Access network construction method based on rail transit infrastructure service state detection

Abstract

The invention discloses an access network construction method based on rail transit infrastructure service state detection in the technical field of rail transit detection. The access network construction method comprises the following steps: step 1, network architecture design: constructing a self-adaptive access network architecture comprising a data acquisition layer, a data transmission layer, a data processing layer and an application layer; step 2, a self-adaptive access mechanism: dynamically adjusting the positions and the number of access points according to the distribution condition and the service requirements of the rail transit infrastructure; and step 3, establishing a wireless communication mode: in a data transmission layer, based on a mobile communication technology, establishing an evolution process from analog to digital and from frequency division multiple access to code division multiple access (CDMA) by adopting the wireless communication mode. The rail transit service information is collected through the non-contact sensing equipment, the rail transit service state is detected by means of sound waves, lasers and the like, and the flatness of the rail and the integrity of materials are evaluated.

Description

Technical Field

[0001] The present invention belongs to the technical field of rail transit detection, and in particular to an access network construction method based on service status detection of rail transit infrastructure. Background Art

[0002] With the rapid development of the economy, rail transit and economic development complement each other. Compared with road construction, road management methods are slightly backward and the technical level is limited. After the road is built, the subsequent maintenance work has not reached a certain level. Factors such as bad weather, road overload, and natural aging can cause cracks, holes and other adverse consequences on the track. This will not only affect the durability of the elevated road, but also endanger social security.

[0003] In urban rail transit, due to the high speed of trains, high traffic density, large passenger flow, complex line conditions, large proportion of plane curves and longitudinal slopes, small radius curves and large slope lines are inevitable. Therefore, the stress environment of the main track components is harsh, which will cause damage to the main components such as rails, fasteners, integral roadbeds, switches, etc.

[0004] As an important component of the track, the health of the rail is of great significance to the safe operation of urban rail trains. Therefore, it is necessary to conduct timely inspections and maintenance to eliminate safety hazards in train operations. The service status of the rail plays a vital role in the normal operation of rail transit. In order to ensure its safety and maintainability, regular systematic structural assessments and interventions are required. Monitoring the service status of rail transit can ensure the normal operation of rail transit, but due to its large structure and size, inspections are time-consuming, expensive, and dangerous, which has intensified the need for intelligent and accurate monitoring of the service status of rail transit.

[0005] Existing rail transit detection access network means often require the deployment of multiple detection points. Due to the long distance of rail transit, this not only means that a large amount of physical resources and funds must be invested in construction, including detection equipment, communication lines, power supply, etc., but also requires a large amount of manpower for installation, debugging and maintenance. Since rail transit lines often pass through various complex environments, such as mountainous areas, rivers, tunnels, etc., it is extremely difficult to lay and maintain detection points in these places, which further increases the cost. At the same time, due to the large number of detection points and their wide distribution, once some of the detection points are damaged due to various reasons (such as natural disasters, equipment aging, human damage, etc.), it is difficult to repair or replace them in time, resulting in the inability to continue to monitor the infrastructure status of the area, which may cause safety hazards. For this reason, it is necessary to propose an access network construction method based on rail transit infrastructure service status detection that does not require the deployment of a large number of detection points and has stable transmission. Summary of the invention

[0006] In order to solve the problem that there are a large number of detection points and a large amount of resources and funds need to be invested in their construction, the purpose of the present invention is to provide an access network construction method based on the service status detection of rail transit infrastructure, which collects rail transit service information through non-contact sensing equipment, detects the service status of rail transit by means of sound waves and lasers, and evaluates the flatness of the track and the integrity of the material.

[0007] In order to achieve the above object, the technical solution of the present invention is as follows: an access network construction method based on service status detection of rail transit infrastructure comprises the following steps:

[0008] Step 1: Network architecture design: Construct an adaptive access network architecture including data collection layer, data transmission layer, data processing layer and application layer. The data collection layer is used to collect the service status data of rail transit infrastructure in real time.

