A wireless signal automatic inspection method for a tunnel scene and a related device

Abstract

This application provides a method, apparatus, device, and readable storage medium for automatic wireless signal inspection in tunnel scenarios. Addressing the typical problem of inaccessible GPS signals in enclosed environments like tunnels, this application introduces geomagnetic information positioning technology. By fully utilizing the stable geomagnetic characteristics within tunnels, it achieves high-precision, continuous, and reliable position estimation, fundamentally overcoming the applicability bottleneck of traditional positioning methods in tunnel scenarios. It integrates automatic wireless signal reception and parameter analysis capabilities, enabling real-time acquisition and processing of multi-dimensional wireless signal characteristics, including signal strength, frequency, and attenuation characteristics, without manual intervention. On one hand, positioning information provides spatial reference for signal perception; on the other hand, the multi-dimensional characteristics of wireless signals can also assist in positioning correction and confidence enhancement. This significantly enhances the system's overall perception and judgment capabilities in complex tunnel environments.

Description

Technical Field

[0001] This application relates to the field of wireless signal inspection technology, and in particular to an automatic wireless signal inspection method and related equipment in a tunnel setting. Background Technology

[0002] In industries such as power, rail transit, and urban utility tunnels, numerous wireless communication devices are deployed within tunnels to carry critical control information. Since the quality of wireless signals directly affects business continuity and equipment operational safety, any anomaly in the wireless signal within a tunnel could lead to communication disruptions or even major safety accidents. Therefore, inspecting the quality of wireless signals in tunnels is essential. Currently, various industries primarily rely on manual inspections to monitor wireless signal quality in tunnel scenarios, resulting in a low level of overall automation. Furthermore, the long tunnel sections and harsh working environments further exacerbate the difficulty of inspections, highlighting the urgent need for automated inspection of wireless signals in tunnel environments.

[0003] Existing manual inspection methods typically involve staff carrying spectrum analyzers, handheld radios, and other equipment during off-peak hours (such as 0:00 to 4:00 AM, common in the rail transit industry) to walk along the tunnel to designated locations to collect wireless parameters. The data is then recorded in paper forms and compiled into an inspection report. Each pair of staff can complete approximately 3 to 5 kilometers of tunnel inspection per night. However, this method suffers from low efficiency and discontinuous collection of wireless signal parameters.

[0004] With the continuous improvement of industry automation, there is an urgent need for a device or method that can automatically acquire wireless signal parameters and tunnel section location information in tunnels and manage them in a unified manner, so as to replace manual inspection and realize the automation of wireless signal detection in tunnel scenarios. Summary of the Invention

[0005] This application aims to at least solve one of the aforementioned technical defects. In view of this, this application provides an automatic wireless signal inspection method and related equipment for tunnel scenarios, which is used to solve the technical defect of poor wireless signal quality inspection effect in tunnel scenarios in the prior art.

[0006] An automatic wireless signal inspection method for tunnel scenarios includes: collecting a first set of target parameters for each preset fixed location of a target train in a target tunnel, each first set of target parameters including ideal geomagnetic signal parameters, wireless signal parameters, and mileage parameters for each fixed location of the target train in the target tunnel; determining mileage data and target geomagnetic signal parameters corresponding to each fixed location traversed by the target train in the target tunnel based on each first set of target parameters; calculating a first analysis result by combining each of the target geomagnetic signal parameters with preset first target data, wherein the preset first target data includes station information for each preset fixed location of the target train in the target tunnel; acquiring the coverage status information of the rail transit wireless signal in the target tunnel in real time during the operation of the target train; framing the coverage status information of the rail transit wireless signal in the target tunnel with each target geomagnetic signal parameter in real time to obtain a first data packet; and analyzing the first data packet using a preset rail transit wireless signal analysis model. The data packet yields a second analysis result. A pre-defined rail transit wireless signal analysis model is trained using the training second data packet as a sample label and the third analysis result included in the second data packet as a sample label. The training second data packet is a data packet composed of frames of the coverage status information of the rail transit wireless signal in the training tunnel and the calibrated training geomagnetic signal parameters at each pre-defined fixed point passed by the training train in the training tunnel. The pre-defined rail transit wireless signal analysis model analyzes a pre-defined reference data packet to obtain a fourth analysis result. The pre-defined reference data packet includes the ideal geomagnetic signal parameters, ideal wireless signal parameters, and ideal mileage parameters of the target train in the target tunnel. The second and fourth analysis results are compared and analyzed, and based on the first analysis result, an automatic wireless signal inspection scheme for the target tunnel is determined. Based on the automatic wireless signal inspection scheme for the target tunnel, the wireless signal of the target tunnel is automatically inspected to determine the wireless signal fault status of the target tunnel.

[0007] Preferably, based on each first target parameter set, determining the mileage data and target geomagnetic signal parameters corresponding to each fixed point location passed by the target train in the target tunnel includes: constructing a first correspondence between the ideal geomagnetic signal parameters and mileage parameters corresponding to each fixed point location in the target tunnel based on each first target parameter set; collecting in real time the actual geomagnetic signal parameters and actual wireless signal parameters of each preset fixed point location passed by the target train during its operation in the target tunnel; and comparing and analyzing the actual geomagnetic signal parameters of each fixed point location passed by the target train in the target tunnel based on the ideal geomagnetic signal parameters of each preset fixed point location in the target tunnel and the first correspondence, to determine the mileage data of each fixed point location passed by the target train in the target tunnel.

[0008] Preferably, the first analysis result is obtained by calculating the parameters of each target geomagnetic signal with the preset first target data, including: determining the ideal mileage information corresponding to each fixed point location of the target tunnel based on the first target data and the first correspondence; determining the actual mileage information corresponding to each target geomagnetic parameter based on the parameters of each target geomagnetic signal and the first correspondence; calculating the actual north component information and east component information corresponding to each target geomagnetic signal parameter with the north component information and east component information of the ideal geomagnetic signal at each fixed point location of the target tunnel to obtain the first target mileage information corresponding to each fixed point location of the target train in the target tunnel; wherein, the calculation formula for the error statistics value corresponding to the first target mileage information at each fixed point location of the target train in the target tunnel includes the following: ,in, This represents the error statistics of the ideal geomagnetic signal parameters and the real-time sensed actual geomagnetic signal parameters corresponding to each fixed position of the target train in the target tunnel; This represents the north component information of the actual geomagnetic signal parameters corresponding to each fixed point location of the target train in the target tunnel; This represents the information about the actual geomagnetic signal parameters corresponding to each fixed point of the target train in the target tunnel; This indicates the position of the target train at each fixed point in the target tunnel. Information on the north component of an ideal geomagnetic signal parameter; This indicates the position of the target train at each fixed point in the target tunnel. The information of the eastern component of the ideal geomagnetic signal parameters; based on the ideal geomagnetic signal parameters at each fixed point in the target tunnel and the station information of the target train in the target tunnel, the information of each first target mileage is calibrated to determine the second target mileage information corresponding to each fixed point in the target tunnel; based on the second target mileage information corresponding to each fixed point in the target tunnel and the ideal mileage parameters corresponding to each fixed point, the real-time mileage information of the target train in the target tunnel is determined.

[0009] Preferably, the coverage status information of the rail transit wireless signal of the target tunnel and the parameters of each target geomagnetic signal are framed in real time to obtain a first data packet, including: determining the mileage information of the target train at each fixed point in the target tunnel based on the target geomagnetic signal parameters; framing the mileage information of the target train at each fixed point in the target tunnel with the wireless signal information of the target train at each fixed point in the target tunnel to obtain a second data packet of the target train at each fixed point in the target tunnel; and fusing each second data packet to form a first data packet.

[0010] Preferably, collecting a first set of target parameters for each preset fixed position of the target train in the target tunnel includes: dividing the target tunnel into grids according to preset grid parameters to obtain several target grids corresponding to the target tunnel; collecting data corresponding to the north component signal and east component signal of the geomagnetic signal parameters of each target grid; fusing the data corresponding to the north component signal and east component signal of the geomagnetic signal parameters of each target grid as the ideal geomagnetic signal parameters of each target grid; determining the correspondence between the target grids corresponding to each preset fixed position in the target tunnel; and determining the ideal geomagnetic signal parameters of each preset fixed position of the target tunnel based on the ideal geomagnetic signal parameters of each target grid and the correspondence between the target grids corresponding to each preset fixed position in the target tunnel.

[0011] Preferably, the automatic wireless signal inspection scheme based on the target tunnel automatically inspects the wireless signal of the target tunnel to determine the wireless signal fault status of the target tunnel, including: an automatic inspection method based on the target tunnel, which collects the actual wireless signal parameters and mileage parameters of each location in the target tunnel in real time; analyzes the wireless signal quality of each location in the target tunnel based on the actual wireless signal parameters of any location in the target tunnel; and determines whether there are locations in the target tunnel with abnormal wireless signal quality based on the wireless signal quality of each location in the target tunnel. If there are fault locations with abnormal wireless signal quality in the target tunnel, an alarm is issued for the fault locations with abnormal wireless signal quality and their corresponding wireless signal fault information.

[0012] Preferably, determining whether there are locations in the target tunnel with abnormal wireless signal quality based on the wireless signal quality at each location of the target tunnel includes: determining whether there are locations in the target tunnel where the signal-to-noise ratio (SNR) of the wireless signal is less than a preset first threshold, or where the difference between the signal strength of the wireless signal and the preset ideal signal strength corresponding to that location is less than a preset second threshold; if there are locations in the target tunnel where the SNR of the wireless signal is less than the preset first threshold, or where the difference between the signal strength of the wireless signal and the preset ideal signal strength corresponding to that location is less than the preset second threshold, and / or where the SNR of the wireless signal is less than the preset first threshold, and the difference between the signal strength of the wireless signal at that location and the preset ideal signal strength corresponding to that location is less than the preset second threshold, then it is determined that there is a wireless signal abnormality at the locations in the target tunnel where the SNR of the wireless signal is less than the preset first threshold, or where the difference between the signal strength of the wireless signal and the preset ideal signal strength corresponding to that location is less than the preset second threshold, and the locations with wireless signal abnormalities are determined as locations in the target tunnel where the wireless signal quality is abnormal.

