RTK server integrity monitoring method based on CORS network

Abstract

The invention discloses an RTK server integrity monitoring method based on a CORS network, and relates to the field of satellite navigation, and the method comprises the steps: obtaining observation data obtained by a plurality of reference stations in the CORS network in the same region in each epoch in an observation time period; based on a distributed monitoring scheme and a hierarchical monitoring thought, performing fault monitoring on receiver faults, signal quality abnormity and star clock abnormity in observation data of each reference station, determining a fault judgment matrix, and performing combined judgment by integrating the fault judgment matrixes of the reference stations; the method comprises the following steps: monitoring ephemeris faults, reference station position faults, regional ionosphere anomalies and regional troposphere anomalies by using multi-station observation data to obtain a first-stage monitoring result, and selecting a reference station and an adjacent reference station according to the approximate position of a user to determine an initial differential correction number; and monitoring the comprehensive fault contained in the observation data of each reference station, and determining the differential correction number and the integrity parameter which are finally broadcasted to the user, so that the integrity of the RTK server can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation, and in particular to a RTK server integrity monitoring method based on a CORS network. Background Art

[0002] Providing high-precision and high-integrity positioning services is the development direction of satellite navigation systems for future industry applications. The demand for high-precision and high-integrity positioning technology in the fields of autonomous driving, drones, maritime, railways, etc. is increasing day by day. Real-Time Kinematic (RTK) is the most mature and widely used high-precision positioning technology of the Global Navigation Satellite System (GNSS). It can achieve centimeter-level high-precision positioning and has good dynamic characteristics. Compared with high-precision positioning such as Precise Point Positioning (PPP) and PPP-RTK, it has irreplaceable advantages and is also the positioning technology currently used by most users of autonomous driving car manufacturers. However, the inherent vulnerability of satellite navigation systems limits their application in the field of life safety. Therefore, the study of RTK integrity is the key to determining whether it can be used in fields such as autonomous driving.

[0003] There are currently two aspects of RTK integrity research. Although RTK system operators are actively responding to user needs and promoting the construction of high-precision positioning service performance specifications, they are still under discussion and have not established a complete theoretical system at the system level to solve the application problems of RTK in autonomous driving. Academic research on RTK integrity mainly focuses on the research and improvement of user-side algorithms, mainly including the improvement of fault monitoring and identification methods and the optimization of protection level (PL), as well as integrity monitoring related to integer ambiguity fixation.

[0004] However, current research on RTK integrity remains at the level of conceptual discussion and user-side algorithms. There are problems such as unclear definition of conceptual indicators and complex algorithms that are not universal. There is also a lack of systematic analysis frameworks and solutions, which makes it impossible to truly promote the practical application of RTK in the field of life safety.

[0005] From the perspective of system construction, detailed and comprehensive integrity monitoring on the server side is the core of ensuring the integrity of correction numbers, and it is also the blind spot in various current research and technologies. Highly reliable correction numbers and integrity parameters are the key to ensuring positioning integrity. Based on this, an RTK server integrity improvement technology is urgently needed. Summary of the invention

[0006] The purpose of this application is to provide an RTK server integrity monitoring method based on the CORS network, which can effectively improve the integrity of the RTK server.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides an RTK server integrity monitoring method based on a CORS network, comprising:

[0009] Observation data obtained by multiple reference stations in the CORS network in the same area at each epoch during an observation period are obtained; the observation data include satellite ephemeris and pseudorange carrier measurement values.

[0010] Based on the distributed monitoring scheme and hierarchical monitoring ideas, fault monitoring is carried out on the receiver failures, signal quality abnormalities and satellite clock abnormalities in the observation data of each reference station, and the fault discrimination matrix of each type of fault is determined.

[0011] Based on the data processing center, joint judgment is carried out by integrating the fault judgment matrices corresponding to each reference station, and multi-station observation data are used to monitor ephemeris faults, reference station position faults, regional ionospheric anomalies and regional tropospheric anomalies to obtain the first-level monitoring results; the first-level monitoring results are the judgment results for each single fault.

[0012] Based on the first-level monitoring results, a base station and an adjacent reference station are selected according to the approximate position of the user, and an initial differential correction number is determined; the initial differential correction number includes a differential correction number from the base station to the user and a differential correction number from the base station to the adjacent reference station.

[0013] Based on the initial differential correction numbers, the comprehensive faults contained in the observation data of each reference station are monitored to determine the differential correction numbers and integrity parameters that are ultimately broadcast to the user.

[0014] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0015] This application adopts a distributed monitoring solution and a hierarchical monitoring approach to carry out abnormal monitoring. In the distributed monitoring solution, each reference station independently obtains observation information and performs partial fault monitoring, that is, single fault monitoring. The data processing center uses the comprehensive observation data and the monitoring results of each reference station to make a comprehensive judgment on the fault, thereby achieving effective judgment of satellite segment faults, transmission segment faults and ground segment faults. In addition, using the hierarchical monitoring approach, after completing the monitoring of each independent fault, another round of monitoring is performed on the comprehensive fault source in the observation data to eliminate the situation where multiple small faults are superimposed to cause larger faults, and to ensure that the generated differential correction numbers and integrity parameters meet the required performance indicators, thereby effectively improving the integrity of the RTK server. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 Flow chart of the RTK server integrity monitoring method based on CORS network provided in this application;

[0018] Figure 2 A schematic diagram of the base station hierarchical integrity monitoring process provided for this application;

[0019] Figure 3 This is a schematic diagram of the first-level monitoring process of the RTK server provided by this application;

[0020] Figure 4 Schematic diagram of the second-level monitoring process of the RTK server provided by this application;

[0021] Figure 5 A schematic diagram of the signal quality abnormality joint monitoring process provided by this application;

[0022] Figure 6 ARS star clock anomaly monitoring process diagram provided for this application;

[0023] Figure 7 Schematic diagram of the Type A ephemeris fault anomaly monitoring process provided by this application;

[0024] Figure 8 Schematic diagram of the Type B ephemeris fault anomaly monitoring process provided by this application;

[0025] Fig. 9 A schematic diagram of the abnormal monitoring of the reference station position provided for this application;

[0026] Fig.10 Schematic diagram of the regional tropospheric residual anomaly monitoring process provided for this application;

[0027] Fig.11 This is the ionospheric anomaly monitoring flow chart provided for this application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] Example 1

[0031] The embodiment of the present application provides an RTK server integrity monitoring method based on a CORS network. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, Figure 1 As shown, the method includes the following steps.

[0032] S1: Obtain observation data obtained by multiple reference stations in the CORS network in the same area at each epoch during an observation period; the observation data includes satellite ephemeris and pseudorange carrier measurement values.

[0033] S2: Based on the distributed monitoring scheme and hierarchical monitoring ideas, fault monitoring is performed on the receiver failures, signal quality anomalies, and satellite clock anomalies in the observation data of each reference station to determine the fault discrimination matrix for each type of fault.

[0034] S3: Based on the data processing center, the fault discrimination matrices corresponding to each reference station are integrated to carry out joint discrimination, and multi-station observation data are used to monitor ephemeris faults, reference station position faults, regional ionospheric anomalies and regional tropospheric anomalies to obtain the first-level monitoring results; the first-level monitoring results are the discrimination results for each single fault.

[0035] S4: Based on the first-level monitoring results, a base station and an adjacent reference station are selected according to the approximate position of the user to determine an initial differential correction number; the initial differential correction number includes a differential correction number from the base station to the user and a differential correction number from the base station to the adjacent reference station.

