A simulation method, device, equipment and medium for railway infrastructure deformation

Abstract

The present invention provides a method, device, equipment, and medium for simulating the deformation of railway infrastructure, relating to the field of safety monitoring technology. The method comprises: obtaining historical load parameters and historical spatiotemporal development curves of target facilities; obtaining an initial spatiotemporal development curve; adjusting a line model, using the corresponding foundation deformation simulation model as a target deformation simulation model; and forming a sample set based on all operating condition data and corresponding deformation amounts; training a preset neural network model based on the sample set to obtain a target deformation prediction model; and inputting the target operating condition data into the target deformation prediction model to obtain the deformation amount of the target facility. The present invention constructs a line model and applies loads based on similarity theory, and employs a non-foundation deformation monitoring device to collect the changing patterns of the line model under various load conditions. This simplifies the control of the entire deformation process, and can simulate the foundation deformation of various lines at a low cost and with relatively accurate simulation results.

Description

Technical Field

[0001] The present invention relates to the field of safety monitoring technology, and in particular to a method, device, equipment and medium for simulating deformation of railway infrastructure. Background Art

[0002] Railway lines are ultra-long strip structures with complex and changeable geological environments below them. Foundation deformation can cause changes in the structural stress state, thereby affecting the structural smoothness. Existing technologies for simulating railway foundation deformation can only simulate longitudinal settlement deformation, which is significantly different from objective reality. In terms of deformation data collection, existing technical methods mostly use strain gauges for deformation data collection, which is complicated and easily affected by the external environment. Summary of the Invention

[0003] The present invention aims to provide a method, device, equipment and medium for simulating railway infrastructure deformation to improve the above-mentioned problems. To achieve the above-mentioned objectives, the present invention adopts the following technical solutions:

[0004] In a first aspect, the present application provides a method for simulating deformation of railway infrastructure, comprising:

[0005] Obtaining historical load parameters and a historical spatiotemporal development curve of a target facility, wherein the historical spatiotemporal development curve is used to characterize spatiotemporal deformation characteristics of the target facility;

[0006] Substituting the historical load parameters into a pre-built foundation deformation simulation model to obtain an initial spatiotemporal development curve, the foundation deformation simulation model at least comprising a line model and a deformation model, the line model being a scaled-down model corresponding to the target facility constructed based on geometric similarity theory, and the deformation model being used to apply loads to the line model;

[0007] Adjusting the line model until the overall similarity between the initial spatiotemporal development curve and the historical spatiotemporal development curve is greater than a first set threshold, and using the corresponding basic deformation simulation model as a target deformation simulation model;

[0008] Bringing different working condition data into the target deformation simulation model to obtain corresponding deformations, and forming a sample set based on all working condition data and corresponding deformations, wherein the working condition data includes lateral load, longitudinal load and shear load;

[0009] Training a preset neural network model based on the sample set, and stopping the training when the preset model indicators meet the set conditions to obtain a target deformation prediction model;

[0010] The target working condition data is input into the target deformation prediction model to obtain the deformation amount of the target facility.

[0011] In a second aspect, the present application further provides a simulation device for railway infrastructure deformation, comprising:

[0012] A first acquisition unit is configured to acquire historical load parameters and a historical spatiotemporal development curve of a target facility, wherein the historical spatiotemporal development curve is used to characterize spatiotemporal deformation characteristics of the target facility;

[0013] a first importing unit, configured to import the historical load parameters into a pre-constructed foundation deformation simulation model to obtain an initial spatiotemporal development curve, wherein the foundation deformation simulation model comprises at least a line model and a deformation model, wherein the line model is a scaled-down model corresponding to the target facility constructed based on geometric similarity theory, and the deformation model is used to apply loads to the line model;

[0014] an adjusting unit, configured to adjust the line model until the overall similarity between the initial spatiotemporal development curve and the historical spatiotemporal development curve is greater than a first set threshold, and use the corresponding basic deformation simulation model as a target deformation simulation model;

[0015] a second input unit, configured to input different working condition data into the target deformation simulation model to obtain corresponding deformations, and to form a sample set based on all working condition data and corresponding deformations, wherein the working condition data includes lateral load, longitudinal load, and shear load;

[0016] A training unit, configured to train a preset neural network model based on the sample set, and stop training when the preset model indicators meet the set conditions to obtain a target deformation prediction model;

[0017] The input unit is used to input the target working condition data into the target deformation prediction model to obtain the deformation amount of the target facility.

