Track detection data matching method, device and track system for different train numbers

Abstract

The present invention provides a method, device and track system for matching track detection data of different trains, the method comprising the steps of preprocessing the collected original signal data, aligning the time domain signal data in the time domain, extracting the signal data of multiple trains from the aligned data, calculating the train speed and expansion coefficient of each train, segmenting the data according to the expansion coefficient, and expanding or compressing the data according to the expansion coefficient. Among them, automatic time domain alignment can be performed, and the train speed and expansion coefficient of each train can be calculated according to the aligned data, and then the corresponding parts can be automatically segmented from the data of each train according to the expansion coefficient, and the segmented data can be correspondingly expanded or compressed according to the expansion coefficient, so as to unify the length of the data of different trains, ensure the consistency of analysis and visualization of the data of different trains, and facilitate the corresponding big data analysis.

Description

Technical Field

[0001] The present invention belongs to the technical field of track detection, and in particular relates to a method and device for matching track detection data of different train numbers, and a track system. Background Art

[0002] In the operation of rail transit, it is very important to inspect the track to ensure the stable and safe operation of the train. This inspection is usually achieved by installing sensors and data collectors on the track to collect data, and further processing and analyzing the collected data. In order to better detect the status and operation of specific tracks and trains, a variety of different types of sensors are currently being installed on the track. For example, for floating plate track with elastic vibration reduction structure, a variety of sensors are installed to detect fastener loads, rail loads, floating plate vibration acceleration, elastic element working status, etc. Although such a setting can obtain multi-source detection data of the track in actual operation, which is conducive to more detailed and comprehensive research and analysis, there are still some problems:

[0003] First, due to differences in the installation positions of sensors and system time differences of different data acquisition instruments, the collected data has time deviations, which brings difficulties to subsequent data processing and analysis. In addition, due to the fact that most sensors are passive acquisition mode sensors (such as load detection sensors), the number of sensors and data acquisition instruments installed in the track is very large, some existing acquisition instrument time synchronization methods are also not applicable. At present, the time domain alignment of multi-source detection data is mainly achieved through manual operation, which not only has limited alignment accuracy, but is also time-consuming and labor-intensive, and is completely unable to meet the needs of big data analysis.

[0004] Secondly, by comparing and analyzing the data of multiple trains traveling on the same track section, it is possible to find differences in train and track defects and track status at different times, so such comparative analysis is very valuable. However, since multiple trains usually pass through the track section at different speeds, the difference in their detection data is large. Currently, the comparative analysis of multiple train data can only be done manually, which is very inefficient and cannot meet the needs of big data analysis. Summary of the invention

[0005] The present invention is made to solve the above problems, and aims to provide a data matching method that can automatically match the track detection data of different trains, thereby more conveniently realizing big data analysis, as well as a device using the method, and a track system including the device and multiple detection devices. The present invention adopts the following technical solutions:

[0006] The present invention provides a method for aligning track detection data of different train numbers, which is used for matching detection data of different train numbers on a track. The method has the following technical features, and comprises the following steps: step S1, respectively performing corresponding preprocessing on original signals collected by multiple detection devices to obtain corresponding time domain signal data; step S2, searching for predetermined feature points in the time domain signal data and obtaining their time points, and performing time domain alignment on the time domain signal data of the multiple detection devices according to the corresponding relationship between the feature points and the train structure and the predetermined distances between the multiple detection devices; step S3, extracting multiple feature points from the aligned time domain signal data; Step S4, calculating the train speed according to the time difference between the feature points in the train signal data of each train number and the train size data corresponding to the train number, and calculating the expansion coefficient according to the train speed; Step S5, performing data segmentation on each of the train signal data according to the corresponding expansion coefficient to obtain the segmented signal data of each of the train numbers; and Step S6, using a data expansion and compression algorithm to perform corresponding expansion or compression on the segmented signal data of each of the train numbers according to the corresponding expansion coefficient, so as to obtain the matched signal data of multiple train numbers.

[0007] The method for aligning track detection data of different trains provided by the present invention may also have such a technical feature, wherein, in step S4, the train speed is calculated based on the predetermined wheelbase of the train and the time difference between a group of characteristic points generated by the bogie of the train passing through the detection device, and the expansion coefficient = the train speed / predetermined standard speed.

[0008] The track detection data alignment method for different trains provided by the present invention may also have such a technical feature, wherein, in step S5, the duration of the segmented signal data = the forward segmentation duration of the feature point coordinates corresponding to the bogie of the train / the expansion coefficient + the backward segmentation duration of the feature point coordinates corresponding to the bogie of the train / the expansion coefficient.

[0009] The method for aligning track detection data of different train numbers provided by the present invention may also have such a technical feature, wherein step S6 includes the following sub-steps: judging whether the expansion coefficient of the train number is greater than or less than a predetermined standard coefficient; when it is judged to be greater than the standard coefficient, using a data expansion algorithm to perform data expansion on the segmented signal data of the train number according to the expansion coefficient; when it is judged to be less than the standard coefficient, using a data compression algorithm to perform data compression on the segmented signal data of the train number according to the expansion coefficient.

[0010] The method for aligning track detection data of different trains provided by the present invention may also have such a technical feature, wherein the data expansion algorithm is a spline interpolation algorithm, and the data compression algorithm is a maximum triangle three-bucket algorithm.

[0011] The method for aligning track detection data of different trains provided by the present invention may also have such a technical feature, wherein one of the multiple detection devices is used as a reference detection device, and the rest are used as general detection devices, and step S2 includes the following sub-steps: searching for predetermined feature points and their corresponding time points corresponding to the bogie of the train in the time domain signal data corresponding to the reference detection device; calculating the time difference between two adjacent feature points to obtain a feature point time difference array; based on the feature point time difference array, calculating the cross-correlation coefficients between multiple groups of feature points by a cross-correlation algorithm to obtain a cross-correlation array; searching for the maximum value position in the cross-correlation array, and obtaining the time point corresponding to the maximum value position as the reference time point. points to determine the best alignment point between the signal data collected by different detection devices; calculate the train speed of the train according to the characteristic size of the train and the time difference between a group of characteristic points generated by the bogie passing through the reference detection device; calculate the elapsed time of the train passing the predetermined distance according to the predetermined distance between the reference detection device and the general detection device and the train speed; calculate the time shift of the time domain signal data of the general detection device relative to the time domain signal data of the reference detection device according to the elapsed time and the reference time point; perform time domain shift on the time domain signal data of each general detection device according to the corresponding time shift, so as to obtain aligned detection data.

[0012] The method for aligning track detection data of different trains provided by the present invention may also have such technical features, wherein the track includes a lower foundation and a plurality of floating roadbed plates arranged in sequence, the floating roadbed plates are arranged on the lower foundation through a plurality of vibration isolators, the reference monitoring detection device is a load detection device arranged in the middle of the floating roadbed plate, which is used to detect the track load to obtain corresponding load signal data, the characteristic point is a peak in the load signal data, and in step S2, the train speed is calculated based on the wheelbase of the train and the peak time difference between a group of peaks generated by the bogie passing through the reference detection device.