[0036] S5: Based on the initial differential correction numbers, monitor the comprehensive faults contained in the observation data of each reference station to determine the differential correction numbers and integrity parameters that are ultimately broadcast to the user.

[0037] In practical applications, such as Figure 2As shown, the present application includes first-level monitoring and second-level monitoring; wherein, the first-level monitoring is single-item fault monitoring: for each epoch within the observation time period, each reference station independently performs fault monitoring for receiver faults, carrier-to-noise ratio anomalies, and satellite clock anomalies according to the following steps 101 to 110, and outputs a fault discrimination matrix and observation data to a data processing center, which gives a final discrimination result based on the fault discrimination matrices of each station. In addition, the data processing center obtains relevant parameters by constructing a baseline combination, and performs ionospheric anomaly, tropospheric anomaly, and reference station position anomaly monitoring.

[0038] When performing single fault monitoring, the idea of ​​distributed monitoring is adopted, such as Figure 4 As shown, including:

[0039] Step 101: For each epoch in the observation time period, each reference station independently obtains each satellite observation data, including ephemeris information, different frequency carrier observation values, and pseudorange observation values. On this basis, a fault discrimination matrix is ​​established, in which each row represents a satellite, and each column represents different frequency carrier or pseudorange observation data. In this embodiment, the dual-system dual-frequency observation values ​​of the Global Positioning System (GPS) and the Beidou Navigation Satellite System (BDS) are selected. The size of the fault discrimination matrix is ​​96×4, and the initial value of the fault discrimination matrix is ​​0.

[0040] Step 102: Each reference station independently performs signal quality abnormality discrimination based on the carrier-to-noise ratio data. Each reference station first monitors the signal quality abnormality based on the acquired satellite and frequency observation data. For the observed satellites and frequencies with a carrier-to-noise ratio less than the threshold, a carrier-to-noise ratio abnormality mark is added to the corresponding position of the fault discrimination matrix established in step 101, and the observation data is not used in the subsequent processing flow. The threshold value is established through long-term historical carrier-to-noise ratio data statistics, reflecting the lower limit of the normal carrier-to-noise ratio.

[0041] Step 103: Each reference station independently performs cycle slip detection. After excluding the observation data with abnormal carrier-to-noise ratio, for each observation satellite in step 102, the dual-frequency carrier observation data φ1 and φ2 are used to construct the geometry-free test statistic T GF , using dual-frequency carrier and pseudo-range observation data P1, P2 to construct the MW (Melbourne-Wunnema) test statistic T MW For cycle slip detection, the calculation methods of the above two test statistics are shown in formula (1) and formula (2), and the threshold values ​​are obtained based on empirical values ​​and historical data.

[0042] T GF=λ1φ1(t)-λ2φ2(t)-[λ1φ1(t-1)-λ2φ2(t-1)] (1)

[0043]

[0044] After the cycle slip is detected, the cycle slip frequency point is further identified and the cycle slip is repaired.

[0045] Step 104: Each reference station independently performs pseudorange gross error detection. Calculate the pseudorange observation correction of each observation channel in the current epoch, use the pseudorange correction of n epochs (from epoch k-n+1 to the current epoch k) to perform quadratic polynomial fitting, and obtain acceleration, slope and step error test statistics for subsequent judgment, where the threshold is obtained through long-term historical data statistics.

[0046] If for the observation channel of satellite i and frequency point j, the number of times the three test statistics exceed the threshold is greater than or equal to 2, it is determined that the measured pseudorange has a gross error, and a pseudorange gross error fault flag is added to the fault judgment matrix established in step 101, and the observation data is not used in the subsequent processing flow.

[0047] Step 105: Each reference station independently monitors the satellite clock anomaly. Calculate the carrier observation value correction number of each observation channel in the current epoch, use the carrier correction number of n epochs (from epoch k-n+1 to the current epoch k) to perform quadratic polynomial fitting, and obtain the acceleration, slope and step error test statistics for subsequent judgment, where the threshold is obtained through long-term historical data statistics. If for the observation channel of satellite i frequency point j: if the number of exceeding the threshold is greater than or equal to 2, then mark the satellite clock abnormal in the fault judgment matrix established in step 101; if the number of exceeding the threshold is 1, then mark the alarm flag as 1 and transmit it to the data processing center for multi-station joint judgment; if the number of exceeding the threshold is 0, then mark the satellite normal.

[0048] Step 106: The data processing center performs joint judgment based on the fault judgment matrix of each reference station.

[0049] Each reference station sends the processed observation data of each frequency point, ephemeris information, and the fault discrimination matrix constructed according to steps 101-105 to the data processing center. The data processing center first jointly discriminates the signal quality anomaly and the satellite clock anomaly based on the fault discrimination matrix of each reference station.

[0050] For signal quality anomalies, according to their occurrence in multiple stations and multiple satellites and their temporal and spatial correlation, they are classified into six categories: single-station single-satellite occasional failure, single-station single-satellite obstruction failure, single-satellite signal failure, regional interference, reference station receiver failure and regional ionospheric anomaly. The detailed identification process is shown in Example 2.

[0051] For satellite clock anomalies, for satellite i, if at least one reference station marks it as abnormal, or at least two reference stations mark it with alarm mark 1, it is determined to be a faulty satellite; in other cases, it is determined to be normal. The detailed judgment process is shown in Example 3.

[0052] Step 107: The data processing center conducts Type A ephemeris fault anomaly monitoring. At each epoch within the observation time period, the data processing center integrates the observation data of each station, constructs a multi-baseline ephemeris fault anomaly monitor, obtains the test statistic distribution characteristics according to the historical measurement error statistical characteristics and related parameter settings, derives the test threshold according to the assigned false detection rate index, and evaluates the theoretical missed detection rate to determine whether the theoretical missed detection rate meets the index requirements: if the theoretical missed detection rate cannot meet the index requirements, it is necessary to replace the test statistic for re-evaluation. If it still cannot meet the index requirements after multiple rounds of evaluation, a monitor unavailable alarm is given; if the theoretical missed detection rate meets the index requirements, the test statistic value is calculated. If it is less than the threshold, the satellite position is marked as normal, otherwise the satellite position is marked as abnormal. See Example 4 for the detailed judgment process. For satellites with abnormal positions, their observation data are not used.

[0053] Step 108: The data processing center conducts Type B ephemeris fault anomaly monitoring. At the time of ephemeris update, the data processing center first compares the parameters of multi-station navigation messages to eliminate anomalies in the process of information reception, processing and decoding; secondly, it simultaneously conducts ephemeris time correlation monitoring and inter-frequency consistency monitoring to eliminate satellite position anomalies caused by ephemeris annotation. If the time correlation monitoring fails, alarm mark 1 is marked; if the inter-frequency consistency monitoring fails, alarm mark 2 is marked. Finally, the judgment results of the ephemeris time correlation monitor and the inter-frequency consistency monitor are integrated, that is, the ephemeris time correlation monitoring results and the inter-frequency consistency monitoring results. If alarm marks 1 and 2 do not exist, the ephemeris is judged to be normal, otherwise the ephemeris is judged to be abnormal. See Example 4 for the detailed judgment process.