[0009] Step 2: Adaptive access mechanism: dynamically adjust the location and number of access points according to the distribution of rail transit infrastructure and business needs; automatically adjust the data transmission mode and routing selection by monitoring network load and transmission quality;

[0010] Step 3, wireless communication method: In the data transmission layer, based on mobile communication technology, wireless communication methods are used to construct the evolution process from analog to digital and from frequency division multiple access to code division multiple access CDMA;

[0011] Step 4, data processing: In the data processing layer, the collected data is processed and analyzed, including data quality detection, data mining and anomaly detection processes, identifying abnormal conditions and generating alarm information;

[0012] Step 5, Application layer functions: In the application layer, the processed data is displayed and has management functions to import, export, retrieve and evaluate the data.

[0013] Furthermore, in step one, the network architecture design method divides the access network into several independent small networks, and the independent small networks are responsible for the detection tasks in the area, and data aggregation and exchange are performed through a unified gateway.

[0014] Furthermore, in step one, the network architecture sets up redundant lines and self-diagnosis modules in an independent small network; when a fault is detected, the self-diagnosis module automatically switches to a backup line or mode and issues a repair request.

[0015] Furthermore, in step one, the data collection layer collects the status data of the rail transit infrastructure in real time through non-contact sensor equipment.

[0016] Furthermore, in step 2, the adaptive access mechanism adaptively adjusts the bandwidth and computing resources of the access mechanism according to the operation of the rail transit; increases resources during peak hours and reduces bandwidth and computing resources during non-peak hours.

[0017] Furthermore, in step three, the data transmission layer adopts edge computing design, uses data information on the service status of rail transit infrastructure for edge computing, performs preliminary processing on real-time collected data, and adopts data compression and transmission technology; transmits key information to the central data center, integrates key information through the current time period and section, distinguishes between priority information and suboptimal information, and avoids transmission during peak hours and sections with defects and faults.

[0018] Furthermore, in step four, data quality testing compares data from different sources and different stages to discover differences between the data and find out the errors, and uses statistical methods to perform statistical analysis on the sample data to determine whether the data meets the distribution and range of the test.

[0019] Furthermore, in step four, data mining uses classification, regression analysis and clustering methods of data mining to conduct in-depth analysis on the processed data; the classification method divides the data into different categories for track gauge, track profile and geometric dimension classification, the regression analysis method is used to predict trends and correlation analysis, and the clustering analysis clusters similar data together.

[0020] Furthermore, in step 4, anomaly detection uses the One-Class SVM learning algorithm to build an anomaly detection model, learns the behavior pattern of normal data through training data, identifies abnormal data that does not conform to the pattern, and detects and identifies outliers based on data distribution.

[0021] Further, in step 1, the non-contact sensor device includes a base plate, a plurality of rotating shafts are rotatably connected to the base plate, pulleys are fixedly connected to both ends of the rotating shafts, a driving motor is fixedly connected to the top of the base plate, the driving motor signal is connected to the controller, the driving motor is electrically connected to a battery, the battery is located on the main body near the driving motor side, the driving motor output shaft is coaxially fixedly connected to a first gear, a chain is meshed on the first gear, a second gear meshed with the chain is fixedly connected to the rotating shaft near the driving motor, and the first gear and the second gear are matched through a chain transmission;

[0022] An acoustic wave sensor is fixedly connected to the side of the top of the base plate close to the second gear, a GPS receiver is fixedly connected to the top of the acoustic wave sensor, an optical fastener detector is provided at the bottom of the acoustic wave sensor, the optical fastener detector is fixedly connected to the bottom of the base plate, a gyroscope is fixedly connected to the side of the bottom of the base plate close to the battery, a camera is fixedly connected to the side of the base plate away from the acoustic wave sensor, a profile detector is fixedly connected to the side of the bottom of the base plate away from the gyroscope, the acoustic wave sensor, the GPS receiver, the optical fastener detector, the gyroscope, the camera and the profile detector are all electrically connected to the battery, and the acoustic wave sensor, the GPS receiver, the optical fastener detector, the gyroscope, the camera and the profile detector are all connected to the controller signal.

