High-precision tunnel settlement monitoring processing method, system and platform based on geomagnetic field change

Abstract

The invention discloses a geomagnetic field change-based high-precision tunnel settlement monitoring processing method, system and platform. The method comprises the following steps: generating and acquiring first data corresponding to a tunnel, and constructing a feature set corresponding to the tunnel based on the first data; wherein the first data is three-dimensional geomagnetic data in a static state in the tunnel; second data corresponding to the tunnel are generated and acquired in real time, and third data corresponding to the tunnel are generated through matching processing and judgment in combination with a dynamic time warping algorithm; wherein the second data is dynamic geomagnetic data of the tunnel; and the third data is settlement condition monitoring data of the tunnel, and the system and the platform corresponding to the method can judge whether the tunnel is settled or not and the specific settlement position in a high-precision manner.

Description

Technical Field

[0001] The present invention belongs to the technical field of tunnel settlement monitoring and processing, and specifically relates to a high-precision tunnel settlement monitoring and processing method, system and platform based on changes in the geomagnetic field. Background Art

[0002] With the acceleration of urbanization, tunnels have become an indispensable component of modern urban transportation networks. The structural safety of tunnels directly impacts the operational stability of trains. However, over the long term, tunnels can experience settlement or structural deformation due to various factors. This not only threatens the stability of the tunnel itself but also poses a potential risk to its safety.

[0003] Currently, traditional tunnel settlement monitoring methods rely primarily on manual inspections, fixed sensors (such as fiber optic sensors and sedimentometers), and physical monitoring equipment on the ground and underground. While these methods can provide some monitoring support, they also have numerous limitations. First, fixed sensors are expensive to deploy, complex to maintain, and cannot cover the entire tunnel. Second, manual inspections not only rely on human participation and are susceptible to interference from human factors, but also struggle to achieve real-time monitoring, with data lag and error accumulation issues. Furthermore, existing monitoring methods are often limited to a specific area or point, making it impossible to achieve continuous monitoring throughout the entire process, and their ability to respond poorly to changes in the tunnel environment.

[0004] With the rapid development of smart sensors, the Internet of Things, and wireless communication technologies, monitoring methods based on wireless sensor networks have been gradually proposed and applied. Wireless technologies such as Wi-Fi and Bluetooth have become important technologies for tunnel monitoring due to their low cost and ease of deployment. However, these wireless technologies are often affected by factors such as signal attenuation, multipath effects, and environmental interference in enclosed spaces such as tunnels, resulting in low positioning accuracy and an inability to meet the requirements for high-precision, continuous, and reliable settlement monitoring. These limitations are particularly pronounced in tunnels with complex underground environments and severe signal obstruction.

[0005] Therefore, in response to the existing technical problems and defects, it is urgent to design and develop a high-precision tunnel settlement monitoring and processing method, system and platform based on changes in the geomagnetic field. Summary of the Invention

[0006] In order to overcome the shortcomings and difficulties of the above-mentioned existing technologies, the present invention provides a high-precision tunnel settlement monitoring and processing method, system and platform based on changes in the geomagnetic field, aiming to monitor tunnel settlement conditions efficiently, accurately and in real time.

[0007] The first object of the present invention is to provide a high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes; the second object of the present invention is to provide a high-precision tunnel settlement monitoring and processing system based on geomagnetic field changes; the third object of the present invention is to provide a high-precision tunnel settlement monitoring and processing platform based on geomagnetic field changes;

[0008] The first object of the present invention is achieved in that the method comprises the steps of:

[0009] Generate and obtain first data corresponding to the tunnel, and construct a feature set corresponding to the tunnel based on the first data; wherein the first data is three-dimensional geomagnetic data in a static state in the tunnel;

[0010] Generate and acquire second data corresponding to the tunnel in real time, and combine with dynamic time warping algorithm, matching processing and judgment to generate third data corresponding to the tunnel; wherein, the second data is the dynamic geomagnetic data of the tunnel; the third data is the settlement condition monitoring data of the tunnel.

[0011] Furthermore, the generating and acquiring first data corresponding to the tunnel, and constructing a feature set corresponding to the tunnel based on the first data, further includes:

[0012] Preprocessing the first data by combining particle filter denoising technology; wherein the preprocessing includes noise reduction processing and measurement error processing;

[0013] Generate and obtain fourth data corresponding to the tunnel, and create a region label uniquely corresponding to the fourth data; wherein the fourth data is structural feature data of the tunnel, including straight section feature data, turning section feature data, intersection feature data, and fork road feature data;

[0014] Based on the first data, a first corresponding relationship between the feature set and the actual mileage of the tunnel or the tunnel area label is established.

[0015] Furthermore, the generating and acquiring first data corresponding to the tunnel, and constructing a feature set corresponding to the tunnel based on the first data, further includes:

[0016] Generate and obtain fifth data corresponding to the tunnel and spaced at a distance of at least one meter; and generate corresponding first data based on the fifth data; wherein the fifth data is three-dimensional geomagnetic data of each data collection point in the tunnel;

[0017] Generate sixth data corresponding to the fifth data, and establish a second corresponding relationship between the fifth data and the current mileage value of each collection point; wherein the sixth data is geomagnetic field component data, including X-direction geomagnetic field component, Y-direction geomagnetic field component and Z-direction geomagnetic field component.

[0018] Furthermore, the real-time generation and acquisition of second data corresponding to the tunnel, and the generation of third data corresponding to the tunnel by matching, processing, and determining the generation of third data in combination with a dynamic time warping algorithm, further includes:

[0019] Generating and acquiring a first data sequence and a second data sequence corresponding to the tunnel respectively; wherein the first data sequence is geomagnetic data collected in real time, including X-axis direction data and Y-axis direction data; and the second data sequence is geomagnetic data in the feature set;

[0020] Based on the first data sequence and the second data sequence, a matching calculation is performed to generate corresponding seventh data; wherein the seventh data is the minimum distance data between the first data sequence and the second data sequence;

[0021] Based on the seventh data, eighth data corresponding to the second data is generated; wherein the eighth data is the tunnel position data of the dynamic position point, including current position data and mileage data.

[0022] Furthermore, the real-time generation and acquisition of second data corresponding to the tunnel, and the generation of third data corresponding to the tunnel by matching, processing, and determining the generation of third data in combination with a dynamic time warping algorithm, further includes:

[0023] Generate and obtain a third data sequence corresponding to the tunnel; wherein the third data sequence is real-time geomagnetic data collected in the Z-axis direction;

[0024] Generate and obtain ninth data corresponding to the tunnel, and based on the ninth data and in combination with the third data sequence, generate corresponding tenth data through matching calculation; wherein the ninth data is a preset settlement threshold; and the tenth data is the minimum distance data between the third data sequence and the second data sequence.