[0054] Step 109: The data processing center simultaneously carries out reference station position anomaly monitoring, tropospheric anomaly monitoring, and ionospheric anomaly monitoring. In each epoch during the observation period, the data processing center integrates the observation data of each station, selects multiple groups of reference stations to construct double-difference observation equations, and takes reference station AB as an example. Its station-satellite double-difference observation equation for satellite i, j, frequency point f1 is as follows:

[0055]

[0056] Combining the observation equations of each satellite and frequency point, the baseline vector x is solved by continuous filtering. AB , reference station zenith tropospheric delay T r Double difference ionospheric delay Based on multiple sets of reference station baseline vectors, zenith tropospheric delays and double-difference ionospheric delay values, the following monitoring is carried out:

[0057] After the baseline vector is obtained, the difference between the baseline vector solved by the combination of multiple reference stations and the historical storage data is calculated, and all baseline differences related to each reference station are averaged as the test statistic. If the test statistic exceeds the threshold, the reference station position is judged to be abnormal. See Example 5 for the detailed process.

[0058] After obtaining the zenith tropospheric delay of each reference station, the double difference tropospheric residuals of multiple reference station combinations at the minimum elevation angle and the maximum elevation angle are calculated as test statistics. If both are less than the threshold, the troposphere of the service area is marked as normal. Otherwise, the tropospheric abnormal area is determined according to the position of the abnormal reference station and marked. See Example 6 for the detailed process.

[0059] After obtaining the double-difference ionospheric delay value, it is determined whether the current moment is in an active ionospheric period according to the carrier-to-noise ratio joint discrimination result and geomagnetic parameters and other indicators. In the active period and the inactive period, the linear interpolation and plane fitting methods are used to interpolate and fit the ionospheric delay value, and the integrity parameters are calculated. The detailed process is shown in Example 7.

[0060] Step 110: The data processing center conducts comprehensive judgment and correction number generation. According to the judgment results of steps 106-109, the abnormal satellite or reference station data is excluded, and the reference station A and a nearby reference station B are selected according to the approximate position of the user. The differential correction number from the reference station to the user and the differential correction number from the reference station to the selected reference station are calculated using normal observation data. In addition, based on the long-term statistical results, the standard deviation of pseudorange and carrier measurement errors, the standard deviation of tropospheric correction residuals, and the standard deviation of ionospheric correction residuals are given for calculating the differential correction integrity parameters.

[0061] After completing single fault monitoring, the probability of occurrence of various types of faults is the single fault monitor missed detection rate. Although the probability is low, there may still be multiple sub-monitors missed detection resulting in the comprehensive deviation exceeding a specific threshold, so comprehensive fault monitoring is required.

[0062] The second level of monitoring is comprehensive fault monitoring: the data processing center selects the base station and adjacent reference stations according to the user's approximate location, generates differential corrections for them, and uses the observation data and differential corrections of the base station and adjacent reference stations to carry out fault detection based on solution separation according to the following steps 201-204 to eliminate comprehensive faults in the observation values ​​and corrections.

[0063] In the aforementioned step 110, after eliminating the single fault, two groups of differential corrections are generated, one group is for the reference station and the user, and the other group is for the reference station A and the reference station B. The present invention uses the observation data of the reference station A and the reference station B and the generated corrections to perform another round of fault monitoring on the corrections containing various types of comprehensive faults. If the comprehensive error in the correction can meet the performance index requirements, it is considered that the generated differential correction between the reference station A and the user can also meet the integrity index requirements. The known reference station positions can provide more redundant information for the monitoring process and improve the monitoring efficiency. The second-level comprehensive fault monitoring is carried out based on the idea of ​​solution separation and verification, and its process is as follows: Figure 3 As shown, including:

[0064] Step 201: Based on the differential correction data of base station A and reference station B generated by the data processing center And the processed observation data, construct the differential observation equation:

[0065]

[0066] Step 202: Calculate the maximum number of faults that need to be monitored. The probability of occurrence of various types of faults that may be included in is the missed detection rate of the single fault monitor. Although the probability is low, there may still be a situation where multiple sub-monitors miss detection, causing the comprehensive deviation to exceed a specific threshold. Therefore, this paper draws on the idea of ​​the ARAIM algorithm, allocates P_unmonitored, and determines the maximum number of faults that need to be considered.

[0067] Step 203: Calculate the prior probability of various fault combinations and conduct fault detection. Calculate the probability of the fault combination based on the prior probability of various faults, adopt the solution separation idea, calculate the location solutions corresponding to the no-fault and various fault combinations, eliminate the fault subsets that exceed the threshold and have the largest deviation, and then re-test, otherwise repeat the elimination process until the number of eliminated faults reaches the maximum number of faults that need to be monitored. If it still fails to pass the test at this time, an unavailable alarm is given.

[0068] Step 204: Calculate the protection level and make a judgment based on the overall risk allocation. According to the inspection results of step 203, the required integrity risk indicators are combined to calculate the protection level in the positioning domain: if the protection level exceeds the maximum allowable deviation of the given positioning domain, mark the epoch correction number as infeasible; otherwise, trust the generated correction number and broadcast it to the user.

[0069] In an exemplary embodiment, S2 may be replaced by the following steps.

[0070] The signal quality of the observation data is monitored using the carrier-to-noise ratio, and the observation data whose carrier-to-noise ratio exceeds a threshold is marked.

[0071] Perform pseudorange gross error detection and carrier cycle slip detection on the marked observation data to confirm the current observation data.

[0072] The carrier observation value of the current observation data and the historical observation value are combined to calculate the carrier acceleration-ramp-step value, and the satellite clock fault is determined according to the carrier acceleration-ramp-step value, and the faulty satellite is marked.

[0073] Based on the fault satellite labeling results, the fault discrimination matrix and comprehensive observation data corresponding to each reference station are generated.

[0074] A comprehensive discrimination matrix is ​​determined according to each fault discrimination matrix, and a joint discrimination of the carrier-to-noise ratio of multiple reference stations and a discrimination of the satellite clock fault of multiple reference stations are performed based on the comprehensive discrimination matrix. At the same time, the comprehensive observation data is monitored for anomaly of ephemeris fault, anomaly of reference station position, anomaly of regional troposphere and anomaly of regional ionosphere.

[0075] The first-level monitoring results are determined based on the joint discrimination results and the abnormal monitoring results.

[0076] In summary, the RTK server integrity monitoring method based on the CORS network provided in the present application includes a multi-station joint monitoring method for signal quality anomalies based on the carrier-to-noise ratio (i.e., Example 2, signal quality anomaly monitoring), a multi-station joint monitoring method for satellite clock anomalies (i.e., Example 3, satellite clock anomaly monitoring), a multi-station joint monitoring method for ephemeris anomalies (i.e., Example 4, ephemeris failure anomaly monitoring), a multi-station joint monitoring method for reference station position anomalies (i.e., Example 5, reference station position anomaly monitoring), a multi-station joint monitoring method for regional tropospheric anomalies (i.e., Example 6, regional tropospheric anomaly monitoring) and a multi-station joint monitoring method for regional ionospheric anomalies (i.e., Example 7, regional ionospheric anomaly monitoring).

[0077] Example 2

[0078] In an exemplary embodiment, the joint determination of the carrier-to-noise ratios of multiple reference stations specifically includes the following steps.

[0079] If the single-station single-satellite carrier-to-noise ratio is lower than the first threshold and there is no time correlation, it is determined that a single-station single-satellite signal sporadic failure occurs; wherein the single station is a single reference station.

