fast static positioning algorithm to perform long-distance ionospheric-free combined filtering on the baseline vector to be solved, calculates the baseline solution, and obtains a preset high-precision relative coordinate result, including:

[0023] Determine the combined observation equations for double-difference pseudorange and ionosphere-free conditions:

[0024]

[0025]

[0026] in, The distance difference between all satellite pairs and each station. The tropospheric delay difference between all satellite pairs and each station. This represents the pseudorange error value without ionosphere. For the ionospheric combined ambiguity double difference between all satellite pairs and each station, This represents the ionospheric carrier phase observation error value. These are combined observations of double-difference pseudoranges. These are observations from a double-difference, ionosphere-free combination. For the double difference operator of inter-station difference and inter-satellite difference, These are pseudorange observations. For carrier phase observations, For the absence of ionospheric assemblage, For receiver number, For mobile stations, As a base station, Number the satellite. For reference stars, Non-reference star For tropospheric delay, The geometric distance from the satellite to the receiver. The equivalent wavelength for an ionosphere-free combination. For ionosphere-free composite ambiguity, the parameter is a real number. This includes observation noise, multipath effects, and unmodeled error terms;

[0027] In static Kalman filtering, the state vector It consists of the following real number parameters:

[0028]

[0029] in, The three-dimensional coordinates of the rover station. The station difference for zenith wet delay (ZWD), For double-difference tropospheric delay, derived from the wet mapping function double-difference and The product is obtained as follows: The ionospheric double-difference ambiguity for all satellite pairs is considered constant in the absence of cycle slips. Total number of satellites;

[0030] Cycle slip detection preprocessing is performed during the filtering process. Kalman filtering is used to estimate the state vector containing real ambiguity. The filter outputs a floating-point solution as the solution result vector.

[0031] According to the present invention, a method for detecting the coordinate accuracy of a network RTK reference station converts the preset high-precision relative coordinate results into coordinate values ​​for each reference station, including:

[0032] Extract the list of site names from the baseline list;

[0033] A dictionary is created based on a data structure. The dictionary contains keys and values, where the keys are each site name and the values ​​are a list of all sites containing a baseline.

[0034] Iterate through each baseline in the baseline list, add each baseline to the corresponding list in the dictionary, determine the mapping relationship between each station and all related baselines, and convert to obtain the coordinate values ​​of each base station.

[0035] According to the present invention, a method for detecting the coordinate accuracy of a network RTK reference station is provided. This method compares the coordinate values ​​of each reference station with preset known coordinate values ​​in the system, calculates the coordinate difference of each reference station on each baseline, and performs gross error removal based on the coordinate differences to obtain the processed coordinate difference. The method includes:

[0036] The system retrieves the pre-set known coordinate values ​​of the two reference stations corresponding to a single baseline from the system database, calculates the known coordinate difference, obtains the solution result vector corresponding to the single baseline, and calculates the difference between the pre-set known coordinate values ​​in the system and the coordinate values ​​of each reference station converted from the solution result vector to obtain the coordinate difference value. The negative number of the coordinate difference value is used as the displacement vector of each reference station.

[0037] Based on any standard coordinate system, the coordinate difference is converted into a set of coordinate components;

[0038] Multiple baselines associated with each reference station are determined, and multiple displacement values ​​are collected for any component in the coordinate component set.

[0039] Calculate the median of multiple displacement values, obtain the absolute deviation of the median based on the median, and convert each absolute deviation of the median into a standard deviation estimate according to a preset constant;

[0040] Determine the error threshold, use the error threshold and standard deviation estimate to remove outliers, and iteratively calculate the remaining median and standard deviation estimates to obtain the displacement observations;

[0041] The average coordinate offset of each station is obtained by calculating the average value of the number of baselines that were not removed and the displacement observations.

[0042] Secondly, the present invention also provides a network RTK base station coordinate accuracy detection system, comprising:

[0043] The module is used to build a base station network based on the Delaunay triangulation algorithm and determine the baseline list;

[0044] The calculation module acquires precise satellite orbit information, selects a baseline vector to be solved from the baseline list based on the precise satellite orbit information, and uses a fast static positioning algorithm to solve the baseline vector to be solved to obtain a preset high-precision relative coordinate result.

[0045] The processing module is used to convert the preset high-precision relative coordinate results into coordinate values ​​of each reference station, compare the coordinate values ​​of each reference station with the preset known coordinate values ​​in the system, calculate the coordinate difference of each reference station on each baseline, and perform gross error removal based on the coordinate difference to obtain the processed coordinate difference.

