A grid network RTK positioning method, device and system

Abstract

The present application relates to the field of RTK algorithm positioning technology, and specifically to a grid network RTK positioning method, device and system, the method comprising: collecting the location information of all base stations and positioning users in the RTK service area; collecting the observation data of each base station, and then obtaining the mutation time, mutation point and mutation probability of each mutation point of the sequence composed of the observation data of the screening interval of each base station; calculating the abnormal mutation factor of any category of observation data; and then calculating the associated abnormal factor of any category of observation data; obtaining the comprehensive index of each base station; based on the comprehensive index and the location information of the base station and the positioning user, obtaining the reference confidence of each base station, and obtaining the reference base station according to the reference confidence. The present application solves the problem of improper selection of reference base stations during positioning, which leads to delayed or inaccurate positioning of users.

Description

Technical Field

[0001] The present application relates to the field of RTK algorithm positioning technology, and specifically to a grid network RTK positioning method, device and system. Background Art

[0002] The grid network RTK positioning method is an advanced RTK positioning method based on multiple base stations. It establishes multiple base stations in a certain area, uses the synchronous observation data of these base stations for processing, generates differential data and broadcasts it through the communication network, thereby achieving real-time dynamic positioning.

[0003] Grid networks use observation data from multiple ground base stations to enhance positioning signals. However, with the expansion of network RTK service scope and the increase in the number of users, traditional grid network RTK positioning technology usually uses the spatial position information of the positioning user and the base station, the spatial error estimation of the base station, the virtual base station, etc. as the criteria for base station selection. There is a lack of correlation analysis of the base station status in the grid network, which may lead to user positioning delays or inaccurate positioning problems due to improper selection of reference base stations. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a grid network RTK positioning method, device and system. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a grid network RTK positioning method, the method comprising the following steps:

[0006] Collect the location information of all base stations and positioning users in the RTK service area; collect the observation data of each base station, the observation data includes pseudorange observation data, carrier phase observation data, and base station delay data;

[0007] For any type of observation data, a time series data analysis algorithm is used to obtain the mutation time, mutation point and mutation probability of each mutation point of the sequence composed of the observation data of the screening interval of each base station;

[0008] Based on the mutation probability, normality, seasonality and chaos degree of the mutation point of the observed data, the abnormal mutation factor of any category of observed data is obtained;

[0009] Based on the similarity between the mutation moments and the similarity between the mutation probabilities of the observed data, the associated abnormality factor of any category of observed data is obtained;

[0010] Based on the abnormal mutation factor and the associated abnormal factor, the comprehensive index of each reference station is obtained; based on the comprehensive index and the location information of the reference station and the positioning user, the reference confidence of each reference station is obtained, and the reference reference station is obtained according to the reference confidence.

[0011] Furthermore, the method for obtaining the abnormal mutation factor is:

[0012] Based on the mutation probability and normality of the mutation points in the observed data, the mutation difference coefficient of each mutation point in any category of observed data is obtained;

[0013] Based on the seasonality and disorder degree of the mutation moment of the observed data, the data stability coefficient of any category of observed data is obtained;

[0014] Based on the mutation difference coefficient and the data stability coefficient, an abnormal mutation factor of any category of observed data is obtained, wherein the abnormal mutation factor is positively correlated with the mutation difference coefficient of all mutation points and negatively correlated with the data stability coefficient.

[0015] Further, the method for obtaining the mutation difference coefficient is:

[0016] All data except mutation points in each category of observation data sequence are recorded as normal data, and the average value of all normal data in any category of observation data within the screening interval of the benchmark station is taken as the normal mean value of any category of observation data within the screening interval of each benchmark station;

[0017] The formula for the mutation difference coefficient is: ; In the formula, For the The first type of observation data The mutation difference coefficient of each mutation point; , Respectively The observation data of this type are in the screening interval of the base station. The mutation probability and data value of each mutation point; For the The normal mean of the observed data of this type in the screening interval of the benchmark station.

