Method and system for selecting position of encrypted reference station based on ionosphere precision information

Abstract

The invention discloses an encrypted reference station position selection method and system based on ionosphere precision information, and the method comprises the steps: constructing an ionosphere delay interpolation model in a service area according to the ionosphere delay of each baseline and the position relation between reference stations; based on an ionosphere delay interpolation model, ionosphere precision information of a single epoch of a user side is estimated; ionosphere precision information of multiple epochs is calculated based on historical observation data, and a GNSS double-difference observation model is adopted to estimate a to-be-solved parameter precision matrix of a user side; and calculating a positioning error of a to-be-selected point location in the region based on the to-be-solved parameter precision matrix of the user side, and selecting an encrypted base station construction position meeting the required number according to the positioning error and in combination with the geographic condition of the point location. According to the method, the ionosphere precision information of any point location in the service area is estimated by using the GNSS measured data of the reference station network in the service area, and then the preferential construction position of the encryption reference station is determined, so that the service capability of the newly-built encryption reference station to the whole network is improved to the maximum extent.

Description

Technical Field

[0001] The present invention belongs to the field of global navigation systems and positioning measurement technology, and in particular relates to a method and system for selecting encrypted reference station positions based on ionospheric accuracy information. Background Art

[0002] The satellite navigation base station network is composed of multiple high-precision GNSS (Global Navigation Satellite System) receivers distributed around the world or in specific regions. It uses technologies such as DGNSS (Differential Global Navigation Satellite System), real-time RTK (Real Time Kinematic), and real-time PPP-RTK (Precise Point Positioning-Real Time Kinematic) to provide users with real-time precise positioning services.

[0003] Base stations are the core component of the satellite navigation base station network. They receive signals from medium-, high-, and low-orbit navigation satellites, generate high-precision observation data, and transmit this data to data centers via various communication methods. Data centers utilize the high-precision observation data from regional base stations to generate various navigation and positioning products. The quality and accuracy of these products are key factors influencing the service capabilities of the base station network. After the base station network is established, to further enhance the base station network's service capabilities, especially to improve user positioning in areas where the base station network's service capabilities are weak, additional encrypted base stations can be added to the existing base station network. Constructing encrypted stations in appropriate locations can supplement data in weak areas of the base station network and enhance the overall network's service capabilities. Conversely, not doing so will result in a waste of resources and have limited impact on the base station network's service capabilities. Therefore, the selection of the appropriate location for the encrypted stations is crucial.

[0004] Traditional encryption station construction either only considers the spatial position relationship between base stations, emphasizing the uniform spatial distribution of base stations, while the actual ionospheric activity is often inconsistent in space; or uses a large amount of user positioning data, which is costly; or relies on empirical formulas to determine areas where the positioning products produced by the base station network have low accuracy, that is, areas where the base station network has weak service capabilities, and builds encryption stations in these areas. However, the actual situation is that the relevant empirical formulas cannot accurately reflect the complex temporal and spatial changes of various error sources (such as ionospheric delay), especially during the atmospherically active midday period and in low-latitude areas. As a result, after the encryption stations are built, the relevant observation data cannot effectively enhance the existing base station network. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method for selecting the location of encrypted reference stations based on ionospheric accuracy information. The method uses measured GNSS observation data, estimates the ionospheric product accuracy information within the service area of the reference station network, calculates the positioning error of the selected points, and finally determines the location of the encrypted reference stations, thereby maximizing the improvement of the service capacity of the entire network by the newly built encrypted reference stations. The method includes the following steps:

[0006] Step 1: Complete the network of reference stations in the service area, resolve the ambiguity on each frequency of the reference station network, and calculate the ionospheric delay on each baseline based on the actual GNSS observation data;

[0007] Step 2: Based on the ionospheric delay on each baseline and the position relationship between the reference stations, an ionospheric delay interpolation model within the service area is constructed;

[0008] Step 3: Based on the ionospheric delay interpolation model constructed in step 2, estimate the ionospheric accuracy information of a single epoch at the user end;

[0009] Step 4: Based on the historical observation data, the calculation method in step 3 is used to estimate the ionospheric accuracy information of multiple epochs, and the GNSS double-difference observation model is used to estimate the accuracy matrix of the unknown parameters at the user end;

[0010] Step 5: Calculate the positioning errors of the candidate points in the area based on the parameter accuracy matrix to be determined on the user side. According to the positioning errors of the candidate points and the geographical conditions of the candidate points, select the locations for the construction of the encrypted reference stations that meet the required number.