[0013] An automatic wireless signal inspection system for tunnel scenarios, applied to any of the methods described above, includes: an automatic train monitoring arrival signal transmission unit, a geomagnetic signal analysis unit, a geomagnetic signal sensing unit, a wireless signal sensing unit, and a wireless sensing and analysis backend. The automatic train monitoring arrival signal transmission unit transmits the station information of the target train within the target tunnel to the geomagnetic signal analysis unit via a vehicle-to-ground wireless signal. The geomagnetic signal analysis unit, based on the station information of the target train, collects in real-time a first set of target parameters for each preset fixed position of the target train within the target tunnel. Each first set of target parameters includes the ideal geomagnetic signal parameters, wireless signal parameters, and mileage for each fixed position of the target train within the target tunnel. The parameters are as follows: The geomagnetic signal sensing unit collects the actual geomagnetic signal parameters and actual wireless signal parameters of the target train at each preset fixed position in the target tunnel in real time and transmits them to the geomagnetic signal analysis unit; The geomagnetic signal analysis unit receives the actual geomagnetic signal parameters and actual wireless signal parameters of the target train at each preset fixed position, and calibrates the actual geomagnetic signal parameters of the target train at each preset fixed position according to the station information of the target train and each first target parameter set, to obtain the target geomagnetic signal and mileage data of the target train at each preset fixed position; It then calculates each target geomagnetic signal and the preset first target data to obtain the first analysis result; and transmits the first analysis result and each target geomagnetic signal to the wireless sensing unit. The analysis backend is responsible for: The wireless signal sensing unit is responsible for real-time sensing of the coverage status of the rail transit wireless signal in the target tunnel, and framing the coverage status of the rail transit wireless signal in the target tunnel with various target geomagnetic signals to obtain a first data packet, which is then transmitted to the wireless sensing analysis backend; The wireless sensing analysis backend receives the first data packet in real time and analyzes it using a preset rail transit wireless signal analysis model to obtain a second analysis result of the wireless signal and geomagnetic signal of the entire operating section of the target tunnel; and obtains a fourth analysis result by analyzing a preset reference data packet using the preset wireless signal analysis model, wherein the preset rail transit wireless signal analysis model uses the training second data packet as the sample label, and the second data packet... The third analysis result included in the training is used as a sample label. The training second data packet is a data packet composed of the coverage status information of the rail transit wireless signal in the training tunnel and the calibrated training geomagnetic signal parameters of each preset fixed point passed by the training train in the training tunnel. The preset reference data packet includes the ideal geomagnetic signal parameters, ideal wireless signal parameters, and ideal mileage parameters of the target train in the target tunnel. The second analysis result and the fourth analysis result are compared and analyzed. Based on the first analysis result, the automatic wireless signal inspection scheme of the target tunnel is determined. Based on the automatic wireless signal inspection scheme of the target tunnel, the wireless signal of the target tunnel is automatically inspected to determine the wireless signal fault status of the target tunnel.

[0014] An automatic wireless signal inspection device for a tunnel scenario includes: one or more processors and a memory; the memory stores computer-readable instructions, which, when executed by one or more processors, implement the steps of any of the automatic wireless signal inspection methods for tunnel scenarios described herein.

[0015] A readable storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to implement the steps of any of the tunnel scenario automatic wireless signal inspection methods described herein.

[0016] As can be seen from the above description, this application can collect a first set of target parameters for each preset fixed location of the target train in the target tunnel. Each first set of target parameters includes ideal geomagnetic signal parameters, wireless signal parameters, and mileage parameters for each fixed location of the target train in the target tunnel. Based on each first set of target parameters, the mileage data and target geomagnetic signal parameters corresponding to each fixed location traversed by the target train in the target tunnel can be determined. This allows for the calculation of each target geomagnetic signal parameter with the preset first target data to obtain a first analysis result. The preset first target data includes station information for each preset fixed location of the target train in the target tunnel. During the operation of the target train, the coverage status information of the rail transit wireless signal in the target tunnel is acquired in real time. The coverage status information of the rail transit wireless signal in the target tunnel is then framed with each target geomagnetic signal parameter in real time to obtain a first data packet. The first data packet is then analyzed using a preset rail transit wireless signal analysis model. The system obtains a second analysis result by training a second data packet as a sample label and using the third analysis result included in the second data packet as a sample label. The training second data packet is a data packet composed of frames of the coverage status information of the rail transit wireless signal in the training tunnel and the calibrated training geomagnetic signal parameters of each preset fixed position passed by the training train in the training tunnel. The system analyzes a preset reference data packet through the preset rail transit wireless signal analysis model to obtain a fourth analysis result. The preset reference data packet includes the ideal geomagnetic signal parameters, ideal wireless signal parameters, and ideal mileage parameters of the target train in the target tunnel. The system then compares and analyzes the second and fourth analysis results, and based on the first analysis result, the automatic wireless signal inspection scheme for the target tunnel can be determined. Finally, based on the automatic wireless signal inspection scheme for the target tunnel, the system can automatically inspect the wireless signal of the target tunnel to determine the wireless signal fault status of the target tunnel.

[0017] Therefore, this application effectively solves the positioning problem in environments without GNSS signals. Addressing the typical issue of inability to receive Global Navigation Satellite System (GNSS) signals in enclosed environments such as tunnels, this invention introduces geomagnetic information positioning technology. It fully utilizes the stable geomagnetic characteristics within tunnels to achieve high-precision, continuous, and reliable position estimation, fundamentally overcoming the applicability bottleneck of traditional positioning methods in tunnel scenarios. It integrates automatic wireless signal reception and parameter analysis capabilities, enabling real-time acquisition and processing of multi-dimensional wireless signal characteristics, including signal strength, frequency, and attenuation characteristics, without manual intervention. This not only enhances the intelligence level of the sensing system but also provides a high-quality data foundation for subsequent environmental modeling and state assessment. It innovatively proposes a fusion strategy between geomagnetic positioning and wireless signal sensing and analysis, enabling the two to complement and synergize at the information level. On one hand, positioning information provides spatial reference for signal sensing; on the other hand, the multi-dimensional characteristics of wireless signals can assist in positioning correction and confidence enhancement. This fusion method significantly enhances the system's overall sensing and judgment capabilities in complex tunnel environments. By achieving self-positioning and automatic signal analysis without relying on GNSS, this invention provides feasible and efficient technical support for the inspection of equipment inside tunnels, continuous monitoring of infrastructure status, and automatic identification of abnormal events. The system can be deployed on robots or mobile terminals, reducing the safety risks and workload of manual inspections, significantly improving the real-time performance, systematic nature, and frequency of inspections, thereby promoting the rapid development of tunnel operation and maintenance towards unmanned, intelligent, and refined processes. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the automatic wireless signal inspection method for tunnel scenarios provided in this application; Figure 2 A schematic diagram of the structure of a first data packet provided in this application; Figure 3 This application provides a system architecture for automatic wireless signal inspection in tunnel scenarios; Figure 4 A schematic diagram of the structure of an automatic wireless signal inspection device in a tunnel scenario, as exemplified in this application; Figure 5 This is a hardware structure block diagram of the wireless signal automatic inspection device for tunnel scenarios disclosed in this application. Detailed Implementation

[0020] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. The method provided by this application can be used in many general or special computing device environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor devices, distributed computing environments including any of the above devices or equipment, etc. This application provides an automatic wireless signal inspection method in a tunnel scenario. This method can be applied to various tunnel signal management systems, and can also be applied to various computer terminals or smart terminals. Its execution subject can be the processor or server of the computer terminal or smart terminal.

[0021] The following is combined Figure 1 This paper introduces the process of the automatic wireless signal inspection method for tunnel scenarios provided in this application, such as... Figure 1 As shown, the process may include the following steps: Step S101: Collect the first target parameter set for each preset fixed position of the target train in the target tunnel.

[0022] Specifically, in practice, available positioning data sources for onboard equipment in the rail transit industry include wheel speed recordings, CBTC location information, and ATS broadcast arrival information. Of these, only ATS broadcast arrival information is publicly available, and this information only synchronizes arrival times; it does not provide the specific location of the vehicle within the tunnel. The tunnel environment completely or partially blocks GNSS signals, making it impossible to obtain absolute latitude and longitude in real time. Therefore, when assessing wireless signal quality, it is essential to know the specific location within the tunnel where the measurement is taking place; otherwise, the signal strength or quality has no spatial significance. The geomagnetic field is a natural characteristic field of the Earth, containing rich geomagnetic information such as magnetic field gradient, geomagnetic components, geomagnetic field strength, and magnetic declination. This information has laid the foundation for the development of geomagnetism and provided sufficient basis and theoretical guidance for geomagnetic navigation and positioning technology. Compared with other navigation methods, geomagnetic navigation has advantages such as being passive, all-weather, all-terrain, and low-energy consumption. Meanwhile, geomagnetic sensors used to measure geomagnetic information have many advantages, such as small size, low cost, and high accuracy. Geomagnetic arrays can sense the Earth's magnetic field information; through multiple magnetic sensing units in the array, common-mode interference can be shielded, and location information can be extracted. In practice, geomagnetic arrays are sensors that are typically deployed in front of the vehicle's cab to minimize the magnetic field interference caused by the vehicle's operation.

[0023] Therefore, to understand the wireless signal conditions of the target tunnel, in practice, a first set of target parameters can be collected for each pre-set fixed location of the target train within the tunnel. This allows for the determination of the mileage data and target geomagnetic signal parameters corresponding to each fixed location traversed by the target train within the tunnel, based on each first set of target parameters. Each first set of target parameters includes the ideal geomagnetic signal parameters, wireless signal parameters, and mileage parameters for each fixed location of the target train within the target tunnel. Each corresponding fixed location within the target tunnel can be pre-determined manually based on actual operational needs.