[0046] The alarm module is used to integrate the processed coordinate differences of each base station on multiple baselines, perform consistency analysis and statistical judgment, determine the base stations with abnormal coordinates by combining preset thresholds, and output diagnostic alarm results.

[0047] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the network RTK base station coordinate accuracy detection method as described above.

[0048] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the network RTK base station coordinate accuracy detection method as described above.

[0049] The network RTK base station coordinate accuracy detection method and system provided by this invention have the following beneficial effects:

[0050] (1) Automation: It realizes full-process automation from networking and calculation to diagnosis, greatly reducing manual intervention;

[0051] (2) High efficiency: By using fast static algorithms and intelligent baseline selection, the detection cycle is greatly shortened, enabling daily or more frequent detection and achieving "near real-time" monitoring.

[0052] (3) High reliability: By using Delaunay triangulation and multiple baseline differences for comprehensive judgment, the risk of misjudgment of a single baseline is avoided, and the accuracy and reliability of the diagnostic results are improved;

[0053] (4) Timeliness: It can quickly detect coordinate anomalies, significantly shortening the window period from the occurrence of a fault to its discovery, and effectively ensuring the continuity and reliability of network RTK services. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating the network RTK base station coordinate accuracy detection method provided by the present invention;

[0056] Figure 2 This is a flowchart of the automatic networking and baseline selection process provided by the present invention;

[0057] Figure 3 This is a flowchart of the long-distance fast static baseline calculation provided by the present invention;

[0058] Figure 4 This is a flowchart of coordinate difference calculation and analysis provided by the present invention;

[0059] Figure 5 This is a flowchart of the automatic diagnosis and alarm process provided by the present invention;

[0060] Figure 6 This is a schematic diagram of the network RTK reference station coordinate accuracy detection system provided by the present invention;

[0061] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0063] Figure 1 This is a flowchart illustrating the network RTK base station coordinate accuracy detection method provided in this embodiment of the invention, as shown below. Figure 1 As shown, it includes:

[0064] Step 100: Establish a base station network based on the Delaunay triangulation algorithm and determine the baseline list;

[0065] Step 200: Obtain precise satellite orbit information, select a baseline vector to be solved from the baseline list based on the precise satellite orbit information, and use a fast static positioning algorithm to solve the baseline vector to be solved to obtain a preset high-precision relative coordinate result;

[0066] Step 300: Convert the preset high-precision relative coordinate results into coordinate values ​​of each reference station, compare the coordinate values ​​of each reference station with the preset known coordinate values ​​in the system, calculate the coordinate difference of each reference station on each baseline, and perform gross error removal based on the coordinate difference to obtain the processed coordinate difference.

[0067] Step 400: Combine the processed coordinate differences of each reference station on multiple baselines, perform consistency analysis and statistical judgment, determine the reference stations with abnormal coordinates by combining the preset threshold, and output the diagnostic alarm results.

[0068] Specifically, in this embodiment of the invention, there are four core steps: First, automatic networking and baseline selection. Taking the BeiDou satellite as an example, the BeiDou satellite system uses the Delaunay triangulation algorithm to automatically network the reference station network and intelligently determine the baseline vectors (i.e., the lines connecting the stations) that need to be calculated, ensuring that the geometric structure of the entire network is effectively covered. Second, fast relative coordinate calculation. For each selected baseline, a fast static positioning algorithm is used for calculation. This algorithm can efficiently calculate the high-precision relative coordinates of each baseline within a certain observation period (e.g., 2 hours). Then, it performs coordinate difference calculation and analysis, converting the calculated relative coordinates of all baselines starting from a certain reference station into the coordinate values ​​of each station, and comparing them with the preset known coordinates in the system to calculate the coordinate difference of each reference station on each baseline. Finally, it performs automatic diagnosis and alarm, comprehensively analyzing the coordinate differences exhibited by each reference station on multiple baselines, performing consistency analysis and statistical judgment, and automatically diagnosing which station's coordinates may be abnormal (error or displacement) based on preset thresholds, generating a diagnostic report or triggering a displacement alarm, thereby achieving automated, near real-time monitoring without manual intervention.

[0069] In one embodiment, such as Figure 2 As shown, step 100 includes:

[0070] Step 1.1: Obtain the coordinates and data of the base station and determine the valid base station.

[0071] The receivers or data centers connected to each reference station via the satellite system automatically collect GNSS multi-frequency pseudorange and carrier phase observations, ephemeris, and other data. The system reads the preset "known" precise coordinates (X0, Y0, Z0) of the station from the system database or configuration file.