[0025] Furthermore, after the real-time generation and acquisition of the second data corresponding to the tunnel, and the matching processing and determination to generate the third data corresponding to the tunnel in combination with the dynamic time warping algorithm, the method further includes:

[0026] Creating a data update mechanism corresponding to the tunnel, and analyzing and processing the third data in real time based on the data update mechanism;

[0027] Based on the third data, eleventh data corresponding to the tunnel is generated in real time; wherein the eleventh data is tunnel settlement warning information data;

[0028] According to the third data and in combination with the sliding window technology, the feature set is dynamically updated and processed in real time.

[0029] The second object of the present invention is achieved as follows: the system is used to implement the high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes, and the system includes:

[0030] a data generation and construction unit, configured to generate and obtain first data corresponding to the tunnel, and construct a feature set corresponding to the tunnel based on the first data; wherein the first data is three-dimensional geomagnetic data in a static state within the tunnel;

[0031] A data processing and generation unit is used to generate and obtain second data corresponding to the tunnel in real time, and combine with a dynamic time warping algorithm to match, process and determine the generation of third data corresponding to the tunnel; wherein, the second data is the dynamic geomagnetic data of the tunnel; and the third data is the settlement condition monitoring data of the tunnel.

[0032] Furthermore, the system further comprises:

[0033] a first processing module, configured to create a data update mechanism corresponding to the tunnel, and analyze and process the third data in real time based on the data update mechanism;

[0034] a first generating module, configured to generate, in real time, eleventh data corresponding to the tunnel based on the third data; wherein the eleventh data is tunnel settlement warning information data;

[0035] A second processing module is configured to dynamically update and process the feature set in real time based on the third data and in combination with a sliding window technology;

[0036] And / or, the data generation construction unit further includes:

[0037] A third processing module is configured to pre-process the first data by combining a particle filter denoising technique; wherein the pre-processing includes noise reduction processing and measurement error processing;

[0038] a first creation module, configured to generate and obtain fourth data corresponding to the tunnel, and to create a region label uniquely corresponding to the fourth data; wherein the fourth data is structural characteristic data of the tunnel, including straight section characteristic data, turning section characteristic data, intersection characteristic data, and fork road characteristic data;

[0039] A second creation module is configured to establish a first correspondence between the feature set and the actual mileage of the tunnel or the tunnel area label based on the first data;

[0040] And / or, the data processing and generating unit further includes:

[0041] a second generating module, configured to respectively generate and obtain a first data sequence and a second data sequence corresponding to the tunnel; wherein the first data sequence is geomagnetic data collected in real time, including X-axis direction data and Y-axis direction data; and the second data sequence is geomagnetic data in the feature set;

[0042] a first calculation module, configured to generate corresponding seventh data by matching calculation based on the first data sequence and the second data sequence; wherein the seventh data is minimum distance data between the first data sequence and the second data sequence;

[0043] The third generating module is used to generate eighth data corresponding to the second data based on the seventh data; wherein the eighth data is the tunnel location data of the dynamic location point, including current location data and mileage data.

[0044] Furthermore, the data generation construction unit further includes:

[0045] a fourth generating module, configured to generate and obtain fifth data corresponding to the tunnel and spaced at a distance of at least one meter; and to generate corresponding first data based on the fifth data; wherein the fifth data is three-dimensional geomagnetic data of each data collection point in the tunnel;

[0046] a third creation module, configured to generate sixth data corresponding to the fifth data, and to establish a second correspondence between the fifth data and the current mileage value of each collection point; wherein the sixth data is geomagnetic field component data, including an X-direction geomagnetic field component, a Y-direction geomagnetic field component, and a Z-direction geomagnetic field component;

[0047] And / or, the data processing and generating unit further includes:

[0048] a fifth generating module, configured to generate and obtain a third data sequence corresponding to the tunnel; wherein the third data sequence is geomagnetic data collected in real time in the Z-axis direction;

[0049] The second calculation module is used to generate and obtain ninth data corresponding to the tunnel, and based on the ninth data and in combination with the third data sequence, match and calculate to generate corresponding tenth data; wherein, the ninth data is a preset settlement threshold; and the tenth data is the minimum distance data between the third data sequence and the second data sequence;.

[0050] The third object of the present invention is achieved as follows: it includes a processor, a memory and a high-precision tunnel settlement monitoring and processing platform control program based on changes in the geomagnetic field; wherein the processor executes the high-precision tunnel settlement monitoring and processing platform control program based on changes in the geomagnetic field, and the high-precision tunnel settlement monitoring and processing platform control program based on changes in the geomagnetic field is stored in the memory, and the high-precision tunnel settlement monitoring and processing platform control program based on changes in the geomagnetic field implements the high-precision tunnel settlement monitoring and processing method based on changes in the geomagnetic field.

[0051] The present invention generates and obtains first data corresponding to a tunnel through a method, and constructs a feature set corresponding to the tunnel based on the first data; wherein the first data is three-dimensional geomagnetic data in a static state in the tunnel; second data corresponding to the tunnel is generated and obtained in real time, and combined with a dynamic time warping algorithm, matching processing and judgment are used to generate third data corresponding to the tunnel; wherein the second data is the dynamic geomagnetic data of the tunnel; the third data is the settlement condition monitoring data of the tunnel, and the system and platform corresponding to the method can accurately determine whether the tunnel has settled and the specific location of the settlement.

[0052] Specifically, the proposed solution first establishes a tunnel feature set using geomagnetic data. Next, the real-time geomagnetic data is accurately matched with a fingerprint database to estimate the current location of trains or pedestrians. Finally, by analyzing geomagnetic variations at different locations, tunnel subsidence can be detected in a timely manner. Compared to traditional wireless technologies, this method offers enhanced anti-interference capabilities. Especially in complex underground environments, geomagnetic monitoring provides more stable and efficient positioning information. Furthermore, the geomagnetic monitoring system is low-cost to deploy and requires no additional wireless network infrastructure, significantly simplifying system construction and maintenance.