[0023] The basic scheme has the following beneficial effects: 1. The present invention divides and partitions the access network reasonably, and divides the access network into multiple independent small networks or subnets by adopting a modular or regional design method. Even if one of the subnets or modules fails, the other parts can still maintain normal operation, thereby minimizing the impact of the failure on the entire access network. By introducing redundant equipment, spare lines and load balancing technology in independent small networks, it is ensured that when equipment fails or the network is congested, the system can automatically switch to spare resources to maintain service continuity and stability. Self-diagnosis modules are deployed in the access network. These modules can check the working status of network equipment and lines regularly or in real time, and collect performance data and fault information. Intelligent routing and fault switching technology are adopted. When the self-diagnosis module detects a fault, the system can automatically switch to a spare line or mode to ensure service continuity and availability. At the same time, the system can also issue a repair request to the administrator or automatic repair tool to repair the fault in time and restore the normal operation of the network.

[0024] 2. The present invention uses non-contact sensing equipment to detect the service status of rail transit by means of sound waves and lasers, thereby reducing interference with train operations. The acoustic wave sensor detects the position and status of an object by emitting sound waves and receiving reflected signals. In rail transit, the flatness of the track and the integrity of the material can be evaluated by measuring the propagation speed and reflection intensity of the sound waves in the material. If there are cracks, wear or other defects in the track, the propagation path and reflection characteristics of the sound waves will change, thus being captured by the sensor. The laser sensor uses the emission and reception of laser beams to measure parameters such as the distance, position, and shape of an object. In rail transit, laser sensors are used to achieve high-precision, high-speed three-dimensional scanning and measurement. By scanning the surface of infrastructure such as tracks, bridges, and tunnels, detailed information such as their geometry and flatness can be obtained. These data can be used to assess the health of the infrastructure and formulate maintenance plans. Potential problems or abnormal conditions of the infrastructure are discovered in a timely manner, and early warning systems are triggered. This early warning mechanism helps to take timely measures for repairs or adjustments, avoid the expansion of faults, and ensure the stable operation of the rail transit system. This automated data collection method improves the accuracy and reliability of data and provides strong support for subsequent monitoring and analysis.

[0025] 3. The present invention ensures that the rail transit system maintains efficient operation during peak hours by adaptively adjusting bandwidth and computing resources, reducing congestion and delays caused by insufficient resources. During peak hours, increasing bandwidth can ensure that key applications of the access network can transmit data in real time and accurately, improving the safety and punctuality of train operation. During non-peak hours, reducing bandwidth and computing resources can reduce the operating cost of the system. This includes not only the maintenance cost of hardware equipment, but also energy consumption and labor costs. Dynamically adjust resource allocation according to actual needs to maximize resource utilization. This can not only improve the overall performance of the system, but also avoid waste of resources. During peak hours, increasing computing resources can accelerate data processing speed and improve the responsiveness of the system; while during non-peak hours, excess resources can be allocated to other applications or tasks. Through the adaptive access mechanism, the system can respond to various operating conditions more flexibly and enhance the stability and reliability of the system. In the event of an emergency or emergency, the system can quickly adjust resource allocation to respond to sudden demands and ensure the normal operation of the rail transit system.

[0026] 4. The edge computing device of the present invention collects the operating status data of the rail transit infrastructure in real time by connecting to cameras, sensors and other devices. Once the data is captured, the edge computing device will immediately perform preliminary processing and analysis. This processing includes image recognition, anomaly detection, pattern recognition, etc., so as to quickly identify potential problems or abnormal conditions of the infrastructure. After preliminary processing, the edge computing device will further screen and integrate the data. Key information is extracted from a large amount of data based on the current time period (such as peak period), road section (such as a section with defects or failures), and other relevant factors. This key information may include detailed information of abnormal events and the health status of the equipment.