[0024] The geomagnetic field distribution within tunnels is relatively stable and unique, forming a fingerprint-like effect influenced by ferromagnetic materials such as steel bars and rails. Pre-collecting ideal geomagnetic signal parameters at known fixed locations is equivalent to establishing a location-geomagnetic characteristic mapping table. When a train passes by, real-time geomagnetic data is measured and compared with the pre-stored ideal values ​​to infer its current location. Tunnels typically have clearly defined mileage markers or calibrated distances. Pre-recording the mileage parameters at each fixed location allows geomagnetic positioning results to be mapped onto a continuous mileage axis, providing a high-precision spatial index for wireless signal quality. Even absolute latitude and longitude obtained at the tunnel entrance or through other means, such as GNSS calibration at the exit followed by inference into the tunnel interior, aligns all data with external geographic coordinate systems, facilitating comprehensive analysis in conjunction with GIS or other external data.

[0025] In practice, to determine whether the wireless signal at a specific location within a target tunnel is good or bad, one cannot rely solely on absolute values ​​but must compare the actual values ​​with ideal reference values. Ideal wireless signal parameters, such as RSRP, SINR, and RSSI, collected beforehand at fixed locations, represent the expected performance under conditions of no interference, normal equipment, and reasonable network configuration. Comparing real-time parameters measured during actual inspections with these ideal parameters allows for quantitative calculation of quality issues such as attenuation, interference, and coverage gaps. Without pre-collecting these ideal parameters, one can only rely on real-time values ​​and cannot distinguish whether the signal is inherently weak at that location or has experienced abnormal attenuation due to equipment malfunction or obstruction. The first target parameter set simultaneously includes four types of heterogeneous data: geomagnetic, wireless signal, mileage, and latitude / longitude information. Pre-collecting these data jointly at the same set of fixed locations achieves time synchronization and spatial registration of multimodal data. During subsequent real-time operation, only the geomagnetic information usable for positioning and the current wireless signal need to be measured. Then, using the pre-established mapping relationship, the ideal wireless signal parameters for the corresponding location can be quickly found and compared. Secondly, the train's arrival information can serve as a timestamp, aligning with train operation records and ground monitoring data to help differentiate wireless signal performance under different conditions such as normal operation, deceleration, and stopping. When the train arrives at the station, its location is known, allowing verification of the accuracy of geomagnetic positioning and correction of accumulated errors. Furthermore, the wireless signal quality in the arrival area often has higher requirements; pre-collecting location data can be used for specialized assessments.

[0026] In practice, the process of collecting the first set of target parameters for each predetermined fixed position of the target train in the target tunnel may include the following steps: Divide the target tunnel into grids according to predetermined grid parameters to obtain several target grids corresponding to the target tunnel. Collect the data corresponding to the north and east components of the geomagnetic signal parameters for each target grid. Merge the data corresponding to the north and east components of the geomagnetic signal parameters for each target grid as the ideal geomagnetic signal parameters for each target grid. Determine the correspondence between the target grids corresponding to each predetermined fixed position in the target tunnel. Based on the ideal geomagnetic signal parameters of each target grid and the correspondence between the target grids corresponding to each predetermined fixed position in the target tunnel, determine the ideal geomagnetic signal parameters for each predetermined fixed position in the target tunnel.

[0027] Specifically, in reality, a tunnel is a continuous space, but the preset geomagnetic points are usually sparse, such as one every 50 or 100 meters, or even one every 1 meter. However, no matter how precise the settings, the preset geomagnetic points are ultimately not continuous. Geomagnetic signals vary continuously in space and exhibit a complex distribution due to the tunnel structure. If geomagnetic measurements are only taken at preset points, the representativeness of the measurement points may be insufficient, for example, if they happen to be selected at a local anomaly, leading to inaccurate fingerprints. Furthermore, the lack of constraints between the geomagnetic points can easily cause ambiguity in subsequent real-time matching.

[0028] For example, a grid can be placed every 1 or 5 meters along the tunnel direction, while the cross-sectional direction may involve a single point, a one-dimensional grid, or subdivision according to the track position. The purpose of gridding is to discretize continuous space into a uniform, dense grid, so that the geomagnetic sampling points cover the entire tunnel section, rather than just a few sparse fixed points. The grid density can be much higher than the fixed point density, for example, 1 meter per grid for every 50 meters of fixed points. This can capture subtle changes in the geomagnetic field and improve the resolution of the fingerprint database. Gridding also provides a regular data structure for subsequent interpolation and fusion. Since the total intensity information of a single component is limited, the three-vector data of the geomagnetic signal is collected for each target grid separately, that is, the X (north), Y (east), and Z (vertical downward) components of the geomagnetic field are collected, or the three components in the sensor coordinate system. This is because the three vectors provide directional features, significantly improving the uniqueness and robustness of the positioning. The three-vector fingerprints at different locations within the tunnel differ significantly, which can effectively distinguish adjacent grids. In practice, portable magnetometers or magnetic sensors mounted on inspection vehicles are usually used, traveling along the tunnel and recording the three-vector values ​​at grid sampling points. Each grid may be traversed multiple times, such as through multiple sampling and averaging, or by performing short-time averaging within a single grid to suppress sensor noise and transient interference. Median filtering and Gaussian smoothing can also be used to obtain stable and representative three-vector values ​​for that grid. A single measurement may be affected by random noise, train vibration, and the movement of surrounding ferromagnetic objects, such as the opening and closing of train doors. The ideal geomagnetic signal parameters obtained after fusion have high repeatability and are suitable as a reference fingerprint. This parameter is the ideal value used in subsequent real-time matching.

[0029] In practice, magnetic arrays consist of multiple sensors. The Earth's magnetic field has seven components: horizontal (H component), north (X component), east (Y component), vertical (Z component), magnetic declination (D), magnetic inclination (I), and total magnetic field strength (T). Other geomagnetic information can be derived from these three components. Each geomagnetic value can form a sphere at that location in space. Assuming the geomagnetic interference is λ, the sphere radius is R, and the three components of the geomagnetic value are... The relationship between these parameters can satisfy... Geomagnetic acquisition equipment can directly measure these three quantities, so attitude correction can be performed using the following formula: .in, This represents the parameter value of the north component of an ideal geomagnetic signal; This represents the eastern component parameter value of an ideal geomagnetic signal; This represents the vertical component parameter value of an ideal geomagnetic signal; This represents the north component parameter value of the corrected target geomagnetic signal; This represents the eastern component parameter value of the corrected target geomagnetic signal; This represents the north component parameter value of the corrected target geomagnetic signal; Indicates the rotation angle in the vertical direction; This indicates the rotation angle in the horizontal direction.

[0030] Each fixed location falls within a grid or is adjacent to multiple grids. It's necessary to determine which grid's fingerprint best represents that location. The grid is dense, but the fixed locations are sparse and may not fall exactly on a grid point. A mapping must be established so that each fixed location obtains a spatially closest grid fingerprint. The nearest neighbor method is typically used: the ideal geomagnetic parameters of the grid containing the fixed location or the nearest grid are used as the ideal geomagnetic signal parameters for that location. Based on the ideal geomagnetic field and its correspondence with the grid, determining the ideal geomagnetic signal parameters for each fixed location provides a stable, high-confidence ideal geomagnetic fingerprint for each preset location, which can be used for subsequent real-time matching and positioning. Simply measuring the geomagnetic field directly at each preset location might result in the complete loss of geomagnetic variation information between two points if the locations are too sparse (e.g., one point every 100 meters). During real-time matching, if the measured geomagnetic field lies between two points, the matching algorithm struggles to determine the accurate location, leading to high ambiguity. It's also possible that the fixed-point location might be situated at a local anomaly, such as a rail joint, where the geomagnetic value is unusual and doesn't represent the general characteristics of that section, leading to systematic biases in subsequent matching. Noise suppression may be impossible; for example, single-point measurements are susceptible to accidental interference, and there's no opportunity to improve the signal-to-noise ratio through multiple measurements or spatial fusion. It can easily lead to low acquisition efficiency: if there are many fixed-point locations, such as one every 10 meters, direct measurement is labor-intensive and difficult to guarantee data consistency. By using a gridded, densely packed approach to collect data from each grid, then fusing the data and mapping it to the fixed points, a complete spatial geomagnetic distribution can be obtained with denser sampling points. Fusion improves the reliability of each grid fingerprint, flexibly providing an ideal reference for any fixed-point location, and also supporting advanced applications such as subsequent interpolation and continuous positioning. The gridded acquisition and fusion of geomagnetic three-vector data aims to characterize the tunnel space with a dense, denoised, and stable fingerprint field, which is then mapped to sparse, pre-defined fixed-point locations, thus providing a high-precision, highly repeatable ideal geomagnetic reference for each fixed point. This is more robust, complete, and engineering feasible than measuring directly at a fixed location.

[0031] For example, the rail transit sections can be first designed as a grid, with each grid cell being 20 meters wide. Taking a 20km single rail transit line as an example, 1000 sub-sections are needed. The three-vector geomagnetic signal of each section is collected using a point-based method. The geomagnetic environment of the rail transit tunnel may be affected by the geological environment, including construction effects. After project acceptance, point-based data collection is performed on the tunnel scene to obtain ideal values ​​at a 1-meter granularity. The ideal values ​​of the three-vector geomagnetic components at each location are then obtained. Here, 'i' refers to the ideal value of the three-vector geomagnetic components obtained by manually marking points at 1-meter intervals after tunnel completion. The range of 'i' can be from the depot to the starting station to the terminal station. The actual measured value of the corresponding three-vector geomagnetic signal obtained during actual vehicle operation in the tunnel scenario is... The rail transit section is divided into 20-meter grids, and the first grid can be defined. Include , i=0,1,2……19; the second grid is defined as containing points: N2 contains , i=0,1,2……19; localization only requires positioning the vehicle to the corresponding grid. Because vehicles move very fast, gridding can reduce abnormal ping-pong bounces during vehicle operation; when At that time, the system was located in grid N1, where, This refers to the first point on the first fingerprint; 1 refers to the first fingerprint; 0 refers to the 0th point. The 119th fingerprint is the 20th fingerprint; YZ is similar to the one above; select consecutive fingerprints. The calculation is performed on 5 points, and when the root mean square value is greater than... and less than This means it falls on the first corresponding grid cell; avoid due to The changes caused a problem with switching between table tennis and ping-pong.