[0018] In a third aspect, the present application further provides a simulation device for railway infrastructure deformation, comprising:

[0019] memory for storing computer programs;

[0020] A processor is configured to implement the steps of the method for simulating railway infrastructure deformation when executing the computer program.

[0021] In a fourth aspect, the present application further provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned simulation method based on railway infrastructure deformation.

[0022] The beneficial effects of the present invention are:

[0023] The present invention constructs a line model and applies loads through similarity theory, and adopts a non-foundation deformation monitoring device to collect the changing rules of the line model under various load conditions, making the control of the entire deformation process relatively simple, and can simulate the foundation deformation of various lines with low cost and more accurate simulation results.

[0024] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 Schematic diagram of the flow of a method for simulating railway infrastructure deformation according to an embodiment of the present invention;

[0027] Figure 2 Schematic diagram of the basic deformation simulation model structure described in an embodiment of the present invention;

[0028] Figure 3 Schematic diagram of the structure of the longitudinal lifting device according to an embodiment of the present invention;

[0029] Figure 4 Schematic diagram of the structure of the lateral displacement device according to an embodiment of the present invention;

[0030] Figure 5 Schematic diagram of the structure of a simulation device for railway infrastructure deformation according to an embodiment of the present invention;

[0031] Figure 6 Schematic diagram of the structure of the simulation equipment for railway infrastructure deformation described in an embodiment of the present invention.

[0032] Markings in the figure:

[0033] 1. Monitoring device; 2. Longitudinal lifting device; 3. Lateral displacement device; 4. Line model; 5. Triangular reaction wall; 6. First lifting rod; 7. Support plate; 8. Ball twisting platform; 9. Flexible thin plate; 10. Second lifting rod; 11. Lateral displacement rod; 100. First acquisition unit; 200. First input unit; 300. Adjustment unit; 400. Second input unit; 500. Training unit; 600. Input unit; 800. Railway infrastructure deformation simulation device; 801. Processor; 802. Memory; 803. Multimedia component; 804. I / O interface; 805. Communication component. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

[0036] Example 1:

[0037] This embodiment provides a method for simulating deformation of railway infrastructure.

[0038] See also Figure 1 , the figure shows that the method includes step S10, step S20, step S30, step S40, step S50 and step S60.

[0039] Step S10. Obtaining historical load parameters and historical spatiotemporal development curves of the target facility, where the historical spatiotemporal development curves are used to characterize the spatiotemporal deformation characteristics of the target facility;

[0040] Specifically, step S10 specifically includes step S11, step S12, step S13 and step S14:

[0041] Step S11. Acquire stratum lithology, ground stress distribution, and sensor deformation data of the target facility area;

[0042] Specifically, the target facility is located in the target area, data on the stratum lithology and ground stress distribution in the target area are collected, and sensors are buried in the target area to monitor the deformation data of the corresponding position of the target facility.

[0043] Step S12: determining historical deformation characteristics of the target facility area based on stratum lithology and ground stress distribution;

[0044] Specifically, we sorted out the experimental and model simulation literature related to the deformation of the target area to clarify the historical deformation characteristics of the target strata in the target area.

[0045] Step S13: drawing a transverse deformation curve and a longitudinal deformation curve based on the sensor deformation data;

[0046] Specifically, mathematical statistics methods are used to analyze the deformation data of the sensor and draw the vertical and horizontal deformation curves of the target stratum in the target area.

