Train integrity detection method and system for constructing dynamic envelope based on multi-particle model

Abstract

The invention provides a train integrity detection method and system for constructing a dynamic envelope based on a multi-mass-point model, and belongs to the technical field of rail transit train operation safety detection. Estimating the real-time length of the baseline by using Kalman filtering; pID control is adopted to analyze and calculate the telescopic displacement of the carriages in the train running process; and comparing the calculated baseline length with the train safety envelope, and judging whether the integrity state of the train is normal or not according to a comparison result. According to the invention, the train length is detected by using the double-difference carrier phase; a train dynamic safety envelope is constructed, a train multi-mass-point model is constructed by researching longitudinal stress analysis such as resistance and workshop acting force of a high-speed train, and compartment extrusion displacement in the train running process is analyzed and calculated through PID control. A train dynamic safety envelope is formed by the data, communication delay and a redundancy threshold value and serves as a judgment basis of train integrity detection, and the efficiency and the real-time performance of train integrity detection are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit train operation safety detection technology, and in particular to a train integrity detection method and system based on a dynamic envelope constructed based on a multi-particle model. Background Art

[0002] During train operation, train formations may separate or cars may become uncoupled due to various reasons, such as mechanical failure, connection failure, or external impact. This loss of integrity not only seriously affects the normal operation of the train but can also cause serious safety accidents. Therefore, real-time and accurate detection of train integrity is of great significance to ensuring railway transportation safety.

[0003] There are three main methods for detecting train integrity:

[0004] 1) Train occupancy detection uses track circuits. The physical pressure of the train wheels on the rails changes the track circuit current, triggering a relay state change to determine whether the track section is occupied. This method can identify stationary cars left behind after coupler separation, but it requires dense deployment of track circuit equipment along the railway line, resulting in high initial construction investment and subsequent maintenance costs. It also poses challenges in terms of equipment complexity and environmental adaptability.

[0005] 2) Determine train integrity and braking status by comparing brake duct pressure. The air brake system is an integral component of train operation control. The air brake systems of each carriage are connected via a main air brake duct, which runs from the front to the rear of the train. Therefore, train integrity can be checked by comparing the air or brake duct pressures at the front and rear of the train. Because the train's air brake ducts run through all carriages, a carriage separation accident could cause the ducts to rupture and leak air, leading to a sharp drop in brake duct pressure. The train driver uses wireless communication to monitor the air pressure in the rear carriage and, combined with the air pressure at the front of the train, can determine the train's integrity. If the brake duct pressure drops below normal due to abnormal leakage, the equipment at the rear of the train will detect a loss of integrity and issue an alarm to ensure train safety.

[0006] 3) GNSS-based integrity testing involves placing a GNSS receiver, a data transmission radio, and an onboard computer on the locomotive, and a GNSS receiver and a data transmission radio at the rear of the train. The rear GNSS receiver simultaneously receives satellite information and transmits its own GNSS data in real time via the data transmission radio. The locomotive radio then transmits this data to the onboard computer. The locomotive GNSS receiver directly transmits the real-time GNSS data to the onboard computer. The onboard computer estimates the train length based on the position information of the leading and trailing trains, compares it with the actual train length, and determines the train's integrity.

[0007] However, satellite-based train integrity detection methods lack mature criteria and theoretical support. The threshold for detection is difficult to define, hindering the full potential of satellite-based train integrity detection. The positioning and detection process treats the train as a single point, ignoring potential changes within the train caused by environmental factors, road conditions, and other factors, which in turn affects the accuracy of train integrity detection. Summary of the Invention

[0008] The object of the present invention is to provide a train integrity detection method and system based on a multi-particle model to construct a dynamic envelope, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] In a first aspect, the present invention provides a train integrity detection method based on a multi-particle model to construct a dynamic envelope, comprising:

[0011] A carrier phase double-difference model is constructed to eliminate the clock errors of the satellite and receiver, as well as the delay errors in the ionosphere and troposphere along the propagation path; the real-time length of the baseline is estimated using Kalman filtering;

[0012] PID control is used to analyze and calculate the expansion and contraction displacement of carriages during train operation;

[0013] Construct a train dynamic safety envelope including telescopic displacement, communication delay, and redundancy threshold as the basis for train integrity detection;

[0014] The calculated baseline length is compared with the train safety envelope, and based on the comparison result, it is determined whether the train integrity status is normal; if it is within the range of the safety envelope, the train integrity status is normal; otherwise, if it exceeds the value of the safety envelope, the integrity status is abnormal.

[0015] As a further limitation of the first aspect of the present invention, the method of using double-difference carrier phase to eliminate the clock errors of the satellite and the receiver, and the delay errors of the ionosphere and the troposphere on the propagation path includes the following steps:

[0016] Establish the carrier phase observation equations at the beginning and end of the column during the propagation process;

[0017] The carrier phases between the first and last antennas in the train are time-aligned and double-differenced to eliminate the clock errors of the satellite and receiver as well as the delays in the ionosphere and troposphere along the propagation path.

[0018] The position of the first antenna is regarded as the initial value of the Taylor expansion of the position of the last antenna, and the distance from the satellite to the first antenna is used to approximately replace the distance from the satellite to the last antenna. The relative positions of the two antennas are expressed by the deviation term in the Taylor expansion.

