Train axle magnetic steel trigger signal mathematical modeling simulation method, system and device

Abstract

This invention discloses a mathematical modeling and simulation method, system, and device for train wheel axle magnet triggering signals: It acquires actual operating data of the magnets and wheel axles; based on preset probability triggering and trigger count relationships, it fits and solves the unknown parameters of the model to obtain the probability triggering model and the trigger count model; it sets different adjustable parameters, including train speed, total number of train wheel axles, number of magnets, magnet sensitivity, and installation parameters of each magnet; using the probability triggering model and the trigger count model, and substituting the adjustable parameters, it simulates the wheel axle magnet triggering process under different operating conditions, obtaining the magnet number, magnet triggering probability, and trigger count at different timestamps under each operating condition, and generating simulation results of the magnet triggering signal including normal triggering and abnormal states such as missed detection and false detection. This invention simulates the normal signal and abnormal signals such as missed detection and false detection of the wheel axle triggering the magnet by quantifying and characterizing the physical laws and interference factors of the wheel axle-magnet interaction.

Description

Technical Field

[0001] This invention relates to magnet triggering simulation technology, and more specifically, to a mathematical modeling simulation method, system, and device for train wheel axle magnet triggering signals. Background Technology

[0002] In the development and application of trackside inspection equipment, the magnetic steel sensor, as a core sensing component, outputs a trigger signal that is crucial for realizing functions such as train arrival detection, speed measurement, and train number association. The triggering principle of the magnetic steel sensor is as follows: when a train axle passes over the magnet, the axle causes a change in the distribution of magnetic field lines within the magnet, which in turn generates an electrical signal. The accuracy of this signal directly affects the overall performance of the trackside inspection equipment.

[0003] However, trackside inspection equipment faces significant limitations in its testing environment during the development and testing phases: the actual train passing scenario is difficult to reproduce, and the real physical process of a wheel axle passing over a magnet cannot be simulated under laboratory conditions. This prevents the early detection of common anomalies in magnet signals during practical applications—most notably missed detections and false detections. Missed detections manifest as no trigger signal generated when the wheel axle passes over the magnet, often caused by factors such as uneven wheel axle material or magnet installation deviations. False detections, on the other hand, manifest as multiple consecutive trigger signals generated during a single wheel axle pass, typically related to environmental factors such as magnetic field interference and signal transmission noise. These problems occur frequently in actual operation, but due to the lack of corresponding simulation testing methods, they are difficult to detect and resolve during the development phase, significantly increasing the commissioning costs and failure risks after the equipment is put into use.

[0004] To address the lack of a suitable testing environment, existing technologies have developed simulation devices. For example, the utility model patent "A Device for Simulating Train Passing Magnet Signals" discloses a simulation device whose control unit can control the signal generation unit based on external commands or preset parameters, thereby simulating active and passive magnet signals. Specifically, parameters can be input via a matrix keypad. After decoding by a comprehensive signal processing circuit, these parameters control the active and passive magnet simulators to generate corresponding simulated signals, which are then output through a signal output interface. Simultaneously, the control unit also controls the tag signal generation unit to generate tag signals with unique car numbers based on information stored in the car number memory, and transmits these signals through an antenna interface. The core advantage of this device is its ability to simulate signals related to various equipment, covering signals corresponding to multiple magnet types and tag information. Its signal triggering depends on a preset sequence, allowing it to be used to test the ability of trackside inspection equipment to receive standard signals.

[0005] However, the aforementioned simulation devices also have significant limitations: their core function is to verify the basic receiving performance of the equipment through trigger signals in a preset sequence. Essentially, they remain "equipment testing tools," only able to determine whether the equipment is receiving standard signals normally. They cannot simulate the abnormal signal characteristics of missed or false detections caused by physical interference, equipment aging, and other factors in actual applications. Therefore, such devices cannot meet the core requirements of trackside detection equipment development—namely, to reproduce abnormal signal phenomena in real-world scenarios and assist developers in optimizing signal processing algorithms and improving the equipment's anti-interference capabilities during the design phase.

[0006] In summary, existing technologies lack a method to accurately simulate the true characteristics of train wheel axle-magnet trigger signals (including normal signals and abnormal signals such as missed detections and false detections). This makes it difficult for trackside inspection equipment to handle signal anomalies in practical applications during the development phase. Therefore, there is an urgent need to construct a simulation method based on mathematical modeling. This method should quantify the physical laws and interference factors of the wheel axle-magnet interaction to generate realistic magnet signals (including normal and abnormal states), providing effective simulation testing support for the development of trackside inspection equipment. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and propose a mathematical modeling simulation method, system and equipment for train wheel axle magnet triggering signals. By quantitatively characterizing the physical laws and interference factors of the wheel axle-magnet interaction, it generates a magnet signal (including normal and abnormal states) that closely resembles reality. It can accurately simulate the real characteristics of train wheel axle triggering magnet signals (including normal signals and abnormal signals such as missed detection and false detection), providing effective simulation test support for the development of trackside detection equipment.