[0018] Furthermore, the method for obtaining the data stability coefficient is:

[0019] The mutation time sequence of all mutation points in any category of observation data within the reference station screening interval is composed of the sequence in time positive order as the mutation time sequence of any category of observation data; any category of observation data within the reference station screening interval is used as input, and the sequence decomposition algorithm is used to obtain the periodic term intensity of all mutation points in any category of observation data;

[0020] The formula for the data stability coefficient is: ; In the formula, For the Data stability coefficient of class observation data; For the The cumulative result of the absolute value of the intensity difference of the periodic term between all mutation points in the screening interval of the reference station for the observation data of the same type; For the The Shannon entropy of the mutation time series corresponding to the observed data; Adjust parameters for the preset.

[0021] Furthermore, the method for obtaining the associated abnormal factor is:

[0022] Based on the similarity between the mutation moments of the observed data, the similarity of the mutation moments of any category of observed data is obtained;

[0023] Based on the similarity between the mutation probabilities of the observed data, the mutation probability correlation of any category of observed data is obtained;

[0024] The associated anomaly factor of any category of observation data is obtained based on the similarity of mutation time and the correlation of mutation probability.

[0025] Furthermore, the method for obtaining the correlation between the mutation time similarity and the mutation probability is as follows:

[0026] The formula for the similarity of mutation moments is: ; In the formula, The first Similarity of mutation moments of category observation data; is the total number of categories of observation data; , They are the first Class and The mutation time sequence of the observational data; is the mutation time sequence and The DTW distance between them; Adjust parameters for presets;

[0027] For the base station screening interval, the moment that is simultaneously the mutation moment of all categories of observation data is taken as the associated mutation moment;

[0028] Arrange the mutation probabilities of all observation data corresponding to the associated mutation moments in any category of observation data in the reference station screening interval in chronological order to form an associated mutation probability sequence of any category of observation data;

[0029] The similarity between the observed data of any category and the associated mutation probability sequences of the observed data of other categories is calculated, and the sum of all the similarities of the observed data of any category is taken as the mutation probability association of the observed data of any category.

[0030] Furthermore, the method for obtaining the comprehensive index of each reference station is:

[0031] For any category of observation data, the product of the abnormal mutation factor and the associated abnormal factor of the observation data is calculated as the abnormal characteristic value of any category of observation data;

[0032] The abnormal characteristic values ​​of all categories of observation data of each reference station in the grid network are used as an evaluation index and as the input of the comprehensive index method. The observation data of all reference stations are processed to obtain the comprehensive index of all reference stations.

[0033] Further, the obtaining of the reference confidence of each reference station and obtaining the reference reference station according to the reference confidence includes:

[0034] Normalize the composite index of all benchmark stations;

[0035] For each reference station in the grid network RTK positioning system, the sum of the normalized comprehensive index of the reference station and the preset adjustment parameter is calculated as a first sum, and the ratio of the inverse of the distance between the reference station and the positioning user and the first sum is calculated as a reference confidence of each reference station;

[0036] A preset number of reference stations with the largest reference confidences are obtained as reference stations for user positioning.

[0037] In the second aspect, an embodiment of the present application provides a grid network RTK positioning device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned grid network RTK positioning methods are implemented.

[0038] In a third aspect, an embodiment of the present application provides a grid network RTK positioning system, the system comprising:

[0039] The data acquisition module is used to collect the location information of all base stations and positioning users in the RTK service area; collect the observation data of each base station, and the observation data includes pseudo-range observation data, carrier phase observation data, and base station delay data;

[0040] The comprehensive index acquisition module is used to obtain the mutation time, mutation point and mutation probability of each mutation point of the sequence composed of the observation data in the screening interval of each base station for any category of observation data using the time series data analysis algorithm; obtain the abnormal mutation factor of any category of observation data based on the mutation probability, normal situation and seasonality and chaos degree of the mutation point of the observation data; obtain the associated abnormal factor of any category of observation data based on the similarity between the mutation time and the similarity between the mutation probabilities of the observation data; obtain the comprehensive index of each base station based on the abnormal mutation factor and the associated abnormal factor;

[0041] The grid network RTK positioning module is used to obtain the reference confidence of each reference station based on the comprehensive index and the location information of the reference station and the positioning user, and obtain the reference reference station according to the reference confidence.