[0019] As a further limitation of the first aspect of the present invention, the double-difference carrier phase error elimination comprises the following steps:

[0020] Perform inter-station differential between the reference station and the mobile station to eliminate errors related to the satellite and propagation path, including satellite clock errors, ionospheric and tropospheric delay errors;

[0021] Assuming that the two receivers observe another satellite at the same time, the single difference equation of the carrier phase is obtained;

[0022] Based on the inter-station difference, the satellite with the best elevation angle is selected as the reference satellite, and the carrier phases corresponding to other satellites are subtracted from the carrier phase of the reference satellite to eliminate the errors related to the receiver.

[0023] As a further limitation of the first aspect of the present invention, based on the measurement equation of the double-difference carrier phase, the system equation and measurement equation of the Kalman filter model are established, including: the state matrix of the Kalman filter system includes the relative positions, relative rates and double-difference integer ambiguities in the three coordinate axis directions in the Earth-centered Earth-fixed coordinate system; determining the state transfer matrix and the noise matrix of the system; obtaining the measurement model of the system, calculating the observation vector of the system and the measurement matrix of the system, and constructing the noise covariance matrix.

[0024] As a further limitation of the first aspect of the present invention, a PID control is used to construct a multi-particle model of the train to analyze and solve the squeeze displacement of the carriage during the operation of the train, including: the solution of the telescopic displacement, the resistance existing in the train during operation includes basic resistance, slope-added resistance, curve-added resistance and tunnel-added resistance; the basic resistance includes wheel-rail friction, air resistance and bearing friction; the slope-added resistance is caused by the slope of the railway line and is related to the gravity of the train; when the train is running on a slope, the slope will generate additional resistance to the train; when going uphill, the gravity of the train will be opposite to the direction of the train's movement, resulting in an increase in the slope-added resistance, which hinders the train's progress; the generation of curve-added resistance is related to the sliding friction between the wheel and rail, the friction between the wheel flange and the rail, and the friction between the bearings and other components; the tunnel-added resistance refers to the additional air resistance caused by the relatively closed tunnel space and restricted air flow when the train is running in the tunnel.

[0025] As a further limitation of the first aspect of the present invention, the communication delay in the train safety envelope is related to the train speed; the positioning communication delay during the train operation refers to the time lag from sending a positioning request to receiving and processing the positioning information when data is exchanged between the train and the ground control system or satellite navigation system; the train speed is calculated based on the collected time lag data; the approximate acceleration of the train during this period is estimated by using the speed values ​​measured at two consecutive sampling moments; and after obtaining the train operation status information, the communication delay is calculated.

[0026] In a second aspect, the present invention provides a train integrity detection system based on a multi-particle model to construct a dynamic envelope, comprising:

[0027] The first building block is used to construct a carrier phase double-difference model to eliminate the clock errors of the satellite and receiver, as well as the delay errors of the ionosphere and troposphere on the propagation path; and use Kalman filtering to estimate the real-time length of the baseline;

[0028] The analysis and settlement module is used to analyze and calculate the expansion and contraction displacement of the carriages during train operation using PID control;

[0029] The second building block is used to construct a train dynamic safety envelope including telescopic displacement, communication delay and redundancy threshold as the basis for train integrity detection;

[0030] The judgment module is used to compare the calculated baseline length with the train safety envelope and determine whether the train integrity status is normal based on the comparison result. If it is within the range of the safety envelope, the train integrity status is normal; otherwise, if it exceeds the value of the safety envelope, the integrity status is abnormal.

[0031] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the train integrity detection method based on the dynamic envelope constructed based on the multi-particle model as described in the first aspect is implemented.

[0032] In a fourth aspect, the present invention provides a computer device comprising a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the train integrity detection method for constructing a dynamic envelope based on a multi-particle model as described in the first aspect.

[0033] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the train integrity detection method for constructing a dynamic envelope based on a multi-particle model as described in the first aspect.

[0034] The beneficial effects of the present invention include utilizing double-difference carrier phase to detect train length; constructing a train dynamic safety envelope, constructing a train multi-particle model by studying the longitudinal force analysis such as the resistance of high-speed trains and the force acting on the vehicle body, and adopting PID control to analyze and solve the extrusion displacement of the vehicle body during the train operation. The dynamic safety envelope of the train is composed of communication delay and redundant threshold value, and is used as the judgment basis for train integrity detection, thereby improving the efficiency and real-time performance of train integrity detection.

[0035] Additional advantages of the present invention will be more clearly given in the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 This is an architecture diagram of a train integrity detection system that constructs a dynamic envelope based on a multi-particle model according to an embodiment of the present invention.

[0038] Figure 2 This is a flow chart of train integrity detection and decision based on building a dynamic envelope based on a multi-particle model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention.

[0040] Those skilled in the art will understand that unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0041] It should also be understood that terms, such as those defined in commonly used dictionaries, should be understood to have a meaning consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless as defined herein.

[0042] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0043] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless otherwise contradictory.