[0008] The objective of this invention can be achieved through the following technical solutions.

[0009] A mathematical modeling and simulation method for triggering signals of train wheel axle magnets includes the following steps: S1 Model Construction: Obtain actual operating data of magnets and wheel axles in the rail section where the trackside detection equipment is located. Based on preset probability triggering and trigger count relationships, use the data to fit and solve for unknown parameters of the model, thus obtaining the probability triggering model and the trigger count model. The probability triggering model is used to quantitatively characterize the physical laws of wheel axle-magnet interaction and the correlation between interference factors and trigger probability. The trigger count model is used to quantitatively characterize the physical laws of wheel axle-magnet interaction and the correlation between interference factors and trigger count. S2 Working Condition Simulation: Different adjustable parameters are set, including train running speed, total number of train axles, number of magnets, magnet sensitivity, and installation parameters of each magnet. Using the constructed probability triggering model and trigger count model, the adjustable parameters are substituted to simulate the triggering process of train axle magnets under different working conditions. The magnet number, magnet triggering probability, and trigger count are obtained at different timestamps under each working condition. The simulation results of magnet triggering signals including normal triggering, missed detection, and false detection are generated.

[0010] Furthermore, the specific process of step S1 is as follows: S11. At least five sets of actual operating data for the magnets and axles were obtained through actual measurements. Each set of data included the magnet installation parameters and the three-dimensional spatial coordinates of the axle. The measured trigger probability and number of triggers, and the magnet installation parameters include the three-dimensional spatial coordinates of the magnet installed on the rail. and the tilt angle of the magnet ;in, For the first The coordinates of the installation position of the magnet along the rail direction. For the first The lateral installation distance of each magnet For the first The height difference of the magnets For the train Carriage No. At time 10:00 Coordinates along the direction of the railway track, For the train Carriage No. The coordinates of the wheel axle center along the horizontal and vertical direction of the rail. For the train Carriage No. The coordinates of the wheel axle center along the direction perpendicular to the rail; (1), In the formula, For train speed, For the train The first carriage The distance between the axle and the preceding axle in the same carriage. For the train The first carriage The distance between the axle and the preceding axle in the same carriage. Indicates from the front of the car to the... The total distance between all axles of the carriage, Indicates the first From the first pair of carriages to the second pair The sum of the distances between the wheel axles; S12, based on the following trigger probability relationship and trigger count relationship, the height difference weighting coefficient is obtained by fitting the actual operating data of the magnet mentioned above. Lateral deviation weighting coefficient Tilt angle weighting coefficient Distance deviation weighting coefficient coefficient of the cubic term ; (2), (3) In the formula, For the train Carriage No. The axle passes through the first The trigger probability when there is a single magnet. For the train Carriage No. The axle passes through the first Number of triggers when there is one magnet; For the first Lateral installation distance deviation of each magnet; For the first The tilt angle of each magnet; For the train Carriage No. The gear axle and the first The normalized spatial distance deviation between individual magnets; where... (4), (5), (6), In the formula, The preset optimal lateral installation distance for the magnets. The preset optimal three-dimensional spatial distance between the wheel axle and the magnet. For the train Carriage No. At time of the wheel axle With the The three-dimensional spatial distance between the magnets; S13, the height difference weighting coefficients obtained above Lateral deviation weighting coefficient Tilt angle weighting coefficient Distance deviation weighting coefficient coefficient of the cubic term Substituting these back into the trigger probability equation and the trigger count equation, we obtain the probability trigger model and the trigger count model.