[0047] Step S14. Determine historical load parameters and historical spatiotemporal development curves based on historical deformation characteristics, transverse deformation curves, and longitudinal deformation curves;

[0048] Specifically, by combining historical deformation characteristics with field monitoring data, the spatiotemporal development laws of the vertical and horizontal deformation of the target strata in the target area are obtained, and the historical spatiotemporal development curve of the foundation deformation is obtained.

[0049] Step S20: Substitute historical load parameters into a pre-built foundation deformation simulation model to obtain an initial spatiotemporal development curve. The foundation deformation simulation model includes at least a line model and a deformation model. The line model is a scaled-down model corresponding to the target facility constructed based on geometric similarity theory. The deformation model is used to apply loads to the line model.

[0050] Specifically, the theory of geometric similarity states that if two buildings have similar shapes, proportions, and structures, then their performance behaviors at the same scale will also be similar. This theory can be used to derive the corresponding test results of the large model from the test results of the small model. The formula is expressed as:

[0051]

[0052] Among them, a1 and b1 are the original line sizes, a , 1 and b , 1 is the line model size, C is the similarity constant;

[0053] Time similarity theory refers to the similarity principle that structures or systems with similar dynamic responses at different time scales must satisfy. According to time similarity theory, if a system has similar dynamic response behaviors at different time scales, then the system is time similar. This theory can be used to predict how the results obtained at the laboratory scale are applicable to large-scale systems in actual engineering. The basic formula of time similarity theory can be expressed as:

[0054]

[0055] Where t1 and t2 are time; L1 and L2 are deformation scales;

[0056] like Figure 2 As shown in FIG, the basic deformation simulation model, the basic deformation simulation device includes a plurality of longitudinal lifting devices 2 and lateral displacement devices 3, such as Figure 3 As shown, the longitudinal lifting device 2 includes a support plate 7 and a first lifting rod 6. The four corner points of the lower surface of the support plate 7 are connected to the first lifting rod 6 through a ball joint. The ball joint platform 8 slides laterally with the movement of the support plate 7. The four corner points of the upper surface of the support plate 7 are fixed with a flexible thin plate 9. The flexible thin plate 9 is a whole and is fixed to multiple support plates 7 at the same time. The first lifting rod 6 is uniformly controlled by the control center, which can realize the control of displacement and application rate; Figure 4 As shown, the lateral displacement device 3 is composed of a second lifting rod 10 and a lateral displacement rod 11. The second lifting rod 10 can longitudinally adjust the application position of the lateral displacement. The lateral displacement device 3 is connected to the triangular reaction wall 5. By adjusting the second lifting rod 10, retention and lateral deformation simulation are achieved. The lifting amplitude and rate are uniformly controlled by the control center.

[0057] Step S30: Adjust the line model until the overall similarity between the initial spatiotemporal development curve and the historical spatiotemporal development curve is greater than a first set threshold, and use the corresponding basic deformation simulation model as the target deformation simulation model;

[0058] Specifically, the geometric similarity theory is used to reduce the line of the target facility to a line model, the historical load is applied using the deformation model, and the deformation space-time development curve under the historical load is obtained. When the overall similarity between the deformation space-time development curve and the historical space-time development curve is high, it is considered that the line model can accurately reflect the deformation state of the actual line.

[0059] Specifically, step S30 includes step S31, step S32, step S33 and step S34:

[0060] Step S31. Based on the spatiotemporal characteristics, the initial spatiotemporal development curve and the historical spatiotemporal development curve are respectively split into a plurality of first units and a plurality of second units, and a plurality of unit sample pairs are obtained in a one-to-one correspondence;

[0061] Step S32. Calculate, based on the dynamic time warping algorithm, a first distance between the velocity feature vectors of the first unit and the second unit in each unit sample pair, and a second distance between the acceleration feature vectors of the first unit and the second unit;