[0080] If the single-station single-satellite carrier-to-noise ratio is lower than the first threshold and there is time correlation, it is determined that a single-station single-satellite obstruction fault occurs.

[0081] If multiple stations simultaneously observe that the carrier-to-noise ratio of a single satellite is lower than the first threshold, it is determined that a single satellite signal failure occurs; wherein the multiple stations are multiple reference stations.

[0082] If the multi-station multi-satellite carrier-to-noise ratio is lower than the first threshold, and the multi-station positions are within the set distance range and the number is less than or equal to 3, it is determined that a regional interference fault occurs.

[0083] If the multi-satellite carrier-to-noise ratio observed by a single station is lower than the first threshold, it is determined that a reference station receiver failure occurs.

[0084] If the carrier-to-noise ratio of multiple stations and multiple satellites is lower than the first threshold, and the multiple stations are not adjacent to each other or the number of adjacent stations is greater than 3, it is determined that a regional ionospheric abnormality fault occurs; wherein the single-satellite signal fault is a satellite segment fault; the regional ionospheric abnormality fault is a propagation segment fault; the single-station single-satellite signal occasional fault, the single-station single-satellite obstruction fault, the regional interference fault and the reference station receiver fault are ground segment faults.

[0085] In practical applications, a multi-station joint monitoring method for signal quality anomalies based on carrier-to-noise ratio is proposed. Figure 5 As shown, including:

[0086] Step 301: Each reference station uses historical observation data to establish a carrier-to-noise ratio anomaly model. The present invention uses the signal-to-noise ratio to carry out signal quality anomaly monitoring. Since the carrier-to-noise ratio level is correlated with the receiver model, environmental factors, and satellite elevation angle, each reference station needs to use historical carrier-to-noise ratio data for independent modeling. The present invention uses interval modeling methods to distinguish all satellite carrier-to-noise ratio data of a single station according to specific elevation angle intervals. In each interval, a specific quantile is selected as the threshold according to the index requirements.

[0087] Step 302: At each epoch in the aforementioned observation time period, each reference station independently performs carrier-to-noise ratio abnormality discrimination, determines the corresponding threshold value according to the satellite elevation angle, and establishes a fault discrimination matrix for each observation satellite and frequency point according to the method in step 101. For observation satellites and frequencies with a carrier-to-noise ratio less than the threshold, a carrier-to-noise ratio abnormality mark is added to the corresponding position of the fault discrimination matrix, and the fault discrimination matrix is ​​transmitted to the data processing center.

[0088] Step 303: The data processing center performs comprehensive judgment based on the carrier-to-noise ratio judgment matrix of each observation station, and classifies the signal anomaly into six types of faults according to the occurrence of the signal anomaly in each satellite and each observation station. The judgment process is as follows:

[0089] 1) Determine whether there is only a single satellite carrier-to-noise ratio anomaly in the current epoch. If so, go to step 2); if not, go to step 4).

[0090] 2) Determine whether multiple stations have observed the satellite anomaly. If so, it is considered that the carrier-to-noise ratio anomaly originates from the satellite end, and the satellite is marked as having a single satellite signal failure, that is, failure type 3; if not, go to step 3).

[0091] 3) Determine whether the single-satellite carrier-to-noise ratio anomaly observed by a single station has time correlation, that is, the phenomenon occurs at repeated intervals in the historical observation period. If so, and auxiliary confirmation is carried out in combination with the actual environment of the observation station, it is considered that the anomaly is caused by the obstruction around the reference station, and it is marked as a single-station single-satellite obstruction fault, that is, fault type 2; if not, it is determined to be a single-station single-satellite signal sporadic fault, that is, fault type 1.

[0092] 4) Determine whether there are multiple reference stations with abnormal multi-satellite carrier-to-noise ratio at the same time. If so, go to step 5; if not, it is considered that there is a fault in the reference station receiver, resulting in a decrease in the carrier-to-noise ratio of multiple satellites received, and the fault type 5 is marked.

[0093] 5) Determine whether the observation stations where the multi-satellite carrier-to-noise ratio anomalies are observed are adjacent. If so, and the number of adjacent observation stations is less than or equal to 3, it is considered that there is regional interference near these observation stations, and the fault type is marked as 4; if not, it means that electromagnetic interference exists in a large range of the entire region, and it is preliminarily determined to be caused by ionospheric anomalies, and the fault type is marked as 6.

[0094] The faults are further classified according to their sources. Among the six types of faults mentioned above, fault type 3 belongs to abnormal signal quality in the satellite segment, fault type 6 belongs to abnormal ionosphere in the propagation segment, and the rest belong to abnormal signal quality in the reference station in the ground segment.

[0095] Step 304: After differentiating the faults, different treatments are performed. For fault types 1 to 5, the observation data of abnormal satellites or abnormal observation stations are excluded; for fault type 6, it is necessary to combine the subsequent ionospheric anomaly monitoring to further confirm whether the ionospheric anomaly occurs and the scope of the impact, but the discrimination result can be used as an indicator of the subsequent monitor to indicate the possible risk of ionospheric anomaly.

[0096] Example 3

[0097] In an exemplary embodiment, the multi-reference station satellite clock fault determination specifically includes:

[0098] Each reference station is instructed to calculate a first test statistic of multiple epochs of each satellite respectively; the first test statistic includes carrier acceleration, slope and step value.

[0099] A second threshold is determined according to the distribution characteristics of the first test statistic and a second false positive rate indicator.

[0100] According to the second threshold value, the satellite corresponding to any reference station is marked to determine the marking result; the marking result includes satellite abnormality, alarm flag is 1 and satellite is normal.

[0101] Comprehensively judge any satellite according to the marking results of all reference stations to determine the satellite judgment result; the satellite judgment result includes whether the satellite is a faulty satellite or a normal satellite.

[0102] In practical applications, a multi-station joint monitoring method for satellite clock anomalies is as follows: Figure 6 As shown, including:

[0103] Step 401: Each reference station independently obtains observation data and calculates carrier observation corrections. In each epoch within the aforementioned observation period, each reference station uses single-frequency carrier observations to calculate carrier observation corrections for each satellite observation channel: first, the satellite position, i.e., the satellite clock error correction, is calculated based on the verified ephemeris, and the station-satellite distance is calculated based on the reference station position, and the station-satellite distance and the satellite clock error correction are subtracted from the carrier observation to obtain the carrier correction at the current moment; secondly, the carrier correction at the reference moment is selected and subtracted from the carrier correction at the current epoch to obtain the normalized carrier correction; finally, the normalized carrier corrections of all satellites are averaged and subtracted from the normalized carrier corrections of each satellite to obtain the carrier correction after eliminating the receiver clock bias.

[0104] Step 402: Each reference station calculates the ARS test statistic of each observed satellite and generates a fault discrimination matrix. For each epoch k in the observation period, the carrier observation correction number of satellite i is fitted with a quadratic polynomial using the carrier correction numbers of n epochs (from epoch k-n+1 to epoch k). The obtained quadratic term coefficients, linear term coefficients and constant term coefficients are taken as the acceleration A, ramp R and step S error test statistics, and the number of them exceeding the threshold is counted: if the number exceeding the threshold is greater than or equal to 2, the satellite clock is marked as abnormal, and the alarm flag is 2; if the number exceeding the threshold is 1, the alarm flag is marked as 1 and transmitted to the data processing center for multi-station joint discrimination; if the number exceeding the threshold is 0, the satellite is marked as normal.