[0011] Furthermore, in step S2, the simulation of the train wheel axle magnet triggering process under one working condition is completed by following the steps S21~S28. The same steps can be used to simulate the train wheel axle magnet triggering process under different working conditions: S21 sets fixed and adjustable parameters. The fixed parameters include the total number of train axles (L) and the number of magnets (C). The adjustable parameters include the train speed. The magnet sensitivity T and the installation parameters of each magnet, including the three-dimensional spatial coordinates of the magnets installed on the rail. and the tilt angle of the magnet ; S22 records the current time t, and substitutes t into the relationship between wheel axle and time (1) to obtain the time of each pair of wheel axles in each carriage. Coordinates along the railway track ; S23 Judgment and If the real-time values ​​are equal, proceed to step S24; otherwise, proceed to step S22, update the current time to t+0.01 seconds and loop. S24 will and Substituting into the three-dimensional spatial distance calculation formula (6) between the magnet and the wheel axle, we obtain the time of each pair of wheel axles in each carriage at time... Three-dimensional spatial distance between each magnet The normalized spatial distance deviation between each pair of wheel axles and each magnet in each car section is further calculated using formula (5). ; S25 uses formula (4) to obtain the lateral installation distance deviation of each magnet. ,Will , , Substituting into the probability triggering model (2), we obtain the triggering probability when each pair of wheel axles of each carriage passes each magnet. ; S26 will Substituting into the trigger count model (3), we obtain the trigger count when each pair of wheel axles of each car passes each magnet. ; S27 Judgment If the value is 0, proceed to step S22, update the current time to t+0.01 seconds and loop; otherwise, obtain the corresponding timestamp and the triggered magnet number. S28 determines whether the time difference between the timestamp obtained in step S27 and the timestamp of the previous record is greater than the magnet sensitivity. If it is greater, the data set is retained; otherwise, the data set is discarded. At the same time, the process jumps to step S22, updates the current time to t+0.01 seconds, and repeats the loop.

[0012] The objective of this invention can also be achieved through the following technical solutions.

[0013] A mathematical modeling and simulation system for triggering signals of train wheel axle magnets includes a model building module and a working condition simulation module; Model building module: Based on the actual operating data of the magnets and wheel axles of the rail section where the trackside detection equipment is located, each set of data includes the three-dimensional spatial coordinates of the wheel axle. The three-dimensional spatial coordinates of the magnet installed on the rail tilt angle of the magnet And the actual trigger probability Trigger count Using preset trigger probability and trigger frequency formulas, the unknown parameters of the model are solved by fitting the data. , , , , Substitute these back into the above-mentioned trigger probability and trigger count relationships to recall the probability trigger model and trigger count model. The probability trigger model is used to quantify the physical laws governing the wheel-axle-magnet interaction and the correlation between interference factors and trigger probability. The trigger count model is used to quantify the physical laws governing the wheel-axle-magnet interaction and the correlation between interference factors and trigger count. Operating condition simulation module: Different adjustable parameters are set, including train running speed, total number of train axles, number of magnets, magnet sensitivity, and installation parameters of each magnet. Using the constructed probability triggering model and trigger count model, the adjustable parameters are substituted to simulate the triggering process of train axle magnets under different operating conditions. The magnet number, magnet triggering probability, and trigger count are obtained at different timestamps under each operating condition. The simulation results of magnet triggering signals, including normal triggering, missed detection, and false detection abnormal states, are generated.

[0014] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described mathematical modeling and simulation method for triggering signals of train wheel axle magnets.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described mathematical modeling and simulation method for triggering signals of train wheel axle magnets.

[0016] Compared with the prior art, the beneficial effects of the technical solution of the present invention are: (1) Existing technologies pre-set certain magnetic signals, and their actual triggering sequence and timing are artificially preset, lacking the possibility of missed or false detections of the magnetics. This invention, based on existing technologies, adds more dimensions of settings, such as the installation parameters of the magnetics, the probability model of the wheel axle triggering the magnetics, and the geometric model of the magnetic detection range. In mathematical modeling, this invention, based on existing technologies, incorporates parameters that affect magnetic triggering in practical applications, constructing a model of the magnetic detection range and a triggering probability model. By adjusting magnetic parameters and magnetic sensitivity, the missed and false detection rates of the magnetics can be increased or decreased.

[0017] (2) The final recorded data of this invention is a list with a timestamp and the serial number of the magnet triggered at that timestamp. The final data is transmitted to the control software of the trackside detection equipment, which allows the trackside equipment to perform software debugging and R&D testing without a field environment.

[0018] (3) This invention includes mathematical modeling and software components, and the output is a simulated magnetic trigger signal with a timestamp. This invention fits a function of the magnetic trigger probability to multiple sets of data under actual conditions through mathematical modeling. Based on this function, it simulates the trigger data of a train passing through the magnetic steel more realistically. This data is beneficial for the software development and functional testing of trackside detection equipment. Attached Figure Description

[0019] Figure 1 This is a flowchart of a single simulation of the triggering process of the train wheel axle magnet in this invention. Detailed Implementation

[0020] The present invention will now be further described with reference to the accompanying drawings.