[0053] By combining the stability of geomagnetic data with sensitivity to environmental changes, the proposed settlement monitoring method offers significant advantages in improving the accuracy and real-time performance of tunnel settlement monitoring, providing a low-cost, efficient, and reliable solution for operations. This method not only effectively addresses the limitations of traditional monitoring technologies but also provides a new technical path for real-time monitoring and early warning of tunnel settlement. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0055] Figure 1A schematic diagram of the process steps of a high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes according to the present invention;

[0056] Figure 2 A schematic diagram of an embodiment of a high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes according to the present invention;

[0057] Figure 3 This is a schematic diagram of the architecture of a high-precision tunnel settlement monitoring and processing system based on geomagnetic field changes according to the present invention;

[0058] Figure 4 This is a schematic diagram of the architecture of a high-precision tunnel settlement monitoring and processing platform based on changes in the geomagnetic field according to the present invention. DETAILED DESCRIPTION

[0059] In order to better understand the purpose, technical solutions and advantages of the present invention, the present invention is further described below with reference to the accompanying drawings and specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

[0060] The present invention may also be implemented or applied through other different specific examples, and the details in this specification may also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0061] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0062] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. Secondly, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0063] Preferably, the high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes of the present invention is applied to one or more terminals or servers. The terminal is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes but is not limited to microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0064] The terminal can be a computing device such as a desktop computer, notebook, PDA, cloud server, etc. The terminal can interact with the client through a keyboard, mouse, remote control, touchpad, or voice control device.

[0065] The present invention provides a high-precision tunnel settlement monitoring and processing method, system and platform based on geomagnetic field changes.

[0066] like Figure 1 , which is a flow chart of a high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes provided by an embodiment of the present invention.

[0067] In this embodiment, the high-precision tunnel settlement monitoring and processing method based on changes in the geomagnetic field can be applied to a terminal or a fixed terminal with a display function. The terminal is not limited to a personal computer, a smart phone, a tablet computer, a desktop computer or an all-in-one computer equipped with a camera, etc.

[0068] The high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes can also be applied in a hardware environment consisting of a terminal and a server connected to the terminal via a network. The network includes, but is not limited to, a wide area network, a metropolitan area network, or a local area network. The high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes according to the embodiments of the present invention can be executed by a server, a terminal, or both.

[0069] For example, for a terminal that needs to perform high-precision tunnel settlement monitoring and processing based on geomagnetic field changes, the high-precision tunnel settlement monitoring and processing function based on geomagnetic field changes provided by the method of the present invention can be directly integrated on the terminal, or a client for implementing the method of the present invention can be installed. For another example, the method provided by the present invention can also be run on a server or other device in the form of a software development kit (SDK), and an interface for the high-precision tunnel settlement monitoring and processing function based on geomagnetic field changes is provided in the form of SDK. The terminal or other device can implement the high-precision tunnel settlement monitoring and processing function based on geomagnetic field changes through the provided interface. The present invention is further elaborated below in conjunction with the accompanying drawings.

[0070] like Figure 1-Figure 2 As shown, the present invention provides a high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes, the method comprising the following steps:

[0071] S01. Generate and obtain first data corresponding to a tunnel, and construct a feature set corresponding to the tunnel based on the first data; wherein the first data is three-dimensional geomagnetic data in a static state in the tunnel;

[0072] S02. Generate and acquire second data corresponding to the tunnel in real time, and combine with dynamic time warping algorithm, match processing and determine to generate third data corresponding to the tunnel; wherein, the second data is the dynamic geomagnetic data of the tunnel; the third data is the settlement condition monitoring data of the tunnel.

[0073] The generating and acquiring first data corresponding to the tunnel, and constructing a feature set corresponding to the tunnel based on the first data, further includes:

[0074] S011. Preprocess the first data in combination with particle filtering denoising technology; wherein the preprocessing includes noise reduction processing and measurement error processing; wherein, Kalman filtering is a recursive filtering method that can estimate the optimal state of the system from noise signals and measurement errors.

[0075] S012. Generate and obtain fourth data corresponding to the tunnel, and create a region label uniquely corresponding to the fourth data; wherein the fourth data is structural characteristic data of the tunnel, including straight section characteristic data, turning section characteristic data, intersection characteristic data, and fork characteristic data; the region label is used to identify the tunnel region to which the location belongs, such as a turning area, straight section, fork, etc.

[0076] S013. Based on the first data, establish a first correspondence between the feature set and the actual mileage of the tunnel or the tunnel area label. To accurately monitor tunnel settlement, the fingerprint library needs to contain three-dimensional geomagnetic data for each location within the tunnel, and this data must be associated with the actual mileage and area label of the location.

[0077] The generating and acquiring first data corresponding to the tunnel, and constructing a feature set corresponding to the tunnel based on the first data, further includes:

[0078] S014. Generate and obtain fifth data corresponding to the tunnel and with an interval distance of at least one meter; and generate corresponding first data based on the fifth data; wherein the fifth data is the three-dimensional geomagnetic data of each data collection point in the tunnel; each data record in the feature set contains the three-dimensional geomagnetic data when the train passes through a specific location.

[0079] S015. Generate sixth data corresponding to the fifth data, and establish a second correspondence between the fifth data and the current mileage value of each collection point; wherein the sixth data is geomagnetic field component data, including an X-direction geomagnetic field component, a Y-direction geomagnetic field component, and a Z-direction geomagnetic field component.

[0080] The real-time generation and acquisition of second data corresponding to the tunnel, and the generation of third data corresponding to the tunnel through matching processing and determination in combination with a dynamic time warping algorithm, further includes:

[0081] S021. Generate and acquire a first data sequence and a second data sequence corresponding to the tunnel, respectively; wherein the first data sequence is geomagnetic data collected in real time, including X-axis direction data and Y-axis direction data; and the second data sequence is geomagnetic data in a feature set;

[0082] S022. Based on the first data sequence and the second data sequence, generate corresponding seventh data by matching calculation; wherein the seventh data is the minimum distance data between the first data sequence and the second data sequence; the calculation formula is as follows:

[0083]

[0084] Where D EDR (A, B) represents the minimum distance between two sets of data. Represents real-time sequence data in the X direction, represents the reference sequence data in the X direction, Represents the real-time sequence data in the Y direction, Represents the real-time sequence data in the Y direction. d() represents the distance calculation function;

[0085] S023. Generate eighth data corresponding to the second data based on the seventh data; wherein the eighth data is the tunnel location data of the dynamic location point, including current location data and mileage data.

[0086] The real-time generation and acquisition of second data corresponding to the tunnel, and the generation of third data corresponding to the tunnel through matching processing and determination in combination with a dynamic time warping algorithm, further includes:

[0087] S024. Generate and obtain a third data sequence corresponding to the tunnel; wherein the third data sequence is real-time geomagnetic data collected in the Z-axis direction; when performing position estimation, the Z-axis data is used as a reference for settlement judgment.

[0088] S025. Generate and obtain ninth data corresponding to the tunnel. Based on the ninth data and in combination with the third data sequence, perform matching calculation to generate corresponding tenth data. The ninth data is a preset settlement threshold. The tenth data is the minimum distance data between the third data sequence and the second data sequence. The calculation formula is as follows:

[0089]

[0090] Where D EDR (z t ,z ref ) represents the minimum distance between data in the z direction, Represents real-time sequence data in the X direction, Indicates the reference sequence data in the X direction.

