Subway train positioning method and system

Abstract

The invention relates to a subway train positioning method and system, and the system comprises a processor which is configured to process the multi-source data, collected by an inertial navigation unit, of a train based on an error Kalman filtering algorithm, so as to preliminarily correct the acceleration and angular velocity; constructing a time-mileage curve of the train and correcting the time-mileage curve based on the first correction proportion; identifying the dynamic change characteristics of turning based on the angular velocity collected by the inertial navigation unit, calculating the total mileage in the turning section, and correcting the total mileage in the turning section based on the second correction proportion of the turning section; abnormal vibration features are identified and marked in a visualized manner based on the high-order change rate of the acceleration and angular velocity of the train. Under the condition of not depending on a GPS (Global Positioning System), acceleration and angular velocity data of the characteristics in the running process of the train are collected and calibrated, and a mileage calibration model based on the track characteristics is constructed, so that accurate train mileage and position information can be obtained in a GPS-free environment.

Description

Technical Field

[0001] The present invention relates to the technical field of urban rail detection, and in particular to a subway train positioning method and system. Background Art

[0002] At present, the positioning system for stability detection in rail transit mainly relies on GPS or Beidou. However, in underground environments such as subway trains, these satellite positioning systems often fail to work properly due to signal shielding, resulting in the inability to obtain the accurate location of the train. In order to solve this problem, transponders and positioning tags are also used as auxiliary positioning devices in existing systems. Transponders are installed along the track, and transmit location information through short-range communication when the train passes, while positioning tags provide auxiliary positioning by sensing the movement of the train. However, the installation and maintenance costs of these external devices are high, and the subway environment has strict restrictions on equipment layout, making it difficult to adapt to comprehensive and long-term application needs.

[0003] Therefore, current stability detection instruments usually use a pure inertial navigation system, that is, an inertial navigation unit (INS) is installed on the train to perform inertial navigation calculations, thereby estimating the mileage without GPS. However, pure inertial navigation also has the problem of error accumulation, making it difficult to maintain the accuracy of the position after long-term operation.

[0004] Moreover, in the subway rail transit system, long-term operation of trains will cause rail wear and track geometry changes, thus causing abnormal vibration of the train. This abnormal vibration not only affects the comfort of passengers, but also poses a potential threat to the safe operation of the train. Therefore, how to accurately detect and locate the abnormal vibration area of ​​the train during track operation has become an important technical requirement for subway safety maintenance. However, the existing positioning system faces problems such as limited signals and error accumulation in the subway environment, making it difficult to accurately monitor and precisely locate the smoothness of train operation.

[0005] The current technologies used for train positioning mainly include GPS and inertial navigation unit (INS) fusion positioning, UWB positioning or GPS and differential GPS system (DGPS) and inertial navigation unit (INS).

[0006] At present, most stability detectors use a fusion positioning method of GPS or Beidou and inertial navigation unit (INS) system, but in underground environments such as subway trains, GPS and Beidou signals are limited, resulting in inability to locate and accurately identify the location where abnormal vibration occurs. Therefore, the system has limited effect in subways.

[0007] Another positioning method for underground trains such as subway trains is to perform real-time correction through UWB or differential GPS systems. However, such systems require the installation of base stations, which brings high installation and maintenance costs. In addition, some subway line environments do not allow the installation of external equipment, so they are not suitable for the positioning needs of subway trains.

[0008] The pure inertial navigation method does not rely on external equipment and is suitable for signal shielding environments such as subway trains. However, the main problem with this method is error accumulation. As time goes by, the error accumulation effect becomes more and more obvious, and generally it may accumulate to about 15% mileage error within 10 minutes. Under the demand for high-precision track detection, the pure inertial navigation solution is difficult to meet the accuracy requirements of long-term operation.

[0009] For example, CN107976697A discloses a train safety positioning method and system based on Beidou / GPS combination, the method comprising: determining the track where the current train is located by reading the transponder configured on the track, checking the accuracy of satellite positioning, and calibrating the GNSS module and the inertial navigation combination module; obtaining the direction of travel of the current train by reading two sets of transponders that the current train passes continuously, or obtaining satellite positioning information and the inertial navigation information of the train by traveling a certain distance at a certain opening speed through the GNSS module and the inertial navigation combination module, thereby parsing the direction of travel of the train; when the track where the current train is located and the direction of travel are determined After that, the satellite positioning information and inertial navigation information of the current train are continuously obtained through the GNSS module and the inertial navigation combination module, and the presence of false or erroneous satellite signals is determined in a predetermined manner; if so, the correct positioning method of the GNSS module and the inertial navigation combination module is selected in combination with the ground differential station system; finally, the tail safety boundary of the current train is set to the most recently triggered electronic fence, and the tail safety boundary plus the length of the vehicle is extended forward to the position of the next electronic fence to obtain the head safety boundary of the train; the head safety boundary and the tail safety boundary together constitute the overall safety footprint of the train, thereby forming the train positioning. This technical solution is a typical positioning solution that integrates GPS and inertial navigation unit (INS). Therefore, in underground environments, the positioning signal is easily unclear and discontinuous, resulting in the inability to locate, and it is difficult to accurately identify the location where the abnormal vibration occurs.

[0010] Therefore, the present invention hopes to provide a subway train positioning method and system, which can realize accurate positioning of the subway by using only an inertial navigation system in an underground environment where GPS signals or Beidou satellite signals are inaccurate.

[0011] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

[0012] In the subway rail transit system, long-term operation of trains will cause rail wear and track geometry changes, which will cause abnormal vibration of the train. This abnormal vibration not only affects the comfort of passengers, but also poses a potential threat to the safe operation of the train. Therefore, accurately detecting and locating the abnormal vibration area of ​​the train during track operation has become an important technical requirement for subway safety maintenance. However, the existing positioning system faces problems such as limited signals and error accumulation in the subway environment, making it difficult to accurately monitor and precisely locate the smoothness of train operation.

[0013] Therefore, the present invention proposes a subway train positioning system, hoping to solve the following problems:

[0014] First, the positioning problem in a GPS-free environment. Since most subway trains run underground, the signals of satellite positioning systems such as GPS and Beidou cannot be covered, causing traditional stability detection equipment to lose its positioning function in underground environments. How to perform high-precision positioning of train operation without external signal support has become one of the technical difficulties.

[0015] Second, the error accumulation problem in pure inertial navigation systems. The inertial navigation unit (INS) can measure the train's motion information, but the errors of the inertial navigation system will gradually accumulate during long-term operation, making it difficult to meet the positioning accuracy requirements.