[0072] Step 1.1.1: Calculate and statistically analyze the observation information, including data integrity rate, number of satellites, and Position Dilution of Precision (PDOP) value.

[0073] In this invention, statistics are typically performed in time intervals of 1 to 5 minutes, and the formula for calculating the data integrity rate is as follows:

[0074]

[0075] in For data integrity rate, The number of observation data received. The desired number of observation data, the number of satellites, and the PDOP value can be obtained using relevant existing formulas and methods.

[0076] Step 1.1.2: Based on the observation information calculated in Step 1.1.1, the coordinates and observation information of all stations can be obtained as follows.

[0077]

[0078] in For the first Information from each site For station number, For the site name, The spatial rectangular coordinates of the site, For the number of satellites, For data integrity rate, This refers to the PDOP value. This indicates whether the label is valid.

[0079] In order to meet the requirements for generating differential data, the number of satellites at the reference station must be greater than a threshold, and the satellite geometric accuracy factor must be less than a threshold. Based on the relevant standards and experience in the construction of BeiDou reference stations, in this embodiment of the invention, stations that meet the following criteria are designated as stations with valid observations.

[0080] when When all three conditions are met simultaneously ,otherwise .

[0081] Step 1.2: Based on the list of valid reference stations, construct a triangulation network using the Delaunay triangulation algorithm.

[0082] Construct a Delaunay triangulation on the horizontal plane using the valid sites obtained in step 1.1. The Delaunay triangulation has the property of "maximum and minimum angle" (i.e., avoids the formation of long and narrow triangles as much as possible), which can ensure that the triangular network formed by the baseline is geometrically superior and stable.

[0083] Step 1.3: Extract the baseline.

[0084] Extract the edges of all triangles from the Delaunay triangulation generated in step 1.2.

[0085] First, baseline generation is performed by traversing each triangle in the triangular mesh and taking its three sides (e.g., ...). , , and (Stand at the three vertices of the triangle respectively) Add to an initial baseline list. ;

[0086] Secondly, baseline deduplication is performed, since an edge may be shared by two adjacent triangles. There will be a large number of duplicate baselines. Duplicates need to be removed to ensure that each baseline is solved only once. For a baseline consisting of two stations... and The defined baseline can have its unique identifier defined as ( By using this sorting and combining method, duplicate baselines are identified and merged.

[0087] Finally, there is the removal of excessively long baselines. The accuracy of baseline calculation is affected by atmospheric errors and satellite orbit errors, and these errors are strongly correlated with the baseline length. Therefore, the accuracy of calculation for excessively long baselines may not meet the requirements. This invention determines whether to remove a certain baseline based on the conditions in the following embodiments, typically setting a threshold of 300 km.

[0088] In one embodiment, such as Figure 3 As shown, step 200 includes:

[0089] Step 2.1 Real-time precise orbit acquisition and correction of BeiDou: This involves receiving real-time precise orbit information from BeiDou B2B signals, decoding it, and correcting the broadcast ephemeris.

[0090] Step 2.1.1 The B2B message follows the format defined by the BeiDou Navigation Satellite System Public Service Performance Standard (BDS-OS-PS-1.0) or related Interface Control Document (ICD). The decoding process mainly includes:

[0091] Frame synchronization: Identifies the start bit of a message.

[0092] Error correction decoding: B2B messages use forward error correction (FEC) encoding and need to be decoded to correct bit errors.

[0093] Information extraction: Extract the precision orbital correction and precision clock error correction from the decoded binary data according to the predefined message structure.

[0094] Step 2.1.2 The decoded B2B information mainly contains two types of corrections: satellite orbit corrections. and satellite clock correction The goal of applying these corrections is to obtain more accurate satellite positions and satellite clock biases.

[0095] The orbital corrections provided by B2B are typically three-dimensional differences in a geocentric fixed coordinate system (such as CGCS2000). These corrections can be directly added to the broadcast ephemeris orbit to obtain the precise orbit.

[0096]

[0097]

[0098]

[0099] in, These are corrected, precise satellite coordinates. These are the satellite coordinates in the broadcast ephemeris orbit. These are orbital corrections for the satellite, decoded from the B2B message. These corrections are typically functions of the satellite signal transmission time t. The B2B message provides the reference time and polynomial coefficients, which can be calculated through interpolation.

[0100] Step 2.2: Calculate the baseline solution for the long-distance ionosphere-free combined filtering solution.