[0044] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0045] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0046] The present invention provides a train integrity detection method based on a dynamic envelope constructed using a multi-particle model. The method includes: using a double-difference carrier phase observation value processing method to effectively eliminate satellite clock errors, receiver clock errors, and ionospheric delay and tropospheric delay errors caused by the signal propagation path. On this basis, a Kalman filter algorithm is used to dynamically estimate the three-dimensional relative position coordinates between the head and tail antennas, and finally the head and tail baseline lengths are obtained through coordinate solution. Then, a dynamic safety envelope of the train is constructed. By studying the longitudinal force analysis such as the resistance of the high-speed train and the vehicle body force, a multi-particle model of the train is constructed. PID control is used to solve the car length calculated during the train operation. If it is within the dynamic envelope range, it indicates that the train integrity state is normal. Conversely, if the solved baseline length exceeds the value given by the dynamic envelope, it indicates that the train integrity state is abnormal and there may be a decoupling situation. The displacement is analyzed and solved, and the dynamic safety envelope of the train is composed of communication delay and redundancy threshold, which is used as the judgment basis for train integrity detection. The present invention is based on the solved moving baseline length and compares it with the train safety envelope to obtain real-time integrity status information of the train. It has the characteristics of good continuity and high reliability, and can effectively reduce the probability of false alarms and missed alarms of train integrity.

[0047] Example 1

[0048] In this embodiment 1, a train integrity detection system for constructing a dynamic envelope based on a multi-particle model is first provided, including: a first construction module, used to construct a carrier phase double-difference model to eliminate the clock errors of the satellite and the receiver, as well as the delay errors of the ionosphere and the troposphere on the propagation path; using Kalman filtering to estimate the real-time length of the baseline; an analysis and settlement module, used to use PID control to analyze and solve the expansion and contraction displacement of the carriage during the train operation; a second construction module, used to construct a train dynamic safety envelope including expansion and contraction displacement, communication delay and redundancy threshold as a basis for train integrity detection; a judgment module, used to compare the solved baseline length with the train safety envelope, and judge whether the integrity status of the train is normal based on the comparison result; if it is within the range of the safety envelope, the integrity status of the train is normal; otherwise, if it exceeds the value of the safety envelope, the integrity status is abnormal.

[0049] In this embodiment, the above-mentioned system is used to implement a train integrity detection method based on a multi-particle model to construct a dynamic envelope, including:

[0050] S1: Construct a carrier phase double-difference model to eliminate the clock errors of the satellite and receiver, as well as the delay errors of the ionosphere and troposphere on the propagation path; use Kalman filtering to estimate the real-time length of the baseline.

[0051] S2: PID control is used to analyze and calculate the expansion and contraction displacement of the carriages during train operation;

[0052] S3: Construct a dynamic safety envelope for the train, including telescopic displacement, communication delay, and redundancy threshold; this serves as the basis for train integrity detection.

[0053] S4: The calculated baseline length is compared with the train safety envelope. Based on the comparison result, the train integrity status is determined to be normal. If it is within the safety envelope, the train integrity status is normal; otherwise, if it exceeds the safety envelope, the train integrity status is abnormal.

[0054] The method of using double-difference carrier phase to eliminate the clock errors of the satellite and the receiver, and the delay errors of the ionosphere and the troposphere on the propagation path includes the following steps:

[0055] During the propagation process, the carrier phase observation equations at the beginning and end of the column are obtained;

[0056]

[0057]

[0058] where φ is the carrier phase measurement; λ is the wavelength of the carrier signal; ρ is the geometric distance from the satellite to the receiver; I is the ionospheric delay error; T is the tropospheric delay error; f is the carrier frequency; δt is the receiver clock error; dt is the satellite clock error; N is the integer ambiguity; ε is the residual error; i is the i-th satellite; 1 and 2 represent the receiver at the end of the train and the receiver at the beginning of the train, respectively.

[0059] The carrier phases between the first and last antennas are time-aligned, and double-difference carrier phases are performed to eliminate clock errors between the satellite and receiver, as well as errors such as ionosphere and troposphere delays along the propagation path.

[0060]

[0061] Where, the true double difference distance can be written as:

[0062]

[0063] The position of the first antenna is considered the initial value of the Taylor expansion of the position of the last antenna, and the distance from the satellite to the first antenna is used to approximate the distance from the satellite to the last antenna. The relative positions of the two antennas are expressed by the deviation term in the Taylor expansion:

[0064]

[0065]

[0066] Where, the deviation between the actual coordinates of the antenna and the approximate coordinates is recorded as (Δx, Δy, Δz), which is the relative position between the two antennas.

[0067] The coefficients of the first-order Taylor expansion represent the direction cosine parameters of the reference antenna pointing to the satellite. The cosine parameters of the three axis directions are defined as (l1, l2, l3), then:

[0068]

[0069] The obtained Substituting the expanded form of into the observation equation, we can get:

[0070]

[0071] The double-difference carrier phase error elimination method comprises the following steps:

[0072] Inter-station differencing: The antenna at the front of the train serves as the base station, and the antenna at the rear of the train serves as the rover. The distance between the two antennas (the train) is approximately 200 meters, far less than the distance between the satellite and the two observation stations. The differences in the propagation paths of the satellite signal to the two antennas are negligible. Inter-station differencing between the base and rover stations eliminates errors related to the satellite and the propagation path, including satellite clock errors and ionospheric and tropospheric delay errors.

[0073]

[0074] Where the subscript 12 represents the difference between the two receivers.