[0016] Third, the problem of identifying and calibrating the characteristic points of the rails. The present invention uses the characteristics of turning points, stopping points, and acceleration mutation points at switches in the subway track as mileage references. Therefore, how to use the natural characteristic points of the track to effectively calibrate the inertial navigation system is also a problem that the present invention needs to solve, which involves two difficulties: (1) identifying the characteristic points of the track from the data of the inertial navigation unit (INS); (2) using the mileage data of the characteristic points to process the data to obtain the calibrated mileage data.

[0017] In view of the deficiencies in the prior art, the present invention provides a subway train positioning system from a first aspect, including a processor, which is configured to: process multi-source data of a train collected by an inertial navigation unit based on an error Kalman filter algorithm to preliminarily correct acceleration and angular velocity; construct a time-mileage curve of the train and correct the time-mileage curve based on a first correction ratio; identify the dynamic change characteristics of a turn based on the angular velocity collected by the inertial navigation unit, wherein the total mileage within the turn section is calculated and the total mileage within the turn section is corrected based on a second correction ratio of the turn section; identify abnormal vibration characteristics based on high-order change rates of the acceleration and angular velocity of the train and mark them in a visual manner.

[0018] Without relying on GPS, the present invention constructs a mileage calibration model based on track characteristics by collecting acceleration and angular velocity data of these characteristics of the train while traveling and calibrating the processed data, thereby ensuring accurate train mileage and position information in a GPS-free environment.

[0019] According to a preferred embodiment, the processor is also configured to: before constructing the time-mileage curve of the train, perform data filtering and smoothing on the acceleration and angular velocity after preliminary correction to remove noise in the data. During the data measurement process of the inertial measurement unit, due to environmental interference, vibration and sensor noise itself, the original acceleration, angular velocity, speed and position information usually contain high-frequency noise. These noises will affect subsequent turn detection, mileage calculation and position estimation. Therefore, the present invention uses smoothing and filtering technology to suppress noise and ensure the stability and accuracy of the data.

[0020] According to a preferred embodiment, the processor is configured to initially correct the acceleration and angular velocity in the following manner: in the initial stage of the processor operation, the train's state at the next moment is predicted based on the preset motion model and the acceleration and angular velocity of the train collected by the inertial navigation unit; the predicted state information is corrected based on the Kalman gain and the error covariance model related to the prediction is updated, thereby correcting the predicted state of the train. The error Kalman filter performs state estimation on the original acceleration and angular velocity data collected by the inertial measurement unit, and dynamically corrects the measurement noise and system error by combining the system's motion model and the actual measurement value of the inertial measurement unit. Through real-time estimation of the noise of the inertial measurement unit, the error Kalman filter algorithm can effectively reduce the drift and random error in the data of the inertial measurement unit, thereby providing more accurate basic data for subsequent data processing.

[0021] According to a preferred embodiment, the processor is configured to correct the time-mileage curve in the following manner: converting acceleration and angular velocity into position and velocity to construct the time-mileage curve; taking the ratio of mileage to the first target mileage as the first correction ratio, scaling the mileage data at each time point in proportion based on the first correction ratio, thereby obtaining the corrected mileage and correcting the time-mileage curve. The time-mileage curve is used to intuitively display the mileage changes of the train.

[0022] According to a preferred embodiment, the processor is configured to identify the dynamic change characteristics of the turn by: performing nonlinear fitting on the angular velocity curve, calculating the rate of change of the angular velocity and the angular acceleration, setting a dynamic threshold based on the current speed of the train and the curvature radius of the track, and determining the start time of the train turning section when the angular velocity is greater than the dynamic threshold; determining the end time of the train turning section based on the rate of change of the angular velocity and the negative change of the angular acceleration. Performing high-order analysis on the angular velocity, using the rate of change of the angular velocity and the second-order derivative to capture the slight changes in the start and end times of the turning section, and dynamically adjusting through the dynamic threshold to adapt to different turning radii and turning speeds.

[0023] According to a preferred embodiment, the processor is configured to correct the total mileage in the turning section by: calculating the total mileage in the turning section based on the speed of the train; taking the ratio of the total mileage in the turning section to the second target distance in the turning section as the second correction ratio; and scaling the total mileage in the turning section proportionally based on the second correction ratio. The present invention makes the mileage in the turning section after correction consistent with the input mileage mark, and dynamically adjusts the scaling ratio to match the real-time changing speed.

[0024] According to a preferred embodiment, the processor corrects the total mileage in the turning section in a manner that further includes: using a transition function to smoothly calibrate the corrected total mileage in the turning section, so that the corrected time-mileage curve is continuous and smoothly connected at the calibration point. This can avoid the corrected time-mileage curve from being discontinuous at the calibration point, so that the time-mileage curve is smoothly connected before and after the calibration point to avoid sudden changes.

[0025] According to a preferred embodiment, the processor is configured to identify abnormal vibration characteristics in the following manner: respectively calculate the third-order derivative functions of the acceleration and angular velocity of the train, the absolute value of the third-order derivative function being the vibration intensity; dynamically adjust the recognition threshold of the vibration point based on the speed of the train; and the position corresponding to the vibration intensity greater than the recognition threshold is the abnormal vibration point.

[0026] The present invention determines the size and color of the visualized vibration point based on the vibration intensity, thereby indicating the severity of the vibration, so that the user can see the specific information of each vibration point at a glance.

[0027] The present invention provides a subway train positioning method from a second aspect, the method comprising: processing the acceleration and angular velocity of the train collected by an inertial navigation unit based on an error Kalman filter algorithm to preliminarily correct the acceleration and angular velocity; constructing a time-mileage curve of the train and correcting the time-mileage curve based on a first correction ratio; identifying the dynamic change characteristics of a turn based on the angular velocity collected by the inertial navigation unit, wherein the total mileage within the turn section is calculated, and the total mileage within the turn section is corrected based on the second correction ratio of the turn section; identifying abnormal vibration characteristics based on the high-order change rate of the acceleration and angular velocity of the train and marking them in a visual manner.