[0027] Edge computing devices prioritize key information. The most preferred information refers to the information that has the greatest impact on the operation of the rail transit system, such as safety-related emergencies; the second-best information refers to the information that has less impact on the operation of the system. By distinguishing this information, edge computing devices can ensure that the most preferred information is transmitted first under limited bandwidth and transmission resources, ensuring that key information can be processed in a timely manner, further improving transmission efficiency.

[0028] In order to reduce the delay and bandwidth requirements of data transmission, edge computing devices use efficient data compression and transmission technologies. These technologies include data compression algorithms, data encoding technologies, network protocol optimization, etc. Through the application of these technologies, edge computing devices can compress raw data into smaller data packets and transmit them through optimized network protocols. This not only reduces the bandwidth requirements for data transmission, but also reduces the risk of network congestion and improves the efficiency and stability of data transmission. Edge computing devices maintain close contact and collaboration with the central data center. Edge computing devices transmit processed key information to the central data center for further analysis and storage. At the same time, the central data center can also send instructions and configuration information to edge computing devices to achieve remote monitoring and control of the rail transit system. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 FIG. 4 is a top view of a non-contact sensor device according to an embodiment of the present invention.

[0030] Figure 2 It is a front view of a non-contact sensor device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following is further described in detail through specific implementation methods:

[0032] The figure marks in the drawings of the specification include: base plate 1, rotating shaft 2, pulley 3, driving motor 4, first gear 5, second gear 6, acoustic wave sensor 7, GPS receiver 8, optical fastener detector 9, gyroscope 10, battery 11, camera 12, profile detector 13.

[0033] The embodiment is basically as shown in the attached Figure 1-Figure 2 As shown: The access network construction method based on the service status detection of rail transit infrastructure includes the following steps:

[0034] Step 1: Network architecture design: Construct an adaptive access network architecture including data collection layer, data transmission layer, data processing layer and application layer. The data collection layer is used to collect the service status data of rail transit infrastructure in real time. The data collection layer collects the status data of rail transit infrastructure in real time through non-contact sensor equipment.

[0035] The network architecture design divides the access network into several independent small networks, which are responsible for the detection tasks in the area and perform data aggregation and exchange through a unified gateway. The network architecture sets up redundant lines and self-diagnosis modules in independent small networks; when a fault is detected, the self-diagnosis module automatically switches to the backup line or mode and issues a repair request.

[0036] Step 2: Adaptive access mechanism: dynamically adjust the location and number of access points according to the distribution of rail transit infrastructure and business needs; automatically adjust the data transmission mode and routing selection by monitoring network load and transmission quality;

[0037] The adaptive access mechanism adaptively adjusts the bandwidth and computing resources of the access mechanism according to the operation of rail transit; it increases resources during peak hours and reduces bandwidth and computing resources during non-peak hours.

[0038] Step 3, wireless communication method: In the data transmission layer, based on mobile communication technology, wireless communication methods are used to construct the evolution process from analog to digital and from frequency division multiple access to code division multiple access CDMA;

[0039] The data transmission layer adopts the design of edge computing, uses the data information of the service status of rail transit infrastructure for edge computing, performs preliminary processing on the real-time collected data, and adopts data compression and transmission technology; transmits key information to the central data center, integrates key information through the current time period and section, distinguishes between priority information and suboptimal information, and avoids transmission during peak hours and sections with defects and faults.

[0040] Step 4, data processing: In the data processing layer, the collected data is processed and analyzed, including data quality detection, data mining and anomaly detection processes, identifying abnormal conditions and generating alarm information.

[0041] Data quality testing compares data from different sources and at different stages to discover differences and identify errors in the data. It uses statistical methods to perform statistical analysis on sample data to determine whether the data meets the distribution and range of the test.

