Steel spring floating slab ballast bed structure monitoring method, device, equipment and medium

Abstract

The invention relates to a steel spring floating slab ballast bed structure monitoring method, device and equipment and a medium, and the method comprises the steps: setting a floating slab point position corresponding table, and obtaining a plurality of train vibration signal sequences when a train passes through a plurality of optical fiber sensor point positions within a preset time; obtaining a wavelet packet energy entropy median of each optical fiber sensor point location according to the spectrograms of the train vibration signal sequences; detecting the wavelet packet energy entropy median of a plurality of target optical fiber sensor point locations corresponding to two adjacent steel spring floating slab ballast bed sections according to a floating slab point location corresponding table to obtain a monitoring result of a shear hinge structure between the two adjacent steel spring floating slab ballast bed sections; according to the method, vibration signals are collected through point locations of the optical fiber sensors, the aggregation effect of the median of the wavelet packet energy entropy is analyzed, the structural state of the shear hinge between adjacent floating slab ballast bed sections can be monitored in real time, the problems that a traditional monitoring method is low in efficiency and limited in coverage range are solved, and the method has the advantage of improving the degradation recognition efficiency of the shear hinge structure.

Description

Technical Field

[0001] This invention relates to the field of urban rail transit monitoring technology, and in particular to a method, device, equipment and medium for monitoring steel spring floating slab track bed structures. Background Technology

[0002] The principle of steel spring floating slab track bed is to reduce the impact vibration between the floating slab and the foundation by adding a helical spring support structure. When the train is running, the track vibration is absorbed by the helical springs through elastic potential energy, and the excess energy that cannot be dissipated is transferred to the roadbed to achieve the purpose of vibration reduction. Setting shear hinges at the ends of the floating slab can reduce the discontinuity of the overall track stiffness at the slab ends, thereby reducing the dynamic response of the train and track. Due to factors such as improper construction techniques, long-term operational loads, and uneven tunnel settlement, the performance of subway floating slab track beds may deteriorate. Shear hinges at the slab ends can significantly reduce the adverse effects caused by the reduction in track bed stiffness, but as the deterioration coefficient of the shear hinges themselves increases, their effect gradually diminishes. Therefore, continuous monitoring of the condition and defects of the shear hinge structure is of great significance for track safety.

[0003] Currently, monitoring of steel spring floating slab track beds in subway tunnels mainly relies on regular manual inspections, lacking widely used intelligent monitoring methods. Inspections can only be conducted during non-operational hours, resulting in limited time and low efficiency. Furthermore, they primarily detect visible problems, failing to identify potential deterioration. Traditional electrical sensor devices, mainly based on point monitoring, have short signal transmission distances, making it difficult to achieve full-time, comprehensive monitoring of the steel spring floating slab track bed sections laid as needed along the track. Therefore, in the rail transit field, there is currently no system available to monitor the shear hinge structure condition and defects of steel spring floating slab track beds covering the entire line.

[0004] Therefore, there is an urgent need to propose a method, device, equipment, and medium for monitoring the structure of steel spring floating slab track bed, in order to solve the technical problem that the monitoring of steel spring floating slab track bed in subway tunnels in the existing technology mainly relies on regular manual inspections and point monitoring by electrical sensor devices, which cannot monitor the structure of steel spring floating slab track bed sections. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, device, equipment and medium for monitoring the structure of steel spring floating slab track bed, so as to solve the technical problem that the monitoring of steel spring floating slab track bed in subway tunnels in the prior art mainly relies on manual periodic inspections and point monitoring by electrical sensor devices, which cannot monitor the structure of steel spring floating slab track bed sections.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides a method for monitoring the structure of a steel spring floating slab track bed, comprising: Set up a table to correspond multiple fiber optic sensor points with floating slab points of steel spring floating slab track bed sections, and obtain multiple train vibration signal sequences when the train passes through the multiple fiber optic sensor points within a preset time. Based on the spectrum diagrams of the multiple train vibration signal sequences, the median wavelet packet energy entropy of each fiber optic sensor point is obtained. According to the floating slab point correspondence table, the median of the wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections is detected to obtain the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections.

[0007] In one possible implementation, obtaining the median wavelet packet energy entropy of each fiber optic sensor location based on the spectrum of the plurality of train vibration signal sequences includes: Based on the spectrum diagrams of the multiple train vibration signal sequences, the wavelet packet energy entropy of each train vibration signal is obtained; Using the preset time as the unit, determine the wavelet packet energy entropy sequence for each fiber optic sensor location; The median of the wavelet packet energy entropy for each fiber optic sensor location is determined based on the wavelet packet energy entropy sequence.