[0075] Assuming that the two receivers observe another satellite j at the same time, the single difference equation of the carrier phase can also be obtained:

[0076]

[0077] Inter-satellite differential: Based on the inter-station differential, the satellite with the best elevation angle is selected as the reference satellite. The carrier phases corresponding to other satellites are subtracted from the carrier phase of the reference satellite to eliminate receiver-related errors such as receiver clock error.

[0078]

[0079] Where the superscript ij represents the difference between the i-th star and the j-th star.

[0080] The system equation and measurement equation of the Kalman filter model are established based on the measurement equation of the double differential load phase;

[0081] System model of Kalman filter:

[0082] X k =AXk-1 +W k

[0083] Where, X k is the state vector of the system; A is the state transfer matrix; W k is the process noise of the system.

[0084] The state matrix of the system includes the relative position, relative velocity and double-difference integer ambiguity of the three coordinate axes in the Earth-centered Earth-fixed coordinate system:

[0085]

[0086] Where, Denote the relative rates in the three directions and assume they obey a first-order Markov distribution; is the matrix of double-difference integer ambiguities.

[0087] State transition matrix:

[0088]

[0089] Wherein the time interval ΔT = 0.1s.

[0090] The noise matrix of the system can be written as:

[0091]

[0092] Where τ is the correlation time.

[0093] Measurement model of the system: Z k =H k X k +V k

[0094] Where Z k is the observation vector of the system; H k is the measurement matrix; V k is the measurement noise, which is assumed to be Gaussian white noise, and its covariance matrix is ​​R.

[0095] The observation vector of the system:

[0096] The measurement matrix of the system:

[0097]

[0098] Noise covariance matrix:

[0099]

[0100] Where the parameter σ is the standard deviation of the L1 carrier noise, and its value is 0.0008m.

[0101] The train safety envelope is composed of telescopic displacement, communication delay, and redundancy threshold.

[0102] L safe =L ref +L delay +L η

[0103] A multi-mass point model of the train is constructed using PID control to analyze and solve the extrusion displacement of the carriages during train operation, including:

[0104] To calculate telescopic displacement, we first analyze the dynamic forces acting on the train during operation. A train experiences basic resistance at all times, primarily from wheel-rail friction, air resistance, and bearing friction. As train speed increases, the effects of air resistance and wheel-rail friction become increasingly significant. Due to their complex composition, they are typically calculated using quadratic trinomials:

[0105] ω0=a+bv+cv 2

[0106] The train is also subject to other additional resistances during operation as follows:

[0107] (1) Additional resistance on the slope

[0108] Slope resistance is caused by the slope of the railway line and is related to the weight of the train. When a train travels on a slope, the slope creates additional resistance. When traveling uphill, the weight of the train opposes the direction of its motion, increasing the slope resistance and hindering its progress. At this point, the train must overcome the force of gravity to maintain or accelerate its motion. The formula for calculating unit slope resistance is as follows:

[0109] ω g =i

[0110] Where:ω g =i is the additional resistance per unit slope, i is the slope in thousandths.

[0111] (2) Curve additional resistance

[0112] When a train travels on a curved track, in addition to the normal resistance encountered during straight-line travel, it also encounters additional resistance, known as curve drag. This additional resistance is primarily due to the following factors: sliding friction between the wheel and rail, friction between the wheel flange and the rail, and friction between bearings and other components. The empirical formula for calculating unit curve drag is as follows:

[0113]

[0114] Where:ωr is the additional resistance per unit ramp, and r is the curve radius.

[0115] (3) Tunnel additional resistance

[0116] Tunnel additional resistance refers to the additional air resistance caused by the confinement of a tunnel and the restricted air flow. Compared to traveling on open roads, air flow is compressed and restricted in tunnels, increasing air resistance. The formula for calculating unit tunnel additional resistance is as follows:

[0117] ω s =0.000013L

[0118] Where:ω s is the additional resistance per unit tunnel, and L is the tunnel length.

[0119] Construct a multi-mass point model of the train to analyze and solve the carriage extrusion displacement, including:

[0120] High-speed train carriages are connected to form a multi-body system via automatic, close-fitting couplers and buffers. During train operation, adjacent carriages interact with each other, generating significant inter-carriage forces. These forces primarily include traction, thrust, and vibration, all of which impact train stability and comfort. To study and calculate these inter-carriage forces, the complex coupler connection system is often simplified into a simple "spring-damper" system.

[0121]

[0122] Where: g i(i+1) is the coupling force between the i-th section and the i+1-th carriage, x i and x i+1 are the positions of the i-th carriage and the i+1-th carriage respectively. and are the velocities of the i-th car and the i+1-th car respectively. k is the elastic coupling coefficient of the car, and d is the damping coupling coefficient of the car.

[0123] The communication delay in the train safety envelope is related to the train speed. The positioning communication delay during train operation refers to the time lag from sending a positioning request to receiving and processing positioning information when exchanging data between the train and the ground control system or satellite navigation system. Satellite navigation receivers generally set a sampling interval of 0.1 seconds. Since satellite receivers are extremely efficient in capturing and tracking signals, the time required is only in the nanosecond range. This short process has a negligible impact on the overall time delay and can be ignored. The collected data can be used to calculate the train speed as:

[0124]

[0125] Given that the sampling interval is relatively short, we can estimate the approximate acceleration of the train during this period by using the speed values ​​measured at two consecutive sampling moments.