[0101] Ionospheric-free ambiguity analysis completely eliminates the first-order ionospheric delay term through linear combination. Double-difference processing eliminates clock errors and hardware delay biases between satellites and receivers. Ionospheric-free ambiguity loses its integer property and is typically estimated as a real-number parameter in data processing. Real-time precise orbit correction largely eliminates the influence of satellite orbit errors in broadcast ephemeris data. Using double-difference processing with ionospheric-free ambiguity analysis is the mainstream method for solving long-baseline ionospheric delay errors. Its core lies in treating ambiguity as a real-number parameter requiring precise estimation, extending the observation time, and using estimation algorithms such as Kalman filtering to achieve convergence, thereby obtaining a high-precision floating-point solution. Although the convergence speed is slower than that of short-baseline solutions with fixed ambiguity, it provides the feasibility of obtaining centimeter-level relative positioning accuracy over long distances.

[0102] Equations for combined observations using double-difference pseudorange and ionosphere-free methods:

[0103]

[0104]

[0105] in, The distance difference between all satellite pairs and each station. The tropospheric delay difference between all satellite pairs and each station. This represents the pseudorange error value without ionosphere. For the ionospheric combined ambiguity double difference between all satellite pairs and each station, This represents the ionospheric carrier phase observation error value. These are combined observations of double-difference pseudoranges. These are observations from a double-difference, ionosphere-free combination. For the double difference operator of inter-station difference and inter-satellite difference, These are pseudorange observations. For carrier phase observations, For the absence of ionospheric assemblage, For receiver number, For mobile stations, As a base station, Number the satellite. For reference stars, Non-reference star For tropospheric delay, The geometric distance from the satellite to the receiver. The equivalent wavelength for an ionosphere-free combination. For ionosphere-free composite ambiguity, the parameter is a real number. This includes observation noise, multipath effects, and unmodeled error terms;

[0106] In static Kalman filtering, the state vector It consists of the following real number parameters:

[0107]

[0108] in:

[0109] Location parameters: Three-dimensional coordinates of the rover ;

[0110] Tropospheric parameters: It is the inter-station difference in zenith wet delay (ZWD), and the double-difference tropospheric delay. This can be represented as a wet mapping function with double difference. and The product;

[0111] Ambiguity parameters: It is the ionospheric double-difference ambiguity of all satellite pairs. Number the satellite. For reference stars, For non-reference stars, these parameters are considered constants when no cycle slip occurs. In The total number of satellites For the absence of ionospheric assemblage, This is the receiver number.

[0112] The filtering process is as follows:

[0113] Preprocessing: Cycle slip detection is performed. Since the ambiguity is real, cycle slip detection is more complex and can be achieved using a geometrically indistinguishable MW combination or an ionospheric residual method.

[0114] Kalman filtering: Directly estimates the state vector containing real-valued ambiguities. Filter convergence means that all real-valued state parameters (including ambiguities) are optimally determined.

[0115] Solution results: The filter outputs a floating-point solution. The final baseline solution accuracy depends on the accuracy of the estimated real ambiguity. After convergence, the accuracy can reach the centimeter or even millimeter level.

[0116] In one embodiment, such as Figure 4 As shown, step 300 includes:

[0117] Step 3.1: Perform a loop at each station to extract all relevant baselines.

[0118] For each base station in the network, all connected baselines are identified so that the stability of the station can be analyzed from the solution results of these baselines. The main process is as follows:

[0119] 1) Obtain the baseline list B_list from step 1.3 (Delaunay triangulation extraction), where each baseline is uniquely identified by two station names (e.g., S1, S2). Also, obtain the list Station_list containing all stations.

[0120] 2) Data Structure: Create a dictionary Its key is each site name, and its value is a list used to store all baselines that contain that site.

[0121] 3) Looping through: For Each baseline in :

[0122] Baseline Add to In the corresponding list.

[0123] Baseline Add to In the corresponding list.

[0124] 4) Output: After processing, It stores the mapping relationship between each station and all relevant baselines.

[0125] Step 3.2: Calculate the coordinate differences of all relevant baselines and remove outliers.

[0126] For a single station to be inspected, the coordinate offset of the station on each baseline is calculated using the solution results of all the baselines connected to it. Unreliable gross errors are then eliminated using statistical methods, and finally a robust estimate of the coordinate change of the station is obtained.