[0042] Data mining uses classification, regression analysis and clustering methods of data mining to conduct in-depth analysis of the processed data; classification methods divide the data into different categories for track gauge, track profile and geometric dimension classification, regression analysis methods are used to predict trends and correlation analysis, and clustering analysis groups similar data together.

[0043] Anomaly detection uses the One-Class SVM learning algorithm to build an anomaly detection model, learns the behavior pattern of normal data through training data, identifies abnormal data that does not conform to the pattern, and detects and identifies outliers based on data distribution.

[0044] Step 5, Application layer functions: In the application layer, the processed data is displayed and has management functions to import, export, retrieve and evaluate the data.

[0045] The non-contact sensor device includes a base plate 1, on which a plurality of rotating shafts 2 are rotatably connected, and pulleys 3 are bolted fixedly connected at both ends of the rotating shafts 2. A driving motor 4 is bolted fixedly connected to the top of the base plate 1, and the driving motor 4 is preferably Y160M-4. The driving motor 4 signal is connected to a controller, and the controller model is preferably OMX931 / 32K. The driving motor 4 is electrically connected to a battery 11, and the battery 11 is bolted fixedly connected to a side of the main body close to the driving motor 4. The output shaft of the driving motor 4 is coaxially bolted fixedly connected to a first gear 5, and a chain is meshed on the first gear 5. A second gear 6 meshed with the chain is fixedly connected to the rotating shaft 2 close to the driving motor 4, and the first gear 5 and the second gear 6 are matched through chain transmission.

[0046] An acoustic wave sensor 7 is bolted and fixedly connected to one side of the top of the bottom plate 1 near the second gear 6. The model of the acoustic wave sensor 7 is preferably 7MH7560-1AA01-AS100. A GPS receiver 8 is bolted and fixedly connected to the top of the acoustic wave sensor 7. The model of the GPS receiver 8 is preferably 1-EGPS-200-P-2. An optical fastener detector 9 is provided at the bottom of the acoustic wave sensor 7. The model of the optical fastener detector 9 is preferably IR-KC-200. The optical fastener detector 9 is bolted and fixedly connected to the bottom of the bottom plate 1. A gyroscope 10 is bolted and fixedly connected to one side of the bottom of the bottom plate 1 near the battery 11. The gyroscope 1 0 model is preferably MPU6050, a camera 12 is bolted to the side of the base plate 1 away from the acoustic wave sensor 7, a profile detector 13 is bolted to the side of the bottom of the base plate 1 away from the gyroscope 10, the model of the profile detector 13 is preferably LT-9001Series, the acoustic wave sensor 7, GPS receiver 8, optical fastener detector 9, gyroscope 10, camera 12 and profile detector 13 are all electrically connected to the battery 11, and the acoustic wave sensor 7, GPS receiver 8, optical fastener detector 9, gyroscope 10, camera 12 and profile detector 13 are all connected to the controller signal.

[0047] The specific implementation process is as follows: first, when testing the service status of rail transit infrastructure, start the drive motor 4, and the drive motor 4 drives the first gear 5 to rotate. Since the first gear 5 and the second gear 6 are matched through chain transmission, the rotation of the drive motor 4 can drive the rotating shaft 2 to rotate, thereby realizing the rotation of the pulley 3, and the non-contact sensor equipment is used through the controller to detect rail transit.

[0048] The ultrasonic sensor 7 is a multifunctional rapid detection and information management developed to address the difficulties in rail transit detection, maintenance and management. It uses the characteristics of ultrasound (such as high frequency, short wavelength, good directionality, etc.) to perform non-contact and wear-free detection. When ultrasound encounters impurities or interfaces during propagation, it will produce significant reflections to form reflected echoes. This characteristic enables the ultrasonic sensor 7 to accurately detect parameters such as the position, distance, and speed of an object. By emitting ultrasound and receiving reflected echoes through the ultrasonic sensor 7, damage such as cracks and peeling on the track surface can be detected.