[0008] In one possible implementation, the step of detecting the median of the wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections according to the floating slab point correspondence table, to obtain the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections, includes: Based on the floating plate point correspondence table, determine the multiple target fiber optic sensor points corresponding to the adjacent two steel spring floating plate track sections; Determine whether the median energy entropy of all wavelet packets at the multiple target fiber optic sensor locations exhibits a clustering effect; the clustering effect is defined as the median energy entropy of all wavelet packets concentrating towards the same median interval. If not, then the monitoring result of the shear hinge structure between the two adjacent steel spring floating slab track sections is determined to be in a deteriorated state; If so, the monitoring result of the shear hinge structure between the two adjacent steel spring floating slab track sections is determined to be in good condition.

[0009] In one possible implementation, determining whether the median energy entropy of all wavelet packets at the plurality of target fiber optic sensor locations exhibits a clustering effect includes: Based on the two adjacent steel spring floating slab track sections, the first steel spring floating slab track section and the second steel spring floating slab track section are determined. Multiple target fiber optic sensor locations on the first steel spring floating slab track bed section are determined as the first measurement area set, and multiple target fiber optic sensor locations on the second steel spring floating slab track bed section are determined as the second measurement area set; The median energy entropy of all wavelet packets in the first and second measurement area sets is compared and analyzed to obtain the analysis results. Determine whether a clustering effect is observed based on the analysis results.

[0010] In one possible implementation, the step of comparing and analyzing the median energy entropy of all wavelet packets in the first and second measurement area sets to obtain the analysis results includes: When the median energy entropy of all wavelet packets in the first and second measurement area sets are in the same median interval, the analysis result is determined to be a clustering effect. When the median interval of the wavelet packet energy entropy median clustering of the first test area set is not the same median interval as the median interval of the wavelet packet energy entropy median clustering of the second test area set, the analysis result is determined to be that no clustering effect has occurred.

[0011] In one possible implementation, acquiring multiple train vibration signal sequences as the train passes the multiple fiber optic sensor locations within a preset time period includes: Set the target extraction time; When the target extraction time is reached, an offline extraction method is adopted to read the vibration signal of the train passing the fiber optic sensor point within a preset time from the local database of the edge server. Multiple train vibration signal sequences were extracted from the vibration signals in a manner that allowed for uniform distribution during the operating period.

[0012] In one possible implementation, after detecting the median of the wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections according to the floating slab point correspondence table, and obtaining the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections, the method further includes: Collect all monitoring results and median energy entropy of all wavelet packets of the shear hinge structure between two adjacent steel spring floating slab track sections at preset time intervals. Based on all the monitoring results, a data distribution diagram of the shear hinge structure and the median wavelet packet energy entropy was drawn. The steel spring floating slab track bed is tracked and given early warning based on the data distribution map.

[0013] Secondly, the present invention also provides a steel spring floating slab track bed structure monitoring device, comprising: The signal acquisition module is used to set up a correspondence table between multiple fiber optic sensor points and floating slab points of the steel spring floating slab track bed section, and to acquire multiple train vibration signal sequences when the train passes through the multiple fiber optic sensor points within a preset time. The wavelet packet energy entropy calculation module is used to obtain the median wavelet packet energy entropy of each fiber optic sensor point based on the spectrum diagram of the multiple train vibration signal sequences. The aggregation detection module is used to detect the median of the wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections according to the floating slab point correspondence table, so as to obtain the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections.

[0014] Thirdly, embodiments of the present invention disclose an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the various steps of the above-described embodiments of the steel spring floating slab track bed structure monitoring method.

[0015] Fourthly, embodiments of the present invention disclose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the various steps of the above-described embodiments of the steel spring floating slab track bed structure monitoring method.

[0016] The beneficial effects of this invention are as follows: A correspondence table is set up between multiple fiber optic sensor locations and floating slab locations of steel spring floating slab track sections to obtain multiple train vibration signal sequences when a train passes through multiple fiber optic sensor locations within a preset time period; based on the spectrum diagram of multiple train vibration signal sequences, the median wavelet packet energy entropy of each fiber optic sensor location is obtained; according to the floating slab location correspondence table, the median wavelet packet energy entropy of multiple target fiber optic sensor locations corresponding to two adjacent steel spring floating slab track sections is detected to obtain the monitoring results of the shear hinge structure between two adjacent steel spring floating slab track sections; by collecting vibration signals from fiber optic sensor locations and analyzing the aggregation effect of the median wavelet packet energy entropy, the structural state of the shear hinge between adjacent floating slab track sections can be monitored in real time, solving the problems of low efficiency and limited coverage of traditional monitoring methods, and having the advantage of improving the efficiency of shear hinge structure deterioration identification. Attached Figure Description