[0028] The method of the present invention has a number of significant advantages, including improving positioning accuracy, reducing error accumulation, and realizing high-precision positioning in a GPS-free environment by combining an inertial navigation system with subway track feature points and using an error Kalman filter algorithm to correct the data of an inertial navigation unit (INS), and controlling the error within 5%. In addition, the present invention gets rid of the dependence on external signals and reduces costs. Compared with existing GPS and UWB solutions, it does not need to rely on external signals or install additional base station equipment, eliminates the problem of underground environment limiting GPS and Beidou signals, and avoids the high installation and maintenance costs of base stations, showing good economy and operability. The present invention also improves the convenience and effectiveness of subway track detection. The positioning and stability detector used is highly integrated, and with the error Kalman filter algorithm and the calibration strategy of track feature points, it can generate a time-mileage curve that is closer to the real train, which is convenient for accurate detection and maintenance of subway line track anomalies in a GPS-free environment, and greatly improves the accuracy and effectiveness of train stability detection.

[0029] According to a preferred embodiment, the method further comprises: before constructing the time-mileage curve of the train, performing data filtering and smoothing on the acceleration and angular velocity after preliminary correction to remove noise in the data.

[0030] During the data measurement process of the inertial measurement unit, due to environmental interference, vibration and sensor noise, the original acceleration, angular velocity, speed and position information usually contain high-frequency noise. These noises will affect the subsequent turn detection, mileage calculation and position estimation. Therefore, the present invention adopts smoothing and filtering technology to suppress noise and ensure the stability and accuracy of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the module connection relationship of the subway train positioning system provided by the present invention;

[0032] Figure 2 It is a schematic diagram of the flow of the subway train positioning method provided by the present invention;

[0033] Figure 3 It is a subway track map provided by the present invention;

[0034] Figure 4 It is a schematic diagram of the orientation of the inertial measurement unit (IMU) provided by the present invention placed flat on a train;

[0035] Figure 5 is a schematic diagram of angular velocity change provided by the present invention;

[0036] Figure 6 It is a schematic diagram showing operation test data of a subway line provided by the present invention;

[0037] Figure 7 is a schematic diagram comparing the mileage-time curves before and after correction provided by the present invention;

[0038] Figure 8 is a graph of absolute velocity versus time provided by the present invention;

[0039] Fig. 9 is a test chart of the total mileage data of the preliminary correction in the example data provided by the present invention;

[0040] Fig.10 is a test chart of the total mileage data calculated in the example data provided by the present invention;

[0041] Fig.11 This is a table diagram of example data obtained through testing of the subway train positioning system provided by the present invention.

[0042] Reference numerals list

[0043] 100: processor; 110: initial correction module; 120: curve generation module; 130: turn recognition module; 140: abnormal vibration recognition module; 200: stability detector; 300: memory. DETAILED DESCRIPTION

[0044] The following is a detailed description with reference to the accompanying drawings.

[0045] The present invention explains some of the noun terms.

[0046] Stability Detection System: It is a data acquisition hardware system that integrates an inertial measurement unit (IMU), a buck module, a microcontroller, and a power supply. It is specially designed to measure and analyze the running stability of transportation vehicles, especially trains. The stability detection system captures the acceleration and attitude of the train through the inertial measurement unit (IMU), the buck module ensures voltage stability, and the microcontroller processes the signals from the sensor and performs control tasks. In addition, the stability detection system also communicates with the host computer, which is used to process the data collected by the stability detection system and perform in-depth analysis, including data trend analysis, anomaly detection, and report generation.

[0047] The core of the stability detection system also includes a mileage calibration algorithm based on absolute mileage and speed. The algorithm determines the running speed of the train by calculating the arithmetic mean of the running speeds at multiple sensor locations and removes abnormal speed values ​​when they are found to improve measurement accuracy. This algorithm helps to reduce calculation errors, ensure the measurement accuracy of the train's running speed, and provide important data support and analysis tools for the stability and safety of the train.

[0048] IMU: Abbreviation for Inertial Measurement Unit. An IMU is an electronic device that measures and reports the three fundamental linear motions (acceleration) and three fundamental angular motions (angular velocity) of an object.

[0049] 6-axis Inertial Measurement Unit (IMU): A sensor system that integrates three acceleration axes (X, Y, Z) and three angular velocity axes (X, Y, Z) to fully measure the motion state of an object in three-dimensional space. The acceleration axis is responsible for capturing the linear acceleration of the object in three mutually perpendicular directions, while the angular velocity axis measures the rotation rate of the object around these three axes. This design enables the 6-axis Inertial Measurement Unit (IMU) to provide accurate data on the speed, position, and attitude of an object.

[0050] Multi-source data: including three-axis acceleration and three-axis angular velocity (angle) collected by the inertial measurement unit (IMU), the actual mileage between two stations, mileage information of turning points, etc.

[0051] Error Kalman filter algorithm: used to process the data collected by the inertial measurement unit (IMU). The error Kalman filter algorithm is the application of the Kalman filter algorithm to process measurement data with errors. The Kalman filter algorithm is an optimal estimation algorithm for estimating the state of a dynamic system, especially in the presence of noise and uncertainty.

[0052] In practical applications, observation data are often affected by various noises, which can be random or systematic. The error Kalman filter algorithm describes the statistical characteristics of these errors, such as their mean and covariance, through mathematical models, and then uses this information to optimize state estimation and reduce the impact of errors.

[0053] Example 1

[0054] The current technologies used for train positioning mainly include GPS and inertial navigation unit (INS) fusion positioning, UWB positioning or GPS and differential GPS system (DGPS) and inertial navigation unit (INS).

[0055] At present, most stability detectors 200 use a fusion positioning method of GPS or Beidou and an inertial navigation unit (INS), but in underground environments such as subway trains, GPS and Beidou signals are limited, resulting in the inability to locate and accurately identify the location where abnormal vibration occurs. Therefore, the effect of this system in subways is limited.

[0056] Another method of positioning subway trains is to perform real-time correction through UWB or differential GPS systems, but such systems require the installation of base stations, which brings high installation and maintenance costs. In addition, some subway line environments do not allow the installation of external equipment, so they are not suitable for the positioning needs of subway trains.

[0057] The pure inertial navigation method does not rely on external equipment and is suitable for signal shielding environments such as subway trains. However, the main problem with this method is error accumulation. As time goes by, the error accumulation effect becomes more and more obvious, and generally it may accumulate to about 15% mileage error within 10 minutes. Under the demand for high-precision track detection, the pure inertial navigation solution is difficult to meet the accuracy requirements of long-term operation.