[0049] Camera 12 is mainly used for collecting high-definition image data of the whole section of the tunnel, fusing the collected images with the three-dimensional laser point cloud data, and automatically detecting the data that is difficult to obtain with the three-dimensional laser scanning data. It is suitable for the inspection of the service status of railway track plates, using related technologies such as multi-channel high-definition camera photography and splicing, computer image processing and analysis, etc., to achieve the inspection items including track plate surface cracks, sleeper block drop and other track plate surface diseases, and to digitally inspect, record, manage and analyze the status of the line track plates.

[0050] The optical fastener detector 9 is mainly used for the inspection of rail line fasteners, automatically detecting the integrity of the fastener system and the fastening status of the spring bar, fastener abnormalities and other fastener system defects, automatically scanning the three-dimensional data of the fastener system to analyze the defect change trend of the fastener system, and scientifically managing the subway line fastener system. Its results can be used as the basis for the maintenance plan arrangement and maintenance of the rail line fastener system, and it is a fast and effective inspection tool for fastener inspection.

[0051] The gyroscope 10 is used to analyze the ratio of the running angular velocity and linear velocity of the rail surface, determine the severity of wave wear, issue real-time alarms for data anomalies, and calculate and reproduce the geometric information of the rail tread. A more accurate error model is used to pre-process the data collected by the sensor; different chord lengths are selected to calculate and restore the wave wear value of the rail tread; and the rail tread state is evaluated based on the calculated wave wear information.

[0052] The profile detector 13 uses a high-precision line structure laser to collect track section profile data, uses the acquired high-precision structured light point cloud data to perform track profile analysis, obtains rail side wear and vertical wear related data and outputs a detection report.

[0053] When the rail transit is built, non-contact sensor equipment is used to detect the initial service status of the track, including track profile, track geometry, gauge, level, superelevation and other information, and the information is transmitted to the data center through wireless communication. After the rail transit is in operation, when detection is required, non-contact sensor equipment is used again to collect information, and the collected data is processed and analyzed through the data processing layer. When an abnormality is detected, an alarm signal is issued. By detecting the horizontal convergence of the ring segment, the misalignment value of the ring segment, the ovality of the ring segment, the equipment limit intrusion status, the seepage disease status, the tunnel block drop status, the tunnel crack disease, the track fastener status, the sleeper disease status, the ballast crack status, the ballast block drop status, the track corrugation status, the rail profile status and other diseases, etc.; it can timely discover and make corresponding warnings in the early stage of the occurrence of full-section diseases in rail transit, adopt targeted and applicable safety evaluation algorithms, correct possible deviations, and determine appropriate maintenance measures, so as to provide effective decision-making support for the management and maintenance of rail transit operations.

[0054] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0055] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for constructing an access network based on service status detection of rail transit infrastructure, characterized in that: The following steps are involved: Step 1: Network architecture design: Construct an adaptive access network architecture including data collection layer, data transmission layer, data processing layer and application layer. The data collection layer is used to collect the service status data of rail transit infrastructure in real time. Step 2: Adaptive access mechanism: dynamically adjust the location and number of access points according to the distribution of rail transit infrastructure and business needs; automatically adjust the data transmission mode and routing selection by monitoring network load and transmission quality; Step 3, wireless communication method: In the data transmission layer, based on mobile communication technology, wireless communication methods are used to construct the evolution process from analog to digital and from frequency division multiple access to code division multiple access CDMA; Step 4, data processing: In the data processing layer, the collected data is processed and analyzed, including data quality detection, data mining and anomaly detection processes, identifying abnormal conditions and generating alarm information; Step 5, Application layer functions: In the application layer, the processed data is displayed and has management functions to import, export, retrieve and evaluate the data.