[0126]

[0127] After obtaining the train operation status information, its communication delay is calculated as follows:

[0128]

[0129] Example 2

[0130] This embodiment 2 provides a train integrity detection method based on a dynamic envelope constructed by a multi-particle model, refines the train integrity detection threshold, and improves the accuracy and real-time performance of the detection. The following technical solutions are adopted: first, the double-difference carrier phase equation is used and Kalman filtering is used to detect the train length; secondly, a train dynamic safety envelope is constructed, and a train multi-particle model is constructed by studying the longitudinal force analysis such as the resistance of the high-speed train and the vehicle interaction force. PID control is used to analyze and solve the carriage extrusion displacement during the train operation. The dynamic safety envelope of the train is composed of communication delay and redundancy threshold to serve as the judgment basis for train integrity detection. Finally, a train integrity detection evaluation and verification is performed through decoupling simulation to simulate the train integrity detection time after decoupling at different speeds and different carriages.

[0131] The double-difference carrier phase equation used can eliminate the influence of satellite clock error, receiver clock error, and ionospheric delay error and tropospheric delay error during the propagation process. During the propagation process, the first and last receivers of the train observe the same satellite at the same time, and their observation equation is:

[0132]

[0133] where φ is the carrier phase measurement; λ is the wavelength of the carrier signal; ρ is the geometric distance from the satellite to the receiver; I is the ionospheric delay error; T is the tropospheric delay error; f is the carrier frequency; δt is the receiver clock error; dt is the satellite clock error; N is the integer ambiguity; ε is the residual error; i is the i-th satellite; 1 and 2 represent the receiver at the end of the train and the receiver at the beginning of the train, respectively.

[0134] By performing a differential in the above equation and performing another differential for a different observation satellite j, we can not only eliminate the errors in the propagation path such as the troposphere and ionosphere, but also offset the errors related to the receiver. The resulting double difference equation is as follows:

[0135]

[0136] At the same time, the coordinates of the receiver are [x1y1z1], and the coordinates of the satellite are [x i y i z i ], then the distance between the receiver and the satellite is:

[0137]

[0138] Because the train length (typically a few hundred meters) is very small compared to the distance between the satellite and the antenna (approximately 20,000 kilometers), the calculation can be simplified: the position of the first antenna can be used as an approximate reference point for the position of the last antenna. Specifically, the position of the first antenna can be used as the initial value of the Taylor expansion of the position of the last antenna, and the distance from the satellite to the first antenna can be used to approximate the distance from the satellite to the last antenna. The relative position of the two antennas is expressed by the deviation term in the Taylor expansion:

[0139]

[0140]

[0141] Substituting the above formula into the observation equation, it can be written as:

[0142]

[0143] Where,

[0144] Furthermore, the train integrity detection envelope is calculated. The train integrity detection safety envelope refers to the dynamic spatial range set during train operation to ensure train safety and detect train integrity. During the process of ensuring train integrity, the change in train length is always within the safety envelope. Once the train decouples and its length exceeds the safety envelope, an alarm is immediately issued and the train integrity is lost. The envelope is composed as follows:

[0145] L safe =L ref +L delay +L η

[0146] Where, L ref is the expansion and contraction distance of the carriage under the multi-mass point modeling solution, L delay is the communication delay equivalent distance, L η is the redundancy threshold, and considering the actual distance between the receiver and the front and rear of the train, it is set to 10m in this embodiment.

[0147] The telescopic displacement of the train carriage is obtained by PID control to simulate the actual stress conditions during train operation.

[0148] Predictive control is used to predict the train speed at the next moment. Based on the predicted speed, the control amount at the current moment is adjusted to reduce the speed tracking error.

[0149] The predicted speed at time k is obtained through the prediction model, and feedback correction is performed with the speed obtained through the solution. The speed after feedback correction can be obtained through the feedback correction link. The input of the PID controller is the error e between the target speed and the corrected predicted speed, and the output of the controller is u. The control process is as follows:

[0150]

[0151] Where K p , K i , K d are the proportional gain, integral gain and differential gain of the controller respectively.

[0152] The state transfer equation of the train multi-mass model is as follows

[0153] X k+1 =AX(k)+BU(k)

[0154] The displacement difference between adjacent carriages [x ref1 x ref2 ...x ref(n-1) ] Τ as a state variable.

[0155] The prediction model is expressed as:

[0156] X(k+1|k)=AX(k|k)+BU(k|k)

[0157] X(k+2|k)=AX(k+1|k)+BU(k+1|k)=

[0158] A 2 X(k|k)+ABU(k|k)+BU(k+1|k)

[0159] …

[0160]

[0161] Where N p is the prediction step length, X(k) is the system state of the train at time k, and U is the control quantity

[0162] Feedback correction is used to improve the control performance of a system during operation. In this method, the system continuously monitors the error between the actual output and the expected output and adjusts the controller's behavior based on this error. Specifically, each time the system is running, an error value is calculated and fed back to the controller, which then adjusts the prediction model to make the next prediction more accurate and the control performance closer to the expected level. The corrected speed is as follows:

[0163] y r (k) = y f (k)+λe(k)

[0164] Where y r (k) is the corrected velocity, y f (k) is the predicted velocity, λ is the correction vector, and e(k) is the error vector.