[0042] This application has at least the following beneficial effects:

[0043] The present application encodes the network RTK grid through Geohash coding, which can quickly and accurately locate the grid where the user and the base station are located and retrieve relevant data, so as to facilitate the subsequent screening of the reference base station for user positioning; the abnormal mutation factor of each category of observation data is obtained through the abnormal mutation characteristics of the observation data in the base station screening interval, and the associated abnormal factor is obtained through the associated abnormal characteristics between different categories of observation data, and the abnormal characteristic value of each category of observation data is obtained in this way, which comprehensively considers the abnormal mutation status of the observation data itself and the abnormal association status between the observation data, and more accurately reflects the degree of data quality deterioration of the base station due to satellite signal errors; the comprehensive index of each base station is obtained, and the reference base station for user positioning is screened out in combination with the distance information between the base station and the positioning user, and the positioning is completed. On the basis of considering the data quality provided by the base station, the analysis of communication bandwidth and computing resources is added, which provides a more accurate basis for selecting the base station for user positioning, solves the problem of improper selection of reference base stations during positioning leading to user positioning delay or inaccurate positioning, and realizes a gridded network RTK positioning method, device and system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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 creative work.

[0045] Figure 1 A flowchart of a grid network RTK positioning method provided in one embodiment of the present application;

[0046] Figure 2 A schematic diagram of the distribution of reference stations provided in one embodiment of the present application;

[0047] Figure 3 A schematic diagram of the grid network RTK positioning system service working mode provided for one embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the grid network RTK positioning method, device and system proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0049] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0050] The following is a detailed description of a specific scheme of a grid network RTK positioning method, device and system provided by the present application in conjunction with the accompanying drawings.

[0051] See also Figure 1 , which shows a flowchart of a grid network RTK positioning method provided by an embodiment of the present application, the method comprising the following steps:

[0052] Step 100: Encode the network RTK grid using Geohash encoding.

[0053] In order to prevent the problem of low efficiency in RTK grid data management and search in existing grid-based network RTK positioning methods, this embodiment divides the network RTK service area into several grid areas of equal area and assigns a Geohash code to each grid. The purpose is to facilitate the subsequent use of the Geohash multidimensional space point indexing algorithm, so that the grid where the user and the base station are located can be quickly and accurately located and relevant data can be retrieved, thereby realizing efficient management and retrieval of network RTK grid data.

[0054] Figure 2 This is a schematic diagram of the base station distribution. The horizontal and vertical coordinates represent the longitude and latitude of the base station, respectively. The hexagon represents the base station, the circle represents the positioning user, and the letters am represent the distance between each base station and the positioning user.

[0055] Step 200: Acquire relevant data of the reference station in the grid network RTK positioning process, and pre-process the acquired reference station data.

[0056] In the grid network RTK positioning system of this embodiment, this embodiment obtains the location information of all reference stations and positioning users in the grid network through the grid area divided in step 200 and the Geohash multidimensional space point index algorithm;

[0057] When the user initiates a positioning request, the observation data of each base station in the grid network within the screening interval is obtained through the GNSS receiver. The observation data includes the pseudo-range observation data, carrier phase observation data, and reference station delay data of the base station. The above data collection time interval is 0.1S, and the reference station screening interval is 1min. In order to prevent the acquired data from being lost due to network fluctuations or external noise interference during transmission, the present application adopts the median filling method to fill the missing values ​​of the acquired pseudo-range observation data, carrier phase observation data, and reference station delay data of the base station; further, the Z-Score standardization is used to unify the dimensions of all observation data. Since the median filling method and Z-Score standardization are both well-known technologies, the specific acquisition process will not be described in detail.

[0058] At this point, the pseudo-range observation data sequence, carrier phase observation data sequence, and reference station delay data sequence based on the time sequence of each reference station in the grid network can be obtained according to the above method.

[0059] Step 300: Obtain the abnormal mutation factor of each category of observation data through the abnormal mutation characteristics of the observation data within the reference station screening interval.