[0127] Step 3.2.1: Calculate the coordinate difference of a single baseline:

[0128] For a line of inspection stations One endpoint is [the first endpoint], and the other endpoint is [the second endpoint]. baseline :

[0129] Known coordinate difference: Retrieve the known coordinate vectors of the two stations from the system database. and Their difference is:

[0130]

[0131] Observation coordinate difference: Obtain the solution vector of the baseline from the fast static baseline solution results in step 2. Right now or ;

[0132] Coordinate difference vector: The coordinate difference values ​​represented on this baseline are:

[0133]

[0134] Where, vector Reflects from baseline From the perspective of the inspection station Compared to the reference station The "displacement". Theoretically, if all station coordinates are correct and have not moved, It should be the zero vector.

[0135] Step 3.2.2: Calculate the stations to be inspected The displacement vector.

[0136] For the inspection station It is on a baseline The displacement vector shown in This can be derived from the above formula. Assume a reference station. If the coordinates are correct (because all stations are being inspected, this is a relative judgment), then the difference between the observed value and the known value is attributed to the station being inspected. Displacement:

[0137]

[0138] To simplify the representation of calculating the difference between its coordinate components For example, in the Northeast-Eastern (ENU) coordinate system:

[0139]

[0140]

[0141]

[0142] in From the baseline The station to be inspected is obtained by reverse calculation from the solution results. coordinates The coordinate components are for the northeastern sky (ENU).

[0143] Step 3.2.3: Outlier removal.

[0144] For each inspection station It iterates through each of its coordinate components (E, N, U):

[0145] Data collection: Assuming there is Article and The relevant baselines are for the eastern component. It can be collected Displacement values: ;

[0146] Calculate the median and robust standard deviation:

[0147] Median:

[0148]

[0149] Calculate the absolute deviation of the median and convert it into a robust estimator of the standard deviation.

[0150]

[0151] (usually taken) This makes it possible for normally distributed data, Consistent with standard deviation);

[0152] Remove gross errors: Set any threshold (e.g.) or Observations outside this range are marked as gross errors and removed from subsequent analyses. If Then it is believed It is a gross error;

[0153] Iteration: After removing outliers, recalculate the median and MAD using the remaining data, and check again until no new outliers are found.

[0154] Step 3.2.4: Output the results.

[0155] For each site After gross error removal, a set of "clean" displacement observations was obtained. The weighted average or arithmetic mean of these values ​​can be calculated as the final estimate of the station's coordinate offset:

[0156]

[0157]

[0158]

[0159] in, This is the number of baselines that were not removed. For any baseline, its variance can be calculated to assess the accuracy of the estimate.

[0160] Finally, the output of step 3 is the average coordinate offset for each station. The data, along with its accuracy indicators and a list of raw data marked with gross errors, will provide a direct basis for decision-making in subsequent steps (determining whether the site coordinates are correct and whether they have moved).

[0161] In one embodiment, such as Figure 5 As shown, step 400 includes:

[0162] Step 4.1: The system integrates the coordinate differences shown by each reference station on multiple baselines, and performs consistency analysis and statistical judgment.

[0163] The average coordinate offset of each station calculated in step 3 Statistical analysis is performed to determine whether the displacement is within the range of random error or is confirmed as a significant anomalous change. The core of this step is statistical hypothesis testing. We perform this test for each site. For each coordinate component, establish the null hypothesis and alternative hypothesis.

[0164] Step 4.1.1: Establish hypotheses.

[0165] Null hypothesis ( ):stand The coordinates did not undergo significant displacement, and the observed offset was... ( Can represent This is caused by random observation noise.

[0166] Alternative hypothesis ( ): Site The coordinates have undergone a significant displacement, and the observed offset is... It's true.

[0167] Step 4.1.2: Calculate the test statistic.

[0168] A quantitative indicator is needed to determine whether to reject the null hypothesis. The most common method is to calculate the magnitude of the displacement vector and perform a significance test. Displacement on the horizontal plane is usually a composite of east and north components, and its magnitude... displacement modulus in the elevation direction That is The absolute values ​​are respectively:

[0169]

[0170]

[0171] Step 4.1.3: Uncertainty assessment and threshold setting.

[0172] The displacement alone is insufficient to make a judgment; it must be combined with its uncertainty (precision), using the standard deviation of the average value calculated in step 3. .

[0173] Horizontal confidence ellipse: Checking horizontal displacement points Does it fall within the confidence ellipse of the null hypothesis? Calculate the variance-covariance matrix of the horizontal displacement. :

[0174]

[0175] in, For variance, For covariance.