[0058] The present invention provides a subway train positioning method and system, in particular, a subway train positioning method and system based on an error Kalman filter algorithm and multi-source data mileage correction. The present invention can also provide an electronic device for subway train positioning. The present invention can also provide a storage medium storing a coding program of a subway train positioning method. The present invention can also provide a processor 100 for a coding program of a subway train positioning method. The present invention can also provide a terminal that can be connected to the processor 100. The present invention can also provide a subway control system, which contains the subway train positioning system of the present invention or executes the subway train positioning method of the present invention. Preferably, the present invention can also provide a subway, on which the subway train positioning system of the present invention is installed, or an electronic device that executes the subway train positioning method of the present invention is installed.

[0059] The present invention hopes to solve the following problems:

[0060] First, the positioning problem in a GPS-free environment. Since most subway trains run underground, the signals of satellite positioning systems such as GPS and Beidou cannot be covered, causing traditional stability detection equipment to lose its positioning function in underground environments. How to perform high-precision positioning of train operation without external signal support has become one of the technical difficulties.

[0061] Second, the error accumulation problem in pure inertial navigation systems. The inertial navigation unit (INS) can measure the train's motion information, but the errors of the inertial navigation system will gradually accumulate during long-term operation, making it difficult to meet the positioning accuracy requirements.

[0062] Third, the problem of identifying and calibrating the characteristic points of the rails. The present invention uses the characteristics of turning points, stopping points, and acceleration mutation points at switches in the subway track as mileage references. Therefore, how to use the natural characteristic points of the track to effectively calibrate the inertial navigation system is also a problem that the present invention needs to solve, which involves two difficulties: (1) identifying the characteristic points of the track from the data of the inertial navigation unit (INS); (2) using the mileage data of the characteristic points to process the data to obtain the calibrated mileage data.

[0063] In view of the shortcomings of the prior art, the present invention provides a subway train positioning system, such as Figure 1 As shown, the system includes a processor 100. The processor 100 is configured with multiple modules, including an initial correction module 110, a curve generation module 120, a turn recognition module 130 and an abnormal vibration recognition module 140.

[0064] Preferably, when the processor 100 is an independent processor, the processor 100 may be a CPU, a GPU, or hardware formed by a combination thereof, or may be a dedicated integrated chip capable of executing the subway train positioning method of the present invention. In this case, the initial correction module 110, the curve generation module 120, the turn recognition module 130, and the abnormal vibration recognition module 140 are different execution task modules inside the processor 100.

[0065] Preferably, when the processor 100 is a non-independent processor, it can be composed of a plurality of dedicated integrated chips and / or processing hardware (CPU and / or GPU, storage module) connected in communication. The initial correction module 110, the curve generation module 120, the turn recognition module 130 and the abnormal vibration recognition module 140 are independent processors or at least two modules are integrated in the same processor 100. Preferably, different independent processors are connected via communication lines.

[0066] Preferably, the processor 100 can be connected to a terminal to receive data input by a user and display data of the processing process and processing result of the processor 100 .

[0067] Preferably, if Figure 1 As shown, the processor 100 of the present invention is communicatively connected to the inertial measurement unit (IMU) of the train, and is used to receive multi-source data of the train collected by the inertial measurement unit (IMU).

[0068] Preferably, the subway train positioning system of the present invention may include a processor 100 and an inertial measurement unit (IMU) or an inertial navigation unit (INS). Further preferably, the subway train positioning system of the present invention may include a processor 100, an inertial measurement unit and a memory 300.

[0069] The subway train of the present invention is simply referred to as a train in the following content.

[0070] Preferably, the stability detector 200 on the train is equipped with an inertial measurement unit (IMU), a voltage reduction module, a power supply and an embedded chip. Alternatively, the stability detector 200 on the train is equipped with an inertial navigation unit (INS), a voltage reduction module, a power supply and an embedded chip.

[0071] The inertial measurement unit (IMU) or inertial navigation unit (INS) collects high-frequency signals, and the embedded chip uses the error Kalman filter algorithm to process the collected data, thereby reducing the random error of the inertial measurement unit (IMU) or inertial navigation unit (INS) and improving data accuracy. The characteristic points of the subway track, such as the change of the track curve when the train turns, the stop time and location of the station can be used as key reference points for mileage calibration: the location of the turn and the start or end time of the turn section are obvious and easy to collect, and the stop time and location provide a clear mileage calibration benchmark.

[0072] like Figure 2 As shown, the processor 100 of the present invention is configured to perform the following steps.

[0073] S100: The initial correction module 110 processes the multi-source data of the train collected by the inertial navigation unit based on the error Kalman filter algorithm to preliminarily correct the acceleration and angular velocity.

[0074] The error Kalman filter performs state estimation on the raw acceleration and angular velocity data collected by the inertial measurement unit (IMU), and dynamically corrects the measurement noise and system error by combining the system's motion model and the actual measurement value of the inertial measurement unit (IMU). Through real-time estimation of the noise of the inertial measurement unit (IMU), the error Kalman filter algorithm can effectively reduce the drift and random error in the inertial measurement unit (IMU) data, thereby providing more accurate basic data for subsequent data processing.

[0075] S200: The curve generating module 120 constructs a time-mileage curve of the train and corrects the time-mileage curve based on a first correction ratio.

[0076] S300: The turn recognition module 130 recognizes the dynamic change characteristics of the turn based on the angular velocity collected by the inertial navigation unit. Preferably, the turn recognition module 130 calculates the total mileage within the turn segment, and corrects the total mileage within the turn segment based on the second correction ratio of the turn segment.

[0077] S400: The abnormal vibration identification module 140 identifies abnormal vibration features based on the high-order change rates of the acceleration and angular velocity of the train and marks them in a visual manner.

[0078] Without relying on GPS, the present invention constructs a mileage calibration model based on track characteristics by collecting acceleration and angular velocity data of these characteristics of the train while traveling and calibrating the processed data, thereby ensuring accurate train mileage and position information in a GPS-free environment.

[0079] The present invention describes step S100 in detail.

[0080] S110: Status initialization.

[0081] In the initial stage of the system operation of the present invention, the state vector [x0] is set according to the initial acceleration, angular velocity and position data of the inertial measurement unit (IMU). The state error covariance matrix [P0] is initialized according to the characteristics of the inertial measurement unit (IMU). The state error covariance matrix includes several initial state vectors. The initial state vector may include the initial position, velocity and attitude of the train.

[0082] S120: Prediction status (time update).

[0083] The next state of the train is predicted based on the preset motion model and the acceleration and angular velocity of the train collected by the inertial navigation unit.

[0084] The time update formula is:

[0085] in, represents the predicted state, x k-1 represents the last predicted state, A represents the state transfer matrix, u k represents the input data (such as acceleration and angular velocity) measured by the inertial measurement unit (IMU), and B represents the input matrix.