[0062] Step S33. Determine a local distance based on the first distance and the second distance;

[0063] Step S34. Sum the local similarities to obtain the overall similarity;

[0064] Specifically, the two spatiotemporal development curves used to compare the overall similarity are divided into multiple segments. Considering that the spatiotemporal development curve uses time as the horizontal coordinate and deformation as the vertical coordinate, the time warping algorithm can be used to calculate the local similarity of each segment separately, and then integrate to obtain the overall similarity, so as to judge whether the two spatiotemporal development curves are similar. When the overall similarity does not reach the set threshold, the line model construction parameters need to be adjusted until the overall similarity of the two lines reaches the set threshold.

[0065] Step S40: Different working condition data are introduced into the target deformation simulation model to obtain corresponding deformations, and a sample set is formed based on all working condition data and corresponding deformations. The working condition data includes lateral load, longitudinal load, and shear load;

[0066] Specifically, in the present application, a non-contact deformation monitoring device is used for monitoring and collection, and the collection steps are simple and the collection accuracy is high.

[0067] Specifically, step S40 includes step S41, step S42, step S43, step S44, step S45 and step S46:

[0068] Step S41. Acquire a first speckle image of the target deformation simulation model based on the monitoring device, where the first speckle image is a corresponding image before the working condition data is introduced;

[0069] Step S42: obtaining a second speckle image of the target deformation simulation model based on the monitoring device, where the second speckle image is a corresponding image after the working condition data is input;

[0070] Step S43: Convert the first speckle image and the second speckle image into sub-pixel images, and determine the target contour to obtain the first contour and the second contour. The target contour is used to represent the overall contour of the line model.

[0071] Specifically, the load combination working condition is determined according to the actual loading state of the line, so as to control the deformation rate of the lateral and longitudinal displacements of the deformation model over time. The load applied by the deformation model is the load after similarity transformation, which can be a single load type or an equivalent load after conversion of multiple load combinations.

[0072] A non-contact deformation monitoring device is used to collect the initial image of the line model and the image after the load is applied, and the initial contour position of the line model and the contour position after the load is applied are determined therefrom.

[0073] Step S44. Determine corresponding points on the first contour and the second contour based on a feature point matching algorithm to obtain a plurality of point pairs;

[0074] Specifically, in the present application, the deformation difference between the initial contour and the contour after load application is calculated by determining a plurality of corresponding points of the two contours, respectively calculating the position difference between the corresponding points, and thus obtaining the position difference of the contours.

[0075] Specifically, step S44 includes step S441, step S442, step S443 and step S444:

[0076] Step S441: compress the second contour to obtain a third contour, where the degree of overlap between the third contour and the first contour is greater than a second set threshold;

[0077] Step S442: Mark the overlapping points of the first contour and the third contour to obtain multiple target points;

[0078] Step S443: Decompress the third contour to obtain a fourth contour containing the target point.

[0079] Step S444. The corresponding target points in the first contour and the fourth contour are taken as a point pair;

[0080] Specifically, the contour after load application is compressed, usually using transverse compression and longitudinal compression, and the compressed contour is overlapped with the initial contour. There will be overlapping positions, and the overlapping positions are marked with points. It is believed that the marked points are the corresponding points between the two contours.

[0081] Step S45. Calculate the relative position difference between each point pair to determine multiple target displacements, where the target displacements carry corresponding point information;

[0082] Step S46: determining a deformation amount based on a plurality of target displacement amounts;

[0083] Specifically, step S46 includes step S461, step S462, step S463, step S464, step S465 and step S466:

[0084] Step S461: Divide the area where the target facility is located into multiple modules to obtain multiple grid units;

[0085] Step S462: Based on the point information carried by the target displacement, the target displacement is correspondingly set in the grid unit to obtain a first grid;

[0086] Step S463. Perform an iterative filling process on the blank grid cells in the first grid that do not contain data to obtain a second grid, wherein the iterative filling process is used to perform filling based on iterative calculations based on data in grid cells adjacent to the blank grid cells;

