Beidou PPP-RTK multiple risk source credible probability allocation method and device

Abstract

The Beidou PPP-RTK multiple risk source trusted probability allocation method and device relate to the field of trusted probability allocation. In order to solve the problem in the prior art that the trusted probability allocation method based on the traditional civil aviation field is no longer applicable due to the large difference between the technical means of the Beidou PPP-RTK carrier phase layer and the navigation and positioning method of the civil aviation pseudorange layer, and trusted monitoring is therefore difficult to carry out, the technical solution provided by the present invention is: Beidou PPP-RTK multiple risk source trusted probability allocation method, the method includes: constructing a Beidou PPP-RTK trusted monitoring fault tree model; based on the model, obtaining an error distribution model; based on the model, constructing a protection level equation with multiple hypothesis solutions separated; constructing an optimal allocation model; solving the optimal allocation model to obtain an allocation result; constructing a protection level to determine whether a stable state has been reached; and outputting the final allocation result. It is suitable for the research work on the trusted probability allocation method for Beidou PPP-RTK, and is also suitable for ensuring the credibility of Beidou PPP-RTK navigation and positioning services.

Description

Technical Field

[0001] It involves the field of credible probability allocation, specifically the field of credible probability allocation of multiple risk sources. Background Art

[0002] With the iterative upgrades of the Global Navigation Satellite System (GNSS) and the development of high-precision positioning technology, satellite navigation now has the ability to provide users with high-precision positioning services. PPP-RTK is a high-precision positioning technology that has been gaining popularity in recent years. It combines the fast convergence of RTK technology with the wide coverage of PPP technology. By densely deploying observation stations in the service area, the service platform generates and broadcasts vectored satellite clock orbits, satellite deviations, and regional atmospheric enhancement information to users. This ensures that users within the service area can complete convergence within 1 minute and achieve centimeter-level positioning accuracy. It has wide applications in unmanned farms, autonomous driving, marine ranching, and other fields.

[0003] Ensuring the reliability of positioning results while providing high-precision positioning services is key to the construction and operation of satellite navigation systems. Trusted monitoring originated in the civil aviation field. It can promptly alert users when a navigation and positioning system fails and cannot complete scheduled operations, thereby meeting the user's required navigation performance (RNP). PPP-RTK applications frequently involve areas related to life safety, and ensuring the reliability of PPP-RTK positioning services is imperative. Due to the significant differences between the technical means of Beidou PPP-RTK carrier phase layer and the navigation and positioning methods of civil aviation pseudorange layer, PPP-RTK vectored positioning faces more potential risk sources. Furthermore, there is currently no authoritative technical indicator system related to Beidou PPP-RTK reliability. The trusted probability allocation method based on the traditional civil aviation field is no longer applicable, making trusted monitoring difficult to carry out. Therefore, it is urgent to develop a trusted probability allocation method for Beidou PPP-RTK.

[0004] In order to ensure the credibility of Beidou PPP-RTK navigation and positioning services and support the expansion of PPP-RTK services to life safety-related fields such as sparsely populated and unmanned operations, the present invention proposes a Beidou PPP-RTK multiple risk source credible probability allocation method. In response to the problems of the lack of an authoritative indicator system for multiple risk sources of Beidou PPP-RTK credible services and the difficulty in ensuring the availability of coordinated monitoring of risk sources, the present invention is based on a credible monitoring fault tree model that clarifies potential risk sources. By setting multiple risk source failure modes, constructing a system-side protection level, quantifying risk source monitoring performance, and taking the equivalence of risk source monitoring performance as the principle, the credible probability allocation of Beidou PPP-RTK multiple risk sources is achieved. The present invention clarifies the multiple risk sources that affect the credibility of positioning and constructs a complete credible probability allocation system. It is of great significance to strictly guarantee the credibility of positioning results while improving the availability of Beidou PPP-RTK navigation and positioning services. Summary of the Invention

[0005] To address the existing technical issues of Beidou PPP-RTK, which faces more potential risk sources due to the significant differences between the technical means of the carrier phase layer of Beidou PPP-RTK and the navigation and positioning methods of the pseudorange layer of civil aviation, and the lack of an authoritative technical indicator system for Beidou PPP-RTK credibility, the credibility probability allocation method based on the traditional civil aviation field is no longer applicable, making credibility monitoring difficult to carry out, the present invention provides the following technical solutions:

[0006] A BeiDou PPP-RTK multiple risk source credible probability allocation method, the method comprising:

[0007] Step 1: Build a BeiDou PPP-RTK trusted monitoring fault tree model;

[0008] Step 2, based on the model, obtaining an error distribution model;

[0009] Step 3, based on the model, constructing a protection level equation with multiple hypothesis solutions separated;

[0010] Step 4, constructing an optimal allocation model based on the equation;

[0011] Step 5, simplifying the optimal allocation model;

[0012] Step 6: solving the optimal allocation model to obtain an allocation result;

[0013] Step 7: construct multiple batches of protection levels and determine whether a stable state has been reached;

[0014] Step 8: Output the allocation result that reaches a stable state as the final allocation result.

[0015] Furthermore, a preferred embodiment is provided, in step 2, the method for obtaining the error distribution model is specifically: constructing multiple risk source failure modes according to the fault tree model, and obtaining the error distribution model according to the failure modes.

[0016] Furthermore, a preferred embodiment is provided, in step 5, the method of simplifying the optimal allocation model is specifically: based on the Lagrange multiplier method.

[0017] Furthermore, a preferred embodiment is provided, in step 6, the method for settling the optimal allocation model is specifically: based on a dichotomy method.

[0018] Based on the same inventive concept, the present invention also provides a Beidou PPP-RTK multiple risk source credible probability allocation device, the device comprising:

[0019] Module 1 is used to build a BeiDou PPP-RTK trusted monitoring fault tree model;

[0020] Module 2, for obtaining an error distribution model based on the model;

[0021] Module 3 is used to construct a protection level equation with multiple hypothesis solutions separated based on the model;

[0022] Module 4 is used to construct an optimal allocation model based on the equation;

[0023] Module 5, used to simplify the optimal allocation model;

[0024] Module 6, used to solve the optimal allocation model and obtain the allocation result;

[0025] Module 7 is used to construct multiple batch protection levels and determine whether a stable state has been reached;

[0026] Module 8 is used to output the allocation result that reaches a stable state as the final allocation result.

[0027] Furthermore, a preferred embodiment is provided, in the module 2, the method for obtaining the error distribution model is specifically: constructing a multiple risk source failure mode according to the fault tree model, and obtaining the error distribution model according to the failure mode.

[0028] Furthermore, a preferred embodiment is provided, in the module 5, the method for simplifying the optimal allocation model is specifically: based on the Lagrange multiplier method.

[0029] Furthermore, a preferred embodiment is provided, in the module 6, the method for settling the optimal allocation model is specifically: based on a dichotomy method.

[0030] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program. When the computer processor processes the computer program in the storage medium, the computer executes the Beidou PPP-RTK multiple risk source credible probability allocation method.

[0031] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium, wherein the storage medium stores a computer program. When the processor of the computer processes the computer program in the storage medium, the computer executes the Beidou PPP-RTK multiple risk source credible probability allocation method.

[0032] The present invention is beneficial in that:

[0033] The Beidou PPP-RTK multiple risk source trusted probability allocation method provided by the present invention addresses the problems of the lack of an authoritative indicator system for multiple risk sources of Beidou PPP-RTK trusted services and the difficulty in ensuring the availability of coordinated monitoring of risk sources. By constructing a Beidou PPP-RTK trusted monitoring fault tree, the present invention designs a trusted probability allocation scheme under the concurrent situation of multiple risk sources, improves the availability of the trusted service platform, and establishes a Beidou PPP-RTK trusted service multiple risk source indicator system.

[0034] It is applicable to the research work on the trusted probability distribution method for Beidou PPP-RTK, and is also applicable to ensuring the credibility of Beidou PPP-RTK navigation and positioning services. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram of the BeiDou PPP-RTK trusted monitoring fault tree model mentioned in Implementation 11;

[0036] Figure 2 This is a flow chart of the Beidou PPP-RTK multiple risk source credible probability allocation method mentioned in Implementation Method 11. DETAILED DESCRIPTION

[0037] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically:

[0038] Implementation method 1: This implementation method provides a BeiDou PPP-RTK multiple risk source credible probability allocation method, the method comprising:

[0039] Step 1: Build a BeiDou PPP-RTK trusted monitoring fault tree model;

[0040] Step 2, based on the model, obtaining an error distribution model;

[0041] Step 3, based on the model, constructing a protection level equation with multiple hypothesis solutions separated;

[0042] Step 4, constructing an optimal allocation model based on the equation;

[0043] Step 5, simplifying the optimal allocation model;

[0044] Step 6: solving the optimal allocation model to obtain an allocation result;

[0045] Step 7: construct multiple batches of protection levels and determine whether a stable state has been reached;

[0046] Step 8: Output the allocation result that reaches a stable state as the final allocation result.