[0013] The method for aligning track detection data of different trains provided by the present invention may also have such technical features, wherein the track includes a lower foundation, a plurality of floating roadbed plates arranged in sequence, and rails arranged on the floating roadbed plates, and the floating roadbed plates are arranged on the lower foundation through a plurality of vibration isolators, and the detection device includes: a plurality of load detection devices, which are arranged below the rails and are used to detect the load of the track and obtain load signal data; a plurality of vibration isolator detection devices, which are respectively arranged in a plurality of the vibration isolators and are at least used to detect the distance from the vibration isolator to the lower foundation and obtain the vibration isolator distance signal data; a plurality of vibration acceleration detection devices, arranged in the middle of the floating ballast plate, for detecting the vibration acceleration of the track to obtain vibration acceleration signal data; a plurality of rail displacement detection devices, respectively arranged under the rails, for detecting the distance from the devices to the bottom surface of the rails to obtain rail displacement distance signal data; in step S1, data smoothing, data cleaning and data zeroing are performed on the load signal data, and electrical signal conversion, data smoothing, data cleaning and data zeroing are performed on the vibration isolator distance signal data and the rail displacement distance signal data, respectively.

[0014] The present invention provides a device for aligning track detection data of different train numbers, which is used to match the detection data of different train numbers on the track. The device has the following technical features: a signal data acquisition unit, which is used to respectively acquire the original signal data collected by multiple detection devices; a data preprocessing unit, which is used to perform corresponding preprocessing on the original signal data to obtain corresponding time domain signal data; a data alignment processing unit, which is used to search for predetermined feature points in the time domain signal data and obtain their time points, and perform time domain alignment on the time domain signal data of multiple detection devices according to the corresponding relationship between the feature points and the train structure and the predetermined distances between the multiple detection devices; a train number signal extraction unit, which is used to extract the time domain signal data from the aligned The train signal data of multiple train numbers are extracted from the time domain signal data; an expansion coefficient acquisition unit is used to calculate the train speed according to the time difference between the characteristic points in the train signal data of each train number and the train size data corresponding to the train number, and calculate the expansion coefficient according to the train speed; a train data segmentation unit is used to perform data segmentation on the train signal data of each train number according to the corresponding expansion coefficient to obtain the segmented signal data of each train number; and a train data matching processing unit is used to adopt a data expansion and compression algorithm to perform corresponding expansion or compression on the segmented signal data of each train number according to the corresponding expansion coefficient, so as to obtain the matched signal data of multiple train numbers.

[0015] The present invention provides a track system having the following technical features: a track; a plurality of detection devices arranged in the track for collecting detection data of the track and a plurality of train numbers; and a track detection data matching device for performing data matching on the detection data of the plurality of train numbers, wherein the track detection data matching device is the above-mentioned track detection data matching device capable of matching detection data of different train numbers.

[0016] The present invention has the following functions and effects.

[0017] According to the method, device and track system for aligning track detection data of different trains provided by the present invention, the method includes the steps of preprocessing the collected original signal data, aligning the time domain signal data in the time domain, extracting the signal data of multiple trains from the aligned data, calculating the train speed and expansion coefficient of each train, segmenting the data according to the expansion coefficient, and expanding or compressing the data according to the expansion coefficient. Among them, since the characteristic points in the time domain signal correspond to the event that the specific structure of the train passes through the detection device, and the time domain signal alignment is achieved in combination with the predetermined distance between the detection devices, the automatic time domain alignment of the detection data can be achieved, and no manual operation is required to achieve the data time domain alignment, the alignment accuracy is higher, and a lot of manpower and time can be saved. Further, the train speed and expansion coefficient of each train can be calculated according to the aligned data, and then the corresponding part can be automatically segmented from the data of each train according to the expansion coefficient, and the segmented data is correspondingly expanded or compressed according to the expansion coefficient, thereby unifying the length of the data of different trains with different speeds, ensuring the consistency of analysis and visualization of the data of different trains, which is conducive to the corresponding big data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of a method for matching track detection data of different train numbers in an embodiment of the present invention;

[0019] Figure 2 is a top view of a track system in an embodiment of the present invention;

[0020] Figure 3 is a cross-sectional view of a track system in an embodiment of the present invention;

[0021] Figure 4 is a schematic diagram of the structure of a train in an embodiment of the present invention;

[0022] Figure 5 is a structural schematic diagram of a vibration isolator detection device in an embodiment of the present invention;

[0023] Figure 6is a structural diagram of a fixed tooling and a distance measuring sensor in an embodiment of the present invention;

[0024] Figure 7 is an installation position diagram of a load detection device in an embodiment of the present invention;

[0025] Figure 8 is a structural exploded view of a load detection device in an embodiment of the present invention;

[0026] Fig. 9 is a flow chart of step S1 in an embodiment of the present invention;

[0027] Fig.10 is a flow chart of step S2 in an embodiment of the present invention;

[0028] Fig.11 is an example diagram of a time history curve of a load detection device in the middle of a plate in an embodiment of the present invention;

[0029] Fig.12 is an example diagram of a time history curve of a load detection device at a plate end in an embodiment of the present invention;

[0030] Fig.13 is a schematic diagram of split signal data of two train numbers in an embodiment of the present invention;

[0031] Fig.14 is a flow chart of step S6 in an embodiment of the present invention;

[0032] Fig.15 is a schematic diagram of a spline interpolation algorithm in an embodiment of the present invention;

[0033] Fig.16 It is a logic block diagram of a track detection data matching device for different train numbers in an embodiment of the present invention.

[0034] Reference numerals:

[0035] Track system 1000; vibration isolator detection device 100; fixing tool 10; base plate 11; flat plate portion 111; clearance notch 1112; fitting protrusion 112; fixing protrusion 113; first sensor bracket 121; first fixing plate portion 1211; first mounting plate portion 1212; second sensor bracket 122; second fixing plate portion 1221; second mounting plate portion 1222; positioning assembly 13; positioning member 131; mounting hole 1131; support Component 14; support plate 15; first distance measuring sensor 21; second distance measuring sensor 22; power supply unit 30; data acquisition instrument 40; load detection device 200; loading pad 211; sensor fixing plate 212; load sensor 213; cable 214; load transfer column 215; base plate 216; fixing hole 2161; spike installation hole 2162; fixing plate fastening screw 217; base fixing screw 218; spike 221; vibration acceleration detection device 300; rail vibration acceleration sensor 310; roadbed vibration acceleration sensor 320; foundation vibration acceleration sensor 330; rail displacement detection device 400; track detection data matching device 700 for different train numbers; signal data acquisition unit 710; data preprocessing unit 720; data alignment processing unit 730; train number signal extraction unit 740; expansion coefficient acquisition unit 750; train number data segmentation unit 760; train number data matching processing unit 77 0; matching device communication unit 780; matching device control unit 790; train 800; carriage 810; bogie 820; wheelset 822; wheel 8221; axle 8222; track 900; lower foundation 910; floating track plate 920; isolator 930; sleeve 931; elastic element 932; unit support plate 933; short rail sleeper 940; rail 950; rail pad 960; track fastener 970; time curves C1, C2. DETAILED DESCRIPTION

[0036] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the track detection data matching method, device and track system for different train numbers of the present invention are specifically described below in combination with embodiments and drawings.

