Multivariate joint monitoring system under high-speed rail global communication scenario

Abstract

The application relates to the technical field of multivariate joint measurement and sensor signal processing, and discloses a multivariate joint monitoring system in a high-speed rail global communication scene, which comprises a physical characteristic extraction module, an aerodynamic load sensing module and a processor. The processor utilizes mechanical impact pulse signals generated by physical displacement to synthesize physical mileage beat trigger sampling sequences, and locks the spatial domain alignment relationship between a monitored carrier parameter and a displacement origin in a power supply system. The processor further calculates a fluid pressure signal gradient to identify a medium fluctuation state, determines a circuit compensation gain to perform residual error elimination on the monitored carrier parameter, and peels off signal artifacts induced by environmental load. The application eliminates the interference of aerodynamic pressure sudden change on the monitored signal, enables the system to distinguish environmental factors and equipment faults, and enhances the accuracy of power supply circuit load state determination.

Description

Technical Field

[0001] This invention belongs to the field of multivariate joint measurement and sensor signal processing technology, and particularly relates to a multivariate joint monitoring system in the context of high-speed rail full-domain communication. Background Technology

[0002] Currently, as a typical multivariate joint measurement scenario, distributed sampling clock architecture is often used to record communication link parameters and environmental parameters to maintain the operational safety of power distribution systems and communication loads. For such comprehensive measurement tasks involving multiple heterogeneous variables such as mechanical shock, fluid pressure, and electromagnetic carrier waves, how to achieve accurate alignment and correlation of two or more different physical parameters in a complex dynamic environment remains a common problem in the current measurement technology field. When the train speed exceeds 350 km / h, the mechanical vibration sensor and the electromagnetic acquisition unit will have a spatiotemporal offset due to the inherent clock drift. When the train passes through tunnels, deep road cuts, or is subjected to strong electromagnetic interference from the power supply system, the external timing reference is prone to interruption or accuracy drop, causing the multivariate monitoring data to become out of focus on the spatial axis.

[0003] While using high-precision clocks or increasing data cache redundancy can partially align spatiotemporal coordinates, these measures increase the power load and hardware design complexity of onboard equipment, and cannot eliminate mileage correlation errors caused by the lack of external timing references. Industry solutions to such problems typically compromise on the chronological order of data recording, making it difficult to physically separate the coupling between environmental factors and the performance degradation of the load equipment, thus limiting the accuracy of fault identification. Even if the issues of hardware integration and multi-loop parallel deployment of monitoring devices are resolved, architectural flaws still exist in the monitoring logic's handling of multi-source heterogeneous data synchronization in high-dynamic motion scenarios. For example, see publication number CN108. Chinese invention patent 572289A discloses an online power quality monitoring device for a high-speed railway traction power supply system. It uses a functional drawer plug-in in conjunction with an industrial control computer to achieve synchronous acquisition of voltage and current signals from multiple circuits. The sampling process of the scheme is bound to a clock beat in a fixed time dimension, and no intrinsic correlation is established between the monitoring parameters and the physical mileage of the track. When the train passes through a tunnel or the external time reference fails due to strong electromagnetic interference, the spatial mapping residual caused by time drift cannot be eliminated. The monitoring device lacks a physical compensation mechanism for sudden changes in external aerodynamic loads, making it difficult to remove signal artifacts induced by medium fluctuations from the acquired features, which affects the accuracy of the equipment performance degradation judgment.

[0004] Therefore, how to utilize the intrinsic physical beat during train operation to achieve native synchronous monitoring of communication characteristics and environmental parameters, and then construct a joint measurement mechanism capable of handling two or more heterogeneous variables, becomes the technical problem to be solved by this invention. Summary of the Invention

[0005] This invention provides a multi-variable joint monitoring system for a high-speed rail full-domain communication scenario, characterized in that the system includes:

[0006] The physical feature extraction module, located on the load-bearing component of the transport platform, is used to acquire the mechanical impact pulse signal generated by the physical displacement of the transport platform;

[0007] A pneumatic load sensing module is located at the electromagnetic energy exchange interface of the power supply system circuit device to acquire the fluid pressure signal around the electromagnetic energy exchange interface.

[0008] The processor is connected to the physical feature extraction module and the aerodynamic load sensing module, respectively. The processor is used to synthesize a trigger sampling sequence based on the physical mileage beat by spatially resampling the interval between pulse peaks according to the spatial periodic characteristics of the mechanical impact pulse signal. This achieves strong spatial alignment between the monitoring carrier parameter reflecting the circuit operating status in the power supply system circuit device under clock crystal oscillator drift and the physical displacement origin. The processor is also used to calculate the instantaneous gradient of the fluid pressure signal, identify the medium fluctuation state of the electromagnetic energy exchange interface based on the instantaneous gradient, determine the circuit compensation gain corresponding to the change in the equivalent dielectric constant in the signal transmission path, and use the circuit compensation gain to perform residual elimination on the spatially aligned and sampled monitoring carrier parameter. This removes monitoring carrier amplitude artifacts induced by sudden changes in the fluid pressure signal, generates circuit monitoring feature values ​​characterizing the load stability of the power supply system circuit device, and outputs a monitoring trajectory characterizing the performance of the power supply system circuit device.