[0086] S130: Prediction error covariance.

[0087] Error covariance matrix of the predicted state for:

[0088] Among them, A T represents the transpose of the state transfer matrix; Q represents the process noise covariance matrix, which is used to describe the uncertainty in the system model.

[0089] S140: Correct the predicted state.

[0090] The predicted state information is corrected based on the Kalman gain, and the error covariance model associated with the prediction is updated.

[0091] The predicted state is corrected based on the acceleration and angular velocity data collected by the inertial measurement unit (IMU).

[0092] First calculate the Kalman gain K k :

[0093] Among them, H T represents the measurement matrix, R represents the measurement noise covariance matrix, and H represents the state observation matrix.

[0094] Use the Kalman gain to correct the predicted state and error covariance:

[0095]

[0096] Among them, z k represents the actual measurement value of the inertial measurement unit (IMU), x k Represents the revised state estimate.

[0097] Update the error covariance:

[0098] S150: Repeat the prediction and update steps.

[0099] The Kalman filter predicts the state and corrects (updates) it at every moment, and continuously reduces the impact of noise and drift on the system state estimation through a recursive process. This filtering process continues until the data processing is completed.

[0100] After the error Kalman filter processing of the inertial measurement unit (IMU) data, the system obtains the acceleration and angular velocity data after preliminary error correction. Subsequently, these filtered inertial measurement unit (IMU) data will be further smoothed and integrated to generate the train's time-mileage curve, and mileage calibration will be performed in combination with track feature points, ultimately achieving high-precision train running trajectory and abnormal vibration detection.

[0101] Preferably, the execution steps of the processor 100 may further include S160. S160 is executed between steps S100 and S200.

[0102] S160: Before constructing the time-mileage curve of the train, data filtering and smoothing are performed on the acceleration and angular velocity after preliminary correction to remove noise in the data.

[0103] During the data measurement process of the inertial measurement unit (IMU), the original acceleration, angular velocity, speed and position information usually contain high-frequency noise due to environmental interference, vibration and sensor noise. These noises will affect the subsequent turn detection, mileage calculation and position estimation. Therefore, the present invention adopts smoothing and filtering technology to suppress noise and ensure the stability and accuracy of the data.

[0104] The present invention hereby explains step S160 as follows.

[0105] S161: Perform low-pass filtering.

[0106] Acceleration and angular velocity data are often affected by high-frequency noise.

[0107] The acceleration is expressed as:

[0108] The angular velocity is expressed as:

[0109] Therefore, the acceleration and angular velocity signals are low-pass filtered to remove high-frequency noise.

[0110] Preferably, a Butterworth filter is used for low-pass filtering. The characteristic of low-pass filtering is smooth frequency response, which can effectively filter out high-frequency noise without introducing phase shift. Assuming that the filter is of 4th order and the filter cutoff frequency is ω c , then the transfer function of the filter is:

[0111]

[0112] The transfer function describes how the filter responds to signals of different frequencies.

[0113] Where s represents the complex frequency variable, ζ represents the damping coefficient, ω c Indicates the cutoff frequency of the filter. For example, the ζ value of the Butterworth filter is fixed, and ζ = 0.707 is usually selected.

[0114] Low-frequency vibration: Low-frequency vibration caused by uneven tracks, asymmetric wheels, etc. Usually less than 20Hz.

[0115] The above high-frequency noise may come from sensor noise, electrical noise, etc., and is usually greater than 50Hz.

[0116] Preferably, when using a Butterworth low-pass filter, the cutoff frequency ω can be chosenc , such as the cutoff frequency ω c The frequency response of the Butterworth filter is then used to determine how to filter out the high frequency noise.

[0117] ω c =2π×30rad / s≈188.4rad / s. This is the cutoff frequency ω of the low-pass filter at a frequency of 30Hz. c The numerical value of .

[0118] For example, suppose you choose the cutoff frequency ω c The cutoff frequency is 30Hz, which determines the frequency at which the filter starts to attenuate.

[0119] Substituting each cutoff frequency into the filter's transfer function, calculate the corresponding gain. This represents the filter's response at that frequency.

[0120] For signals below the cutoff frequency, the filter provides almost no attenuation.

[0121] For signals above the cutoff frequency, the filter will gradually attenuate the signal and reduce high-frequency noise.

[0122] Specifically, when the cutoff frequency ω is selected c When it is 30Hz, the frequency components of 1Hz to 30Hz in the vibration signal of the train acceleration are basically unaffected, while the high-frequency noise exceeding 30Hz will be filtered out.

[0123] The transfer function after filtering out high frequency noise becomes:

[0124]

[0125] Set to s = jω, where ω is the actual frequency of the signal.

[0126] Substituting the complex frequency variable s=jω into the transfer function, the processing effect of the filter on the signal can be calculated.

[0127] In the present invention, the acceleration and angular velocity data are low-pass filtered by the filter, and the filtered signal is:

[0128]

[0129]

[0130] Among them, * represents the convolution operation, and They represent the acceleration and angular velocity signals after filtering respectively.

[0131] S162: Smoothing the data.

[0132] The filtered acceleration and angular velocity signals may still have slight noise fluctuations, so smoothing is used to further suppress residual noise and enhance data stability. Smoothing uses sliding average method and double smoothing technology.

[0133] S1621: Sliding average method.

[0134] The sliding average method is a commonly used smoothing technique that eliminates short-term fluctuations by averaging the data within a window. For position, velocity, and angular velocity data, the sliding average smoothing formula is:

[0135]

[0136] Wherein, N represents the window size, and x(t+k) represents the value at the Kth point after the original signal x(t). k is an index, which represents the t+kth value of the original signal N moments before and after time t. The data measured by the present invention are angular velocity and acceleration data, so only the angular velocity and acceleration data are smoothed.

[0137] S1622: Secondary smoothing.

[0138] In order to further improve the smoothing effect, the system performs secondary smoothing on the data. First, the preliminary smoothed data is subjected to a second sliding average:

[0139]

[0140] This secondary smoothing process can further remove high-frequency noise while maintaining signal stability. Especially in turn detection and mileage calibration, using smoothed data can reduce jitter and ensure the accuracy of subsequent calculations.

[0141] S163: Overall formula description of the data processing process.

[0142] As shown above, the system processes the raw inertial measurement unit (IMU) data A two-step process is performed.

