Multi-source data-based tunneling equipment autonomous navigation positioning method and system

Abstract

The invention provides a tunneling equipment autonomous navigation positioning method and system based on multi-source data, and relates to the technical field of inertial navigation, the method comprises the following steps: connecting multi-source monitoring equipment, obtaining a multi-source data set, carrying out space-time alignment, and establishing a monitoring data network; butting the transparent geologic model, and fusing the transparent geologic model with a monitoring data network to construct a virtual model; fitting the physical structure parameters and the operation state parameters of the tunneling equipment to the virtual model, positioning the tunneling equipment, and performing tunneling path optimization search; performing attitude and inertia analysis in the virtual model based on the planned path to obtain a path attitude strategy; and carrying out operation navigation control on the tunneling equipment. According to the method and the device, the technical problem of high difficulty in long-time navigation and positioning in a complex environment due to lack of autonomous navigation of the tunneling equipment is solved, and accurate positioning of the tunneling equipment in the complex environment is realized through fusion of multi-source monitoring and the virtual model, so that the tunneling efficiency and safety are improved.

Description

Technical Field

[0001] This application relates to the technical field of inertial navigation, and particularly to an autonomous navigation and positioning method and system for tunneling equipment based on multi-source data. Background Art

[0002] Tunneling equipment is a large-scale mechanical equipment used for rock or soil layer excavation operations in engineering environments such as underground tunnels and mines, including various models such as roadheaders, continuous miner load haul dump units (LHDs), continuous miners, shield machines, shuttle cars, roof bolters, and crushers. Due to the complex working environment of tunneling equipment, which works in long and narrow coal mine roadways for a long time, it will face complex environments such as satellite signal rejection, enclosed roadway environment, long-term vibration, electromagnetic interference, high temperature and humidity, insufficient light, and high dust concentration. Usually relying on a single navigation source, it leads to great difficulty in navigation and positioning during long-term operations, low accuracy, and affects the operation efficiency and safety.

[0003] In summary, there is a technical problem in the prior art that due to the lack of autonomous navigation of tunneling equipment, it is difficult to perform long-term navigation and positioning in complex environments. Summary of the Invention

[0004] The purpose of this application is to provide an autonomous navigation and positioning method and system for tunneling equipment based on multi-source data, so as to solve the technical problem in the prior art that due to the lack of autonomous navigation of tunneling equipment, it is difficult to perform long-term navigation and positioning in complex environments.

[0005] In view of the above problems, this application provides an autonomous navigation and positioning method and system for tunneling equipment based on multi-source data.

[0006] In a first aspect, this application provides an autonomous navigation and positioning method for tunneling equipment based on multi-source data. The autonomous navigation and positioning method for tunneling equipment based on multi-source data is implemented through an autonomous navigation and positioning system for tunneling equipment based on multi-source data. Among them, the autonomous navigation and positioning method for tunneling equipment based on multi-source data includes: connecting multi-source monitoring devices, obtaining a multi-source data set, performing spatio-temporal alignment on the multi-source data set, and establishing a monitoring data network; docking a transparent geological model, obtaining geological information such as thickness, faults, water-rich area ranges, and roadway surrounding rock stresses, and fusing it with the monitoring data network to construct a virtual model, where the virtual model is used to simulate the tunneling scenario; fitting the physical structure parameters and operating state parameters of the tunneling equipment to the virtual model, performing tunneling equipment positioning through the virtual model, and performing real-time data of the monitoring data network in combination with the positioning data to optimize the search for the tunneling path to obtain a planned path; performing attitude and inertial analysis in the virtual model based on the planned path to obtain a path attitude strategy, where the path attitude strategy is used to describe the attitude control parameters of the tunneling equipment in the planned path; and performing operation navigation control of the tunneling equipment according to the path attitude strategy.

[0007] Optionally, the multi-source monitoring device includes: inertial navigation, lidar, visual navigation, and geomagnetic sensors.