[0009] Preferably, the processor follows the following quantization rules when performing residual removal: ,in, For circuit monitoring characteristic values, The original amplitude of the monitoring carrier parameter for spatial domain aligned sampling is given by λ, which is a preset dielectric correction factor, and ΔP is the amplitude of pressure change of the fluid pressure signal within a unit sampling mileage. The processor adjusts the reading frequency of the fluid pressure signal in real time by calculating the frequency of the physical mileage beat, so that the distribution density of heterogeneous monitoring variables in the power supply system circuit device on the spatial axis remains constant.

[0010] Preferably, the system further includes: a drive frequency analysis module connected to the execution motor of the carrier platform, used to obtain the drive frequency parameters of the execution motor; and a processor connected to the drive frequency analysis module, used to perform spatial step length analysis continuity compensation for the physical mileage beat using the drive frequency parameters when the signal-to-noise ratio of the mechanical impact pulse signal output by the physical feature extraction module is lower than a preset threshold, so as to maintain the geometric consistency of the trigger sampling sequence on the smooth running section.

[0011] Preferably, the system further includes: a spatial filter, integrated inside the processor, wherein the tap coefficients of the spatial filter are set based on the fixed geometric axis of the load-bearing component; and a processor, which uses the spatial filter to perform two-point differential operation on the mechanical impact pulse signal to eliminate random fluid noise generated by high-speed movement through spatial geometric constraints and lock the physical displacement origin.

[0012] Preferably, the processor is also used to extract the attenuation envelope characteristics of the mechanical impact pulse signal and calculate the energy attenuation rate of the pulse wake; the processor matches the energy attenuation rate with a preset dynamic model to determine the operating interface interaction quality index characterizing the contact impedance state, and uses the operating interface interaction quality index as a correction parameter for the circuit monitoring characteristic value.

[0013] Preferably, the processor is also used to extract the frequency drift state in the random vibration spectrum of the electromagnetic energy exchange interface and construct a correlation model between the intrinsic frequency offset of the electromagnetic energy exchange interface and the instantaneous gradient of the fluid pressure signal; the processor uses the correlation model to correct the fluid pressure signal in real time to eliminate pressure sensing noise caused by structural vibration coupling.

[0014] Preferably, the system further includes: a status determination unit connected to the processor; the status determination unit is used to receive the circuit monitoring characteristic value after residual elimination and map it to the stored circuit fault characteristic spectrum; the status determination unit outputs an alarm command for abnormal power supply system circuit load when the circuit monitoring characteristic value deviates from the circuit fault characteristic spectrum and the instantaneous gradient of the fluid pressure signal is in a constant range.

[0015] Preferably, the processor identifies periodic feature points generated by the track joint by performing autocorrelation calculation on the mechanical impact pulse signal, and uses the periodic feature points as the reference origin of the spatial step to calibrate the cumulative position deviation of the triggered sampling sequence.

[0016] Preferably, when the processor detects that the transport platform has entered a tunnel or bridge, it increases the sampling weight of the aerodynamic load sensing module and dynamically adjusts the trigger threshold of the trigger sampling sequence according to the non-uniform compression of the fluid density.

[0017] Preferably, the system further includes: a virtual space synchronization sequence synthesis module, integrated into the processor; the virtual space synchronization sequence synthesis module is used to combine the real-time running speed of the carrier platform with the preset location feature database to generate virtual synchronization pulses corresponding to the geographical location coordinates, and use the virtual synchronization pulses as a redundancy check reference for triggering the sampling sequence to ensure that the system maintains temporal and spatial synchronization between multivariable circuit parameters in the event of sensor failure.

[0018] Furthermore, compared with the prior art, the multi-variable joint monitoring system for high-speed rail full-domain communication scenarios of the present invention has the following advantages:

[0019] 1. In multivariate joint monitoring, by selecting the periodic mechanical impact pulse sequence generated by wheel-rail interaction as the trigger reference for system sampling, replacing the absolute time reference, the monitoring parameters collected by each sensing unit are bound in situ with the physical mileage of the track. This achieves essential synchronization of mechanical and electromagnetic variables on the spatial axis, demonstrating the synergy of the measuring device in processing multi-physical field parameters. It eliminates the spatiotemporal mapping deviation caused by clock crystal drift during high-speed movement. When the train speed changes, the sampling frequency is adjusted synchronously with the physical displacement frequency, keeping the distribution density of monitoring data on the spatial axis constant. This ensures strong spatial alignment between heterogeneous parameters even in the absence of external timing signals.