[0087] Step S464: Determine the lateral displacement and the longitudinal displacement based on the second grid;

[0088] Step S465: Determine the lateral strain, longitudinal strain, and longitudinal shear strain based on the lateral displacement and the longitudinal displacement;

[0089] Step S466: Determine the deformation amount based on the transverse strain, the longitudinal strain, and the longitudinal shear strain;

[0090] Specifically, the area where the target facility is located is divided into a grid style, and the target displacement calculated based on the corresponding point is filled into the corresponding position of the grid. It is considered that the line model is a linear material. When deformation occurs, the deformation amount at the adjacent position also changes linearly. Therefore, the unfilled positions in the grid can be filled by iterative filling in four directions. The deformation amount at each position of the target facility is obtained based on the completely filled image.

[0091] Step S50: training the preset neural network model based on the sample set. When the preset model indicators meet the set conditions, the training is stopped to obtain the target deformation prediction model.

[0092] Step S60: Input the target working condition data into the target deformation prediction model to obtain the deformation of the target facility;

[0093] Specifically, by inputting the target working condition data into the trained target deformation prediction model, a more accurate deformation prediction value can be obtained.

[0094] Example 2:

[0095] like Figure 5 As shown, this embodiment provides a simulation device for railway infrastructure deformation, the device comprising:

[0096] The first acquisition unit 100 is used to acquire historical load parameters and historical spatiotemporal development curves of the target facility, where the historical spatiotemporal development curves are used to characterize spatiotemporal deformation characteristics of the target facility.

[0097] The first input unit 200 is used to input historical load parameters into a pre-built foundation deformation simulation model to obtain an initial spatiotemporal development curve. The foundation deformation simulation model includes at least a line model and a deformation model. The line model is a scaled-down model corresponding to the target facility built based on geometric similarity theory. The deformation model is used to apply loads to the line model.

[0098] An adjusting unit 300 is configured to adjust the line model until the overall similarity between the initial spatiotemporal development curve and the historical spatiotemporal development curve is greater than a first set threshold, and use the corresponding basic deformation simulation model as a target deformation simulation model;

[0099] The second input unit 400 is used to input different working condition data into the target deformation simulation model to obtain corresponding deformations, and to form a sample set based on all working condition data and corresponding deformations, wherein the working condition data includes lateral load, longitudinal load and shear load;

[0100] The training unit 500 is used to train the preset neural network model based on the sample set, and when the preset model indicators meet the set conditions, the training is stopped to obtain the target deformation prediction model;

[0101] The input unit 600 is used to input the target working condition data into the target deformation prediction model to obtain the deformation of the target facility.

[0102] In a specific embodiment disclosed in this application, the first acquiring unit 100 includes:

[0103] The second acquisition unit is used to obtain the stratum lithology, ground stress distribution and sensor deformation data of the area where the target facility is located;

[0104] The first determining unit is configured to determine historical deformation characteristics of the area where the target facility is located based on stratum lithology and ground stress distribution;

[0105] a drawing unit, for drawing a transverse deformation curve and a longitudinal deformation curve based on the sensor deformation data;

[0106] The second determining unit is used to determine the historical load parameters and the historical time-space development curve based on the historical deformation characteristics, the transverse deformation curve and the longitudinal deformation curve.

[0107] In a specific embodiment disclosed in the present application, the second bringing-in unit 400 includes:

[0108] a third acquiring unit, configured to acquire a first speckle image of the target deformation simulation model based on the monitoring device, where the first speckle image is a corresponding image before the working condition data is introduced;

[0109] a fourth acquiring unit, configured to acquire a second speckle image of the target deformation simulation model based on the monitoring device, where the second speckle image is a corresponding image after the working condition data is input;

[0110] a conversion unit, configured to convert the first speckle image and the second speckle image into a sub-pixel image, and determine a target contour to obtain a first contour and a second contour, wherein the target contour is used to represent an overall contour of the line model;

[0111] A matching unit, configured to determine corresponding points on the first contour and the second contour based on a feature point matching algorithm to obtain a plurality of point pairs;

[0112] A first calculation unit is used to calculate the relative position difference between each point pair and determine multiple target displacements, where the target displacements carry corresponding point information;

[0113] The third determining unit is configured to determine a deformation amount based on a plurality of target displacement amounts.