[0021] The mathematical modeling and simulation method for train wheel axle magnet triggering signals of the present invention mainly includes the following two parts: model construction and working condition simulation: S1: Model Building The model construction process assumes the following: During train operation, the wheel axle remains rigid and runs smoothly on the rails, without considering changes in the wheel axle's running posture due to wear, deformation, etc.; the magnet's installation position is fixed and will not be displaced due to train vibration, environmental factors, etc., but a set height difference, tilt angle, and lateral installation distance deviation are allowed during installation; the trigger probability of this model only considers the height difference, tilt angle, lateral installation distance, and spatial distance between the wheel axle and the magnet, without considering other interference factors, such as electromagnetic interference, the influence of ambient temperature and humidity on the magnet's performance, etc.

[0022] The actual operating data of the magnets and wheel axles in the rail section where the trackside detection equipment is located are obtained. Based on preset probability triggering and trigger frequency relationships, the unknown parameters of the model are solved using the data fitting, resulting in a probability triggering model and a trigger frequency model. The probability triggering model is used to quantify the physical laws governing the wheel axle-magnet interaction and the correlation between interference factors and the triggering probability. The trigger frequency model is used to quantify the physical laws governing the wheel axle-magnet interaction and the correlation between interference factors and the trigger frequency. The specific process is as follows: S11. At least five sets of actual operating data for the magnets and axles were obtained through actual measurements. Each set of data included the magnet installation parameters and the three-dimensional spatial coordinates of the axle. The measured trigger probability and number of triggers, and the magnet installation parameters include the three-dimensional spatial coordinates of the magnet installed on the rail. and the tilt angle of the magnet .in, The axis is the direction of the rail (the direction of train travel), with the starting position of the train's first wheel axle as the zero coordinate. The axis is horizontal and perpendicular to the direction of the rail (pointing towards the inside of the rail is positive), with the inner surface of the rail as the zero coordinate. The axis is vertical (positive upwards), with the height of the rail surface as the zero coordinate. For the first A magnet along the direction of the rail ( The installation position coordinates of the shaft; For the first The lateral installation distance of the first magnet indicates the lateral installation distance of the first magnet. The distance from the center of each magnet to the side of the rail in the y-axis direction is used to describe the installation of the magnet in the lateral position. For the first The height difference of the first magnet indicates the height difference of the second magnet. The height difference between the upper surface of the magnet and the upper surface of the rail reflects the positional deviation of the magnet in the vertical direction. Let be the magnet attitude parameters, representing the first . The tilt angle of each magnet along the horizontal direction perpendicular to the rail (y-axis) reflects the tilt of the magnet in the horizontal plane during installation. For the train Carriage No. At time 10:00 Coordinates along the direction of the railway track, For the train Carriage No. The position coordinates of the wheel axle along the horizontal and vertical direction of the rail. For the train Carriage No. The coordinates of the position of the wheel axle center along the direction perpendicular to the rail. It changes over time. The y-axis is approximately a straight line in front of the rack, without any bends, therefore... The default value is constant and does not change over time. It does not change with time and is a fixed value by default. The following formula (1) is the relationship between the wheel and axle and time, denoted as S-form.

[0023] (1) In the formula, For runtime, The train's speed is also the speed of the wheel and axle, which moves at a constant linear speed in the horizontal direction (x-axis). For the train The first carriage The distance between the axle and the preceding axle in the same carriage. For the train The first carriage The distance between the axle and the preceding axle in the same carriage. Indicates from the front of the car to the... The total distance between all axles of the carriage, Indicates the first From the first pair of carriages to the second pair The sum of the distances between the wheel axles.

[0024] S12, based on the following trigger probability relationship and trigger count relationship, the height difference weighting coefficient is obtained by fitting the actual operating data of the magnet mentioned above. Lateral deviation weighting coefficient Tilt angle weighting coefficient Distance deviation weighting coefficient coefficient of the cubic term .

[0025] Trigger probability Height difference with magnet Inclination angle Horizontal installation distance The three-dimensional spatial distance between the wheel axle and the magnet Closely related, the following trigger probability relationship is constructed, denoted as P: (2) In the formula, For the train Carriage No. The axle passes through the first The trigger probability when there is a single magnet; For the first The lateral installation distance deviation of each magnet is used to measure the deviation between the actual lateral installation distance of the magnet and the optimal lateral installation distance. The larger the distance deviation, the higher the triggering probability may be affected. For the first The tilt angle of each magnet; For the train Carriage No. The gear axle and the first Normalized spatial distance deviation between individual magnets.