[0020] 2. By establishing a correlation model between the intrinsic frequency offset of the antenna support and the aerodynamic pressure gradient, real-time compensation for the operating environment characteristics of the communication load in the power supply system is achieved. The processor analyzes the frequency drift state in the random vibration spectrum of the antenna support, identifies the instantaneous changes in air density around the antenna, and performs residual elimination on the signal path loss parameter accordingly. This process removes the false interference on signal strength caused by aerodynamic loads generated by the vehicle passing through tunnels or passing trains, enabling the status determination unit to distinguish between environmentally induced artifacts and equipment performance failures, thereby improving the accuracy of power distribution load status determination under complex geographical conditions.

[0021] 3. Combining the operating frequency parameters of the traction motor with the mechanical vibration characteristics, a virtual space synchronous sequence synthesis mechanism is constructed. On smooth road sections where the signal-to-noise ratio of physical impact pulses decreases, the frequency characteristics of the traction system are used to perform continuous compensation for spatial step length analysis. In conjunction with the spatial filter of the bogie with fixed wheelbase, two-point differential operation is performed to eliminate high-energy random aerodynamic noise generated by ultra-high speed driving with spatial geometric constraints. This ensures that the sampling trigger source still locks the physical displacement origin under extreme dynamic pressure environment and maintains the geometric consistency of the monitoring trajectory in the entire range. Attached Figure Description

[0022] Figure 1 This is a flowchart of a multivariable signal processing and monitoring trajectory generation method according to the present invention;

[0023] Figure 2 This is a sequence diagram of the interaction between system configuration parameter update and verification in this invention. Detailed Implementation

[0024] The principles and spirit of the present invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0025] A multivariate joint monitoring system for high-speed rail full-domain communication scenarios, the system includes:

[0026] The physical feature extraction module, located on the load-bearing component of the transport platform, is used to acquire the mechanical impact pulse signal generated by the physical displacement of the transport platform;

[0027] A pneumatic load sensing module is located at the electromagnetic energy exchange interface of the power supply system circuit device to acquire the fluid pressure signal around the electromagnetic energy exchange interface.

[0028] The processor is connected to the physical feature extraction module and the aerodynamic load sensing module, respectively. The processor is used to synthesize a trigger sampling sequence based on the physical mileage beat by spatially resampling the interval between pulse peaks according to the spatial periodic characteristics of the mechanical impact pulse signal. This achieves strong spatial alignment between the monitoring carrier parameter reflecting the circuit operating status in the power supply system circuit device under clock crystal oscillator drift and the physical displacement origin. The processor is also used to calculate the instantaneous gradient of the fluid pressure signal, identify the medium fluctuation state of the electromagnetic energy exchange interface based on the instantaneous gradient, determine the circuit compensation gain corresponding to the change in the equivalent dielectric constant in the signal transmission path, and use the circuit compensation gain to perform residual elimination on the spatially aligned and sampled monitoring carrier parameter. This removes monitoring carrier amplitude artifacts induced by sudden changes in the fluid pressure signal, generates circuit monitoring feature values ​​characterizing the load stability of the power supply system circuit device, and outputs a monitoring trajectory characterizing the performance of the power supply system circuit device.

[0029] Preferably, the processor follows the following quantization rules when performing residual removal: ,in, For circuit monitoring characteristic values, The original amplitude of the monitoring carrier parameter for spatial domain aligned sampling is given by λ, which is a preset dielectric correction factor, and ΔP is the amplitude of pressure change of the fluid pressure signal within a unit sampling mileage. The processor adjusts the reading frequency of the fluid pressure signal in real time by calculating the frequency of the physical mileage beat, so that the distribution density of heterogeneous monitoring variables in the power supply system circuit device on the spatial axis remains constant.

[0030] Preferably, the system further includes: a drive frequency analysis module connected to the execution motor of the carrier platform, used to obtain the drive frequency parameters of the execution motor; and a processor connected to the drive frequency analysis module, used to perform spatial step length analysis continuity compensation for the physical mileage beat using the drive frequency parameters when the signal-to-noise ratio of the mechanical impact pulse signal output by the physical feature extraction module is lower than a preset threshold, so as to maintain the geometric consistency of the trigger sampling sequence on the smooth running section.

[0031] Preferably, the system further includes: a spatial filter, integrated inside the processor, wherein the tap coefficients of the spatial filter are set based on the fixed geometric axis of the load-bearing component; and a processor, which uses the spatial filter to perform two-point differential operation on the mechanical impact pulse signal to eliminate random fluid noise generated by high-speed movement through spatial geometric constraints and lock the physical displacement origin.