[0105] Step 403: The data processing center receives the fault matrices of each reference station and conducts comprehensive judgment. The judgment criteria are: for satellite i, if there is at least one reference station that marks the alarm mark 2, or at least two reference stations mark it with the alarm mark 1, it is judged as a faulty satellite; in other cases, it is judged to be normal. For a faulty satellite, all its carrier and pseudorange observation values ​​are eliminated.

[0106] Example 4

[0107] In an exemplary embodiment, the monitoring process of satellite ephemeris failure specifically includes the following steps.

[0108] When monitoring Type A satellite ephemeris fault anomalies, a multi-baseline anomaly detector is constructed based on multi-station observation data to carry out fault monitoring, including:

[0109] Based on multi-station observation data, a dual-bandwidth lane carrier observation equation is constructed for each satellite, and the second test statistic is selected.

[0110] According to the statistical characteristics of historical measurement errors and the selected number of wide-lane ambiguity filtering epochs, the distribution characteristics of the second test statistic are determined, and the test threshold is determined according to the second false positive rate indicator to evaluate the theoretical missed detection rate.

[0111] Determine whether the theoretical missed detection rate meets the index requirements.

[0112] If so, solve the wide lane ambiguity of the dual-frequency wide lane carrier observation equation and calculate the value of the second test statistic.

[0113] If the value of the second test statistic is less than the test threshold, the satellite position is marked as normal.

[0114] If the value of the second test statistic is not less than the test threshold, the satellite position is marked as abnormal.

[0115] If not, replace the second test statistic and re-evaluate the theoretical missed detection rate until the theoretical missed detection rate meets the index requirement. If the theoretical missed detection rate still does not meet the index requirement after multiple rounds of evaluation, output an alarm indicating that the multi-baseline anomaly detector is unavailable.

[0116] When monitoring Type B satellite ephemeris fault anomalies, multi-frequency ephemeris data and historical ephemeris are used to design a composite monitor based on time correlation and consistency between frequencies to carry out fault monitoring, including:

[0117] Based on multi-frequency ephemeris data, ephemeris time correlation monitoring and inter-frequency consistency monitoring are carried out simultaneously.

[0118] In the time correlation monitoring, the satellite positions are extrapolated and calculated using the current ephemeris and the verified historical ephemeris, and the position difference between the two satellite positions is determined as the third test statistic.

[0119] A third threshold is determined according to the distribution characteristics of the third test statistic and a third false positive rate indicator.

[0120] A first alarm indicator is marked according to the third test statistic and the third threshold value.

[0121] In the inter-frequency point consistency monitoring, satellite positions are calculated respectively using ephemeris parameters of different frequency points, and the position difference of different satellite positions is determined as a fourth test statistic.

[0122] A fourth threshold is determined according to the distribution characteristics of the fourth test statistic and a fourth false positive rate indicator.

[0123] A second alarm indicator is marked according to the fourth test statistic and the fourth threshold.

[0124] A comprehensive alarm flag is determined according to the first alarm flag and the second alarm flag; the comprehensive alarm flag includes abnormal ephemeris and normal ephemeris.

[0125] In practical applications, a multi-station joint monitoring method for ephemeris anomalies includes monitoring of satellite maneuver anomalies, namely Type A faults, and ephemeris annotation anomalies, namely Type B faults.

[0126] Type A ephemeris fault abnormal monitoring process is as follows Figure 7 As shown, including:

[0127] Step 501: Construct a dual-frequency wide-lane carrier observation equation and select a test statistic. In each epoch within the aforementioned observation period, the data processing center integrates the carrier observation data of each reference station, selects a group of observation stations with a longer distance, and constructs a double-difference wide-lane carrier observation equation. According to the constructed observation equation, the test statistic is selected as: the wide-lane carrier measurement value minus the projection of the baseline on the reference station inter-satellite single difference direction vector, and then minus the product of the double-difference wide-lane ambiguity and the wide-lane wavelength.

[0128] Step 502: According to the statistical characteristics of historical measurement errors and related parameter settings, the distribution characteristics of the test statistic are obtained. It can be seen from the expression in step 501 that the test statistic includes not only the ephemeris error term, but also the ionospheric residual, the tropospheric residual and the observation noise term. In addition, the ambiguity-related terms need to be subtracted from the formula, and there may be errors in the ambiguity fixation process. Therefore, the test statistic does not obey a simple normal distribution, but presents a "multi-peak" form, and different "peaks" reflect the ambiguity fixation deviation. In order to obtain the test statistic threshold, it is necessary to obtain the distribution characteristics of the test statistic, and further it is necessary to obtain the distribution characteristics of the ambiguity.

[0129] The present invention uses a continuous filtering and rounding method to fix the wide lane ambiguity. First, a double-difference ionosphere-free combined observation is constructed, and the wide lane ambiguity is obtained by averaging the multi-epoch observations. It can be seen that the wide lane ambiguity estimation error obeys the normal distribution, and its standard deviation can be calculated based on the original observation noise. If the ambiguity filtering time is long, the probability of its fixed error is low. Therefore, when considering its impact on the distribution of the test statistic, only the case of a deviation of ±1 week can be considered. At this point, combined with the formula in step 501, the distribution characteristics of the test statistic can be obtained.

[0130] Step 503: Derivation of the test threshold according to the assigned false detection rate index, and evaluation of whether the theoretical missed detection rate meets the index requirements. After obtaining the distribution characteristics of the test statistic, the threshold value can be solved according to the given false detection rate index. Further, according to the distribution characteristics of the test statistic and the threshold value, the curve of the change of the theoretical false detection rate with the deviation can be calculated, and the performance of the monitor can be evaluated accordingly: 1) If the theoretical missed detection rate meets the performance index requirements, the wide lane ambiguity is solved and the test statistic value is calculated to determine whether it is less than the threshold value: if it is less than the threshold, the satellite position is marked as normal; otherwise, the satellite position is marked as abnormal; 2) If the theoretical missed detection rate cannot meet the index requirements, it is necessary to replace the test statistic for re-evaluation, select four reference stations to construct a double baseline test statistic, return to step 502 for re-performance evaluation, if it still cannot meet the missed detection rate performance index, continue to increase the number of baselines, and so on until the number of baselines exceeds 5. If it still cannot meet the requirements, the monitor is given an unavailable alarm.

[0131] Type B ephemeris fault abnormal monitoring process is as follows Figure 8 As shown, including:

[0132] Step 601: At the time of ephemeris update, the data processing center first compares the parameters of multi-station navigation messages for consistency, and eliminates abnormalities in the process of information reception, processing and decoding. For the navigation ephemeris parameters broadcast by satellite i at frequency j, if the message parameters provided by each reference station are consistent, the test is passed; if they are inconsistent, the principle of minority obeys majority is adopted to select the parameters provided by the majority of reference stations; if the message parameters of all reference stations are inconsistent, an ephemeris unavailable alarm is directly given, and the alarm flag is 1.

[0133] Step 602: synchronously carry out time correlation monitoring and frequency consistency monitoring on the navigation ephemeris of each satellite to eliminate satellite position anomalies caused by ephemeris annotation.

[0134] Time correlation monitoring process:

[0135] 1) Determine whether there is a verified historical ephemeris. If so, proceed to step 2). If not, proceed to step 3).

