A tunnel personnel device positioning monitoring method, device, equipment and medium

Abstract

The application provides a tunnel personnel equipment positioning monitoring method and device, equipment and medium, relates to the technical field of high-precision positioning, and the method comprises the following steps: acquiring the real-time moving distance of the rail car relative to any one fixed base station; determining the first positioning data of the rail car based on the moving distance; determining a plurality of first distances measured by the mobile base station, a plurality of second distances measured by each mobile beacon, and a plurality of third distances measured by the fixed base station, wherein the first distance is the distance between the mobile base station and the mobile beacon, the second distance is the distance between any two mobile beacons, and the third distance is the distance between the selected fixed base station and the mobile beacon; based on the first distance, the second distance, the third distance and the first positioning data, analyzing and calculating the second positioning data of each positioning target. The application can realize high-precision positioning of tunnel construction.

Description

Technical Field

[0001] This application relates to the technical field of high-precision positioning, specifically to a method, device, equipment, and medium for monitoring the positioning of personnel and equipment in tunnels. Background Technology

[0002] Tunnel construction refers to the entire process of building underground passage structures for transportation, municipal pipelines, water conservancy, or energy transmission through mechanical excavation, blasting, support and reinforcement, and lining in underground or mountainous areas. The construction environment is usually enclosed and narrow, accompanied by complex conditions such as high humidity, high dust, and low light. Construction methods include open-cut, cut-and-cover, shield tunneling, and New Austrian Tunneling Method (NATM). A reasonable technical path must be selected based on geological conditions, tunnel purpose, and cross-sectional characteristics. It is also necessary to coordinate with multiple auxiliary systems such as measurement and control, risk warning, ventilation and drainage, personnel positioning, and safety monitoring to achieve the goal of structural stability, controllable progress, and guaranteed safety in underground space construction.

[0003] The enclosed environment inside tunnels means that Global Navigation Satellite System (GNSS) signals cannot reach the area, rendering conventional GNSS positioning technology completely ineffective. Furthermore, because tunnel walls are typically constructed of metal or rock, electromagnetic waves encounter severe reflection, attenuation, and multipath interference, resulting in a highly nonlinear wireless signal propagation model. This makes it difficult for traditional wireless positioning methods based on received signal strength or time of arrival to achieve stable accuracy. In addition, visual degradation factors such as fog, water vapor, and dust often occur within tunnels, causing image-based or laser reflection-based sensing and positioning technologies to suffer feature loss and recognition failures under complex conditions, further exacerbating the challenges to the stability and robustness of the positioning system. Therefore, a method for achieving high-precision positioning within tunnels is needed. Summary of the Invention

[0004] This application provides a method, device, equipment, and medium for monitoring the positioning of personnel and equipment in tunnels, which can achieve high-precision positioning during tunnel construction.

[0005] The first aspect of this application provides a method for locating and monitoring personnel and equipment in a tunnel, the method comprising:

[0006] Obtain the real-time moving distance of the railcar relative to any one of the fixed base stations;

[0007] Based on the travel distance, the first positioning data of the railcar is determined;

[0008] The system determines multiple first distances calculated by the mobile base station, multiple second distances calculated by each of the mobile beacons, and multiple third distances calculated by the fixed base station, wherein the first distance is the distance between the mobile base station and the mobile beacon, the second distance is the distance between any two mobile beacons, and the third distance is the distance between the fixed base station and the mobile beacon after filtering.

[0009] Based on the first distance, the second distance, the third distance, and the first positioning data, the second positioning data of each of the positioning targets is analyzed and calculated.

[0010] Based on the above technical solutions, preferably, determining the first positioning data of the railcar based on the moving distance specifically includes:

[0011] A preset coordinate system is constructed based on the fixed base station, wherein a first coordinate axis is determined by a connecting line connecting the fixed base stations at both ends of the construction tunnel, the first coordinate axis coincides with the connecting line, and a second coordinate axis of the preset coordinate system is perpendicular to the first axis.

[0012] If it is determined that the connecting line of the fixed track coincides, then the coordinate point of the track vehicle in the preset coordinate system is determined according to the real-time moving distance;

[0013] Convert the coordinates of the coordinate points into the first positioning data in the world coordinate system.

[0014] Based on the above technical solutions, preferably, determining the first positioning data of the railcar based on the moving distance further includes:

[0015] If it is determined that the connecting lines of the fixed track do not coincide, then a trajectory curve is constructed in the preset coordinate system based on the trajectory data of the fixed track.

[0016] Determine the trajectory function corresponding to the trajectory curve;

[0017] Based on the real-time movement distance, the coordinates of the track vehicle in the preset coordinate system are determined;

[0018] Determine the projection point of the coordinate point on the trajectory curve, and determine the coordinates of the projection point according to the trajectory function;

[0019] The coordinates of the projection point are converted into the first positioning data in the world coordinate system.

[0020] Based on the above technical solutions, preferably, the step of analyzing and calculating the second positioning data of each positioning target based on the first distance, the second distance, the third distance, and the first positioning data specifically includes:

[0021] Time synchronization is performed on multiple first distances, multiple second distances, and multiple third distances;

[0022] In each positioning cycle, a spatial vector relationship is established between the first positioning data and each first distance to construct a circular constraint with the railcar as the center and the first distance as the radius, which is used to limit the possible location area of ​​the positioning target;

[0023] A graph structure model is constructed with the coordinate data of all the moving beacons as variable nodes. Each moving beacon is a location node, and each of the first distance, the second distance, and the third distance corresponds to a ranging constraint edge, forming a polygonal geometric relationship between the positioning targets. The first positioning data is fixed in the graph structure model as a known anchor point, and the first distance between the known anchor point and each moving beacon constitutes an anchoring vector.

[0024] An objective function is constructed based on the circular constraint. Various ranging errors corresponding to the first distance, the second distance, and the third distance are defined as residual terms. The residual terms are minimized by the unified objective function.

[0025] The graph structure model is optimized by the objective function to obtain the spatial coordinates of the moving beacon in the preset coordinate system.

[0026] The spatial coordinate output is converted into the second positioning data in the world coordinate system.

[0027] Based on the above technical solutions, preferably, before determining the plurality of first distances calculated by the mobile base station, the second distances calculated by each of the mobile beacons, and the plurality of third distances calculated by the fixed base station, the method further includes:

[0028] A preset distance threshold is determined based on the location of the fixed base station;

[0029] Obtain multiple initial distances calculated by the fixed base station, wherein the initial distance is the distance between the fixed base station and the mobile beacon;

[0030] By filtering multiple initial distances using the preset distance threshold, initial distances smaller than the preset distance threshold are retained to obtain multiple third distances.

[0031] Based on the above technical solutions, preferably, the step of analyzing and calculating the second positioning data of each positioning target based on the first distance, the second distance, the third distance, and the first positioning data further includes:

[0032] Time synchronization processing is performed on multiple first distances, multiple second distances, and multiple third distances;

[0033] In each positioning cycle, a spatial vector relationship is established between the first positioning data and each first distance, and a circular constraint is constructed with the railcar as the center and the first distance as the radius to limit the possible location area corresponding to the mobile beacon.

[0034] A graph structure model is constructed with the coordinate data of all the moving beacons as variable nodes, wherein each moving beacon is a location node, each of the first distance, the second distance and the third distance constitutes a ranging constraint edge, and the first positioning data is fixed in the graph structure model as a known anchor point and forms an anchor vector with each of the moving beacons;

[0035] A confidence weight is introduced for the third distance, and an objective function with a weighted residual term is constructed based on the confidence weight. All kinds of ranging errors corresponding to the first distance, the second distance, and the weighted third distance are uniformly included in the objective function for minimization.