[0114] In a specific embodiment disclosed in this application, the matching unit includes:

[0115] a compression unit, configured to compress the second contour to obtain a third contour, wherein the degree of overlap between the third contour and the first contour is greater than a second set threshold;

[0116] a marking unit, configured to mark overlapping points of the first contour and the third contour to obtain a plurality of target points;

[0117] a decompression unit, configured to perform decompression processing on the third contour to obtain a fourth contour carrying the target point;

[0118] As a unit, it is used to take the corresponding target points in the first contour and the fourth contour as a point pair.

[0119] In a specific embodiment disclosed in this application, the third determining unit includes:

[0120] A division unit is used to divide the area where the target facility is located into multiple modules to obtain multiple grid units;

[0121] A setting unit, configured to set the target displacement into a corresponding grid unit based on the point information carried by the target displacement to obtain a first grid;

[0122] A filling unit, configured to iteratively fill blank grid cells in the first grid that do not contain any data, to obtain a second grid, wherein the iterative filling process is configured to indicate filling after iterative calculation based on data in grid cells adjacent to the blank grid cells;

[0123] a fourth determining unit, configured to determine a lateral displacement and a longitudinal displacement based on the second grid;

[0124] Determine the transverse strain, longitudinal strain and longitudinal shear strain based on the transverse displacement and longitudinal displacement;

[0125] The fifth determining unit is configured to determine the deformation amount based on the transverse strain, the longitudinal strain, and the longitudinal shear strain.

[0126] In a specific embodiment disclosed in this application, the adjustment unit 300 includes:

[0127] A splitting unit is used to split the initial spatiotemporal development curve and the historical spatiotemporal development curve into a plurality of first units and a plurality of second units respectively based on spatiotemporal characteristics, and obtain a plurality of unit sample pairs in one-to-one correspondence;

[0128] A second calculation unit is configured to calculate a first distance between respective velocity feature vectors of a first unit and a second unit in each unit sample pair, and a second distance between respective acceleration feature vectors based on a dynamic time warping algorithm;

[0129] a sixth determining unit, configured to determine a local distance based on the first distance and the second distance;

[0130] The third calculation unit is used to sum up the local similarities to obtain the overall similarity.

[0131] It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0132] Example 3:

[0133] Corresponding to the above method embodiment, this embodiment further provides a simulation device for railway infrastructure deformation. The simulation device for railway infrastructure deformation described below and the simulation method for railway infrastructure deformation described above can refer to each other.

[0134] Figure 6 FIG. 8 is a block diagram of a simulation device 800 for railway infrastructure deformation according to an exemplary embodiment. Figure 6 As shown, the railway infrastructure deformation simulation device 800 may include: a processor 801 , a memory 802 , and may further include one or more of a multimedia component 803 , an I / O interface 804 , and a communication component 805 .

[0135] The processor 801 is used to control the overall operation of the railway infrastructure deformation simulation device 800 to complete all or part of the steps of the railway infrastructure deformation simulation method described above. The memory 802 is used to store various types of data to support the operation of the railway infrastructure deformation simulation device 800. This data may include, for example, instructions for any application or method operating on the railway infrastructure deformation simulation device 800, as well as application-related data such as contact information, sent and received messages, images, audio, video, etc. The memory 802 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 storage, flash memory, magnetic disk or optical disk. The multimedia component 803 can include a screen and an audio component. The screen can be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component can include a microphone, and the microphone is used to receive external audio signals. The received audio signals can be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be a keyboard, a mouse, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the simulation device 800 for railway infrastructure deformation and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0136] In an exemplary embodiment, the railway infrastructure deformation simulation device 800 can be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned railway infrastructure deformation simulation method.