[0032] Preferably, the processor is also used to extract the attenuation envelope characteristics of the mechanical impact pulse signal and calculate the energy attenuation rate of the pulse wake; the processor matches the energy attenuation rate with a preset dynamic model to determine the operating interface interaction quality index characterizing the contact impedance state, and uses the operating interface interaction quality index as a correction parameter for the circuit monitoring characteristic value.

[0033] Preferably, the processor is also used to extract the frequency drift state in the random vibration spectrum of the electromagnetic energy exchange interface and construct a correlation model between the intrinsic frequency offset of the electromagnetic energy exchange interface and the instantaneous gradient of the fluid pressure signal; the processor uses the correlation model to correct the fluid pressure signal in real time to eliminate pressure sensing noise caused by structural vibration coupling.

[0034] Preferably, the system further includes: a status determination unit connected to the processor; the status determination unit is used to receive the circuit monitoring characteristic value after residual elimination and map it to the stored circuit fault characteristic spectrum; the status determination unit outputs an alarm command for abnormal power supply system circuit load when the circuit monitoring characteristic value deviates from the circuit fault characteristic spectrum and the instantaneous gradient of the fluid pressure signal is in a constant range.

[0035] Preferably, the processor identifies periodic feature points generated by the track joint by performing autocorrelation calculation on the mechanical impact pulse signal, and uses the periodic feature points as the reference origin of the spatial step to calibrate the cumulative position deviation of the triggered sampling sequence.

[0036] Preferably, when the processor detects that the transport platform has entered a tunnel or bridge, it increases the sampling weight of the aerodynamic load sensing module and dynamically adjusts the trigger threshold of the trigger sampling sequence according to the non-uniform compression of the fluid density.

[0037] Preferably, the system further includes: a virtual space synchronization sequence synthesis module, integrated into the processor; the virtual space synchronization sequence synthesis module is used to combine the real-time running speed of the carrier platform with the preset location feature database to generate virtual synchronization pulses corresponding to the geographical location coordinates, and use the virtual synchronization pulses as a redundancy check reference for triggering the sampling sequence to ensure that the system maintains temporal and spatial synchronization between multivariable circuit parameters in the event of sensor failure.

[0038] Example 1: In high-speed railway operation, the transport platform passes through tunnel cluster sections at a speed of no less than 350 km / h. The external satellite timing signal is interrupted due to mountain shielding and electromagnetic interference in the tunnel. Due to the microsecond-level offset of the independent clock crystal oscillators of each sensing unit in the monitoring system, the sampling points are dynamically deviated on the spatial axis, making it impossible to map the communication link parameters to the corresponding track mileage points. In addition, the increased aerodynamic load in the tunnel causes local air density fluctuations in the radome. The resulting medium property offset causes instantaneous attenuation of the monitoring signal strength, making the monitoring data unable to reflect the load performance status of the circuit device. The physical feature extraction module captures the mechanical impact pulse signal generated by wheel-rail interaction. The processor resamples the pulse peak interval according to the spatial periodic characteristics of the mechanical impact pulse signal to generate a trigger sampling sequence based on the physical mileage beat. This aligns the monitoring carrier parameter of the power supply system circuit device with the physical displacement origin in the spatial domain. Using the beat signal generated by wheel-rail physical impact as the logical trigger point, the asynchronous sampling driven by the time axis is converted into synchronous sampling driven by the spatial axis, reducing the impact of clock offset on the correlation accuracy of multivariable monitoring data.

[0039] The processor calculates the instantaneous gradient of the fluid pressure signal sensed by the aerodynamic load sensing module, identifies the medium fluctuation state at the electromagnetic energy exchange interface, determines the circuit compensation gain corresponding to the change in the equivalent dielectric constant along the signal transmission path, and performs residual elimination. During residual elimination, the processor follows quantization rules. ,in, For circuit monitoring characteristic values, The original amplitude of the monitoring carrier parameter for spatial domain aligned sampling is given by λ, which is a preset dielectric correction factor determined by measuring the carrier signal attenuation rate under different pressure gradients in a laboratory environment and performing linear fitting. ΔP is the amplitude of the pressure change of the fluid pressure signal within a unit sampling distance. Calibrating the dielectric correction factor λ requires establishing a mapping model between the fluid pressure gradient and electromagnetic wave propagation loss. A dynamic pressure environment from 0 kPa to 100 kPa is simulated using a pressure transmitter chamber, and a vector network analyzer is used simultaneously to acquire the monitoring carrier signal in the 3.5 GHz to 5 GHz frequency band. The parameter change curves are fitted using the least squares method to establish a linear relationship between the pressure increment ΔP and the amplitude attenuation ΔA. The regression coefficient k is extracted as the initial value of the factor λ. The processor monitors the instantaneous gradient of the fluid pressure signal in real time. ,like Exceeding the turbulence threshold determined by wind tunnel experiments Triggering the nonlinear compensation operator The circuit compensation gain is corrected, and the attenuation index α is calibrated based on the Reynolds number of the antenna radome aerodynamic shape. This ensures that the residual elimination process covers the nonlinear compression effect of high subsonic air density. The monitoring carrier amplitude artifacts are removed, and high-fidelity circuit monitoring characteristic values ​​are generated. The processor uses the trigger sampling sequence generated by the mechanical impact pulse signal as the sampling logic reference. The circuit compensation gain is used to correct the monitoring carrier amplitude error caused by the sudden change of fluid pressure signal. The circuit monitoring characteristic values ​​that characterize the load stability of the power supply system circuit device are obtained, and the monitoring trajectory that characterizes the performance of the power supply system circuit device is output. This enables the monitoring system to distinguish between environmental fluctuations and equipment failures under tunnel conditions.