[0008] Optionally, identify the acquisition timestamp, acquisition position center, acquisition range, and acquisition parameters of the multi-source monitoring device; perform time alignment on the multi-source data set according to the acquisition timestamp; determine the position center coordinates, acquisition direction, and acquisition distance relationship according to the acquisition position center, acquisition range, and acquisition parameters; establish a standard coordinate system, project the monitoring data in the multi-source data set according to the position center coordinates, acquisition direction, and acquisition distance relationship, and perform data spatial alignment according to the coordinate relationship; construct the monitoring data network according to the time alignment and data spatial alignment relationship.

[0009] Optionally, align the position center coordinates with the standard coordinate system for center point coordinate alignment, and determine the acquisition coordinates according to the acquisition direction and acquisition distance; project the monitoring data into the standard coordinate system according to the acquisition coordinates of the monitoring data to locate the coordinate relationship of the monitoring data.

[0010] Optionally, collect the size, shape, dimensions of each component, and connection structure of the tunneling equipment to obtain physical structure parameters; analyze the speed, power, operating characteristics, and morphological change characteristics of the tunneling equipment in the operating state to obtain operating state parameters; establish the correlation between the operating state parameters and the physical structure parameters; perform coordinate conversion according to the position relationship between the position center coordinates and the physical structure parameters to determine the coordinate alignment relationship of the tunneling equipment, and fit the physical structure parameters of the tunneling equipment into the virtual model; fit the operating state parameters into the virtual model according to the correlation between the operating state parameters and the physical structure parameters.

[0011] Optionally, obtain the geomagnetic interference characteristics based on the monitoring data of the geomagnetic sensor; establish the influence relationship between the geomagnetic interference and inertial navigation; perform inertial navigation data interference compensation according to the geomagnetic interference characteristics and the influence relationship; import the inertial monitoring data corrected by the interference compensation into the monitoring data network.

[0012] Optionally, based on the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters, construct a fitness evaluation function, establish a path optimization search module according to the processing logic of the optimization algorithm relying on the fitness evaluation function, and add the path optimization search module to the virtual model; use the tunneling task requirement parameters and positioning data as input parameters, activate the path optimization search module to identify passable and obstacle characteristics in the virtual model according to the geological structure and the monitoring data network, and obtain the path with the highest evaluation of the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters through iterative search, and output the planned path.

[0013] Optionally, according to the planned path, identify the path tunneling environment characteristics in the virtual model; perform constraint analysis on the path tunneling environment characteristics according to the physical structure parameters and operating state parameters to obtain tunneling constraint conditions; perform corresponding analysis of path postures according to the tunneling task requirement parameters and the tunneling constraint conditions to obtain the attitude control parameters of each path node; smoothly connect the attitude control parameters of each path node according to the planned path to obtain the path attitude strategy.

[0014] Optionally, simulate the operating state parameters according to the control range of the attitude control parameters to obtain the morphological change characteristics; simulate the influence effect of the physical structure parameters and the morphological change characteristics on the path tunneling environment characteristics; set the environmental influence effect threshold based on the tunneling task requirement parameters and the geological influence characteristics; constrain the influence effect according to the environmental influence effect threshold to obtain the constrained control range of the attitude control parameters and obtain the tunneling constraint conditions.

[0015] In a second aspect, the present application also provides a tunneling equipment autonomous navigation and positioning system based on multi-source data, which is used to execute the tunneling equipment autonomous navigation and positioning method based on multi-source data as described in the first aspect. Among them, the tunneling equipment autonomous navigation and positioning system based on multi-source data includes: a data acquisition and alignment module, which is used to connect multi-source monitoring devices, obtain a multi-source data set, perform spatio-temporal alignment on the multi-source data set, and establish a monitoring data network; a virtual model construction module, which is used to dock with a transparent geological model, obtain geological information such as thickness, fault, water-rich area range, and roadway surrounding rock stress, and fuse it with the monitoring data network to construct a virtual model, and the virtual model is used to simulate the tunneling scenario; a mining path planning module, which is used to fit the physical structure parameters and operating state parameters of the tunneling equipment to the virtual model, perform positioning of the tunneling equipment through the virtual model, and perform real-time data of the monitoring data network in combination with the positioning data to optimize the search for the tunneling path to obtain a planned path; an attitude strategy determination module, which is used to perform attitude and inertia analysis in the virtual model based on the planned path to obtain a path attitude strategy, and the path attitude strategy is used to describe the attitude control parameters of the tunneling equipment in the planned path; an operation navigation control module, which is used to perform operation navigation control of the tunneling equipment according to the path attitude strategy.