[0011] Furthermore, in step 1, the ambiguity on each frequency of the reference station network is resolved. When the ambiguity is fixed, the ionospheric delay on each baseline is calculated as:

[0012]

[0013] Where, represents the ionospheric delay on the L1 signal between the reference stations, f1 and f2 represent the frequencies of the L1 and L2 signals respectively, λ1 and λ2 represent the wavelengths of the L1 and L2 signals respectively, N1 and N2 represent the ambiguity on the L1 and L2 signals between the reference stations respectively, and Represent the carrier observation values of L1 and L2 signals respectively.

[0014] Furthermore, in step 2, an indirect adjustment method is used to construct an ionospheric delay interpolation model within the service area. The specific calculation method is as follows:

[0015] V r =B r XL r (2)

[0016]

[0017] Where V r represents the ionospheric delay residual in the indirect adjustment model; B r To construct a matrix, where ΔX i and ΔY i Respectively represent the components on the X-axis and Y-axis of the plane corresponding to the baseline; L r is the reference value vector, where I i represents the ionospheric delay of the corresponding baseline; X is the fitting parameter vector, where a1 and a2 are the fitting parameters of the linear interpolation model, and n is the number of baselines.

[0018] The fitting parameter vector after adjustment is calculated using the least squares method Right now:

[0019]

[0020] Among them, P r is the weight matrix, and is calculated as follows:

[0021]

[0022] Where, d i Indicates the plane distance corresponding to the baseline.

[0023] Furthermore, in step 3, the coordinate difference matrix B between the user terminal and the main reference station is used. u , calculate the ionospheric delay at the user end. The specific calculation formula is as follows:

[0024]

[0025] Where, I ru is the ionospheric delay of the user end, ΔX is the coordinate difference between the user end and the master reference station on the X axis, and ΔY is the coordinate difference between the user end and the master reference station on the Y axis.

[0026] There is an error between the ionospheric delay at the user end calculated by equation (8) and the actual ionospheric delay, which is recorded as V u ,but:

[0027]

[0028] Where, L u is the actual ionospheric delay at the user end, E u is a unit vector.

[0029] According to the error propagation law, the user-side ionospheric delay error V u The variance of is expressed as:

[0030]

[0031] Where, is the variance of the ionospheric delay product error at the user end, D LL is the ionospheric delay variance of the baseline and user side.

[0032] In actual operation, the server will use the regional ionospheric delay interpolation model to calculate the ionospheric delay product for each user terminal. Therefore, the number of user terminals in the model is considered to be 1, and D LL is an n+1 square matrix, that is:

[0033]

[0034] Where, is the unit weighted variance of the ionospheric delay residuals between n baselines and the baselines of the user and the master reference station, P r is the weight matrix, d u is the plane distance between the user terminal and the master reference station.

[0035] Considering that the user is located inside the network, the properties of its ionospheric delay residual are similar to those of the surrounding stations, so its ionospheric delay residual can be approximated as the average value of the ionospheric delay residuals of other reference stations, and thus Estimated to be:

[0036]

[0037] Where, is the unit weight variance of the ionospheric delay residual on n baselines, V n is the ionospheric delay residual on n baselines, P r is the weight matrix, and t is the necessary number of observations.

[0038] The variance of the ionospheric delay error is calculated according to equations (10)-(14): That is, the user-side ionospheric accuracy information at each epoch.