[0032] A more optimized approach involves marking points at different times during the initial step, thereby obtaining a curve showing the magnetic field changes over time at each location. The vehicle carries a geomagnetic induction curve, and the XYZ components at the corresponding time are used as theoretical values ​​during operation. Even more optimized, the geomagnetic acquisition sensor uses a geomagnetic array to acquire geomagnetic data, calculating the average value from sensors in the XYZ directions as the geomagnetic information for that location. This average is then compared with the theoretically marked geomagnetic parameters to determine the location.

[0033] Step S102: Based on each first target parameter set, determine the mileage data and target geomagnetic signal parameters corresponding to each fixed point position passed by the target train in the target tunnel.

[0034] Specifically, as described above, each first target parameter set contains mileage parameters corresponding to each fixed point the target train passes through in the target tunnel. The mileage information for each fixed point is known and recorded during data acquisition, such as mileage marker readings or precise measurements within the tunnel. Therefore, the mileage parameter itself is a field in the first target parameter set. When the matching algorithm subsequently determines which fixed point the train has matched to, the mileage parameter for that point can be directly read. The target geomagnetic signal parameters are pre-acquired ideal geomagnetic features. The ideal geomagnetic signal parameters in the first target parameter set are the geomagnetic fingerprints corresponding to the fixed points, such as magnetic field strength, tilt angle, and deflection angle, serving as a reference benchmark for subsequent real-time geomagnetic measurements. When the real-time train measures a geomagnetic signal vector, it performs similarity matching with all pre-stored ideal geomagnetic signal parameters, such as dynamic time warping and nearest neighbor analysis, to find the most similar fixed point. At this point, the ideal geomagnetic signal parameters of that fixed point are called the target geomagnetic signal parameters for the current real-time measurement, i.e., the expected geomagnetic value to be measured. Therefore, after collecting each set of first target parameters, the mileage data and target geomagnetic signal parameters corresponding to each fixed point position of the target train in the target tunnel can be determined based on each set of first target parameters.

[0035] The process of determining the mileage data and target geomagnetic signal parameters corresponding to each fixed point location traversed by the target train in the target tunnel, based on each first target parameter set, may include the following: Based on each first target parameter set, construct a first correspondence between the ideal geomagnetic signal parameters and mileage parameters corresponding to each fixed point location in the target tunnel. Based on the first correspondence, collect in real-time the actual geomagnetic signal parameters and actual wireless signal parameters of each preset fixed point location traversed by the target train in the target tunnel. Based on the ideal geomagnetic signal parameters of each preset fixed point location of the target train in the target tunnel and the first correspondence, compare and analyze the actual geomagnetic signal parameters of each fixed point location traversed by the target train in the target tunnel to determine the mileage data of each fixed point location traversed by the target train in the target tunnel.

[0036] Specifically, establishing the first correspondence between ideal geomagnetic signal parameters and mileage information allows binding the geomagnetic fingerprint within the tunnel to absolute geographic coordinates, forming a fingerprint map. Using the ideal geomagnetic signal parameters at each fixed point, such as magnetic field strength, inclination, and corresponding latitude and longitude, a mapping model or index structure can be determined, enabling the finding of the closest mileage parameters for any geomagnetic vector. Geomagnetic positioning is essentially fingerprint matching; the real-time measured magnetic field vector is used to search for the most similar point in a pre-stored fingerprint database, outputting the latitude and longitude of that point. Without this step, subsequent positioning is impossible. To convert absolute geographic coordinates into more intuitive mileage for engineering purposes, a linear reference transformation function can be determined using the geomagnetic signals from the same set of fixed points and their known mileage. This function is typically a one-dimensional interpolation because latitude and longitude are approximately linearly correlated with mileage along the tunnel direction. In practice, tunnel maintenance typically uses mileage rather than latitude and longitude. Wireless signal quality assessment also requires segmented output based on mileage. Real-time collection of actual geomagnetic signal parameters and actual wireless signal parameters allows obtaining on-site measurement values ​​as the train passes each fixed point. Actual geomagnetic signal parameters can be used for positioning, and actual wireless signal parameters can be used for quality assessment. Without real-time data, everything is meaningless. Based on the ideal geomagnetic field and the second correspondence, comparing the actual geomagnetic field with the actual geomagnetic field allows us to determine the mileage and the target geomagnetic signal. For example, a segment of actual geomagnetic data acquired in real time is matched with the ideal geomagnetic field in the first correspondence. If the match is successful, the latitude and longitude of the current location can be obtained. Using the second correspondence, the mileage data of the current location can be further obtained. The ideal geomagnetic signal parameters of the matched fixed location are the target geomagnetic signal parameters that the train should measure in real time.

[0037] Step S103: Calculate the geomagnetic signal parameters of each target with the preset first target data to obtain the first analysis result.

[0038] Specifically, in practice, within a tunnel, the vehicle's electrical system generates electromagnetic noise. This electromagnetic noise may exhibit common-mode characteristics, while the spatial gradient information of the actual geomagnetic field displays differential-mode characteristics. Therefore, electromagnetic noise may cause common-mode interference to the geomagnetic signal corresponding to each location of the target train. As mentioned above, the target geomagnetic signal parameters are obtained by matching real-time geomagnetic measurements with a first set of target parameters. These parameters provide a continuous position estimate, such as the train's current location within the tunnel. However, this estimate suffers from accumulated errors and cannot automatically identify whether the train is in a specific event state such as arrival at a station. Therefore, to reduce accumulated errors, each target geomagnetic signal parameter can be calculated with preset first target data to obtain a first analysis result. The preset first target data may include station information for each preset fixed location of the target train within the target tunnel.

[0039] In practice, the arrival information included in the preset first target data is a discrete but absolutely accurate event dataset. This information records the train's precise mileage, latitude and longitude, and arrival time or time window at each station / stop. It does not rely on geomagnetic matching but comes from engineering completion data or operating timetables. Calculating the geomagnetic signal parameters of each target with the preset first target data allows for the correction of continuous relative positioning using a discrete absolute reference, and event labels are used to interpret or eliminate abnormal signal fluctuations. In practice, although geomagnetic fingerprint matching is stable, it is affected by factors such as temperature, movement of ferromagnetic materials within tunnels (e.g., temporary construction), and sensor noise, resulting in mileage drift of several meters or even tens of meters after long-distance operation. When station information is fused, it becomes clear what the train is currently stopping at the platform and what its mileage should be. If the mileage given by geomagnetic positioning deviates from this value, a forced correction is performed. This correction result is an important component of the first analysis result. When the train arrives at a station, due to stopping, platform screen doors opening and closing, passengers boarding and alighting, and changes in pantograph position, the wireless signal undergoes significant reflection, attenuation, or multipath changes. These changes, if directly compared with ideal wireless signal parameters, can be misjudged as coverage faults. By integrating arrival information, these changes can be marked or segmented in the first analysis result, evaluated separately during arrival periods, and even alarms can be masked, with strict fault assessment only performed on non-arrival sections. This avoids a large number of false alarms. Secondly, the first target parameter set was collected during a historical period, while the tunnel environment may change slowly after operation. Station information provides a long-term, stable, absolute reference. For example, each time a train passes the same station, calculations can be used to infer whether the geomagnetic fingerprint database needs a local update. If the deviation between the actual geomagnetic field and the ideal value in the database increases after multiple consecutive arrivals, it indicates that the geomagnetic environment of that section has changed and recalibration is necessary. This is reflected in the first analysis result as a confidence level or a calibration requirement. Without integrating arrival information to calibrate the geomagnetic signal parameters at each fixed location, the first analysis result is merely a series of geomagnetic reference values ​​over a continuous mileage, lacking contextual semantics. After fusion, the first analysis result can be output in the mileage range A~B, where the geomagnetic matching confidence level is high; at mileage C, i.e., the station, an arrival event is detected, and the fluctuation of wireless signal parameters is considered normal parking behavior and is not counted as a fault. Such results are more valuable for subsequent automated judgment.

[0040] The process of calculating the first analysis result by comparing the target geomagnetic signal parameters with preset first target data may include the following: Based on the first target data and the first correspondence, determine the ideal mileage information corresponding to each fixed point location in the target tunnel. Based on the target geomagnetic signal parameters and the first correspondence, determine the actual latitude and longitude information corresponding to each target geomagnetic parameter. Calculate the north and east component information corresponding to each target geomagnetic parameter with the north and east component information of the ideal geomagnetic signal parameters at each fixed point location in the target tunnel to obtain the first target mileage information corresponding to each fixed point location of the target train in the target tunnel. Based on the ideal geomagnetic signal parameters at each fixed point location in the target tunnel and the station information of the target train in the target tunnel, calibrate each first target mileage information to determine the second target mileage information corresponding to each fixed point location of the target train in the target tunnel. Based on the second target mileage information corresponding to each fixed point location of the target train in the target tunnel and the ideal mileage parameters corresponding to each fixed point location, determine the real-time mileage information of the target train in the target tunnel.

[0041] The formula for calculating the error statistics value corresponding to the first target mileage information at each fixed point of the target train in the target tunnel includes the following: ,in, This represents the statistical error between the ideal geomagnetic signal parameters and the actual geomagnetic signal parameters sensed in real time at each fixed point of the target train in the target tunnel. This represents the north component information of the actual geomagnetic signal parameters corresponding to each fixed point location of the target train in the target tunnel; This represents the eastern component information of the actual geomagnetic signal parameters corresponding to each fixed point location of the target train in the target tunnel; This indicates the position of the target train at each fixed point in the target tunnel. Information on the north component of an ideal geomagnetic signal parameter; This indicates the position of the target train at each fixed point in the target tunnel. The eastern component information of an ideal geomagnetic signal parameter. For example, the northern and eastern component information of each actually obtained geomagnetic signal parameter are calculated with 30 parameters before and after its location; that is, compared with... , ... , , ... The calculations are performed separately, and the minimum value is taken to determine the train's position mileage parameters within the tunnel section. The mileage position within the tunnel section is then determined based on the mileage parameters in the ideal model. Simultaneously, ATS arrival data is used as calibration data. After the train enters a station, it receives information about the specific station, which naturally includes the mileage information for that station. Upon receiving the ATS data, the mileage parameters for that station are used to calibrate the perceived mileage parameters. The perceived mileage parameters are then replaced with those for that station to calibrate the train's position.