[0016] One or more technical solutions provided in the present application have at least the following beneficial effects: By connecting multi-source monitoring devices, a multi-source data set is obtained. The multi-source data set is subjected to spatio-temporal alignment to establish a monitoring data network. A transparent geological model is docked to obtain geological information such as thickness, faults, the scope of water-rich areas, and roadway surrounding rock stress, and is fused with the monitoring data network to construct a virtual model, which is used to simulate the tunneling scenario. The physical structure parameters and operating state parameters of the tunneling equipment are fitted to the virtual model. The tunneling equipment is positioned through the virtual model, and the tunneling path is optimized and searched in real time according to the positioning data combined with the real-time data of the monitoring data network to obtain a planned path. Based on the planned path, attitude and inertial analysis are performed in the virtual model to obtain a path attitude strategy, which is used to describe the attitude control parameters of the tunneling equipment in the planned path. The operation navigation control of the tunneling equipment is carried out according to the path attitude strategy. That is to say, by integrating multi-source navigation data and combining a transparent geological model, including formation thickness, fault location, the scope of water-rich areas, and roadway surrounding rock stress, a virtual model is constructed. The physical structure parameters and operating state parameters of the tunneling equipment are input into the virtual model to achieve the positioning of the tunneling equipment. According to the positioning data and real-time monitoring data, the tunneling path is optimized and searched. According to the obtained planned path, attitude and inertial analysis are performed in the virtual model to obtain a path attitude strategy, guiding the tunneling equipment to complete the tunneling task safely and efficiently, improving the accuracy of the navigation and positioning of the tunneling equipment in a complex environment, and thus improving the tunneling efficiency and safety.

[0017] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically given below. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0019] Figure 1 It is a schematic flow chart of the method for autonomous navigation and positioning of a tunneling equipment based on multi-source data according to the present application; Figure 2 It is a schematic structural diagram of the system for autonomous navigation and positioning of a tunneling equipment based on multi-source data according to the present application.

[0020] Explanation of the reference numerals: data collection and alignment module 11 , virtual model building module 12 , mining path planning module 13 , posture strategy determination module 14 , operation navigation control module 15 . DETAILED DESCRIPTION

[0021] This application solves the technical problem in the prior art that long-term navigation and positioning are difficult in complex environments due to the lack of autonomous navigation of tunneling equipment by providing an autonomous navigation and positioning method and system for tunneling equipment based on multi-source data. By integrating multi-source navigation data and combining transparent geological models, including stratum thickness, fault location, water-rich area range, and tunnel surrounding rock stress, a virtual model is constructed, and the physical structure parameters and operating status parameters of the tunneling equipment are input into the virtual model to achieve the positioning of the tunneling equipment. Based on the positioning data and real-time monitoring data, the tunneling path is optimized and searched. According to the obtained planned path, the posture and inertia analysis are performed in the virtual model to obtain the path posture strategy, guide the tunneling equipment to complete the tunneling task safely and efficiently, and improve the navigation and positioning accuracy of the tunneling equipment in complex environments, thereby improving the tunneling efficiency and safety.

[0022] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0023] For example, please refer to the attached Figure 1 The present application provides an autonomous navigation and positioning method for tunneling equipment based on multi-source data, wherein the autonomous navigation and positioning method for tunneling equipment based on multi-source data is executed by an autonomous navigation and positioning system for tunneling equipment based on multi-source data, and the autonomous navigation and positioning method for tunneling equipment based on multi-source data specifically includes the following steps: S100: connecting multi-source monitoring devices, acquiring multi-source data sets, performing spatiotemporal alignment on the multi-source data sets, and establishing a monitoring data network.

[0024] Furthermore, the present application S100 includes: The multi-source monitoring equipment includes: inertial navigation, laser radar, visual navigation, and geomagnetic sensor.