[0039] Furthermore, the observation equation of the user side in step 4 is:

[0040]

[0041] Where i and j represent satellite numbers, r and u represent receiver numbers, is the double-difference pseudorange observation value between satellites i and j and between receivers r and u, is the double difference distance between satellites i and j and between receivers r and u, is the ionospheric delay between satellites i and j and between receivers r and u, is the double-difference carrier observation value between satellites i and j and between receivers r and u, is the double-difference ambiguity between satellites i and j and between receivers r and u, and λ represents the frequency of the corresponding carrier signal.

[0042] The user-side indirect adjustment model is constructed as follows:

[0043]

[0044] Where V is the residual vector of the equation, is the double difference unit vector on the line connecting receivers r, u and satellites i, j, Δx ru is the coordinate change of the receiver r, u approximate coordinates, is the ionospheric delay between satellites i and j and between receivers r and u, is the double-difference ambiguity between satellites i and j and between receivers r and u, is the double-difference pseudorange observation value between satellites i and j and between receivers r and u, is the double difference geometric distance between receiver r, u and satellite i, j calculated from the user's approximate coordinates, is the a priori ionospheric delay between satellites i and j and between receivers r and u, is the double-difference carrier observation value between satellites i and j and between receivers r and u, is the a priori double-difference ambiguity between satellites i and j and between receivers r and u, and λ represents the frequency of the corresponding carrier signal.

[0045] make

[0046] Then formula (17) can be abbreviated as:

[0047] V=BX * -L (18)

[0048] According to the altitude angle model or the signal-to-noise ratio random error model, the observation value weight matrix P is calculated and the related parameters to be solved are obtained using the least squares method:

[0049]

[0050] Let A=(B T PB) -1 B T P, according to the error propagation law, the accuracy matrix of the parameters to be determined is obtained for:

[0051]

[0052] in:

[0053]

[0054] Where D L is the observation precision matrix; is the random error of the observation value, which can be expressed as follows in the GNSS double-difference observation model:

[0055]

[0056] Where, is the random error of the pseudorange or carrier observation value corresponding to satellite i. When the observation value is pseudorange, When the observation value is carrier, 0.001; the variance of the ionospheric delay error under multiple epochs is calculated from historical observation data Find the RMS value of the diagonal elements corresponding to satellite i, and get

[0057] Furthermore, in step 5, Add the first three elements of the diagonal to get the positioning error D XYZ , calculate the D of all candidate points in the service area XYZ , according to the D of the selected point XYZ , combined with the geographical conditions of the selected points, select the location for the construction of encrypted base stations that meet the required number.

[0058] The present invention also provides an encrypted reference station position selection system based on ionospheric precision information, which is used to implement the encrypted reference station position selection method based on ionospheric precision information as described above.

[0059] Furthermore, the invention comprises a processor and a memory, wherein the memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute the above-mentioned method for selecting the encrypted reference station position based on ionospheric accuracy information.

[0060] Alternatively, it includes a readable storage medium having a computer program stored thereon, and when the computer program is executed, it implements the above-mentioned method for selecting the encrypted reference station position based on ionospheric accuracy information.

[0061] Compared with the prior art, the present invention has the following advantages:

[0062] The present invention uses the GNSS measured data of the reference station network in the service area to estimate the ionospheric accuracy information of any point in the service area, and then determines the areas (points) within the coverage area of the reference station network that are greatly affected by the ionosphere and have weak service capabilities. These areas (points) are then used as priority areas for the construction of encrypted reference stations, thereby maximizing the service capabilities of the newly built encrypted reference stations for the entire network. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0064] Figure 1 This is a flow chart of a method for selecting an encrypted reference station position based on ionospheric accuracy information according to an embodiment of the present invention.

[0065] Figure 2 This is a diagram of the positioning error results of the candidate points calculated according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are further described below with reference to the accompanying drawings and embodiments. It is obvious that the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0067] Example 1

[0068] like Figure 1 As shown, an embodiment of the present invention provides a method for selecting an encrypted reference station position based on ionospheric accuracy information, comprising the following steps:

[0069] Step 1: Complete the base station network in the service area, resolve the ambiguity on each frequency of the base station network, and calculate the ionospheric delay on each baseline based on the actual GNSS observation data.

