A railway auxiliary positioning method and system for signal fault scenarios

Abstract

The application discloses a railway auxiliary positioning method and system for a signal fault scene, and the method comprises the following steps: a train auxiliary positioning system acquires train positioning quality parameters and train Beidou positioning signals, normalizes the train positioning quality parameters, and calculates a fault diagnosis coefficient; a sliding window threshold is set according to the fault diagnosis coefficient; a train auxiliary fitting algorithm is designed to obtain a final fitting position of the train; and finally, the train position is calculated by combining the train inertial navigation position and the final fitting position of the train. The application calculates the fault diagnosis coefficient from multiple normalized train positioning quality parameters, and then judges the fault severity of the positioning signal, thereby improving the richness and calculation efficiency of the positioning signal quality judgment, designing an auxiliary fitting algorithm, continuously iterating the fitting parameters of the train auxiliary fitting algorithm, combining the train inertial navigation position to calculate the train position, and further improving the accuracy of the scheme.

Description

Technical Field

[0001] This invention belongs to the field of railway positioning technology, and in particular relates to a railway auxiliary positioning method and system for signal failure scenarios. Background Technology

[0002] With the ever-increasing speed of logistics and transportation, the railway transportation system, as a convenient and efficient mode of transport, plays an increasingly important role in social production and daily life. Accurate train positioning is crucial for ensuring operational safety and improving transportation efficiency during train operation. Currently, railway trains mainly rely on satellite positioning systems (such as GPS and BeiDou) to obtain real-time location information. However, these positioning signals are highly susceptible to interference from various factors in the complex railway operating environment, leading to signal failures. This results in a significant decrease in positioning accuracy, or even the complete loss of positioning signals.

[0003] When train positioning signals malfunction, existing railway positioning systems often struggle to quickly and accurately determine a train's location. Traditional positioning methods have significant limitations in signal failure scenarios. For example, relying on a single parameter to determine signal quality results in large errors; using fixed beacons for positioning fails to provide continuous positioning; and positioning based on track circuits has low accuracy, making it unsuitable for the high-speed, high-efficiency operation requirements of modern railways. These methods also suffer from poor real-time performance, low computational efficiency, and a single source of information for determining positioning signal quality. Therefore, a new railway-assisted positioning method is needed. Summary of the Invention

[0004] This invention provides a railway auxiliary positioning method and system for signal failure scenarios, in order to solve the technical problems of current railway auxiliary positioning data being limited, having poor real-time performance, and low accuracy in signal failure scenarios.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] On one hand, the present invention provides a railway-assisted positioning method for signal failure scenarios, the railway-assisted positioning method for signal failure scenarios includes:

[0007] S1: The train auxiliary positioning system acquires the train positioning quality parameters and the train Beidou positioning signal, normalizes the train positioning quality parameters, and calculates the fault diagnosis coefficient from the normalized train positioning quality parameters.

[0008] S2: Set the sliding window threshold based on the fault diagnosis coefficient;

[0009] S3: Calculate the position of each sliding window using the train's Beidou positioning signal, design a train-assisted fitting algorithm, calculate the fitting parameters of the train-assisted fitting algorithm based on the position of the sliding window in the sliding window threshold, calculate the fitting position based on the fitting parameters, and obtain the final fitted position of the train.

[0010] S4: The train auxiliary positioning system obtains the train's inertial navigation position, and calculates the train's position by combining the train's inertial navigation position and the train's final fitted position.

[0011] S5: Output train position.

[0012] Preferably, step S1 includes:

[0013] The train auxiliary positioning system acquires train positioning quality parameters and train BeiDou positioning signals;

[0014] Train positioning quality parameters include positioning signal strength, satellite geometry distribution, and clock error;

[0015] Normalization of train positioning quality parameters includes:

[0016] Normalize the positioning signal strength P, and calculate the normalized positioning signal strength. The specific calculation formula is as follows:

[0017] ;

[0018] ;

[0019] in, This represents the average value of the positioning signal strength, where i is an integer. Indicates the number of positioning signals. Indicates the strength of the i-th positioning signal. A normalized parameter representing the strength of the positioning signal;

[0020] Normalize the satellite geometric distribution W, and calculate the normalized satellite geometric distribution. The specific calculation formula is as follows:

[0021] ;

[0022] in, Normalized parameters representing the geometric distribution of satellites;

[0023] Normalize the clock error S and calculate the normalized clock error. The specific calculation formula is as follows:

[0024] ;

[0025] in, This represents the clock error of the i-th positioning signal. Normalized parameter representing clock error;

[0026] The fault diagnosis coefficient is calculated from the normalized train positioning quality parameters.