[0042] Specifically, to obtain high-precision real-time mileage for each fixed or continuous position of the train within the tunnel for subsequent spatial calibration of wireless signal quality, the core challenges include inherent errors in geomagnetic matching, the need for absolute reference points for latitude-longitude to mileage conversion, and the need for mileage itself to be aligned with operational events. Based on the first target data and the first correspondence, ideal mileage information is determined; this ideal mileage information serves as the absolute benchmark for all subsequent fusion and calibration. Without it, it is impossible to determine or correct real-time positioning deviations. Based on the target geomagnetic signal parameters and the first correspondence, the actual mileage information is determined; this is the raw output of the real-time positioning system, but it contains noise and drift. Only by obtaining this information can it be compared and fused with the ideal mileage parameters. The instantaneous error of real-time positioning is corrected using the absolute benchmark. Ideal mileage parameters are discrete but accurate, while actual mileage parameters are continuous but have errors. Fusing the two forces alignment at known fixed positions, eliminating drift and ensuring smooth transitions between fixed points, maintaining continuity. The fusion only utilizes ideal mileage parameters from the geomagnetic fingerprint database. Arrival information is another independent, high-precision absolute position reference. When the train arrives at the station, its actual mileage information is the platform mileage information. If there is a deviation between the first target mileage information and the arrival mileage information, it indicates that there are still systematic errors in geomagnetic positioning or previous fusion. Secondary calibration using the arrival information can further eliminate accumulated errors. The final determined second target mileage information is the mileage information after geomagnetic reference correction and arrival event absolute correction. Finally, based on the second target mileage information and ideal mileage parameters, the real-time mileage information is determined.

[0043] Step S104: During the operation of the target train, the coverage status information of the rail transit wireless signal in the target tunnel is obtained in real time.

[0044] Specifically, to better understand the wireless signal quality of the target tunnel, the coverage status information of the rail transit wireless signal in the target tunnel can be acquired in real time during the target train's operation. This real-time acquisition of the target tunnel's rail transit wireless signal coverage status information is crucial for the entire integrated positioning and sensing system to achieve dynamic monitoring, anomaly diagnosis, and proactive maintenance. Rail transit has extremely high requirements for the reliability of wireless communication, such as train control, dispatch voice, and emergency communication. Real-time coverage status information, such as RSRP, SINR, RSSI, and packet loss rate, can directly determine whether there are coverage blind spots, weak signal areas, or interference areas in the current tunnel section, avoiding the impact of communication degradation on train operation safety. The pre-collected first target parameter set contains the ideal wireless signal parameters for each fixed location. Comparing the real-time acquired coverage status information with the ideal values ​​point by point allows for quantitative calculation of signal attenuation, signal-to-noise ratio degradation, and whether abnormal fluctuations or interruptions occur, thereby quickly locating problems such as wireless equipment failure, cable damage, and antenna obstruction. Furthermore, knowing only the strength of the wireless signal without location information is meaningless. After obtaining the current real-time mileage through geomagnetic positioning, the coverage status information is bound to that mileage point, enabling dynamic updates to the tunnel wireless signal coverage map. This allows for the identification of coverage trends that change over time or due to environmental factors, such as attenuation caused by tunnel water seepage, providing maintenance personnel with precise fault location mileage. In unmanned or robotic inspection scenarios, real-time acquisition of coverage status information is a prerequisite for online diagnostics. Once a section's wireless signal is detected to be consistently below a preset threshold, an alarm can be immediately generated, triggering a retest or work order, avoiding the lag of manual data review. Changes in wireless signal coverage status are sometimes also related to train position, direction of travel, or tunnel structure. Real-time signal characteristics can serve as auxiliary features, working with geomagnetic signals to optimize the confidence of location matching and improve positioning robustness. The wireless signal coverage information for the tunnel can include signal strength, signal-to-noise ratio, frequency band, frequency point, and cell number.

[0045] Step S105: In real time, the coverage status information of the rail transit wireless signal of the target tunnel is framed with the geomagnetic signal parameters of each target to obtain the first data packet.

[0046] Specifically, in practice, the wireless signal coverage status information of tunnels, such as RSRP, SINR, and RSSI, is measured in real time, with clear timestamps and corresponding train mileage. Target geomagnetic signal parameters are also obtained based on the train's position at the same time point. Therefore, to more accurately analyze the wireless signal quality of the target tunnel, the coverage status information of the target tunnel's rail transit wireless signal and various target geomagnetic signal parameters can be framed in real time to obtain the first data packet. If they are not encapsulated in the same data frame, it is difficult to determine which set of geomagnetic references and which location the current wireless signal corresponds to during subsequent processing. Framing is equivalent to giving both the same spatiotemporal label, ensuring that there will be no misalignment during subsequent analysis. Trains or inspection robots continuously generate a large number of geomagnetic matching results and wireless signal measurements. If transmitted separately, two independent data streams need to be maintained, and timestamp synchronization needs to be repeated, increasing communication overhead and storage complexity. Framing packages the two types of data into a single data packet with a fixed structure, which can be sent using a unified data channel, such as via serial port, network, or vehicle-to-ground wireless transmission. The receiving end can also parse the data frame by frame without needing separate alignment.

[0047] When data packets arrive at the server or vehicle-mounted processing unit, a single first data packet contains all the information needed to determine the wireless signal quality at a location, such as specific location information, measured wireless signal values, and geomagnetic references. The processing program can independently complete tasks such as ideal-to-measured comparison, anomaly detection, and confidence calculation packet by packet, without needing to search for related information across data packets. This is highly beneficial for parallel processing or real-time pipeline designs. In cases of wireless network instability or storage media fragmentation, if wireless signals and geomagnetic parameters are stored / transmitted separately, the loss of one data point can lead to incorrect alignment of all subsequent data. After framing, each data packet is self-contained; even if one packet is lost, it will not affect the correct parsing of other packets. Even if the packets arrive in the wrong order, they can still be correctly reconstructed because the frame contains spatiotemporal information. Framing defines a clear data structure, such as a frame header, geomagnetic parameter segment, wireless coverage status segment, timestamp, and checksum. If additional sensor data, such as temperature, vibration, or image features, needs to be added later, only new fields need to be added to the frame, without redesigning the entire data flow architecture.

[0048] The process of framing the coverage status information of the target tunnel's rail transit wireless signal with the parameters of each target geomagnetic signal to obtain the first data packet may include the following: Based on each target geomagnetic signal parameter, determine the mileage information of the target train at each fixed point in the target tunnel. Framute the mileage information of the target train at each fixed point in the target tunnel with the wireless signal information of the target train at each fixed point in the target tunnel to obtain the second data packet of the target train at each fixed point in the target tunnel. Merge each second data packet to form the first data packet.

[0049] Specifically, in transforming discrete, multi-source raw data into transmissible and analyzable structured data packets, it is necessary to ensure the spatial alignment and clear structure of the data. Therefore, the coverage status information of the target tunnel's rail transit wireless signal is framed in real time with the geomagnetic signal parameters of each target to obtain the first data packet, which can be used for subsequent model analysis. The challenge is that geomagnetic signal parameters themselves are not spatial coordinates and cannot be directly used for location marking, and wireless signal measurements must be bound to precise mileage parameters to be meaningful. However, the data comes from different sensors, and the sampling frequency and time reference may be inconsistent. Geomagnetic signal parameters are essentially feature vectors, such as X, Y, and Z components, not location information. If geomagnetic parameters are directly included during framing, the receiver cannot know which spatial location this set of data corresponds to. Wireless signal quality analysis requires explicit location labels, such as latitude, longitude, or mileage. Therefore, geomagnetic signal parameters must first be converted into mileage parameters as location indexes for all subsequent data. Without this conversion, the framed data packet will lack spatial reference and cannot be used for location-related coverage analysis. Wireless signal measurements occur at location and are naturally tightly coupled with the current location coordinates. Binding them into an independent data pair first ensures that they won't be misaligned during transmission or storage. After forming the second data packet, each packet is self-contained; even if some packets arrive out of order or are lost, it won't affect the correct parsing of other packets. When subsequently merging multiple second data packets, there's no need to realign the positions and signals. If second data packets aren't formed, and all latitude and longitude coordinates are directly mixed with all wireless signals, it will be impossible to distinguish which signal corresponds to which location later. Figure 2 As shown, Figure 2 A schematic diagram illustrating the structure of the first data packet is provided.

[0050] In practical communication or storage, it's often desirable to transmit data in batches rather than sending each second data packet individually, as this is inefficient and costly. Data fusion adds common metadata to the data across the entire tunnel segment, such as inspection task IDs, train speed and direction, and weather information. This information doesn't need to be repeatedly stored in each second data packet. This allows subsequent models to process data from a complete segment at a time, improving analysis efficiency. Without fusion, downstream systems would receive a large number of scattered small packets, requiring them to manage the order and relationships themselves, increasing complexity.

[0051] Step S106: Analyze the first data packet using a preset rail transit wireless signal analysis model to obtain the second analysis result.

[0052] Specifically, as described above, the first data packet itself is merely structured raw data and has not yet been transformed into evaluation conclusions or diagnostic information that can be directly used for decision-making. Therefore, in order to better understand the wireless signal quality of the target tunnel, the first data packet can be analyzed using a pre-set rail transit wireless signal analysis model to obtain the second analysis result. The pre-set rail transit wireless signal analysis model is trained using the training second data packet as the sample label and the third analysis result included in the second data packet as the sample label. The training second data packet is a data packet composed of frames formed by combining the coverage status information of the rail transit wireless signal in the training tunnel with the calibrated training geomagnetic signal parameters at each pre-set fixed position passed by the training train in the training tunnel.