[0165] By analyzing the stress conditions of the train during actual operation, using the prediction model and feedback correction to adjust the input of the system for the next epoch, and updating the state variables through the multi-particle model, the real-time relative displacement of the train carriages at each epoch during operation is obtained, which is used for subsequent train safety envelope and train integrity detection.

[0166] In summary, the online train integrity detection method based on the dynamic safety envelope provided in this embodiment first uses the double-difference carrier phase to detect the train length; secondly, the train dynamic safety envelope is constructed, and the longitudinal force analysis such as the resistance of the high-speed train and the vehicle-to-vehicle force is studied to construct a multi-particle model of the train. PID control is used to analyze and solve the carriage extrusion displacement during the train operation. The dynamic safety envelope of the train is composed of communication delay and redundant threshold, which serves as the judgment basis for train integrity detection, which can improve the efficiency and real-time performance of train integrity detection.

[0167] Example 3

[0168] This embodiment 3 provides a method for online train integrity detection based on a dynamic safety envelope, comprising the following steps:

[0169] Step 1: Collect the original data information of the column head and column tail positioning units and pre-process them.

[0170] Step 2: Use the double-difference carrier phase equation to solve the positioning problem. After obtaining the relative position of the two baselines, solve the train length L for each epoch during the train operation.

[0171]

[0172] Step 3: Construct a multi-mass point model of the train, use the controller to solve the carriage displacement during the train operation, and construct the train integrity detection safety envelope based on its communication delay at different epochs.

[0173] L safe =L ref +L delay +L η

[0174] Step 4: Compare and judge, compare with the known safety envelope and make a train integrity judgment decision.

[0175] During the propagation of satellite signals, the first and last receivers in the train observe the same satellite at the same time, and the observation equation is:

[0176]

[0177] where φ is the carrier phase measurement; λ is the wavelength of the carrier signal; ρ is the geometric distance from the satellite to the receiver; I is the ionospheric delay error; T is the tropospheric delay error; f is the carrier frequency; δt is the receiver clock error; dt is the satellite clock error; N is the integer ambiguity; ε is the residual error; i is the i-th satellite; 1 and 2 represent the receiver at the end of the train and the receiver at the beginning of the train, respectively.

[0178] By performing a differential in the above equation and performing another differential for a different observation satellite j, we can not only eliminate the errors in the propagation path such as the troposphere and ionosphere, but also offset the errors related to the receiver. The resulting double difference equation is as follows:

[0179]

[0180] At the same time, the coordinates of the receiver are [x1y1z1], and the coordinates of the satellite are [x i y i z i ], then the distance between the receiver and the satellite is:

[0181]

[0182] Because the train length (typically a few hundred meters) is very small compared to the distance between the satellite and the antenna (approximately 20,000 kilometers), the calculation can be simplified: the position of the first antenna can be used as an approximate reference point for the position of the last antenna. Specifically, the position of the first antenna can be used as the initial value of the Taylor expansion of the position of the last antenna, and the distance from the satellite to the first antenna can be used to approximate the distance from the satellite to the last antenna. The relative position of the two antennas is expressed by the deviation term in the Taylor expansion:

[0183]

[0184]

[0185] Substituting the above formula into the observation equation, it can be written as:

[0186]

[0187] Where,

[0188] According to the measurement equation of double differential load phase, the system equation and measurement equation of Kalman filter model are established;

[0189] System model of Kalman filter: X k =AX k-1 +W k Where, X k is the state vector of the system; A is the state transfer matrix; W k is the process noise of the system.

[0190] The state matrix of the system includes the relative position, relative velocity and double-difference integer ambiguity of the three coordinate axes in the Earth-centered Earth-fixed coordinate system:

[0191]

[0192] Where, Denote the relative rates in the three directions and assume they obey a first-order Markov distribution; is the matrix of double-difference integer ambiguities.

[0193] State transition matrix:

[0194]

[0195] Wherein the time interval ΔT = 0.1s.

[0196] The noise matrix of the system can be written as:

[0197]

[0198] Where τ is the correlation time.

[0199] Measurement model of the system: Z k =H k X k +V k Where Z k is the observation vector of the system; H k is the measurement matrix; V k is the measurement noise, which is assumed to be Gaussian white noise, and its covariance matrix is ​​R.

[0200] The observation vector of the system:

[0201] The measurement matrix of the system:

[0202]

[0203] Noise covariance matrix:

[0204]

[0205] Where the parameter σ is the standard deviation of the L1 carrier noise, and its value is 0.0008m.

[0206] Furthermore, the train integrity detection envelope is calculated. The train integrity detection safety envelope refers to the dynamic spatial range set during train operation to ensure train safety and detect train integrity. During the process of ensuring train integrity, the change in train length is always within the safety envelope. Once the train decouples and its length exceeds the safety envelope, an alarm is immediately issued and the train integrity is lost. The envelope is composed as follows:

[0207] L safe =L ref +L delay +L η

[0208] Where, L ref is the expansion and contraction distance of the carriage under the multi-mass point modeling solution, L delay is the communication delay equivalent distance, L η is the redundancy threshold, considering the actual distance between the receiver and the front and rear of the train, which is taken as 10m in this paper.