[0027] Preferably, the calculation of the fault diagnosis coefficient from the normalized train positioning quality parameters includes:

[0028] Positioning signal strength after normalization Normalized satellite geometric distribution and normalized clock error The fault diagnosis coefficient Q is calculated using the following formula:

[0029] ;

[0030] in, , and All are fault assignment parameters;

[0031] , and The following relationship must be satisfied:

[0032] .

[0033] Preferably, step S2 includes:

[0034] The sliding window threshold H is set based on the fault diagnosis coefficient Q, and the specific expression is as follows:

[0035] ;

[0036] And based on the fault diagnosis coefficient Q, it is determined whether an auxiliary location method needs to be executed, including:

[0037] Initialize the auxiliary flag (FLAG), and set the number of sampling times within the sliding window where the fault diagnosis coefficient is less than 30. The total number of sampling times within the sliding window is The expression for FLAG is as follows:

[0038] ;

[0039] in, Indicates other situations, when When, it indicates that the signal failure rate is low within a continuous period of time, and no auxiliary positioning method is required. "At this time" indicates that the signal has a high failure rate within a continuous period of time, and an auxiliary positioning method needs to be executed.

[0040] Preferably, step S3 includes:

[0041] when The auxiliary positioning method is executed, and each sliding window contains 5 seconds of train Beidou positioning signal;

[0042] Initialize the positioning signal acquisition frequency f, that is, the frequency f within each sliding window. Each train's BeiDou positioning signal D is the average of n positioning signals at that moment, where D={x,y,z}, where x represents the train's position on the X-axis in the track coordinate system, y represents the train's position on the Y-axis in the track coordinate system, and z represents the train's position on the Z-axis in the track coordinate system.

[0043] The position of each sliding window is calculated using the train's BeiDou positioning signal, and the position of the j-th sliding window is calculated. The specific expression is as follows:

[0044] ;

[0045] in, This represents the BeiDou positioning signal of the i-th train in the j-th sliding window, where j is less than or equal to H;

[0046] The train-assisted fitting algorithm is designed. First, the level coefficient and trend coefficient are calculated. Then, the fitting parameters are calculated from the level coefficient and trend coefficient. The specific expression is as follows:

[0047] ;

[0048] ;

[0049] ;

[0050] in, Let 'b' represent the expected coefficient and 'b' represent the fitted parameters. Indicates the m-th The expected coefficient, Indicates the m-th The expected coefficient, Indicates the m-th Fitting parameters, This represents the m-th level coefficient. This indicates the position of the m-th sliding window. This indicates the position of the (m+1)th sliding window. This represents the (m-1)th level coefficient. This represents the m-th trend coefficient. This represents the (m-1)th trend coefficient;

[0051] initialization , 、{ }and{ Iterative calculation The value of is expressed as follows:

[0052] ;

[0053] in, Indicates the first one The expected coefficient, Indicates the s-th The expected coefficient, Indicates the Hth The expected coefficient, Indicates the first one The expected coefficient, Indicates the s-th The expected coefficient, Indicates the Hth The expected coefficient, Indicates the first one Fitting parameters, Indicates the s-th Fitting parameters, Indicates the Hth The fitting parameters are: max{} represents finding the maximum value, min{} represents finding the minimum value, and s represents the sequence number, which is an integer.

[0054] Based on the fitting parameters Calculate the value of the sliding window at the H+1th time. Then the final fitted position of the train at fitting time T is... for +[( - ) / ],in, Indicates the Hth sliding window. The train's BeiDou positioning signal, where T represents the fitted position and time.

[0055] Preferably, step S4 includes:

[0056] Train auxiliary positioning system obtains train inertial navigation position Train inertial navigation position Calculated by the inertial measurement unit carried by the train;

[0057] From the train's inertial navigation position and the final fitted position of the train Jointly calculate train position The specific expression is as follows:

[0058] ;

[0059] in, and To calculate the coefficients.