[0036] The graph structure model is optimized by the objective function to obtain the spatial coordinate output of each of the moving beacons in the preset coordinate system, and the spatial coordinate output is converted into the second positioning data in the world coordinate system.

[0037] Based on the above technical solution, preferably, the railcar is equipped with a wheel speed sensor assembly and an inertial measurement unit assembly, and the acquisition of the real-time movement distance of the railcar relative to any one of the fixed base stations specifically includes:

[0038] Based on the instantaneous speed signal output by the wheel speed sensor assembly, multiple first cumulative driving distances are obtained through time integration;

[0039] Based on the inertial measurement unit component, the attitude change of the railcar is obtained, and multiple nonlinear velocity drift amounts of the railcar are determined;

[0040] Align multiple first cumulative driving distances with multiple nonlinear speed drift amounts at time nodes, and compensate the first cumulative driving distances using the nonlinear speed drift amounts to obtain multiple second cumulative driving distances;

[0041] Acquire multiple fourth distances calculated by the fixed base station or the mobile base station, wherein the multiple second cumulative driving distances are aligned one-to-one with the multiple fourth distances, and the fourth distance is the distance between the mobile base station and the fixed base station;

[0042] The integral function of the second cumulative travel distance is corrected based on multiple fourth distances to obtain the travel distance function of the railcar, wherein the integral function is a time integral function based on the instantaneous speed signal and the nonlinear speed drift.

[0043] When the mobile base station and the fixed base station cannot establish a communication connection, the real-time mobile distance is calculated using the mobile distance function.

[0044] A second aspect of this application provides a tunnel personnel and equipment positioning and monitoring device. The device is used to execute a tunnel personnel and equipment positioning and monitoring method as described in any of the above-described methods. The device is a server, communicatively connected to a mobile base station, multiple mobile beacons, and multiple fixed base stations. The fixed base stations are located at both ends of the construction tunnel, the mobile base stations are located on a track vehicle, the track vehicle is located on a fixed track within the construction tunnel, and the mobile beacons are located at positioning targets within the construction tunnel. The device includes an acquisition module, a processing module, and an output module, wherein:

[0045] The acquisition module is used to acquire the real-time movement distance of the railcar relative to any one of the fixed base stations;

[0046] The processing module is used to determine the first positioning data of the railcar based on the moving distance;

[0047] The processing module is used to determine multiple first distances calculated by the mobile base station, multiple second distances calculated by each of the mobile beacons, and multiple third distances calculated by the fixed base station, wherein the first distance is the distance between the mobile base station and the mobile beacon, the second distance is the distance between any two mobile beacons, and the third distance is the distance between the fixed base station and the mobile beacon after filtering.

[0048] The output module is used to analyze and calculate the second positioning data of each of the positioning targets based on the first distance, the second distance, the third distance, and the first positioning data.

[0049] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any of the foregoing.

[0050] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any of the preceding descriptions.

[0051] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0052] 1. This application constructs a multilateral ranging network with a railcar as a dynamic anchor point, a fixed base station as a global reference, and multiple mobile beacons as positioning targets. It combines the first positioning data calculated from the real-time movement distance of the railcar with multi-source ranging information between the mobile base station and various beacons to form a spatial constraint graph structure. Through unified time synchronization and residual optimization calculations on a server, it accurately analyzes the spatial position of each positioning target in a preset coordinate system or even the world coordinate system. Even under complex conditions such as GNSS signal failure in tunnels, severe electromagnetic interference, and degraded visual perception, it can still achieve stable, continuous, and high-precision positioning of construction personnel and equipment by relying on redundant ranging paths, graph optimization calculations, and dynamic signal filtering mechanisms, effectively supporting safety supervision and operational collaboration during tunnel construction.

[0053] 2. By constructing a preset coordinate system based on fixed base stations, and when the track vehicle is collinear with this coordinate system, the real-time movement distance of the track vehicle is used to directly map its position in the preset coordinate system, and then convert it into position data in the world coordinate system. This enables the track vehicle to achieve fast and high-precision positioning in simple structural and straight track scenarios, and serves as a global anchor point to support subsequent positioning calculations.

[0054] 3. When the trajectory of the railcar does not coincide with the line connecting the fixed base station, by constructing the trajectory curve and establishing the trajectory function, the moving distance of the railcar can be accurately mapped to the spatial position under complex paths. Then, the corresponding coordinates are accurately calculated through trajectory projection, ensuring that stable and accurate first positioning data of the railcar can still be obtained in nonlinear track structures.

[0055] 4. By integrating the first positioning data of the track vehicle with multiple types of ranging data, a constraint graph structure is constructed on the basis of unified time synchronization, and spatial calculation is performed by graph optimization. This enables the system to effectively identify the relative and absolute positions of multiple positioning targets in complex tunnel environments, and achieve high-precision synchronous positioning of multiple targets.

[0056] 5. By setting a distance threshold based on the location of a fixed base station, the initial ranging data is filtered out, and low-confidence ranging results greater than the threshold are removed. Only high-quality third distances are retained as effective constraint inputs, which significantly improves the accuracy and stability of the positioning data used for subsequent graph optimization calculations.

[0057] 6. By introducing a confidence weight for the third distance during the graph structure optimization process, the influence of ranging data on the optimization results is controlled by weighting the residual terms. This achieves dynamic suppression of unstable or low-quality data, thereby enhancing the robustness of the entire positioning system to abnormal ranging and improving the accuracy of the final second positioning data.

[0058] 7. By integrating the outputs of the wheel speed sensor assembly and the inertial measurement unit assembly, the travel distance of the railcar is inferred in real time. The accumulated error is corrected by combining external ranging data, and a travel distance function is constructed to ensure that the railcar can still output accurate real-time travel distance continuously in areas where the railcar cannot communicate with the fixed base station, thereby ensuring the continuity and reliability of the railcar positioning. Attached Figure Description

[0059] Figure 1 This is a schematic flowchart of a tunnel personnel and equipment positioning and monitoring method disclosed in an embodiment of this application;

[0060] Figure 2 This is a schematic diagram of a tunnel personnel and equipment positioning and monitoring device disclosed in an embodiment of this application;

[0061] Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application.

[0062] Explanation of reference numerals in the attached drawings: 201, acquisition module; 202, processing module; 203, output module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Implementation

[0063] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0064] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0065] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0066] Tunnel construction refers to the construction of underground passages for transportation, municipal pipelines, or energy transmission through mechanical excavation, support reinforcement, and lining within underground or mountainous areas. The construction environment is generally enclosed and narrow, accompanied by complex conditions such as high humidity, high dust levels, and low light. Conventional GNSS positioning technology is completely ineffective inside tunnels, and electromagnetic wave propagation is severely interfered with by metallic rock masses. Furthermore, the wireless signal model is highly nonlinear, making it difficult to guarantee the accuracy of traditional wireless positioning methods. Simultaneously, visual and laser sensing technologies are prone to losing feature points in environments with fog, water vapor, and dust, further leading to unstable positioning. Therefore, there is an urgent need to develop a high-precision, robust positioning method suitable for the complex environment of tunnels.

[0067] This embodiment discloses a method for positioning and monitoring personnel and equipment in tunnels, referring to... Figure 1 This includes the following steps S110-S140:

[0068] S110: Obtain the real-time moving distance of the railcar relative to any fixed base station.

[0069] The tunnel personnel and equipment positioning and monitoring method disclosed in this application is applied to a server. The server includes, but is not limited to, electronic devices such as mobile phones, tablets, wearable devices, and PCs (Personal Computers), and can also be a backend server running a tunnel personnel and equipment positioning and monitoring method. The server can be implemented using a standalone server or a server cluster composed of multiple servers.

