Method and system for optimizing dropper arrangement of high-speed railway overhead line system based on machine learning

Abstract

The invention relates to the field of intelligent operation and maintenance of a high-speed railway overhead line system, provides a machine learning-based optimization method and system for dropper arrangement of the high-speed railway overhead line system, and aims to solve the problems of insufficient pantograph-catenary coupling stability and relatively low abrasion uniformity of the overhead line system. The method comprises the steps that contact force space distribution characteristics of a pantograph and a contact net are collected, and contact force balance constraint conditions are generated; constructing a line parameter compensation mechanism by combining the track ultrahigh value, the vertical curve curvature and the elevation data of the suspension point acquired by the laser scanner, and outputting a compensation three-dimensional coordinate of the suspension point of the overhead line system; a programmable gate array is adopted to implement parallel acceleration on neural network weight calculation and space coordinate transformation, contact force constraint and compensation coordinates are iteratively processed, and dropper arrangement parameters are generated; based on the segmented tension value and the node offset compensation value, driving equipment to finish accurate adjustment and positioning installation of the dropper. According to the technical scheme provided by the invention, the pantograph-catenary coupling stability and the wear uniformity of the catenary are improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent operation and maintenance of high-speed railway contact networks, and in particular to a method and system for optimizing the arrangement of high-speed railway contact network suspension strings based on machine learning. Background Art

[0002] Under the complex line conditions of high-speed railways (such as large slopes and small curve radius sections), the catenary suspension arrangement needs to adapt to the high-frequency changes in track geometry and pantograph contact force. Due to the nonlinear superposition of the spatial deformation of the catenary caused by sudden changes in line curvature and slope differences, the traditional static suspension parameter model is difficult to meet the requirements of pantograph coupling stability. An optimization method that can integrate track geometry parameters and contact force distribution and realize adaptive adjustment of suspension arrangement is urgently needed.

[0003] In the prior art, an offline optimization method based on three-dimensional point cloud scanning and finite element simulation is proposed. Track geometric parameters (such as superelevation and vertical curve curvature) are obtained through periodic scanning, a static dropper position calculation model is established, and the contact pressure distribution is predicted in combination with finite element simulation. The dropper node spacing and tension parameters are adjusted offline.

[0004] The defects of the existing technology are that it relies on periodic scanning data and offline simulation optimization, and is unable to respond to the high-frequency changes in the contact force of the pantograph slide, resulting in the adjustment of the suspension string parameters lagging behind the actual pantograph-net coupling state; the track geometry parameter compensation and contact force distribution optimization are processed in stages, and a coupling correction mechanism for contact force and track deformation has not been established. The sudden change in the contact network tension gradient in the large slope section is prone to cause the accumulation of compensation errors; the finite element simulation has a large amount of calculation and has not introduced hardware acceleration technology, resulting in a long optimization cycle in complex line scenarios, which is difficult to meet the requirements of high-speed railway operation and maintenance. Summary of the invention

[0005] The embodiments of the present application provide a method and system for optimizing the arrangement of high-speed railway contact network hanger strings based on machine learning, so as to solve the problems of insufficient bow-net coupling stability and low contact network wear uniformity in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for optimizing the arrangement of high-speed railway overhead contact network droppers based on machine learning, comprising: The pressure sensor at the overhead contact network suspension point is used to capture the spatial distribution characteristics of the contact force between the pantograph slide and the overhead contact network, and the contact force equilibrium constraint condition including the contact pressure standard deviation optimization objective is generated. Based on the contact force balance constraint condition, the track superelevation value and the vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track are used to perform parameter compensation processing on the suspension string position calculation model, and the line parameter compensation mechanism is generated in combination with the elevation data of the contact network suspension point to obtain the compensated three-dimensional coordinates of the contact network suspension point; The pipeline architecture of the programmable gate array hardware platform is used to perform parallel acceleration processing on the weight calculation and spatial coordinate transformation of the neural network, and the contact force equilibrium constraint condition and the compensated three-dimensional coordinate are synchronously iterated to generate the string arrangement parameters that meet the bow-net coupling relationship; Based on the segmented tension values ​​between the contact network suspension points and the compensation values ​​of the contact network node offsets in the suspension network arrangement parameters, the contact network construction equipment is driven to perform suspension network length adjustment and positioning installation.

[0007] Optionally, the weight calculation of the neural network and the spatial coordinate transformation are accelerated in parallel by using the pipeline architecture of the programmable gate array hardware platform, and the contact force equilibrium constraint condition and the compensated three-dimensional coordinate are iteratively calculated synchronously to generate the string arrangement parameters that satisfy the bow-net coupling relationship, including: Decompose the weight calculation of the neural network into an axial weight subtask related to the contact force distribution direction, and decompose the spatial coordinate transformation into a geometric transformation subtask based on the longitudinal curvature of the track; In the pipeline architecture of the programmable gate array hardware platform, an independent first parallel computing channel is allocated to the axial weight subtask, and an independent second parallel computing channel is allocated to the geometric transformation subtask; In the first parallel calculation channel, the axial weight coefficient of the contact force equilibrium constraint condition is adjusted according to the suspension point elevation data of the compensated three-dimensional coordinates; In the second parallel calculation channel, a segmented interpolation process is performed on the lateral offset of the compensated three-dimensional coordinates based on the longitudinal curvature of the track to generate a suspension point spatial deformation compensation amount; Through the built-in cross-channel data interaction unit of the programmable gate array hardware platform, the dot product calculation of the contact force direction vector and the spatial deformation vector is completed in a single clock cycle, and the axial weight coefficient and the suspension point spatial deformation compensation amount are coupled to generate a coupling operation result; According to the coupling operation result, the suspension point spacing in the compensated three-dimensional coordinates is relaxed and iterated. When the tension gradient change rate of adjacent suspension points is less than a preset convergence threshold, the final distribution sequence of the suspension string node offset compensation value is output to generate the suspension string arrangement parameters.

[0008] Optionally, through the built-in cross-channel data interaction unit of the programmable gate array hardware platform, the dot product calculation of the contact force direction vector and the spatial deformation vector is completed in a single clock cycle, and the axial weight coefficient and the suspension point spatial deformation compensation amount are coupled to generate a coupling operation result, including: The contact force direction vector is axially decomposed to obtain the first longitudinal direction component of the track and the second transverse direction component of the track, and the spatial deformation vector is divided into multiple continuous geometric units based on the spacing between adjacent suspension points of the compensated three-dimensional coordinates, and the transverse offset gradient of each segment is extracted; In the cross-channel data interaction unit, the first direction component and the lateral offset gradient are superimposed section by section, and at the same time, the second direction component and the elevation change rate of the suspension point spatial deformation compensation amount are projected to generate a superposition result and a projection operation result; Scaling the superposition result based on the longitudinal curvature of the track to generate a deformation coupling coefficient, and performing a scalar and vector mixed operation on the projection operation result and the deformation coupling coefficient through a preset vector synthesis circuit to obtain a mixed operation result; The mixed operation result is discretized and integrated according to the distribution density of the suspension point spatial deformation compensation amount to generate a coupling operation result.

[0009] Optionally, according to the coupling operation result, the suspension point spacing in the compensated three-dimensional coordinates is relaxed and iterated, and when the tension gradient change rate of adjacent suspension points is less than a preset convergence threshold, a final distribution sequence of the suspension string node offset compensation value is output to generate the suspension string arrangement parameters, including: Based on the longitudinal curvature of the track and the distribution density of the spatial deformation compensation amount of the suspension point, the compensated three-dimensional coordinates are divided into a plurality of tension balance areas, and an initial relaxation factor is assigned to each area; In the tension balance area, the initial relaxation factor is adjusted according to the tension gradient change rate of adjacent suspension points, wherein the relaxation step reduction strategy when the track superelevation value is greater than a preset threshold constrains the step length of the initial relaxation factor to obtain an adjusted initial relaxation factor; The adjusted initial relaxation factor is used to perform a bidirectional chain iterative calculation on the suspension point spacing in each tension balance area, and when the suspension point spacing adjustment triggers the offset compensation value of the adjacent suspension string node to change in the opposite direction, a suspension point density adaptive local rebalancing factor is generated based on the tension gradient change rate and the relaxation step reduction strategy; The local rebalancing factor is weightedly integrated with the deformation coupling coefficient in the coupling operation result, and the lateral offset cumulative error of the suspension point spatial deformation compensation amount is synchronously detected. If the lateral offset cumulative error exceeds the tolerance range corresponding to the curvature of the vertical curve of the track, the lateral offset gradient is reset based on the segmented results of the multiple continuous geometric units; When the tension gradient change rates of adjacent suspension points in all tension balance areas are less than a preset convergence threshold, a mapping relationship of the suspension string node offset compensation values ​​is generated based on the suspension point spacing adjustment amount, the local rebalancing factor and the reset lateral offset gradient, and the mapping relationship is locked to generate the suspension string arrangement parameters.

[0010] Optionally, the adjusted initial relaxation factor is used to perform a bidirectional chain iterative calculation on the suspension point spacing in each tension balance area. When the suspension point spacing adjustment triggers a reverse change in the offset compensation value of the adjacent suspension string node, a suspension point density adaptive local rebalancing factor is generated based on the tension gradient change rate and the relaxation step reduction strategy, including: Mark the suspension point spacing in the tension balance area in segments based on the longitudinal curvature of the track, and use the change in the longitudinal curvature of the track between adjacent suspension points as a direction switching threshold for chain iteration; In the bidirectional chain iterative calculation, the suspension point spacing is adjusted in sequence along the positive longitudinal direction of the track. When the suspension point spacing adjustment exceeds the direction switching threshold, the iterative calculation is switched to the reverse longitudinal direction of the track. In each iteration process, according to the inverse change relationship between the suspension point spacing adjustment amount and the offset compensation value of the adjacent suspension string node, the ratio of the current suspension point density to the tension gradient change rate is calculated to generate the suspension point density change coefficient; Based on the product of the suspension point density variation coefficient and the adjusted initial relaxation factor, the step length constraint range of the relaxation step length reduction strategy is corrected to generate a relaxation step length correction amount of the current iteration step; The relaxation step length correction amount and the tension gradient change rate are weightedly superimposed, and the superimposed result is subjected to curvature compensation processing in combination with the track superelevation value to generate a local rebalancing factor for suspension point density adaptation.