[0070] The ambiguity on each frequency can be solved by using a multi-epoch filtering algorithm, a single-epoch algorithm, or an inter-reference station ambiguity fixing algorithm such as TCAR. This embodiment uses a multi-epoch filtering algorithm to solve the ambiguity on each frequency. After the ambiguity is fixed, the ionospheric delay on each baseline is calculated as:

[0071]

[0072] Where, I f1 represents the ionospheric delay on the L1 signal between the reference stations, f1 and f2 represent the frequencies of the L1 and L2 signals respectively, λ1 and λ2 represent the wavelengths of the L1 and L2 signals respectively, N1 and N2 represent the ambiguity on the L1 and L2 signals between the reference stations respectively, and Represent the carrier observation values of L1 and L2 signals respectively.

[0073] Step 2: Based on the ionospheric delay on each baseline and the position relationship between the reference stations, an ionospheric delay interpolation model within the service area is constructed.

[0074] The ionospheric delay interpolation model in the service area is constructed by indirect adjustment. The specific calculation method is as follows:

[0075] V r =B r XL r (2)

[0076]

[0077] Where V r represents the ionospheric delay residual in the indirect adjustment model; B r To construct a matrix, where ΔX i and ΔY i Respectively represent the components on the X-axis and Y-axis of the plane corresponding to the baseline; L r is the reference value vector, where I i represents the ionospheric delay of the corresponding baseline; X is the fitting parameter vector, where a1 and a2 are the fitting parameters of the linear interpolation model, and n is the number of baselines.

[0078] The fitting parameter vector after adjustment is calculated using the least squares method Right now:

[0079]

[0080] Among them, P r is the weight matrix, and is calculated as follows:

[0081]

[0082] Where, d i Indicates the plane distance corresponding to the baseline.

[0083] Step 3: Based on the ionospheric delay interpolation model constructed in step 2, the ionospheric accuracy information of a single epoch at the user end is estimated.

[0084] According to the coordinate difference matrix B between the user terminal and the main reference station u , calculate the ionospheric delay at the user end. The specific calculation formula is as follows:

[0085]

[0086] Where, I ru is the ionospheric delay of the user end, ΔX is the coordinate difference between the user end and the master reference station on the X axis, and ΔY is the coordinate difference between the user end and the master reference station on the Y axis.

[0087] There is an error between the ionospheric delay at the user end calculated by equation (8) and the actual ionospheric delay, which is recorded as V u ,but:

[0088]

[0089] Where, L u is the actual ionospheric delay at the user end, E u is a unit vector.

[0090] According to the error propagation law, the user-side ionospheric delay error V u The variance of can be expressed as:

[0091]

[0092] Where, is the variance of the ionospheric delay product error at the user end, D LL is the ionospheric delay variance of the baseline and user side.

[0093] In actual operation, the server will use the regional ionospheric delay interpolation model to calculate the ionospheric delay product for each user terminal. Therefore, the number of user terminals in the model can be regarded as 1, then D LL is an n+1 square matrix, that is:

[0094]

[0095] Where, is the unit weighted variance of the ionospheric delay residuals between n baselines and the baselines of the user and the master reference station, P r is the weight matrix, calculated by formula (7); d u is the plane distance between the user terminal and the master reference station.

[0096] Considering that the user is located inside the reference station network, the properties of its ionospheric delay residual are similar to those of the surrounding stations, so its ionospheric delay residual can be approximated as the average value of the ionospheric delay residuals of other reference stations, and thus Estimated to be:

[0097]

[0098] Where, is the unit weight variance of the ionospheric delay residual on n baselines, V n is the ionospheric delay residual on n baselines, P r is the weight matrix, t is the necessary number of observations;

[0099] The variance of the ionospheric delay error is calculated according to equations (10)-(14): That is, the user-side ionospheric accuracy information at each epoch.

[0100] Step 4: Based on the historical observation data, use the calculation method in step 3 to estimate the ionospheric accuracy information of multiple epochs, and use the GNSS double-difference observation model to estimate the accuracy matrix of the unknown parameters on the user side.