[0047] Implementation method 2. This implementation method is a further limitation of the Beidou PPP-RTK multiple risk source credible probability allocation method provided in implementation method 1. In step 2, the method for obtaining the error distribution model is specifically: constructing a multiple risk source failure mode according to the fault tree model, and obtaining the error distribution model according to the failure mode.

[0048] Implementation method three: This implementation method further limits the Beidou PPP-RTK multiple risk source credible probability allocation method provided in implementation method one. In step 5, the method for simplifying the optimal allocation model is specifically: based on the Lagrange multiplier method.

[0049] Implementation method 4: This implementation method further limits the Beidou PPP-RTK multiple risk source credible probability allocation method provided in implementation method 1. In step 6, the method for settling the optimal allocation model is specifically: based on the dichotomy method.

[0050] Implementation 5: This implementation provides a BeiDou PPP-RTK multiple risk source credible probability allocation device, the device comprising:

[0051] Module 1 is used to build a BeiDou PPP-RTK trusted monitoring fault tree model;

[0052] Module 2, for obtaining an error distribution model based on the model;

[0053] Module 3 is used to construct a protection level equation with multiple hypothesis solutions separated based on the model;

[0054] Module 4 is used to construct an optimal allocation model based on the equation;

[0055] Module 5, used to simplify the optimal allocation model;

[0056] Module 6, used to solve the optimal allocation model and obtain the allocation result;

[0057] Module 7 is used to construct multiple batch protection levels and determine whether a stable state has been reached;

[0058] Module 8 is used to output the allocation result that reaches a stable state as the final allocation result.

[0059] Implementation method six. This implementation method is a further limitation of the Beidou PPP-RTK multiple risk source trusted probability distribution device provided in implementation method five. In module 2, the method for obtaining the error distribution model is specifically: constructing a multiple risk source failure mode according to the fault tree model, and obtaining the error distribution model according to the failure mode.

[0060] Implementation method seven: This implementation method is a further limitation of the Beidou PPP-RTK multiple risk source credible probability allocation device provided in implementation method five. In module 5, the method for simplifying the optimal allocation model is specifically: based on the Lagrange multiplier method.

[0061] Implementation method eight: This implementation method is a further limitation of the Beidou PPP-RTK multiple risk source credible probability allocation device provided in implementation method five. In module 6, the method for settling the optimal allocation model is specifically: based on the dichotomy method.

[0062] Implementation method nine. This implementation method provides a computer storage medium for storing a computer program. When the computer processor processes the computer program in the storage medium, the computer executes the Beidou PPP-RTK multiple risk source trusted probability allocation method provided in any one of implementation methods one to four.

[0063] Implementation method ten. This implementation method provides a computer, including a processor and a storage medium, in which a computer program is stored. When the processor of the computer processes the computer program in the storage medium, the computer executes the Beidou PPP-RTK multiple risk source trusted probability allocation method provided in any one of implementation methods one to four.

[0064] Implementation 11. This implementation provides a specific implementation of the BeiDou PPP-RTK multiple risk source credible probability allocation method provided in Implementation 1, and is also used to explain Implementations 1 to 4. Specifically:

[0065] It is important to note that the concepts repeatedly mentioned in the steps are defined as follows: the credible budget is the overall credible probability requirement; the credible probability is the credible probability of a certain epoch failure mode k evaluated by the allocation algorithm; and the credible index is the convergence result of the credible probability within the specified period as an indicator.

[0066] Step 1: Build a BeiDou PPP-RTK trusted monitoring fault tree model;

[0067] The construction of BeiDou PPP-RTK trusted monitoring fault tree is the premise of the trusted probability distribution of BeiDou PPP-RTK multiple risk sources. According to the multiple risk sources of BeiDou PPP-RTK in trusted monitoring stations, service platforms, communication links and user terminals, clustering and sorting are carried out according to the different risk source levels, and the key risk sources that PPP-RTK needs to monitor are identified. The BeiDou PPP-RTK trusted monitoring fault tree is constructed as shown in the attached figure. Figure 1 shown.