[0011] Optionally, in each iteration process, according to the inverse change relationship between the suspension point spacing adjustment amount and the offset compensation value of the adjacent suspension string node, the ratio of the current suspension point density to the tension gradient change rate is calculated to generate the suspension point density change coefficient, including: The suspension point spacing adjustment amount is processed in sections based on the change in the longitudinal curvature of the track to generate an adjustment ratio of the current suspension point spacing; According to the reverse change direction of the offset compensation value of the adjacent suspension string nodes, extract the reverse change interval of the suspension point spacing adjustment amount and the offset compensation value, and calculate the suspension point density change gradient within the reverse change interval; Performing a ratio operation on the suspension point density change gradient and the tension gradient change rate, and performing weighted correction on the adjustment ratio and the comparison value operation result to generate an initial value of the initial suspension point density change coefficient; The initial suspension point density variation coefficient is subjected to curvature compensation correction based on the track superelevation value to generate the suspension point density variation coefficient.

[0012] Optionally, based on the contact force equilibrium constraint condition, the track superelevation value and the vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track are used to perform parameter compensation processing on the suspension string position calculation model, and the line parameter compensation mechanism is generated in combination with the elevation data of the contact network suspension point to obtain the compensated three-dimensional coordinates of the contact network suspension point, including: The track superelevation value and vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track are used to perform segmented interpolation processing on the elevation data of the overhead contact network suspension point to generate the elevation change gradient of the suspension point; According to the contact pressure standard deviation optimization target in the contact force equilibrium constraint condition, the influence coefficient of the track superelevation value on the lateral offset of the overhead contact suspension point is extracted to generate a lateral offset compensation factor; The elevation change gradient of the suspension point and the lateral offset compensation factor are weightedly superimposed, and the superposition result is corrected by curvature compensation in combination with the vertical curve curvature to generate the suspension point spatial deformation compensation amount; Based on the change in the longitudinal curvature of the track, the spatial deformation compensation amount of the suspension point is processed in sections to generate the initial compensation coordinates of the overhead line suspension point; According to the contact pressure distribution direction in the contact force equilibrium constraint condition, the initial compensation coordinate is axially corrected to generate the compensated three-dimensional coordinate of the contact network suspension point.

[0013] In a second aspect, the embodiment of the present application provides a high-speed railway contact network dropper string arrangement optimization system based on machine learning, including: The contact constraint generation module is used to capture the spatial distribution characteristics of the contact force between the pantograph slide and the contact network using the pressure sensor at the contact network suspension point, and generate the contact force equilibrium constraint condition including the contact pressure standard deviation optimization target; A laser parameter compensation correction module is used to perform parameter compensation processing on the suspension string position calculation model based on the contact force equilibrium constraint condition, using the track superelevation value and the vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track, and generating a line parameter compensation mechanism in combination with the elevation data of the contact network suspension point to obtain the compensated three-dimensional coordinates of the contact network suspension point; A hardware acceleration iterative optimization module is used to utilize the pipeline architecture of the programmable gate array hardware platform to perform parallel acceleration processing on the weight calculation and spatial coordinate transformation of the neural network, synchronously iterate the contact force equilibrium constraint condition and the compensated three-dimensional coordinate, and generate the string arrangement parameters that satisfy the bow-net coupling relationship; The suspension string parameter execution driving module is used to drive the contact network construction equipment to perform suspension string length adjustment and positioning installation based on the segmented tension value between the contact network suspension points and the suspension string node offset compensation value in the suspension string arrangement parameters.

[0014] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for optimizing the arrangement of high-speed railway contact network suspension strings based on machine learning as described in the first aspect above.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a method for optimizing the arrangement of high-speed railway contact network suspension strings based on machine learning as described in the first aspect.

[0016] In an embodiment of the present application, a pressure sensor at a contact network suspension point is used to capture the spatial distribution characteristics of the contact force between the pantograph slide and the contact network, and a contact force balance constraint condition including a contact pressure standard deviation optimization target is generated; based on the contact force balance constraint condition, the track superelevation value and the vertical curve curvature obtained by the laser scanner along the longitudinal movement of the track are used to perform parameter compensation processing on the suspension string position calculation model, and a line parameter compensation mechanism is generated in combination with the elevation data of the contact network suspension point to obtain the compensated three-dimensional coordinates of the contact network suspension point; the pipeline architecture of the programmable gate array hardware platform is used to perform parallel acceleration processing on the weight calculation and spatial coordinate transformation of the neural network, and the contact force balance constraint condition and the compensated three-dimensional coordinates are synchronously iterated to generate suspension string arrangement parameters that satisfy the bow-network coupling relationship; based on the segmented tension value between the contact network suspension points and the suspension string node offset compensation value in the suspension string arrangement parameters, the contact network construction equipment is driven to perform suspension string length adjustment and positioning installation.

[0017] The technical solution of this application has the following beneficial effects: The spatial distribution characteristics of the contact force are captured by pressure sensors, and constraints with the standard deviation of the contact pressure as the optimization target are established to achieve balanced control of the bow-net contact force. The compensation mechanism is generated by combining the geometric parameters of the laser scanning track and the elevation data of the suspension point to accurately correct the spatial deformation error of the contact network and improve the positioning accuracy of the suspension string under complex line conditions. The programmable gate array pipeline architecture is used to parallelly process the neural network weights and spatial coordinate transformations to achieve millisecond-level synchronous iteration of the contact force constraints and compensation coordinates to ensure the accuracy of the coupled optimization. Executable parameters are generated based on the segmented tension value and the offset compensation value, which directly drives the construction equipment to complete the precise adjustment of the suspension string and form a closed-loop engineering implementation link.

[0018] Furthermore, by decomposing the weight calculation of the neural network into the contact force axial weight subtask and the spatial coordinate transformation into the track curvature geometric transformation subtask, independent parallel computing channels are allocated in the programmable gate array hardware platform; the contact force constraint weight coefficient is adjusted in the axial weight channel, and the deformation compensation amount is generated based on the track curvature in the geometric transformation channel; the cross-channel data interaction unit is used to realize the dot product calculation and coupling operation of the contact force direction vector and the deformation vector, and finally the suspension string arrangement parameters are generated through relaxation iterative convergence.

[0019] Through the above method, the bottleneck of traditional serial computing efficiency is broken through, and the coordinated optimization of contact force constraint and track deformation compensation is achieved through hardware acceleration, which solves the lag problem of suspension string parameter adjustment in complex line scenarios, improves the bow-net coupling stability and contact network wear uniformity, and shortens the optimization response time to milliseconds to meet the requirements of high-speed railway operation and maintenance.

[0020] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 A flow chart of a method for optimizing the arrangement of high-speed railway contact network hanger strings based on machine learning provided in the present application is shown; Figure 2 A structural schematic diagram of a high-speed railway contact network dropper arrangement optimization system based on machine learning provided by the present application is shown; Figure 3 A schematic diagram of the structure of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0024] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to different types.

[0025] The technical solution of the present application is applicable to the optimization scenario of suspension string arrangement under complex line conditions (such as large slopes and small curve radius). The research and development idea of ​​the present application is to capture the spatial distribution characteristics of the contact force between the pantograph slide and the contact network through a pressure sensor, and construct a constraint condition with the contact pressure standard deviation as the optimization target; synchronously integrate the track superelevation, vertical curve curvature and suspension point elevation data obtained by the laser scanner to generate three-dimensional coordinates for compensation of the contact network spatial deformation; use the pipeline architecture of the programmable gate array hardware platform to accelerate the weight calculation and spatial coordinate transformation of the neural network in parallel, and realize millisecond-level synchronous iterative optimization of contact force constraints and compensation coordinates; finally, based on the segmented tension value and offset compensation value, the construction equipment is driven to complete the precise adjustment of the suspension string, forming a full-link optimization logic of "perception and deformation compensation, hardware acceleration and closed-loop execution", to solve the problem of instability of complex line pantograph-network coupling.

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0027] Figure 1 A flowchart of a method for optimizing the arrangement of high-speed railway overhead contact network droppers based on machine learning is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes: 101. Use the pressure sensor at the overhead contact network suspension point to capture the spatial distribution characteristics of the contact force between the pantograph slide and the overhead contact network, and generate the contact force equilibrium constraint condition including the contact pressure standard deviation optimization objective; In this step, the pressure sensor refers to a plurality of pressure sensors arranged at the contact network suspension point, which can simultaneously detect the contact force components between the pantograph slide and the contact network in different directions (such as longitudinal, transverse, and vertical).

[0028] The contact pressure standard deviation optimization target is to establish a balance target by statistically analyzing the standard deviation of the contact force distribution, which is used to constrain the spatial fluctuation range of the contact force.

[0029] In an embodiment of the present application, first, a high-precision pressure sensor array is deployed at each suspension point of the contact network to collect dynamic contact force data when the pantograph slide passes in real time, and the spatial frequency domain characteristics of the contact force fluctuation are extracted by Fourier transform; secondly, the sliding window method is used to standardize the continuously monitored contact force data, calculate the standard deviation of the contact pressure in each suspension point area, and set the minimization of the standard deviation as the optimization goal; then, a contact force distribution prediction model is constructed based on the random forest algorithm, and the suspension point position characteristics (such as span, slope, and suspension mass) that have a significant impact on the contact force fluctuation are screened out through feature importance analysis; finally, the key features output by the prediction model are combined with the standard deviation optimization goal to generate a contact force balance constraint condition containing the suspension point position weight coefficient, and the constraint condition expression is that the weighted sum of the standard deviations of the contact forces of each suspension point is less than a preset threshold.

[0030] In an actual case, taking a high-speed railway section with a steep slope as an example, a triaxial pressure sensor array was deployed at the contact network suspension point with a slope of 25‰ to detect the contact force when the pantograph slide passed. When the train passed at 350km / h, the sensor captured a vertical pressure peak of 120N and a lateral offset force gradient change rate of 0.8N / m (exceeding the constraint threshold). The system automatically triggered the contact force standard deviation optimization target generation module, adjusted the lateral offset force gradient constraint value to 0.6N / m, and generated the corresponding equilibrium constraint condition.

[0031] 102. Based on the contact force equilibrium constraint condition, the track superelevation value and the vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track are used to perform parameter compensation processing on the suspension string position calculation model, and the line parameter compensation mechanism is generated in combination with the elevation data of the contact network suspension point to obtain the compensated three-dimensional coordinates of the contact network suspension point; In this step, the line parameter compensation mechanism refers to a mathematical model that corrects the spatial position of the overhead contact network by combining the track geometric parameters (superelevation value, vertical curve curvature) with the elevation data of the suspension point.

[0032] Compensated three-dimensional coordinates refer to the three-dimensional spatial coordinates of the contact network suspension point generated after parameter compensation, including lateral offset and elevation correction.