[0053] The first data packet provides information, not conclusions. It may include location, geomagnetic signal baseline and confidence level, measured wireless signal values ​​such as RSRP and SINR, and event flags, such as whether a train has arrived at the station or whether calibration has been performed. However, this data is merely a collection of multi-source parameters and does not determine whether the current signal quality of the target tunnel is acceptable, whether it exceeds normal fluctuation ranges, whether specific fault modes exist (such as coverage voids, interference, equipment aging), or whether early warning or repair is required.

[0054] In practice, assessing the quality of wireless signals in rail transit involves complex standards and scenario adaptations, with different thresholds potentially existing in different tunnel sections. The acceptable standards differ between arrival and non-arrival states. When geomagnetic confidence is low, reliance on location accuracy should be reduced to avoid misjudgments. Historical data needs to be considered to determine trends; for example, signal attenuation at the same location for several consecutive days may indicate a hardware malfunction. The first data packet is analyzed using a pre-defined rail transit wireless signal analysis model. This allows for understanding the signal quality level, anomaly type, confidence level, and recommended actions for the target tunnel.

[0055] Step S107: Analyze the preset reference data packet using the preset rail transit wireless signal analysis model to obtain the fourth analysis result.

[0056] Specifically, the second analysis result obtained from the first data packet analysis reflects the current actual wireless signal quality. However, the second analysis result alone cannot reveal the gap between the current quality and the design expectations or engineering acceptance standards. Therefore, after determining the second analysis result, a fourth analysis result can be obtained by analyzing a preset reference data packet using a preset rail transit wireless signal analysis model. The preset reference data packet includes the ideal geomagnetic signal parameters, ideal wireless signal parameters, and ideal mileage parameters of the target train in the target tunnel. The reference data packet represents the wireless signal performance that the tunnel should have under ideal conditions, without faults or interference, and with standard configuration. Inputting it into the same analysis model, the fourth analysis result is the theoretical expected score or grade. By comparing the second and fourth analysis results, the quality deviation can be quantitatively determined. For example, if the actual coverage grade is medium while the ideal grade is excellent, it can be determined as an abnormal coverage attenuation.

[0057] Every analytical model, whether a rule engine or a machine learning model, has its inherent decision-making metrics such as thresholds, weights, and classification boundaries. Directly looking at the absolute value of the second analysis result may be influenced by model design, making direct comparison between different models difficult. However, by feeding a reference data package as standard input into the same model, the resulting fourth analysis result is equivalent to the model's zero drift or baseline output. Subsequent analyses of all measured data can then be compared with the fourth analysis result to obtain a model-normalized evaluation metric, making evaluation results comparable across different times, tunnels, and model versions.

[0058] Step S108: Compare and analyze the second and fourth analysis results, and determine the automatic wireless signal inspection scheme for the target tunnel based on the first analysis result.

[0059] Specifically, the fourth analysis result represents the conclusion on wireless signal quality under ideal or standard conditions, while the second analysis result represents the conclusion on wireless signal quality obtained from actual measurements. The first analysis result represents the location, mileage confidence, and event label after fusion of geomagnetic and arrival information. Therefore, the second and fourth analysis results can be further compared and analyzed, and based on the first analysis result, an automatic wireless signal inspection scheme for the target tunnel can be determined. Comparing the second and fourth analysis results yields the quality deviation. Combining this with the first analysis result, the deviation can be mapped to a specific location, whether it falls within the arrival event, and whether the geomagnetic positioning is reliable, ultimately determining an executable inspection scheme, such as immediately repairing the coverage void at mileage 12.3km or focusing on a specific section during the next inspection.

[0060] In practice, if the second analysis result is "good," but the ideal result should be "excellent," it indicates there is still room for improvement and it needs to be included in the inspection focus. If the second analysis result is "good," and the ideal result is also "good," it indicates that it meets expectations and the regular inspection frequency can be maintained. Comparison is necessary to distinguish between normal compliance and compliance with but deteriorated standards. The ideal benchmarks differ for different tunnels and different operators' equipment. Directly comparing absolute levels across tunnels is meaningless. The fourth analysis result provides a baseline for comparison with its own ideal value; the deviation value is the universally applicable anomaly indicator. The first analysis result provides the indispensable location and event context when comparing deviations. The comparison between the second and fourth analysis results can only determine if there is a deviation in the wireless signal at a certain point in the target tunnel, but the first analysis result provides the mileage and geomagnetic confidence level at that point. If the geomagnetic confidence level is low, it indicates that the current positioning may be inaccurate, and the deviation may stem from positioning errors rather than actual signal problems. In this case, the inspection plan should prioritize recalibrating the positioning rather than blindly dispatching repairs. The first analysis result indicates whether it falls within the arrival event interval. If the deviation only occurs at the arrival point and falls within the preset allowable fluctuation range for arrival, then no repair is triggered; it is only marked as a normal event impact. If the deviation occurs in a non-destination section, a precise inspection task is triggered. The first analysis result also includes the continuous location trajectory after mileage correction. Comparing the deviation can generate a deviation-mileage curve, thereby identifying whether it is a point fault or a section of coverage attenuation.

[0061] Step S109: Based on the automatic wireless signal inspection scheme of the target tunnel, automatically inspect the wireless signal of the target tunnel to determine the wireless signal fault status of the target tunnel.

[0062] Specifically, as described above, this application can determine the automatic wireless signal inspection scheme for the target tunnel based on its basic parameters. After determining the automatic wireless signal inspection scheme, the wireless signal of the target tunnel is automatically inspected based on this scheme to determine any wireless signal faults. This improves the efficiency and quality of wireless signal monitoring of the target tunnel.

[0063] In practice, the automatic wireless signal inspection scheme based on the target tunnel automatically inspects the wireless signal in the target tunnel to determine the wireless signal fault status. This can be achieved by first collecting real-time actual wireless signal parameters and mileage parameters at each location within the target tunnel using the automatic inspection method. Then, based on the actual wireless signal parameters at any location within the target tunnel, the wireless signal quality at each location is analyzed. Subsequently, based on the wireless signal quality at each location within the target tunnel, it is determined whether there are any locations with abnormal wireless signal quality. If such locations are found, an alarm is triggered to identify the locations with abnormal wireless signal quality and their corresponding wireless signal fault information.

[0064] For example, based on the wireless signal quality at each location in the target tunnel, it can be determined whether there are locations in the target tunnel where the signal-to-noise ratio (SNR) of the wireless signal is less than a preset first threshold, or whether the difference between the signal strength of the wireless signal at that location and the preset ideal signal strength at that location is less than a preset second threshold. If there are locations in the target tunnel where the SNR of the wireless signal is less than the preset first threshold, or where the difference between the signal strength of the wireless signal at that location and the preset ideal signal strength at that location is less than the preset second threshold, and / or where the SNR of the wireless signal at that location is less than the preset first threshold, and the difference between the signal strength of the wireless signal at that location and the preset ideal signal strength at that location is less than the preset second threshold, then it is determined that there is a wireless signal anomaly at these locations, and these locations with wireless signal anomalies are identified as locations in the target tunnel where the wireless signal quality is abnormal. The preset first threshold can be set to [5dB, 10dB]; the preset second threshold can be set to 20dB.

[0065] The following is combined Figure 3 This application introduces an optional system architecture for automatic wireless signal inspection of tunnels, as provided in its embodiments. This system can be applied to any of the aforementioned automatic wireless signal inspection methods for tunnel scenarios, such as... Figure 3As shown, the system architecture may include: an Automatic Train Monitoring (ATS) arrival signal transmission unit, a geomagnetic signal analysis unit, a geomagnetic signal sensing unit, a wireless signal sensing unit, and a wireless sensing and analysis backend. The ATS arrival signal transmission unit is responsible for transmitting the station information of the target train within the target tunnel to the geomagnetic signal analysis unit via vehicle-to-ground wireless signals. The geomagnetic signal analysis unit, based on the station information of the target train, collects in real-time a first set of target parameters for each preset fixed position of the target train within the target tunnel. Each first set of target parameters includes the ideal geomagnetic signal parameters, wireless signal parameters, and mileage parameters for each fixed position of the target train within the target tunnel. The geomagnetic signal sensing unit collects in real-time the actual geomagnetic signal parameters and actual wireless signal parameters of the target train at each preset fixed position within the target tunnel and transmits them to the geomagnetic signal analysis unit. The geomagnetic signal analysis unit can receive the actual geomagnetic signal parameters and actual wireless signal parameters of the target train at each preset fixed location, and calibrate the actual geomagnetic signal parameters of the target train at each preset fixed location based on the station information of the target train and each first target parameter set, to obtain the target geomagnetic signal and mileage data of the target train at each preset fixed location; calculate each target geomagnetic signal and preset first target data to obtain the first analysis result; and transmit the first analysis result and each target geomagnetic signal to the wireless sensing analysis backend; the wireless signal sensing unit can be responsible for sensing the coverage status of the rail transit wireless signal of the target tunnel in real time, and frame the coverage status of the rail transit wireless signal of the target tunnel with each target geomagnetic signal to obtain the first data packet, and transmit the first data packet to the wireless sensing analysis backend. The wireless sensing and analysis backend receives the first data packet in real time and analyzes it using a preset rail transit wireless signal analysis model to obtain the second analysis results of the wireless signal and geomagnetic signal of the entire operating section of the target tunnel. It then uses the preset wireless signal analysis model to analyze a preset reference data packet to obtain a fourth analysis result. The preset rail transit wireless signal analysis model is trained using the training second data packet as sample labels and the third analysis result included in the second data packet as sample labels. The training second data packet is a data packet composed of frames of the coverage status information of the rail transit wireless signal in the training tunnel and the calibrated training geomagnetic signal parameters at each preset fixed point passed by the training train in the training tunnel. The preset reference data packet includes the ideal geomagnetic signal parameters, ideal wireless signal parameters, and ideal mileage parameters of the target train in the target tunnel. The second and fourth analysis results are compared and analyzed, and based on the first analysis result, an automatic wireless signal inspection scheme for the target tunnel is determined. Based on the automatic wireless signal inspection scheme for the target tunnel, the wireless signal of the target tunnel is automatically inspected to determine the wireless signal fault status of the target tunnel.