[0060] On the other hand, the present invention also provides a railway auxiliary positioning system for signal failure scenarios, the railway auxiliary positioning system for signal failure scenarios comprising:

[0061] The signal acquisition module acquires the train positioning quality parameters and the train Beidou positioning signal from the train auxiliary positioning system, normalizes the train positioning quality parameters, and calculates the fault diagnosis coefficient from the normalized train positioning quality parameters.

[0062] The sliding window threshold calculation module sets the sliding window threshold based on the fault diagnosis coefficient.

[0063] The fitting auxiliary algorithm module calculates the position of each sliding window based on the train's Beidou positioning signal, designs a train-assisted fitting algorithm, calculates the fitting parameters of the train-assisted fitting algorithm based on the position of the sliding window in the sliding window threshold, calculates the fitting position based on the fitting parameters, and obtains the final fitted position of the train.

[0064] The output module obtains the train's inertial navigation position from the train's inertial navigation position, calculates the train's position by combining the train's inertial navigation position and the final fitted position, and outputs the train's position.

[0065] The beneficial effects of the technical solution provided by this invention include at least the following:

[0066] 1. This invention employs multiple parameters of the positioning signal to achieve railway assisted positioning in signal fault scenarios. It acquires train positioning quality parameters and train BeiDou positioning signals through a positioning-assisted positioning system. To achieve quantitative analysis of positioning signal quality, the train positioning quality parameters are normalized. Fault diagnosis coefficients are calculated from the normalized train positioning quality parameters to determine the severity of the positioning signal fault. The combined use of multiple train positioning quality parameters not only enhances the richness and information dimensions of signal fault diagnosis but also improves the accuracy of fault judgment. This allows for timely determination of whether to use an assisted positioning method, reducing the computational cost of the train system. Compared to other railway assisted positioning methods for signal fault scenarios, this solution provides more targeted data, taking a multi-signal-dimensional approach to fault judgment, resulting in higher accuracy.

[0067] 2. This invention further reduces the computational load of assisted positioning by setting sliding windows. Each sliding window is set as a calculation unit. Different numbers of sliding windows are selected for calculation based on the quality of the positioning signal. The ratio of the number of signals with better quality to the total number of signals in the entire sliding window is used to determine whether to perform assisted positioning. This not only avoids the consumption of train system computing resources and power system caused by assisted positioning, but also reduces the risk of accidents caused by the failure of the train to perform assisted positioning in time, thus improving the safety of train operation. Compared with the traditional solution, this solution uses sliding windows to divide the historical data of the train into grids instead of processing all the data, which further improves the real-time performance of this solution. The use of big data analysis of the historical position of the train further improves the accuracy of this solution.

[0068] 3. This scheme designs an auxiliary fitting algorithm. Based on the position of the sliding window in the sliding window threshold, the fitting parameters of the train auxiliary fitting algorithm are iteratively solved to obtain the final fitted position of the train. In order to improve the accuracy of the train position, the train inertial navigation position is added when solving the train position, taking advantage of the accurate temporary positioning information of the inertial measurement unit. The train position is calculated by combining the train inertial navigation position and the final fitted position of the train, thereby improving the calculation accuracy of this scheme. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of the overall execution flow of a railway auxiliary positioning method for signal fault scenarios provided in an embodiment of the present invention.

[0070] Figure 2 This is an implementation diagram of train fitting position calculation provided in an embodiment of the present invention. Detailed Implementation

[0071] The present invention will be further described below with reference to the accompanying drawings, but this is not intended to limit the present invention in any way. Any modifications or substitutions made based on the teachings of the present invention shall fall within the protection scope of the present invention.

[0072] Example 1:

[0073] This embodiment provides a railway-assisted positioning method for signal failure scenarios, which can be implemented by electronic devices, such as... Figure 1 As shown. Specifically, the method in this embodiment includes the following steps:

[0074] The train auxiliary positioning system acquires train positioning quality parameters and train BeiDou positioning signals;

[0075] Train positioning quality parameters include positioning signal strength, satellite geometry distribution, and clock error;

[0076] Normalization of train positioning quality parameters includes:

[0077] Normalize the positioning signal strength P, and calculate the normalized positioning signal strength. The specific calculation formula is as follows:

[0078] ;

[0079] ;

[0080] in, This represents the average value of the positioning signal strength, where i is an integer. Indicates the number of positioning signals. Indicates the strength of the i-th positioning signal. A normalized parameter representing the strength of the positioning signal;