[0026] (3) (4) in, The preset optimal lateral installation distance for the magnet represents the lateral installation distance value that maximizes the trigger probability when the magnet is installed near the side of the rail. It is generally determined based on actual testing and experience. The preset optimal three-dimensional spatial distance between the wheel axle and the magnet represents the ideal three-dimensional spatial distance between the wheel axle and the magnet. At this distance, the trigger probability is relatively ideal, which is derived from experiments or theories. For the train Carriage No. At time of the wheel axle With the The three-dimensional spatial distance between the magnets is given by the following formula (5), which is the formula for the relative position of the magnets and the wheel axle, denoted as formula D.

[0027] (5) Calculate the first using the Pythagorean theorem. Carriage No. At time 10:00 With the The three-dimensional spatial distance between the centers of the magnets This is the absolute value of the deviation between the current distance and the optimal distance. Spatial distance deviation has a significant impact on the trigger probability.

[0028] The correlation between the magnet's triggering characteristics and its installation position is as follows: ① When the magnet's installation posture, height, and lateral offset all meet the optimal standards, the triggering probability is 100% and the triggering frequency is 1; ② When the magnet's installation posture, height, and lateral offset are less than the optimal standards, the triggering probability is less than 100% and the triggering frequency is less than 1; ③ When the magnet's installation posture, height, and lateral offset are greater than the optimal standards, the triggering probability is greater than 100% and the triggering frequency is greater than 1.

[0029] According to the magnet triggering model, when the magnet is installed near the optimal standard, the probability of magnet triggering changes little. However, when the magnet deviates significantly from the standard position, the probability value changes greatly with distance. Based on this situation, a triggering frequency expression is designed, Equation (6), which adopts a cubic function model and is denoted as N-form.

[0030] (6) In the formula, For the train Carriage No. The axle passes through the first The number of triggers for each magnet is a non-negative integer. When, is the baseline probability of the reference position, in units of %; coefficient The smaller the curve exist The smaller the rate of change at a given point. When the trigger probability is 100%, the trigger count is 1. In this invention, when the probability is greater than 100, the trigger count is greater than 1, which simulates a false positive; when the probability is less than 100, the trigger count is less than 1, which simulates a false negative.

[0031] S13, the height difference weighting coefficients obtained above Lateral deviation weighting coefficient Tilt angle weighting coefficient Distance deviation weighting coefficient coefficient of the cubic term Substituting these back into formulas (2) and (6), we obtain the probability triggering model and the trigger count model.

[0032] Trigger probability parameters: The weight represents the influence of the height difference on the trigger probability. The value range is usually [0, 1] and is determined through a large number of experiments or empirical data. The larger the weight, the more significant the influence of the height difference on the trigger probability. This represents the weight of the distance between the magnet and the side of the rail on the trigger probability. It takes values ​​in the interval [0, 1] and is used to measure the magnitude of the effect of the lateral distance on the trigger probability. This represents the weight of the tilt angle on the trigger probability, with a value range of [0, 1]. It reflects the degree of influence of the tilt angle on the trigger probability, and the weight is adjusted according to the actual situation. This represents the weight of the spatial distance between the wheel axle and the magnet on the trigger probability, with a value range of [0, 1], reflecting the importance of spatial distance in the trigger probability calculation.

[0033] S2: Working Condition Simulation Different adjustable parameters are set, including train running speed, total number of train axles, number of magnets, magnet sensitivity, and installation parameters of each magnet. Using the constructed probability triggering model and trigger count model, the adjustable parameters are substituted to simulate the triggering process of train axle magnets under different working conditions. The magnet number, magnet triggering probability, and trigger count are obtained at different timestamps under each working condition. The simulation results of magnet triggering signals including normal triggering, missed detection, and false detection are generated.

[0034] This invention quantifies the physical laws and interference factors of the wheel-axle-magnet interaction by constructing a probability triggering model and a trigger count model, thereby simulating normal triggering signals, missed detections, and false detections. Normal trigger signal: When the installation height difference of the magnets... Lateral installation distance deviation and tilt angle All are within the preset optimal range, and the train speed is within the optimal range. When parameters such as wheel and axle position match ideal working conditions, the model outputs the trigger probability. 100%, number of triggers This is a standard signal of 1, corresponding to the scenario where the wheel axle passes normally over the magnet.

[0035] Missed detection simulation: By increasing the installation height difference of the magnets Lateral installation distance deviation and tilt angle Alternatively, reducing the magnet sensitivity parameter can decrease the trigger probability calculated by the probability triggering model. Below 100%, number of triggers A value less than 1 indicates a missed detection scenario where no trigger signal is received when the axle passes by; the greater the installation deviation, the lower the magnet sensitivity and the higher the probability of missed detection.