[0091] After the second data corresponding to the tunnel is generated and acquired in real time, and the third data corresponding to the tunnel is generated by matching and determining the dynamic time warping algorithm, the method further includes:

[0092] S03. Creating a data update mechanism corresponding to the tunnel, and analyzing and processing the third data in real time based on the data update mechanism;

[0093] S04. Based on the third data, generating eleventh data corresponding to the tunnel in real time; wherein the eleventh data is tunnel settlement warning information data;

[0094] S05. Based on the third data and in combination with a sliding window technique, the feature set is dynamically updated in real time. The present invention utilizes a sliding window and real-time data update mechanism to ensure rapid processing of new geomagnetic data while the train is in motion, and to perform accurate positioning and settlement detection by updating the fingerprint library.

[0095] Specifically, in an embodiment of the present invention, a high-precision tunnel settlement monitoring method based on geomagnetic field changes is proposed. Unlike traditional wireless positioning technologies such as Wi-Fi and Bluetooth, this method relies entirely on geomagnetic field signals for monitoring, and has the advantage of not requiring additional infrastructure support. The geomagnetic field is a naturally occurring physical phenomenon on the earth. It has strong stability and unforgeability, and can sensitively reflect tiny structural changes in the tunnel, especially vertical changes such as settlement. By arranging magnetic field sensors in the tunnel, collecting geomagnetic data in real time, and comparing these data with historical geomagnetic fingerprints, the method of this scheme can accurately determine whether the tunnel has settled and the specific location of the settlement.

[0096] In other words, the present invention proposes a high-precision tunnel settlement monitoring method based on geomagnetic field variations. This method first establishes a tunnel magnetic field signature set using magnetic field data. Then, the current position of a train or pedestrian is accurately matched with the real-time magnetic field data. Finally, by analyzing magnetic field variations at different locations, tunnel settlement can be detected in a timely manner. Compared with traditional wireless technologies, this method has stronger anti-interference capabilities. Especially in complex underground environments, magnetic field monitoring provides more stable and efficient positioning information. Furthermore, the magnetic field monitoring system has low deployment costs and does not require additional wireless network infrastructure, greatly simplifying system construction and maintenance.

[0097] By combining the stability of magnetic field data with sensitivity to environmental changes, the proposed settlement monitoring method offers significant advantages in improving the accuracy and real-time performance of tunnel settlement monitoring, providing a low-cost, efficient, and reliable solution for train operations. This method not only effectively addresses the limitations of traditional monitoring technologies but also provides a new technical path for real-time monitoring and early warning of tunnel settlement.

[0098] Tunnel settlement is detected by using geomagnetic sensors to collect real-time geomagnetic data from the tunnel and analyzing changes in this data. To ensure high accuracy and robustness, this solution utilizes a variety of advanced data processing technologies, covering several key steps: data acquisition and preprocessing, establishing a geomagnetic fingerprint database, geomagnetic data matching and position estimation, and settlement monitoring and change analysis. Each step incorporates specific algorithms and formula derivations to ensure the scientific nature and feasibility of the method.

[0099] Data acquisition and preprocessing: To monitor tunnel settlement, geomagnetic sensors must be deployed within the tunnel to collect real-time geomagnetic data. This data includes the geomagnetic field components in the X, Y, and Z directions. The collected geomagnetic data often contains noise, so effective data preprocessing is required to improve the accuracy of subsequent analysis.

[0100] Data preprocessing. Since magnetic field signals are susceptible to various interferences during the acquisition process, such as electromagnetic noise and sensor errors, advanced filtering techniques will be used in the preprocessing stage to remove noise and improve signal reliability. To achieve this goal, this solution uses particle filtering denoising technology.

[0101] Particle filtering is a recursive Bayesian estimation method based on the Monte Carlo method that can handle estimation problems with nonlinear and non-Gaussian noise in high-dimensional space. Particle filtering estimates the state of a system by simulating multiple particles (samples) and performing weighted updates based on the observed values.

[0102] Assume that at time t, the currently observed magnetic field data is Z t , the recursive process of particle filtering is as follows:

[0103] Importance sampling: According to the state x at the previous moment t-1 Generate N particles And through the state transition model p(x t |x t-1 )Predict the particle state at the current moment:

[0104]

[0105] in, is the state transfer function, i represents a particle index in the particle filter, u t-1 is the control input, is the process noise.

[0106] Weighted update: By observing the model p(z t |x t ) Calculate the weight of each particle, the weight of the particle With the current observation value Z t The matching degree is related to:

[0107]

[0108] Normalized weights: Normalize the weights:

[0109]

[0110] represents the normalized weight of the i-th particle at time t, represents the sum of the weights of all particles at time t, and N represents the total number of particles.

[0111] State estimation: Perform weighted averaging based on the particle weights to obtain the estimated state x^t\hat{x}_tx^t at the current moment:

[0112]

[0113] represents the estimated system state at time t, Represents the state of the i-th particle at time t. Particle filtering does not need to assume linear systems and Gaussian noise, so it can effectively handle nonlinear systems and complex noise environments.

[0114] Through particle filtering, filtered magnetic field data can be obtained, making subsequent analysis more accurate and effectively removing interference caused by measurement errors and noise.

[0115] Establishing a magnetic field feature set: The magnetic field feature set is a core component of this solution and the foundation for tunnel settlement monitoring. The data in the feature set must be accurately collected and stored to provide accurate data support for position estimation and settlement detection during real-time monitoring. To accurately monitor tunnel settlement, the feature set must contain 3D magnetic field data for each location within the tunnel, and this data must be associated with the actual mileage and area labels of each location. By comparing it with historically collected data, the feature set can provide effective baseline data for subsequent settlement monitoring.

[0116] Magnetic field feature set storage: Each data record in the magnetic field feature set contains the three-dimensional magnetic field data when the train passes a specific location. The feature set data items include the magnetic field strength values ​​in three directions (X, Y, Z). In addition, the mileage information (in meters) and area label of the location need to be recorded. By associating magnetic field data and mileage information, the feature set can provide real-time positioning support. The feature set information of each collection point can be expressed as:

[0117] Features i ={(M xi ,M yi ,M zi ),Mileage i ,A i}(5)

[0118] Where: M xi ,M yi ,M zi Respectively represent the magnetic field strength of the X, Y, and Z axes at position i; Mileage i It is the mileage information of the location, indicating the cumulative mileage of the train when passing through the location (unit: meter); A i This is the region label for the location, used to identify the tunnel area to which the location belongs, such as a turning area, a straight section, or a fork in the road. Through this data structure, the magnetic field feature set can effectively associate the actual geographic location with the magnetic field data within the tunnel, ensuring high-precision position estimation.