[0070] In this application, the server acts as the central control and computing node, connecting in real time with multiple communication components, including a fixed base station located at the tunnel entrance, a mobile base station configured on the railcar, and multiple mobile beacons placed on the positioning targets (including construction personnel and work equipment). These three types of nodes together constitute a complete positioning and sensing system. The functional division and spatial deployment of this architecture are as follows:

[0071] First, multiple fixed base stations are set up at both ends of the construction tunnel, serving as the origin and reference points of a pre-defined coordinate system, providing global static spatial anchoring capabilities. These fixed base stations maintain high-speed and stable communication with the server via wired or wireless means, and possess high-precision ranging capabilities, enabling them to acquire distance information between themselves and any mobile node in real time. Simultaneously, they perform time synchronization, ensuring a unified clock reference throughout. As global reference points, the spatial positions of these fixed base stations are precisely calibrated during the initialization phase, serving as the basis for mapping all subsequent positioning data.

[0072] Secondly, the mobile base station is deployed on a track vehicle that operates along a fixed track within the tunnel, exhibiting a stable and predictable trajectory. The mobile base station possesses high-frequency ranging capabilities, continuously acquiring relative distance information between itself and multiple mobile beacons. It then uses an inertial measurement unit and wheel speed sensors to collaboratively estimate the track vehicle's real-time displacement, thereby obtaining the track vehicle's initial positioning data. This mobile base station acts as a dynamic anchor point, providing relative coordinate references in areas where the fixed base station signal is weak or interrupted. Furthermore, it performs periodic corrections using the ranging residuals between itself and the fixed base station, improving the continuity and accuracy of the track vehicle's positioning.

[0073] Secondly, multiple mobile beacons are fixed to various positioning targets, including positioning tags worn by construction workers and positioning modules on the work equipment. Each mobile beacon has the ability to communicate and measure distances bidirectionally with both fixed and mobile base stations, and can obtain the relative positional relationship with the track vehicle and other mobile beacons in real time. Under the unified scheduling of the server, a polygonal ranging map structure that can be used for topology optimization is formed. The positioning calculation result of the mobile beacons is the second positioning data of each target in the tunnel coordinate system, which is the core output object.

[0074] Finally, the server, acting as a centralized data processing center, receives ranging data and status information collected by all base stations and beacons. By constructing a multi-source ranging constraint map, fusing the first positioning data from the railcar, and various spacing relationships, it analyzes the two-dimensional or three-dimensional spatial positions of all positioning targets in real time within a preset coordinate system. The server is also responsible for tasks such as time synchronization, filter state updates, residual optimization calculations, and positioning result output, ensuring continuous, accurate, and low-error positioning capabilities for people and equipment under conditions of high interference, weak sensing, and strong structural constraints.

[0075] By constructing a multi-node heterogeneous collaborative system consisting of "fixed base stations + mobile base stations + mobile beacons + central servers", a tunnel positioning and monitoring system with capabilities such as global anchoring, local compensation, autonomous sensing, and joint optimization has been formed. It has the advantages of high precision, high robustness, and adaptability to strong interference environments, and can be widely used in key aspects of tunnel construction such as personnel scheduling, safety supervision, equipment tracking, and emergency response.

[0076] Furthermore, this application adopts ultra-wideband (UWB) positioning technology as the core ranging method, aiming to solve problems such as the unavailability of GNSS signals, severe electromagnetic interference, and visual perception failure in tunnel construction environments. Through a high-precision, low-latency, and multipath-resistant UWB positioning mechanism, a highly reliable real-time positioning system for personnel and equipment suitable for complex tunnel conditions is constructed.

[0077] At the deployment level, the fixed base stations at both ends of the tunnel are equipped with built-in UWB communication and ranging modules. Serving as spatial anchoring sources for the tunnel coordinate system, they possess stable UWB transmission and reception capabilities. They are responsible for periodically sending UWB pulse signals to various mobile nodes (including mobile base stations and mobile beacons) and accurately calculating the straight-line distances between themselves and each node using Time Difference of Arrival (TDOA) or Time of Flight (TOF) measurement mechanisms. This provides a global benchmark for track vehicle position estimation and mobile beacon positioning. The mobile base stations on the track vehicles, through embedded UWB transceiver modules, maintain real-time UWB communication with the fixed base stations during operation. After leaving the signal coverage area of ​​the fixed base stations, they perform trajectory inference through local inertial measurement units and wheel speed sensors. Upon receiving UWB ranging data again, they perform state correction, thereby achieving continuous displacement calculation and accurate closed-loop operation.

[0078] Multiple mobile beacons are installed on key positioning targets at the construction site, including locations such as workers' helmets or belts and structural components of construction equipment. Each mobile beacon has a built-in UWB tag, enabling UWB signal reception and active ranging. Real-time UWB ranging operations are performed between the mobile beacons and the mobile base station to obtain the first distance, aiding in establishing the dynamic relative spatial relationship between the construction targets and the track vehicle. Simultaneously, mobile beacons can perform UWB ranging between themselves according to a preset scheduling strategy to obtain the second distance, thereby constructing a topology graph structure between targets. When a usable link is established between a beacon and a fixed base station, a third distance is obtained, providing additional global positioning constraints. Based on the above multilateral ranging information, the server constructs a multi-source ranging optimization graph model with the track vehicle as the reference anchor point and the fixed base station as the absolute coordinate source. Weighted least squares or graph optimization algorithms (such as nonlinear optimization based on factor graphs) are used to infer the actual spatial position of each mobile beacon, achieving high-precision second positioning data output for all positioning targets.

[0079] The UWB positioning technology used in this application boasts sub-meter level positioning accuracy, is less affected by dust, humidity, and water vapor interference, and features strong resistance to multipath interference, large bandwidth, narrow instantaneous pulse, concentrated energy, and low latency. It is particularly suitable for target positioning needs in enclosed environments. During operation, the UWB ranging device coordinates communication according to preset time slots or TDOA scheduling strategies to avoid co-channel interference, and performs continuous trajectory calculation in conjunction with the inertial measurement unit under high-frequency dynamic updates. Furthermore, the UWB ranging process and the server-side state optimization calculation operate collaboratively, dynamically adjusting the ranging confidence weights to further enhance the robustness and stability of the overall positioning results.

[0080] In one possible implementation, obtaining the real-time travel distance of the railcar relative to any fixed base station specifically includes: obtaining multiple first cumulative travel distances through time integration based on the instantaneous speed signal output by the wheel speed sensor component; obtaining the attitude change of the railcar based on the inertial measurement unit component to determine multiple nonlinear speed drifts of the railcar; aligning the multiple first cumulative travel distances with the multiple nonlinear speed drifts in time, and compensating the first cumulative travel distances with the nonlinear speed drifts to obtain multiple second cumulative travel distances; obtaining multiple fourth distances measured by the fixed base station or the mobile base station, wherein the multiple second cumulative travel distances are aligned one-to-one with the multiple fourth distances, and the fourth distances are the distances between the mobile base station and the fixed base station; correcting the integral function of the second cumulative travel distances based on the multiple fourth distances to obtain the railcar's travel distance function, wherein the integral function is a time integral function based on the instantaneous speed signal and the nonlinear speed drifts; and calculating the real-time travel distance using the travel distance function when the mobile base station and the fixed base station cannot establish a communication connection.

[0081] Specifically, wheel speed sensor components and inertial measurement unit components are integrated on the railcar to enable continuous and high-precision dynamic inference of the real-time movement distance of the railcar when the fixed base station signal is unavailable in the enclosed space of the tunnel.