[0033] In the embodiment of the present application, first, a laser scanner scans the track geometric parameters with millimeter-level accuracy along the longitudinal direction of the track, extracts the track superelevation value and the vertical curve curvature through point cloud registration technology, and uses a cubic spline interpolation algorithm to perform continuous processing on discrete measurement points; secondly, the processed track parameters are input into the suspension string position calculation model, a three-dimensional coordinate system is established based on the elevation data of the suspension point, and the deviation matrix between the track parameters and the original coordinates of the suspension point is calculated through the least squares method; then, an adaptive compensation algorithm is designed: a lateral offset compensation coefficient α = superelevation value / standard track gauge is introduced for the track superelevation value, and a weighted correction of the inverse of the curvature radius is used for the vertical curve curvature to generate a parameter compensation vector containing the lateral offset and the elevation correction; finally, the compensation vector is superimposed on the original three-dimensional coordinates of the suspension point, and the compensated three-dimensional coordinates of the compensated suspension point are obtained through the spatial coordinate transformation matrix calculation to form a line parameter compensation mechanism.

[0034] Continuing with the above example, in a section with a small curve radius (R=600m), the laser scanner detected that the track superelevation value was 80mm and the vertical curve curvature was 1 / 800m⁻¹; combined with the measured value of the suspension point elevation (12.5m), the system converted the superelevation value into a lateral offset compensation coefficient (+16mm), and interpolated and corrected the elevation data according to the curvature (-2.1mm), and finally generated the compensated three-dimensional coordinates as (lateral +16mm, elevation 10.4m).

[0035] 103. Using the pipeline architecture of the programmable gate array hardware platform to perform parallel acceleration processing on the weight calculation and spatial coordinate transformation of the neural network, synchronously iteratively calculate the contact force equilibrium constraint condition and the compensated three-dimensional coordinates, and generate the string arrangement parameters that meet the bow-net coupling relationship; In this step, pipeline architecture refers to the hardware design of parallel processing tasks in field-programmable gate arrays (FPGAs), allowing the weight calculation of the neural network to be performed synchronously with the spatial coordinate transformation.

[0036] Synchronous iterative operation refers to the coupling calculation of contact force constraints and compensated three-dimensional coordinates within the same hardware clock cycle.

[0037] In the embodiment of the present application, firstly, a dual-pipeline architecture is deployed on the FPGA platform for parallel acceleration: the first pipeline uses a fixed-point arithmetic unit to process the neural network weight calculation, decomposes the contact force direction into the longitudinal / lateral components of the track, and dynamically adjusts the axial weight coefficient based on the gradient descent method; the second pipeline is configured with a floating-point arithmetic unit to perform spatial coordinate transformation, and uses the CORDIC algorithm to realize the real-time conversion of the track curvature to the spatial deformation variable; secondly, hardware-level data fusion is realized through a cross-channel data interaction unit: the dot product operation of the contact force direction vector and the spatial deformation vector is completed in a single clock cycle, and the ping-pong buffer technology is used to realize the wait-free interaction of the two-channel data; then, a relaxation iteration algorithm is established: the suspension point spacing is initialized to the design value, the tension gradient is calculated according to the coupling operation result, and when it is detected that the tension gradient change rate of adjacent suspension points exceeds the threshold, a bidirectional chain iteration is triggered (forward iteration adjusts the suspension point spacing, and reverse iteration corrects the offset compensation value); finally, the convergence judgment condition is set to the tension gradient change rate being less than 0.5% for three consecutive iterations, and the string arrangement parameters that meet the bow-net coupling requirements are output, including the theoretical coordinates and allowable offset of each string node.

[0038] Continuing with the above example, in the continuous curve section (R=800m), the FPGA hardware platform performs a dot product operation (100×12=1200N·mm) on the longitudinal component of the contact force (100N) and the lateral offset (+12mm), and adjusts the longitudinal weight coefficient to 0.15 according to the elevation data (-1.5m). After three iterations, the tension gradient change rate drops to 0.04N / m (less than the threshold value of 0.05), and the generated dropper node offset compensation value is +8mm.

[0039] 104. Based on the segmented tension value between the contact network suspension points and the compensation value of the node offset of the contact network in the contact network arrangement parameters, drive the contact network construction equipment to perform the contact network length adjustment and positioning installation.

[0040] In this step, the segmented tension value refers to the tension distribution parameter between the overhead contact network suspension points divided by the longitudinal sections of the track, such as the tension target value for each 10m section.

[0041] The offset compensation value refers to the lateral or vertical adjustment of the suspension node relative to the theoretical position, such as +5mm (right deviation) or -3mm (downward movement).

[0042] In the embodiment of the present application, first, the segmented tension value in the suspension string arrangement parameters is analyzed, and the tension adjustment instruction is generated by the PID controller: when the measured tension deviates from the theoretical value by more than 50N, the hydraulic servo system is driven to adjust the suspension string length, and the control accuracy reaches ±1mm; secondly, the suspension string node offset compensation value is converted into a construction positioning coordinate, and a laser locator is used to guide the installation robot arm, and three-dimensional space positioning is achieved through triangulation, and the lateral positioning error is controlled within ±3mm; then, a construction closed-loop verification mechanism is established: the contact force re-measurement is started immediately after the installation is completed. If it is detected that the contact pressure standard deviation has risen by more than 10% of the optimized value, the parameter re-optimization process is automatically triggered; finally, the construction data (including the final suspension string length, positioning coordinates, and measured tension) is uploaded to the cloud database through the industrial Internet of Things platform, and a digital twin model is generated for subsequent operation and maintenance.

[0043] Continuing with the above example, in a certain large ramp section (slope 30‰), the system generates segmented tension values ​​for Section 1 (12.5kN) and Section 2 (12.2kN), and the offset compensation value is +10mm in the lateral direction. After the construction equipment receives the command, the hydraulic mechanism accurately adjusts the suspension string tension to the target value, and the laser positioning device guides the robotic arm to install the suspension string node to the position of +10mm in lateral offset, with the error controlled within ±0.5mm.

[0044] In summary, steps 101 to 104 achieve adaptive adjustment of the string arrangement under complex line conditions through contact force perception, track geometry parameter compensation, hardware accelerated iterative optimization, and construction closed-loop execution. In scenarios such as large ramps and small curve radii, the system responds to contact force fluctuations and track deformation, and shortens the optimization cycle to milliseconds through FPGA hardware acceleration, ensuring uniformity of string tension distribution and node positioning accuracy, significantly improving the stability of the bow-net coupling, and reducing non-uniform wear of the contact network.

[0045] In order to improve the optimization capability of the suspension string arrangement under complex line conditions of high-speed railways, the contact force constraint and track deformation compensation are collaboratively accelerated through the pipeline architecture of the programmable gate array hardware platform, and the relaxation iterative mechanism is combined to generate high-precision suspension string parameters.

[0046] In some embodiments, in step 103, the weight calculation of the neural network and the spatial coordinate transformation are accelerated in parallel by using the pipeline architecture of the programmable gate array hardware platform, and the contact force balance constraint condition and the compensated three-dimensional coordinate are iteratively calculated synchronously to generate the string arrangement parameters that satisfy the bow-net coupling relationship, including: 201. Decompose the weight calculation of the neural network into an axial weight subtask related to the contact force distribution direction, and decompose the spatial coordinate transformation into a geometric transformation subtask based on the longitudinal curvature of the track; In step 201, the axial weight subtask refers to the weight calculation task of the neural network decomposed according to the contact force distribution direction (such as longitudinal and lateral directions), which is used to adjust the weight coefficient of the contact force constraint condition.

[0047] The geometric transformation subtask refers to the task of performing segmented transformation of the contact network spatial coordinates based on the longitudinal curvature of the track, which is used to compensate for the contact network deviation caused by the geometric deformation of the track.

[0048] In an embodiment of the present application, first, the weight calculation of the neural network is directionally decomposed, the main direction of the contact force distribution is extracted as the axial reference through principal component analysis, and the weight update amount is projected onto the two orthogonal axes of the longitudinal and transverse directions of the track to form an axial weight subtask; secondly, the geometric features of the spatial coordinate transformation are decoupled, and the differential geometry method is used to calculate the influence factor of the longitudinal curvature of the track on the coordinate transformation, and the mapping relationship between curvature and deformation is established, and the curvature-based geometric transformation subtask is decomposed.

[0049] 202. In the pipeline architecture of the programmable gate array hardware platform, an independent first parallel computing channel is allocated to the axial weight subtask, and an independent second parallel computing channel is allocated to the geometric transformation subtask; In step 202, the parallel computing channels refer to independently running logic units in the FPGA hardware, which are used to process different computing tasks simultaneously.

[0050] In an embodiment of the present application, first, a dual-channel pipeline architecture is designed in an FPGA hardware platform, the first channel is configured with a fixed-point arithmetic unit and preloads an axial weight calculation instruction set, and the second channel deploys a floating-point arithmetic unit and stores a geometric transformation parameter lookup table; secondly, independent data storage areas are allocated to the two channels through a hardware description language, the first channel is connected to an elevation data bus for compensating three-dimensional coordinates, and the second channel is connected to a track curvature sensor input interface; then, clock domain crossing technology is used to achieve parallel clock synchronization of the dual channels to ensure that the calculation rhythms of the two channels are aligned; finally, a hardware-level handshake protocol is deployed between channels, and when any channel completes the current calculation task, the cross-channel data interaction unit is started by triggering the status register.

[0051] 203. In the first parallel calculation channel, adjusting the axial weight coefficient of the contact force equilibrium constraint condition according to the suspension point elevation data of the compensated three-dimensional coordinates; In step 203, the suspension point elevation data refers to the rate of change of the vertical height of the overhead contact network suspension point along the longitudinal direction of the track (such as the elevation change per meter).

[0052] In an embodiment of the present application, first, the elevation data set of the suspension points that compensate for the three-dimensional coordinates is read in the first parallel calculation channel, and the high-frequency noise in the elevation data is removed by Gaussian filtering; secondly, the filtered elevation data is multiplied point by point with the initial value of the axial weight in the contact force equilibrium constraint condition, and the axial weight influence factor of each suspension point is calculated; then, the weight coefficient is dynamically adjusted based on the gradient descent method: if the elevation change rate of a suspension point exceeds 20% of the average value of the adjacent points, its axial weight coefficient is increased by 0.1 times the standard deviation; finally, the adjusted axial weight coefficient is written to the output buffer of the first channel, and a ready signal is sent to the cross-channel data interaction unit at the same time.