[0017] Figure 1 A schematic flowchart of an embodiment of the steel spring floating slab track bed structure monitoring method provided by the present invention; Figure 2 This is a schematic diagram of an embodiment of the floating plate location correspondence table provided by the present invention; Figure 3 For the present invention Figure 1A schematic flowchart of an embodiment of step S103; Figure 4 For the present invention Figure 3 A schematic flowchart of an embodiment of step S302; Figure 5 A schematic diagram of an embodiment of the data distribution map provided by the present invention; Figure 6 A schematic diagram of an embodiment of the steel spring floating slab track bed structure monitoring device provided by the present invention; Figure 7 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0018] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0019] like Figure 1 As shown, a specific embodiment of the present invention discloses a method for monitoring the structure of a steel spring floating slab track bed, comprising: S101. Set up a table corresponding to multiple fiber optic sensor points and floating slab points of the steel spring floating slab track bed section, and obtain multiple train vibration signal sequences when the train passes through multiple fiber optic sensor points within a preset time.

[0020] The steel spring floating slab track bed structure monitoring method provided in this application embodiment can be applied to a steel spring floating slab track bed structure monitoring system. The steel spring floating slab track bed structure monitoring system can be a software system running on a terminal device. The terminal device can be a server, tablet computer, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), mobile phone, etc. This application embodiment does not impose any restrictions on the specific type of terminal device.

[0021] Among them, fiber optic sensor points refer to the monitoring area formed by fiber optic sensor points continuously arranged along the track. Specifically, wavelength division multiplexing technology can be used to divide multiple monitoring areas, with each monitoring area covering a specific floating slab track bed section.

[0022] Specifically, a fiber optic vibration sensor cable is laid along the subway track to detect vibration signals at corresponding locations in real time. The spacing between the fiber optic sensor points is required to be no more than 5 meters. For a common 25-meter-long steel spring floating slab track bed, this allows for at least five vibration monitoring intervals. A floating slab point mapping table is established in the database, as shown in the table below. Figure 2 As shown, Figure 2 The floating slab location correspondence table includes direction, floating slab mileage, floating slab number, offset, and fiber optic sensor location number. This table records the specific fiber optic sensor location measurement area numbers covered by each steel spring floating slab track section (e.g., floating slab numbers 1-5). (For example, floating slab number 1 corresponds to fiber optic sensor location numbers 100-104, floating slab number 2 corresponds to fiber optic sensor location numbers 105-109, floating slab number 3 corresponds to fiber optic sensor location numbers 110-114, floating slab number 4 corresponds to fiber optic sensor location numbers 115-119, and floating slab number 5 corresponds to fiber optic sensor location numbers 120-124). The shear hinge is located at the connection between floating slab number 1 and floating slab number 2, and its influence range typically covers several measurement areas on both sides. Instruments in the subway station equipment room are responsible for real-time acquisition of vibration signals from the fiber optic sensors. The required output sampling frequency is not less than 500 Hz. In a specific embodiment of this invention, the sampling frequency of the instrument used is 700 Hz.

[0023] S102. Based on the spectrum diagrams of multiple train vibration signal sequences, obtain the median wavelet packet energy entropy of each fiber optic sensor location.

[0024] The median wavelet packet energy entropy refers to the median value obtained by sorting the wavelet packet energy entropy values ​​of all train vibration signals in a certain measurement area within a preset time period. Specifically, it can be calculated by calculating the signal spectrum using a Fast Fourier Transform and then applying the information entropy formula. This indicator can eliminate single measurement errors and reflect the concentration trend of vibration energy distribution in the measurement area. The preset time period can be one day.

[0025] S103. Based on the floating slab point correspondence table, the median of wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections is detected to obtain the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections.

[0026] Among them, clustering detection refers to analyzing whether the median wavelet packet energy entropy of corresponding test areas of adjacent track bed sections has statistical consistency. Specifically, the interval overlap analysis method can be used. When the median wavelet packet energy entropy of multiple test areas is concentrated in the same value interval, it is determined that there is a clustering effect, indicating that the structural connection status is good.

[0027] Specifically, fiber optic sensors are first deployed along the track to establish a mapping table between the measurement area numbers and the floating slab track sections. When a train passes, the fiber optic sensors collect vibration signals and transmit them to the processing terminal. Spectral analysis is performed on all vibration signals from each measurement area within a preset time period. The median of the wavelet packet energy entropy value for each signal is then taken as the characteristic value of that measurement area. For two adjacent floating slab track sections, the median wavelet packet energy entropy dataset of their corresponding measurement areas is retrieved, and statistical tests are used to determine whether the data distribution exhibits significant clustering. If the difference in the median distribution intervals between adjacent track section measurement areas exceeds a threshold, the shear hinge structure is deemed to have deteriorated.