[0136] 2) Use the current ephemeris and historical ephemeris to extrapolate and calculate the satellite position, and use the three-dimensional position difference calculated by the two as the test statistic. If two of the test statistics exceed the threshold, the alarm mark 2 is marked; in other cases, the ephemeris data is marked as normal. The test threshold is determined according to the distribution characteristics of the historical test statistics and the false alarm rate index.

[0137] 3) If there is no historically verified ephemeris, no time correlation judgment is performed, but the current ephemeris is initialized and verified. The specific method is: within 2 minutes after the ephemeris is updated, the new ephemeris parameters are used to select the observation data of a reference station for pseudo-range single-point positioning, and the average value of the three-dimensional positioning error obtained by the two is used as the test statistic. If two of the test statistics exceed the threshold, the current ephemeris is considered unusable, and the subsequent updated ephemeris also needs to be initialized and verified; if only 1 or 0 test statistics exceed the threshold, the current ephemeris is considered usable, and the subsequent updated ephemeris can carry out Type B anomaly monitoring normally.

[0138] Frequency consistency monitoring process:

[0139] 1) Determine whether the ephemeris of different frequency points is complete. If it is complete, go to step 2). If it is incomplete, mark the alarm mark 3.

[0140] 2) Use different frequency point ephemeris parameters to calculate the satellite position, and use the three-dimensional position difference calculated by the two as the test statistic. If two of the test statistics exceed the threshold, the alarm mark 2 is marked; in other cases, the ephemeris data is marked as normal. The test statistic threshold is determined according to the distribution characteristics of the historical test statistics and the false alarm rate index.

[0141] Step 603: Based on the above determination results, if any alarm mark exists for satellite i, the satellite ephemeris is determined to be abnormal; otherwise, the satellite ephemeris is determined to be normal.

[0142] Example 5

[0143] In an exemplary embodiment, the monitoring process of the reference station position anomaly specifically includes the following steps.

[0144] A reference station combination is selected, and continuous filtering is performed with the baseline vector, the zenith pair process error of each reference station and the ambiguity as unknowns to calculate the difference between the baseline vector of multiple reference station combinations and the historical stored data.

[0145] The average value of all differences associated with each reference station is taken as the fifth test statistic, and the maximum allowable reference station position deviation allocated in the performance indicator is taken as the fifth threshold.

[0146] Whether the reference station position is abnormal is determined according to the fifth test statistic and the fifth threshold value.

[0147] In practical applications, a multi-station joint monitoring method for abnormal reference station positions is as follows: Fig. 9 As shown, including:

[0148] Step 701: Obtain observation data from each reference station and construct the station-satellite double difference observation equation. During the aforementioned observation period, at one-hour intervals, the data processing center integrates the carrier observation data from each reference station and constructs multiple sets of station-satellite double difference observation equations as shown in formula (3). In order to verify the effectiveness, each reference station constructs the observation equation with at least three adjacent reference stations.

[0149] Step 702: Solve for each group of baseline vectors and construct a test statistic. According to the constructed observation equation, a continuous filtering solution is performed with the baseline vector, the zenith tropospheric delay value of each station and the ambiguity as unknowns.

[0150] This embodiment assumes that there are five reference stations AE, and each reference station calculates four sets of baseline vectors. For reference station A, four sets of baseline vectors can be obtained according to the above solution process: L AB,cal ,L AC,cal ,L AD,cal ,L AE,cal , according to the historical baseline vector L stored at the reference station AB,res ,L AC,res ,L AD,res ,L AE,res , taking baseline AB as an example, according to L AB,res and L AB,cal The difference between the two can be calculated as AB ={δx AB ,δy AB ,δz AB}, where δx AB ,δy AB ,δz AB Under normal circumstances, they all obey a normal distribution with a mean of 0 and a standard deviation equal to the standard deviation of the positioning error in that direction, that is, The same goes for the other baselines.

[0151] If the position of reference station A is abnormal, its 3D position error will appear in all baselines based on A. If all baseline differences based on A are averaged, we can get where ε A ={δx A ,δy A ,δz A} is the offset vector from the storage position of reference station A to the abnormal position. At the same time, since the averaging process also adjusts the position error, assuming that the standard deviation of the position error of each baseline is the same, we have

[0152] For the baseline vectors centered at the other observation stations, only the baseline pointing to reference station A has an anomaly. Taking reference station B as an example, averaging them yields

[0153] Taking into account the possibility that multiple reference station locations may be abnormal at the same time, for the sake of conservatism and convenience, the absolute values ​​of the horizontal and vertical errors of each baseline corresponding to each reference station are averaged as the test statistic to determine whether there is an abnormality in the reference station location.

[0154] Step 703: Calculate the test statistic value of each reference station and make a judgment based on the test threshold: if the test statistic is less than the threshold, mark the reference station as being in a normal position; if the test statistic exceeds the threshold, further check whether the test statistic of the adjacent reference station exceeds the test statistic at the historical normal time. If the test statistic of most reference stations exceeds the historical normal value, the reference station is judged to be abnormal, and its observation data needs to be excluded and its position is relocated and solved. The threshold value is obtained according to the performance indicator allocation.

[0155] Example 6

[0156] In an exemplary embodiment, the process of monitoring tropospheric anomalies specifically includes the following steps.

[0157] A reference station combination is selected, and continuous filtering is performed with the baseline vector, the zenith pair process error of each reference station, and the ambiguity as unknowns to calculate the double-difference tropospheric residuals of each reference station combination at the minimum and maximum elevation angles.

[0158] The double difference process residual is used as a sixth test statistic, and the maximum allowable tropospheric residual allocated in the performance indicator is used as a sixth threshold.

[0159] Whether the process is abnormal is determined according to the sixth test statistic and the sixth threshold.

[0160] In practical applications, a multi-station joint monitoring method for regional tropospheric anomalies is proposed. The process is as follows: Fig.10 As shown, including:

[0161] Step 801: Obtain observation data from each reference station and construct the station-satellite double difference observation equation as shown in formula (3). In each epoch of the aforementioned observation period, the data processing center integrates the carrier observation data from each reference station and constructs multiple sets of station-satellite double difference observation equations to ensure that each reference station constructs an observation equation with at least one adjacent reference station.

[0162] Step 802: Solve each group of baseline vectors and construct a test statistic. Perform continuous filtering and solving according to the constructed observation equation to extract the zenith tropospheric delay value of each reference station. The extracted zenith tropospheric delay includes two parts: dry component and wet component. The dry component accounts for 90% of the overall tropospheric delay of the station and can be accurately corrected by the model; while the wet component is regional depending on the region and climate. Generally, the wet component of the zenith direction tropospheric delay is used as an estimated parameter and projected to the tilt direction through a projection function. Since the dry component can be accurately corrected, tropospheric anomalies are more likely to appear in the wet component part and are greatly affected by the elevation angle. For this reason, the 15° elevation angle and 90° elevation angle conditions are selected to obtain the tropospheric double difference residual as the test statistic of the tropospheric anomaly. This value is independent of the actual satellite position and can reflect the maximum value of the tropospheric delay correction residual between reference stations.

[0163] Step 803: Joint identification of tropospheric anomalies by multiple stations. Calculate the maximum value of the double-difference tropospheric correction residual of each reference station combination for anomaly identification: if the test statistics of each reference station are less than the threshold, the troposphere in the marked area is normal; if there is at least one group of test statistics exceeding the threshold, the abnormal area is marked according to the distribution of the reference stations, that is, the union of the circular areas with the reference station combination baseline as the diameter is the abnormal area. If the user is located in the abnormal area, the tropospheric delay value needs to be additionally corrected, and the standard deviation of the tropospheric residual error is amplified.