[0037] Figure 1 It is a flow chart of the track detection data matching method for different train numbers in this embodiment.

[0038] like Figure 1 As shown, the method comprises the following steps:

[0039] Step S1, pre-processing the original signals collected by various detection devices respectively to obtain corresponding time domain signal data.

[0040] Step S2, searching for predetermined feature points in the time domain signal data and obtaining their time points, and performing time domain alignment on the time domain signal data of multiple detection devices according to the correspondence between the feature points and the train structure and the predetermined distances between the multiple detection devices.

[0041] Step S3, extracting train number signal data of multiple train numbers from the aligned time domain signal data.

[0042] Step S4, for each train number, the train speed is calculated according to the time difference between the characteristic points in the train number signal data and the train size data corresponding to the train number, and the expansion coefficient is calculated according to the train speed.

[0043] Step S5, segmenting the train signal data of each train number according to the corresponding expansion coefficient to obtain segmented signal data of each train number.

[0044] Step S6, using a data expansion and compression algorithm to expand or compress the segmented signal data of each train number according to the corresponding expansion coefficient, so as to obtain matched (unified) signal data of multiple train numbers.

[0045] In order to better illustrate the present method, the algorithm flow of the present method will be specifically described below by way of example in combination with an actual track structure.

[0046] Figure 2 is a top view of the track system in this embodiment, Figure 3 is a cross-sectional view of the track system in this embodiment.

[0047] like Figure 2 and Figure 3 As shown, taking a floating plate track arranged in a circular tunnel as an example, the track system 1000 includes a track 900, a vibration isolator detection device 100, a load detection device 200, a vibration acceleration detection device 300, and a rail displacement detection device 400.

[0048] The track 900 includes a lower foundation 910 , a plurality of floating track plates 920 , a plurality of vibration isolators 930 , a plurality of short sleepers 940 , and two steel rails 950 .

[0049] The lower foundation 910 is arranged at the bottom of the circular tunnel, and its upper surface is roughly flat. The floating track plate 920 is roughly in the shape of a rectangular plate as a whole, and is a precast concrete plate. Multiple floating track plates 920 are connected end to end and arranged in sequence along the tunnel. The short sleepers 940 are concrete short sleepers, which are arranged on the upper surface of the floating track plate 920 to form two rows, and the multiple short sleepers 940 in each row are arranged at roughly equal intervals. The rails 950 are I-shaped rails, and the two rails 950 are respectively placed on the two rows of short sleepers 940 and fixed by rail fasteners arranged on the rectangular sleepers 940.

[0050] A plurality of vibration isolators 930 are embedded in the floating track plate 920. The vibration isolators 930 include a sleeve 931, an elastic element 932 and other mounting components pre-buried in the floating track plate 920. The elastic element 932 is arranged at the bottom of the sleeve 931, and the lower end of the elastic element 932 extends out of the sleeve 931 and abuts against the lower foundation 910. The vibration isolators 930 are distributed on both sides of the width direction of the floating track plate 920 to form two rows, and each vibration isolator 930 is located between two adjacent short sleepers 940 and is closer to one side edge of the floating track plate 920 in the width direction relative to the short sleepers 940. The floating track plate 920 is arranged on the lower foundation 910 through a plurality of vibration isolators 930, and there is a certain gap between the bottom surface of the floating track plate 920 and the upper surface of the lower foundation 910.

[0051] Optionally, an elastic pad for vibration isolation may be provided between the rail 950 and the short sleeper 940 .

[0052] In this embodiment, the size of the floating track plate 920 is 4710×2600×320mm (length×width×thickness). There are eight short sleepers 940 in a row on each floating track plate 920, and the distance between two adjacent short sleepers 940 is 595mm, which is the distance between two adjacent sets of track fasteners. There are four vibration isolators 930 in a row on each floating track plate 920, two of which are respectively arranged near the two side plate ends (both ends in the length direction) of the floating track plate 920, and the distance between two adjacent vibration isolators 930 is 1190mm. The elastic element 932 in the vibration isolator 930 is a rubber spring or includes a rubber spring.

[0053] Figure 4 is a schematic diagram of the structure of the train in this embodiment. In addition, Figure 3 The structure diagram of the train is also shown in FIG.

[0054] like Figure 3 and Figure 4 As shown, the train 800 includes multiple carriages 810 and multiple bogies 820. Multiple bogies 820 are arranged at the bottom of each carriage 810, and the bogies 820 include a support frame, a wheel pair 822, a spring device and a shock absorber, etc. The wheel pair 822 includes a wheel 8221 and an axle 8222.

[0055] In this embodiment, the train 800 has six carriages 810, and each carriage 810 has two bogies 820 at the bottom. The total length of the train is 118.8m, the vehicle fixed distance (the distance between two bogies at the bottom of a carriage) is 12.6m, and the wheelbase (the distance between two wheel pairs in a bogie, also called fixed wheelbase) is 2300mm.

[0056] Figure 5is a schematic diagram of the structure of the vibration isolator detection device in this embodiment, Figure 6 is a structural diagram of the fixed tooling and the distance measuring sensor in this embodiment.

[0057] like Figure 5 As shown, the vibration isolator 930 also includes a unit support plate 933, which is a metal flat plate, embedded in the sleeve 931 and abutted against a circle of steps near the bottom of the sleeve 931, and the upper end of the elastic unit 932 abuts against the unit support plate 933. The edge of the unit support plate 933 has a plurality of arc-shaped notches. The diameter of the elastic element 932 is smaller than the inner diameter of the sleeve 931, and is arranged approximately coaxially with the sleeve 931.

[0058] The vibration isolator detection device 100 is disposed in the vibration isolator 930 and is used to detect the dynamic deformation of the elastic element 932 (rubber spring). Figure 5 and Figure 6 As shown, the vibration isolator detection device 100 includes a fixing device 10 , a plurality of first distance measuring sensors 21 , a plurality of second distance measuring sensors 22 , corresponding power supply units 30 and a data acquisition device 40 .

[0059] The fixing tool 10 includes a base plate 11 , a plurality of first sensor brackets 121 , a plurality of second sensor brackets 122 , and a positioning assembly 13 .

[0060] The base plate 11 includes a disc-shaped flat plate portion 111, a plurality of engaging protrusions 112 formed on the edge of the flat plate portion 111, and a plurality of fixing protrusions 113, wherein the fixing protrusions 113 have mounting holes 1131. The edge of the flat plate portion 111 also has a plurality of clearance notches 1112.

[0061] The first sensor bracket 121 is L-shaped as a whole, and has a first fixing plate portion 1211 and a first mounting plate portion 1212 which are perpendicular to each other. The first fixing plate portion 1211 is fixedly mounted on the base plate 11, and the first mounting plate portion 1212 is vertically located above the base plate 11 and next to a clearance notch 1112. The first distance measuring sensor 21 is mounted on the outer end of the first mounting plate portion 1212, with its detection end facing downward, and can detect the distance from the sensor to the unit support plate 933 through the clearance notch 1112.