[0119] Region division and feature set labeling: To ensure the accuracy and reliability of magnetic field data, region division in the tunnel is a crucial step. The basic goal of region division is to divide the tunnel into multiple regions with relatively stable magnetic field distribution and assign a unique region label A to each region. i In this way, the magnetic field data in each area can be accurately marked, making it easier to match and compare it with the magnetic field data collected in real time.

[0120] Principles of Regionalization: Regionalization must consider the tunnel's structural characteristics, the changing trends of magnetic field data, and actual operational needs. Generally speaking, tunnel structural features include straight sections, curves, intersections, and forks, which often lead to significant variations in magnetic field data. Therefore, regionalization must not only consider the tunnel's geometry but also the stability and changing trends of magnetic field data.

[0121] Straight area: Generally, magnetic field data is more stable within a straight section, so the straight section is divided into a region and marked.

[0122] Turning area: When turning, the shape change of the tunnel will affect the magnetic field, resulting in large fluctuations in the magnetic field data. Therefore, the turning area needs to be divided into a separate area.

[0123] Forks and intersections: These places produce more complex magnetic field changes and usually need to be marked as a separate area.

[0124] Through these detailed regional divisions, the magnetic field data of each specific location in the tunnel can be accurately associated with the corresponding regional label A. i The magnetic field data fluctuations caused by the difference in spatial position are reduced by correlating them.

[0125] Magnetic field data collection and mileage assignment: Magnetic field data collection is a core step in the feature set construction process. Ensuring the accuracy of the collected magnetic field data is crucial for subsequent settlement monitoring and position estimation. Inside the tunnel, magnetic field sensors are installed on trains, recording the magnetic field components in the X, Y, and Z directions in real time, and marking them with a timestamp and the train's mileage information. Data collection is typically performed at key locations in the tunnel, such as straight sections, turns, and intersections, with magnetic field data collected every 10 meters to ensure representative data.

[0126] During the magnetic field data collection process, key locations, including tunnel entrances and exits, curves, and intersections, are densely sampled to ensure comprehensive coverage of magnetic field variations across different tunnel areas. Each set of collected magnetic field data is de-noised using appropriate filtering algorithms to ensure data accuracy. To accurately locate this data, the magnetic field data at each collection point must be correlated with the current mileage. The odometer records this data in real time during train travel and matches it to the magnetic field data.

[0127] Once a magnetic field signature set is established, its data serves as a baseline for subsequent settlement monitoring and real-time position estimation. By comparing it with real-time magnetic field data, the signature set not only accurately matches locations but also detects potential settlement changes. Each time a train passes through, the collected magnetic field data is compared with the data in the signature set, accurately calculating the train's current position and enabling real-time monitoring of tunnel settlement.

[0128] Magnetic Field Data Matching and Position Estimation: In tunnels, real-time magnetic field data must be compared with pre-established magnetic field signature data to estimate the train's current position through matching calculations. Since the train's trajectory is fixed and its position can be expressed in mileage, the core task of this component is to compare the collected magnetic field data with historically recorded magnetic field data to determine the train's current position and mileage.

[0129] Sequence Matching Algorithm: To achieve accurate location information, this solution uses the Edit Distance for Real sequences (EDR) method to match magnetic field data. EDR is a classic trajectory similarity calculation method that calculates the similarity between two time series data (i.e., magnetic field data sequences) to find the optimal matching point and estimate the train's position. The advantage of the EDR method is that it can effectively handle noise and irregular variations in time series data, making it particularly suitable for complex magnetic field data matching tasks in tunnels.

[0130] Assume that the real-time magnetic field data sequence is {x t ,y t ,z t}, and the magnetic field data sequence in the feature set is {x ref ,y ref ,z ref Each set of magnetic field data contains the magnetic field strength in three directions: X, Y and Z. Therefore, the real-time data sequence {x t ,y t ,z t} and the data sequence {x ref ,y ref ,z ref} can be expressed as:

[0131]

[0132] In order to achieve high-precision positioning, we first match the real-time data with the magnetic field data in the feature set. Since the geometric structure of the tunnel usually determines the changes in the X-axis and Y-axis magnetic field data, it is most appropriate to use the X-axis and Y-axis data for preliminary positioning judgment. These data are usually related to the shape of the tunnel (such as straight sections, turns, etc.), so they can effectively help estimate the current position of the train. Specifically, the X-axis and Y-axis data collected in real time are matched with the data in the feature set using the EDR method. The EDR method calculates the minimum edit distance between the two sets of data, and the matching process can be expressed as:

[0133]

[0134] By calculating the matching distance of the X-axis and Y-axis data, we can estimate the current position and mileage of the train and determine the tunnel location of the train. In the formula, D EDR (A, B) represents the minimum distance between two sets of data. Represents real-time sequence data in the X direction, represents the reference sequence data in the X direction, Represents the real-time sequence data in the Y direction, Represents real-time sequence data in the Y direction. d() represents the distance calculation function.

[0135] Axis changes for settlement detection: Although X- and Y-axis data are primarily used for position estimation, Z-axis (vertical) magnetic field data is key in this solution for monitoring vertical changes, such as settlement. Settlement can cause vertical magnetic field changes, so comparing real-time Z-axis data with historical signature data can help detect potential settlement areas.

[0136] After the position estimation is complete, the Z-axis data is used to further verify whether settlement has occurred. Settlement is usually manifested as a change in the Z-axis magnetic field strength, and this change can be sensitively detected by comparing it with historical data. By using the EDR algorithm to calculate the minimum edit distance between the real-time Z-axis data and the Z-axis data of the historical data in the feature set, we can evaluate whether the Z-axis change exceeds the preset settlement threshold. This process is expressed by the following formula:

[0137]

[0138] Through this matching method, the change in the Z axis can sensitively reflect the change in the magnetic field caused by factors such as sedimentation. When the change in the Z axis exceeds the set threshold, the system will mark the location as a potential sedimentation area; where DEDR (z t ,z ref ) represents the minimum distance between data in the z direction, Represents real-time sequence data in the X direction, Indicates the reference sequence data in the X direction.

[0139] Position Estimation and Subsidence Detection: When performing position estimation, Z-axis data serves as a reference for subsidence detection. Position matching, accomplished through the EDR algorithm, first uses X- and Y-axis data for preliminary position estimation, followed by Z-axis data to further determine whether subsidence has occurred. Real-time Z-axis data is compared with historical data in the feature set. When the change exceeds a set threshold, the system identifies a potential subsidence area and triggers subsidence detection.