[0082] First, based on the instantaneous velocity signals output by the wheel speed sensor components on the railcar, continuous sampling is performed within a preset time step. The velocity signals are then numerically accumulated using a time integration method to obtain multiple first cumulative travel distances. These first cumulative travel distances represent the displacement estimation results of the railcar under ideal straight-line travel conditions, but they do not consider factors such as track curvature, attitude changes, and wheel slippage, resulting in a deviation that increases over time.

[0083] Secondly, the attitude change information of the track vehicle at each time point is acquired based on the inertial measurement unit (IMU) component, including changes in dynamic attitude parameters such as heading angle, pitch angle, and roll angle. Based on the rate of change of attitude angles and their deviation from the track direction, the nonlinear velocity drift generated by the track vehicle at each time point is calculated. This nonlinear velocity drift is used to measure the proportion of the effective component of the wheel speed sensor output velocity signal in the track direction, thus forming a reference factor for velocity projection correction.

[0084] Next, the aforementioned multiple first cumulative travel distances are aligned one by one with the nonlinear velocity drift amounts obtained at the corresponding time nodes, and the first cumulative travel distances are corrected based on the drift amount at each time node to obtain multiple second cumulative travel distances. This correction process involves vector-projecting each instantaneous velocity according to the attitude error to form an equivalent travel distance in the direction of the track's main axis. The summation of these equivalent travel distances constitutes a second cumulative travel distance that more closely approximates the actual length of the railcar's travel path.

[0085] Subsequently, fourth distance measurements between multiple mobile and fixed base stations of the railcar are acquired as a reference correction data source. These fourth distances are the actual distances between the railcar and the static reference point, obtained periodically or irregularly based on the UWB ranging mechanism. Their accuracy is generally better than the results of wheel speed and inertial integral inference, and multiple second cumulative travel distances correspond strictly one-to-one with the fourth distances at different time points. The server or edge computing module constructs residual sequences for these two quantities at multiple time points and analyzes the trend and offset model of the cumulative error.

[0086] Based on this, multiple second-cumulative travel distances are used as variables to construct an integral function model. This integral function is a function expression based on the continuous accumulation of instantaneous velocity signals and nonlinear velocity drift in the time domain. Then, using the deviation data between multiple fourth distances and the second-cumulative travel distances at corresponding time points, nonlinear regression correction is performed on this integral function to establish a travel distance function model. This model performs global deviation compensation on the initial integral function by fitting actual distance measurements, thereby forming a function expression that is more consistent with the actual trajectory of the railcar.

[0087] Finally, during the operation of the railcar, when the mobile base station is unable to establish a communication connection with any fixed base station due to environmental obstruction, long-distance penetration limitations, or multipath interference, based on the established moving distance function model, the speed signal output by the current wheel speed sensor component and the attitude change output by the inertial measurement unit component of the railcar are used as input parameters. The function model is called in real time to calculate the real-time moving distance of the railcar, ensuring that the railcar still has a stable and continuous railcar position reasoning capability in the communication failure scenario, and maintaining the coordinate reference support capability for other mobile beacons at the construction site.

[0088] In summary, through steps such as velocity integration, attitude correction, ranging calibration, and function regression, this scheme establishes a real-time inference mechanism for track vehicle movement distance that can maintain robustness, continuity, and high accuracy even in tunnel environments with GNSS absence, limited communication, and multi-source drift. This mechanism constitutes a key dynamic anchoring benchmark in tunnel positioning.

[0089] S120, based on the distance traveled, determines the first positioning data of the railcar.

[0090] In one possible implementation, determining the first positioning data of the track vehicle based on the travel distance specifically includes: constructing a preset coordinate system based on fixed base stations, wherein a first coordinate axis is determined by connecting the fixed base stations at both ends of the construction tunnel, the first coordinate axis coincides with the connecting line, and a second coordinate axis of the preset coordinate system is perpendicular to the first axis; if it is determined that the fixed track connecting line coincides, then the coordinate point of the track vehicle in the preset coordinate system is determined according to the real-time travel distance; and the coordinates of the coordinate point are converted into the first positioning data in the world coordinate system.

[0091] Specifically, firstly, a preset coordinate system is constructed during the initialization phase based on fixed base stations deployed at both ends of the construction tunnel. By acquiring the spatial coordinates of the two fixed base stations, the connection vector between them is calculated and defined as the direction of the first coordinate axis of the preset coordinate system. This first coordinate axis coincides perfectly with the connecting line and is used to describe the longitudinal trajectory of the track vehicle along the tunnel. Simultaneously, a direction vector orthogonal to the first coordinate axis is constructed and defined as the direction of the second coordinate axis of the preset coordinate system. This second coordinate axis lies in the tunnel cross-sectional plane and is used to describe the lateral position of the track vehicle in a two-dimensional coordinate system. Although the actual track vehicle only moves longitudinally, the coordinate system maintains two-dimensional descriptive capabilities to accommodate subsequent multi-target positioning requirements.

[0092] Subsequently, if the tunnel design information, track layout drawings, or sensor calibration results confirm that the fixed track coincides with the connecting line, meaning the track laying direction is consistent with the first coordinate axis direction determined by the fixed base station, then the real-time movement distance of the track vehicle can be directly used as its position coordinate in the first coordinate axis direction. The track vehicle's starting point is set as the origin of the preset coordinate system, the track vehicle's real-time movement distance is used as the coordinate value in the first coordinate axis direction at that moment, and the second coordinate axis coordinate value is set as an initial fixed lateral value or a preset calibration value. In this way, the track vehicle's movement distance is mapped to a coordinate point in a two-dimensional preset coordinate system.

[0093] Finally, to ensure the universality of the track vehicle's position data, the track vehicle coordinates in the aforementioned two-dimensional preset coordinate system are mapped to the world coordinate system through affine transformation or a coordinate transformation matrix. During the coordinate transformation process, a pre-established coordinate system transformation relationship is invoked to project the coordinate components of the track vehicle on the first and second coordinate axes onto the corresponding coordinate axes in the world coordinate system, forming first positioning data with absolute spatial reference significance. This first positioning data can be used for subsequent calls to modules such as construction personnel positioning mapping, construction task scheduling visualization, equipment scheduling path constraints, and dynamic safety early warning, ensuring the stability and reliability of the track vehicle as a core reference point for dynamic anchoring throughout the tunnel positioning process.

[0094] In one possible implementation, determining the first positioning data of the track vehicle based on the travel distance further includes: if it is determined that the fixed track connection lines do not coincide, constructing a trajectory curve in a preset coordinate system based on the trajectory data of the fixed track; determining the trajectory function corresponding to the trajectory curve; determining the coordinate point of the track vehicle in the preset coordinate system based on the real-time travel distance; determining the projection point of the coordinate point on the trajectory curve, and determining the coordinates of the projection point based on the trajectory function; and converting the coordinates of the projection point into the first positioning data in the world coordinate system.

[0095] Specifically, firstly, when it is confirmed through tunnel structure drawings, track construction calibration data, or initial trajectory sampling that the connecting line formed by the fixed track and the fixed base station on which the railcar operates does not coincide, the linear projection model will no longer be used. Instead, a trajectory curve will be constructed based on the discrete coordinate points of the track centerline in a preset coordinate system. This trajectory curve accurately describes the actual direction of the track centerline in the tunnel plane, recording the bending, turning, and changing trends of the track along the longitudinal direction, serving as the spatial basis for the railcar's path.