[0081] In this embodiment, The value is 8;

[0082] Normalize the satellite geometric distribution W, and calculate the normalized satellite geometric distribution. The specific calculation formula is as follows:

[0083] ;

[0084] in, Normalized parameters representing the geometric distribution of satellites;

[0085] Normalize the clock error S and calculate the normalized clock error. The specific calculation formula is as follows:

[0086] ;

[0087] in, This represents the clock error of the i-th positioning signal. Normalized parameter representing clock error;

[0088] It should be further explained that, since the calculation ranges of positioning signal strength, satellite geometric distribution and clock error are inconsistent, the above data must be normalized in order to achieve quantitative analysis of the data, thereby improving the reliability of data calculation. In addition, normalization can also avoid interference to the calculation of other data due to the excessive size of one data, thereby improving the accuracy of this scheme in signal quality judgment and train position calculation.

[0089] Positioning signal strength after normalization Normalized satellite geometric distribution and normalized clock error The fault diagnosis coefficient Q is calculated using the following formula:

[0090] ;

[0091] in, , and All are fault assignment parameters;

[0092] , and The following relationship must be satisfied:

[0093] ;

[0094] In this embodiment, , , .

[0095] It should be further explained that, in order to avoid extreme cases, the normalized positioning signal strength can be adjusted. Normalized satellite geometric distribution and normalized clock error The fault diagnosis coefficient Q is calculated for priority testing. Based on the priority testing results, the fault allocation parameters are then determined. The calculation method for priority testing is as follows:

[0096] Calculate the normalized positioning signal strength Priority coefficient Normalized satellite geometric distribution Priority coefficient and normalized clock error Priority coefficient The specific calculation formula is as follows:

[0097] ;

[0098] ;

[0099] ;

[0100] when , or If any item in the list is less than 0.1, the fault allocation parameter for the corresponding signal is set to 0, and the fault allocation parameters for other signals are then determined.

[0101] like When it is 0, this setting It needs to be redefined. and Size.

[0102] After obtaining the fault diagnosis coefficients, the sliding window threshold H can be set based on the fault diagnosis coefficients. The specific expression is as follows:

[0103] ;

[0104] If the fault diagnosis coefficient is less than 30, the sliding window threshold is equal to 0.5 times the fault diagnosis coefficient plus 5; if the fault diagnosis coefficient is greater than or equal to 30 and less than 60, the sliding window threshold is equal to the fault diagnosis coefficient; if the fault diagnosis coefficient is greater than or equal to 60, the sliding window threshold is equal to 60.

[0105] It should be further explained that the fault diagnosis coefficient indicates the quality of the positioning signal. When the fault diagnosis coefficient is less than 30, the positioning signal quality is good; when the fault diagnosis coefficient is greater than or equal to 30 and less than 60, the positioning signal quality is poor; and when the fault diagnosis coefficient is greater than or equal to 60, the positioning signal quality is very poor. A fault diagnosis coefficient is obtained at each sampling moment, and a sliding window is extracted backward from this moment as the end point to judge the quality of the positioning signal within the sliding window. In order to judge the quality of the positioning signal within the sliding window, an auxiliary flag bit needs to be set.

[0106] And based on the fault diagnosis coefficient Q, it is determined whether an auxiliary location method needs to be executed, including:

[0107] Initialize the auxiliary flag (FLAG), and set the number of sampling times within the sliding window where the fault diagnosis coefficient is less than 30. The total number of sampling times within the sliding window is The expression for FLAG is as follows:

[0108] ;

[0109] in, Indicates other situations, when When, it indicates that the signal failure rate is low within a continuous period of time, and no auxiliary positioning method is required. "At this time" indicates that the signal has a high failure rate within a continuous period of time, and an auxiliary positioning method needs to be executed.

[0110] when The auxiliary positioning method is executed, and each sliding window contains 5 seconds of train Beidou positioning signal;

[0111] Initialize the positioning signal acquisition frequency f, that is, the frequency f within each sliding window. Each train's BeiDou positioning signal D is the average of n positioning signals at that moment, where D={x,y,z}, where x represents the train's position on the X-axis in the track coordinate system, y represents the train's position on the Y-axis in the track coordinate system, and z represents the train's position on the Z-axis in the track coordinate system.