2. The access network construction method based on rail transit infrastructure service status detection according to claim 1 is characterized in that: In step one, the network architecture design method divides the access network into several independent small networks. The independent small networks are responsible for the detection tasks in the area and perform data aggregation and exchange through a unified gateway.

3. The access network construction method based on rail transit infrastructure service status detection according to claim 2 is characterized in that: In step one, the network architecture sets up redundant lines and self-diagnosis modules in an independent small network; when a fault is detected, the self-diagnosis module automatically switches to a backup line or mode and issues a repair request.

4. The access network construction method based on rail transit infrastructure service status detection according to claim 3 is characterized in that: In step 1, the data collection layer collects the status data of the rail transit infrastructure in real time through non-contact sensor devices.

5. The access network construction method based on rail transit infrastructure service status detection according to claim 4 is characterized in that: In step 2, the adaptive access mechanism adaptively adjusts the bandwidth and computing resources of the access mechanism according to the operation of rail transit; it increases resources during peak hours and reduces bandwidth and computing resources during non-peak hours.

6. The access network construction method based on rail transit infrastructure service status detection according to claim 5 is characterized in that: In step three, the data transmission layer adopts edge computing design, uses data information on the service status of rail transit infrastructure for edge computing, performs preliminary processing on real-time collected data, and adopts data compression and transmission technology; transmits key information to the central data center, integrates key information through the current time period and section, distinguishes between priority information and suboptimal information, and avoids transmission during peak hours and sections with defects and faults.

7. The access network construction method based on rail transit infrastructure service status detection according to claim 6 is characterized in that: In step four, data quality testing compares data from different sources and different stages to discover differences and find errors in the data, and uses statistical methods to perform statistical analysis on sample data to determine whether the data meets the distribution and range of the test.

8. The access network construction method based on rail transit infrastructure service status detection according to claim 7 is characterized in that: In step 4, data mining uses classification, regression analysis and clustering methods of data mining to conduct in-depth analysis of the processed data; the classification method divides the data into different categories for track gauge, track profile and geometric dimension classification, the regression analysis method is used to predict trends and correlation analysis, and the clustering analysis clusters similar data together.

9. The access network construction method based on rail transit infrastructure service status detection according to claim 8, characterized in that: In step 4, anomaly detection uses the One-Class SVM learning algorithm to build an anomaly detection model, learns the behavior pattern of normal data through training data, identifies abnormal data that does not conform to the pattern, and detects and identifies outliers based on data distribution.

10. The method for constructing an access network based on service status detection of rail transit infrastructure according to claim 9, characterized in that: In step 1, the non-contact sensor device includes a base plate, a plurality of rotating shafts are rotatably connected to the base plate, pulleys are fixedly connected to both ends of the rotating shafts, a driving motor is fixedly connected to the top of the base plate, a controller is connected to the driving motor signal, a battery is electrically connected to the driving motor, the battery is located on the main body near the driving motor side, a first gear is coaxially fixedly connected to the output shaft of the driving motor, a chain is meshed on the first gear, a second gear meshed with the chain is fixedly connected to the rotating shaft near the driving motor, and the first gear and the second gear are matched through chain transmission; An acoustic wave sensor is fixedly connected to the side of the top of the base plate close to the second gear, a GPS receiver is fixedly connected to the top of the acoustic wave sensor, an optical fastener detector is provided at the bottom of the acoustic wave sensor, the optical fastener detector is fixedly connected to the bottom of the base plate, a gyroscope is fixedly connected to the side of the bottom of the base plate close to the battery, a camera is fixedly connected to the side of the base plate away from the acoustic wave sensor, a profile detector is fixedly connected to the side of the bottom of the base plate away from the gyroscope, the acoustic wave sensor, the GPS receiver, the optical fastener detector, the gyroscope, the camera and the profile detector are all electrically connected to the battery, and the acoustic wave sensor, the GPS receiver, the optical fastener detector, the gyroscope, the camera and the profile detector are all connected to the controller signal.

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