[0025] Furthermore, the present application also includes the following steps: Identify the acquisition timestamp, acquisition position center, acquisition range, and acquisition parameters of the multi-source monitoring device; perform time alignment on the multi-source dataset according to the acquisition timestamp; determine the position center coordinates, acquisition direction, and acquisition distance relationship according to the acquisition position center, acquisition range, and acquisition parameters; establish a standard coordinate system, project each monitoring data in the multi-source dataset according to the position center coordinates, acquisition direction, and acquisition distance relationship, and perform data spatial alignment according to the coordinate relationship; construct the monitoring data network according to the time alignment and data spatial alignment relationship.

[0026] Align the position center coordinates with the center point coordinates of the standard coordinate system, and determine the acquisition coordinates according to the acquisition direction and acquisition distance; project the monitoring data into the standard coordinate system according to the acquisition coordinates of the monitoring data, and locate the coordinate relationship of the monitoring data.

[0027] Specifically, connect multi-source monitoring devices such as inertial navigation, lidar, visual navigation, and geomagnetic sensors to the tunneling equipment. These devices have different measurement principles and advantages. Inertial navigation uses an inertial measurement unit to measure the acceleration and angular velocity of an object, and calculates the position and attitude information of the object through integration; lidar measures the distance between the tunneling equipment and the surrounding environment by emitting laser beams and receiving reflected signals, and generates a three-dimensional environmental map; visual navigation takes pictures of the surrounding environment images through a camera, and realizes positioning and map construction through feature point extraction and matching; the geomagnetic sensor measures the intensity and direction of the earth's magnetic field, and is used to assist in heading determination and counteract inertial navigation drift.

[0028] Parse the timestamp, corresponding installation center coordinates, sensing range, and respective internal parameters of each piece of data from the original logs of inertial navigation, lidar, visual navigation, and geomagnetic sensors. The acquisition timestamp is the accurate time mark stamped by each sensor at the sampling moment; the acquisition position center is the installation position of each sensor on the tunneling equipment, serving as the spatial reference point of the data; the acquisition range is the spatial area that each sensor can effectively monitor; the acquisition parameters are the setting parameters of the sensor during the data acquisition process, such as resolution, frequency, etc.

[0029] Time alignment is to synchronize data from different devices according to their acquisition timestamps to ensure that all data can be compared and analyzed within the same time frame. Spatial alignment is to project data from different devices into a unified coordinate system to ensure that all data can be integrated and analyzed within the same spatial framework. By comparing and adjusting the data timestamps of each device, it is ensured that the data of all sensors can be analyzed within a unified time frame. According to the acquisition timestamps, a time synchronization algorithm (such as the NTP Network Time Protocol) is used to perform time alignment on the multi-source data set to ensure that all data is on the same time basis. A time synchronization algorithm is an algorithm used to adjust the time of different devices to align it with a unified time basis. By transmitting time synchronization messages in the network, comparing the times of different devices, and adjusting their times to match a unified time basis. For example, if the time of the inertial navigation system is 50 milliseconds faster than that of the lidar, adjust the time of the inertial navigation system to synchronize it with the lidar and other devices.

[0030] According to the acquisition position center, acquisition range, and acquisition parameters, determine the position center coordinates, acquisition direction, and acquisition distance relationship of each monitoring device. The position center coordinates are the coordinate values of the acquisition position center; the acquisition direction is the direction during data acquisition, such as the scanning direction of the lidar and the field of view direction of the vision sensor; the acquisition distance relationship is the distance relationship between the data acquisition point and the position center, determining the maximum distance and range that the device can perceive, and is used to describe the spatial detection ability of the device.

[0031] Construct a standard coordinate system. Select a fixed coordinate system as the standard coordinate system. Usually, a known reference point is selected as the origin, and the coordinate system is constructed through preset coordinate axes (such as the X-axis, Y-axis, and Z-axis). According to the position center coordinates of each device, convert the position of each sensor into coordinates in the standard coordinate system. Through coordinate transformation and adjustment, such as rotation and translation, multiple monitoring devices can be spatially aligned. According to the acquisition direction and acquisition distance, determine the acquisition coordinates of each sensor's data. That is to say, all sensor data is calculated according to its own center coordinates + direction + distance to obtain the coordinate points (acquisition coordinates), and then uniformly transformed into this standard coordinate system to determine the three-dimensional position of each sensor data in the unified coordinate system.