[0112] In this embodiment, f=10;

[0113] The position of each sliding window is calculated using the train's BeiDou positioning signal, and the position of the j-th sliding window is calculated. The specific expression is as follows:

[0114] ;

[0115] in, This represents the BeiDou positioning signal of the i-th train in the j-th sliding window, where j is less than or equal to H;

[0116] In this embodiment, the position of the third sliding window The specific expression is as follows:

[0117] ;

[0118] The train-assisted fitting algorithm is designed. First, the level coefficient and trend coefficient are calculated. Then, the fitting coefficient is calculated from the level coefficient and trend coefficient. The specific expression is as follows:

[0119] ;

[0120] ;

[0121] ;

[0122] in, The expected coefficient, b represents the fitted parameter, Indicates the m-th The expected coefficient, Indicates the m-th The expected coefficient, Indicates the m-th Fitting parameters, This represents the m-th level coefficient. This indicates the position of the m-th sliding window. This indicates the position of the (m+1)th sliding window. This shows the (m-1)th level coefficient. This represents the m-th trend coefficient. This represents the (m-1)th trend coefficient;

[0123] initialization , 、{ }and{ Iterative calculation The value of is expressed as follows:

[0124] ;

[0125] in, Indicates the first one The expected coefficient, Indicates the s-th The expected coefficient, Indicates the Hth The expected coefficient, Indicates the first one The expected coefficient, Indicates the s-th The expected coefficient, Indicates the Hth The expected coefficient, Indicates the first one Fitting parameters, Indicates the s-th Fitting parameters, Indicates the Hth The fitting parameters are: max{} represents finding the maximum value, min{} represents finding the minimum value, and s represents the sequence number, which is an integer.

[0126] It should be further explained that, in this embodiment, the calculation of the fitting parameters is analyzed in detail. When m=1,

[0127] ;

[0128] ;

[0129] ;

[0130] because , , , and The value of is known, therefore, only in the above formula The value of is an unknown, which can be obtained using three formulas. By iterating through the values ​​in this way, we can eventually obtain... The value;

[0131] It should be noted that when extracting a sliding window forward, the sequence numbers are arranged from largest to smallest. In addition, to improve the adaptability and fault tolerance of this scheme, if there is no positioning signal when calculating the fitting parameters, the positioning signal at the corresponding time is removed. When calculating the FLAG, if there is no positioning signal, the number of positioning signals with good quality is calculated and the value of the FLAG is obtained while keeping the total number of time points unchanged. This can avoid the risk of method failure due to the absence of positioning signal at a certain time. Furthermore, since the fitting position is calculated based on the position of the sliding window, this characteristic can be used to fit the missing signal in a local range, further improving the applicability of this scheme.

[0132] Based on the fitting parameters Calculate the value of the sliding window at the H+1th time. Then the final fitted position of the train at fitting time T is... for +[( - ) / ],in, Indicates the Hth sliding window. The train's BeiDou positioning signal, T represents the fitted position and time; the implementation diagram for calculating the train's fitted position is shown below. Figure 2 As shown;

[0133] Train auxiliary positioning system obtains train inertial navigation position The train's inertial navigation position is calculated by the inertial measurement unit carried by the train;

[0134] Calculate train position The specific expression is as follows:

[0135] ;

[0136] in, and To calculate the coefficients;

[0137] Output train position.

[0138] Example 2:

[0139] This embodiment provides a railway auxiliary positioning system for signal failure scenarios, which includes the following modules:

[0140] The signal acquisition module acquires the train positioning quality parameters and the train Beidou positioning signal from the train auxiliary positioning system, normalizes the train positioning quality parameters, and calculates the fault diagnosis coefficient from the normalized train positioning quality parameters.

[0141] The sliding window threshold calculation module sets the sliding window threshold based on the fault diagnosis coefficient.

[0142] The fitting auxiliary algorithm module calculates the position of each sliding window based on the train's Beidou positioning signal, designs a train-assisted fitting algorithm, calculates the fitting parameters of the train-assisted fitting algorithm based on the position of the sliding window in the sliding window threshold, calculates the fitting position based on the fitting parameters, and obtains the final fitted position of the train.

[0143] The output module obtains the train's inertial navigation position from the train's inertial navigation position, calculates the train's position by combining the train's inertial navigation position and the final fitted position, and outputs the train's position.