[0060] In the grid network RTK positioning system, when the satellite is at a low altitude angle or in an active period of the ionosphere, the satellite signal loss increases and it is difficult for the base station to lock the satellite signal. In addition, the delays in the ionosphere and troposphere will cause errors in the propagation of satellite signals, causing cycle slips and signal loss in the pseudorange observation data and carrier phase observation data. After the signal is lost and the signal is recaptured, the signal tracking needs to be reinitialized. This process may be affected by a stronger multipath effect, resulting in a large degree of periodic deviation and abnormal fluctuation in the pseudorange observation data, carrier phase observation data and base station delay data.

[0061] Based on the above analysis, specifically, any reference station screening interval is taken as an example for subsequent analysis, and each category of observation data sequence is used as the input of the Bayesian online change point detection algorithm to obtain each mutation point, mutation time and mutation probability of each mutation point in the pseudorange observation data sequence, and all data except the mutation point in each category of observation data sequence are recorded as normal data, and the average value of all normal data in any category of observation data in the reference station screening interval is taken as the normal mean value of any category of observation data in the reference station screening interval.

[0062] The mutation moments of all mutation points in any category of observation data within the reference station screening interval are arranged in a sequence in chronological order as the mutation moment sequence of any category of observation data; any category of observation data within the reference station screening interval is taken as input, and the STL (Seasonal and Trend decomposition using Loess) sequence decomposition algorithm is used to obtain the periodic term intensity of all mutation points in any category of observation data. Since the Bayesian online change point detection algorithm and the STL sequence decomposition algorithm are both well-known technologies, the specific acquisition process will not be described in detail.

[0063] For any mutation point in any category of observed data, the mutation difference coefficient can be calculated as follows: ; In the formula, For the The first type of observation data The mutation difference coefficient of each mutation point; , Respectively The observation data of this type are in the screening interval of the base station. The mutation probability and data value of each mutation point; For the The normal mean of the observed data of this type in the screening interval of the benchmark station.

[0064] It should be noted that when the base station observation data in the grid network RTK positioning system produces cycle slips and signal loss due to satellite signal propagation errors, the more serious the situation, the more significant the mutation degree of the data mutation point in the observation data, the larger the mutation difference coefficient, that is, the greater the mutation probability of the mutation point, and the greater the difference between the mutation point data and the normal mean.

[0065] The data stability coefficient of any type of observation data within the base station screening interval can be calculated as follows: ; In the formula, For the Data stability coefficient of class observation data; For the The cumulative result of the absolute value of the intensity difference of the periodic term between all mutation points in the screening interval of the reference station for the observation data of the same type; For the The Shannon entropy of the mutation time series corresponding to the observed data; It is a preset adjustment parameter, and in this embodiment, the value is 0.1 to prevent the denominator from being 0.

[0066] It should be noted that when the base station observation data in the grid network RTK positioning system produces cycle slips and signal loss due to satellite signal propagation errors, the more serious the situation, the worse the stability of the observation data, the greater the difference in the intensity of the periodic terms between all mutation point data in the observation data, and the more chaotic the distribution of data mutation time in the observation data.

[0067] The abnormal mutation factor of any category of observation data within the base station screening interval can be determined by the mutation difference coefficient and the data stability coefficient; the abnormal mutation factor is positively correlated with the mutation difference coefficient and negatively correlated with the data stability coefficient.

[0068] It should be noted that the positive correlation relationship indicates that the dependent variable increases as the independent variable increases, and decreases as the independent variable decreases; the negative correlation relationship indicates that the dependent variable decreases as the independent variable increases, and increases as the independent variable decreases.

[0069] Specifically, the present application proposes an embodiment in which the abnormal mutation factor is the ratio of the mutation difference coefficient to the data stability coefficient.

[0070] Furthermore, the present application proposes another embodiment, in which the difference between the mutation difference coefficient and the data stability coefficient is calculated as the first difference, and an exponential function with a natural constant as the base and the first difference as the exponent is used as the abnormal mutation factor.