[0036] False positive simulation: By adjusting the installation height difference of the magnets Lateral installation distance deviation and tilt angle Alternatively, the sensitivity parameters of the magnet can be increased to improve the triggering probability calculated by the probability triggering model. Multiple times greater than 100%, trigger count A value greater than 1 indicates a false detection scenario where multiple abnormal triggers occur when the axle passes by; the smaller the installation deviation, the higher the magnet sensitivity and the higher the probability of false detection.

[0037] The specific process is as follows: Figure 1 As shown, the simulation of the train wheel axle magnet triggering process under one working condition is completed by following the steps S21~S28. The same steps can be used to simulate the train wheel axle magnet triggering process under different working conditions: S21 sets fixed and adjustable parameters. The fixed parameters include the total number of train axles (L) and the number of magnets (C). The adjustable parameters include the train speed. The magnet sensitivity T and the installation parameters of each magnet, including the three-dimensional spatial coordinates of the magnets installed on the rail. and the tilt angle of the magnet .

[0038] S22 records the current time t. Substituting t into S, i.e. formula (1), we obtain the time of each pair of wheel axles in each carriage at time t. Coordinates along the railway track .

[0039] S23 Judgment and If the real-time values ​​are equal, proceed to step S24; otherwise, proceed to step S22, update the current time to t+0.01 seconds and loop, where 0.01 seconds is the cycle period of the timing task.

[0040] pass and By comparing the heights of the two magnets, it can be determined whether the axle has entered the detection range of the magnet. However, determining the detection range requires careful consideration of the height difference of the magnets. and tilt angle The impact on the detection range can be considered as a hollow cylinder that deforms due to height difference and tilt angle.

[0041] S24 will and Substituting into equation D, i.e., formula (5), we obtain the time of each pair of wheel axles in each car at time... Three-dimensional spatial distance between each magnet The normalized spatial distance deviation between each pair of wheel axles and each magnet in each car section was further calculated using formula (4). .

[0042] S25 uses formula (3) to obtain the lateral installation distance deviation of each magnet. ,Will , , Substituting into the probability triggering model, i.e., the above P-formula (2), we obtain the triggering probability when each pair of wheel axles of each car passes each magnet. .

[0043] S26 will Substituting into the trigger count model, i.e., the N-formula (6) above, we obtain the trigger count when each pair of wheel axles of each car passes each magnet. .

[0044] S27 Judgment If the value is 0, proceed to step S22, update the current time to t+0.01 seconds and loop; otherwise, obtain the corresponding timestamp and the triggered magnet number.

[0045] S28 determines whether the time difference between the timestamp obtained in step S27 and the timestamp of the previous record is greater than the magnet sensitivity T. If it is greater, the data set is retained; otherwise, the data set is discarded. At the same time, the process jumps to step S22, updates the current time to t+0.01 seconds, and repeats the loop.

[0046] The magnet sensitivity T is a preset value, representing the minimum interval between two consecutive triggers of the magnet, measured in milliseconds. It is adjustable; increasing the T value reduces the probability of the magnet being triggered multiple times when the wheel axle passes over it, while decreasing the T value accommodates scenarios with excessively high train speeds. The final retained data is a list with timestamps and the sequence numbers of the magnets triggered at those timestamps. Transmitting this final data to the control software of the trackside detection equipment allows for software debugging and development testing of the trackside equipment without the need for on-site testing.

[0047] Based on the above-mentioned method and process principles, this invention also proposes a mathematical modeling and simulation system for train wheel axle magnet triggering signals, including a model construction module and a working condition simulation module.

[0048] Model building module: Based on the actual operating data of the magnets and wheel axles of the rail section where the trackside detection equipment is located, each set of data includes the three-dimensional spatial coordinates of the wheel axle. The three-dimensional spatial coordinates of the magnet installed on the rail tilt angle of the magnet And the actual trigger probability Trigger count Using the preset trigger probability relationship (2) and trigger count relationship (6), the unknown parameters of the model are solved by fitting the data. , , , , Re-enter formulas (2) and (6), remembering the probability triggering model and the trigger count model; the probability triggering model is used to quantify the physical laws of the wheel axle-magnet action and the relationship between interference factors and triggering probability, and the trigger count model is used to quantify the physical laws of the wheel axle-magnet action and the relationship between interference factors and trigger count.

[0049] Operating condition simulation module: Different adjustable parameters are set, including train running speed, total number of train axles, number of magnets, magnet sensitivity, and installation parameters of each magnet. Using the constructed probability triggering model and trigger count model, the adjustable parameters are substituted to simulate the triggering process of train axle magnets under different operating conditions. The magnet number, magnet triggering probability, and trigger count are obtained at different timestamps under each operating condition. The simulation results of magnet triggering signals, including normal triggering, missed detection, and false detection abnormal states, are generated.