[0101] The observation equation on the user side can be expressed as:

[0102]

[0103]

[0104] Where i and j represent satellite numbers, r and u represent receiver numbers, is the double-difference pseudorange observation value between satellites i and j and between receivers r and u, is the double difference distance between satellites i and j and between receivers r and u, is the ionospheric delay between satellites i and j and between receivers r and u, is the double-difference carrier observation value between satellites i and j and between receivers r and u, is the double-difference ambiguity between satellites i and j and between receivers r and u, and λ represents the frequency of the corresponding carrier signal.

[0105] The user-side indirect adjustment model is constructed as follows:

[0106]

[0107] Where V is the residual vector of the equation, is the double difference unit vector on the line connecting receivers r, u and satellites i, j, Δx ru is the coordinate change of the receiver r, u approximate coordinates, is the ionospheric delay between satellites i and j and between receivers r and u, is the double-difference ambiguity between satellites i and j and between receivers r and u, is the double-difference pseudorange observation value between satellites i and j and between receivers r and u, is the double difference geometric distance between receiver r, u and satellite i, j calculated from the user's approximate coordinates, is the a priori ionospheric delay between satellites i and j and between receivers r and u, is the double-difference carrier observation value between satellites i and j and between receivers r and u, is the a priori double-difference ambiguity between satellites i and j and between receivers r and u, and λ represents the frequency of the corresponding carrier signal.

[0108] make

[0109] Then formula (17) can be simplified as:

[0110] V=BX * -L (18)

[0111] The observation weight matrix P is calculated according to the altitude angle model or the signal-to-noise ratio random error model, and the relevant parameters to be determined can be solved by the least squares method:

[0112]

[0113] Let A=(B T PB) -1 B T P, according to the error propagation law, the accuracy matrix of the parameters to be determined is obtained for:

[0114]

[0115] in:

[0116]

[0117] Where D L is the observation precision matrix; is the random error of the observation value. In the GNSS double-difference observation model (Formulas (15)-(16)), It can be expressed as:

[0118]

[0119] Where, is the random error of the pseudorange or carrier observation value corresponding to satellite i. When the observation value is pseudorange, When the observation value is carrier, 0.001; the variance of the ionospheric delay error under multiple epochs is calculated from historical observation data Find the RMS value of the diagonal elements corresponding to satellite i, and get

[0120] Step 5: Calculate the positioning errors of the candidate points in the area based on the parameter accuracy matrix to be determined on the user side. According to the positioning errors of the candidate points and the geographical conditions of the candidate points, select the locations for the construction of the encrypted reference stations that meet the required number.

[0121] The specific locations of the selected points can be evenly divided according to the longitude and latitude, or they can be conditionally designated according to the relevant requirements of the builder. The sum of the first three elements of the diagonal is the positioning error D of the selected point. XYZIn this embodiment, the latitude and longitude grid of the service area is divided into 10-second intervals to obtain all the points to be selected. The positioning error D of all the points to be selected is calculated. XYZ , and the error results are as follows Figure 2 shown. Figure 2 In the figure, the black inverted triangle point is the established benchmark station, D XYZ The larger the value, the redder the color, which means that the point is more likely to be used to build an encrypted base station. XYZ , combined with the geographical conditions of the point (preferably a plain and unobstructed area), select the location for the construction of encrypted base stations that meets the required number.

[0122] Example 2

[0123] Based on the same inventive concept, the present invention also provides an encrypted reference station position selection system based on ionospheric precision information, including a processor and a memory, the memory being used to store program instructions, and the processor being used to call the program instructions in the memory to execute the above-mentioned encrypted reference station position selection method based on ionospheric precision information.

[0124] Example 3

[0125] Based on the same inventive concept, the present invention also provides an encrypted reference station position selection system based on ionospheric precision information, including a readable storage medium, on which a computer program is stored. When the computer program is executed, the encrypted reference station position selection method based on ionospheric precision information as described above is implemented.