[0068] Step 2: Based on the fault tree model constructed in step 1, clarify the multiple risk source failure modes and obtain the error distribution model; set the multiple risk source failure modes to sort out the fault tree model in step 1.

[0069] The risk source type is associated with the fault hypothesis used to quantify the fault detection performance. According to the method of multiple hypothesis solution separation, the fault mode k corresponds to the fault hypothesis H ij : Risk source i occurs on satellite j. The prior failure probability of failure mode k is denoted as P ap,k .

[0070] In addition, the Gaussian distribution model provided by the residual envelope system can be used to obtain the error distribution of fault mode k: And the distribution of test statistics: To construct the protection level equation for multiple hypothesis separation;

[0071] Where N represents Gaussian distribution, represents the variance of the error distribution, μ represents the expectation of the detection statistic distribution, represents the variance of the distribution of the test statistic.

[0072] Step 3: Based on the fault mode, construct the protection level equation with multiple hypothesis solutions;

[0073] Apply the fault mode and distribution model constructed in step 2 and build protection levels corresponding to different fault modes along the Beidou PPP-RTK trusted monitoring fault tree in step 1:

[0074] XPL k =K HMI，k σ k +B k +K cont，k σ ss，k +B ss，k (1)

[0075] Where B k 、B ss，k is the maximum deviation of the distribution model, which depends on the result of the residual envelope system; K HMI，k , K cont，kThey are respectively the credible probability scalar multiplier and the continuity probability scalar multiplier, and are represented by the Gaussian inverse cumulative distribution function Q -1 Obtain:

[0076]

[0077]

[0078] Where, IR k is the credible probability assigned to failure mode k, CR k is the continuity probability assigned to fault mode k. The dimension of the protection level (HPL or VPL) depends on the application budget direction. Here, XPL is used to reflect the fault detection performance of multiple risk sources.

[0079] Step 4: Based on the protection level equation constructed in step 3, the optimal allocation model is constructed;

[0080] Using the protection level formula in step 3, combined with the credibility probability and continuity probability constraints, and based on the principle of equivalence of fault detection performance for multiple risk sources, the optimal allocation model can be constructed as follows:

[0081] Minimize XPL

[0082] XPL=K HMI，k σ k +B k +K cont，k σ ss，k +B ss，k

[0083] subject to

[0084]

[0085] Where N mod is the total number of failure modes; IR k The total value is subject to the credible budget IR req Constraint, CR k The total value is subject to the continuity budget CR req Constraints are called credibility constraint equations and continuity constraint equations. The analytical formula (3) is easy to understand. The allocation problem is equivalent to the minimum value problem (optimal problem), which can be solved using the Lagrange multiplier method.

[0086] Step 5: Use the Lagrange multiplier method to simplify the optimal allocation model constructed in step 4;

[0087] According to the steps of the Lagrange multiplier method, the Lagrange equation is constructed using formula (3). The optimal state can be obtained by setting its gradient to 0. The optimal state is transformed equivalently so that the credible probability and continuity probability can be directly solved using the dichotomy method, further simplifying the allocation problem. The simplified results are the credible probability and continuity probability expressed in the form of corresponding quantiles,

[0088]

[0089]

[0090] Where μ is the Lagrange multiplier introduced to construct the Lagrange equation, β k B k +B ss，k , c k For intermediate variables:

[0091]

[0092] Step 6: Use the dichotomy method to solve the optimal allocation model and obtain the allocation result of this batch;

[0093] The simplified results expressed as quantiles in step 5 can be converted to IR using the cumulative distribution function k , CR k ,

[0094] IR k =2P ap，k Q(-K HMI，k )

[0095] CR k =2Q(-K cont，k ) (7)

[0096] Using the conversion relationship in Equation (7), we can perform a binary search for the unknown variables XPL and μ along the credibility constraint equation and continuity constraint equation in Equation (3). During the binary search, for each XPL value, we must first search for a μ value that satisfies the credibility constraint equation to obtain the unknown variable value that satisfies both constraint equations. Substituting the search results into Equations (4) and (5) to calculate the quantile, and then substituting the quantile into Equation (7) to obtain the credibility probability and continuity probability.

[0097] Step 7: Construct multiple batches of protection levels and determine whether a stable state has been reached.