[0096] Subsequently, the trajectory curve is functionalized in a preset coordinate system to form a trajectory function. This trajectory function takes the track starting point as the parameterization starting point, uses the distance the track vehicle travels along the track as the input variable, and outputs the coordinates of the track vehicle in a two-dimensional preset coordinate system. The trajectory function can be fitted using piecewise spline functions, Bezier curves, or B-spline models to ensure smooth continuity at changes in track curvature and to possess differentiability and high fitting accuracy.

[0097] Next, during the operation of the track vehicle, the fusion results of the wheel speed sensor assembly and the inertial measurement unit assembly are used to obtain the real-time distance traveled by the track vehicle relative to the starting point, which is recorded as the current travel distance. This travel distance is used as the input value of the trajectory function. The trajectory function is called in a preset coordinate system to calculate the corresponding coordinate point on the track curve, determining the coordinate position of the track vehicle in the preset coordinate system at that moment. This coordinate point represents the ideal position of the track vehicle on the theoretical track curve, which is the result of the trajectory function mapping based on the travel distance.

[0098] Subsequently, considering that the track vehicle may deviate from the ideal trajectory due to inertial errors or lateral disturbances, a trajectory projection process is further performed on the actual position of the track vehicle in a preset coordinate system. Specifically, using the coordinate point derived from the current movement distance of the track vehicle as a reference point, the projection point with the closest Euclidean distance to that point is searched on the trajectory curve. This projection point is considered the actual positioning projection position of the track vehicle on the trajectory curve. This step enhances the adaptability to track geometric constraints and effectively suppresses coordinate offsets caused by lateral drift errors.

[0099] Finally, the coordinates of the track vehicle projection points in the preset coordinate system are transformed according to the established coordinate transformation relationship between the preset coordinate system and the world coordinate system. This transformation relationship can be expressed by an affine transformation matrix or a combination of coordinate rotation and translation, mapping the track vehicle coordinates obtained from the trajectory function to the world coordinate system, which has an absolute spatial reference, forming the final output of the track vehicle's first positioning data. This first positioning data serves as the global position identifier of the track vehicle and participates in subsequent core calculation stages such as multi-beacon joint positioning, relative position calculation of personnel and equipment, and visualized construction scheduling.

[0100] S130, determine multiple first distances calculated by mobile base stations, multiple second distances calculated by each mobile beacon, and multiple third distances calculated by fixed base stations.

[0101] A mobile base station equipped with UWB communication and ranging capabilities is deployed on the track vehicle. The mobile base station periodically emits UWB pulse signals and receives response pulse signals from each mobile beacon. Based on the time difference between signal transmission and reception, the mobile base station calculates the initial distance for each mobile beacon at the current moment. This initial distance is the relative straight-line distance between the mobile base station and a single mobile beacon, and is crucial data reflecting the real-time spatial distance between the track vehicle and construction personnel or equipment. The mobile base station records each ranging result and uploads it to a server in real-time via a communication module, used to construct ranging constraints with the track vehicle as the anchor point.

[0102] Secondly, each mobile beacon possesses independent UWB ranging capabilities. Following a pre-defined time scheduling strategy, each beacon employs a polling or broadcast response mechanism to perform bidirectional ranging between any two mobile beacons, calculating a second distance. This second distance is the relative straight-line distance between any two mobile beacons, and it captures the spatial topology between beacons, forming a crucial component in constructing constraint edges between nodes in the global localization graph. In this process, the ranging results from all beacons are time-synchronized and transmitted to the server to establish a spatial association model between localization targets.

[0103] Secondly, multiple fixed base stations are deployed at both ends of the tunnel. These fixed base stations possess stable UWB ranging capabilities and can periodically measure distances with some mobile beacons to calculate a third distance. Due to the complex tunnel environment, the UWB signals of the fixed base stations may be affected by factors such as obstruction, reflection, and multipath interference. Therefore, the ranging data acquired by the fixed base stations needs to be filtered based on criteria such as signal-to-noise ratio, beacon response quality, and the rate of change of ranging residuals, retaining only the third distance that meets the confidence requirements. This third distance represents the actual distance between the fixed base station and a specific mobile beacon and is a key observation for achieving global anchoring of the positioning target.

[0104] S140, based on the first distance, second distance, third distance and first positioning data, analyze and calculate the second positioning data of each positioning target.

[0105] In one possible implementation, based on the first distance, second distance, third distance, and first positioning data, the second positioning data of each positioning target is analyzed and calculated. Specifically, this includes: time synchronization of multiple first distances, multiple second distances, and multiple third distances; in each positioning cycle, establishing a spatial vector relationship between the first positioning data and each first distance, constructing a circular constraint with the track vehicle as the center and the first distance as the radius to limit the possible location area of ​​the positioning target; constructing a graph structure model with the coordinate data of all moving beacons as variable nodes, where each moving beacon is a location node, and each first distance, second distance, and third distance corresponds to a ranging constraint edge, forming a polygonal geometric relationship between the positioning targets; the first positioning data is fixed in the graph structure model as a known anchor point, and the first distance between the known anchor point and each moving beacon constitutes an anchoring vector; constructing an objective function based on the circular constraint, defining various ranging errors corresponding to the first distance, second distance, and third distance as residual terms, and minimizing the residual terms through a unified objective function; optimizing the graph structure model through the objective function to obtain the spatial coordinate output of the moving beacon in a preset coordinate system; and converting the spatial coordinate output into second positioning data in the world coordinate system.

[0106] Specifically, firstly, time synchronization is performed on multiple first distances, multiple second distances, and multiple third distances. After receiving ranging data from mobile base stations, multiple mobile beacons, and fixed base stations, the server aligns them according to a unified timestamp to ensure that each ranging value has temporal consistency within the same positioning period. This synchronization process is completed by the server's internal time management module, and abnormal ranging values ​​with excessive time drift are eliminated by combining a synchronization error threshold, thereby ensuring the logical consistency and physical accuracy of the input data for subsequent graph structure optimization.

[0107] Subsequently, in each positioning cycle, the server spatially correlates the current positioning data of the track vehicle with each first distance. Using the first positioning data as the center and the first distance as the radius, a circular constraint region is constructed in a preset coordinate system, limiting the spatial range in which each moving beacon may appear centered on the track vehicle. Each circular constraint represents a ranging spherical shell between the moving beacon and the track vehicle, used to form the initial geometric boundary values ​​in subsequent graph optimization, improving solution stability and suppressing error drift.

[0108] Next, the server constructs a graph structure model in a preset coordinate system, defining all moving beacons as location nodes, with each node representing the coordinates of a positioning target to be solved. Simultaneously, each ranging data point (first distance, second distance, and third distance) is mapped as ranging constraint edges, connecting the track vehicle anchor point to moving beacon nodes, between moving beacons, and between fixed base stations and moving beacon nodes, thus forming an information graph structure with redundant constraints. In this graph structure, the track vehicle's first positioning data is the known anchor point, and its first distances to all moving beacons constitute fixed boundary conditions, giving the graph structure global spatial reference capabilities.

[0109] Subsequently, based on the constructed circular constraints and graph topology, a unified objective function is defined. This objective function uses various ranging errors as residual terms, where the residual for each ranging edge is defined as the difference between the Euclidean distance between moving beacon nodes and the corresponding ranging value, reflecting the deviation of the current graph structure model in fitting actual physical distances. The residual terms for the three types of ranging are defined as follows:

[0110] First distance residual term:

[0111]

[0112] Second distance residual term:

[0113]

[0114] Third distance residual term:

[0115]

[0116] in, For the first The first distance between the mobile beacon and the railcar For the first The and the first The second distance between the mobile beacons For the first The fixed base station and the first The third distance between mobile beacons For the first The coordinates of a moving beacon in a preset coordinate system This is the initial positioning data for the railcar.