[0062] The structure of the second sensor bracket 122 is substantially the same as that of the first sensor bracket 121, and it has a second fixing plate portion 1221 and a second mounting plate portion 1222 which are perpendicular to each other. The second fixing plate portion 1221 is fixedly mounted on the base plate 11, and the second mounting plate portion 1222 vertically extends from the outside of the base plate 11 to the bottom of the base plate 11. The second distance measuring sensor 22 is mounted on the outer end of the second mounting plate portion 1222, with its detection end facing downward, and can detect the distance from the sensor to the lower foundation through the corresponding arc-shaped notch on the unit support plate 933.

[0063] In this embodiment, the first distance measuring sensor 21 and the second distance measuring sensor 22 are both laser distance measuring sensors.

[0064] The positioning assembly 13 includes a plurality of thin rod-shaped positioning members 131, which are respectively inserted into a plurality of corresponding mounting holes on the base plate 11, and the mounting positions of the positioning members 131 in the corresponding mounting holes are adjustable. When installing the base plate 11, the plurality of positioning members 131 can be used to keep the surface direction of the base plate 11 perpendicular to the axial direction of the sleeve, and the leveling of the base plate 11 can be further achieved by adjusting the positioning members 131.

[0065] The support member 14 is in the shape of a circular cylinder, and has external thread protrusions at both ends. The support plate 15 is in the shape of a circular plate, and is used to support the power supply unit and the data acquisition unit. One end of the support member 14 is installed in the mounting hole in the middle of the flat plate portion 111, and the other end is installed in the mounting hole in the middle of the support plate 15, so that the support plate 15 is installed above the flat plate portion 111, and the support plate 15 is parallel to the flat plate portion 111.

[0066] like Figure 2 As shown, in this embodiment, the eight vibration isolators 930 in a floating track bed slab 920 are all provided with vibration isolator detection devices 100. Optionally, the two vibration isolators 930 at the plate ends of an adjacent floating track bed slab 920 are also provided with vibration isolator detection devices 100.

[0067] Figure 7 2 is an installation position diagram of the load detection device in this embodiment.

[0068] like Figure 7 As shown, the overall shape of the load detection device 200 is consistent with the track iron pad, and it is installed in the track structure instead of the track iron pad, specifically between the short sleeper 940 and the rail 950. When the rail pad 960 is provided, it is located between the short sleeper 940 and the rail pad 960. The rail fastener 970 is fixed on the load detection device 200.

[0069] Figure 8 It is a structural exploded view of the load detection device in this embodiment.

[0070] like Figure 8 As shown, the load detection device 200 includes a loading pad 211, a sensor fixing plate 212, a plurality of load sensors 213 and their cables 214, a plurality of load transfer columns 215, a base plate 216, a plurality of fixing plate fastening screws 217, a plurality of base fixing screws 218, and also includes a corresponding data acquisition instrument (not shown in the figure).

[0071] Among them, the lower surface of the loading pad 211 and the upper surface of the sensor fixing plate 212 have correspondingly arranged multiple sensor fixing grooves and wiring grooves. After being assembled, corresponding accommodating cavities and wiring channels are formed. Multiple load sensors 213 are respectively embedded in each accommodating cavity, and a load transfer column 215 is arranged at the bottom. Its cables 214 are gathered together through the wiring channel and led out from one side of the entire device. According to the distribution of the accommodating cavities, multiple load sensors 213 form two rows, which are respectively located below the two sides of the rail 950.

[0072] The base plate 216 has a plurality of fixing holes 2161 and a pair of spike mounting holes 2162 . The loading pad 211 , the sensor fixing plate 212 , and the base plate 216 are stacked and assembled and fixed on the short rail sleeper 940 through a plurality of spikes 221 .

[0073] The load sensor 213 may be a pressure sensor or a piezoelectric sensor. In this embodiment, it is a pressure sensor. There are six load sensors 213 in one device, forming two rows, with three in each row.

[0074] like Figure 2 As shown, in this embodiment, a load detection device 200 is provided at each short sleeper 940 on a floating track plate 920 .

[0075] like Figure 2 and Figure 3 As shown, there are two vibration acceleration detection devices 300, which are respectively arranged in the plate (middle in the length direction) of the floating track plate 920, and are respectively located on both sides (both sides in the width direction) of the floating track plate 920. The vibration acceleration detection device 300 includes a rail vibration acceleration sensor 310, a roadbed vibration acceleration sensor 320, and a foundation (tunnel wall) vibration acceleration sensor 330, which are respectively used to collect vibration acceleration data at predetermined positions above the rail 950, the floating track plate 920, and the lower foundation 910, and also includes a corresponding data acquisition instrument (not shown in the figure).

[0076] The rail vibration acceleration sensor 310 is installed at the outer side of the rail 950 .

[0077] The track bed vibration acceleration sensor 320 is installed on the upper surface of the floating track plate 920 and is located at the edge in the width direction thereof.

[0078] The foundation vibration acceleration sensor 330 is installed on the tunnel wall above the lower foundation 910.

[0079] like Figure 2 and Figure 3As shown, there are two rail displacement detection devices 400, which are also respectively arranged in the middle of the floating track plate 920 and are relatively close to both sides of the floating track plate 920. The rail displacement detection device 400 includes a rail displacement ranging sensor and a corresponding data acquisition instrument. The rail displacement ranging sensor is arranged on the upper surface of the floating track plate 920 and is located directly below the rail 950. Its detection end faces upward to the bottom surface of the rail 950, and is used to detect the displacement of the rail in the middle of the plate.

[0080] The method of the present invention will be described in detail below in conjunction with the above-mentioned track system 1000.

[0081] Step S1, pre-processing the original signal data collected by various detection devices respectively to obtain corresponding time domain signal data.

[0082] Fig. 9 is a flow chart of step S1 in this embodiment.

[0083] like Fig. 9 As shown, the preprocessing of the original signal data collected by various detection devices is different. Among them, the load detection device 200 collects the load signal data of the track. For the track load signal data, step S1 includes the following sub-steps.

[0084] Step SA1, downloading load signal data from a data acquisition instrument corresponding to the load detection device.

[0085] Step SA2: preview the load signal data.

[0086] Step SA3, smoothing the load signal data, wherein a low-pass filter or a moving average smoothing algorithm may be used to smooth the data to reduce noise.

[0087] Step SA4, performing data cleaning on the smoothed load signal data, including removing abnormal values ​​therein.

[0088] Step SA5, reset the cleaned load signal data to zero, wherein the load signal data is a time domain signal (time history curve), whose two axes are time and load value, respectively. By translating the time history curve as a whole along the axis of the load value, the load value of the time history curve is made to be 0 when the train does not pass through the load detection device, thereby eliminating the influence of the train passing on the installation status of the load detection device.

[0089] For the elastic element distance signal data collected by the vibration isolator detection device 100 , step S1 includes the following sub-steps.

[0090] Step SB1, downloading elastic element distance signal data, which is electrical signal data, from a data acquisition instrument corresponding to the vibration isolator detection device.