[0140] At the same time, if the change in the Z axis exceeds the set threshold The position may have sunk. Calculate the change in the Z axis:

[0141]

[0142] Indicates the Z-axis magnetic field intensity value collected in real time at the current moment. That is, during the monitoring process, the actual observed value of the vertical direction (Z-axis) magnetic field field at a certain position in the tunnel at the current moment. Indicates the Z-axis magnetic field strength value in the historical record. That is, the historical magnetic field data used as a benchmark is usually the vertical direction (Z-axis) magnetic field data collected under normal tunnel conditions (when there is no settlement or other significant structural changes). This location is then marked as a potential subsidence area. By combining position estimation and subsidence detection, the system can accurately monitor tunnel subsidence and provide real-time warnings. Based on this approach, the system not only provides highly accurate position estimation but also enables real-time monitoring and early warning of tunnel subsidence, providing strong support for tunnel safety.

[0143] System Optimization and Real-Time Feedback: To ensure the magnetic field monitoring system can efficiently and accurately monitor tunnel settlement and provide timely warnings, this solution combines sliding window technology with a real-time data update mechanism. These technologies enable rapid processing of new magnetic field data in a short period of time, ensuring the system's real-time performance and stability. Specifically, the sliding window technology analyzes the latest data in the time series in real time. As a train passes, new magnetic field data is added to the analysis queue, while the oldest data is removed, ensuring real-time data processing. The sliding window size and step size are optimized through experimentation to balance data processing efficiency and accuracy. Each time a new data point arrives, the window is updated and settlement monitoring results are immediately fed back, ensuring the system can rapidly respond to changes in magnetic field data. Furthermore, the real-time data update mechanism promptly integrates newly collected magnetic field data, compares it with historical data in the feature set, and updates the feature set using a matching algorithm. This real-time update not only ensures the timeliness of the feature set but also enables the system to quickly adapt to changes in magnetic field data, enabling real-time position estimation and settlement detection. Once new magnetic field data is collected, the system immediately performs position estimation and settlement monitoring analysis, providing rapid settlement warnings. Through these optimization measures, the system can not only improve real-time monitoring capabilities, but also maintain high accuracy, providing reliable support for tunnel settlement detection.

[0144] This proposal proposes a high-precision tunnel settlement monitoring method based on magnetic field variations. This method aims to accurately monitor tunnel settlement by collecting magnetic field data in real time. Compared to traditional wireless positioning technologies, this method overcomes the low accuracy and susceptibility to interference of existing technologies in complex underground environments by leveraging the stability and unforgeability of magnetic field signals. By deploying magnetic field sensors to collect magnetic field data in the X, Y, and Z directions in real time and comparing it with historical feature sets, this method can accurately estimate the train's position and effectively monitor the occurrence of settlement.

[0145] During implementation, this approach incorporates particle filtering technology to denoise magnetic field data, ensuring data reliability. EDR (Electronic Decoding Detection) is employed for magnetic field data matching, improving positioning accuracy and stability. Furthermore, sliding window technology and a real-time data update mechanism optimize the real-time nature and responsiveness of data processing. In particular, changes in Z-axis magnetic field data can sensitively reflect potential subsidence areas, providing important early warning information for tunnel safety.

[0146] This method offers advantages such as low cost, high efficiency, and ease of deployment, providing a more accurate real-time settlement monitoring solution than traditional methods. With further technological development, magnetic field-based monitoring methods will play an increasingly important role in tunnel maintenance and safety management, providing solid technical support for the safe and stable operation of the system.

[0147] To achieve the above objectives, the present invention also provides a high-precision tunnel settlement monitoring and processing system based on geomagnetic field changes, such as Figure 3 As shown, the system specifically includes:

[0148] a data generation and construction unit, configured to generate and obtain first data corresponding to the tunnel, and construct a feature set corresponding to the tunnel based on the first data; wherein the first data is three-dimensional geomagnetic data in a static state within the tunnel;

[0149] A data processing and generation unit is used to generate and obtain second data corresponding to the tunnel in real time, and combine it with a dynamic time warping algorithm to match, process and determine the generation of third data corresponding to the tunnel; wherein, the second data is the dynamic geomagnetic data of the tunnel; and the third data is the settlement condition monitoring data of the tunnel.

[0150] The system further includes: a first processing module, configured to create a data update mechanism corresponding to the tunnel, and to analyze and process the third data in real time based on the data update mechanism;

[0151] a first generating module, configured to generate, in real time, eleventh data corresponding to the tunnel based on the third data; wherein the eleventh data is tunnel settlement warning information data;

[0152] A second processing module is configured to dynamically update and process the feature set in real time based on the third data and in combination with a sliding window technology;

[0153] And / or, the data generation construction unit further includes:

[0154] A third processing module is configured to pre-process the first data by combining a particle filter denoising technique; wherein the pre-processing includes noise reduction processing and measurement error processing;

[0155] a first creation module, configured to generate and obtain fourth data corresponding to the tunnel, and to create a region label uniquely corresponding to the fourth data; wherein the fourth data is structural characteristic data of the tunnel, including straight section characteristic data, turning section characteristic data, intersection characteristic data, and fork road characteristic data;

[0156] A second creation module is configured to establish a first correspondence between the feature set and the actual mileage of the tunnel or the tunnel area label based on the first data;

[0157] And / or, the data processing and generating unit further includes:

[0158] a second generating module, configured to respectively generate and obtain a first data sequence and a second data sequence corresponding to the tunnel; wherein the first data sequence is geomagnetic data collected in real time, including X-axis direction data and Y-axis direction data; and the second data sequence is geomagnetic data in the feature set;

[0159] a first calculation module, configured to generate corresponding seventh data by matching calculation based on the first data sequence and the second data sequence; wherein the seventh data is minimum distance data between the first data sequence and the second data sequence;

[0160] The third generating module is used to generate eighth data corresponding to the second data based on the seventh data; wherein the eighth data is the tunnel location data of the dynamic location point, including current location data and mileage data.

[0161] The data generation construction unit further includes:

[0162] a fourth generating module, configured to generate and obtain fifth data corresponding to the tunnel and spaced at a distance of at least one meter; and to generate corresponding first data based on the fifth data; wherein the fifth data is three-dimensional geomagnetic data of each data collection point in the tunnel;

[0163] a third creation module, configured to generate sixth data corresponding to the fifth data, and to establish a second correspondence between the fifth data and the current mileage value of each collection point; wherein the sixth data is geomagnetic field component data, including an X-direction geomagnetic field component, a Y-direction geomagnetic field component, and a Z-direction geomagnetic field component;

[0164] And / or, the data processing and generating unit further includes:

[0165] a fifth generating module, configured to generate and obtain a third data sequence corresponding to the tunnel; wherein the third data sequence is geomagnetic data collected in real time in the Z-axis direction;

[0166] The second calculation module is used to generate and obtain ninth data corresponding to the tunnel, and based on the ninth data and in combination with the third data sequence, match and calculate to generate corresponding tenth data; wherein, the ninth data is a preset settlement threshold; and the tenth data is the minimum distance data between the third data sequence and the second data sequence;.