[0040] Example 2: In a test environment including a dynamic simulation bench for the transport platform and a multi-dimensional heterogeneous variable acquisition array, a data acquisition module with a sampling rate of 100 MS / s was used to simultaneously acquire mechanical impact pulse signals, fluid pressure signals, and monitoring carrier parameters. The spatial sampling step size of the trigger sampling sequence was limited by the highest spatial frequency component of the monitoring carrier parameter on the spatial axis and the real-time throughput rate of the processor. By calculating the signal attenuation envelope characteristics of typical road sections, the reference spatial interval of the trigger sampling sequence was determined to be 5 cm, which reduced the data computation load of the system while ensuring resampling fidelity. When the simulated operating speed of the transport platform increased from 250 km / h to 400 km / h, the logic gate trigger pulse was adjusted in real time according to the physical frequency of the captured mechanical impact pulse signal. To improve response time and maintain consistency in the distribution density of monitoring data, a Gaussian random vibration interference with a signal-to-noise ratio of 20dB is superimposed at the signal input of the physical feature extraction module to simulate physical impact noise caused by track irregularities. Simultaneously, a simulation condition is set where a train passes through a group of ultra-long tunnels at a speed of 350km / h. At this point, the initial pressure value of the sensed fluid pressure signal is 101.325kPa. Due to the air compression effect inside the tunnel, the maximum instantaneous pressure gradient of the fluid pressure signal is 15.68kPa / m. The corresponding amplitude of the original monitoring carrier parameter experiences a 12.86dB attenuation in the 3.5GHz band. In the control group logic without residual elimination, this attenuation characteristic is identified as a signal drop caused by equipment load failure, generating an erroneous warning.

[0041] The processor executes a residual elimination procedure, utilizing its integrated spatial filter to perform two-point differential operations on the noisy mechanical impact pulse signal, extracting physical displacement features and synthesizing a trigger sampling sequence. Within a continuous observation period of 500ms, the spatial positioning error of the trigger sampling sequence relative to the track physical joint decreases from 18.5cm in the random time sampling mode to 1.2cm. Based on the amplitude variation of the 15.68kPa / m fluid pressure signal, the processor calculates the circuit compensation gain under a preset dielectric correction factor λ=0.85, and follows the quantization rules. Real-time amplitude reconstruction was performed on the sampling points to remove signal artifacts induced by aerodynamic density fluctuations and generate corrected circuit monitoring characteristic values. When the pressure gradient of the fluid pressure signal was in the range of 0 to 20.5 kPa / m, the residual between the circuit monitoring characteristic value and the reference value remained below 1.25%. When the pressure gradient exceeded the performance inflection point of 25.4 kPa / m, the compensation efficiency of λ tended to saturate due to the influence of the fluid around the radome entering a turbulent state, and the residual increased to 5.12%. By comparing the performance of the test group with that of the control group with the aerodynamic load sensing module removed, the accuracy of the test group in judging the equipment load performance status improved from 74.2% to 97.6%, and no data spatial position defocusing induced by sampling time drift occurred in the 400 km / h operating environment.

[0042] Example 3: This example combines Figures 1 to 2 This section describes a multivariate joint monitoring system for high-speed rail full-domain communication scenarios, such as... Figure 1 As shown, the signal processing logic begins with two independent acquisition branches: the aerodynamic load sensing module located at the electromagnetic energy exchange interface is responsible for outputting the fluid pressure signal, and the physical feature extraction module located at the carrier platform bearing component is responsible for outputting the mechanical impact pulse signal. The fluid pressure signal is input to the medium fluctuation state identification stage, and the instantaneous gradient of the fluid pressure signal is calculated to determine the circuit compensation gain. At the same time, the mechanical impact pulse signal enters the spatial domain resampling and sequence synthesis stage, which is used to synthesize the physical mileage beat trigger sampling sequence and lock the monitoring carrier parameter and displacement origin, thereby performing spatial domain aligned sampling. The data from the above two paths converge to the residual elimination and feature generation stage. By using the circuit compensation gain to process the monitoring carrier parameter sampled by spatial domain alignment, the signal artifacts induced by environmental loads are removed and circuit monitoring feature values ​​are generated. Finally, the monitoring trajectory output stage outputs the monitoring trajectory characterizing the performance of the power supply system circuit device.