[0143] Low-pass filtering: Remove high-frequency noise through a Butterworth low-pass filter:

[0144]

[0145]

[0146] Secondary smoothing: The filtered signal is further smoothed by two sliding averages:

[0147]

[0148]

[0149] x can represent acceleration a or angular velocity ω. When x represents acceleration a, x smooth (t) represents the filtered acceleration a, x smooth2 (t) represents the acceleration a after secondary smoothing.

[0150] When x represents the angular velocity ω, x smooth (t) represents the filtered angular velocity ω, x smooth2 (t) represents the angular velocity ω after the secondary smoothing process.

[0151] Through the above low-pass filtering and secondary smoothing, the system can effectively remove high-frequency noise in the inertial measurement unit (IMU) data, ensuring that in the calculation of key data such as acceleration and angular velocity, noise interference is reduced and a smoother signal is provided. This processing method is particularly suitable for turn detection and mileage calibration in inertial navigation, improving the stability and accuracy of the system.

[0152] Figure 3 A subway track map is shown. Figure 3 XK0+139 in the figure means 0 km and 139 m. XK1 means 1 kilometer (1 km). Figure 3 XK1+355.944 in the figure means that the turn starts at 1355.944m. That is to say, there is a turning point between the two stations: XK1+355.944.

[0153] like Figure 4 As shown in FIG. 1 , when the inertial measurement unit (IMU) is placed flat on the train, the X direction or Y direction of the inertial measurement unit (IMU) is the running direction of the train. The arc arrow represents the angular velocity around the Z axis.

[0154] When the train passes through a curve, the inertial measurement unit (IMU) will detect and record the change in angular velocity, such as Figure 5 As shown. Figure 5 In the figure, the horizontal axis represents time and the vertical axis represents the magnitude of the angular velocity around the z-axis. The angular velocity changes significantly in the turning section.

[0155] The present invention describes step S200 as follows.

[0156] S210: Convert the acceleration and angular velocity into position and velocity to construct a time-mileage curve.

[0157] By numerically integrating the acceleration and angular velocity data, the acceleration and angular velocity data of the train at different time points are converted into position and velocity information through integration operations to generate the time-mileage curve of the train.

[0158] S211: The acceleration data is integrated to obtain the velocity.

[0159] The integration formula is: V(t)=V0+∫a(t)dt.

[0160] Among them, V(t) represents the velocity at time t, a(t) represents the acceleration data, and V0 represents the initial velocity.

[0161] S212: Perform a second integration on the speed data to obtain the mileage of the train.

[0162] S(t)=S0+∫V(t)dt=S0+∫(V0+∫a(t)dt)dt.

[0163] Among them, S(t) represents the mileage of the train at time t, and S0 represents the initial mileage.

[0164] S220: Taking the ratio of the mileage to the first target mileage as a first correction ratio.

[0165] like Figure 6 As shown, the operation test data of XX Metro Line 9 is shown. Figure 6 The information displayed includes total mileage settings, query time point, query results, running mileage, running time, correction file options, algorithm selection box and other information.

[0166] S221: Input the first target mileage.

[0167] Enter the actual mileage S in "Total Mileage Settings" target . S target Used to correct the total mileage calculated by the Inertial Measurement Unit (IMU).

[0168] S222: Total mileage correction.

[0169] Calculate the mileage S(t) obtained by the inertial measurement unit (IMU) integration and the first target mileage S entered by the user target The ratio of , obtains the first correction ratio k.

[0170]

[0171] When k>1, it means that the calculated mileage is less than the first target mileage and needs to be stretched; when k<1, it means that the calculated mileage is greater than the first target mileage and needs to be compressed.

[0172] S230: scaling the mileage data at each time point based on the first correction ratio, thereby obtaining a corrected mileage and correcting the time-mileage curve. The time-mileage curve is used to intuitively display the mileage changes of the train.

[0173] S231: Use the first correction ratio k to scale the mileage data at each time point in equal proportion.

[0174] S corrected (t) = S(t) × k.

[0175] Among them, S corrected (t) represents the corrected mileage.

[0176] S232: Draw a time-mileage curve.

[0177] like Figure 7 As shown, a corrected mileage-time curve is generated and displayed to intuitively show the change in mileage of the train.

[0178] Figure 7 In the table on the right, the horizontal axis represents time and the vertical axis represents mileage. The table shows the curve formed by the original data and the curve formed by the corrected data.

[0179] Compare the mileage-time curves before and after correction. The corrected mileage-time curve can more accurately show the mileage changes of the train.

[0180] The present invention describes step S300 as follows.

[0181] In order to more accurately identify the moment when the train enters and leaves the curve, the Z-axis angular velocity ω z (t) Performs high-level analysis using the rate of change of angular velocity and second-order derivatives to capture small changes in the start and end points of a turn, and dynamically adjusts thresholds to accommodate different turn radii and turn speeds.

[0182] S310: Perform nonlinear fitting on the angular velocity curve to calculate the rate of change of the angular velocity and the angular acceleration.

[0183] The change of Z-axis angular velocity over time can be modeled by a third-order Bezier curve:

[0184] ω z (t) = a3t 3 +a2t 2 +a1t+a0.

[0185] Among them, a0, a1, a2, and a3 represent the coefficients obtained by the least squares fitting method. Here, the dynamic change characteristics of the turn are captured by performing nonlinear fitting on the angular velocity curve.

[0186] S320: A dynamic threshold is set based on the current speed of the train and the curvature radius of the track. When the angular velocity is greater than the dynamic threshold, the start time of the train turning section is determined; and the end time of the train turning section is determined based on the rate of change of the angular velocity and the negative change of the angular acceleration.

[0187] S321: Determine the starting time within the turning section.

[0188] The start time of the turn segment is determined by the first and second derivatives of the angular velocity.

[0189] Calculate the rate of change of angular velocity on the Z axis and acceleration Its definition is as follows:

[0190]

[0191]

[0192] Set the dynamic threshold Δω threshold (t), the dynamic threshold is based on the current speed of the train and the curvature radius of the track and is calculated as:

[0193]

[0194] Among them, α represents the proportional coefficient, V(t) represents the current speed, and R(t) represents the curvature radius of the track.

[0195] Preferably, α is used to adjust the sensitivity of dynamic detection of turns. It controls the size of the threshold, thereby determining how to detect the change in the angular velocity of the train during the turn. When the speed of the train and the curvature radius of the track change, the dynamic threshold will be adjusted accordingly. The value range of α is 0.8 to 1.5.