[0050] Based on the above-mentioned method and process principles, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the steps of the above-mentioned mathematical modeling and simulation method for triggering signals of train wheel axle magnets.

[0051] Based on the above-mentioned method and process principles, the present invention also proposes a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the above-mentioned mathematical modeling and simulation method for triggering signals of train wheel axle magnets.

[0052] For trackside detection equipment, the magnetic sensors used require a change in the magnetic field lines within the magnet when a train axle passes over it, thus triggering a signal. This is used to detect train arrival and measure speed. However, during the experimental phase, such an environment is unavailable for testing the performance of trackside detection equipment. This invention uses a software solution to simulate the trigger signal of a train passing over a magnet, allowing for testing of the trackside detection equipment's functionality during the development phase.

[0053] Because the trigger signal of the magnetic steel sensor may be missed or falsely detected in actual use, the most common missed detection is when no trigger signal is generated when the train wheel axle passes over it, while false detection is more likely to generate multiple trigger signals when the wheel axle passes over the magnet once. These problems often occur in actual use, but due to the lack of actual testing environment, these problems cannot be encountered only in the development stage. This invention uses a trigger probability and trigger number model to simulate these problems, so that the signal received during development is closer to the real use environment.

[0054] This invention simulates the magnet trigger signal in a real-world environment during the development phase, eliminating the need to pre-set the magnet signal sequence and triggering entirely through model simulation. Using a mathematical model to more realistically simulate the signal allows for the addition of noise to simulate missed and false detections in real-world usage, thereby optimizing the software and improving its robustness. Since the quality of the magnet trigger signal directly affects the proper functioning of the device, modifying the trigger probabilities of missed and false detections allows for the accurate determination of the magnet signal's impact on device functionality.

[0055] Although the functions and working processes of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific functions and working processes described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims, and all of these are within the protection scope of the present invention.

Claims

1. A mathematical modeling and simulation method for triggering signals of train wheel axle magnets, characterized in that, Includes the following steps: S1 Model Construction: Obtain actual operating data of magnets and wheel axles in the rail section where the trackside detection equipment is located. Based on preset probability triggering and trigger count relationships, use the data to fit and solve for unknown parameters of the model, thus obtaining the probability triggering model and the trigger count model. The probability triggering model is used to quantitatively characterize the physical laws of wheel axle-magnet interaction and the correlation between interference factors and trigger probability. The trigger count model is used to quantitatively characterize the physical laws of wheel axle-magnet interaction and the correlation between interference factors and trigger count. S2 Working Condition Simulation: Different adjustable parameters are set, including train running speed, total number of train axles, number of magnets, magnet sensitivity, and installation parameters of each magnet. Using the constructed probability triggering model and trigger count model, the adjustable parameters are substituted to simulate the triggering process of train axle magnets under different working conditions. The magnet number, magnet triggering probability, and trigger count are obtained at different timestamps under each working condition. The simulation results of magnet triggering signals including normal triggering, missed detection, and false detection are generated.

2. The mathematical modeling and simulation method for train wheel axle magnet triggering signals according to claim 1, characterized in that, The specific process of step S1: S11. At least five sets of actual operating data for the magnets and axles were obtained through field measurements. Each set of data included the magnet installation parameters and the three-dimensional spatial coordinates of the axle. The measured trigger probability and number of triggers, and the magnet installation parameters include the three-dimensional spatial coordinates of the magnet installed on the rail. and the tilt angle of the magnet ;in, For the first The coordinates of the installation position of the magnet along the rail direction. For the first The lateral installation distance of each magnet For the first The height difference of the magnets For the train Carriage No. At time 10:00 Coordinates along the direction of the railway track, For the train Carriage No. The coordinates of the wheel axle center along the horizontal and vertical direction of the rail. For the train Carriage No. The coordinates of the position of the wheel axle center along the direction perpendicular to the rail; （1）， In the formula, For train speed, For the train The first carriage The distance between the axle and the preceding axle in the same carriage. For the train The first carriage The distance between the axle and the preceding axle in the same carriage. Indicates from the front of the car to the... The total distance between all axles of the carriage, Indicates the first From the first pair of carriages to the second pair The sum of the distances between the wheel axles; S12, based on the following trigger probability relationship and trigger count relationship, the height difference weighting coefficient is obtained by fitting and solving using the actual operating data of the above-mentioned magnet. Lateral deviation weighting coefficient Tilt angle weighting coefficient Distance deviation weighting coefficient coefficient of the cubic term ; （2）， （3） In the formula, For the train Carriage No. The axle passes through the first The trigger probability when there is a single magnet. For the train Carriage No. The axle passes through the first Number of triggers when there is one magnet; For the first Lateral installation distance deviation of each magnet; For the first The tilt angle of each magnet; For the train Carriage No. The gear axle and the first The normalized spatial distance deviation between individual magnets; where... （4）， （5）， （6）， In the formula, The preset optimal lateral installation distance for the magnets. The preset optimal three-dimensional spatial distance between the wheel axle and the magnet. For the train Carriage No. At time of the wheel axle With the The three-dimensional spatial distance between the magnets; S13, the height difference weighting coefficients obtained above Lateral deviation weighting coefficient Tilt angle weighting coefficient Distance deviation weighting coefficient coefficient of the cubic term Substituting these back into the trigger probability equation and the trigger count equation, we obtain the probability trigger model and the trigger count model.