[0053] 204. In the second parallel calculation channel, a segmented interpolation process is performed on the lateral offset of the compensated three-dimensional coordinate based on the longitudinal curvature of the track to generate a suspension point spatial deformation compensation amount; In step 204, the segmented interpolation process refers to interpolating and correcting the lateral offset according to the range of longitudinal curvature variation of the track (such as curvature radius <800m, 800-1500m).

[0054] In an embodiment of the present application, first, the longitudinal curvature data set of the track is loaded in the second parallel calculation channel, and the curvature measurement points are processed continuously using the cubic spline interpolation algorithm; secondly, the lateral offset of the compensated three-dimensional coordinates is divided into multiple continuous intervals according to the curvature change points, and a local coordinate system is established in each interval; then, the compensation gradient of the lateral offset in each interval is calculated based on the curvature radius, and when the curvature radius is less than 1000 meters, the secondary compensation mode is started, and an additional 5% of the offset is added as a safety margin; finally, the compensation amount of each interval is fitted with a B-spline curve to generate a smooth spatial deformation compensation amount, which is stored in the result register of the second channel.

[0055] 205. The dot product calculation of the contact force direction vector and the spatial deformation vector is completed within a single clock cycle through the cross-channel data interaction unit built into the programmable gate array hardware platform, and the axial weight coefficient is coupled with the suspension point spatial deformation compensation amount to generate a coupling operation result; In step 205, the cross-channel data interaction unit refers to a hardware module inside the FPGA that implements data interaction between different computing channels.

[0056] In an embodiment of the present application, first, the cross-channel data interaction unit simultaneously reads the axial weight coefficient matrix of the first channel and the spatial deformation compensation vector of the second channel; secondly, the parallel processing of the vector dot product operation is realized at the hardware level: the contact force direction vector is decomposed into a 16-bit fixed-point format, and the spatial deformation vector is converted into a 32-bit floating-point format, and the synchronous calculation of the component products is completed through a dedicated multiplier array; then, a carry-preserving addition tree is used to complete the cumulative summation of the product results within a single clock cycle to generate a coupling intermediate value in scalar form; finally, the axial weight coefficient and the coupling intermediate value are weightedly superimposed, the dimensional difference is eliminated through processing, and a coupling operation result with unified dimension is output.

[0057] 206. Perform relaxation iteration on the suspension point spacing in the compensated three-dimensional coordinates according to the coupling operation result, and when the tension gradient change rate of adjacent suspension points is less than a preset convergence threshold, output a final distribution sequence of the suspension string node offset compensation values ​​to generate suspension string arrangement parameters.

[0058] In step 206, the relaxation iteration refers to adjusting the suspension point spacing by a stepwise approximation method until the tension gradient change rate meets the convergence condition.

[0059] In the embodiment of the present application, first, the coupling operation result is input into the relaxation iterative controller, and the suspension point spacing is initialized to the range of ±10% of the design standard value; secondly, a bidirectional chain iteration is performed along the longitudinal direction of the track: during the forward iteration, the suspension point spacing is increased according to the tension gradient, and during the reverse iteration, the spacing is reduced in combination with the offset compensation value, and the step size of each iteration is adaptively adjusted to 1 / 5 of the current tension gradient change rate; then, the tension gradient change rate of adjacent suspension points is monitored in real time, and when the change rate is less than a preset threshold (such as 0.5%) in three consecutive iterations, it is determined to be a convergence state; finally, the current suspension point spacing distribution is locked, the offset compensation value of each suspension string node is extracted to form a final distribution sequence, and the suspension string arrangement parameters including node coordinates, compensation amount and tension value are output.

[0060] Here is a specific example: In a section where a small curve radius (R=600m) overlaps with a large ramp (25‰), the laser scanner detects a track superelevation value of 85mm and a vertical curve curvature of 1 / 600m⁻¹. The pressure sensor measures a longitudinal contact force peak of 130N and a lateral offset force gradient of 0.9N / m. The system decomposes the task and distributes it to the FPGA dual channels through steps 201-202: the first channel adjusts the longitudinal weight to 0.15 based on the elevation data (-0.3m / 10m); the second channel interpolates the curvature 1 / 600m⁻¹ section to generate a lateral offset of +18mm. The cross-channel interaction unit completes the dot product operation (130×18=2340N·mm). After three relaxation iterations, the tension gradient change rate drops to 0.45N / m, generating an offset compensation sequence (+12mm, +11mm, +10mm) to drive the construction equipment to complete the precise installation of the suspension string.

[0061] In summary, steps 201 to 206 achieve millisecond-level optimization response of contact force constraint and track deformation compensation under complex line conditions through the coordination of hardware acceleration and relaxation iteration, significantly improve the accuracy of suspension string arrangement parameters, ensure uniform distribution of pantograph contact force, reduce the risk of abnormal contact network wear, and adapt to the operation requirements of high-speed trains.

[0062] In order to further improve the efficiency of the coupling operation of contact force and track deformation, the collaborative calculation of contact force direction and spatial deformation is realized through the cross-channel data interaction unit of the programmable gate array hardware platform, and high-precision coupling results are generated in combination with discrete integration.

[0063] In some embodiments, in step 205, the cross-channel data interaction unit built into the programmable gate array hardware platform completes the dot product calculation of the contact force direction vector and the spatial deformation vector in a single clock cycle, and couples the axial weight coefficient with the suspension point spatial deformation compensation amount to generate a coupling operation result, including: 301. Axially decompose the contact force direction vector to obtain a first direction component in the longitudinal direction of the track and a second direction component in the transverse direction of the track, and divide the spatial deformation vector into multiple continuous geometric units based on the spacing between adjacent suspension points of the compensated three-dimensional coordinates, and extract the transverse offset gradient of each segment; In step 301, axial decomposition refers to decomposing the contact force direction vector into two orthogonal components according to the longitudinal direction of the track (the direction in which the train travels) and the transverse direction (perpendicular to the track direction).

[0064] Continuous geometric unit means dividing the track longitudinally into multiple geometric calculation intervals based on the distance between adjacent suspension points (such as 10 meters), and each interval corresponds to an independent spatial deformation correction parameter.

[0065] In an embodiment of the present application, first, the contact force direction vector is decomposed into the track coordinate system by using the coordinate system projection method, and the longitudinal first direction component along the extension direction of the track and the lateral second direction component perpendicular to the track are obtained by calculating the direction cosines; secondly, based on the spacing data of adjacent suspension points in the compensated three-dimensional coordinates, the spatial deformation vector is divided into multiple continuous geometric units according to the principle of equal spacing, and the length of each unit is set to 1 / 5 of the spacing between adjacent suspension points; then, the change gradient of the lateral offset is calculated in each geometric unit, and the central difference method is used to calculate the lateral offset difference between the starting point and the end point of the unit and divided by the unit length to generate the lateral offset gradient of each segment, thereby providing input data for cross-channel interaction.

[0066] 302. In the cross-channel data exchange unit, the first direction component and the lateral offset gradient are superimposed section by section, and at the same time, the second direction component and the elevation change rate of the suspension point spatial deformation compensation amount are projected to generate a superposition result and a projection operation result; In step 302, the projection operation refers to performing data mapping calculation in an orthogonal direction between the lateral contact force component and the suspension point elevation change rate (vertical height change / track longitudinal length).

[0067] In an embodiment of the present application, first, the cross-channel data interaction unit reads the longitudinal first direction component data from the first parallel computing channel, and simultaneously obtains the segmented lateral offset gradient value from the second channel; secondly, the segment-by-segment superposition of the components and gradients is realized at the hardware level: the longitudinal component and the lateral gradient value corresponding to each geometric unit are scalarly superimposed through a fixed-point adder to generate superposition results of each segment; then, the elevation change rate of the lateral second direction component and the spatial deformation compensation amount of the suspension point are synchronously processed: the second component is projected to the elevation change direction through a vector projection circuit, and the product of the projection length and the elevation change rate is calculated to generate a projection operation result in scalar form; finally, the superposition result sequence and the projection result matrix are written into the superposition result queue and the projection result buffer respectively to trigger subsequent processing signals.

[0068] 303. Scaling the superposition result based on the longitudinal curvature of the track to generate a deformation coupling coefficient, and performing a scalar and vector mixed operation on the projection operation result and the deformation coupling coefficient through a preset vector synthesis circuit to obtain a mixed operation result; In step 303, the deformation coupling coefficient refers to a correction factor reflecting the effect of the longitudinal curvature of the track on the contact force and deformation coupling strength.

[0069] Scalar and vector mixed operation refers to the joint calculation of the scalar projection result and the vector deformation coefficient.

[0070] In an embodiment of the present application, first, the longitudinal curvature value of the current section is extracted from the track parameter database, and the superposition result is multiplied by the inverse of the curvature radius through the curvature scaling factor calculator to generate a deformation coupling coefficient; secondly, a preset vector synthesis circuit reads the projection operation result and the deformation coupling coefficient at the same time, and a hardware multiplier is used to multiply the deformation coupling coefficient as a scalar with the projection result vector element by element; then, the product result is superimposed with the original space deformation vector through a parallel adder, wherein the longitudinal components are directly added and the lateral components are weighted averaged; finally, the operation result is saturated to limit the output value within a preset physical reasonable range, and a mixed operation result including the longitudinal deformation amount and the lateral correction amount is generated.

[0071] 304. Discretize and integrate the mixed operation result according to the distribution density of the suspension point spatial deformation compensation amount to generate a coupling operation result.

[0072] In step 304, the discretization integration process refers to performing segmented cumulative calculations on the mixed results according to the distribution density of the spatial deformation compensation amount of the suspension points (such as the number of suspension points per meter).

[0073] In the embodiment of the present application, first, the distribution density of the spatial deformation compensation amount of the suspension point is statistically analyzed, and the high-density area is determined by the histogram analysis method and divided into integral intervals, each interval containing 3-5 suspension points; secondly, the trapezoidal integration method is used in each integral interval, and the mixed operation results are weighted and accumulated according to the density weight, and the longitudinal component is linearly integrated, and the transverse component is applied with quadratic interpolation; then, the integration results are processed to eliminate the magnitude deviation caused by the density difference in different intervals, and generate a standardized coupling value; finally, the discrete integration results are reconstructed into a continuous distribution curve through the spatial interpolation algorithm, and the coupling operation results containing the coupling strength values ​​of each suspension point are output to complete the final fusion of multi-dimensional data.