[0028] Compared with existing technologies, this embodiment provides a correspondence table between multiple fiber optic sensor locations and floating slab locations of steel spring floating slab track sections. It acquires multiple train vibration signal sequences when a train passes multiple fiber optic sensor locations within a preset time period. Based on the spectrum of the multiple train vibration signal sequences, the median wavelet packet energy entropy of each fiber optic sensor location is obtained. The median wavelet packet energy entropy of multiple target fiber optic sensor locations corresponding to two adjacent steel spring floating slab track sections is detected according to the floating slab location correspondence table, yielding the monitoring results of the shear hinge structure between two adjacent steel spring floating slab track sections. By collecting vibration signals from fiber optic sensor locations and analyzing the aggregation effect of the median wavelet packet energy entropy, the structural state of the shear hinge between adjacent floating slab track sections can be monitored in real time. This solves the problems of low efficiency and limited coverage of traditional monitoring methods, and has the advantage of improving the efficiency of shear hinge structure deterioration identification.

[0029] In some embodiments of the present invention, step S101 includes: Set the target extraction time. The target extraction time refers to the pre-set periodic data collection trigger node, which can be implemented using a timer or a scheduled task to control the timing of data collection.

[0030] When the target extraction time is reached, an offline extraction method is adopted to read the vibration signal of the train passing the fiber optic sensor point within the preset time from the local database of the edge server.

[0031] Offline extraction refers to obtaining vibration signals stored in a local database through a non-real-time transmission path. This can be achieved using batch data reading technology to avoid the network resource consumption caused by real-time transmission.

[0032] Multiple train vibration signal sequences were extracted from the vibration signals in a manner that allowed for uniform distribution during the operating period.

[0033] The uniform distribution of operating time periods refers to covering different train operation density stages with the extracted vibration signal samples. This can be achieved by dividing the time window and combining it with a random sampling algorithm to ensure the time period representativeness of the data samples.

[0034] Specifically, by setting periodically triggered data extraction times (daily early morning hours), the waste of storage resources caused by continuous data collection can be avoided. When the preset time point is reached, the system automatically reads historical vibration data in batches from the local storage of the edge server, without relying on a real-time transmission channel. Subsequently, time windows are divided according to the operating period, such as peak hours, off-peak hours, and nighttime hours. Vibration signal samples are extracted proportionally from each time window to form a data set covering different operating states. This step is performed in the signal processing module of the edge server. Extraction can be real-time or offline. For example, if the target extraction time is daily early morning hours, offline extraction can be used. The signal data of the previous day (preset time) is read from the local database of the edge server, and 20 train vibration signal segments are extracted in a way that is evenly distributed according to the operating period and saved to the edge database.

[0035] In some embodiments of the present invention, step S102 includes: Based on the spectrum diagrams of multiple train vibration signal sequences, the wavelet packet energy entropy of each train vibration signal is obtained.

[0036] Among them, wavelet packet energy entropy refers to the signal complexity metric obtained through spectrum analysis. Specifically, it can be calculated using fast Fourier transform combined with Shannon entropy formula, and is used to quantify the energy distribution characteristics of vibration signals.

[0037] The wavelet packet energy entropy sequence of each fiber optic sensor point is determined using a preset time unit.

[0038] Among them, the wavelet packet energy entropy sequence refers to the sequence formed by arranging the energy entropy values ​​of multiple wavelet packets collected within a preset time period in chronological order. Specifically, it can be achieved by segmenting the continuous signal by setting a fixed time window, and is used to reflect the temporal changes in the vibration state of the survey area.

[0039] The median of the wavelet packet energy entropy at each fiber optic sensor location is determined based on the wavelet packet energy entropy sequence.

[0040] The median of wavelet packet energy entropy refers to the middle value selected from the wavelet packet energy entropy sequence. Specifically, it can be achieved by using a sorting algorithm to extract the median of the sequence. It is used to eliminate extreme value interference and characterize the typical features of the vibration state of the test area.

[0041] Specifically, within a preset time period, vibration signals from trains passing through track sections are collected using fiber optic sensor locations. Spectral analysis is performed on each signal, and the corresponding wavelet packet energy entropy value is calculated. The fiber optic sensor location number, signal time, characteristic parameters, and extraction time are written into the edge database. This step is executed in the signal processing module of the edge server. All wavelet packet energy entropy values ​​generated within the same measurement area within a fixed time window are constructed into a chronological sequence, and the median value of this sequence is determined using a sorting algorithm. This median value serves as the vibration characteristic parameter for that location on that date (referred to as the wavelet packet energy entropy median), effectively eliminating the influence of single measurement errors or sudden interference, and providing a reliable data foundation for subsequent shear hinge state assessment. The fiber optic sensor location number, date, and wavelet packet energy entropy median are written into the edge database and pushed to the central big data module.

[0042] In some embodiments of the present invention, such as Figure 3 As shown, step S103 includes: S301. Determine the locations of multiple target fiber optic sensors corresponding to two adjacent steel spring floating slab track sections according to the floating slab location correspondence table.