[0167] In the system solution embodiment of the present invention, the method steps involved in the high-precision tunnel settlement monitoring and processing based on the changes in the geomagnetic field have been described above in detail. That is to say, the functional modules in the system are used to implement the steps or sub-steps in the above method embodiment, which will not be repeated here.

[0168] To achieve the above objectives, the present invention also provides a high-precision tunnel settlement monitoring and processing platform based on geomagnetic field changes, such as Figure 4 As shown, it includes a processor, a memory, and a high-precision tunnel settlement monitoring and processing platform control program based on geomagnetic field changes; wherein, the processor executes the high-precision tunnel settlement monitoring and processing platform control program based on geomagnetic field changes, the high-precision tunnel settlement monitoring and processing platform control program based on geomagnetic field changes is stored in the memory, and the high-precision tunnel settlement monitoring and processing platform control program based on geomagnetic field changes implements the high-precision tunnel settlement monitoring and processing method steps based on geomagnetic field changes. For example:

[0169] S01. Generate and obtain first data corresponding to a tunnel, and construct a feature set corresponding to the tunnel based on the first data; wherein the first data is three-dimensional geomagnetic data in a static state in the tunnel;

[0170] S02. Generate and acquire second data corresponding to the tunnel in real time, and combine with dynamic time warping algorithm, match processing and determine to generate third data corresponding to the tunnel; wherein, the second data is the dynamic geomagnetic data of the tunnel; the third data is the settlement condition monitoring data of the tunnel.

[0171] The specific details of the steps have been explained above and will not be repeated here.

[0172] In an embodiment of the present invention, the built-in processor of the high-precision tunnel settlement monitoring and processing platform based on geomagnetic field changes can be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor utilizes various interfaces and circuits to connect various components, and executes or executes programs or units stored in the memory, as well as calls data stored in the memory, to perform various functions and process data for high-precision tunnel settlement monitoring based on geomagnetic field changes.

[0173] The memory is used to store program codes and various data. It is installed in a high-precision tunnel settlement monitoring and processing platform based on changes in the geomagnetic field, and realizes high-speed and automatic access to programs or data during operation.

[0174] The memory includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electronically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0175] The present invention generates and obtains first data corresponding to a tunnel through a method, and constructs a feature set corresponding to the tunnel based on the first data; wherein the first data is three-dimensional geomagnetic data in a static state in the tunnel; second data corresponding to the tunnel is generated and obtained in real time, and combined with a dynamic time warping algorithm, matching processing and judgment are used to generate third data corresponding to the tunnel; wherein the second data is the dynamic geomagnetic data of the tunnel; the third data is the settlement condition monitoring data of the tunnel, and the system and platform corresponding to the method can accurately determine whether the tunnel has settled and the specific location of the settlement.

[0176] Specifically, the proposed solution first establishes a tunnel feature set using geomagnetic data. Next, the real-time geomagnetic data is accurately matched with a fingerprint database to estimate the current location of trains or pedestrians. Finally, by analyzing geomagnetic variations at different locations, tunnel subsidence can be detected in a timely manner. Compared to traditional wireless technologies, this method offers enhanced anti-interference capabilities. Especially in complex underground environments, geomagnetic monitoring provides more stable and efficient positioning information. Furthermore, the geomagnetic monitoring system is low-cost to deploy and requires no additional wireless network infrastructure, significantly simplifying system construction and maintenance.

[0177] By combining the stability of geomagnetic data with sensitivity to environmental changes, the proposed settlement monitoring method offers significant advantages in improving the accuracy and real-time performance of tunnel settlement monitoring, providing a low-cost, efficient, and reliable solution for operations. This method not only effectively addresses the limitations of traditional monitoring technologies but also provides a new technical path for real-time monitoring and early warning of tunnel settlement.

[0178] In other words, the proposed settlement monitoring method based on geomagnetic field variations can provide a highly accurate, real-time solution for tunnel settlement detection. By combining data processing technology, real-time feedback mechanisms, and settlement monitoring algorithms, the system provides reliable support for operational safety and provides effective data for tunnel settlement early warning and maintenance.

[0179] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes, characterized in that: The method comprises the steps of: Generate and obtain first data corresponding to the tunnel, and construct a feature set corresponding to the tunnel based on the first data; wherein the first data is three-dimensional geomagnetic data in a static state in the tunnel; Generate and acquire second data corresponding to the tunnel in real time, and combine with dynamic time warping algorithm, matching processing and judgment to generate third data corresponding to the tunnel; wherein, the second data is the dynamic geomagnetic data of the tunnel; the third data is the settlement condition monitoring data of the tunnel.

2. A high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes according to claim 1, characterized in that: The generating and acquiring first data corresponding to the tunnel, and constructing a feature set corresponding to the tunnel based on the first data, further includes: Preprocessing the first data by combining particle filter denoising technology; wherein the preprocessing includes noise reduction processing and measurement error processing; Generate and obtain fourth data corresponding to the tunnel, and create a region label uniquely corresponding to the fourth data; wherein the fourth data is structural feature data of the tunnel, including straight section feature data, turning section feature data, intersection feature data, and fork road feature data; Based on the first data, a first corresponding relationship between the feature set and the actual mileage of the tunnel or the tunnel area label is established.

3. A high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes according to claim 1 or 2, characterized in that: The generating and acquiring first data corresponding to the tunnel, and constructing a feature set corresponding to the tunnel based on the first data, further includes: Generate and obtain fifth data corresponding to the tunnel and spaced at a distance of at least one meter; and generate corresponding first data based on the fifth data; wherein the fifth data is three-dimensional geomagnetic data of each data collection point in the tunnel; Generate sixth data corresponding to the fifth data, and establish a second corresponding relationship between the fifth data and the current mileage value of each collection point; wherein the sixth data is geomagnetic field component data, including X-direction geomagnetic field component, Y-direction geomagnetic field component and Z-direction geomagnetic field component.

4. The high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes according to claim 1 is characterized in that: The real-time generation and acquisition of second data corresponding to the tunnel, and the generation of third data corresponding to the tunnel through matching processing and determination in combination with a dynamic time warping algorithm, further includes: Generating and acquiring a first data sequence and a second data sequence corresponding to the tunnel respectively; wherein the first data sequence is geomagnetic data collected in real time, including X-axis direction data and Y-axis direction data; and the second data sequence is geomagnetic data in the feature set; Based on the first data sequence and the second data sequence, a matching calculation is performed to generate corresponding seventh data; wherein the seventh data is the minimum distance data between the first data sequence and the second data sequence; the calculation formula is as follows: Where D EDR (A, B) represents the minimum distance between two sets of data. Represents real-time sequence data in the X direction, represents the reference sequence data in the X direction, Represents the real-time sequence data in the Y direction, Represents the real-time sequence data in the Y direction. d() represents the distance calculation function; Based on the seventh data, eighth data corresponding to the second data is generated; wherein the eighth data is the tunnel position data of the dynamic position point, including current position data and mileage data.