[0144] As used herein, the term "preferred" is meant as an example, illustration, or illustration. Any aspect or design described herein as "preferred" need not be construed as being more advantageous than other aspects or designs. Rather, the use of the term "preferred" is intended to present the concept in a specific manner. As used in this application, the term "or" is intended to mean an inclusive "or" rather than an exclusionary "or." That is, unless otherwise specified or clear from the context, "X uses A or B" naturally includes either of the permutations. That is, if X uses A; X uses B; or X uses both A and B, then "X uses A or B" is satisfied in any of the foregoing examples.

[0145] Furthermore, although this disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art based on a reading and understanding of this specification and the accompanying drawings. This disclosure includes all such modifications and variations and is limited only by the scope of the appended claims. In particular, with respect to the various functions performed by the aforementioned components (e.g., elements, etc.), the terminology used to describe such components is intended to correspond to any component (unless otherwise indicated) that performs the specified function of said component (e.g., is functionally equivalent to it), even if structurally not equivalent to the disclosed structure performing the functions in the exemplary implementations of this disclosure shown herein. Moreover, although specific features of this disclosure have been disclosed with respect to only one of several implementations, such features may be combined with one or more features of other implementations that may be desirable and advantageous for a given or particular application. Furthermore, with regard to the use of the terms “comprising,” “having,” “containing,” or variations thereof in the Detailed Description or claims, such terms are intended to be included in a manner similar to the term “including.”

[0146] The functional units in this invention embodiment can be integrated into a processing module, or each unit can exist physically separately, or multiple units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. The aforementioned devices or systems can execute the storage methods in the corresponding method embodiments.

[0147] In summary, the above embodiments are one implementation of the present invention, but the implementation of the present invention is not limited to the embodiments described above. Any changes, modifications, substitutions, combinations, or simplifications made that deviate from the spirit and principle of the present invention should be considered equivalent substitutions and are included within the protection scope of the present invention.

Claims

1. A railway-aided localization method for signal failure scenarios, characterized in that, Includes the following steps: S1: The train auxiliary positioning system acquires the train positioning quality parameters and the train Beidou positioning signal, normalizes the train positioning quality parameters, and calculates the fault diagnosis coefficient from the normalized train positioning quality parameters. S2: Set the sliding window threshold based on the fault diagnosis coefficient; S3: Calculate the position of each sliding window using the train's BeiDou positioning signal, design a train-assisted fitting algorithm, calculate the fitting parameters of the train-assisted fitting algorithm based on the position of the sliding window within the sliding window threshold, calculate the fitting position based on the fitting parameters, and obtain the final fitted position of the train, including: The train-assisted fitting algorithm is designed. First, the level coefficient and trend coefficient are calculated. Then, the fitting parameters are calculated from the level coefficient and trend coefficient. The specific expression is as follows: ； ； ； in, Let b represent the expected coefficient and b represent the fitted parameters. Indicates the m-th The expected coefficient, Indicates the m-th The expected coefficient, Indicates the m-th Fitting parameters, This represents the m-th level coefficient. This indicates the position of the m-th sliding window. This indicates the position of the (m+1)th sliding window. This represents the (m-1)th level coefficient. This represents the m-th trend coefficient. This represents the (m-1)th trend coefficient; initialization , 、{ }and{ Iterative calculation The value of is expressed as follows: ； in, Indicates the first one The expected coefficient, Indicates the s-th The expected coefficient, Indicates the Hth The expected coefficient, Indicates the first one The expected coefficient, Indicates the s-th The expected coefficient, Indicates the Hth The expected coefficient, Indicates the first one Fitting parameters, Indicates the s-th Fitting parameters, Indicates the Hth The fitting parameters are: max{} represents finding the maximum value, min{} represents finding the minimum value, and s represents the sequence number, which is an integer. Based on the fitting parameters Calculate the value of the sliding window at the H+1th time. Then the final fitted position of the train at fitting time T is... for +[( - ) / ],in, Indicates the Hth sliding window. The train's BeiDou positioning signal, where T represents the fitted position and time; and f represents the positioning signal acquisition frequency. S4: The train auxiliary positioning system obtains the train's inertial navigation position, and calculates the train's position by combining the train's inertial navigation position and the train's final fitted position. S5: Output train position.