[0074] Here is a specific example: In a section where a large ramp (slope 30‰) and a small curve radius (R=500m) overlap, the laser scanner measured a track superelevation of 90mm and a vertical curve curvature of 1 / 500m⁻¹. The pressure sensor collected a longitudinal contact force of 150N and a lateral contact force of 40N. The spacing between suspension points was 10 meters and the distribution density was 3 points / 10 meters. In step 301, the longitudinal component 150N and the transverse component 40N are decomposed, and the transverse offset gradient +1.5mm / m is extracted after the geometric unit is divided; in step 302, the longitudinal component and the gradient are superimposed to obtain 150×1.5=225N·mm / m, and the transverse component and the elevation change rate (-0.3m / 10m=-0.03) are projected to obtain 40×(-0.03)=-1.2N·m / m; in step 303, the deformation coupling coefficient 225×0.2=45 is generated according to the curvature 1 / 500m⁻¹ (scaling factor 0.2), and the mixed operation result is -1.2×45=-54N·m² / m; in step 304, discrete integration is performed at a density of 3 points / 10 meters (-54÷3=-18 / point), and the 3-point integral is accumulated to obtain -54N·m², and the coupling operation result is generated to drive the suspension string offset compensation.

[0075] In summary, steps 301 to 304 realize efficient coupling calculation of contact force and track deformation through coordinated processing of axial decomposition, projection operation and discrete integration, significantly improve the efficiency and accuracy of optimization of suspension string parameters under complex line conditions, ensure the stability of pantograph coupling, and adapt to the operation requirements of high-speed trains in sharp bends and steep slopes.

[0076] In order to further improve the convergence efficiency of the suspension string arrangement parameters, the adaptive adjustment of the suspension point spacing is achieved through the tension balance area division and relaxation iteration mechanism, and the high-precision suspension string parameters are generated in combination with the lateral offset error detection.

[0077] In some embodiments, in step 206, the suspension point spacing in the compensated three-dimensional coordinates is relaxed and iterated according to the coupling operation result, and when the tension gradient change rate of adjacent suspension points is less than a preset convergence threshold, a final distribution sequence of the suspension string node offset compensation value is output to generate the suspension string arrangement parameters, including: 401. Based on the longitudinal curvature of the track and the distribution density of the spatial deformation compensation amount of the suspension point, the compensated three-dimensional coordinate is divided into a plurality of tension balance areas, and an initial relaxation factor is allocated to each area; In step 401, the tension balance area refers to an independent optimization interval divided according to the longitudinal curvature of the track and the distribution density of the spatial deformation compensation amount of the suspension point, which is used for local tension gradient balancing processing.

[0078] The initial relaxation factor refers to the initial parameter that reflects the adjustment rate of the suspension point spacing. The larger the value, the larger the adjustment range allowed in a single iteration.

[0079] In an embodiment of the present application, firstly, based on the track longitudinal curvature data set and the distribution density heat map of the spatial deformation compensation amount of the suspension point, the K-means clustering algorithm is used to divide the compensated three-dimensional coordinates into multiple tension balance areas, each area contains 5-8 adjacent suspension points; secondly, an initial relaxation factor is assigned to each area, and the initial value is calculated according to the average curvature in the area: for every 0.001m⁻¹ increase in curvature, the relaxation factor decreases by 0.1, and the initial value is smoothed by a Gaussian distribution model; then, a regional boundary marking mechanism is established, and a transition buffer is inserted when the curvature difference between adjacent areas exceeds a set threshold; finally, the division result and the corresponding initial relaxation factor are written into the regional management register to provide initialization parameters for iterative calculation.

[0080] 402. In the tension balance area, the initial relaxation factor is adjusted according to the tension gradient change rate of adjacent suspension points, wherein the relaxation step reduction strategy when the track superelevation value is greater than a preset threshold performs step-size constraint on the initial relaxation factor to obtain an adjusted initial relaxation factor; In step 402, the relaxation step reduction strategy refers to a step limit rule designed for areas with large track superelevation values ​​(such as superelevation>80mm) to prevent over-adjustment of parameters during the iteration process.

[0081] In the embodiment of the present application, first, in the divided tension balance area, the tension gradient change rate of adjacent suspension points is monitored in real time, and the sliding window method is used to calculate the mean and standard deviation of the change rate in the current window; secondly, the relaxation factor is dynamically adjusted according to the change rate: if the change rate exceeds 1.2 times of the previous iteration cycle, the relaxation factor is increased by 0.05, otherwise it is reduced by 0.03; then, the track superelevation value constraint condition is introduced, and when the superelevation value is detected to be greater than a preset threshold (such as 150mm), the relaxation step reduction strategy is activated to limit the adjustment range to 50% of the normal value; finally, the effective range of the relaxation factor is limited to [0.2,1.8] through the saturation function, and the adjusted initial relaxation factor table is generated and updated to the area management register.

[0082] 403. Perform bidirectional chain iterative calculation on the suspension point spacing in each tension balance area using the adjusted initial relaxation factor, and when the suspension point spacing adjustment triggers the offset compensation value of the adjacent suspension string node to change in the opposite direction, generate a suspension point density adaptive local rebalancing factor based on the tension gradient change rate and the relaxation step reduction strategy; In step 403, the bidirectional chain iterative calculation refers to an iterative method of alternately adjusting the suspension point spacing in the forward and reverse directions along the longitudinal direction of the track to balance the tension distribution.

[0083] The local rebalancing factor refers to the regional adaptive adjustment coefficient generated according to the tension gradient change rate and step size constraint.

[0084] In the embodiment of the present application, first, the bidirectional chain iteration is initialized using the adjusted relaxation factor: the forward iteration gradually increases the suspension point spacing along the track extension direction, and the reverse iteration reduces the spacing in the reverse direction, and each adjustment step is the product of the relaxation factor and the current spacing; secondly, the changing trend of the offset compensation value of the suspension string node is monitored in real time, and when it is detected that the compensation values ​​of adjacent nodes are adjusted in opposite directions (such as the compensation of the front node increases and the compensation of the rear node decreases), a local rebalancing calculation is triggered; then, the density influence coefficient is calculated based on the current tension gradient change rate, and the product of the relaxation factor and the density influence coefficient is used as the local rebalancing factor.

[0085] 404. Weighted fusion is performed on the local rebalancing factor and the deformation coupling coefficient in the coupling operation result, and the lateral offset cumulative error of the suspension point spatial deformation compensation amount is synchronously detected. If the lateral offset cumulative error exceeds the tolerance range corresponding to the curvature of the vertical curve of the track, the lateral offset gradient is reset based on the segmented results of the multiple continuous geometric units. In step 404, the lateral offset cumulative error refers to the cumulative deviation value of the suspension point spatial deformation compensation amount in the lateral direction of the track (eg, +15 mm exceeds the allowable ±10 mm).

[0086] In the embodiment of the present application, first, the local rebalancing factor and the deformation coupling coefficient in the coupling operation result are weighted and fused, and the weight ratio is dynamically allocated according to the regional curvature: for every 0.001m⁻¹ increase in curvature, the weight of the deformation coupling coefficient increases by 5%; secondly, the lateral offset cumulative error of the spatial deformation compensation amount of the suspension point is synchronously calculated, and the moving average method is used to calculate the error cumulative value of the most recent 10 suspension points; then, when it is detected that the cumulative error exceeds the vertical curve curvature tolerance range (such as ±5mm), the lateral offset gradient reset mechanism is triggered: based on the original segmented geometric unit data, the lateral offset gradient of each segment is recalculated; finally, the reset gradient value is updated to the compensation parameter register of the second parallel calculation channel, and the error accumulation counter is cleared.

[0087] 405. When the tension gradient change rates of adjacent suspension points in all tension balance areas are less than a preset convergence threshold, a mapping relationship of the suspension string node offset compensation values ​​is generated based on the suspension point spacing adjustment amount, the local rebalancing factor and the reset lateral offset gradient, and the mapping relationship is locked to generate the suspension string arrangement parameters.

[0088] In step 405, the mapping relationship refers to a mathematical correlation model between the suspension point spacing adjustment amount, the local rebalancing factor and the lateral offset gradient.

[0089] In an embodiment of the present application, first, convergence detection is performed in parallel in all tension balance areas, and the moving average of the tension gradient change rate of adjacent suspension points is calculated. When the change rate of all areas is less than a preset threshold (such as 0.5%) for three consecutive iterations, it is determined that the system has reached a stable state; secondly, the suspension point spacing adjustment amount, the local rebalancing factor and the reset lateral offset gradient of each area are integrated, and a continuous distribution curve of the offset compensation value of the suspension string node is generated through a cubic spline interpolation algorithm; then, a compensation value mapping relationship table is established, the theoretical coordinates are bound to the allowable offset range, and a compensation range is set for each node; finally, the mapping relationship table is locked and the suspension string arrangement parameters including node numbers, coordinates, compensation amounts and tension values ​​are generated to complete the optimization calculation process.

[0090] Here is a specific example: In a section where continuous sharp bends (R=400m, R=450m) overlap with a large slope (28‰), the track superelevation is 95mm, the vertical curve curvature is 1 / 400m⁻¹, and the suspension point distribution density is 4 points / 10 meters. Step 401 divides the tension balance area into three areas (curvature 0.0025m⁻¹, 0.0022m⁻¹, 0.0018m⁻¹), and assigns initial relaxation factors of 0.8, 0.75, and 0.7; Step 402 reduces the factors to 0.7, 0.65, and 0.6 because the ultra-high value exceeds the threshold; Step 403 bidirectional iteration triggers the reverse change of the offset (+6mm→-1mm), and generates a local rebalancing factor of 0.3; Step 404 detects the cumulative error of the lateral offset of +13mm (out of tolerance ±10mm), and resets the gradient to +0.7mm / m; Step 405 generates a mapping relationship (+6×0.3×0.7=+1.26mm), outputs the offset sequence of +1.26mm, +1.2mm, and +1.15mm, and drives the suspension string to be accurately installed.

[0091] In summary, steps 401 to 405 achieve rapid convergence and high-precision generation of the suspension string arrangement parameters under complex line conditions through relaxation iteration and closed-loop correction of lateral errors, significantly improve the stability of the bow-net coupling, ensure uniform distribution of contact network tension, and meet the continuous operation requirements of high-speed trains in sharp bends and steep slopes.

[0092] In some embodiments, in step 403, the suspension point spacing in each tension balance area is calculated in a bidirectional chain iterative manner using the adjusted initial relaxation factor. When the suspension point spacing adjustment triggers a reverse change in the offset compensation value of the adjacent suspension string node, a suspension point density adaptive local rebalancing factor is generated based on the tension gradient change rate and the relaxation step reduction strategy, including: 501. Mark the suspension point spacing in the tension balance area in sections based on the longitudinal curvature of the track, and use the change in the longitudinal curvature of the track between adjacent suspension points as a direction switching threshold for chain iteration; In step 501, segment marking refers to dividing the suspension point spacing in the tension balance area into intervals according to the longitudinal curvature of the track (such as a curvature value of 0.002m⁻¹ corresponding to a curvature radius of 500m), and each interval corresponds to an independent iterative direction switching rule.