[0043] Among them, such as Figure 2 The floating slabs numbered 1 and 2 are two adjacent steel spring floating slab track sections. The corresponding multiple target fiber optic sensor points are fiber optic sensor point numbers 100-104 and fiber optic sensor point numbers 105-109. Similarly, all adjacent steel spring floating slab track sections can be processed in the same way. An example is used to illustrate this embodiment of the invention.

[0044] S302. Determine whether the median energy entropy of all wavelet packets at multiple target fiber optic sensor locations exhibits a clustering effect; the clustering effect is expressed as the median energy entropy of all wavelet packets concentrating towards the same median interval.

[0045] The clustering effect refers to the convergence of the numerical distribution of the median wavelet packet energy entropy in different measurement areas within the same structural unit. Specifically, it can be achieved by calculating the dispersion or interval overlap of the median wavelet packet energy entropy. When the dispersion is lower than a set threshold or the interval overlap is higher than a set proportion, a clustering effect is determined to exist.

[0046] S303. If not, then the monitoring result of the shear hinge structure between two adjacent steel spring floating slab track sections is determined to be in a deteriorated state.

[0047] The median wavelet packet energy entropy refers to the median statistic of the wavelet packet energy entropy sequence calculated from the vibration signal spectrum. Specifically, it can be calculated using a fast Fourier transform combined with the Shannon entropy formula to determine the spectral energy distribution characteristics of the vibration signal, which is used to characterize the stability of the structural vibration state. Deterioration state determination refers to the situation where the median distribution of wavelet packet energy entropy between adjacent track bed sections is inconsistent, indicating that the shear hinge has failed to effectively transfer vibration energy. This state classification can be achieved by setting a difference threshold or using statistical testing methods.

[0048] S304. If so, then the monitoring result of the shear hinge structure between two adjacent steel spring floating slab track sections is determined to be in good condition.

[0049] Specifically, during implementation, the first step is to locate the set of test areas associated with adjacent track bed sections based on the fiber optic sensor point mapping table, and extract the median wavelet packet energy entropy data of each test area within a preset time period. By comparing the distribution characteristics of the median wavelet packet energy entropy of the two sets of test areas, if there is a significant difference in the median intervals of the two sets of data, it is determined that the clustering effect has disappeared. In this case, the shear hinge is obstructed due to structural damage, resulting in the transmission of vibration energy between adjacent track bed sections, which is manifested as an increase in the dispersion of the wavelet packet energy entropy distribution. If the median intervals of the two sets of data highly overlap, it is determined that the clustering effect exists, indicating that the shear hinge is maintaining a normal working state. This judgment process can be continuously executed through a sliding time window to achieve dynamic monitoring of the structural state.

[0050] Furthermore, in this embodiment of the invention, when determining whether an aggregation effect is exhibited, the test area located at the end of the plate is removed.

[0051] In some embodiments of the present invention, such as Figure 4 As shown, step S302 includes: S401. Based on two adjacent steel spring floating slab track sections, determine the first steel spring floating slab track section and the second steel spring floating slab track section.

[0052] The first and second steel spring floating slab track sections refer to two adjacent floating slab track sections connected by shear hinges. This division can be made using a floating slab point correspondence table to distinguish the vibration signal sources of adjacent track sections. For example... Figure 2 The floating plates in the middle are numbered 1 and 2.

[0053] S402. The multiple target fiber optic sensor locations on the first steel spring floating slab track bed section are determined as the first measurement area set, and the multiple target fiber optic sensor locations on the second steel spring floating slab track bed section are determined as the second measurement area set.

[0054] The first and second measurement area sets refer to the sets of fiber optic sensor locations corresponding to the first and second steel spring floating slab track sections, respectively. These sets can be specifically divided based on the physical locations of the fiber optic sensor locations, and are used to collect vibration data from the two track sections respectively. For example... Figure 2 The floating plates are numbered 1 and 2. The first measurement area set L1 includes measurement areas with fiber optic sensor point numbers 100-104, and the second measurement area set L2 includes measurement areas with fiber optic sensor point numbers 105-109.

[0055] S403. Compare and analyze the median energy entropy of all wavelet packets in the first and second measurement area sets to obtain the analysis results.

[0056] Among them, comparative analysis refers to the statistical distribution comparison of the median wavelet packet energy entropy of two test area sets. Specifically, it can be implemented by interval overlap calculation or clustering algorithm to determine whether the vibration characteristics of the two track bed sections are consistent.

[0057] S404. Determine whether a clustering effect is observed based on the analysis results.