[0043] like Figure 2As shown, system parameter maintenance follows the configuration update sequence logic. This process begins with the operation and maintenance management platform initiating a configuration update request to the configuration management module. The configuration management module then submits the new configuration parameters to the parameter verification unit for parameter validity checking. If the parameter verification passes, the parameter verification unit reports successful verification. The configuration management module then requests configuration write permissions from the system core. After obtaining authorization from the system core for configuration updates, the configuration management module distributes the new configuration parameters to each downstream monitoring subsystem. Each monitoring subsystem applies the new configuration and confirms the configuration to the configuration management module. The configuration management module triggers the logger to record the configuration change. After the log recording is completed, the configuration management module reports successful configuration update to the operation and maintenance management platform. If parameter verification fails, the parameter verification unit directly returns an error message, and the configuration management module immediately reports configuration update failure to the operation and maintenance management platform.

[0044] Example 4: During the operation of a high-speed railway transport platform transitioning from an elevated bridge section with periodic joints to a fully welded long rail section, the energy amplitude of the mechanical impact pulse signal acquired by the physical feature extraction module decreases, resulting in a logical switching action from mechanical impact pulse signal-driven to drive frequency analysis compensation-driven. A spatial filter is used to perform geometric correspondence to the physical mileage beat, and the set of tap coefficients {W} of the spatial filter is based on the fixed geometric wheelbase of the transport platform's load-bearing components. Confirmed, time delay interval is met. Where τ is the time delay interval, To maintain a fixed geometric wheelbase, where v is the instantaneous velocity extracted by the processor, the processor performs a two-point difference operation using spatial geometric constraints to remove random disturbance components generated by the vibration of the load-bearing component and establish the physical displacement origin. The spatial filter performs the two-point difference operation based on the fixed geometric wheelbase of the load-bearing component. A spatial delay operator is constructed to transform the mechanical impact pulse signal x(t) into an equally spaced spatial sequence x(s) through a circular buffer queue, and tap coefficients are set. and The number of sampling points corresponding to the delay tap position k The differential calculation y(s) = x(s) - x(sN) is performed to filter out in-phase random fluid noise that is positively correlated with the operating speed. By identifying the pulse envelope abrupt change points in the y(s) sequence that conform to the physical characteristics of the wheel-rail joint, the origin of the physical displacement is locked. If the offset δ between adjacent feature points exceeds the length of the reference track... 0.5%, the processor integrates the Kalman filter algorithm to fuse and execute the motor drive frequency. The processor performs cumulative error correction and outputs a physical odometer-triggered sampling sequence with spatial geometric constraints. This enables strong alignment of the carrier parameter with the physical displacement coordinates during clock drift. When synthesizing the trigger sampling sequence, the processor executes the following logical steps: It uses a high-speed counter with a main frequency of 100MHz to obtain the arrival times of adjacent peak values ​​of the mechanical shock pulse signal. and ; Calculate the instantaneous spatial step size mapping operator Where ΔS is the instantaneous spatial step size mapping operator, The preset physical spacing of the track joints. and Given the arrival times of two adjacent pulses, the processor uses a step accumulator with a step size of 1 / ΔS, and accumulates the data until the preset spatial resampling interval is reached. At that time, a trigger pulse is sent to the acquisition front end of the power supply system circuit device to generate a trigger sampling sequence that characterizes the equidistant features of the spatial domain.

[0045] The processor executes a signal-to-noise ratio (SNR) determination procedure to maintain sampling continuity and switches the threshold. The calibration process is as follows: The background electrical noise energy of the system is collected while the launch platform is stationary. And at an operating speed of 350 km / h, signal energy including track joint impact was collected. The switching threshold is set at 0.5 times the ratio of the two. ;in, The dimensionless switching threshold. For signal energy, The background electrical noise energy is the measured signal-to-noise ratio. Below At that time, read the motor drive frequency parameters output by the drive frequency analysis module. The equivalent spatial step size is calculated using the transfer function of motor speed and physical mileage. Spatial step size analysis continuity compensation is performed for the physical mileage beat to maintain the geometric consistency of the trigger sampling sequence in the state of missing physical beats. Circuit monitoring feature values ​​that characterize the load stability of the power supply system circuit device are generated so that the circuit monitoring feature values ​​can still be mapped to the physical track mileage on a smooth running surface.