[0093] The direction switching threshold refers to the critical value of the curvature change that triggers the iteration direction to switch from the positive longitudinal direction of the track to the negative direction (for example, switching when the curvature change exceeds 0.0005m⁻¹).

[0094] In the embodiment of the present application, first, continuous measurement data of the longitudinal curvature of the track is obtained, and the sliding window method is used to calculate the curvature change between adjacent suspension points, and the window width is set to the spacing between 3 suspension points; secondly, a direction switching threshold is set according to the size of the curvature change, and when the curvature change between adjacent points exceeds 0.002m⁻¹, it is marked as a segment boundary; then, the calculation interval is divided according to the segment mark in the tension balance area, and the curvature change threshold is stored at the starting point of each interval; finally, the marking result is written into the iteration control register to provide a direction switching trigger condition for the bidirectional chain iteration.

[0095] 502. In the bidirectional chain iterative calculation, the suspension point spacing is adjusted in sequence along the longitudinal positive direction of the track. When the suspension point spacing adjustment amount exceeds the direction switching threshold, the iterative calculation is switched to the longitudinal reverse direction of the track; In step 502, the bidirectional chain iterative calculation refers to an iterative method of alternately adjusting the suspension point spacing along the longitudinal positive direction (train travel direction) and the reverse direction of the track to balance the tension distribution.

[0096] In the embodiment of the present application, first, the bidirectional chain iteration direction is initialized to the longitudinal positive direction of the track, and the spacing is gradually increased starting from the starting suspension point, and the amount of each adjustment is the product of the current relaxation factor and the standard spacing; secondly, the suspension point spacing adjustment amount is monitored in real time, and when the cumulative adjustment amount exceeds the curvature change threshold of the corresponding interval, the direction switching interrupt is triggered; then, the current iteration state is saved to the stack, and the reverse iteration mode is switched to reduce the spacing in reverse starting from the current suspension point.

[0097] 503. In each iteration process, according to the inverse change relationship between the suspension point spacing adjustment amount and the offset compensation value of the adjacent suspension string node, the ratio of the current suspension point density to the tension gradient change rate is calculated to generate the suspension point density change coefficient; In step 503, the suspension point density variation coefficient refers to a correction factor reflecting the relationship between the suspension point distribution density (such as the number of suspension points per 10 meters) and the tension gradient variation rate.

[0098] In an embodiment of the present application, firstly, the adjustment amount of the suspension point spacing and the direction of change of the offset compensation value of the adjacent suspension string nodes are synchronously recorded in each iteration. When it is detected that the offset of the adjacent nodes shows an opposite change (such as the compensation of node A increases and the compensation of node B decreases), the suspension point density in the current area is extracted; secondly, the ratio of the density to the rate of change of the tension gradient is calculated, and the exponential weighted average method is used to eliminate the influence of instantaneous fluctuations; then, the ratio is processed to the [0,1] interval through the Sigmoid function to generate the suspension point density change coefficient; finally, the coefficient is stored in a temporary register for subsequent step correction.

[0099] 504. Based on the product of the suspension point density variation coefficient and the adjusted initial relaxation factor, the step length constraint range of the relaxation step length reduction strategy is corrected to generate a relaxation step length correction amount of the current iteration step; In step 504, the relaxation step correction amount refers to the iterative step correction value adjusted according to the density variation coefficient and the relaxation factor.

[0100] In the embodiment of the present application, first, the adjusted initial relaxation factor table is read to extract the relaxation factor value corresponding to the current iteration step; secondly, the density variation coefficient is scalar multiplied by the relaxation factor, and the calculation result is constrained by a limiter to be between 50% and 150% of the original step range; then, the correction strategy is dynamically selected according to the track superelevation state: when the superelevation value exceeds 120mm, an additional 20% reduction factor is applied; finally, a relaxation step correction value including the upper and lower limits of the step size is generated and updated to the iterative controller.

[0101] 505. Perform weighted superposition on the relaxation step length correction amount and the tension gradient change rate, perform curvature compensation processing on the superposition result in combination with the track superelevation value, and generate a local rebalancing factor for suspension point density self-adaptation.

[0102] In step 505, curvature compensation processing refers to geometrically correcting iterative parameters based on the track superelevation value to eliminate the interference of track deformation on tension distribution.

[0103] In the embodiment of the present application, first, the relaxation step correction amount and the real-time tension gradient change rate are weighted and superimposed, and the weight ratio is dynamically adjusted according to the number of iterations (initial weight 0.7:0.3, and the change rate weight is increased by 0.05 every 10 iterations); secondly, the track superelevation compensation module is introduced to perform curvature compensation on the superimposed result: when the superelevation value is in the [100mm, 150mm] interval, the compensation coefficient is 1.2, and in other intervals it is 1.0; then, the compensated value is projected to the direction of change of the suspension point density through a vector rotation operation to generate a direction-sensitive local rebalancing factor.

[0104] Here is a specific example: In a section where a small curve radius (R=450m) overlaps with a large ramp (30‰), the longitudinal curvature of the track is 0.0022m⁻¹, and the density of the suspension points is 4 points / 10 meters. Step 501 sets the curvature change of 0.0004m⁻¹ as the direction switching threshold; Step 502 triggers the threshold when the spacing is adjusted by +4mm in the positive direction, and switches to the reverse direction to adjust -3mm; Step 503 calculates the density change coefficient (4 points÷0.7N / m≈5.71×0.1=0.571); Step 504 generates the relaxation step correction (0.571×0.6=0.343mm); Step 505 generates the local rebalancing factor (0.343×0.95≈0.326) in combination with the superelevation value of 95mm (compensation coefficient 0.95), and finally drives the compensation value of the node offset of the suspension string to be adjusted to +0.326mm, +0.31mm, and +0.29mm.

[0105] In summary, steps 501 to 505 significantly improve the stability and accuracy of the suspension string spacing adjustment under complex line conditions through the bidirectional chain iteration and density adaptive rebalancing mechanism, effectively suppress the sudden change of tension gradient, ensure the uniformity of the pantograph coupling, and adapt to the high-frequency operation requirements of high-speed trains in sharp bends and steep slopes.

[0106] In some embodiments, in step 503, during each iteration, the ratio of the current suspension point density to the tension gradient change rate is calculated according to the inverse change relationship between the suspension point spacing adjustment amount and the offset compensation value of the adjacent suspension string node, and the suspension point density change coefficient is generated, including: 601. Performing segment processing on the suspension point spacing adjustment amount based on the track longitudinal curvature variation to generate an adjustment ratio of the current suspension point spacing; In step 601, segmentation processing refers to dividing the suspension point spacing adjustment amount into intervals according to the change in the longitudinal curvature of the track (such as the change in the radius of curvature), and standardizing the adjustment amount to a preset range according to the intervals.

[0107] In the embodiment of the present application, first, continuous measurement data of the change in the longitudinal curvature of the track is obtained, and the data is divided into multiple sections by using an equally spaced sampling method, and the length of each section is set to twice the distance between adjacent suspension points; secondly, the distribution of the adjustment amount of the suspension point distance is statistically analyzed in each section, the average value and standard deviation of the adjustment amount are calculated, and the ratio of the standard deviation to the average value is used as the adjustment ratio of the section; then, the abnormal adjustment amount is eliminated, and when the adjustment amount of a certain point exceeds 3 times the standard deviation of the section average value, a linear interpolation method is used for correction; finally, the adjustment ratio of each section is stored in the ratio parameter table in the order of track mileage, providing a benchmark parameter for subsequent density calculation.

[0108] 602. According to the reverse change direction of the offset compensation value of the adjacent suspension string nodes, extract the reverse change interval of the suspension point spacing adjustment amount and the offset compensation value, and calculate the suspension point density change gradient within the reverse change interval; In step 602, the reverse change interval refers to a track longitudinal interval in which the offset compensation values ​​of adjacent dropper node points reverse direction (eg, suddenly change from a positive value to a negative value).

[0109] The gradient of suspension point density change refers to the rate of change of suspension point density (such as the number of suspension points per 10 meters) in the reverse change range with the spacing adjustment amount.

[0110] In an embodiment of the present application, first, the direction of change of the offset compensation value of adjacent suspension string nodes is monitored in real time. When it is detected that the compensation value of the front node increases and the compensation value of the rear node decreases, it is marked as a reverse change interval; secondly, the spacing adjustment data of all suspension points in the interval are extracted, and the density change in the window (the default window size is 5 suspension points) is calculated by the sliding window method; then, the central difference method is used to calculate the suspension point density change gradient, that is, the ratio of the density difference between two adjacent points to the spacing.

[0111] 603. Perform a ratio operation on the suspension point density change gradient and the tension gradient change rate, and perform weighted correction on the ratio calculation result in combination with the adjustment ratio to generate an initial value of the initial suspension point density change coefficient; In step 603, weighted correction refers to weighting the ratio of the density gradient to the tension gradient according to the normalized adjustment ratio.

[0112] In the embodiment of the present application, first, the density change gradient data is read from the gradient register, and the current tension gradient change rate measurement value is obtained at the same time, and the two are ratio-calculated (density gradient / tension change rate); secondly, the adjustment ratio of the corresponding segment in the proportional parameter table is retrieved, and the ratio calculation result is multiplied by the adjustment ratio coefficient using a weighted correction algorithm; then, the correction result is normalized and mapped to the [0,1] interval through a Sigmoid function; finally, the initial value of the initial suspension point density change coefficient is generated and written into the coefficient buffer area to wait for curvature compensation.

[0113] 604. Perform curvature compensation correction on the initial suspension point density variation coefficient based on the track superelevation value to generate the suspension point density variation coefficient.

[0114] In step 604, curvature compensation correction refers to geometric correction of the density variation coefficient based on the track superelevation value to eliminate the interference of the track lateral deformation on the parameter calculation.

[0115] In the embodiment of the present application, first, the superelevation value data of the current section is obtained from the track parameter database, and a mapping relationship table between the superelevation value and the curvature compensation coefficient is established (for every 10mm increase in the superelevation value, the compensation coefficient increases by 0.05); secondly, the initial density change coefficient matrix is ​​read, and the corresponding compensation coefficient is obtained by looking up the table according to the superelevation value of the section; then, the initial coefficient is multiplied by the compensation coefficient to generate the density change coefficient after curvature compensation; finally, the compensation result is saturated, the coefficient value is limited to the physically reasonable range [0.2,1.5], and the final suspension point density change coefficient is output to the optimization controller.