[0176] Calculate the square of the Mahalanobis distance It follows a chi-square distribution with 2 degrees of freedom. :

[0177]

[0178] Judgment: If At the significance level The null hypothesis is rejected, assuming a significant horizontal displacement has occurred, for example, hour, .

[0179] Z-test for elevation: For elevation components, use the Z-test (or t-test, if the number of observations is small):

[0180]

[0181] Judgment: If If so, it is considered that a significant displacement has occurred in the elevation direction. It is a preset threshold, usually taken as... (At a 99.73% confidence level) or (More stringent).

[0182] Simplified threshold method: In practical engineering, a more intuitive simplified threshold method is often used, which sets fixed empirical thresholds for the horizontal and vertical directions respectively. and judge: or ;

[0183] Horizontal threshold : Can be set according to network precision, for example, 5 mm ~ 10 mm; Elevation threshold It is usually 1.5 to 2 times the horizontal threshold, for example, 10 mm to 20 mm, because GNSS is usually less accurate in the elevation direction.

[0184] Step 4.2: Based on the preset threshold, automatically diagnose which station's coordinates may be abnormal (error or displacement), and generate a diagnostic report or trigger a displacement alarm, thereby achieving automated, near real-time monitoring without manual intervention.

[0185] Based on the statistical judgment results of step 4.1, the diagnostic decision is automatically executed, and a report that can be viewed manually or a system alarm is triggered.

[0186] When a site is diagnosed as "mobile" or "potentially mobile," the system automatically triggers the following actions:

[0187] Generate a structured diagnostic report, which typically includes: Site ID: S_a; Detection time: YYYY-MM-DDHH:MM:SS; Horizontal displacement: H_a ± σ_H (unit: mm); Elevation displacement: V_a ± σ_U (unit: mm); 3D displacement: D_3D,a (unit: mm); Number of baselines used: m; Number of outliers removed: n – m; Statistical test values: M_a^2,Z_U,a; Diagnostic conclusion: "Coordinate anomaly: suspected displacement".

[0188] Trigger an alarm: The site will be highlighted in red on the monitoring software interface, an alarm email or SMS will be sent to the maintenance personnel, and the event will be recorded in the system log database.

[0189] Optional automated operation: Automatically disable the site from the current network solution to prevent it from polluting the entire network RTK service, prompting for manual intervention and verification.

[0190] The following describes the network RTK base station coordinate accuracy detection system provided by the present invention. The network RTK base station coordinate accuracy detection system described below can be referred to in correspondence with the network RTK base station coordinate accuracy detection method described above.

[0191] Figure 6 This is a schematic diagram of the network RTK reference station coordinate accuracy detection system provided in an embodiment of the present invention, as shown below. Figure 6 As shown, it includes: a construction module 61, a calculation module 62, a processing module 63, and an alarm module 64, wherein:

[0192] The construction module 61 is used to network reference stations based on the Delaunay triangulation algorithm and determine the baseline list; the calculation module 62 acquires the precise orbit information of the satellite, selects the baseline vector to be solved from the baseline list based on the precise orbit information of the satellite, and uses a fast static positioning algorithm to solve the baseline vector to be solved to obtain the preset high-precision relative coordinate results; the processing module 63 is used to convert the preset high-precision relative coordinate results into the coordinate values ​​of each reference station, compares the coordinate values ​​of each reference station with the preset known coordinate values ​​in the system, calculates the coordinate difference of each reference station on each baseline, performs gross error elimination based on the coordinate difference, and obtains the processed coordinate difference; the alarm module 64 is used to integrate the processed coordinate difference of each reference station on multiple baselines, perform consistency analysis and statistical judgment, determine the reference stations with abnormal coordinates based on a preset threshold, and output the diagnostic alarm results.

[0193] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7 As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740. The processor 710 can call logic instructions in the memory 730 to execute a network RTK base station coordinate accuracy detection method. This method includes: establishing a base station network based on the Delaunay triangulation algorithm to determine a baseline list; acquiring precise satellite orbit information; selecting a baseline vector to be solved from the baseline list based on the precise satellite orbit information; using a fast static positioning algorithm to solve the baseline vector to obtain a preset high-precision relative coordinate result; converting the preset high-precision relative coordinate result into coordinate values ​​for each base station; comparing the coordinate values ​​of each base station with preset known coordinate values ​​in the system; calculating the coordinate difference for each base station on each baseline; performing gross error elimination based on the coordinate difference to obtain a processed coordinate difference; combining the processed coordinate differences for each base station on multiple baselines; performing consistency analysis and statistical judgment; determining abnormal coordinate base stations based on a preset threshold; and outputting a diagnostic alarm result.