[0046] Example 5: Inside the pressure transmitter chamber simulating an atmospheric environment, the processor executes a benchmark calibration process for the dielectric correction factor λ, establishing a propagation attenuation benchmark for electromagnetic waves under different air densities. Technicians adjust the compression ratio inside the pressure transmitter chamber to obtain sampled values ​​of the fluid pressure signal at a gradient from 0 kPa to 50 kPa. Simultaneously, a vector network analyzer is used to measure the amplitude offset of the monitoring carrier parameter at the electromagnetic energy exchange interface of the power supply system circuit device. The processor uses a linear regression algorithm to calculate the correspondence between the amplitude offset of the monitoring carrier parameter and the gradient of the fluid pressure signal, determines the proportional coefficient of the regression equation, and sets it as the preset dielectric correction factor λ. The processor writes the calibrated λ into a non-volatile configuration register, which serves as a real-time operation operator for residual elimination performed by the transport platform under tunnel cluster and strong aerodynamic load conditions.

[0047] When the monitoring system is installed at different physical locations on the carrier platform, the processor executes the spatial domain initial phase locking procedure for the sampling sequence to eliminate spatiotemporal alignment errors caused by sensor mounting position deviations. When the carrier platform passes through a test track section with fixed physical joints at a calibration speed of 30 km / h, the physical feature extraction module acquires mechanical impact pulse signals reflecting wheel-rail interaction characteristics, and determines the first pulse rising edge as the starting reference point for spatial domain sampling. Based on the fixed geometric wheelbase of the carrier platform's load-bearing components... With the preset spatial resampling interval The geometric phase difference between the sampling trigger signal and the track mileage reference is calculated, and the preset count initial value of the internal timer is adjusted accordingly to make the synthesized trigger sampling sequence intrinsically aligned with the physical displacement origin on the geometric axis.

[0048] Example 6: In the sensor deployment scenario of the power supply system circuit device of the launch platform, the processor performs physical offset calibration of the aerodynamic load sensing module relative to the electromagnetic energy exchange interface to eliminate the spatial phase compensation caused by the difference in the mounting position of the sensing unit; the geometric distance between the sensing center of the aerodynamic load sensing module and the electromagnetic beam center of the electromagnetic energy exchange interface in the direction of track mileage is measured using a laser rangefinder. According to geometric distance The synchronization correction time difference between the fluid pressure signal acquisition time and the monitoring carrier parameter acquisition time is determined by the real-time moving speed v of the transport platform. ,in, To synchronize and correct time differences, Let v be the geometric distance and v be the real-time movement speed. The processor will synchronously correct for the time difference. Pre-installed in the processor's timing scheduling logic, the calculation of the fluid pressure signal gradient during the residual elimination process is aligned with the interference area of ​​the power supply system circuit device in the physical space dimension, thus removing measurement artifacts induced by installation pose deviations.

[0049] When the carrier platform operates in a high-speed environment, causing the frequency of aerodynamic interference to expand in the frequency domain, the processor performs dynamic sliding sampling window length calibration based on the fluid pressure signal gradient, utilizing the system's hardware sampling frequency. With the maximum operating speed of the carrier platform Calculate the optimal number of data points to characterize the pressure change amplitude within a unit sampling distance. ,in, To determine the optimal number of data points, The hardware sampling frequency, The radome's characteristic geometric diameter is preset. For maximum operating speed, the processor uses the optimal number of data points. The data sequence of fluid pressure signal is captured and the root mean square gradient is calculated. The output is used to compensate the gain of the circuit for driving residual elimination. The mechanical impact pulse signal is detected to recover from the smooth section to the joint impact characteristic, and the measured signal-to-noise ratio is higher than that of the target. At the instant of receiving the first pulse rising edge, the spatial resampling step accumulator is reset, and the sampling logic is restored from the analytical mode based on the execution motor drive frequency to the synchronous mode based on the physical mileage beat, so as to achieve continuous geometric alignment between the monitoring trajectory and the track mileage coordinate system.

[0050] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments 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 this application without departing from the spirit of this application and the scope of protection of this invention, and all of these forms are within the protection scope of this application.