[0126] In specific implementation, the method proposed in the technical solution of the present invention can be automatically run by those skilled in the art using computer software technology. System devices that implement the method, such as computer-readable storage media that store the corresponding computer program of the technical solution of the present invention and computer equipment that runs the corresponding computer program, should also be within the scope of protection of the present invention.

[0127] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Persons skilled in the art may make various modifications, additions, or substitutions to the described specific embodiments without departing from the spirit of the present invention or exceeding the scope of the appended claims.

Claims

1. A method for selecting encrypted reference station positions based on ionospheric accuracy information, characterized in that: The following steps are involved: Step 1: Complete the network of reference stations in the service area, resolve the ambiguity on each frequency of the reference station network, and calculate the ionospheric delay on each baseline based on the actual GNSS observation data; Step 2: Based on the ionospheric delay on each baseline and the position relationship between the reference stations, an ionospheric delay interpolation model within the service area is constructed; Step 3: Based on the ionospheric delay interpolation model constructed in step 2, estimate the ionospheric accuracy information of a single epoch at the user end; Step 4: Based on the historical observation data, the calculation method in step 3 is used to estimate the ionospheric accuracy information of multiple epochs, and the GNSS double-difference observation model is used to estimate the accuracy matrix of the unknown parameters at the user end; Step 5: Calculate the positioning errors of the candidate points in the area based on the parameter accuracy matrix to be determined on the user side. According to the positioning errors of the candidate points and the geographical conditions of the candidate points, select the locations for the construction of the encrypted reference stations that meet the required number.

2. The method for selecting an encrypted reference station position based on ionospheric accuracy information according to claim 1, wherein: In step 1, the ambiguity at each frequency of the reference station network is resolved. Once the ambiguity is fixed, the ionospheric delay on each baseline is calculated as: Where, represents the ionospheric delay on the L1 signal between the reference stations, f1 and f2 represent the frequencies of the L1 and L2 signals respectively, λ1 and λ2 represent the wavelengths of the L1 and L2 signals respectively, N1 and N2 represent the ambiguity on the L1 and L2 signals between the reference stations respectively, and Represent the carrier observation values of L1 and L2 signals respectively.

3. The method for selecting encrypted reference station positions based on ionospheric accuracy information according to claim 1, wherein: In step 2, the indirect adjustment method is used to construct the ionospheric delay interpolation model within the service area. The specific calculation method is as follows: V r =B r ·X-L r (2) Where V r represents the ionospheric delay residual in the indirect adjustment model; B r To construct a matrix, where ΔX i and ΔY i Respectively represent the components on the X-axis and Y-axis of the plane corresponding to the baseline; L r is the reference value vector, where I i represents the ionospheric delay of the corresponding baseline; X is the fitting parameter vector, where a1 and a2 are the fitting parameters of the linear interpolation model, and n is the number of baselines; The fitting parameter vector after adjustment is calculated using the least squares method Right now: Among them, P r is the weight matrix, and is calculated as follows: Where, d i Indicates the plane distance corresponding to the baseline.

4. The method for selecting encrypted reference station positions based on ionospheric accuracy information according to claim 3, wherein: In step 3, the coordinate difference matrix B between the user terminal and the main reference station is used. u , calculate the ionospheric delay at the user end. The specific calculation formula is as follows: Where, I ru is the ionospheric delay of the user terminal, ΔX is the coordinate difference between the user terminal and the master reference station on the X axis, and ΔY is the coordinate difference between the user terminal and the master reference station on the Y axis; There is an error between the ionospheric delay at the user end calculated by equation (8) and the actual ionospheric delay, which is recorded as V u ,but: Where, L u is the actual ionospheric delay at the user end, E u is a unit vector.