[0116] Here is a specific example: In a section where a large ramp (slope 28‰) and a small curve radius (R=450m) overlap, the track longitudinal curvature changes by 0.0003m⁻¹, the suspension point spacing adjustment is +6mm, and the offset compensation value changes suddenly from +4mm to -1mm. Step 601 normalizes the adjustment amount to a ratio of 0.8; Step 602 determines that the reverse change interval is 125-130 meters, and calculates the density change gradient (5 points / 10 meters÷(-5mm)=-1 point / mm); Step 603 generates the initial coefficient (-1÷0.5N / m=-2×0.8=-1.6); Step 604 based on the superelevation value of 95mm (compensation coefficient 0.95), the correction coefficient is -1.6×0.95=-1.52, and the drive string node offset is adjusted to -1.52mm, -1.45mm, and -1.4mm.

[0117] In summary, steps 601 to 604 significantly improve the calculation accuracy of the suspension point density variation coefficient through the coordinated processing of segmentation and curvature compensation, effectively suppress the overshoot risk of suspension string parameter adjustment under complex line conditions, ensure the balance of the pantograph contact force distribution, and adapt to the continuous operation requirements of high-speed trains in sharp bends and steep slopes.

[0118] In some embodiments, in step 102, based on the contact force equilibrium constraint condition, the track superelevation value and the vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track are used to perform parameter compensation processing on the suspension string position calculation model, and the line parameter compensation mechanism is generated in combination with the elevation data of the contact network suspension point to obtain the compensated three-dimensional coordinates of the contact network suspension point, including: 701. Using the track superelevation value and vertical curve curvature obtained by moving the laser scanner along the longitudinal direction of the track, the elevation data of the overhead contact network suspension point are subjected to segmented interpolation processing to generate the elevation change gradient of the suspension point; In step 701, the segmented interpolation process refers to dividing the elevation data of the suspension point into intervals at fixed longitudinal intervals of the track (eg, every 10 meters), and using an interpolation algorithm to fill in the data discontinuities.

[0119] The gradient of the suspension point elevation change refers to the rate of change of the vertical height of the contact network suspension point along the longitudinal unit length of the track (such as a 0.2 meter elevation change per meter).

[0120] In the embodiment of the present application, first, the laser scanner collects the original data of the track superelevation value and the vertical curve curvature at intervals of 10 cm along the longitudinal direction of the track, and the equipment vibration noise is eliminated by Kalman filtering; secondly, based on the mileage coordinates of the contact network suspension points, the cubic spline interpolation algorithm is used to perform continuous processing on the discrete elevation data to generate a suspension point elevation sequence at intervals of each meter; then, a differential operation is performed on the interpolated elevation data to calculate the elevation difference between adjacent suspension points and divide it by the spacing to generate the suspension point elevation change gradient.

[0121] 702. According to the contact pressure standard deviation optimization target in the contact force equilibrium constraint condition, extract the influence coefficient of the track superelevation value on the lateral offset of the overhead contact suspension point, and generate a lateral offset compensation factor; In step 702, the lateral offset compensation factor refers to a correction coefficient that reflects the effect of the track superelevation value on the lateral offset of the contact network (eg, for every 10 mm increase in superelevation value, the lateral offset compensation increases by 2 mm).

[0122] In the embodiment of the present application, first, the target value of the contact pressure standard deviation is extracted from the contact force equilibrium constraint condition, and a mapping relationship table between the standard deviation and the lateral offset is established; secondly, the random forest algorithm is used to analyze the influence weight of the track superelevation value on the lateral offset, and the top three key influencing parameters are selected by ranking the feature importance; then, based on the multivariate linear regression model, the lateral offset change coefficient corresponding to the unit change of the superelevation value is calculated to generate the lateral offset compensation factor α=Δx / Δh (Δh is the superelevation change).

[0123] 703. Perform weighted superposition on the elevation change gradient of the suspension point and the lateral offset compensation factor, perform curvature compensation correction on the superposition result in combination with the vertical curve curvature, and generate a suspension point spatial deformation compensation amount; In step 703, curvature compensation correction refers to geometric correction of the weighted superposition result based on the curvature of the vertical curve to eliminate the interference of the vertical deformation of the track on the spatial deformation.

[0124] In the embodiment of the present application, firstly, the elevation change gradient curve is processed so that its dimension is consistent with the lateral offset compensation factor; secondly, a dynamic weight allocation strategy is designed: a 60% weight is given to the elevation gradient in the straight segment, and the lateral compensation factor is increased to a 55% weight in the curved segment; then, the weighted superposition result is input into the curvature compensation module, and the compensation coefficient β=1 / (R·k) (R is the curvature radius, k is the material elastic coefficient) is calculated using the vertical curve curvature radius, and the superposition result is nonlinearly amplified; finally, the mutation point is smoothed by a spatial convolution operation, and the spatial deformation compensation amount of the suspension point including the lateral displacement Δx and the elevation correction Δz is output.

[0125] 704. Performing piecewise integration processing on the spatial deformation compensation amount of the suspension point based on the change amount of the longitudinal curvature of the track to generate the initial compensation coordinates of the overhead line suspension point; In step 704, the segmented integration process refers to segmented accumulation calculation of the spatial deformation compensation amount according to the change in the longitudinal curvature of the track.

[0126] In the embodiment of the present application, firstly, the spatial deformation compensation amount is divided into continuous integration intervals according to the rate of change of the longitudinal curvature of the track, and each interval corresponds to a track segment with the same curvature sign; secondly, the trapezoidal integration method is used in each interval to accumulate and calculate the Δx and Δz components respectively along the mileage direction of the track, and the longitudinal integration step is set to 1 / 10 of the suspension point spacing; then, the integration result is superimposed with the original suspension point coordinates to generate preliminary compensation coordinates; finally, the system cumulative error is eliminated through coordinate translation transformation to ensure that the coordinate closure error between the starting point and the end point is less than 2 mm, thereby forming the initial compensation coordinates.

[0127] 705. According to the contact pressure distribution direction in the contact force equilibrium constraint condition, the initial compensation coordinate is axially corrected to generate the compensated three-dimensional coordinate of the contact network suspension point.

[0128] In step 705 , the axial correction refers to adjusting the axial component of the compensation coordinate according to the contact pressure distribution direction (eg, longitudinal dominant or transverse dominant).

[0129] In the embodiment of the present application, first, the main direction vector of the contact pressure is extracted from the contact force equilibrium constraint condition and decomposed into the longitudinal and vertical components in the orbital coordinate system; secondly, an axial correction model is established: it includes the longitudinal coordinate correction amount and the vertical correction amount; then, the rigid body rotation algorithm is used to spatially transform the initial compensation coordinates according to the correction amount to ensure that the correction direction is consistent with the contact force distribution; finally, the coordinate accuracy verification is performed: the contact pressure standard deviation of the corrected coordinates is calculated by the back propagation algorithm. If the target value is not reached, steps 702-705 are repeated until the compensated three-dimensional coordinates that meet the mechanical requirements are generated.

[0130] Here is a specific example: In a section where a small curve radius (R=500m) overlaps with a large ramp (25‰), the laser scanner measured a track superelevation value of 85mm, a vertical curve curvature of 1 / 500m⁻¹, and an actual measured suspension point elevation of 12.6m. Step 701 generates an elevation change gradient of -0.3m / 10m; Step 702 calculates a lateral offset compensation factor of +17mm; Step 703 weighted superposition obtains -0.3×0.6+17×0.4=6.62, and the deformation after curvature compensation is 6.95mm; Step 704 integrates to generate initial coordinates (lateral +17mm, elevation 12.3m); Step 705 corrects the elevation to 12.0m based on 80% of the longitudinal contact force, and outputs the compensated three-dimensional coordinates (lateral +17mm, elevation 12.0m).

[0131] In summary, steps 701 to 705 achieve high-precision calculation of contact network spatial deformation compensation under complex line conditions through the integration of laser scanning data and contact force constraints, significantly improve the positioning accuracy of the suspension string, effectively balance the distribution of the bow-net contact force, and adapt to the high-frequency deformation compensation needs of high-speed trains in sharp bends and steep slopes.

[0132] Figure 2 A structural schematic diagram of a high-speed railway contact network suspension string arrangement optimization system based on machine learning is provided for an embodiment of the present application, such as Figure 2 As shown, the device comprises: The contact constraint generation module 21 is used to capture the spatial distribution characteristics of the contact force between the pantograph slide plate and the contact network by using the pressure sensor at the contact network suspension point, and generate a contact force equilibrium constraint condition including a contact pressure standard deviation optimization target; The laser parameter compensation correction module 22 is used to perform parameter compensation processing on the suspension string position calculation model based on the contact force balance constraint condition, using the track superelevation value and the vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track, and generating a line parameter compensation mechanism in combination with the elevation data of the contact network suspension point to obtain the compensated three-dimensional coordinates of the contact network suspension point; The hardware acceleration iterative optimization module 23 is used to utilize the pipeline architecture of the programmable gate array hardware platform to perform parallel acceleration processing on the weight calculation and spatial coordinate transformation of the neural network, synchronously iterate the contact force balance constraint condition and the compensated three-dimensional coordinate, and generate the string arrangement parameters that meet the bow-net coupling relationship; The suspension string parameter execution driving module 24 is used to drive the contact network construction equipment to perform suspension string length adjustment and positioning installation based on the segmented tension value between the contact network suspension points and the suspension string node offset compensation value in the suspension string arrangement parameters.

[0133] Figure 2 The above-mentioned optimization device for high-speed railway contact network suspension string arrangement based on machine learning can perform Figure 1The implementation principle and technical effect of the optimization method for the arrangement of high-speed railway overhead contact network suspension strings based on machine learning described in the illustrated embodiment will not be repeated. The specific manner in which each module and unit performs operations in the optimization device for the arrangement of high-speed railway overhead contact network suspension strings based on machine learning in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0134] In one possible design, Figure 2 The optimization device for high-speed railway contact network suspension string arrangement based on machine learning in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0135] The processing component 32 is used for the above Figure 1 The embodiment provides a method for optimizing the arrangement of high-speed railway contact network hanger strings based on machine learning.

[0136] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0137] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0138] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0139] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0140] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0141] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0142] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a method for optimizing the arrangement of high-speed railway contact network hanger strings based on machine learning.