[0117] All residual terms are uniformly incorporated into the objective function, the specific expression of which is as follows:

[0118]

[0119] in: The weighting coefficients are used for various ranging residuals; the error is minimized using a nonlinear least squares optimization method, thereby guiding the graph structure to converge to the optimal solution that satisfies all ranging relationships.

[0120] Then, iterative optimization of the objective function is performed. By calling solvers such as the Gauss-Newton method or the Levenberg-Marquardt algorithm, all position nodes in the graph structure model are updated, gradually reducing the ranging residuals, and finally solving for the optimal spatial coordinates of each moving beacon in the preset coordinate system. This spatial coordinate result is the two-dimensional absolute position of each positioning target in the tunnel coordinate system, which is used to construct a multi-target position status map within the current positioning cycle.

[0121] Finally, to achieve cross-call functionality, visualization, and standardized processing of subsequent positioning results, the spatial coordinates output under the aforementioned preset coordinate system are uniformly converted into spatial position coordinates in the world coordinate system based on the coordinate system mapping relationship established during the initial orbit calibration stage. This conversion process can be accomplished using an affine transformation matrix, ensuring that the output second positioning data has global coordinate reference significance and can be directly used for higher-level applications such as safety warnings, scheduling visualization, and 3D reconstruction.

[0122] In summary, this technical solution uses the first positioning data of the track vehicle as a dynamic anchor point, combines multiple ranging values ​​to construct a constraint graph, and aims to minimize the ranging residual to complete the unified reasoning and world coordinate system mapping of the moving beacon position, resulting in high-precision, highly consistent, and highly robust tunnel personnel and equipment positioning results.

[0123] In one possible implementation, before determining the multiple first distances calculated by the mobile base station, the second distances calculated by each mobile beacon, and the multiple third distances calculated by the fixed base station, the method further includes: determining a preset distance threshold based on the location of the fixed base station; obtaining multiple initial distances calculated by the fixed base station, wherein the initial distances are the distances between the fixed base station and the mobile beacons; filtering the multiple initial distances through the preset distance threshold, retaining the initial distances that are less than the preset distance threshold, and obtaining multiple third distances.

[0124] Specifically, a preset distance threshold is determined based on the deployment location of the fixed base station. This preset distance threshold is used to limit the effective ranging range of the fixed base station. This preset distance threshold can be set according to the tunnel geometry, the UWB signal power of the fixed base station, the signal-to-noise ratio attenuation model, or historical data statistics. Typically, this threshold is set inside the maximum effective ranging radius of the fixed base station in open conditions to ensure that the collected data maintains physical validity and ranging accuracy despite factors such as line-of-sight obstruction, humidity interference, and metal reflection.

[0125] Subsequently, the fixed base station establishes UWB ranging links with all mobile beacons within its range according to a preset ranging period, acquiring multiple initial distance data. Each initial distance corresponds to the instantaneous ranging result between the fixed base station and a mobile beacon within the current time window. The fixed base station records these initial distances and sends them to the server, which uniformly receives, classifies, and timestamps them for archiving. Since the initial distances have not undergone confidence level assessment and contain some ranging values ​​significantly affected by environmental noise, they cannot be directly used in the positioning optimization process.

[0126] Next, a distance threshold filtering operation is performed on all initial distances. The server sequentially compares each initial distance with a preset distance threshold for the corresponding fixed base station, retaining ranging data below the threshold and discarding data above the threshold. This filtering process can employ a hard threshold filtering rule, i.e., if a certain initial distance... If the data is unstable, it is considered unstable ranging data and removed from the dataset; if If the data is not specified, it is retained and marked as the third distance, which is used as the mapping input in the subsequent distance constraint diagram.

[0127] Finally, all the initial distances that passed the screening were categorized into multiple third distances and stored in a structured manner according to the correspondence between fixed base stations and mobile beacons. Each third distance has high ranging confidence and physical consistency, and can be used together with the first positioning data of the rail vehicle to construct the spatial constraints of fixed anchor points in the subsequent positioning map structure, further enhancing the global convergence capability and anti-drift performance of the positioning.

[0128] Furthermore, based on the first distance, second distance, third distance, and first positioning data, the second positioning data of each positioning target is analyzed and calculated. Specifically, this includes: performing time synchronization processing on multiple first distances, multiple second distances, and multiple third distances; establishing a spatial vector relationship between the first positioning data and each first distance in each positioning cycle, constructing a circular constraint with the railcar as the center and the first distance as the radius to limit the possible location area of ​​the corresponding moving beacon; and constructing a graph structure model with the coordinate data of all moving beacons as variable nodes, where each moving beacon is a location node, and each line... The first distance, the second distance, and the third distance each constitute a ranging constraint edge. The first positioning data is fixed in the graph structure model as a known anchor point and forms an anchor vector with each moving beacon. A confidence weight is introduced for the third distance, and an objective function with a weighted residual term is constructed based on the confidence weight. All kinds of ranging errors corresponding to the first distance, the second distance, and the weighted third distance are uniformly included in the objective function for minimization. The graph structure model is optimized through the objective function to obtain the spatial coordinate output of each moving beacon in the preset coordinate system, and the spatial coordinate output is converted into the second positioning data in the world coordinate system.

[0129] Specifically, a confidence weighting mechanism is introduced for the third distance. The confidence weight is determined by the server based on the magnitude of each third distance value; a larger value indicates lower confidence, reflecting the reliability of the ranging data between the fixed base station and the mobile beacon. When constructing the objective function, the server multiplies this confidence weight into the corresponding third distance residual term, thus constructing an objective function with weighted residual terms. This objective function integrates the error terms corresponding to the three types of ranging data: the first distance residual, the second distance residual, and the weighted third distance residual. These are then uniformly incorporated into a nonlinear least squares optimization model for minimization, improving overall solution accuracy and enhancing the ability to suppress unstable ranging data.

[0130] Subsequently, a graph optimization engine, such as the Gauss-Newton method or the Levenberg-Marquardt method, is invoked to iteratively solve the objective function, gradually optimizing all position node variables in the graph structure model, and finally obtaining the optimal spatial coordinates of all moving beacons in the preset coordinate system. This optimization process combines polygon ranging constraints, anchor point positioning data, and signal confidence adjustment to ensure that the position solution of each positioning target has accuracy, stability, and robustness.

[0131] Finally, based on the coordinate mapping relationship between the preset coordinate system and the world coordinate system established during the initialization phase, the spatial coordinates of each optimized moving beacon are transformed and output as second positioning data in the world coordinate system. This second positioning data has a unified spatial reference benchmark and can be directly used in application modules such as scheduling visualization, positioning display, and safety early warning, enabling real-time, high-precision spatial management of personnel and equipment in the tunnel construction environment.

[0132] This embodiment also discloses a tunnel personnel and equipment positioning and monitoring device, referring to... Figure 2 The device includes an acquisition module 201, a processing module 202, and an output module 203. The device is a server, communicatively connected to a mobile base station, multiple mobile beacons, and multiple fixed base stations. The fixed base stations are located at both ends of the construction tunnel, the mobile base stations are located on a track vehicle, the track vehicle is located on a fixed track within the construction tunnel, and the mobile beacons are located at positioning targets within the construction tunnel. The device is used to execute any of the tunnel personnel and equipment positioning and monitoring methods described above, wherein:

[0133] The acquisition module 201 is used to acquire the real-time moving distance of the railcar relative to any fixed base station.

[0134] Processing module 202 is used to determine the first positioning data of the railcar based on the travel distance.