[0058] Specifically, during the monitoring process, the fiber optic sensor locations corresponding to two adjacent track bed sections are first determined using a floating slab location correspondence table, and the locations are then divided into a first set of locations and a second set of locations. Subsequently, the median wavelet packet energy entropy of all locations in each of the two sets is extracted, and statistical methods are used to analyze whether their distribution intervals overlap. For example, if the median wavelet packet energy entropy of both sets is concentrated within the same numerical interval, it indicates that the vibration energy distribution of the two track bed sections is coordinated, and the shear hinge structure is in good condition; if the median wavelet packet energy entropy of the two sets is distributed in different intervals, it indicates that there are significant differences in the vibration characteristics of the two track bed sections, and the shear hinge may be deteriorating.

[0059] In some embodiments of the present invention, step S403 includes: When the median energy entropy of all wavelet packets in the first and second measurement regions falls within the same median interval, the analysis result is determined to be a clustering effect.

[0060] The median interval refers to a numerical range defined based on historical data or preset rules. Specifically, it can be achieved by using statistical methods to divide the wavelet packet energy entropy median into multiple continuous intervals, for example, defining 0.5-1.5 as the same interval. This division is used to quantify the distribution characteristics of the wavelet packet energy entropy median, thereby determining the correlation between data from different survey areas.

[0061] When the median interval of the wavelet packet energy entropy median clustering of the first test area set is not the same median interval as the median interval of the wavelet packet energy entropy median clustering of the second test area set, the analysis result is determined to be that no clustering effect has occurred.

[0062] Among them, the clustering effect refers to the concentration trend of the median wavelet packet energy entropy of multiple survey areas in terms of numerical distribution. Specifically, it can be calculated by interval overlap or probability density function. This effect reflects the consistency of structural vibration energy transfer between adjacent track bed sections.

[0063] Specifically, after obtaining the median wavelet packet energy entropy of the first and second test area sets, the median of each test area is categorized into a preset interval. If the medians of both sets are concentrated in the same interval, it indicates that the vibration energy distribution of adjacent track sections is consistent, and the shear hinge structure has not deteriorated. If the medians of the two sets are distributed in different intervals, it indicates that the vibration energy transmission is abnormal, and the shear hinge structure may experience a decrease in stiffness or connection failure. For example, when the median of the first test area set L1 is concentrated in the 0.8-1.2 interval, while the median of the second test area set L2 is distributed in the 1.6-2.0 interval, the value sets of L1 and L2 show obvious separation, indicating that they have not formed a clustering effect, thus triggering a deterioration warning. When the median sets of the first test area set L1 and the second test area set L2 are both distributed in the 0.8-1.2 interval, the L1 and L2 sets are inseparable, indicating that they have formed a clustering effect, suggesting that the shear hinge structure between the two floating slab track sections is in good condition.

[0064] In some embodiments of the present invention, after step S103, the method further includes: Collect all monitoring results and the median of all wavelet packet energy entropy of the shear hinge structure between two adjacent steel spring floating slab track sections at preset time intervals.

[0065] The preset time refers to the time interval between the periodic collection of monitoring results and the median of wavelet packet energy entropy. Specifically, it can be achieved by using a fixed period or dynamically adjusting the period, such as every 24 hours or setting different periods according to the train operation density, to ensure the continuity of data collection.

[0066] Based on all monitoring results, a data distribution map of the shear hinge structure and the median wavelet packet energy entropy was plotted.

[0067] Among them, the data distribution map refers to a visual chart that correlates the monitoring results at different time points with the corresponding median wavelet packet energy entropy. Specifically, it can be implemented using scatter plots, line graphs, or heat maps to intuitively show the dynamic relationship between the shear hinge structure state and the median wavelet packet energy entropy.

[0068] The steel spring floating slab track bed is tracked and an early warning is issued based on the data distribution map.

[0069] Among them, tracking and early warning refer to the monitoring results that identify abnormal trends or deviations from the normal range based on the data distribution map. Specifically, this can be achieved by setting thresholds or using machine learning algorithms for pattern recognition. For example, an early warning signal is triggered when the median of the wavelet packet energy entropy exceeds the preset range for multiple consecutive cycles.