[0071] It should be noted that when the base station observation data in the grid network RTK positioning system produces cycle slips and signal loss due to satellite signal propagation errors, the more serious the abnormal mutation of the observation data is, the greater the difference between the mutation point and the normal data in the observation data, the greater the difference between the intensity of the periodic terms of the mutation point, that is, the more significant the mutation difference coefficient is, and the worse the data stability coefficient is.

[0072] At this point, the abnormal mutation factor of each category of observation data can be obtained according to the above method.

[0073] Step 400: Obtaining a correlation anomaly factor by screening correlation anomaly features between different categories of observation data within a reference station screening interval.

[0074] Generally speaking, the more consistent the time when any category of observation data at the base station is with the other categories of observation data, the more likely it is that the abnormal conditions between different categories of observation data at the base station are associated, and the worse the quality of the satellite signal received by the base station.

[0075] Therefore, the similarity of the mutation time of any category of observation data within the reference station screening interval can be calculated as follows: ; In the formula, The first Similarity of mutation moments of category observation data; is the total number of categories of observed data, which is fixed to 3 in this embodiment; , They are the first Class and The mutation time sequence of the observational data; is the mutation time sequence and The DTW distance between them; It is a preset adjustment parameter, and in this embodiment, the value is 0.1 to prevent the denominator from being 0;

[0076] It should be noted that when the signal quality received by the base station in the grid network RTK positioning system is worse, the abnormal correlation between the observation data obtained by the base station is more significant, and the data mutation moments in different categories of observation data obtained by the base station are more consistent, that is, the DTW distance between the corresponding mutation moment sequences of the observation data is smaller.

[0077] The moment that is also the mutation moment of all categories of observation data within the base station screening interval is recorded as the associated mutation moment, and the mutation probabilities of the mutation points corresponding to all associated mutation moments in any category of observation data within the base station screening interval are sequenced in the positive order of the associated mutation moments as the associated mutation probability sequence of any category of observation data.

[0078] Similarly, when the probability of anomalies between any category of observation data at the base station and other categories of observation data is more consistent, it is more likely that the anomalies between different categories of observation data at the base station are associated, and the quality of the satellite signal received by the base station is worse.

[0079] Specifically, the mutation probability correlation of any category of observation data within the base station screening interval can be calculated as follows: ; In the formula, For the The mutation probability correlation of category observation data; , Respectively , The associated mutation probability sequence corresponding to the class observation data; is the probability sequence of associated mutations and The cosine similarity between .

[0080] It should be noted that when the signal quality received by the base station in the grid network RTK positioning system is worse, the abnormal correlation between the observation data obtained by the base station is more significant, the mutation conditions between different categories of observation data are more similar, and the difference in data mutation probability between different categories of observation data obtained by the base station is smaller, that is, the correlation mutation probability sequences corresponding to different categories of observation data are more similar.

[0081] Furthermore, when the time when any category of observation data of the reference station produces an abnormal condition with other categories of observation data is more consistent with the abnormal probability, the abnormal condition between any category of observation data of the reference station and other categories of observation data is more likely to be related to each other, and the quality of the satellite signal received by the reference station is worse.

[0082] For any category of observation data within the base station screening interval, the associated anomaly factor can be determined by the similarity of mutation moment and the correlation of mutation probability. Specifically, in one embodiment of the present application, the associated anomaly factor is the product of the similarity of mutation moment and the correlation of mutation degree; when the signal quality received by the base station in the grid network RTK positioning system is worse, the more significant the abnormal correlation between the observation data obtained by the base station, the larger the associated anomaly factor, the more consistent the data mutation moments in different categories of observation data obtained by the base station, and the smaller the difference in data mutation probability between different categories of observation data obtained by the base station, that is, the greater the similarity of mutation moment, the greater the correlation of mutation probability.

[0083] At this point, the associated anomaly factor for each category of observation data can be obtained according to the above method.

[0084] Step 500: Obtain the abnormal characteristic value of each category of observation data according to the abnormal mutation factor and the associated abnormal factor, and thereby obtain the comprehensive index of the reference station.