[0091] Step SB2, performing conversion calculation on the elastic element distance signal data of the electrical signal data to convert it into digital signal data.

[0092] Step SB3, previewing the elastic element distance signal data of the digital signal data.

[0093] Step SB4, smoothing the elastic element distance signal data.

[0094] Step SB5, cleaning the smoothed elastic element distance signal data, including removing abnormal values ​​therein.

[0095] Step SB6, returning the distance signal data of the cleaned elastic element to zero.

[0096] The specific operations of step SB3 to step SB6 are substantially the same as those of the above-mentioned step SA2 to step SA5, and thus will not be described again. The corresponding steps of the following other detection devices are also the same.

[0097] For the rail displacement distance signal data collected by the rail displacement detection device 400, step S1 includes the following sub-steps.

[0098] Step SC1, downloading rail displacement distance signal data, which is electrical signal data, from a data acquisition instrument corresponding to the rail displacement detection device.

[0099] Step SC2, converting and calculating the rail displacement distance signal data of the electrical signal data to convert it into digital signal data.

[0100] Step SC3: previewing the rail displacement distance signal data of the digital signal data.

[0101] Step SC4, smoothing the rail displacement distance signal data.

[0102] Step SC5, cleaning the smoothed rail displacement distance signal data, including removing abnormal values ​​therein.

[0103] Step SC6, returning the displacement distance signal data of the cleaned rail to zero.

[0104] For the vibration acceleration signal data collected by the vibration acceleration detection device 300, step S1 includes the following sub-steps.

[0105] Step SD1, downloading vibration acceleration signal data from a data acquisition instrument corresponding to the vibration acceleration detection device.

[0106] Step SD2, previewing the vibration acceleration signal data.

[0107] Step S2, searching for predetermined feature points in the time domain signal data and obtaining their time points, and performing time domain alignment on the time domain signal data of multiple detection devices according to the correspondence between the feature points and the train structure and the predetermined distances between the multiple detection devices.

[0108] Fig.10 is a flow chart of step S2 in this embodiment.

[0109] like Fig.10 As shown, step S2 specifically includes the following sub-steps.

[0110] Step S2-1, searching for predetermined feature points corresponding to the train bogie and their corresponding time points in the time domain signal data corresponding to the reference detection device.

[0111] In this embodiment, the load detection device 200 located in the middle of the floating track plate 920 (the middle in the length direction) is selected as the reference detection device, and its time domain signal data is also the load time history curve, and the peak and the corresponding coordinate (time point) are found in the curve.

[0112] Fig.11 is an example diagram of the time history curve of the load detection device in the middle of the plate in this embodiment. Fig.12 It is an example diagram of the time history curve of the load detection device at the plate end in this embodiment.

[0113] like Fig.11 and Fig.12 As shown, the time history curve collected by the load detection device 200 in the middle of the plate has no obvious abnormality, and the corresponding relationship between the wave peak and the train bogie is clear and definite. This is because the load detection device 200 (pad device) in the middle of the plate is surrounded by other pad devices to bear the load together, so the middle of the plate is relatively stable. The time history curve collected by the load detection device 200 at the end of the plate is more complicated. In addition to the wave peaks corresponding to the two wheel pairs of the bogie, there are many other relatively small wave peaks, which makes the relationship between the train structure and the time history curve not as good as the time history curve. Fig.11 So clear and Fig.11 and Fig.12 The waveform shapes are quite different. This is because the load detection device 200 at the plate end only has other pad devices to bear the load on one side close to the middle of the plate, but not on the other side, so the stability is relatively worse. On the other hand, when the train passes the plate end, the plate end will be warped by the force, that is, the position close to the middle of the plate sinks and the end of the plate is lifted, so there will be abnormalities in the collected data.

[0114] Therefore, in this embodiment, the load detection device 200 in the middle of the plate is selected as the reference detection device, and the time coordinate of its time domain signal data is used as the reference to perform time domain translation on the time domain signal data of other detection devices, thereby performing multi-source data alignment. For the convenience of description, the other detection devices except the load detection device 200 in the middle of the plate (reference detection device) are referred to as general detection devices.

[0115] In this embodiment, the characteristic point to be searched is the peak in the load signal data. All peak data in the time domain signal (load signal data) are found through the peak detection algorithm. The peak data includes the coordinates of the peak (ie, the time point) and the value of the peak (ie, the load value).

[0116] Step S2-2, based on the found feature points, calculate the time difference between two adjacent feature points to obtain a feature point time difference array.

[0117] In this embodiment, based on the time point of the peak found, the time difference between two adjacent peaks is calculated to obtain the peak time difference array of each group of peaks. Peak time difference array = [peak 2 coordinate - peak 1 coordinate, peak 3 coordinate - peak 2 coordinate, peak 4 coordinate - peak 3 coordinate, ...].

[0118] Step S2-3, based on the feature point time difference array, the cross-correlation coefficients between the multiple groups of feature points are calculated by a cross-correlation algorithm to obtain a cross-correlation array.

[0119] Specifically, firstly, a cross-correlation array is created for storing the cross-correlation calculation results, and then the cross-correlation coefficients between multiple groups of peaks are calculated, wherein, for each possible lag value (the time lag of each group of peaks relative to the first group of peaks), the sum of the products of the overlapping parts of the group of peaks and the first group of peaks is calculated as the lag value, and the result of the lag value is stored in the cross-correlation array as the corresponding cross-correlation coefficient.

[0120] Step S2-4, searching for the maximum value position in the cross-correlation array, and obtaining the time point corresponding to the maximum value position as the reference time point, so as to determine the best alignment point between the signal data collected by different detection devices.

[0121] Step S2-5, calculates the train speed of the train based on the time difference between the characteristic points (peak time difference) between the wheelbase of the train and a group of characteristic points (peaks) generated by the train bogie passing through the reference detection device, wherein the groups of peaks generated by the bogie (including two wheelsets) of the train passing through the reference detection device can be obtained through a cross-correlation algorithm, and then the train speed can be calculated in combination with the known wheelbase.

[0122] Step S2-6, calculating the time it takes for the train to pass the predetermined distance based on the predetermined distance between the reference detection device and the general detection device and the train speed.

[0123] Step S2-7, calculating the time shift of the time domain signal data of the general detection device relative to the time domain signal data of the reference detection device according to the elapsed time and the reference time point.

[0124] Step S2-8, performing time domain shift on the time domain signal data of each general detection device according to the corresponding time shift amount, so as to obtain multi-source detection data after time domain alignment.

[0125] Among them, the time shift amount = the distance between the general detection device and the reference detection device / the train speed + the offset value of the reference time point relative to the coordinate origin.

[0126] Step S3, extracting train number signal data of multiple train numbers from the aligned time domain signal data.

[0127] In this embodiment, the load detection device 200 is configured to start collecting load signal data for a period of time when a predetermined critical load value is detected, and the critical load value is, for example, 1 kN, that is, when a train passes through, the load detection device 200 detects the pressure generated by the train and automatically starts data collection for a period of time, so that the data of each train can be separated very conveniently. The same is true for other detection devices.