[0135] The processing module 202 is used to determine multiple first distances calculated by the mobile base station, multiple second distances calculated by each mobile beacon, and multiple third distances calculated by the fixed base station. The first distance is the distance between the mobile base station and the mobile beacon, the second distance is the distance between any two mobile beacons, and the third distance is the distance between the fixed base station and the mobile beacon after filtering.

[0136] The output module 203 is used to analyze and calculate the second positioning data of each positioning target based on the first distance, the second distance, the third distance and the first positioning data.

[0137] In one possible implementation, the processing module 202 is used to construct a preset coordinate system based on a fixed base station, wherein a first coordinate axis is determined by a connecting line connecting the fixed base stations at both ends of the construction tunnel, the first coordinate axis coincides with the connecting line, and a second coordinate axis of the preset coordinate system is perpendicular to the first axis.

[0138] The processing module 202 is used to determine the coordinates of the track vehicle in the preset coordinate system based on the real-time movement distance if it is determined that the fixed track connection line coincides.

[0139] Output module 203 is used to convert the coordinates of the coordinate points into first positioning data in the world coordinate system.

[0140] In one possible implementation, the processing module 202 is used to construct a trajectory curve in a preset coordinate system based on the trajectory data of the fixed track if it is determined that the fixed track connection lines do not coincide.

[0141] Processing module 202 is used to determine the trajectory function corresponding to the trajectory curve.

[0142] The processing module 202 is used to determine the coordinates of the track vehicle in the preset coordinate system based on the real-time movement distance.

[0143] The processing module 202 is used to determine the projection point of the coordinate point on the trajectory curve, and to determine the coordinates of the projection point according to the trajectory function.

[0144] Processing module 202 is used to convert the coordinates of the projected points into first positioning data in the world coordinate system.

[0145] In one possible implementation, the processing module 202 is used to perform time synchronization on a plurality of first distances, a plurality of second distances, and a plurality of third distances.

[0146] The processing module 202 is used to establish a spatial vector relationship between the first positioning data and each first distance in each positioning cycle, and construct a circular constraint with the railcar as the center and the first distance as the radius to limit the possible location area of ​​the positioning target.

[0147] The processing module 202 is used to construct a graph structure model with the coordinate data of all moving beacons as variable nodes. Each moving beacon is a location node, and each first distance, second distance and third distance corresponds to a ranging constraint edge, forming a polygonal geometric relationship between positioning targets. The first positioning data is fixed in the graph structure model as a known anchor point, and the first distance between the known anchor point and each moving beacon constitutes the anchoring vector.

[0148] The processing module 202 is used to construct an objective function based on circular constraints, and to define various ranging errors corresponding to the first distance, the second distance and the third distance as residual terms. The residual terms are minimized through a unified objective function.

[0149] The processing module 202 is used to optimize the graph structure model through an objective function to obtain the spatial coordinates of the moving beacon in a preset coordinate system.

[0150] Output module 203 is used to convert spatial coordinate output into second positioning data in the world coordinate system.

[0151] In one possible implementation, the processing module 202 is used to determine a preset distance threshold based on the location of the fixed base station.

[0152] The acquisition module 201 is used to acquire multiple initial distances calculated by the fixed base station, where the initial distance is the distance between the fixed base station and the mobile beacon.

[0153] The processing module 202 is used to filter multiple initial distances by a preset distance threshold, retain the initial distances that are less than the preset distance threshold, and obtain multiple third distances.

[0154] In one possible implementation, the processing module 202 is used to perform time synchronization processing on a plurality of first distances, a plurality of second distances, and a plurality of third distances.

[0155] The processing module 202 is used to establish a spatial vector relationship between the first positioning data and each first distance in each positioning cycle, and construct a circular constraint with the railcar as the center and the first distance as the radius to limit the possible location area of ​​the corresponding moving beacon.

[0156] The processing module 202 is used to construct a graph structure model with the coordinate data of all moving beacons as variable nodes. Each moving beacon is a location node, and each first distance, second distance and third distance constitute a ranging constraint edge. The first positioning data is fixed in the graph structure model as a known anchor point and forms an anchor vector with each moving beacon.

[0157] The processing module 202 is used to introduce confidence weights into the third distance, construct an objective function with weighted residual terms based on the confidence weights, and incorporate various ranging errors corresponding to the first distance, the second distance, and the weighted third distance into the objective function for minimization.

[0158] The processing module 202 is used to optimize the graph structure model through the objective function, obtain the spatial coordinate output of each moving beacon in the preset coordinate system, and convert the spatial coordinate output into second positioning data in the world coordinate system.

[0159] In one possible implementation, the processing module 202 is used to obtain multiple first cumulative driving distances by time integration based on the instantaneous speed signal output by the wheel speed sensor assembly.

[0160] The processing module 202 is used to acquire the attitude change of the track vehicle based on the inertial measurement unit component and determine multiple nonlinear velocity drifts of the track vehicle.

[0161] The processing module 202 is used to align multiple first cumulative driving distances with multiple nonlinear speed drifts in time, and to compensate for the first cumulative driving distances by the nonlinear speed drifts to obtain multiple second cumulative driving distances.

[0162] The acquisition module 201 is used to acquire multiple fourth distances calculated by a fixed base station or a mobile base station, wherein multiple second cumulative driving distances are aligned with multiple fourth distances, and the fourth distance is the distance between the mobile base station and the fixed base station.

[0163] The processing module 202 is used to correct the integral function of the second cumulative travel distance based on multiple fourth distances to obtain the travel distance function of the railcar, wherein the integral function is a time integral function based on the instantaneous speed signal and the nonlinear speed drift.

[0164] The output module 203 is used to calculate the real-time mobile distance using a mobile distance function when the mobile base station and the fixed base station cannot establish a communication connection.

[0165] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0166] This embodiment also discloses an electronic device, as shown in the reference. Figure 3 The electronic device may include: at least one processor 301, at least one communication bus 302, user interface 303, network interface 304, and at least one memory 305.

[0167] The communication bus 302 is used to enable communication between these components.

[0168] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0169] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0170] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications. The GPU is responsible for rendering and drawing the content required for display. The modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0171] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the various method embodiments described above, etc. The data storage area may store data involved in the various method embodiments described above. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. As a computer storage medium, the memory 305 may include an operating system, a network communication module, a user interface 303 module, and an application program for a tunnel personnel and equipment positioning and monitoring method.

[0172] exist Figure 3In the illustrated electronic device, the user interface 303 is primarily used to provide an input interface for the user and to acquire user input data. The processor 301 can be used to call an application program stored in the memory 305 that represents a method for locating and monitoring personnel and equipment in a tunnel. When executed by one or more processors 301, the electronic device performs one or more methods as described in the above embodiments.

[0173] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0174] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0175] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0176] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0177] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0178] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 305 and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory 305 includes various media capable of storing program code, such as a USB flash drive, external hard drive, magnetic disk, or optical disk.

[0179] This application also discloses a computer-readable storage medium storing instructions. When executed by one or more processors 301, these instructions cause an electronic device to perform one or more methods as described in the above embodiments.