[0085] When the abnormal mutation factor and the associated abnormal factor of any category of observation data of the base station are higher, the abnormal mutation condition of any category of observation data is more significant, the associated abnormal condition is more significant, the cycle slip and signal loss condition of the base station observation data in the grid network RTK positioning system is more serious, the satellite signal quality obtained by the base station is worse, and it is less suitable to be used as a reference base station for user positioning.

[0086] Furthermore, for any category of observed data, the product of the abnormal mutation factor of the observed data and the associated abnormal factor is calculated as the abnormal characteristic value of any category of observed data.

[0087] It should be noted that when the data quality of any base station in the grid network RTK positioning system is worse due to satellite signal errors, the base station should not be used to solve user positioning requests. The larger the abnormal characteristic value, the larger the abnormal mutation factor of the observation data, and the larger the associated abnormal factor of the observation data.

[0088] The abnormal characteristic values ​​of all categories of observation data of each base station in the grid network are taken as an evaluation index and input, and the comprehensive index of each base station is obtained by the CIM (Comprehensive Insex Method) comprehensive index method, wherein the weight of the abnormal characteristic value of each category of observation data of the base station is obtained by the entropy weight method. Since the CIM comprehensive index method is a well-known technology, the specific process will not be described in detail.

[0089] Get the comprehensive index of all base stations in the grid network RTK positioning system. The larger the comprehensive index of the base station, the more significant the abnormal mutation of all categories of observation data obtained by the base station, the higher the correlation abnormality, the worse the data quality caused by the satellite signal error, and the less suitable it should be as a reference base station for user positioning requests, otherwise it will affect the real-time and accuracy of user positioning.

[0090] At this point, the comprehensive index of all reference stations in the grid network can be obtained according to the above method.

[0091] Step 600: Filter out reference reference stations for user positioning according to the comprehensive index of the reference stations and the distance between the reference stations and the positioning user.

[0092] In the grid network RTK positioning system, GNSS signals will be affected by various errors during the propagation process. The closer the distance between the base station and the positioning user, the more accurate differential correction information can be provided, the more stable and efficient the data communication between the base station and the user, and the communication bandwidth and computing resources required for positioning information can be reduced.

[0093] Specifically, according to the above analysis, the reference confidence of each reference station is obtained according to the reference station location information and the comprehensive index, which can be calculated as follows: ; In the above formula, It is the first Reference confidence level of each base station; For the The inverse of the distance between the base station and the positioning user; The first Normalized results of the comprehensive index of the base stations; To adjust the parameters, the value is taken as 0.1 in this application to prevent the denominator from being 0.

[0094] It should be noted that the greater the reference confidence, the smaller the distance between the base station and the positioning user, and the smaller the comprehensive index of the base station, the higher the data quality of the base station.

[0095] Select N reference stations with the largest reference confidence in the grid network RTK positioning system as reference reference stations for user positioning. The larger N is, the more accurate the positioning for the user will be, and the communication bandwidth and computing resources will be improved. In this embodiment, N is 3.

[0096] When the user sends a positioning request, the reference base station is selected in the above manner, and the satellite signal error is corrected according to the differential data provided by the reference base station to achieve accurate positioning of the user. Since the network RTK positioning method is a well-known technology, the specific acquisition process will not be described in detail.

[0097] Among them, the grid network RTK positioning system service working mode diagram is as follows Figure 3 shown.

[0098] When the user sends a positioning request to the grid network RTK positioning system, the grid where the user's position is located is obtained according to the Geohash multidimensional space point index algorithm in the above steps, and the grids where all the reference stations in the grid network RTK system are located and related data information are obtained at the same time, and the reference reference stations are selected according to the above method;

[0099] The GNSS satellite signals are acquired through the reference base station, and the observation data such as pseudorange and carrier phase are recorded. The base station then transmits the collected observation data to the data processing module of the grid network RTK positioning system through GB / T 39607-2020 "Satellite Navigation Positioning Base Station Data Transmission and Interface Protocol". The data processing module of the grid network RTK positioning system processes the acquired reference base station observation data, including baseline settlement, ambiguity fixation, error estimation and modeling, to generate differential correction information;

[0100] The observation data of the virtual reference station is generated by screening the observation data of the reference base station, which is used to simulate an actual reference base station near the user's location. The differential correction information and the virtual reference station data are encoded and broadcast to the user in real time through the communication network. After the user device receives the differential data and the virtual reference station data from the grid network RTK positioning system data processing module, the positioning solution is performed based on the GNSS satellite signal and differential data received by the device to obtain accurate position information, and output it to the user or related applications. Since the grid network RTK positioning method is a well-known technology, the specific acquisition process will not be described in detail in this application.