[0098] For the credibility and continuity probability distribution results from step 6, a method for calculating protection levels for multiple risk sources across multiple batches is used, and the principle of protection level consistency is used to determine whether the distribution results have reached a stable state. Specifically, when the protection levels for the same risk source across multiple batches converge, the distribution performance reaches a stable state. If a stable state is reached, the converged result is considered the credibility probability distribution result for the current cycle and is considered a credibility indicator, thereby solidifying the credibility risk allocation system for multiple risk sources in PPP-RTK.

[0099] The present invention addresses the problems of the lack of an authoritative indicator system for multiple risk sources in Beidou PPP-RTK trusted services and the difficulty in ensuring the availability of coordinated risk source monitoring. By setting multiple risk source failure modes, constructing a system-side protection level, quantifying risk source monitoring performance, and based on the principle of risk source monitoring performance equivalence, the present invention implements Beidou PPP-RTK multiple risk source trusted probability allocation and constructs a trusted monitoring fault tree model. The present invention clarifies the multiple risk sources that affect positioning credibility and constructs a complete trusted probability allocation system, thereby strictly ensuring the credibility of positioning results while improving the availability of Beidou PPP-RTK navigation and positioning services.

[0100] The above further describes the technical solution provided by the present invention in detail through several specific embodiments, which is only for the purpose of highlighting the advantages and benefits of the present invention. However, the above-mentioned embodiments are not intended to limit the present invention. Any changes and improvements to the technical solution provided by the present invention, combinations of embodiments and equivalent replacements based on the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. BeiDou PPP-RTK multiple risk source credible probability allocation method, characterized by: The method comprises: Step 1: Build a BeiDou PPP-RTK trusted monitoring fault tree model; Step 2, based on the model, obtaining an error distribution model; Step 3: constructing a protection level equation with multiple hypothesis solutions separated based on the error distribution model; Step 4, constructing an optimal allocation model based on the equation; Step 5, simplifying the optimal allocation model; Step 6: solving the optimal allocation model to obtain an allocation result; Step 7: construct multiple batches of protection levels and determine whether a stable state has been reached; Step 8: Output the allocation result that reaches a stable state as the final allocation result.

2. The BeiDou PPP-RTK multiple risk source credible probability allocation method according to claim 1, characterized in that: In step 2, the method for obtaining the error distribution model is specifically: constructing multiple risk source failure modes according to the fault tree model, and obtaining the error distribution model according to the failure modes.

3. The BeiDou PPP-RTK multiple risk source credible probability allocation method according to claim 1, characterized in that: In step 5, the method for simplifying the optimal allocation model is specifically: based on the Lagrange multiplier method.

4. The BeiDou PPP-RTK multiple risk source credible probability allocation method according to claim 1, characterized in that: In step 6, the method for settling the optimal allocation model is specifically: based on the dichotomy method.

5. Beidou PPP-RTK multiple risk source credible probability distribution device, characterized by: The device comprises: Module 1 is used to build a BeiDou PPP-RTK trusted monitoring fault tree model; Module 2, for obtaining an error distribution model based on the model; Module 3 is used to construct a protection level equation with multiple hypothesis solutions separated based on the error distribution model; Module 4 is used to construct an optimal allocation model based on the equation; Module 5, used to simplify the optimal allocation model; Module 6, used to solve the optimal allocation model and obtain the allocation result; Module 7 is used to construct multiple batch protection levels and determine whether a stable state has been reached; Module 8 is used to output the allocation result that reaches a stable state as the final allocation result.

6. The BeiDou PPP-RTK multiple risk source credible probability allocation device according to claim 5, characterized in that: In the module 2, the method for obtaining the error distribution model is specifically: constructing a multiple risk source failure mode according to the fault tree model, and obtaining the error distribution model according to the failure mode.

7. The BeiDou PPP-RTK multiple risk source credible probability allocation device according to claim 5, characterized in that: In the module 5, the method for simplifying the optimal allocation model is specifically: based on the Lagrange multiplier method.

8. The BeiDou PPP-RTK multiple risk source credible probability allocation device according to claim 5, characterized in that: In the module 6, the method for settling the optimal allocation model is specifically: based on the dichotomy method.

9. A computer storage medium for storing a computer program, characterized in that When the computer processor processes the computer program in the storage medium, the computer executes the Beidou PPP-RTK multiple risk source trust probability allocation method described in claims 1-4.

10. A computer comprising a processor and a storage medium, wherein the storage medium stores a computer program, characterized in that: When the processor of the computer processes the computer program in the storage medium, the computer executes the Beidou PPP-RTK multiple risk source trust probability allocation method described in claims 1-4.

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