[0180] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truths. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for positioning and monitoring personnel and equipment in a tunnel, characterized in that, The method is applied to a server, which is communicatively connected to a mobile base station, multiple mobile beacons, and multiple fixed base stations. The fixed base stations are located at both ends of the construction tunnel, the mobile base stations are located on a railcar, the railcar is located on a fixed track within the construction tunnel, and the mobile beacons are located at positioning targets within the construction tunnel. The method includes: Obtain the real-time moving distance of the railcar relative to any one of the fixed base stations; Based on the travel distance, the first positioning data of the railcar is determined; The system determines multiple first distances calculated by the mobile base station, multiple second distances calculated by each of the mobile beacons, and multiple third distances calculated by the fixed base station, wherein the first distance is the distance between the mobile base station and the mobile beacon, the second distance is the distance between any two mobile beacons, and the third distance is the distance between the fixed base station and the mobile beacon after filtering. Based on the first distance, the second distance, the third distance, and the first positioning data, the second positioning data of each of the positioning targets is analyzed and calculated; The step of analyzing and calculating the second positioning data for each positioning target based on the first distance, the second distance, the third distance, and the first positioning data specifically includes: Time synchronization is performed on multiple first distances, multiple second distances, and multiple third distances; In each positioning cycle, a spatial vector relationship is established between the first positioning data and each first distance to construct a circular constraint with the railcar as the center and the first distance as the radius, which is used to limit the possible location area of ​​the positioning target; A graph structure model is constructed with the coordinate data of all the moving beacons as variable nodes. Each moving beacon is a location node, and each of the first distance, the second distance, and the third distance corresponds to a ranging constraint edge, forming a polygonal geometric relationship between the positioning targets. The first positioning data is fixed in the graph structure model as a known anchor point, and the first distance between the known anchor point and each moving beacon constitutes an anchoring vector. An objective function is constructed based on the circular constraint. Various ranging errors corresponding to the first distance, the second distance, and the third distance are defined as residual terms. The residual terms are minimized by the unified objective function. The graph structure model is optimized by the objective function to obtain the spatial coordinates of the moving beacon in the preset coordinate system. The spatial coordinate output is converted into the second positioning data in the world coordinate system.

2. The method for positioning and monitoring personnel and equipment in a tunnel according to claim 1, characterized in that, The determination of the first positioning data of the railcar based on the travel distance specifically includes: A preset coordinate system is constructed based on the fixed base station, wherein a first coordinate axis is determined by a connecting line connecting the fixed base stations at both ends of the construction tunnel, the first coordinate axis coincides with the connecting line, and a second coordinate axis of the preset coordinate system is perpendicular to the first axis. If it is determined that the connecting line of the fixed track coincides, then the coordinate point of the track vehicle in the preset coordinate system is determined according to the real-time moving distance; Convert the coordinates of the coordinate points into the first positioning data in the world coordinate system.

3. The method for positioning and monitoring personnel and equipment in a tunnel according to claim 2, characterized in that, The step of determining the first positioning data of the railcar based on the travel distance specifically includes: If it is determined that the connecting lines of the fixed track do not coincide, then a trajectory curve is constructed in the preset coordinate system based on the trajectory data of the fixed track. Determine the trajectory function corresponding to the trajectory curve; Based on the real-time movement distance, the coordinates of the track vehicle in the preset coordinate system are determined; Determine the projection point of the coordinate point on the trajectory curve, and determine the coordinates of the projection point according to the trajectory function; The coordinates of the projection point are converted into the first positioning data in the world coordinate system.

4. The method for positioning and monitoring personnel and equipment in a tunnel according to claim 1, characterized in that, Before determining the plurality of first distances calculated by the mobile base station, the second distances calculated by each of the mobile beacons, and the plurality of third distances calculated by the fixed base station, the method further includes: A preset distance threshold is determined based on the location of the fixed base station; Obtain multiple initial distances calculated by the fixed base station, wherein the initial distance is the distance between the fixed base station and the mobile beacon; By filtering multiple initial distances using the preset distance threshold, initial distances smaller than the preset distance threshold are retained to obtain multiple third distances.

5. A method for positioning and monitoring personnel and equipment in a tunnel according to claim 4, characterized in that, The step of analyzing and calculating the second positioning data for each positioning target based on the first distance, the second distance, the third distance, and the first positioning data further includes: Time synchronization processing is performed on multiple first distances, multiple second distances, and multiple third distances; In each positioning cycle, a spatial vector relationship is established between the first positioning data and each first distance, and a circular constraint is constructed with the railcar as the center and the first distance as the radius to limit the possible location area corresponding to the mobile beacon. A graph structure model is constructed with the coordinate data of all the moving beacons as variable nodes, wherein each moving beacon is a location node, each of the first distance, the second distance and the third distance constitutes a ranging constraint edge, and the first positioning data is fixed in the graph structure model as a known anchor point and forms an anchor vector with each of the moving beacons; A confidence weight is introduced for the third distance, and an objective function with a weighted residual term is constructed based on the confidence weight. All kinds of ranging errors corresponding to the first distance, the second distance, and the weighted third distance are uniformly included in the objective function for minimization. The graph structure model is optimized by the objective function to obtain the spatial coordinate output of each of the moving beacons in the preset coordinate system, and the spatial coordinate output is converted into the second positioning data in the world coordinate system.

6. The method for positioning and monitoring personnel and equipment in a tunnel according to claim 1, characterized in that, The railcar is equipped with a wheel speed sensor assembly and an inertial measurement unit assembly. The process of acquiring the real-time movement distance of the railcar relative to any one of the fixed base stations specifically includes: Based on the instantaneous speed signal output by the wheel speed sensor assembly, multiple first cumulative driving distances are obtained through time integration; Based on the inertial measurement unit component, the attitude change of the railcar is obtained, and multiple nonlinear velocity drift amounts of the railcar are determined; Align multiple first cumulative driving distances with multiple nonlinear speed drift amounts at time nodes, and compensate the first cumulative driving distances using the nonlinear speed drift amounts to obtain multiple second cumulative driving distances; Acquire multiple fourth distances calculated by the fixed base station or the mobile base station, wherein the multiple second cumulative driving distances are aligned one-to-one with the multiple fourth distances, and the fourth distance is the distance between the mobile base station and the fixed base station; The integral function of the second cumulative travel distance is corrected based on multiple fourth distances to obtain the travel distance function of the railcar, wherein the integral function is a time integral function based on the instantaneous speed signal and the nonlinear speed drift. When the mobile base station and the fixed base station cannot establish a communication connection, the real-time mobile distance is calculated using the mobile distance function.

7. A tunnel personnel and equipment positioning and monitoring device, characterized in that, The device is used to execute a tunnel personnel and equipment positioning and monitoring method as described in any one of claims 1-6. The device is a server, and the server is communicatively connected to a mobile base station, multiple mobile beacons, and multiple fixed base stations. The fixed base stations are located at both ends of the construction tunnel, the mobile base stations are located on a track vehicle, the track vehicle is located on a fixed track within the construction tunnel, and the mobile beacons are located at positioning targets within the construction tunnel. The device includes an acquisition module (201), a processing module (202), and an output module (203), wherein: The acquisition module (201) is used to acquire the real-time moving distance of the railcar relative to any one of the fixed base stations; The processing module (202) is used to determine the first positioning data of the railcar based on the moving distance; The processing module (202) is used to determine multiple first distances calculated by the mobile base station, multiple second distances calculated by each of the mobile beacons, and multiple third distances calculated by the fixed base station, wherein the first distance is the distance between the mobile base station and the mobile beacon, the second distance is the distance between any two mobile beacons, and the third distance is the distance between the fixed base station and the mobile beacon after filtering. The output module (203) is used to analyze and calculate the second positioning data of each of the positioning targets based on the first distance, the second distance, the third distance and the first positioning data.

8. An electronic device, characterized in that, The device includes a processor (301), a communication bus (302), a user interface (303), a network interface (304), and a memory (305). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are both used to communicate with other devices. The communication bus (302) is used to realize the connection and communication between the components within the electronic device. The processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device performs the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-6.

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