[0101] Based on the same inventive concept as the above method, an embodiment of the present application also provides a grid network RTK positioning device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above-mentioned grid network RTK positioning methods are implemented.

[0102] Similarly, based on the same inventive concept as the above method, an embodiment of the present application provides a grid network RTK positioning system, the system comprising:

[0103] The data acquisition module is used to collect the location information of all base stations and positioning users in the RTK service area; collect the observation data of each base station, and the observation data includes pseudo-range observation data, carrier phase observation data, and base station delay data;

[0104] The comprehensive index acquisition module is used to obtain the mutation time, mutation point and mutation probability of each mutation point of the sequence composed of the observation data in the screening interval of each base station for any category of observation data using the time series data analysis algorithm; obtain the abnormal mutation factor of any category of observation data based on the mutation probability, normal situation and seasonality and chaos degree of the mutation point of the observation data; obtain the associated abnormal factor of any category of observation data based on the similarity between the mutation time and the similarity between the mutation probabilities of the observation data; obtain the comprehensive index of each base station based on the abnormal mutation factor and the associated abnormal factor;

[0105] The grid network RTK positioning module is used to obtain the reference confidence of each reference station based on the comprehensive index and the location information of the reference station and the positioning user, and obtain the reference reference station according to the reference confidence.

[0106] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0108] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A grid network RTK positioning method, characterized in that: The method comprises the following steps: Collect the location information of all base stations and positioning users in the RTK service area; collect the observation data of each base station, the observation data includes pseudorange observation data, carrier phase observation data, and base station delay data; For any type of observation data, a time series data analysis algorithm is used to obtain the mutation time, mutation point and mutation probability of each mutation point of the sequence composed of the observation data of the screening interval of each base station; Based on the mutation probability, normality, seasonality and chaos degree of the mutation point of the observed data, the abnormal mutation factor of any category of observed data is obtained; Based on the similarity between the mutation moments and the similarity between the mutation probabilities of the observed data, the associated abnormality factor of any category of observed data is obtained; Obtain a comprehensive index of each reference station based on the abnormal mutation factor and the associated abnormal factor; obtain a reference confidence of each reference station based on the comprehensive index and the location information of the reference station and the positioning user, and obtain a reference reference station based on the reference confidence; The method for obtaining the abnormal mutation factor is: Based on the mutation probability and normality of the mutation points in the observed data, the mutation difference coefficient of each mutation point in any category of observed data is obtained; Based on the seasonality and disorder degree of the mutation moment of the observed data, the data stability coefficient of any category of observed data is obtained; Obtaining an abnormal mutation factor of any category of observed data based on the mutation difference coefficient and the data stability coefficient, wherein the abnormal mutation factor is positively correlated with the mutation difference coefficients of all mutation points and negatively correlated with the data stability coefficient; The method for obtaining the associated abnormal factor is: Based on the similarity between the mutation moments of the observed data, the similarity of the mutation moments of any category of observed data is obtained; Based on the similarity between the mutation probabilities of the observed data, the mutation probability correlation of any category of observed data is obtained; The associated anomaly factor of any category of observation data is obtained based on the similarity of mutation time and the correlation of mutation probability.

2. A grid network RTK positioning method as claimed in claim 1, characterized in that: The method for obtaining the mutation difference coefficient is: All data except mutation points in each category of observation data sequence are recorded as normal data, and the average value of all normal data in any category of observation data within the screening interval of the benchmark station is taken as the normal mean value of any category of observation data within the screening interval of each benchmark station; The formula for the mutation difference coefficient is: ; In the formula, For the The first type of observation data The mutation difference coefficient of each mutation point; , Respectively The observation data of this type are in the screening interval of the base station. The mutation probability and data value of each mutation point; For the The normal mean of the observed data of this type in the screening interval of the benchmark station.