[0070] Specifically, the aforementioned monitoring conditions may change slowly or abruptly over time. After the shear hinge structure of the adjacent steel spring floating slab track section completes clustered monitoring, historical data is integrated into a time-series dataset by periodically collecting monitoring results and the corresponding median wavelet packet energy entropy. In the data distribution map, the horizontal axis can represent time or monitoring period, and the vertical axis can represent the numerical range of the median wavelet packet energy entropy. Different monitoring results are distinguished by color or markers. By analyzing the distribution density and trend of the median wavelet packet energy entropy in the data distribution map, it is determined whether the shear hinge structure is experiencing gradual deterioration, thereby identifying its trend changes and abrupt changes. When the median wavelet packet energy entropy continuously deviates from the clustered range or the distribution density significantly decreases in the data distribution map, an early warning mechanism is triggered and maintenance recommendations are generated. For trend changes, they can be tracked through visualization and predicted using common trend analysis methods. For abrupt changes, warning messages can be generated to remind users to pay attention and take appropriate action. Figure 5 As shown, the x-axis represents the date, and the y-axis represents the wavelet packet energy entropy. Fiber optic sensor point number 105 is located at the shear hinge structure of the end joint between floating slab 1 and floating slab 2, so its value varies significantly and can be disregarded. Values ​​101-104 belong to floating slab 1, and values ​​106-109 belong to floating slab 2. Because the floating slab track bed structure changes slowly, the figure is based on data from the first day of each quarter for simplicity. Figure 5 In the data, the median wavelet packet energy entropy of the other fiber optic sensor points, except for point 105, was concentrated before January 1, 2025. Starting from January 1, 2025, the median wavelet packet energy entropy of the other fiber optic sensor points began to separate towards both ends, falling into different median intervals. This indicates that the shear hinge between floating slab 1 and floating slab 2 has been damaged due to structural damage. The structural damage to the shear hinge has hindered the transmission of vibration energy between adjacent track bed sections, which is manifested as an increase in the dispersion of wavelet packet energy entropy distribution. This indicates that the floating slab track bed structure has undergone severe deterioration.

[0071] To better implement the steel spring floating slab track bed structure monitoring method in this embodiment of the invention, correspondingly, this embodiment of the invention also provides a steel spring floating slab track bed structure monitoring device, such as... Figure 6As shown, the steel spring floating slab track bed structure monitoring device 600 includes: The signal acquisition module 601 is used to set up a correspondence table between multiple fiber optic sensor points and floating slab points of the steel spring floating slab track bed section, and to acquire multiple train vibration signal sequences when the train passes through multiple fiber optic sensor points within a preset time. The wavelet packet energy entropy calculation module 602 is used to obtain the median wavelet packet energy entropy of each fiber optic sensor point based on the spectrum of multiple train vibration signal sequences. The aggregation detection module 603 is used to detect the median of wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections according to the floating slab point correspondence table, so as to obtain the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections.

[0072] The steel spring floating slab track bed structure monitoring device 600 provided in the above embodiments can realize the technical solutions described in the above embodiments of the steel spring floating slab track bed structure monitoring method. The specific implementation principles of each module or unit can be found in the corresponding content in the above embodiments of the steel spring floating slab track bed structure monitoring method, and will not be repeated here.

[0073] like Figure 7 As shown, the present invention also provides an electronic device 700. The electronic device 700 includes a processor 701, a memory 702, and a display 703. Figure 7 Only some components of the electronic device 700 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0074] In some embodiments, memory 702 may be an internal storage unit of electronic device 700, such as a hard disk or memory of electronic device 700. In other embodiments, memory 702 may also be an external storage device of electronic device 700, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 700.

[0075] Furthermore, the memory 702 may include both internal storage units of the electronic device 700 and external storage devices. The memory 702 is used to store application software and various types of data installed on the electronic device 700.

[0076] In some embodiments, processor 701 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in memory 702 or process data, such as the steel spring floating slab track bed structure monitoring method of the present invention.

[0077] In some embodiments, display 703 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 703 is used to display information from electronic device 700 and to display a visual user interface. Components 701-703 of electronic device 700 communicate with each other via a system bus.

[0078] In some embodiments of the present invention, when the processor 701 executes the steel spring floating slab track bed structure monitoring program in the memory 702, the following steps can be implemented: Set up a table to correspond multiple fiber optic sensor points with floating slab points of steel spring floating slab track bed sections, and obtain multiple train vibration signal sequences when the train passes multiple fiber optic sensor points within a preset time. Based on the spectrum diagrams of multiple train vibration signal sequences, the median wavelet packet energy entropy of each fiber optic sensor location is obtained. Based on the floating slab point correspondence table, the median of wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections is detected to obtain the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections.

[0079] It should be understood that when the processor 701 executes the steel spring floating slab track bed structure monitoring program in the memory 702, in addition to the functions mentioned above, it can also perform other functions, as can be found in the description of the corresponding method embodiments above.

[0080] Furthermore, the embodiments of the present invention do not specifically limit the type of electronic device 700 mentioned. Electronic device 700 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, electronic device 700 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0081] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions of the steel spring floating slab track bed structure monitoring method provided in the above-described method embodiments.

[0082] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0083] The above provides a detailed description of the steel spring floating slab track structure monitoring method, device, equipment, and medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for monitoring the shear hinge structure of a steel spring floating slab track bed, characterized in that, include: Set up a table to correspond multiple fiber optic sensor points with floating slab points of steel spring floating slab track bed sections, and obtain multiple train vibration signal sequences when the train passes through the multiple fiber optic sensor points within a preset time. Based on the spectrum diagrams of the multiple train vibration signal sequences, the median wavelet packet energy entropy of each fiber optic sensor point is obtained. According to the floating slab point correspondence table, the median of the wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections is detected to obtain the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections.