[0194] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0195] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a network RTK reference station coordinate accuracy detection method provided by the above methods. The method includes: establishing a reference station network based on the Delaunay triangulation algorithm to determine a baseline list; acquiring precise satellite orbit information; selecting a baseline vector to be solved from the baseline list based on the precise satellite orbit information; solving the baseline vector to be solved using a fast static positioning algorithm to obtain a preset high-precision relative coordinate result; converting the preset high-precision relative coordinate result into coordinate values ​​of each reference station; comparing the coordinate values ​​of each reference station with preset known coordinate values ​​in the system; calculating the coordinate difference of each reference station on each baseline; performing gross error elimination based on the coordinate difference to obtain a processed coordinate difference; combining the processed coordinate differences of each reference station on multiple baselines; performing consistency analysis and statistical judgment; determining reference stations with abnormal coordinates based on a preset threshold; and outputting a diagnostic alarm result.

[0196] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0197] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0198] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A network RTK reference station coordinate precision detection method, characterized in that, The method comprises the following steps: Delaunay triangulation algorithm is used to network the reference stations to determine the baseline list; Satellite precise orbit information is obtained, and based on the satellite precise orbit information, a baseline vector to be solved is selected from the baseline list, a fast static positioning algorithm is used to solve the baseline vector to be solved, and a preset high-precision relative coordinate result is obtained; The preset high-precision relative coordinate result is converted into coordinate values of each reference station, the coordinate values of each reference station are compared with preset known coordinate values in the system, coordinate difference values of each reference station on each baseline are calculated, rough error elimination is performed based on the coordinate difference values, and processed coordinate difference values are obtained; The processed coordinate difference values of each reference station on multiple baselines are integrated, consistency analysis and statistical judgment are performed, coordinate abnormal reference stations are determined in combination with a preset threshold, and a diagnostic alarm result is output. 2.The network RTK reference station coordinate precision detection method according to claim 1, characterized in that, Delaunay triangulation algorithm is used to network the reference stations to determine the baseline list, including: A receiver or data center connected to each reference station in the satellite system collects GNSS multi-frequency pseudorange and observation value information, and obtains preset known coordinate values in the system, wherein the observation value information includes data integrity rate, satellite number and PDOP value, the coordinate and observation value information set of all reference stations are obtained from the observation value information, and the effective reference station list is screened by using the satellite number threshold, satellite geometric accuracy factor threshold and PDOP threshold; A Delaunay triangulation network is constructed based on the effective reference station list; Each triangle in the Delaunay triangulation network is traversed, and three edges in the triangle are extracted to the initial baseline list; For the first reference station and the second reference station corresponding to the two vertices on each edge, the unique identifier is determined by the maximum value and the minimum value between the two reference stations, the unique identifier is arranged and combined, the repeated baselines are identified and merged, the overlong baseline threshold is used to remove the overlong baselines, and the baseline list is output. 3.The network RTK reference station coordinate precision detection method of claim 1, wherein, Satellite precise orbit information is obtained, and based on the satellite precise orbit information, a baseline vector to be solved is selected from the baseline list, a fast static positioning algorithm is used to solve the baseline vector to be solved, and a preset high-precision relative coordinate result is obtained, including: Real-time precise orbit information in the received satellite signal is decoded, and the broadcast ephemeris is corrected to obtain B2B decoding correction information; B2B decoding correction information is used to perform long-distance ionosphere-free combined filtering solution on the baseline vector to be solved by using the fast static positioning algorithm, calculate the baseline solution, and obtain the preset high-precision relative coordinate result. 4.The network RTK reference station coordinate precision detection method according to claim 3, characterized in that, Real-time precise orbit information in the received satellite B2B signal is decoded, and the broadcast ephemeris is corrected to obtain B2B decoding correction information, including: B2B text is received, frame synchronization is performed by identifying the starting bit of the B2B text, error correction decoding is performed by using forward error correction coding, and from the decoded binary data, the precise orbit correction number and the precise clock difference correction number are extracted according to the predefined text structure; The B2B decoding correction information includes satellite orbit correction number and satellite clock difference correction number. 5.The network RTK reference station coordinate precision detection method according to claim 3, characterized in that, Utilizing B2B decoding correction information, a fast static positioning algorithm is employed to perform long-distance ionosphere-free combined filtering on the baseline vector to be solved, calculating the baseline solution and obtaining a preset high-precision relative coordinate result, including: Determine the combined observation equations for double-difference pseudorange and ionosphere-free conditions: wherein, is a double difference value of the distance between all satellite pairs and stations, is a double difference value of the tropospheric delay between all satellite pairs and stations, is a value of the ionosphere-free pseudorange error, is a double difference value of the ionosphere-free combined ambiguity between all satellite pairs and stations, is a value of the ionosphere-free carrier phase observation error, is a double difference pseudorange combined observation, is a double difference ionosphere-free combined observation, is a double difference operator of the inter-station and inter-satellite difference, is a pseudorange observation, is a carrier phase observation, is an ionosphere-free combined flag, is a receiver number, is a rover station, is a reference station, is a satellite number, is a reference satellite, is a non-reference satellite, is a tropospheric delay, is a geometric distance from the satellite to the receiver, is an equivalent wavelength of the ionosphere-free combination, is an ionosphere-free combined ambiguity being a real parameter, is an observation noise, multipath effect and un-modeled error term; In static Kalman filtering the state vector consists of the following real parameters: where is the three-dimensional coordinates of the flow station, is the inter-station difference of zenith wet delay, ZWD, is the double-difference tropospheric delay, obtained from the double-difference of the wet mapping function and is the ionosphere-free double-difference ambiguity for all satellite pairs, which is considered constant when no cycle slips occur;​ Cycle slip detection preprocessing is performed during the filtering process. Kalman filtering is used to estimate the state vector containing real ambiguity. The filter outputs a floating-point solution as the solution result vector. 6.The network RTK reference station coordinate precision detection method according to claim 1, characterized in that, Converting the preset high-precision relative coordinate results into coordinate values ​​for each reference station includes: Extract the list of site names from the baseline list; A dictionary is created based on a data structure. The dictionary contains keys and values, where the keys are each site name and the values ​​are a list of all sites containing a baseline. Iterate through each baseline in the baseline list, add each baseline to the corresponding list in the dictionary, determine the mapping relationship between each station and all related baselines, and convert to obtain the coordinate values ​​of each base station.