[0164] Example 7

[0165] In an exemplary embodiment, the ionospheric anomaly monitoring process specifically includes the following steps.

[0166] The observation equation is constructed based on multi-station observation data.

[0167] The observation equation is solved by filtering, and the ambiguity is fixed to extract the double difference ionospheric delay value.

[0168] The combined results of carrier-to-noise ratio and electromagnetic field parameters are used to determine whether the current moment is in an active ionospheric period.

[0169] During the active and inactive periods of the ionosphere, the linear interpolation and plane fitting methods are used to interpolate and fit the double-difference ionospheric delay values, and the integrity parameters are calculated.

[0170] A multi-station joint monitoring method for regional ionospheric anomalies, the process is as follows Fig.11 As shown, including:

[0171] Step 901: Obtain observation data from each reference station and construct a double-difference observation equation. In each epoch of the aforementioned observation period, the data processing center integrates the carrier observation data from each reference station and selects multiple groups of reference stations to construct a double-difference observation equation, ensuring that each reference station constructs an observation equation with at least one adjacent reference station.

[0172] Step 902: Solve the integer ambiguity and extract the double-difference ionospheric delay value. For the selected observation station combination, first solve the double-difference wide-lane integer ambiguity, and verify whether the double-difference wide-lane integer ambiguity is accurate by checking whether the sum of the closed baseline double-difference wide-lane integer ambiguity composed of three reference stations is equal to 0; secondly, use the relationship satisfied by the ionosphere-free combination integer ambiguity, the wide-lane double-difference integer ambiguity and the L1 carrier double-difference integer ambiguity to obtain the L1 double-difference ambiguity floating-point solution through filtering and further solve the fixed solution; finally, based on the calculated ambiguity fixed solution, use the ionosphere-free combination formed by the dual-frequency carrier phase measurement value to estimate the double-difference ionospheric delay.

[0173] Step 903: Comprehensively determine whether the current moment is in an active ionospheric period by combining various indicators. After extracting the ionospheric delay, it needs to be interpolated to compensate for the ionospheric residual from the user to the base station and calculate the relevant integrity parameters. The appropriate interpolation methods are different in the active ionospheric period and the quiet period, so it is necessary to first determine the current ionospheric state. This patent uses the aforementioned carrier-to-noise ratio anomaly judgment result and the geomagnetic index for judgment. If it exceeds the set threshold, it is considered that the current moment is in an active ionospheric period; otherwise, it is considered to be in a quiet ionospheric period.

[0174] Step 904: Select different methods to perform ionospheric delay value interpolation fitting according to the ionospheric activity state, and calculate integrity parameters.

[0175] If it is determined that the current ionosphere is active, the linear fitting method is used for interpolation fitting, and the integrity parameters of the ionosphere correction term are further calculated. The steps are as follows: 1) For satellite j, the difference between the ionosphere vertical delay value estimated based on linear fitting and the vertical delay value calculated based on dual-frequency observations is calculated. 2) Within a time interval (m epochs), perform statistical analysis on the vertical error sequence composed of the difference values, and calculate the mean and standard deviation of the user's vertical delay value; 3) For the observation data of the mth epoch, estimate the absolute error deviation of the vertical delay at the user based on the absolute value of the IPP residual; 4) Generate a conservative error limit boundary E for the IPP of each CORS station involved in positioning i ; 5) Get the integrity parameter GIVE of satellite j, that is, the estimated error, the maximum error max in the k sequences k (E k ) and quantization error sum.

[0176] If it is determined that the current period is a quiet ionospheric period, the plane fitting method is used for interpolation fitting, and the integrity parameters of the ionospheric correction items are further calculated. The steps are as follows: 1) The ionospheric vertical delay value is estimated by the IPP of the CORS station within the fitting threshold around the user station, and n Kriging fitting coefficients are found to achieve the linear unbiased minimum mean square error estimation of the double-difference ionospheric vertical delay between stations at the ionospheric penetration point; 2) The Kriging variance is calculated based on the estimated Kriging coefficients; 3) The GIVE, i.e., the integrity parameter, which characterizes the uncertainty of the ionospheric delay estimation, is obtained based on the required envelope rate index, comprehensive Kriging variance, undersampling variance, and quantization error parameters.

[0177] Step 905: Output the ionospheric fitting results and integrity parameters at the current moment.

[0178] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0179] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for monitoring the integrity of an RTK server based on a CORS network, characterized in that: The RTK server integrity monitoring method based on the CORS network includes: Obtaining observation data obtained by multiple reference stations in the CORS network in the same region at each epoch during an observation period; the observation data includes satellite ephemeris and pseudorange carrier measurement values; Based on the distributed monitoring scheme and hierarchical monitoring ideas, fault monitoring is performed on the receiver faults, signal quality anomalies and satellite clock anomalies in the observation data of each reference station, and the fault discrimination matrix of each type of fault is determined; Based on the data processing center, the fault discrimination matrix corresponding to each reference station is integrated to carry out joint discrimination, and the multi-station observation data is used to monitor the ephemeris fault, reference station position fault, regional ionosphere anomaly and regional troposphere anomaly to obtain the first-level monitoring results; the first-level monitoring results are the discrimination results for each single fault; Based on the first-level monitoring results, a base station and an adjacent reference station are selected according to the approximate position of the user, and an initial differential correction number is determined; the initial differential correction number includes a differential correction number from the base station to the user and a differential correction number from the base station to the adjacent reference station; Based on the initial differential correction numbers, the comprehensive faults contained in the observation data of each reference station are monitored to determine the differential correction numbers and integrity parameters that are ultimately broadcast to the user.

2. The RTK server integrity monitoring method based on CORS network according to claim 1 is characterized in that: Based on the distributed monitoring scheme and hierarchical monitoring ideas, fault monitoring is performed on the receiver failures, signal quality anomalies, and satellite clock anomalies in the observation data of each reference station, and the fault discrimination matrix of each type of fault is determined to obtain the first-level monitoring results, including: Performing signal quality monitoring on the observation data using the carrier-to-noise ratio, and marking the observation data whose carrier-to-noise ratio exceeds a threshold; Perform pseudo-range gross error detection and carrier cycle slip detection on the marked observation data to confirm the current observation data; Calculate the carrier acceleration-ramp-step value based on the carrier observation value of the current observation data and the historical observation value, and identify the satellite clock fault according to the carrier acceleration-ramp-step value, and mark the faulty satellite; Based on the fault satellite annotation results, the fault discrimination matrix and comprehensive observation data corresponding to each reference station are generated; Based on the data processing center, the fault discrimination matrix corresponding to each reference station is integrated to carry out joint discrimination, and multi-station observation data is used to monitor ephemeris faults, reference station position faults, regional ionosphere anomalies, and regional troposphere anomalies to obtain the first-level monitoring results, including: Determine a comprehensive discrimination matrix according to each fault discrimination matrix, and perform joint discrimination of carrier-to-noise ratios of multiple reference stations and discrimination of satellite clock faults of multiple reference stations based on the comprehensive discrimination matrix, and at the same time, perform ephemeris fault anomaly monitoring, reference station position fault anomaly monitoring, regional tropospheric anomaly monitoring, and regional ionospheric anomaly monitoring on the comprehensive observation data; The first-level monitoring results are determined based on the joint discrimination results and the abnormal monitoring results.