[0137] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, the program instructions implement the steps of the aforementioned method for simulating railway infrastructure deformation. For example, the computer-readable storage medium may be the aforementioned memory 802 including the program instructions. The program instructions may be executed by the processor 801 of the railway infrastructure deformation simulation device 800 to perform the aforementioned method for simulating railway infrastructure deformation.

[0138] Example 4:

[0139] Corresponding to the above method embodiment, this embodiment further provides a readable storage medium. The readable storage medium described below and the railway infrastructure deformation simulation method described above can refer to each other.

[0140] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for simulating railway infrastructure deformation of the above-mentioned method embodiment.

[0141] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0142] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0143] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for simulating deformation of railway infrastructure, characterized in that: include: Obtaining historical load parameters and a historical spatiotemporal development curve of a target facility, wherein the historical spatiotemporal development curve is used to characterize spatiotemporal deformation characteristics of the target facility; Substituting the historical load parameters into a pre-built foundation deformation simulation model to obtain an initial spatiotemporal development curve, the foundation deformation simulation model at least comprising a line model and a deformation model, the line model being a scaled-down model corresponding to the target facility constructed based on geometric similarity theory, and the deformation model being used to apply loads to the line model; Adjusting the line model until the overall similarity between the initial spatiotemporal development curve and the historical spatiotemporal development curve is greater than a first set threshold, and using the corresponding basic deformation simulation model as a target deformation simulation model; Bringing different working condition data into the target deformation simulation model to obtain corresponding deformations, and forming a sample set based on all working condition data and corresponding deformations, wherein the working condition data includes lateral load, longitudinal load and shear load; Training a preset neural network model based on the sample set, and stopping the training when the preset model indicators meet the set conditions to obtain a target deformation prediction model; Inputting target working condition data into the target deformation prediction model to obtain the deformation amount of the target facility; The historical load parameters and historical spatiotemporal development curve of the target facility are obtained. The historical spatiotemporal development curve is used to characterize the spatiotemporal deformation characteristics of the target facility, including: Acquiring stratum lithology, ground stress distribution, and sensor deformation data of the area where the target facility is located; Determining historical deformation characteristics of the area where the target facility is located based on the formation lithology and the ground stress distribution; Drawing a transverse deformation curve and a longitudinal deformation curve based on the sensor deformation data; Determining the historical load parameters and the historical spatiotemporal development curve based on the historical deformation characteristics, the transverse deformation curve, and the longitudinal deformation curve; Different working condition data are brought into the target deformation simulation model to obtain corresponding deformation amounts, and a sample set is formed based on all working condition data and corresponding deformation amounts. The basic deformation simulation model also includes a monitoring device, which is used to measure the deformation amount of the line model, including: Acquiring a first speckle image of the target deformation simulation model based on a monitoring device, where the first speckle image is a corresponding image before the working condition data is introduced; Acquiring a second speckle image of the target deformation simulation model based on a monitoring device, where the second speckle image is a corresponding image after the working condition data is input; Converting the first speckle image and the second speckle image into sub-pixel images, and determining a target contour to obtain a first contour and a second contour, wherein the target contour is used to represent an overall contour of the line model; Determine corresponding points on the first contour and the second contour based on a feature point matching algorithm to obtain a plurality of point pairs; Calculating the relative position difference between each point pair to determine multiple target displacements, each of which carries corresponding point information; The deformation amount is determined based on a plurality of target displacement amounts.