[0128] Step S4, calculating the train speed according to the time difference between the characteristic points in the train signal data of each train number and the train size data corresponding to the train number, and calculating the expansion coefficient according to the train speed.

[0129] In this embodiment, since the train speed of the train has been calculated in step S2-5 based on the peak time difference between the wheelbase of the train and a group of wave peaks corresponding to the first group of bogies of the train, the expansion coefficient can be directly calculated using the train speed in step S4. In an alternative scheme, step S2 can also use a method that does not involve the calculation of the train speed to perform data time domain alignment, for example, the train speed is directly measured by another speed detection device in the track. Then in step S4, the train speed can be calculated in the same way as steps S2-5, or the train speed measured by the speed detection device can be directly used, or both methods can be used at the same time, and the train speeds obtained by the two methods can be used as references to each other.

[0130] In this embodiment, in step S4, the expansion factor of each train is calculated according to the train speed of each train and the predetermined standard speed. Expansion factor = train speed / standard speed. For example, if the standard speed is set to 80 km / h, and the train speed is calculated to be 60 km / h through step S2-5, the expansion factor of the train is 60 / 80 = 0.75.

[0131] Step S5, segmenting the train signal data of each train number according to the corresponding expansion coefficient to obtain segmented signal data of each train number.

[0132] The duration of the segmented signal data = the forward segmentation duration of the peak coordinates corresponding to the first group of bogies of the train / expansion factor + the backward segmentation duration of the peak coordinates corresponding to the first group of bogies of the train / expansion factor. In this embodiment, the unit of the duration is seconds.

[0133] Fig.13 It is a schematic diagram of the segmented signal data of two train numbers in this embodiment.

[0134] like Fig.13 As shown, the train speed of one train is relatively fast, so the interval between the multiple bogies of the train passes the detection device is short, and the distance between the two peaks in its time curve C1 is smaller. The train speed of the other train is relatively slow, so the distance between the two peaks in its time curve C2 is larger.

[0135] According to the expansion coefficients of the two trains, the faster train will extract a relatively shorter data segment, while the slower train will extract a relatively longer data segment. It can be seen intuitively that although the two data segments have different lengths, their waveforms are similar. After subsequent processing, the lengths of the two data segments will be consistent and the peak coordinates will be unified.

[0136] Step S6, using a data expansion and compression algorithm to expand or compress the segmented signal data of each train number according to the corresponding expansion coefficient, so as to obtain the signal data of multiple matched train numbers.

[0137] Fig.14 is a flow chart of step S6 in this embodiment.

[0138] like Fig.14 As shown, step S6 specifically includes the following sub-steps:

[0139] Step S6-1, determining whether the expansion coefficient of the train number is greater than or less than a predetermined standard coefficient.

[0140] Step S6-2: When it is determined in step S6-1 that the value is greater than the standard coefficient, a data expansion algorithm is used to expand the segmented signal data of the train number according to the expansion coefficient.

[0141] Among them, the data expansion algorithm is a spline interpolation algorithm. For trains with faster speeds (that is, the train speed is greater than the predetermined standard speed), the spline interpolation algorithm is used to expand the data, increase the data in the horizontal direction (time axis direction), and make its time curve relatively flatter, which has an effect similar to reducing the speed of the train.

[0142] Fig.15 Schematic diagram of the spline interpolation algorithm in this embodiment.

[0143] like Fig.15 As shown, the spline interpolation algorithm fits the data points through piecewise polynomials to ensure the smoothness of the data at the connections between each segment, and is suitable for processing continuous and smooth signals. For example, in this embodiment, the time domain signal data generated by the train passing through each detection device is usually continuous, so these signals can be effectively reconstructed through the spline interpolation algorithm.

[0144] Step S6-3: When it is determined in step S6-1 that the value is smaller than the standard coefficient, a data compression algorithm is used to compress the segmented signal data of the train number according to the expansion coefficient.

[0145] Among them, the data compression algorithm is the Largest-Triangle-Three-Bucket (LTTB) algorithm. For trains with slower speeds (i.e., train speeds less than the predetermined standard speed), the Largest-Triangle-Three-Bucket algorithm is used for data compression (sampling), reducing the horizontal data and making the time course curve relatively steeper, which has an effect similar to increasing the speed of the train. In addition, the algorithm can maintain the overall trend and shape of the data while reducing the number of data points, thus avoiding the loss of key information when compressing the data.

[0146] In an alternative solution, one of the train numbers may be randomly selected as the reference train number, or the reference train number may be selected according to a predetermined selection standard, such as selecting a train number with a train speed closest to a predetermined standard speed as the reference train number (i.e., among multiple train numbers, its expansion coefficient is closest to 1). Similarly, by using the above-mentioned data expansion and compression algorithm to adjust the expansion coefficients of other train numbers to be consistent with the expansion coefficient of the reference train number, a similar effect can be achieved.

[0147] Through the above steps, the detection signal data can be flexibly expanded and compressed in the horizontal direction (time axis) according to the train speed, so as to unify the length of data under different train numbers and different speeds, and ensure the consistency of analysis and visualization of data from different train numbers.

[0148] After the detection data of different trains are matched and unified, the matched detection data can be used to realize automatic detection of track and vehicle defects, optimal design of track structure and train structure, etc. Some application methods are exemplified below.

[0149] For example, load data after matching multiple train numbers can be used to automatically detect and judge train wheel defects. The waveforms of different bogies of different train numbers can be compared. If the waveforms of multiple bogies are similar, and the waveform of one bogie is greatly different from the waveforms of other bogies (low similarity), the wheel of the bogie is determined to be an abnormal wheel, and the abnormal wheel can be located according to the time of the abnormal waveform, and the information of the wheel can be output (for example, output train number + bogie number).

[0150] For another example, the overall curves of different trains can be compared. If the overall curves of multiple trains are similar, but the overall curve of one train is less similar to the overall curves of other trains, the train of this train is judged as an abnormal train, so that possible defects of the train can be discovered.

[0151] Fig.16 It is a logic block diagram of the track detection data matching device for different train numbers in this embodiment.

[0152] like Fig.16 As shown, a device corresponding to the above method can be formed, and the track detection data matching device 700 for different train numbers includes a signal data acquisition unit 710, a data preprocessing unit 720, a data alignment processing unit 730, a train number signal extraction unit 740, an expansion coefficient acquisition unit 750, a train number data segmentation unit 760, a train number data matching processing unit 770, a matching device communication unit 780, and a matching device control unit 790.

[0153] The signal data acquisition unit 710 is used to acquire the corresponding signal data collected by each detection device from each data acquisition instrument.

[0154] The data preprocessing unit 720 performs corresponding preprocessing on the acquired signal data using the method of step S1 to obtain corresponding time domain signal data.

[0155] The data alignment processing unit 730 performs time domain alignment on the time domain signal data of multiple detection devices using the method of step S2 above, and outputs the aligned time domain signal data.

[0156] The train number signal extraction unit 740 extracts the train number signal data of two or more train numbers from the aligned time domain signal data using the method of step S3 above.

[0157] The expansion coefficient acquisition unit 750 calculates the expansion coefficient of each extracted train signal data using the method of step S4.