3. The RTK server integrity monitoring method based on CORS network according to claim 2 is characterized in that: The multi-reference station carrier-to-noise ratio joint determination specifically includes: If the single-station single-satellite carrier-to-noise ratio is lower than the first threshold and there is no time correlation, it is determined that a single-station single-satellite signal sporadic failure occurs; wherein the single station is a single reference station; If the single-station single-satellite carrier-to-noise ratio is lower than the first threshold and there is a time correlation, it is determined that a single-station single-satellite obstruction fault occurs; If multiple stations simultaneously observe that the carrier-to-noise ratio of a single satellite is lower than the first threshold, it is determined that a single satellite signal failure occurs; wherein the multiple stations are multiple reference stations; If the multi-station multi-satellite carrier-to-noise ratio is lower than the first threshold, and the multi-station positions are within the set distance range and the number is less than or equal to 3, it is determined that a regional interference fault occurs; If the multi-satellite carrier-to-noise ratio observed by a single station is lower than the first threshold, it is determined that a reference station receiver failure occurs; If the carrier-to-noise ratio of multiple stations and multiple satellites is lower than the first threshold, and the multiple stations are not adjacent to each other or the number of adjacent stations is greater than 3, it is determined that a regional ionospheric abnormality fault occurs; wherein the single-satellite signal fault is a satellite segment fault; the regional ionospheric abnormality fault is a propagation segment fault; the single-station single-satellite signal occasional fault, the single-station single-satellite obstruction fault, the regional interference fault and the reference station receiver fault are ground segment faults.

4. The RTK server integrity monitoring method based on CORS network according to claim 2 is characterized in that: The multi-reference station star clock fault determination specifically includes: Instruct each reference station to calculate a first test statistic of multiple epochs of each satellite; the first test statistic includes carrier acceleration, slope, and step value; Determining a second threshold value according to the distribution characteristics of the first test statistic and a second false positive rate indicator; According to the second threshold, a satellite corresponding to any reference station is marked to determine a marking result; the marking result includes satellite abnormality, an alarm flag of 1, and a satellite normal; Comprehensively judge any satellite according to the marking results of all reference stations to determine the satellite judgment result; the satellite judgment result includes whether the satellite is a faulty satellite or a normal satellite.

5. The RTK server integrity monitoring method based on CORS network according to claim 1 is characterized in that: The monitoring process of the satellite ephemeris failure specifically includes: When monitoring Type A satellite ephemeris fault anomalies, a multi-baseline anomaly detector is constructed based on multi-station observation data to carry out fault monitoring, including: Based on multi-station observation data, a dual-bandwidth lane carrier observation equation is constructed for each satellite, and the second test statistic is selected; Determine the distribution characteristics of the second test statistic according to the statistical characteristics of the historical measurement errors and the number of epochs of the selected wide lane ambiguity filter, determine the test threshold according to the second false positive rate indicator, and evaluate the theoretical missed detection rate; Determine whether the theoretical missed detection rate meets the index requirement; determine the distribution characteristics of the second test statistic according to the statistical characteristics of historical measurement errors and the number of selected wide lane ambiguity filtering epochs, and determine the test threshold according to the second false positive rate index to evaluate the theoretical missed detection rate; If yes, solving the widelane ambiguity of the dual-frequency widelane carrier observation equation and calculating the value of the second test statistic; If the value of the second test statistic is less than the test threshold, the satellite position is marked as normal; If the value of the second test statistic is not less than the test threshold, marking the satellite position as abnormal; If not, replace the second test statistic and re-evaluate the theoretical missed detection rate until the theoretical missed detection rate meets the index requirement; if after multiple rounds of evaluation, the theoretical missed detection rate still does not meet the index requirement, output an unavailable alarm for the multi-baseline anomaly detector; When monitoring Type B satellite ephemeris fault anomalies, multi-frequency ephemeris data and historical ephemeris are used to design a composite monitor based on time correlation and consistency between frequencies to carry out fault monitoring, including: Based on multi-frequency ephemeris data, ephemeris time correlation monitoring and inter-frequency consistency monitoring are carried out simultaneously; In the time correlation monitoring, the satellite positions are extrapolated and calculated using the current ephemeris and the verified historical ephemeris, and the position difference between the two satellite positions is determined as a third test statistic; Determining a third threshold value according to the distribution characteristics of the third test statistic and a third false positive rate indicator; Marking a first alarm indicator according to the third test statistic and the third threshold value; In the inter-frequency point consistency monitoring, satellite positions are calculated respectively using ephemeris parameters of different frequency points, and position differences of different satellite positions are determined as a fourth test statistic; Determining a fourth threshold value according to the distribution characteristics of the fourth test statistic and a fourth false positive rate indicator; Marking a second alarm indicator according to the fourth test statistic and the fourth threshold value; A comprehensive alarm flag is determined according to the first alarm flag and the second alarm flag; the comprehensive alarm flag includes abnormal ephemeris and normal ephemeris.

6. The RTK server integrity monitoring method based on CORS network according to claim 1 is characterized in that: The monitoring process of the abnormal position of the reference station specifically includes: Select a reference station combination, take the baseline vector, the zenith pair process error of each reference station and the ambiguity as unknowns, carry out continuous filtering solution, and calculate the difference between the baseline vector of multiple reference station combinations and the historical storage data; Taking the average of all differences associated with each reference station as the fifth test statistic, and taking the maximum allowable measurement error allocated in the performance indicator as the fifth threshold; Whether the reference station position is abnormal is determined according to the fifth test statistic and the fifth threshold value.

7. The RTK server integrity monitoring method based on CORS network according to claim 1, characterized in that: The monitoring process of tropospheric anomalies specifically includes: Select a reference station combination, take the baseline vector, the zenith pair process error of each reference station and the ambiguity as unknowns, carry out continuous filtering solution, and calculate the double difference tropospheric residual of each reference station combination at the minimum elevation angle and the maximum elevation angle; Using the double difference process residual as a sixth test statistic, and using the maximum allowable measurement error allocated in the performance indicator as a sixth threshold; Whether the process is abnormal is determined according to the sixth test statistic and the sixth threshold.

8. The RTK server integrity monitoring method based on CORS network according to claim 1, characterized in that: The monitoring process of the ionospheric anomaly specifically includes: Construct observation equations based on multi-station observation data; filtering to solve the observation equation, fix the ambiguity, and extract the double difference ionospheric delay value; Determine whether the current moment is in an active ionosphere period based on the carrier-to-noise ratio joint judgment result and electromagnetic field parameters; During the active and inactive periods of the ionosphere, the linear interpolation and plane fitting methods are used to interpolate and fit the double-difference ionospheric delay values, and the integrity parameters are calculated.

9. The RTK server integrity monitoring method based on CORS network according to claim 1, characterized in that: Based on the initial differential corrections, the comprehensive faults contained in the observation data of each reference station are monitored to determine the differential corrections and integrity parameters that are ultimately broadcast to the user, specifically including: Calculating the maximum number of faults that need to be monitored based on the first-level monitoring result and the initial differential correction number; Based on the maximum number of faults that need to be monitored, combine the faults and calculate the corresponding risk of each fault combination; Calculate the protection level based on the corresponding risk of each fault combination; The differential correction number and integrity parameter finally broadcast to the user are determined according to the protection level and the maximum allowable error.

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