3. A grid network RTK positioning method as claimed in claim 1, characterized in that: The method for obtaining the data stability coefficient is: The mutation time sequence of all mutation points in any category of observation data within the reference station screening interval is composed of the sequence in time positive order as the mutation time sequence of any category of observation data; any category of observation data within the reference station screening interval is used as input, and the sequence decomposition algorithm is used to obtain the periodic term intensity of all mutation points in any category of observation data; The formula for the data stability coefficient is: ; In the formula, For the Data stability coefficient of class observation data; For the The cumulative result of the absolute value of the intensity difference of the periodic term between all mutation points in the screening interval of the reference station for the observation data of the same type; For the The Shannon entropy of the mutation time series corresponding to the observed data; Adjust parameters for the preset.

4. A grid network RTK positioning method as claimed in claim 1, characterized in that: The method for obtaining the correlation between the mutation time similarity and the mutation probability is as follows: The formula for the similarity of mutation moments is: ; In the formula, The first Similarity of mutation moments of category observation data; is the total number of categories of observation data; , They are the first Class and The mutation time sequence of the observational data; is the mutation time sequence and The DTW distance between them; Adjust parameters for presets; For the base station screening interval, the moment that is simultaneously the mutation moment of all categories of observation data is taken as the associated mutation moment; Arrange the mutation probabilities of all observation data corresponding to the associated mutation moments in any category of observation data in the reference station screening interval in chronological order to form an associated mutation probability sequence of any category of observation data; The similarity between the observed data of any category and the associated mutation probability sequences of the observed data of other categories is calculated, and the sum of all the similarities of the observed data of any category is taken as the mutation probability association of the observed data of any category.

5. A grid network RTK positioning method as claimed in claim 1, characterized in that: The method for obtaining the comprehensive index of each reference station is: For any category of observation data, the product of the abnormal mutation factor and the associated abnormal factor of the observation data is calculated as the abnormal characteristic value of any category of observation data; The abnormal characteristic values ​​of all categories of observation data of each reference station in the grid network are used as an evaluation index and as the input of the comprehensive index method. The observation data of all reference stations are processed to obtain the comprehensive index of all reference stations.

6. A grid network RTK positioning method as claimed in claim 1, characterized in that: The obtaining of the reference confidence of each reference station and obtaining the reference reference station according to the reference confidence comprises: Normalize the composite index of all benchmark stations; For each reference station in the grid network RTK positioning system, the sum of the normalized comprehensive index of the reference station and the preset adjustment parameter is calculated as a first sum, and the ratio of the inverse of the distance between the reference station and the positioning user and the first sum is calculated as a reference confidence of each reference station; A preset number of reference stations with the largest reference confidences are obtained as reference stations for user positioning.

7. A grid network RTK positioning device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a grid network RTK positioning method as described in any one of claims 1-6 are implemented.

8. A grid network RTK positioning system, implementing the method as claimed in claim 1, characterized in that: The positioning system comprises: The data acquisition module is used to collect the location information of all base stations and positioning users in the RTK service area; collect the observation data of each base station, and the observation data includes pseudo-range observation data, carrier phase observation data, and base station delay data; The comprehensive index acquisition module is used to obtain the mutation time, mutation point and mutation probability of each mutation point of the sequence composed of the observation data in the screening interval of each base station for any category of observation data using the time series data analysis algorithm; obtain the abnormal mutation factor of any category of observation data based on the mutation probability, normal situation and seasonality and chaos degree of the mutation point of the observation data; obtain the associated abnormal factor of any category of observation data based on the similarity between the mutation time and the similarity between the mutation probabilities of the observation data; obtain the comprehensive index of each base station based on the abnormal mutation factor and the associated abnormal factor; The grid network RTK positioning module is used to obtain the reference confidence of each reference station based on the comprehensive index and the location information of the reference station and the positioning user, and obtain the reference reference station according to the reference confidence.

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