7. The network RTK reference station coordinate precision detection method according to claim 6, characterized in that, By comparing the coordinate values ​​of each reference station with the preset known coordinate values ​​in the system, the coordinate difference of each reference station on each baseline is calculated. Gross errors are then removed based on these coordinate differences to obtain the processed coordinate difference, including: The system retrieves the pre-set known coordinate values ​​of the two reference stations corresponding to a single baseline from the system database, calculates the known coordinate difference, obtains the solution result vector corresponding to the single baseline, and calculates the difference between the pre-set known coordinate values ​​in the system and the coordinate values ​​of each reference station converted from the solution result vector to obtain the coordinate difference value. The negative number of the coordinate difference value is used as the displacement vector of each reference station. Based on any standard coordinate system, the coordinate difference is converted into a set of coordinate components; Multiple baselines associated with each reference station are determined, and multiple displacement values ​​are collected for any component in the coordinate component set. Calculate the median of multiple displacement values, obtain the absolute deviation of the median based on the median, and convert each absolute deviation of the median into a standard deviation estimate according to a preset constant; Determine the error threshold, use the error threshold and standard deviation estimate to remove outliers, and iteratively calculate the remaining median and standard deviation estimates to obtain the displacement observations; The average coordinate offset of each station is obtained by calculating the average value of the number of baselines that were not removed and the displacement observations.

8. A network RTK reference station coordinate accuracy detection system, characterized in that, include: The module is used to build a base station network based on the Delaunay triangulation algorithm and determine the baseline list; The calculation module acquires precise satellite orbit information, selects a baseline vector to be solved from the baseline list based on the precise satellite orbit information, and uses a fast static positioning algorithm to solve the baseline vector to be solved to obtain a preset high-precision relative coordinate result. The processing module is used to convert the preset high-precision relative coordinate results into coordinate values ​​of each reference station, compare the coordinate values ​​of each reference station with the preset known coordinate values ​​in the system, calculate the coordinate difference of each reference station on each baseline, and perform gross error removal based on the coordinate difference to obtain the processed coordinate difference. The alarm module is used to integrate the processed coordinate differences of each base station on multiple baselines, perform consistency analysis and statistical judgment, determine the base stations with abnormal coordinates by combining preset thresholds, and output diagnostic alarm results.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the network RTK base station coordinate accuracy detection method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the network RTK base station coordinate accuracy detection method as described in any one of claims 1 to 7.

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