[0158] The train number data segmentation unit 760 uses the method of step S5 to segment the data of each train number signal according to the corresponding expansion coefficient and outputs the segmented signal data.

[0159] The train number data matching processing unit 770 uses the method of step S6 to perform data expansion or data compression on each segmented signal data according to the corresponding expansion coefficient, and outputs the matched signal data of multiple train numbers.

[0160] The train wheel defect judgment unit 771 uses the above-mentioned application method to judge whether the train wheels have defects based on the matched signal data of multiple train numbers, and outputs the corresponding wheel information when it is judged that a certain wheel has a defect.

[0161] The communication unit 780 of the matching device is used to communicate with other devices, for example, the signal data of multiple trains after matching can be sent to other computing and analysis devices for further processing and analysis. Optionally, the track detection data matching device 700 may further include a computing and analysis unit for further processing and analysis based on the signal data of multiple trains after matching, such as visualizing the signal data of multiple trains, or finding a train whose waveform is obviously different from the waveforms of multiple other trains as a train with a fault condition, etc. Optionally, the track detection data matching device 700 does not directly connect with each data acquisition instrument and obtain the corresponding signal data collected by it, but receives signal data from other devices through the communication unit 780 of the matching device.

[0162] The matching device control unit 790 is used to control the operation of the above-mentioned functional units.

[0163] In this embodiment, the track detection data matching device 700 is a laptop computer provided with a corresponding computer program. The track system 1000 also includes the track detection data matching device 700 .

[0164] This embodiment has the following functions and effects.

[0165] According to the track detection data matching method, device and track system of different trains provided in this embodiment, the method includes the steps of preprocessing the collected original signal data, aligning the time domain signal data in the time domain, extracting the signal data of multiple trains from the aligned data, calculating the train speed and expansion coefficient of each train, segmenting the data according to the expansion coefficient, and expanding or compressing the data according to the expansion coefficient. Among them, since the characteristic points in the time domain signal correspond to the event that the specific structure of the train passes through the detection device, and the time domain signal alignment is achieved in combination with the predetermined distance between the detection devices, the automatic time domain alignment of the detection data can be achieved, and no manual operation is required to achieve the data time domain alignment, the alignment accuracy is higher, and a lot of manpower and time can be saved. Further, the train speed and expansion coefficient of each train can be calculated according to the aligned data, and then the corresponding part can be automatically segmented from the data of each train according to the expansion coefficient, and the segmented data is correspondingly expanded or compressed according to the expansion coefficient, so as to unify the length of the data of different trains with different speeds, ensure the consistency of analysis and visualization of the data of different trains, and thus facilitate the corresponding big data analysis.

[0166] In the embodiment, different preprocessing is performed on the raw data collected by various detection devices, including data smoothing, elimination of abnormal data, and data zeroing. After preprocessing, the noise in the raw data can be reduced, and the impact of passing trains on the installation status of each detection device can be eliminated, which is conducive to obtaining more accurate alignment results later.

[0167] Furthermore, in the data time domain alignment, the load detection device installed in the middle of the floating track plate is used as a reference detection device. Since there are multiple pad devices in the middle of the track plate that bear the load together and are relatively stable, there are basically no abnormal peaks in the time curve collected by the load detection device in the middle of the plate, and there are very few other abnormal values. The correspondence between multiple peaks and the train bogie is clear and definite. Therefore, it is used as the reference detection device, and the search for characteristic points (peaks) is more convenient and accurate. Correspondingly, the subsequently calculated train speed and time shift are also more accurate, and the multi-source detection data can be more accurately aligned in the time domain.

[0168] Furthermore, the maximum correlation algorithm can effectively determine the optimal alignment point between signals. Due to the structural characteristics of the floating track plate, the time history curves collected by the load detection device in the middle of the plate and the load detection device at the end of the plate are very different. It is impossible to use the data in the middle of the plate to assist the correspondence and confirmation of the wheel position in the data at the end of the plate through algorithms such as peak matching. However, by using the characteristic that the bogie will produce two peaks with equal time intervals when passing through the load detection device, the maximum correlation algorithm can well determine the optimal alignment point between the very different signals, and then calculate the vehicle speed and other data through the peak data in the middle of the plate, which can assist in confirming the wheel position in the data at the end of the plate.

[0169] Furthermore, since the various detection devices are fixedly installed in the track structure, the known device spacing, the calculated train speed, and the reference time point when the train passes the reference detection device can be used to obtain accurate time shifts with very little calculation complexity, which can be used to perform automated time domain alignment on large-scale multi-source detection data.

[0170] Furthermore, for faster trains, the spline interpolation algorithm is used for data expansion (interpolation) to increase the number of data points in the horizontal direction. This algorithm is applicable to complex time-course curves, and it has high precision and high stability, is insensitive to changes in input data, and can approximate the original data well. For slower trains, the maximum triangle three-bucket algorithm is used for data compression (sampling) to reduce the data points in the horizontal direction. This algorithm can well maintain the overall trend and shape of the data, thereby avoiding the loss of key information when compressing the data. Therefore, the above two algorithms can flexibly realize the expansion and compression of the data in the horizontal direction while maintaining the trend and characteristics of the data, effectively avoiding the expansion and compression operations from affecting the subsequent further data processing and analysis.

[0171] In the embodiment, the track is a floating plate track, and the multi-source detection data includes track load data, elastic unit related distance data, rail / roadbed plate / tunnel wall vibration acceleration data, and rail displacement distance data. After data alignment processing, these data can be used to analyze the wheel-rail contact, rail deformation, elastic unit installation and deformation, overall vibration reduction effect, etc., which is conducive to keeping the floating plate track in a more ideal state.

[0172] The above embodiments are only used to illustrate the specific implementation of the present invention, and the present invention is not limited to the description scope of the above embodiments. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

[0173] For example, in the above embodiment, the detection device includes a vibration isolator detection device, a load detection device, a vibration acceleration detection device, and a rail displacement detection device. In an alternative solution, the detection device may include only one or more of these types, and the specific structure and installation position of each detection device may also be different. For example, for a non-floating ballast plate track, there may be no vibration isolator detection device; for a track using a vibration isolation pad, the vibration isolator detection device may also use different detection elements and different structures.

[0174] In the above embodiment, the load detection device in the middle of the plate is used as the reference detection device, and the wave crest corresponding to the wheel is found from its time domain signal as the feature point for time domain alignment. In an alternative solution, other detection devices can also be used as reference detection devices, as long as there are waveform features in their time domain signals that are relatively easy to identify and can clearly correspond to specific structures on the train. The feature points can also use other waveform features, such as troughs, specific shape curve segments, etc.

[0175] In the above embodiment, the spline interpolation algorithm and the maximum triangle three-bucket algorithm are respectively used to perform data expansion and data compression. In an alternative solution, other existing interpolation algorithms and sampling algorithms can also be used to implement data expansion and data compression.

[0176] In the above embodiment, the device for matching track detection data of different train numbers is a laptop computer. In an alternative solution, it may also be other computing and analysis equipment, and may also be a remote device.