[0209] The telescopic displacement of the train carriage is obtained by PID control to simulate the actual stress conditions during train operation.

[0210] Predictive control is used to predict the train speed at the next moment. Based on the predicted speed, the control amount at the current moment is adjusted to reduce the speed tracking error.

[0211] The predicted speed at time k is obtained through the prediction model, and feedback correction is performed with the speed obtained through the solution. The speed after feedback correction can be obtained through the feedback correction link. The input of the PID controller is the error e between the target speed and the corrected predicted speed, and the output of the controller is u. The control process is as follows:

[0212]

[0213] Where K p , K i , K d are the proportional gain, integral gain and differential gain of the controller respectively.

[0214] The state transfer equation of the train multi-mass model is as follows

[0215] X k+1=AX(k)+BU(k)

[0216] The displacement difference between adjacent carriages [x ref1 x ref2 ...x ref(n-1) ] Τ as a state variable.

[0217] The prediction model is expressed as:

[0218] X(k+1|k)=AX(k|k)+BU(k|k)

[0219] X(k+2|k)=AX(k+1|k)+BU(k+1|k)=

[0220] A 2 X(k|k)+ABU(k|k)+BU(k+1|k)

[0221] …

[0222]

[0223] Where N p is the prediction step length, X(k) is the system state of the train at time k, and U is the control quantity

[0224] Feedback correction is used to improve the control performance of a system during operation. In this method, the system continuously monitors the error between the actual output and the expected output and adjusts the controller's behavior based on this error. Specifically, each time the system is running, an error value is calculated and fed back to the controller, which then adjusts the prediction model to make the next prediction more accurate and the control performance closer to the expected level. The corrected speed is as follows:

[0225] y r (k) = y f (k)+λe(k)

[0226] Where y r (k) is the corrected velocity, y f (k) is the predicted velocity, λ is the correction vector, and e(k) is the error vector.

[0227] By analyzing the stress conditions of the train during actual operation, using the prediction model and feedback correction to adjust the input of the system for the next epoch, and updating the state variables through the multi-particle model, the real-time relative displacement of the train carriages at each epoch during operation is obtained, which is used for subsequent train safety envelope and train integrity detection.

[0228] Example 4

[0229] This embodiment 4 provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the train integrity detection method based on the dynamic envelope constructed by the multi-particle model as described above is implemented. The method includes:

[0230] A carrier phase double-difference model is constructed to eliminate the clock errors of the satellite and receiver, as well as the delay errors in the ionosphere and troposphere along the propagation path; the real-time length of the baseline is estimated using Kalman filtering;

[0231] PID control is used to analyze and calculate the expansion and contraction displacement of carriages during train operation;

[0232] Construct a train dynamic safety envelope including telescopic displacement, communication delay, and redundancy threshold as the basis for train integrity detection;

[0233] The calculated baseline length is compared with the train safety envelope, and based on the comparison result, it is determined whether the train integrity status is normal; if it is within the range of the safety envelope, the train integrity status is normal; otherwise, if it exceeds the value of the safety envelope, the integrity status is abnormal.

[0234] Example 5

[0235] This embodiment 5 provides a computer device, including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the train integrity detection method based on the multi-particle model to construct a dynamic envelope, the method comprising:

[0236] A carrier phase double-difference model is constructed to eliminate the clock errors of the satellite and receiver, as well as the delay errors in the ionosphere and troposphere along the propagation path; the real-time length of the baseline is estimated using Kalman filtering;

[0237] PID control is used to analyze and calculate the expansion and contraction displacement of carriages during train operation;

[0238] Construct a train dynamic safety envelope including telescopic displacement, communication delay, and redundancy threshold as the basis for train integrity detection;

[0239] The calculated baseline length is compared with the train safety envelope, and based on the comparison result, it is determined whether the train integrity status is normal; if it is within the range of the safety envelope, the train integrity status is normal; otherwise, if it exceeds the value of the safety envelope, the integrity status is abnormal.

[0240] Example 6

[0241] This embodiment 6 provides an electronic device, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the train integrity detection method based on a multi-particle model to construct a dynamic envelope, as described above. The method includes:

[0242] A carrier phase double-difference model is constructed to eliminate the clock errors of the satellite and receiver, as well as the delay errors in the ionosphere and troposphere along the propagation path; the real-time length of the baseline is estimated using Kalman filtering;

[0243] PID control is used to analyze and calculate the expansion and contraction displacement of carriages during train operation;

[0244] Construct a train dynamic safety envelope including telescopic displacement, communication delay, and redundancy threshold as the basis for train integrity detection;

[0245] The calculated baseline length is compared with the train safety envelope, and based on the comparison result, it is determined whether the train integrity status is normal; if it is within the range of the safety envelope, the train integrity status is normal; otherwise, if it exceeds the value of the safety envelope, the integrity status is abnormal.

[0246] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0247] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0248] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0249] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide the functions for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0250] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solutions disclosed in the present invention without the need for creative work should be included in the scope of protection of the present invention.