[0143] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0144] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0145] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the arrangement of high-speed railway overhead contact wires based on machine learning, characterized in that: include: The pressure sensor at the overhead contact network suspension point is used to capture the spatial distribution characteristics of the contact force between the pantograph slide and the overhead contact network, and the contact force equilibrium constraint condition including the contact pressure standard deviation optimization objective is generated. Based on the contact force balance constraint condition, the track superelevation value and the vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track are used to perform parameter compensation processing on the suspension string position calculation model, and the line parameter compensation mechanism is generated in combination with the elevation data of the contact network suspension point to obtain the compensated three-dimensional coordinates of the contact network suspension point; Iterate the contact force equilibrium constraint condition and the compensated three-dimensional coordinate synchronously to generate the string arrangement parameters satisfying the bow-net coupling relationship; Based on the segmented tension values ​​between the contact network suspension points and the compensation values ​​of the contact network node offsets in the suspension network arrangement parameters, the contact network construction equipment is driven to perform suspension network length adjustment and positioning installation.

2. The method according to claim 1, characterized in that The contact force equilibrium constraint condition and the compensated three-dimensional coordinate are synchronously iterated to generate the string arrangement parameters satisfying the pantograph-catenary coupling relationship, including: Decompose the weight calculation of the neural network into an axial weight subtask related to the contact force distribution direction, and decompose the spatial coordinate transformation into a geometric transformation subtask based on the longitudinal curvature of the track; In the pipeline architecture of the programmable gate array hardware platform, an independent first parallel computing channel is allocated to the axial weight subtask, and an independent second parallel computing channel is allocated to the geometric transformation subtask; In the first parallel calculation channel, the axial weight coefficient of the contact force equilibrium constraint condition is adjusted according to the suspension point elevation data of the compensated three-dimensional coordinates; In the second parallel calculation channel, a segmented interpolation process is performed on the lateral offset of the compensated three-dimensional coordinates based on the longitudinal curvature of the track to generate a suspension point spatial deformation compensation amount; Through the built-in cross-channel data interaction unit of the programmable gate array hardware platform, the dot product calculation of the contact force direction vector and the spatial deformation vector is completed in a single clock cycle, and the axial weight coefficient and the suspension point spatial deformation compensation amount are coupled to generate a coupling operation result; According to the coupling operation result, the suspension point spacing in the compensated three-dimensional coordinates is relaxed and iterated. When the tension gradient change rate of adjacent suspension points is less than a preset convergence threshold, the final distribution sequence of the suspension string node offset compensation value is output to generate the suspension string arrangement parameters.

3. The method according to claim 2, characterized in that Through the built-in cross-channel data interaction unit of the programmable gate array hardware platform, the dot product calculation of the contact force direction vector and the spatial deformation vector is completed in a single clock cycle, and the axial weight coefficient and the suspension point spatial deformation compensation amount are coupled to generate a coupling operation result, including: The contact force direction vector is axially decomposed to obtain the first longitudinal direction component of the track and the second transverse direction component of the track, and the spatial deformation vector is divided into multiple continuous geometric units based on the spacing between adjacent suspension points of the compensated three-dimensional coordinates, and the transverse offset gradient of each segment is extracted; In the cross-channel data interaction unit, the first direction component and the lateral offset gradient are superimposed section by section, and at the same time, the second direction component and the elevation change rate of the suspension point spatial deformation compensation amount are projected to generate a superposition result and a projection operation result; Scaling the superposition result based on the longitudinal curvature of the track to generate a deformation coupling coefficient, and performing a scalar and vector mixed operation on the projection operation result and the deformation coupling coefficient through a preset vector synthesis circuit to obtain a mixed operation result; The mixed operation result is discretized and integrated according to the distribution density of the suspension point spatial deformation compensation amount to generate a coupling operation result.

4. The method according to claim 3, characterized in that According to the coupling operation result, the suspension point spacing in the compensated three-dimensional coordinate is relaxed and iterated. When the tension gradient change rate of adjacent suspension points is less than a preset convergence threshold, a final distribution sequence of the suspension string node offset compensation value is output to generate the suspension string arrangement parameters, including: Based on the longitudinal curvature of the track and the distribution density of the spatial deformation compensation amount of the suspension point, the compensated three-dimensional coordinates are divided into a plurality of tension balance areas, and an initial relaxation factor is assigned to each area; In the tension balance area, the initial relaxation factor is adjusted according to the tension gradient change rate of adjacent suspension points, wherein the relaxation step reduction strategy when the track superelevation value is greater than a preset threshold constrains the step length of the initial relaxation factor to obtain an adjusted initial relaxation factor; The adjusted initial relaxation factor is used to perform a bidirectional chain iterative calculation on the suspension point spacing in each tension balance area, and when the suspension point spacing adjustment triggers the offset compensation value of the adjacent suspension string node to change in the opposite direction, a suspension point density adaptive local rebalancing factor is generated based on the tension gradient change rate and the relaxation step reduction strategy; The local rebalancing factor is weightedly integrated with the deformation coupling coefficient in the coupling operation result, and the lateral offset cumulative error of the suspension point spatial deformation compensation amount is synchronously detected. If the lateral offset cumulative error exceeds the tolerance range corresponding to the curvature of the vertical curve of the track, the lateral offset gradient is reset based on the segmented results of the multiple continuous geometric units; When the tension gradient change rates of adjacent suspension points in all tension balance areas are less than a preset convergence threshold, a mapping relationship of the suspension string node offset compensation values ​​is generated based on the suspension point spacing adjustment amount, the local rebalancing factor and the reset lateral offset gradient, and the mapping relationship is locked to generate the suspension string arrangement parameters.

5. The method according to claim 4, characterized in that The adjusted initial relaxation factor is used to perform bidirectional chain iterative calculation on the suspension point spacing in each tension balance area. When the suspension point spacing adjustment triggers the offset compensation value of the adjacent suspension string node to change in the opposite direction, a suspension point density adaptive local rebalancing factor is generated based on the tension gradient change rate and the relaxation step reduction strategy, including: Mark the suspension point spacing in the tension balance area in segments based on the longitudinal curvature of the track, and use the change in the longitudinal curvature of the track between adjacent suspension points as a direction switching threshold for chain iteration; In the bidirectional chain iterative calculation, the suspension point spacing is adjusted in sequence along the positive longitudinal direction of the track. When the suspension point spacing adjustment exceeds the direction switching threshold, the iterative calculation is switched to the reverse longitudinal direction of the track. In each iteration process, according to the inverse change relationship between the suspension point spacing adjustment amount and the offset compensation value of the adjacent suspension string node, the ratio of the current suspension point density to the tension gradient change rate is calculated to generate the suspension point density change coefficient; Based on the product of the suspension point density variation coefficient and the adjusted initial relaxation factor, the step length constraint range of the relaxation step length reduction strategy is corrected to generate a relaxation step length correction amount of the current iteration step; The relaxation step length correction amount and the tension gradient change rate are weightedly superimposed, and the superimposed result is subjected to curvature compensation processing in combination with the track superelevation value to generate a local rebalancing factor for suspension point density adaptation.

6. The method according to claim 5, characterized in that In each iteration process, according to the inverse change relationship between the suspension point spacing adjustment amount and the offset compensation value of the adjacent suspension string node, the ratio of the current suspension point density to the tension gradient change rate is calculated to generate the suspension point density change coefficient, including: The suspension point spacing adjustment amount is processed in sections based on the change in the longitudinal curvature of the track to generate an adjustment ratio of the current suspension point spacing; According to the reverse change direction of the offset compensation value of the adjacent suspension string nodes, extract the reverse change interval of the suspension point spacing adjustment amount and the offset compensation value, and calculate the suspension point density change gradient within the reverse change interval; Performing a ratio operation on the suspension point density change gradient and the tension gradient change rate, and performing weighted correction on the adjustment ratio and the comparison value operation result to generate an initial value of the initial suspension point density change coefficient; The initial suspension point density variation coefficient is subjected to curvature compensation correction based on the track superelevation value to generate the suspension point density variation coefficient.

7. The method according to claim 1, characterized in that Based on the contact force equilibrium constraint condition, the track superelevation value and vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track are used to perform parameter compensation processing on the suspension string position calculation model. The line parameter compensation mechanism is generated in combination with the elevation data of the contact network suspension point to obtain the compensated three-dimensional coordinates of the contact network suspension point, including: The track superelevation value and vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track are used to perform segmented interpolation processing on the elevation data of the overhead contact network suspension point to generate the elevation change gradient of the suspension point; According to the contact pressure standard deviation optimization target in the contact force equilibrium constraint condition, the influence coefficient of the track superelevation value on the lateral offset of the overhead contact suspension point is extracted to generate a lateral offset compensation factor; The elevation change gradient of the suspension point and the lateral offset compensation factor are weightedly superimposed, and the superposition result is corrected by curvature compensation in combination with the vertical curve curvature to generate the suspension point spatial deformation compensation amount; Based on the change in the longitudinal curvature of the track, the spatial deformation compensation amount of the suspension point is processed in sections to generate the initial compensation coordinates of the overhead line suspension point; According to the contact pressure distribution direction in the contact force equilibrium constraint condition, the initial compensation coordinate is axially corrected to generate the compensated three-dimensional coordinate of the contact network suspension point.

8. A high-speed railway contact network dropper arrangement optimization system based on machine learning, characterized in that: include: The contact constraint generation module is used to capture the spatial distribution characteristics of the contact force between the pantograph slide and the contact network using the pressure sensor at the contact network suspension point, and generate the contact force equilibrium constraint condition including the contact pressure standard deviation optimization target; A laser parameter compensation correction module is used to perform parameter compensation processing on the suspension string position calculation model based on the contact force equilibrium constraint condition, using the track superelevation value and the vertical curve curvature obtained by the laser scanner moving along the longitudinal direction of the track, and generating a line parameter compensation mechanism in combination with the elevation data of the contact network suspension point to obtain the compensated three-dimensional coordinates of the contact network suspension point; A hardware acceleration iterative optimization module is used to utilize the pipeline architecture of the programmable gate array hardware platform to perform parallel acceleration processing on the weight calculation and spatial coordinate transformation of the neural network, synchronously iterate the contact force equilibrium constraint condition and the compensated three-dimensional coordinate, and generate the string arrangement parameters that satisfy the bow-net coupling relationship; The suspension string parameter execution driving module is used to drive the contact network construction equipment to perform suspension string length adjustment and positioning installation based on the segmented tension value between the contact network suspension points and the suspension string node offset compensation value in the suspension string arrangement parameters.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for optimizing the arrangement of high-speed railway contact network suspension strings based on machine learning as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a method for optimizing the arrangement of high-speed railway contact network hanger strings based on machine learning is implemented as described in any one of claims 1 to 7.

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