[0196] When the angular velocity change rate reaches the threshold, the train starts to turn:

[0197]

[0198] The start time of the train turning section depends not only on the change of angular velocity, but also on the positive change of angular acceleration to ensure the dynamic characteristics of the turn.

[0199] S322: Determine the end time within the turning section.

[0200] The end time of the turning segment is determined by the rate of change of angular velocity and the negative change of acceleration.

[0201] The specific formula is as follows:

[0202]

[0203] At this time, the angular velocity decreases and the angular acceleration is negative, indicating that the train is leaving the turning section.

[0204] A high-level analysis of the angular velocity is performed, using the rate of change of the angular velocity and the second-order derivative to capture the slight changes in the start and end times of the turn segment, and dynamically adjusts to adapt to different turning radii and turning speeds through dynamic thresholds.

[0205] S330: Correct the total mileage in the turning section.

[0206] S331: Calculate the total mileage in the turning section based on the speed of the train.

[0207] In order to calculate the total mileage of the train in each curve, the speed of the train is integrated. The speed V(t) is obtained by integrating the acceleration data of the inertial measurement unit (IMU) twice:

[0208]

[0209] Where a(t) represents acceleration and V0 represents initial velocity.

[0210] Further integrating the speed gives the total mileage within the turning section:

[0211]

[0212] The calculation process of the total mileage in the turning section not only takes the speed into account, but also corrects the nonlinear change of acceleration by integrating the acceleration.

[0213] S332: Taking the ratio of the total mileage in the turning segment to the second target distance in the turning segment as the second correction ratio.

[0214] The total mileage calculated by the inertial measurement unit (IMU) in the turning section may have a significant error with the total mileage in the actual turning section. The total mileage is calibrated through a nonlinear correction model. Assuming that the second target mileage input by the terminal is S input,bi ,S input, ei,… (representing the mileage of the first turn start and the mileage of the first turn end respectively), the second correction ratio k i (t) is a dynamic function that changes with time to adapt to the changes of train speeds and turning conditions:

[0215]

[0216] The denominator is the total mileage actually integrated within the turning section.

[0217] S333: Proportionally scale the total mileage in the turning segment based on the second correction ratio.

[0218] Specifically, the second correction ratio is used to nonlinearly scale the total mileage in each turning segment:

[0219]

[0220] S(t) represents the total mileage in the original turning section, S corrected (t) represents the total mileage within the corrected turning section. represents the start time of the first turning segment, Indicates the end time of the first turning segment.

[0221] This formula ensures that within each turning segment, the mileage within the corrected turning segment is consistent with the input mileage markers, and dynamically adjusts the scaling to match the real-time changing speed.

[0222] S334: Using a transition function to smoothly calibrate the total mileage in the corrected turning section, so that the corrected time-mileage curve is continuous and smoothly connected at the calibration point.

[0223] The transition function is:

[0224] Among them, β represents the parameter that controls the transition. The corrected total mileage can be expressed as:

[0225]

[0226] S final (t) represents the total mileage in the turning section after smooth calibration. This can avoid discontinuity in the corrected time-mileage curve at the calibration point, so that the time-mileage curve is smoothly connected before and after the calibration point to avoid sudden changes.

[0227] The present invention describes step S400 as follows.

[0228] The identification of abnormal vibration is based on the analysis of the high-order rate of change of acceleration and angular velocity. and angular velocity The third-order derivative of is used to capture the violent vibration changes.

[0229] S410: Calculate the third-order derivative functions of the acceleration and angular velocity of the train respectively, and the absolute value of the third-order derivative function is the vibration intensity.

[0230] To capture the vibration more accurately, calculate the third derivative of the acceleration and the third derivative of the angular velocity

[0231]

[0232]

[0233] These third-order derivatives represent the rate of change of the rate of change of acceleration and angular velocity, respectively. In other words, these third-order derivatives are used to capture the drastic changes of acceleration and angular velocity. According to the magnitude of the rate of change, the vibration point with a large rate of change can be quickly determined, that is, the vibration position and vibration time with a large rate of change of angular velocity and acceleration can be quickly determined, so that the vibration point with a drastic change in a short time can be effectively captured.

[0234] S420: Dynamically adjust the identification threshold of the vibration point based on the speed of the train. The position corresponding to the vibration intensity greater than the identification threshold is the abnormal vibration point.

[0235] According to the different speeds and environmental conditions of the train, the identification threshold of the vibration point and It is also dynamically adjusted:

[0236]

[0237]

[0238] Among them, γ and δ are dynamic adjustment coefficients, γ ≥ 1, δ ≤ 5, to ensure the accuracy of vibration point identification under different speed conditions. V(t) represents the current speed, and R(t) represents the curvature radius of the track.

[0239] γ and δ are dynamic adjustment coefficients used to adjust the identification threshold of vibration points. The role of these two coefficients is to dynamically adjust the identification threshold of acceleration and angular velocity vibration based on the train speed V(t) and the curvature radius R(t) of the track, so as to avoid large vibrations caused by turning or acceleration being misjudged as abnormal vibrations.

[0240] When the train speed is low or the track is straight, the recognition threshold is lower so that smaller vibration changes can be detected.

[0241] Preferably, a point in the time-distance curve of the rate of change of the angular velocity and the rate of change of the acceleration greater than the identification threshold is determined as an abnormal vibration point.

[0242] S430: Mark the vibration point in a visual manner.

[0243] At each detected abnormal vibration point, not only its time and mileage are marked, but also the severity of the vibration is indicated by the depth of color. The present invention determines the size and color of the visualized vibration point based on the vibration intensity, and then indicates the severity of the vibration, so that the user can see the specific information of each vibration point at a glance.

[0244] Figure 8The graph in the figure shows the absolute velocity changing with time, which is also a visual diagram of the abnormal vibration point. The horizontal axis of the curve represents time (seconds), and the vertical axis represents the absolute velocity (m / s). Figure 8 As shown in the curve, the circular points on the curve represent the abnormal vibration points of the train, and the triangular points represent the vibration changes caused by the train turning (which is a normal phenomenon and can be ignored). Through the visualization of abnormal vibration points, users can intuitively view the number and frequency of abnormal vibrations of the train, and can also intuitively determine the location of the abnormal vibration points.

[0245] Through nonlinear correction models and high-order derivative analysis, not only the recognition accuracy of turning moments is improved, but also the accumulation of mileage errors is reduced. In addition, through the third-order derivative analysis of acceleration and angular velocity, abnormal vibration points can be more sensitively identified, providing more accurate track maintenance information.