Claims

1. A train integrity detection method based on a dynamic envelope constructed using a multi-particle model, characterized in that: include: Construct a carrier phase double-difference model to eliminate the clock errors of the satellite and receiver, as well as the delay errors in the ionosphere and troposphere along the propagation path; Use Kalman filtering to estimate the real-time length of the baseline; PID control is used to analyze and calculate the expansion and contraction displacement of carriages during train operation; Construct a train dynamic safety envelope including telescopic displacement, communication delay, and redundancy threshold as the basis for train integrity detection; The calculated baseline length is compared with the train safety envelope, and based on the comparison result, it is determined whether the train integrity status is normal; if it is within the range of the safety envelope, the train integrity status is normal; otherwise, if it exceeds the value of the safety envelope, the integrity status is abnormal.

2. The train integrity detection method based on dynamic envelope construction of a multi-particle model according to claim 1 is characterized in that: The method of using double-difference carrier phase to eliminate the clock errors of the satellite and the receiver, and the delay errors of the ionosphere and the troposphere on the propagation path includes the following steps: Establish the carrier phase observation equations at the beginning and end of the column during the propagation process; The carrier phases between the first and last antennas in the train are time-aligned and double-differenced to eliminate the clock errors of the satellite and receiver as well as the delays in the ionosphere and troposphere along the propagation path. The position of the first antenna is regarded as the initial value of the Taylor expansion of the position of the last antenna, and the distance from the satellite to the first antenna is used to approximately replace the distance from the satellite to the last antenna. The relative positions of the two antennas are expressed by the deviation term in the Taylor expansion.

3. The train integrity detection method based on dynamic envelope construction of a multi-particle model according to claim 2, characterized in that: The double-difference carrier phase error elimination method comprises the following steps: Perform inter-station differential between the reference station and the mobile station to eliminate errors related to the satellite and propagation path, including satellite clock errors, ionospheric and tropospheric delay errors; Assuming that the two receivers observe another satellite at the same time, the single difference equation of the carrier phase is obtained; Based on the inter-station difference, the satellite with the best elevation angle is selected as the reference satellite, and the carrier phases corresponding to other satellites are subtracted from the carrier phase of the reference satellite to eliminate the errors related to the receiver.

4. The train integrity detection method based on dynamic envelope construction of a multi-particle model according to claim 2, characterized in that: According to the measurement equation of double-difference carrier phase, the system equation and measurement equation of the Kalman filter model are established, including: the state matrix of the Kalman filter system includes the relative position, relative velocity and double-difference integer ambiguity of the three coordinate axes in the Earth-centered Earth-fixed coordinate system; determine the state transfer matrix and the noise matrix of the system; obtain the measurement model of the system, calculate the observation vector of the system and the measurement matrix of the system, and construct the noise covariance matrix.

5. The train integrity detection method based on dynamic envelope construction of a multi-particle model according to claim 1 is characterized in that: PID control is used to construct a multi-particle model of the train to analyze and solve the squeezing displacement of the carriages during the operation of the train, including the solution of telescopic displacement. The resistance existing in the train operation includes basic resistance, slope-added resistance, curve-added resistance and tunnel-added resistance; basic resistance includes wheel-rail friction, air resistance and bearing friction; slope-added resistance is caused by the slope of the railway line and is related to the gravity of the train; when the train is running on a slope, the slope will generate additional resistance to the train; when going uphill, the gravity of the train will be opposite to the direction of the train's movement, resulting in an increase in slope-added resistance, which hinders the train's progress; the generation of curve-added resistance is related to the sliding friction between the wheel and rail, the friction between the wheel flange and the rail, and the friction between the bearings and other components; tunnel-added resistance refers to the additional air resistance caused by the relatively closed tunnel space and restricted air flow when the train is running in the tunnel.

6. The train integrity detection method based on dynamic envelope construction of a multi-particle model according to claim 1, characterized in that: The communication delay in the train safety envelope is related to the train speed. The positioning communication delay during train operation refers to the time lag between sending a positioning request and receiving and processing positioning information when exchanging data between the train and the ground control system or satellite navigation system. The train speed is calculated based on the collected time delay data. The approximate acceleration of the train during this period is estimated by using the speed values ​​measured at two consecutive sampling moments; after obtaining the train operation status information, the communication delay is calculated.

7. A train integrity detection system based on a dynamic envelope constructed using a multi-particle model, characterized in that: include: The first building block is used to construct a carrier phase double-difference model to eliminate the clock errors of the satellite and receiver, as well as the delay errors of the ionosphere and troposphere on the propagation path; and use Kalman filtering to estimate the real-time length of the baseline; The analysis and settlement module is used to analyze and calculate the expansion and contraction displacement of the carriages during train operation using PID control; The second building block is used to construct a train dynamic safety envelope including telescopic displacement, communication delay and redundancy threshold as the basis for train integrity detection; The judgment module is used to compare the calculated baseline length with the train safety envelope and determine whether the train integrity status is normal based on the comparison result. If it is within the range of the safety envelope, the train integrity status is normal; otherwise, if it exceeds the value of the safety envelope, the integrity status is abnormal.

8. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the train integrity detection method based on the dynamic envelope constructed by the multi-particle model as described in any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: It includes a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the train integrity detection method based on the dynamic envelope constructed by the multi-particle model as described in any one of claims 1 to 6.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to execute instructions for implementing the train integrity detection method based on a multi-particle model to construct a dynamic envelope as described in any one of claims 1 to 6.

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