Claims

1. A method for matching track detection data of different train numbers, used for matching detection data of different train numbers of tracks, characterized in that: The following steps are involved: Step S1, performing corresponding preprocessing on the original signals collected by multiple detection devices to obtain corresponding time domain signal data; Step S2, searching for a predetermined feature point in the time domain signal data and obtaining its time point, and performing time domain alignment on the time domain signal data of multiple detection devices according to the corresponding relationship between the feature point and the train structure and the predetermined distance between the multiple detection devices; Step S3, extracting train number signal data of multiple train numbers from the aligned time domain signal data; Step S4, calculating the train speed according to the time difference between the characteristic points in the train signal data of each train number and the train size data corresponding to the train number, and calculating the expansion coefficient according to the train speed, wherein the groups of characteristic points generated by the bogie of the train passing through the detection device are found in the time domain signal data by a cross-correlation algorithm, and the train speed is calculated according to the predetermined wheelbase of the train and the time difference between the group of characteristic points generated by the bogie of the train passing through the detection device; Step S5, performing data segmentation on each of the train number signal data according to the corresponding expansion coefficient to obtain segmented signal data of each of the train numbers; and Step S6, using a data expansion and compression algorithm to expand or compress the segmented signal data of each train number according to the corresponding expansion coefficient, so as to obtain matched signal data of multiple train numbers.

2. The method for matching track detection data of different train numbers according to claim 1, characterized in that: in, The expansion factor = the train speed / the predetermined standard speed.

3. The method for matching track detection data of different train numbers according to claim 2, characterized in that: in, In step S5, the duration of the segmented signal data = the forward segmentation duration of the feature point coordinates corresponding to the bogie of the train / the expansion factor + the backward segmentation duration of the feature point coordinates corresponding to the bogie of the train / the expansion factor.

4. The method for matching track detection data of different train numbers according to claim 1, Features: Wherein, step S6 includes the following sub-steps: Determining whether the expansion coefficient of the train number is greater than or less than a predetermined standard coefficient; When it is judged to be greater than the standard coefficient, a data expansion algorithm is used to perform data expansion on the segmented signal data of the train number according to the expansion coefficient; When it is determined to be smaller than the standard coefficient, a data compression algorithm is used to compress the segmented signal data of the train number according to the expansion coefficient.

5. The method for matching track detection data of different train numbers according to claim 4, characterized in that: in, The data expansion algorithm is a spline interpolation algorithm. The data compression algorithm is a maximum triangle three-bucket algorithm.

6. The method for matching track detection data of different train numbers according to claim 1, Features: Among them, one of the multiple detection devices is used as a reference detection device, and the rest are used as general detection devices. Step S2 includes the following sub-steps: Searching for predetermined characteristic points corresponding to the bogie of the train and their corresponding time points in the time domain signal data corresponding to the reference detection device; Calculate the time difference between two adjacent feature points to obtain a feature point time difference array; Based on the feature point time difference array, the cross-correlation coefficients between the plurality of groups of feature points are calculated by a cross-correlation algorithm to obtain a cross-correlation array; Finding the maximum value position in the cross-correlation array, and obtaining the time point corresponding to the maximum value position as a reference time point, so as to determine the best alignment point between the signal data collected by different detection devices; calculating the train speed of the train based on the wheelbase of the train and the time difference between a set of characteristic points generated by the bogie passing through the reference detection device; Calculating the elapsed time of the train passing the predetermined distance according to the predetermined distance between the reference detection device and the general detection device and the speed of the train; Calculating a time shift of the time domain signal data of the general detection device relative to the time domain signal data of the reference detection device according to the elapsed time and the reference time point; The time domain signal data of each general detection device is respectively time-domain shifted according to the corresponding time shift amount, so as to obtain aligned detection data.

7. The method for matching track detection data of different train numbers according to claim 6, characterized in that: in, The track includes a lower foundation and a plurality of floating ballast plates arranged in sequence, wherein the floating ballast plates are arranged on the lower foundation through a plurality of vibration isolators. The reference monitoring device is a load detection device arranged in the middle of the floating track bed plate, and is used to detect the track load and obtain corresponding load signal data. The characteristic point is a peak in the load signal data. In step S2, the train speed is calculated based on the wheelbase of the train and the peak time difference between a group of peaks generated by the bogie passing through the reference detection device.

8. The method for matching track detection data of different train numbers according to claim 1, Features: The track includes a lower foundation, a plurality of floating ballast plates arranged in sequence, and rails arranged on the floating ballast plates. The floating ballast plate is arranged on the lower foundation through a plurality of vibration isolators. The detection device comprises: A plurality of load detection devices are arranged under the rails and used to detect the load of the rails and obtain load signal data; A plurality of vibration isolator detection devices are respectively arranged in the plurality of vibration isolators, and are at least used to detect the distance from the vibration isolator to the lower foundation to obtain vibration isolator distance signal data; A plurality of vibration acceleration detection devices are arranged in the middle of the floating track bed plate, and are used to detect the vibration acceleration of the track and obtain vibration acceleration signal data. A plurality of rail displacement detection devices are respectively arranged under the rails to detect the distance from the device to the bottom surface of the rails and obtain rail displacement distance signal data. In step S1, the load signal data is smoothed, cleaned and reset to zero. The isolator distance signal data and the rail displacement distance signal data are respectively subjected to electrical signal conversion processing, data smoothing, data cleaning and data zeroing processing.

9. A device for matching track detection data of different train numbers, used for matching detection data of different train numbers of tracks, characterized in that: include: A signal data acquisition unit, used to acquire the original signal data collected by the multiple detection devices respectively; A data preprocessing unit, used for performing corresponding preprocessing on the original signal data to obtain corresponding time domain signal data; A data alignment processing unit, used to search for a predetermined feature point in the time domain signal data and obtain its time point, and perform time domain alignment on the time domain signal data of a plurality of the detection devices according to a correspondence between the feature point and the train structure and a predetermined distance between the plurality of the detection devices; A train number signal extraction unit, used to extract train number signal data of multiple train numbers from the aligned time domain signal data; an expansion coefficient acquisition unit, used for calculating the train speed according to the time difference between the characteristic points in the train signal data of each train number and the train size data corresponding to the train number, and calculating the expansion coefficient according to the train speed, wherein the groups of characteristic points generated by the bogie of the train passing through the detection device are searched in the time domain signal data by a cross-correlation algorithm, and the train speed is calculated according to the predetermined wheelbase of the train and the time difference between the group of characteristic points generated by the bogie of the train passing through the detection device; A train number data segmentation unit, used for segmenting the train number signal data of each train number according to the corresponding expansion coefficient to obtain segmented signal data of each train number; as well as The train number data matching processing unit is used to use a data expansion and compression algorithm to expand or compress the segmented signal data of each train number according to the corresponding expansion coefficient, so as to obtain the matched signal data of multiple train numbers.

10. A track system, characterized in that: include: track; A plurality of detection devices are arranged in the track and used to collect detection data of the track and trains of a plurality of train numbers; as well as A track detection data matching device is used to match the detection data of multiple trains. Wherein, the track detection data matching device is the track detection data matching device for different train numbers as described in claim 9.

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