5. A high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes according to claim 1 or 4, characterized in that: The real-time generation and acquisition of second data corresponding to the tunnel, and the generation of third data corresponding to the tunnel through matching processing and determination in combination with a dynamic time warping algorithm, further includes: Generate and obtain a third data sequence corresponding to the tunnel; wherein the third data sequence is real-time geomagnetic data collected in the Z-axis direction; Generate and obtain ninth data corresponding to the tunnel, and generate corresponding tenth data based on the ninth data and in combination with the third data sequence through matching calculation; wherein the ninth data is a preset settlement threshold; and the tenth data is the minimum distance data between the third data sequence and the second data sequence; the calculation formula is as follows: Where D EDR (z t ,z ref ) represents the minimum distance between data in the z direction, Represents real-time sequence data in the X direction, Indicates the reference sequence data in the X direction.

6. The high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes according to claim 1 is characterized in that: After the second data corresponding to the tunnel is generated and acquired in real time, and the third data corresponding to the tunnel is generated by matching and determining the dynamic time warping algorithm, the method further includes: Creating a data update mechanism corresponding to the tunnel, and analyzing and processing the third data in real time based on the data update mechanism; Based on the third data, eleventh data corresponding to the tunnel is generated in real time; wherein the eleventh data is tunnel settlement warning information data; According to the third data and in combination with the sliding window technology, the feature set is dynamically updated and processed in real time.

7. A high-precision tunnel settlement monitoring and processing system based on geomagnetic field changes, characterized in that: The system is used to implement the high-precision tunnel settlement monitoring and processing method based on geomagnetic field changes as described in any one of claims 1 to 6, and the system includes: a data generation and construction unit, configured to generate and obtain first data corresponding to the tunnel, and construct a feature set corresponding to the tunnel based on the first data; wherein the first data is three-dimensional geomagnetic data in a static state within the tunnel; A data processing and generation unit is used to generate and obtain second data corresponding to the tunnel in real time, and combine with a dynamic time warping algorithm to match, process and determine the generation of third data corresponding to the tunnel; wherein, the second data is the dynamic geomagnetic data of the tunnel; and the third data is the settlement condition monitoring data of the tunnel.

8. The high-precision tunnel settlement monitoring and processing system based on geomagnetic field changes according to claim 7, characterized in that: The system further comprises: a first processing module, configured to create a data update mechanism corresponding to the tunnel, and analyze and process the third data in real time based on the data update mechanism; a first generating module, configured to generate, in real time, eleventh data corresponding to the tunnel based on the third data; wherein the eleventh data is tunnel settlement warning information data; A second processing module is configured to dynamically update and process the feature set in real time based on the third data and in combination with a sliding window technology; And / or, the data generation construction unit further includes: A third processing module is configured to pre-process the first data by combining a particle filter denoising technique; wherein the pre-processing includes noise reduction processing and measurement error processing; a first creation module, configured to generate and obtain fourth data corresponding to the tunnel, and to create a region label uniquely corresponding to the fourth data; wherein the fourth data is structural characteristic data of the tunnel, including straight section characteristic data, turning section characteristic data, intersection characteristic data, and fork road characteristic data; A second creation module is configured to establish a first correspondence between the feature set and the actual mileage of the tunnel or the tunnel area label based on the first data; And / or, the data processing and generating unit further includes: a second generating module, configured to respectively generate and obtain a first data sequence and a second data sequence corresponding to the tunnel; wherein the first data sequence is geomagnetic data collected in real time, including X-axis direction data and Y-axis direction data; and the second data sequence is geomagnetic data in the feature set; The first calculation module is configured to generate corresponding seventh data by matching calculation based on the first data sequence and the second data sequence; wherein the seventh data is the minimum distance data between the first data sequence and the second data sequence; the calculation formula is as follows: Where D EDR (A, B) represents the minimum distance between two sets of data. Represents real-time sequence data in the X direction, represents the reference sequence data in the X direction, Represents the real-time sequence data in the Y direction, Represents the real-time sequence data in the Y direction. d() represents the distance calculation function; The third generating module is used to generate eighth data corresponding to the second data based on the seventh data; wherein the eighth data is the tunnel location data of the dynamic location point, including current location data and mileage data.

9. A high-precision tunnel settlement monitoring and processing system based on geomagnetic field changes according to claim 7 or 8, characterized in that: The data generation construction unit further includes: a fourth generating module, configured to generate and obtain fifth data corresponding to the tunnel and spaced at a distance of at least one meter; and to generate corresponding first data based on the fifth data; wherein the fifth data is three-dimensional geomagnetic data of each data collection point in the tunnel; a third creation module, configured to generate sixth data corresponding to the fifth data, and to establish a second correspondence between the fifth data and the current mileage value of each collection point; wherein the sixth data is geomagnetic field component data, including an X-direction geomagnetic field component, a Y-direction geomagnetic field component, and a Z-direction geomagnetic field component; And / or, the data processing and generating unit further includes: a fifth generating module, configured to generate and obtain a third data sequence corresponding to the tunnel; wherein the third data sequence is geomagnetic data collected in real time in the Z-axis direction; The second calculation module is configured to generate and obtain ninth data corresponding to the tunnel, and to generate corresponding tenth data by matching and calculation based on the ninth data and in combination with the third data sequence; wherein the ninth data is a preset settlement threshold; and the tenth data is the minimum distance data between the third data sequence and the second data sequence; the calculation formula is as follows: Where D EDR (z t ,z ref ) represents the minimum distance between data in the z direction, Represents real-time sequence data in the X direction, Indicates the reference sequence data in the X direction.

10. A high-precision tunnel settlement monitoring and processing platform based on geomagnetic field changes, characterized in that: It includes a processor, a memory and a high-precision tunnel settlement monitoring and processing platform control program based on changes in the geomagnetic field; wherein, the high-precision tunnel settlement monitoring and processing platform control program based on changes in the geomagnetic field is executed by the processor, and the high-precision tunnel settlement monitoring and processing platform control program based on changes in the geomagnetic field is stored in the memory, and the high-precision tunnel settlement monitoring and processing platform control program based on changes in the geomagnetic field implements the high-precision tunnel settlement monitoring and processing method based on changes in the geomagnetic field as described in any one of claims 1 to 6.

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