[0246] Preferably, the present invention can ensure that at least three mileage corrections are performed at each station by performing mileage corrections at turning points. Compared with the prior art method of only performing mileage corrections at the end, the time-mileage curve obtained by the present invention is closer to the time-mileage curve of train operation under actual conditions.

[0247] As mentioned above, due to the existence of the turning point, the Z-axis angular velocity data measured by the inertial measurement unit (IMU) when the train passes through this point will be significantly different from when the train moves in a straight line. Based on this principle, the moment when the train enters the turning point can be found, and then the mileage information calculated after data processing can be corrected to the information at the starting point of the turn.

[0248] The present invention also provides sample data obtained by testing the subway train positioning system, such as Fig.11 As shown. Fig.11 The test graph of the total mileage data for preliminary correction is as follows: Fig. 9 As shown ( Fig. 9 The triangle mark in the figure can be ignored); the test graph of the calculated total mileage data is as follows Fig.10 shown.

[0249] The method and system of the present invention can achieve the following objectives:

[0250] First, improve positioning accuracy and reduce error accumulation.

[0251] The present invention combines the inertial navigation system with subway track feature points (such as turning points, parking points, and acceleration mutation points at turnouts) and corrects the data of the inertial navigation unit (INS) through the error Kalman filter algorithm. The track feature points are used as natural calibration references to achieve high-precision positioning in a GPS-free environment and control the error within a reasonable range (5%).

[0252] Second, get rid of dependence on external signals and reduce costs.

[0253] Compared with existing GPS and UWB solutions, the present invention does not need to rely on external signals or install additional base station equipment, completely eliminating the signal limitation problem of GPS and Beidou caused by the underground environment of the subway, while avoiding the high installation and maintenance costs of base stations, and has good economy and operability.

[0254] Third, improve the convenience and effectiveness of train track inspection.

[0255] The positioning and stability detector 200 adopted in the present invention is highly integrated. With the error Kalman filter algorithm and the calibration strategy of the track feature points, it can generate a time-mileage curve that is closer to the actual train, which is convenient for the accurate detection and maintenance of abnormal points on the subway line track in a GPS-free environment, greatly improving the accuracy and effectiveness of train stability detection.

[0256] By achieving the above objectives, the present invention provides a low-cost, high-precision solution for accurate positioning and track detection in underground environments, effectively solving the defects of existing positioning technologies when there is no GPS or when base stations cannot be installed.

[0257] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably" and "according to a preferred embodiment", which means that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept.

Claims

1. A subway train positioning system, characterized in that: The invention comprises a processor (100), wherein the processor (100) is configured to: The multi-source data of the train collected by the inertial navigation unit is processed based on the error Kalman filter algorithm to preliminarily correct the acceleration and angular velocity; constructing a time-mileage curve for the train and correcting the time-mileage curve based on a first correction ratio; Identifying dynamic change characteristics of a turn based on the angular velocity collected by the inertial navigation unit, wherein the total mileage within the turn segment is calculated, and the total mileage within the turn segment is corrected based on a second correction ratio of the turn segment; Abnormal vibration signatures are identified based on the high-order rates of change of the train’s acceleration and angular velocity and marked in a visual manner.

2. The system according to claim 1, characterized in that The processor (100) is also configured to: Before constructing the time-mileage curve of the train, the acceleration and angular velocity after preliminary correction are filtered and smoothed to remove noise in the data.

3. The system according to claim 1 or 2, characterized in that: The processor (100) is configured to preliminarily correct the acceleration and angular velocity in the following manner: In the initial stage of the operation of the processor (100), the next state of the train is predicted based on a preset motion model and the acceleration and angular velocity of the train collected by the inertial navigation unit; The predicted state information is modified based on the Kalman gain and an error covariance model associated with the prediction is updated, thereby correcting the predicted state of the train.

4. The system according to any one of claims 1 to 3, characterized in that: The processor (100) is configured to correct the time-mileage curve in the following manner: Converting the acceleration and angular velocity into position and velocity to construct the time-mileage curve; The ratio of the mileage to the first target mileage is used as a first correction ratio, The mileage data at each time point is proportionally scaled based on the first correction ratio to obtain the corrected mileage and correct the time-mileage curve.

5. The system according to any one of claims 1 to 4, characterized in that: The processor (100) is configured to recognize the dynamic change characteristics of a turn in the following manner: Perform nonlinear fitting on the angular velocity curve. Calculate the rate of change of angular velocity and angular acceleration, Dynamic thresholds are set based on the current speed of the train and the curvature radius of the track. When the angular velocity is greater than the dynamic threshold, determining the start time of the train turning section; The end time of the train turning section is determined based on the rate of change of the angular velocity and the negative change of the angular acceleration.

6. The system according to any one of claims 1 to 5, characterized in that: The processor (100) is configured to correct the total mileage in the turning segment in the following manner: Calculate the total mileage within the curve based on the train's speed; The ratio of the total mileage in the turning section to the second target distance in the turning section is used as the second correction ratio; The total mileage in the turning segment is scaled proportionally based on the second correction ratio.

7. The system according to any one of claims 1 to 6, characterized in that: The processor (100) further comprises: The total mileage in the corrected turning section is smoothly calibrated using a transition function so that the corrected time-mileage curve is continuous and smoothly connected at the calibration point.

8. The system according to any one of claims 1 to 7, characterized in that: The processor (100) is configured to identify abnormal vibration characteristics in the following manner: respectively calculating the third-order derivative functions of the acceleration and angular velocity of the train, wherein the absolute value of the third-order derivative function is the vibration intensity; Dynamically adjust the identification threshold of vibration points based on the speed of the train; The position corresponding to the vibration intensity greater than the identification threshold is the abnormal vibration point.

9. A subway train positioning method, characterized in that: The method comprises: Processing the acceleration and angular velocity of the train collected by the inertial navigation unit based on an error Kalman filter algorithm to preliminarily correct the acceleration and angular velocity; constructing a time-mileage curve for the train and correcting the time-mileage curve based on a first correction ratio; Identifying dynamic change characteristics of a turn based on the angular velocity collected by the inertial navigation unit, wherein the total mileage within the turn segment is calculated, and the total mileage within the turn segment is corrected based on a second correction ratio of the turn segment; Abnormal vibration signatures are identified based on the high-order rates of change of the train’s acceleration and angular velocity and marked in a visual manner.

10. The method according to claim 9, characterized in that The method further comprises: Before constructing the time-mileage curve of the train, the acceleration and angular velocity after preliminary correction are filtered and smoothed to remove noise in the data.

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