3. The mathematical modeling and simulation method for train wheel axle magnet triggering signals according to claim 1, characterized in that, In step S2, the simulation of the train wheel axle magnet triggering process under one working condition is completed by following the steps S21~S28. The same steps can be used to simulate the train wheel axle magnet triggering process under different working conditions. S21 sets fixed and adjustable parameters. The fixed parameters include the total number of train axles (L) and the number of magnets (C). The adjustable parameters include the train speed. The magnet sensitivity T and the installation parameters of each magnet, including the three-dimensional spatial coordinates of the magnets installed on the rail. and the tilt angle of the magnet ; S22 records the current time t, and substitutes t into the relationship between wheel axle and time (1) to obtain the time of each pair of wheel axles in each carriage. Coordinates along the railway track ; S23 Judgment and If the real-time values ​​are equal, proceed to step S24; otherwise, proceed to step S22, update the current time to t+0.01 seconds and loop. S24 will and Substituting into the three-dimensional spatial distance calculation formula (6) between the magnet and the wheel axle, we obtain the time of each pair of wheel axles in each carriage at time... Three-dimensional spatial distance between each magnet The normalized spatial distance deviation between each pair of wheel axles and each magnet in each car section is further calculated using formula (5). ; S25 uses formula (4) to obtain the lateral installation distance deviation of each magnet. ,Will , , Substituting into the probability triggering model (2), we obtain the triggering probability when each pair of wheel axles of each carriage passes each magnet. ; S26 will Substituting into the trigger count model (3), we obtain the trigger count when each pair of wheel axles of each car passes each magnet. ; S27 Judgment If the value is 0, proceed to step S22, update the current time to t+0.01 seconds and loop; otherwise, obtain the corresponding timestamp and the triggered magnet number. S28 determines whether the time difference between the timestamp obtained in step S27 and the timestamp of the previous record is greater than the magnet sensitivity. If it is greater, the data set is retained; otherwise, the data set is discarded. At the same time, the process jumps to step S22, updates the current time to t+0.01 seconds, and repeats the loop.

4. A mathematical modeling and simulation system for train wheel axle magnet triggering signals based on the mathematical modeling and simulation method for train wheel axle magnet triggering signals according to any one of claims 1 to 3, characterized in that, Includes a model building module and a working condition simulation module. Model building module: Based on the actual operating data of the magnets and wheel axles of the rail section where the trackside detection equipment is located, each set of data includes the three-dimensional spatial coordinates of the wheel axle. The three-dimensional spatial coordinates of the magnet installed on the rail tilt angle of the magnet And the actual trigger probability Trigger count Using preset trigger probability and trigger frequency formulas, the unknown parameters of the model are solved by fitting the data. , , , , Re-enter the trigger probability relationship and trigger count relationship, remembering the probability trigger model and trigger count model; the probability trigger model is used to quantitatively characterize the physical laws of the wheel axle-magnet action and the correlation between interference factors and trigger probability, and the trigger count model is used to quantitatively characterize the physical laws of the wheel axle-magnet action and the correlation between interference factors and trigger count; Operating condition simulation module: Different adjustable parameters are set, including train running speed, total number of train axles, number of magnets, magnet sensitivity, and installation parameters of each magnet. Using the constructed probability triggering model and trigger count model, the adjustable parameters are substituted to simulate the triggering process of train axle magnets under different operating conditions. The magnet number, magnet triggering probability, and trigger count are obtained at different timestamps under each operating condition. The simulation results of magnet triggering signals, including normal triggering, missed detection, and false detection abnormal states, are generated.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the mathematical modeling and simulation method for the train wheel axle magnet trigger signal as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the mathematical modeling and simulation method for the train wheel axle magnet trigger signal as described in any one of claims 1 to 3.

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