2. The railway auxiliary positioning method for signal fault scenarios according to claim 1, characterized in that, Step S1 includes: The train auxiliary positioning system acquires train positioning quality parameters and train BeiDou positioning signals; Train positioning quality parameters include positioning signal strength, satellite geometry distribution, and clock error; Normalization of train positioning quality parameters includes: Normalize the positioning signal strength P, and calculate the normalized positioning signal strength. The specific calculation formula is as follows: ； ； in, This represents the average value of the positioning signal strength, where i is an integer. Indicates the number of positioning signals. Indicates the strength of the i-th positioning signal. Normalized parameter representing the strength of the positioning signal; Normalize the satellite geometric distribution W, and calculate the normalized satellite geometric distribution. The specific calculation formula is as follows: ； in, Normalized parameters representing the geometric distribution of satellites; Normalize the clock error S and calculate the normalized clock error. The specific calculation formula is as follows: ； in, This represents the clock error of the i-th positioning signal. Normalized parameter representing clock error; The fault diagnosis coefficient is calculated from the normalized train positioning quality parameters.

3. The railway auxiliary positioning method for signal fault scenarios according to claim 2, characterized in that, The calculation of the fault diagnosis coefficient from the normalized train positioning quality parameters includes: Positioning signal strength after normalization Normalized satellite geometric distribution and normalized clock error The fault diagnosis coefficient Q is calculated using the following formula: ； in, , and All are fault assignment parameters; , and The following relationship must be satisfied: 。 4. The railway auxiliary positioning method for signal failure scenarios according to claim 1, characterized in that, Step S2 includes: The sliding window threshold H is set based on the fault diagnosis coefficient Q, and the specific expression is as follows: ； And based on the fault diagnosis coefficient Q, it is determined whether an auxiliary location method needs to be executed, including: Initialize the auxiliary flag (FLAG), and set the number of sampling times within the sliding window where the fault diagnosis coefficient is less than 30. The total number of sampling times within the sliding window is The expression for FLAG is as follows: ； in, Indicates other situations, when When, it indicates that the signal failure rate is low within a continuous period of time, and no auxiliary positioning method is required. "At this time" indicates that the signal has a high failure rate within a continuous period of time, and an auxiliary positioning method needs to be executed.

5. The railway auxiliary positioning method for signal failure scenarios according to claim 4, characterized in that, Step S3 includes: when The auxiliary positioning method is executed, and each sliding window contains 5 seconds of train Beidou positioning signal; Initialize the positioning signal acquisition frequency f, that is, the frequency f within each sliding window. Each train's BeiDou positioning signal D is the average of n positioning signals at that moment, where D={x,y,z}, where x represents the train's position on the X-axis in the track coordinate system, y represents the train's position on the Y-axis in the track coordinate system, and z represents the train's position on the Z-axis in the track coordinate system. The position of each sliding window is calculated using the train's BeiDou positioning signal, and the position of the j-th sliding window is calculated. The specific expression is as follows: ； in, H represents the BeiDou positioning signal of the i-th train in the j-th sliding window, where j is less than or equal to H.

6. The railway auxiliary positioning method for signal fault scenarios according to claim 1, characterized in that, Step S4 includes: Train auxiliary positioning system obtains train inertial navigation position Train inertial navigation position Calculated by the inertial measurement unit carried by the train; From the train's inertial navigation position and the final fitted position of the train Jointly calculate train position The specific expression is as follows: ； in, and To calculate the coefficients.

7. A railway auxiliary positioning system for signal failure scenarios, characterized in that, include: The signal acquisition module acquires the train positioning quality parameters and the train Beidou positioning signal from the train auxiliary positioning system, normalizes the train positioning quality parameters, and calculates the fault diagnosis coefficient from the normalized train positioning quality parameters. The sliding window threshold calculation module sets the sliding window threshold based on the fault diagnosis coefficient. The fitting auxiliary algorithm module calculates the position of each sliding window based on the train's Beidou positioning signal, designs a train-assisted fitting algorithm, calculates the fitting parameters of the train-assisted fitting algorithm based on the position of the sliding window in the sliding window threshold, calculates the fitting position based on the fitting parameters, and obtains the final fitted position of the train. The output module obtains the train's inertial navigation position from the train's inertial navigation position, calculates the train's position by combining the train's inertial navigation position and the final fitted position, and outputs the train's position. Implement a railway auxiliary positioning method for signal failure scenarios as described in any one of claims 1-6.

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