2. The method for simulating railway infrastructure deformation according to claim 1, characterized in that , based on a feature point matching algorithm, determining corresponding points on the first contour and the second contour, and obtaining a plurality of point pairs, including: compressing the second contour to obtain a third contour, wherein the degree of overlap between the third contour and the first contour is greater than a second set threshold; Marking the overlapping points of the first contour and the third contour to obtain a plurality of target points; Performing a decompression process on the third contour to obtain a fourth contour carrying the target point; The corresponding target points in the first contour and the fourth contour are used as the point pair.

3. A simulation device for railway infrastructure deformation, characterized in that: include: A first acquisition unit is configured to acquire historical load parameters and a historical spatiotemporal development curve of a target facility, wherein the historical spatiotemporal development curve is used to characterize spatiotemporal deformation characteristics of the target facility; a first importing unit, configured to import the historical load parameters into a pre-constructed foundation deformation simulation model to obtain an initial spatiotemporal development curve, wherein the foundation deformation simulation model comprises at least a line model and a deformation model, wherein the line model is a scaled-down model corresponding to the target facility constructed based on geometric similarity theory, and the deformation model is used to apply loads to the line model; an adjusting unit, configured to adjust the line model until the overall similarity between the initial spatiotemporal development curve and the historical spatiotemporal development curve is greater than a first set threshold, and use the corresponding basic deformation simulation model as a target deformation simulation model; a second input unit, configured to input different working condition data into the target deformation simulation model to obtain corresponding deformations, and to form a sample set based on all working condition data and corresponding deformations, wherein the working condition data includes lateral load, longitudinal load, and shear load; A training unit, configured to train a preset neural network model based on the sample set, and stop training when the preset model indicators meet the set conditions to obtain a target deformation prediction model; An input unit, configured to input target operating condition data into the target deformation prediction model to obtain a deformation amount of the target facility; The first acquiring unit includes: A second acquisition unit is used to acquire stratum lithology, ground stress distribution and sensor deformation data of the area where the target facility is located; A first determining unit is configured to determine historical deformation characteristics of the area where the target facility is located based on the formation lithology and the ground stress distribution; a drawing unit, configured to draw a transverse deformation curve and a longitudinal deformation curve based on the sensor deformation data; a second determining unit, configured to determine the historical load parameter and the historical spatiotemporal development curve based on the historical deformation characteristics, the transverse deformation curve, and the longitudinal deformation curve; Wherein, the second bringing-in unit includes: a third acquiring unit, configured to acquire, based on a monitoring device, a first speckle image of the target deformation simulation model, where the first speckle image is a corresponding image before the working condition data is introduced; a fourth acquiring unit, configured to acquire, based on a monitoring device, a second speckle image of the target deformation simulation model, wherein the second speckle image is a corresponding image after the working condition data is input; a conversion unit, configured to convert the first speckle image and the second speckle image into sub-pixel images, and determine a target contour to obtain a first contour and a second contour, wherein the target contour is used to represent an overall contour of the line model; a matching unit, configured to determine corresponding points on the first contour and the second contour based on a feature point matching algorithm to obtain a plurality of point pairs; A first calculation unit is configured to calculate the relative position difference between each pair of points to determine a plurality of target displacements, wherein the target displacements carry corresponding point information; The third determining unit is configured to determine the deformation amount based on a plurality of target displacement amounts.

4. The railway infrastructure deformation simulation device according to claim 3, characterized in that: The matching unit includes: a compression unit, configured to compress the second contour to obtain a third contour, wherein the degree of overlap between the third contour and the first contour is greater than a second set threshold; a marking unit, configured to mark overlapping points of the first contour and the third contour to obtain a plurality of target points; a decompression unit, configured to perform decompression processing on the third contour to obtain a fourth contour carrying the target point; As a unit, it is used to take the corresponding target points in the first contour and the fourth contour as the point pair.

5. A simulation device for railway infrastructure deformation, characterized in that: include: memory for storing computer programs; A processor is configured to implement the steps of the method for simulating railway infrastructure deformation as claimed in any one of claims 1 to 2 when executing the computer program.

6. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for simulating railway infrastructure deformation according to any one of claims 1 to 2.

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