5. The method for selecting an encrypted reference station position based on ionospheric accuracy information according to claim 4, wherein: In step 3, according to the error propagation law, the user-side ionospheric delay error V u The variance of is expressed as: Where, is the variance of the ionospheric delay product error at the user end, D LL is the ionospheric delay variance of the baseline and user side; In actual operation, the server will use the regional ionospheric delay interpolation model to calculate the ionospheric delay product for each user terminal. Therefore, the number of user terminals in the model is considered to be 1, and D LL is an n+1 square matrix, that is: Where, is the unit weighted variance of the ionospheric delay residuals between n baselines and the baselines of the user and the master reference station, P r is the weight matrix, d u is the plane distance between the user terminal and the main reference station; Considering that the user is located inside the network, the properties of its ionospheric delay residual are similar to those of the surrounding stations, so its ionospheric delay residual can be approximated as the average value of the ionospheric delay residuals of other reference stations, and thus Estimated to be: Where, is the unit weight variance of the ionospheric delay residual on n baselines, V n is the ionospheric delay residual on n baselines, P r is the weight matrix, t is the necessary number of observations; The variance of the ionospheric delay error is calculated according to equations (10)-(14): That is, the user-side ionospheric accuracy information at each epoch.

6. The method for selecting encrypted reference station positions based on ionospheric accuracy information according to claim 1, wherein: The observation equation of the user side in step 4 is: Where i and j represent satellite numbers, r and u represent receiver numbers, is the double-difference pseudorange observation value between satellites i and j and between receivers r and u, is the double difference distance between satellites i and j and between receivers r and u, is the ionospheric delay between satellites i and j and between receivers r and u, is the double-difference carrier observation value between satellites i and j and between receivers r and u, is the double-difference ambiguity between satellites i and j and between receivers r and u, and λ represents the frequency of the corresponding carrier signal; The user-side indirect adjustment model is constructed as follows: Where V is the residual vector of the equation, is the double difference unit vector on the line connecting receivers r, u and satellites i, j, Δx ru is the coordinate change of the receiver r, u approximate coordinates, is the ionospheric delay between satellites i and j and between receivers r and u, is the double-difference ambiguity between satellites i and j and between receivers r and u, is the double-difference pseudorange observation value between satellites i and j and between receivers r and u, is the double difference geometric distance between receiver r, u and satellite i, j calculated from the user's approximate coordinates, is the a priori ionospheric delay between satellites i and j and between receivers r and u, is the double-difference carrier observation value between satellites i and j and between receivers r and u, is the a priori double-difference ambiguity between satellites i and j and between receivers r and u, and λ represents the frequency of the corresponding carrier signal.

7. The method for selecting encrypted reference station positions based on ionospheric accuracy information according to claim 6, wherein: In step 4, Then formula (17) can be abbreviated as: V=BX * -L (18) According to the altitude angle model or the signal-to-noise ratio random error model, the observation value weight matrix P is calculated and the related parameters to be solved are obtained using the least squares method: Let A=(B T PB) -1 B T P, according to the error propagation law, the accuracy matrix of the parameters to be determined is obtained for: in: Where D L is the observation precision matrix; is the random error of the observation value, which can be expressed as follows in the GNSS double-difference observation model: Where, is the random error of the pseudorange or carrier observation value corresponding to satellite i. When the observation value is pseudorange, When the observation value is carrier, 0.001; the variance of the ionospheric delay error under multiple epochs is calculated from historical observation data Find the RMS value of the diagonal elements corresponding to satellite i, and get 8. The method for selecting encrypted reference station positions based on ionospheric accuracy information according to claim 7, wherein: In step 5, Add the first three elements of the diagonal to get the positioning error D XYZ , calculate the D of all candidate points in the service area XYZ , according to the D of the selected point XYZ , combined with the geographical conditions of the selected points, select the location for the construction of encrypted base stations that meet the required number.

9. An encrypted reference station location selection system based on ionospheric accuracy information, characterized in that: It includes a processor and a memory, the memory is used to store program instructions, and the processor is used to call the program instructions in the memory to execute the encrypted reference station position selection method based on ionospheric accuracy information as described in any one of claims 1-8.

10. An encrypted reference station position selection system based on ionospheric accuracy information, characterized in that: It includes a readable storage medium, on which a computer program is stored. When the computer program is executed, the method for selecting an encrypted reference station position based on ionospheric accuracy information as described in any one of claims 1 to 8 is implemented.

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