[0066] In practice, the onboard wireless sensing equipment for rail transit adopts a modular architecture of two integrated network elements and two external units, forming a complete sensing-analysis-transmission functional chain. The geomagnetic signal analysis unit, as the core computing node of the onboard system, undertakes key tasks such as real-time calibration of geomagnetic signals, multi-source data fusion, and position calculation. This unit receives raw geomagnetic data from the geomagnetic signal sensing unit via a serial port and simultaneously listens to vehicle arrival information broadcast by the ATS arrival signal transmission unit, serving as the absolute reference benchmark for position calibration. Internally, it integrates attitude correction algorithms, grid positioning algorithms, sliding window filtering, and a multi-source data fusion engine, enabling it to complete complex data processing in real time under high-speed train operation. The wireless signal sensing unit is responsible for framing the precise position information calculated by the geomagnetic signal analysis unit with the wireless environmental sensing data at the protocol layer, supporting standard IoT protocols such as SNMP and MQTT, and transmitting it to the ground backend system via a vehicle-to-ground wireless link. The two integrated network elements are interconnected through a high-speed internal bus, ensuring millisecond-level synchronization between position information and wireless signal quality information, providing a high-precision spatiotemporal correlation data foundation for digital twin analysis in the backend. The geomagnetic signal sensing unit adopts a multi-sensor array architecture and is deployed in the area in front of the vehicle's cab. This location has been optimized for electromagnetic compatibility to minimize interference from strong sources such as traction motors and inverters, while ensuring a wide field of view for the sensors. The magnetic array consists of multiple independent magnetic sensors, each capable of simultaneously acquiring the three orthogonal components of the geomagnetic field: X (north component), Y (east component), and Z (vertical component). The core advantage of the array design lies in its common-mode interference shielding mechanism: electromagnetic noise generated by the vehicle's electrical system exhibits high correlation (common-mode characteristics) at each array unit, while the spatial gradient information of the real geomagnetic field exhibits differential-mode characteristics. Through differential operations, adaptive filtering, and other signal processing algorithms, common-mode interference can be effectively separated and suppressed, extracting pure location-sensitive information. In addition, the array's redundant design provides sensor fault tolerance; when a single sensor malfunctions, it can be automatically discarded and switched to a backup unit. The wireless signal sensing antenna is responsible for real-time scanning of the coverage status of multiple wireless signals along the rail transit line, including dedicated frequency bands used by industry systems, ATS vehicle-to-ground communication links, and public WiFi signals. Antenna design must balance requirements such as wide bandwidth coverage, high gain, and low profile to adapt to the unique propagation characteristics of the tunnel environment. This antenna module works in conjunction with the wireless sensing and analysis backend to continuously collect wireless signal fingerprint data across the entire line, constructing a complete wireless coverage quality map and providing raw data support for the generation of automatic inspection schemes. The ATS arrival signal transmission unit, belonging to the rail transit control and dispatch center, is a key information hub connecting train operation control and the onboard sensing system. This network element broadcasts vehicle arrival information to all trains along the line via the vehicle-to-ground wireless communication network. The information includes a station number sequence (1, 2, 3, …, n, where 1 represents the originating station and n represents the destination station), arrival / departure status flags, estimated arrival time, and other operational data.The wireless sensing and analysis backend is a comprehensive platform integrating digital twin technology, wireless signal propagation models, physical space information mapping, and intelligent application functions, forming the cloud-based brain of the entire system. Before the system is put into operation, the backend calculates and stores the expected wireless signal coverage parameters for each physical location along the entire line based on the line's three-dimensional geographic information, wireless access point deployment parameters, and electromagnetic wave propagation theory, forming an idealized benchmark database. This model represents the optimal wireless coverage state at the initial stage of line opening or after optimization and adjustment, serving as the gold standard for subsequent comparative analysis. During operation, the backend receives location-signal correlation data packets reported in real time from onboard equipment. Through various algorithms such as setting dynamic thresholds, weighted average filtering, signal strength spatiotemporal fitting, and SNR trend analysis, it performs in-depth comparisons between measured data and the ideal model. This comparison can not only identify immediate wireless coverage anomalies but also discover gradual trends in wireless signal performance through the accumulation of long-term data, enabling preventative maintenance strategies. It upgrades traditional manual periodic inspections to an intelligent automatic inspection mode, significantly improving the intelligence level and reliability assurance capabilities of the rail transit communication system's operation and maintenance. The data interaction between the various components of the system presents a three-layer, two-line architecture. The three layers refer to the perception layer (geomagnetic signal sensing unit and wireless signal sensing antenna), the edge computing layer (geomagnetic signal analysis unit and wireless signal sensing unit), and the platform layer (wireless sensing and analysis backend). The two lines refer to the geomagnetic positioning chain and the wireless sensing chain, which converge and merge at the wireless signal sensing unit to form location-signal association data. The geomagnetic signal analysis unit simultaneously receives two inputs: the raw geomagnetic data stream from the geomagnetic signal sensing unit and the arrival broadcast information from the ATS transmitting unit. After internal calibration and fusion processing, it outputs the calibrated location information to the wireless signal sensing unit. As the data aggregation node of the vehicle system, the wireless signal sensing unit performs spatiotemporal alignment and protocol encapsulation of the location tag and wireless quality parameters to form a complete location-signal association data frame for uploading to the backend. This layered and decoupled architecture design ensures the independent evolution capability of each functional module while achieving seamless integration through standardized interfaces. The specific implementation process of the wireless signal automatic inspection system for tunnel scenarios provided in this application can be referred to the aforementioned description of the implementation process of the wireless signal automatic inspection method for tunnel scenarios, and will not be repeated here.

[0067] The automatic wireless signal inspection device for tunnel scenarios provided in this application is described below. The automatic wireless signal inspection device for tunnel scenarios described below can be referred to in conjunction with the automatic wireless signal inspection method for tunnel scenarios described above. See also... Figure 4 , Figure 4 This is a schematic diagram of the structure of an automatic wireless signal inspection device for a tunnel scenario disclosed in this application. Figure 4As shown, the wireless signal automatic inspection device for the tunnel scenario may include: a first collection unit 101, used to collect a first target parameter set for each preset fixed position of the target train in the target tunnel, each first target parameter set including the ideal geomagnetic signal parameters, wireless signal parameters, and mileage parameters of each fixed position of the target train in the target tunnel. The first determining unit 102 is used to determine the mileage data and target geomagnetic signal parameters corresponding to each fixed point location passed by the target train in the target tunnel based on each first target parameter set; the fusion calculation unit 103 is used to calculate the target geomagnetic signal parameters with the preset first target data to obtain a first analysis result, wherein the preset first target data includes the station information of each preset fixed point location of the target train in the target tunnel; the real-time acquisition unit 104 is used to acquire the coverage status information of the rail transit wireless signal in the target tunnel in real time; the framing unit 105 is used to frame the coverage status information of the rail transit wireless signal in the target tunnel with the target geomagnetic signal parameters in real time to obtain a first data packet; the first analysis unit 106 is used to analyze the first data packet through a preset rail transit wireless signal analysis model to obtain a second analysis result, wherein the preset rail transit wireless signal analysis model uses the training second data packet as the sample label, and... The third analysis result included in the second data packet is used as a sample label for training. The training second data packet is a data packet composed of frames of the coverage status information of the rail transit wireless signal in the training tunnel and the calibrated training geomagnetic signal parameters at each preset fixed point passed by the training train in the training tunnel. The second analysis unit 107 is used to analyze a preset reference data packet using a preset rail transit wireless signal analysis model to obtain a fourth analysis result. The preset reference data packet includes the ideal geomagnetic signal parameters, ideal wireless signal parameters, and ideal mileage parameters of the target train in the target tunnel. The comparison analysis unit 108 compares and analyzes the second and fourth analysis results, and determines the automatic wireless signal inspection scheme for the target tunnel based on the first analysis result. The automatic inspection unit 109 is used to automatically inspect the wireless signal of the target tunnel based on the automatic wireless signal inspection scheme for the target tunnel to determine the wireless signal fault status of the target tunnel. The specific processing flow of each unit included in the above-mentioned automatic wireless signal inspection device for tunnel scenarios can be referred to the relevant introduction in the section on automatic wireless signal inspection methods for tunnel scenarios above, and will not be repeated here.

[0068] The automatic wireless signal inspection device for tunnel scenarios provided in this application can be applied to automatic wireless signal inspection equipment in tunnel scenarios, such as terminals like mobile phones and computers. Optionally, Figure 5 The hardware structure block diagram of the wireless signal automatic inspection device in the tunnel scenario is shown. (Refer to...) Figure 5The hardware structure of the automatic wireless signal inspection device in a tunnel scenario may include: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4. In this application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and the processor 1, communication interface 2, and memory 3 communicate with each other through the communication bus 4. The processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement this application; the memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device; wherein, the memory stores a program, which the processor can call, and the program is used to implement various processing flows in the aforementioned automatic wireless signal inspection scheme for the terminal in a tunnel scenario. This application also provides a readable storage medium that stores a program suitable for processor execution, the program being used to implement various processing flows in the aforementioned automatic wireless signal inspection scheme for the terminal in a tunnel scenario. Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The various embodiments described in this specification are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. The various embodiments can be combined with each other. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for automatic wireless signal inspection in a tunnel setting, characterized in that, include: Collect a first set of target parameters for each preset fixed position of the target train in the target tunnel. Each first set of target parameters includes the ideal geomagnetic signal parameters, wireless signal parameters, and mileage parameters of the target train at each fixed position in the target tunnel. Based on each of the first target parameter sets, determine the mileage data and target geomagnetic signal parameters corresponding to each fixed point location traversed by the target train in the target tunnel; The target geomagnetic signal parameters are calculated with the preset first target data to obtain the first analysis result, wherein the preset first target data includes the station information of each preset fixed position of the target train in the target tunnel; During the operation of the target train, the coverage status information of the rail transit wireless signal in the target tunnel is acquired in real time. The coverage status information of the rail transit wireless signal of the target tunnel is framed with the geomagnetic signal parameters of each target in real time to obtain the first data packet; The first data packet is analyzed by a preset rail transit wireless signal analysis model to obtain a second analysis result. The preset rail transit wireless signal analysis model is trained using the training second data packet as a sample label and the third analysis result included in the second data packet as a sample label. The training second data packet is a data packet composed of the coverage status information of the rail transit wireless signal in the training tunnel and the calibrated training geomagnetic signal parameters of each preset fixed position passed by the training train in the training tunnel. The fourth analysis result is obtained by analyzing the preset reference data packet through the preset rail transit wireless signal analysis model. The preset reference data packet includes the ideal geomagnetic signal parameters, ideal wireless signal parameters, and ideal mileage parameters of the target train in the target tunnel. By comparing and analyzing the second analysis result and the fourth analysis result, and based on the first analysis result, a wireless signal automatic inspection scheme for the target tunnel is determined. Based on the automatic wireless signal inspection scheme of the target tunnel, the wireless signal of the target tunnel is automatically inspected to determine the wireless signal fault status of the target tunnel.