2. The method for monitoring the structure of a steel spring floating slab track bed according to claim 1, characterized in that, The step of obtaining the median wavelet packet energy entropy for each fiber optic sensor location based on the spectrum of the multiple train vibration signal sequences includes: Based on the spectrum diagrams of the multiple train vibration signal sequences, the wavelet packet energy entropy of each train vibration signal is obtained; Using the preset time as the unit, determine the wavelet packet energy entropy sequence for each fiber optic sensor location; The median of the wavelet packet energy entropy for each fiber optic sensor location is determined based on the wavelet packet energy entropy sequence.

3. The method for monitoring the structure of a steel spring floating slab track bed according to claim 1, characterized in that, The step of detecting the median of the wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections according to the floating slab point correspondence table, and obtaining the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections, includes: Based on the floating plate point correspondence table, determine the multiple target fiber optic sensor points corresponding to the adjacent two steel spring floating plate track sections; Determine whether the median energy entropy of all wavelet packets at the multiple target fiber optic sensor locations exhibits a clustering effect; the clustering effect is defined as the median energy entropy of all wavelet packets concentrating towards the same median interval. If not, then the monitoring result of the shear hinge structure between the two adjacent steel spring floating slab track sections is determined to be in a deteriorated state; If so, the monitoring result of the shear hinge structure between the two adjacent steel spring floating slab track sections is determined to be in good condition.

4. The method for monitoring the structure of a steel spring floating slab track bed according to claim 3, characterized in that, The determination of whether the median energy entropy of all wavelet packets at the multiple target fiber optic sensor locations exhibits a clustering effect includes: Based on the two adjacent steel spring floating slab track sections, the first steel spring floating slab track section and the second steel spring floating slab track section are determined. Multiple target fiber optic sensor locations on the first steel spring floating slab track bed section are determined as the first measurement area set, and multiple target fiber optic sensor locations on the second steel spring floating slab track bed section are determined as the second measurement area set; The median energy entropy of all wavelet packets in the first and second measurement area sets is compared and analyzed to obtain the analysis results. Determine whether a clustering effect is observed based on the analysis results.

5. The method for monitoring the structure of a steel spring floating slab track bed according to claim 4, characterized in that, The step involves comparing and analyzing the median energy entropy of all wavelet packets in the first and second measurement area sets to obtain the analysis results, including: When the median energy entropy of all wavelet packets in the first and second measurement area sets are in the same median interval, the analysis result is determined to be a clustering effect. When the median interval of the wavelet packet energy entropy median clustering of the first test area set is not the same median interval as the median interval of the wavelet packet energy entropy median clustering of the second test area set, the analysis result is determined to be that no clustering effect has occurred.

6. The method for monitoring the structure of a steel spring floating slab track bed according to claim 1, characterized in that, The acquisition of multiple train vibration signal sequences when the train passes the multiple fiber optic sensor points within a preset time period includes: Set the target extraction time; When the target extraction time is reached, an offline extraction method is adopted to read the vibration signal of the train passing the fiber optic sensor point within a preset time from the local database of the edge server. Multiple train vibration signal sequences were extracted from the vibration signals in a manner that allowed for uniform distribution during the operating period.

7. The method for monitoring the structure of a steel spring floating slab track bed according to claim 1, characterized in that, After detecting the median of the wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections according to the floating slab point correspondence table, and obtaining the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections, the method further includes: Collect all monitoring results and median energy entropy of all wavelet packets of the shear hinge structure between two adjacent steel spring floating slab track sections at preset time intervals. Based on all the monitoring results, a data distribution diagram of the shear hinge structure and the median wavelet packet energy entropy was drawn. The steel spring floating slab track bed is tracked and given early warning based on the data distribution map.

8. A monitoring device for steel spring floating slab track bed structure, characterized in that, include: The signal acquisition module is used to set up a correspondence table between multiple fiber optic sensor points and floating slab points of the steel spring floating slab track bed section, and to acquire multiple train vibration signal sequences when the train passes through the multiple fiber optic sensor points within a preset time. The wavelet packet energy entropy calculation module is used to obtain the median wavelet packet energy entropy of each fiber optic sensor point based on the spectrum diagram of the multiple train vibration signal sequences. The aggregation detection module is used to detect the median of the wavelet packet energy entropy of multiple target fiber optic sensor points corresponding to two adjacent steel spring floating slab track sections according to the floating slab point correspondence table, so as to obtain the monitoring results of the shear hinge structure between the two adjacent steel spring floating slab track sections.

9. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of the steel spring floating slab track structure monitoring method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program implements the steps of the steel spring floating slab track bed structure monitoring method as described in any one of claims 1-7.

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