Claims

1. A multi-variable joint monitoring system for a high-speed rail full-domain communication scenario, characterized in that the system... include: The physical feature extraction module, located on the load-bearing component of the transport platform, is used to acquire the mechanical impact pulse signal generated by the physical displacement of the transport platform; A pneumatic load sensing module is located at the electromagnetic energy exchange interface of the power supply system circuit device to acquire the fluid pressure signal around the electromagnetic energy exchange interface. The processor is connected to the physical feature extraction module and the aerodynamic load sensing module respectively. The processor is used to synthesize a trigger sampling sequence based on the physical mileage beat by spatially resampling the spacing of the pulse peaks according to the spatial periodic characteristics of the mechanical impact pulse signal, so as to achieve strong spatial alignment between the monitoring carrier parameter reflecting the circuit operation status and the physical displacement origin in the power supply system circuit device under the clock crystal oscillator drift state. The processor is also used to calculate the instantaneous gradient of the fluid pressure signal, identify the medium fluctuation state of the electromagnetic energy exchange interface based on the instantaneous gradient, determine the circuit compensation gain corresponding to the change of the equivalent dielectric constant in the signal transmission path, and use the circuit compensation gain to perform residual elimination on the monitoring carrier parameters sampled by spatial domain alignment, remove the monitoring carrier amplitude artifacts induced by the sudden change of fluid pressure signal, generate circuit monitoring feature values ​​characterizing the load stability of the power supply system circuit device, and output the monitoring trajectory characterizing the performance of the power supply system circuit device. Furthermore, when the processor performs residual culling, it follows the following quantization rules: ,in, For circuit monitoring characteristic values, The original amplitude of the monitoring carrier parameter for spatial domain aligned sampling is given by λ, which is a preset dielectric correction factor, and ΔP is the amplitude of pressure change of the fluid pressure signal within a unit sampling mileage. The processor adjusts the reading frequency of the fluid pressure signal in real time by calculating the frequency of the physical mileage beat, so that the distribution density of heterogeneous monitoring variables in the power supply system circuit device on the spatial axis remains constant.

2. The multivariate joint monitoring system for a high-speed rail full-domain communication scenario according to claim 1, characterized in that, The system also includes: a drive frequency analysis module, connected to the execution motor of the carrier platform, used to obtain the drive frequency parameters of the execution motor; and a processor, connected to the drive frequency analysis module, used to perform spatial step length analysis continuity compensation for the physical mileage beat using the drive frequency parameters when the signal-to-noise ratio of the mechanical impact pulse signal output by the physical feature extraction module is lower than a preset threshold, so as to maintain the geometric consistency of the trigger sampling sequence on the smooth running section.

3. The multivariate joint monitoring system for a high-speed rail full-domain communication scenario according to claim 1, characterized in that, The system also includes: a spatial filter, integrated inside the processor, with the tap coefficients of the spatial filter set based on the fixed geometric axis of the load-bearing component; and a processor that uses the spatial filter to perform two-point differential operations on the mechanical impact pulse signal to eliminate random fluid noise generated by high-speed movement through spatial geometric constraints and lock the physical displacement origin.

4. The multivariate joint monitoring system for a high-speed rail full-domain communication scenario according to claim 1, characterized in that, The processor is also used to extract the attenuation envelope characteristics of the mechanical shock pulse signal and calculate the energy decay rate of the pulse trail. The processor matches the energy decay rate with a preset dynamic model to determine the operating interface interaction quality index that characterizes the contact impedance state, and uses the operating interface interaction quality index as a correction parameter for the circuit monitoring characteristic value.

5. A multi-variable joint monitoring system for a high-speed rail full-domain communication scenario according to claim 1, characterized in that, The processor is also used to extract the frequency drift state in the random vibration spectrum of the electromagnetic energy exchange interface and construct a correlation model between the intrinsic frequency offset of the electromagnetic energy exchange interface and the instantaneous gradient of the fluid pressure signal. The processor uses the correlation model to correct the fluid pressure signal in real time to eliminate pressure sensing noise caused by structural vibration coupling.

6. A multi-variable joint monitoring system for a high-speed rail full-domain communication scenario according to claim 1, characterized in that, The system also includes: a status determination unit connected to the processor; a status determination unit for receiving circuit monitoring characteristic values ​​after residual elimination and mapping them to the stored circuit fault characteristic spectrum; and a status determination unit for outputting an alarm command for abnormal power supply system circuit load when the circuit monitoring characteristic values ​​deviate from the circuit fault characteristic spectrum and the instantaneous gradient of the fluid pressure signal is in a constant range.

7. A multi-variable joint monitoring system for a high-speed rail full-domain communication scenario according to claim 1, characterized in that, The processor identifies periodic feature points generated by the track joint by performing autocorrelation calculation on the mechanical impact pulse signal, and uses the periodic feature points as the reference origin of the spatial step to calibrate the cumulative position deviation of the triggered sampling sequence.

8. A multi-variable joint monitoring system for a high-speed rail full-domain communication scenario according to claim 1, characterized in that, When the processor detects that the transport platform has entered a tunnel or bridge, it increases the sampling weight of the aerodynamic load sensing module and dynamically adjusts the trigger threshold of the trigger sampling sequence according to the non-uniform compression of the fluid density.

9. A multi-variable joint monitoring system for a high-speed rail full-domain communication scenario according to claim 1, characterized in that, The system also includes: a virtual space synchronization sequence synthesis module, integrated into the processor; the virtual space synchronization sequence synthesis module is used to combine the real-time running speed of the carrier platform with the preset location feature database to generate virtual synchronization pulses corresponding to the geographical coordinates, and use the virtual synchronization pulses as a redundancy check reference for triggering the sampling sequence to ensure that the system maintains temporal and spatial synchronization between multivariable circuit parameters in the event of sensor failure.

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