2. The method according to claim 1, characterized in that, The step of determining the mileage data and target geomagnetic signal parameters corresponding to each fixed point location traversed by the target train in the target tunnel based on each of the first target parameter sets includes: Based on each of the first target parameter sets, a first correspondence between the ideal geomagnetic signal parameters and mileage parameters corresponding to each fixed point in the target tunnel is constructed. The actual geomagnetic signal parameters and actual wireless signal parameters of the target train are collected in real time at each preset fixed point location as the target train passes through the target tunnel. Based on the ideal geomagnetic signal parameters of each fixed point in the target tunnel and the first correspondence, the actual geomagnetic signal parameters of each fixed point in the target tunnel are compared and analyzed to determine the mileage data of each fixed point in the target tunnel.

3. The method according to claim 2, characterized in that, The step of calculating the geomagnetic signal parameters of each target with preset first target data to obtain a first analysis result includes: Based on the first target data and the first correspondence, the ideal mileage information corresponding to each fixed point of the target tunnel is determined respectively; Based on the target geomagnetic signal parameters and the first correspondence, the actual mileage information corresponding to each target geomagnetic parameter is determined; The north and east component information corresponding to each of the target geomagnetic signal parameters are respectively calculated with the north and east component information of the ideal geomagnetic signal parameters at each fixed point of the target tunnel to obtain the first target mileage information corresponding to each fixed point of the target train in the target tunnel; wherein, the calculation formula for the error statistics value corresponding to the first target longitude mileage information corresponding to each fixed point of the target train in the target tunnel includes the following: ； in, This represents the statistical error between the ideal geomagnetic signal parameters corresponding to each fixed point position of the target train in the target tunnel and the actual geomagnetic signal parameters sensed in real time. This represents the north component information of the actual geomagnetic signal parameters corresponding to each fixed point location of the target train in the target tunnel; This represents the eastern component information of the actual geomagnetic signal parameters corresponding to each fixed point position of the target train in the target tunnel; This indicates the first [missing information] corresponding to each fixed point position of the target train in the target tunnel. Information on the north component of an ideal geomagnetic signal parameter; This indicates the first [missing information] corresponding to each fixed point position of the target train in the target tunnel. Information on the eastern component of an ideal geomagnetic signal parameter; Based on the ideal geomagnetic signal parameters at each fixed point in the target tunnel and the station information of the target train in the target tunnel, the first target mileage information is calibrated to determine the second target mileage information corresponding to each fixed point in the target tunnel; based on the second target mileage information corresponding to each fixed point in the target tunnel and the ideal mileage parameters corresponding to each fixed point, the real-time mileage information of the target train in the target tunnel is determined.

4. The method according to claim 2, characterized in that, The step of framing the coverage status information of the target tunnel's rail transit wireless signal with each of the target geomagnetic signal parameters to obtain a first data packet includes: Based on the target geomagnetic signal parameters, the mileage information of the target train at each fixed point in the target tunnel is determined; The mileage information of the target train at each fixed point in the target tunnel is framed with the wireless signal information of the target train at each fixed point in the target tunnel to obtain the second data packet of the target train at each fixed point in the target tunnel. Each of the second data packets is merged to form the first data packet.

5. The method according to claim 1, characterized in that, The first set of target parameters for collecting the target train at each preset fixed position in the target tunnel includes: The target tunnel is divided into several target grids according to preset grid parameters. Data corresponding to the north component and east component of the geomagnetic signal parameters for each target grid are collected respectively; The data corresponding to the north component signal and the east component vector signal of the geomagnetic signal parameters of each target grid are fused as the ideal geomagnetic signal parameters of each target grid; Determine the correspondence between the target grid and each preset fixed point position in the target tunnel; Based on the ideal geomagnetic signal parameters of each target grid and the correspondence between the target grids and each preset fixed point in the target tunnel, the ideal geomagnetic signal parameters of each preset fixed point in the target tunnel are determined.

6. The method according to claim 1, characterized in that, The automatic wireless signal inspection scheme based on the target tunnel automatically inspects the wireless signal of the target tunnel to determine the wireless signal fault status of the target tunnel, including: Based on the automatic inspection method of the target tunnel, the actual wireless signal parameters and mileage parameters of each location of the target tunnel are collected in real time; Based on the actual wireless signal parameters at any location of the target tunnel, analyze the wireless signal quality at each location of the target tunnel; Based on the wireless signal quality at each location in the target tunnel, it is determined whether there are locations in the target tunnel with abnormal wireless signal quality. If there are fault locations with abnormal wireless signal quality in the target tunnel, an alarm is issued for the fault locations with abnormal wireless signal quality and their corresponding wireless signal fault information.

7. The method according to claim 6, characterized in that, The step of determining whether there are locations with abnormal wireless signal quality in the target tunnel based on the wireless signal quality at each location of the target tunnel includes: Based on the wireless signal quality at each location of the target tunnel, determine whether there is a location in the target tunnel where the signal-to-noise ratio of the wireless signal is less than a preset first threshold, or whether the difference between the signal strength of the wireless signal and the preset ideal signal strength corresponding to that location is less than a preset second threshold. If there is a location in the target tunnel where the signal-to-noise ratio of the wireless signal is less than a preset first threshold, Alternatively, a location where the difference between the signal strength of a wireless signal in the target tunnel and the preset ideal signal strength at that location is less than a preset second threshold. And / or, a location in the target tunnel where the signal-to-noise ratio of a wireless signal is less than a preset first threshold, and the difference between the signal strength of the wireless signal at that location and the preset ideal signal strength at that location is less than a preset second threshold. Then, it is determined that there are locations in the target tunnel where the signal-to-noise ratio of the wireless signal is less than a preset first threshold, or locations where the difference between the signal strength of the wireless signal and the preset ideal signal strength corresponding to that location is less than a preset second threshold, indicating a wireless signal anomaly. These locations are then identified as locations in the target tunnel where the wireless signal quality is abnormal.

8. A wireless signal automatic inspection system for tunnel scenarios, characterized in that, The system applied to the method according to any one of claims 1-7 comprises: a train automatic monitoring arrival signal sending unit, a geomagnetic signal analysis unit, a geomagnetic signal sensing unit, a wireless signal sensing unit, and a wireless sensing and analysis backend; The automatic train monitoring arrival signal sending unit is responsible for sending the station information of the target train in the target tunnel to the geomagnetic signal analysis unit via vehicle-to-ground wireless signal. The geomagnetic signal analysis unit collects, in real time, a first target parameter set for each preset fixed position of the target train in the target tunnel based on the station information of the target train. Each first target parameter set includes the ideal geomagnetic signal parameters, wireless signal parameters, and mileage parameters of the target train at each fixed position in the target tunnel. The geomagnetic signal sensing unit collects the actual geomagnetic signal parameters and actual wireless signal parameters of the target train at each preset fixed position in the target tunnel in real time and transmits them to the geomagnetic signal analysis unit. The geomagnetic signal analysis unit receives the actual geomagnetic signal parameters and actual wireless signal parameters of the target train at each preset fixed location, and calibrates the actual geomagnetic signal parameters of the target train at each preset fixed location based on the station information of the target train and each of the first target parameter sets, to obtain the target geomagnetic signal and mileage data of the target train at each preset fixed location; it then calculates each of the target geomagnetic signals and the preset first target data to obtain a first analysis result; and transmits the first analysis result and each of the target geomagnetic signals to the wireless sensing analysis backend. The wireless signal sensing unit is responsible for sensing the coverage status of the rail transit wireless signal of the target tunnel in real time, framing the coverage status of the rail transit wireless signal of the target tunnel with each of the target geomagnetic signals to obtain a first data packet, and transmitting the first data packet to the wireless sensing analysis backend. The wireless sensing and analysis backend receives the first data packet in real time and analyzes it using a preset rail transit wireless signal analysis model to obtain a second analysis result of the wireless signal and geomagnetic signal of the entire operating section of the target tunnel. It then uses the preset wireless signal analysis model to analyze a preset reference data packet to obtain a fourth analysis result. The preset rail transit wireless signal analysis model is trained using a training second data packet as a sample label and the third analysis result included in the second data packet as a sample label. The training second data packet is a data packet composed of frames of the coverage status information of the rail transit wireless signal in the training tunnel and the calibrated training geomagnetic signal parameters of each preset fixed point passed by the training train in the training tunnel. The preset reference data packet includes the ideal geomagnetic signal parameters, ideal wireless signal parameters, and ideal mileage parameters of the target train in the target tunnel. The backend compares and analyzes the second and fourth analysis results, and based on the first analysis result, determines an automatic wireless signal inspection scheme for the target tunnel. Based on the automatic wireless signal inspection scheme for the target tunnel, it automatically inspects the wireless signal of the target tunnel to determine the wireless signal fault status of the target tunnel.

9. A wireless signal automatic inspection device for tunnel scenarios, characterized in that, include: One or more processors, and a memory; the memory stores computer-readable instructions that, when executed by the one or more processors, implement the steps of the automatic wireless signal inspection method for a tunnel scenario as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to implement the steps of the automatic wireless signal inspection method for a tunnel scenario as described in any one of claims 1 to 7.

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