[0032] According to the acquisition coordinates of the monitoring data, project the data into the standard coordinate system. Place the data points of each sensor in the standard coordinate system according to their acquisition coordinates, thereby achieving the spatial alignment of the monitoring data. For example, for the point cloud data scanned by the lidar, the X, Y, and Z coordinates of each point are calculated according to its position and direction relative to the position center coordinates and projected into the standard coordinate system.

[0033] Combining the results of time alignment and spatial alignment, a monitoring data network is constructed to correlate the data of each sensor in terms of time and space, forming a complete data model. The monitoring data network is a data set formed after the alignment of multi-source sensor data, which is used to represent the real-time position information, attitude, environmental data, etc. of the equipment during the entire monitoring process. Through time alignment and spatial alignment, it is ensured that the sensor data from different devices can be compared and analyzed under a unified standard, improving the efficiency and accuracy of data fusion.

[0034] S200: Dock with the transparent geological model, obtain geological information such as thickness, faults, water-rich area range, and roadway surrounding rock stress, and fuse it with the monitoring data network to construct a virtual model, which is used to simulate the tunneling scenario.

[0035] Specifically, the transparent geological model is a three-dimensional geological model created based on geological exploration data (such as drilling, seismic waves, groundwater monitoring, etc.), which can display underground structures, rock layers, faults, hydrogeology, etc. Through the transparent geological model, geological information such as the thickness, faults, water-rich area range, and roadway surrounding rock stress in the tunneling area is obtained. The thickness refers to the thickness of the rock layer or ore layer, which affects the tunneling difficulty and tool selection; the fault refers to the rupture and displacement of underground rock layers, which may cause geological risks for tunneling equipment; the water-rich area range refers to the area with a high groundwater content, which affects the tunneling operation (such as the risk of water inrush); the roadway surrounding rock stress represents the pressure condition around the underground rock layer, which affects the safety and stability during the tunneling process.

[0036] Fuse these geological information with the monitoring data network, and use data fusion algorithms (such as Kalman filtering, etc.) to effectively combine the real-time monitoring data with the static geological information to form a dynamic virtual tunneling scenario. Kalman filtering is used to fuse data from different sources (such as sensor data and geological data) into an accurate estimate. The monitoring data provides the position information, attitude information, speed, etc. of the tunneling equipment, while the transparent geological model provides detailed information about the underground environment. The combination of the two can better simulate the tunneling process.

[0037] Using a digital twin model, geological information (rock formations, faults, water-rich areas, surrounding rock stress, etc.) and the monitoring data network are integrated into a unified virtual environment to simulate the operating scenarios of tunneling equipment in different geological environments. The monitoring data network has ensured the consistency of the coordinate systems in the real world and the virtual world, and all sensors and geological information can be mapped in the same standard coordinate system, thus ensuring the precise correspondence between the virtual scenario and the real tunneling scenario. Simply put, geological information (such as rock formations, faults, water-rich areas, etc.) is mapped into the virtual model so that the geological environment can be clearly displayed. The virtual model should be able to be dynamically updated according to real-time monitoring data to reflect the motion state of the tunneling equipment and environmental changes. Based on the constructed virtual model, tunneling scenarios are simulated, including planning the optimal path of the tunneling equipment, simulating the behavior of the equipment in different geological environments, monitoring environmental changes during the tunneling process, and predicting potential risks.

[0038] By docking the transparent geological model and the monitoring data network, the constructed virtual model can provide precise tunneling scenario simulation, thereby achieving more precise navigation and positioning and path planning, and improving the safety and efficiency of tunneling operations.

[0039] S300: Fit the physical structure parameters and operating state parameters of the tunneling equipment to the virtual model, position the tunneling equipment through the virtual model, and perform an optimized search for the tunneling path according to the positioning data combined with the real-time data of the monitoring data network to obtain the planned path.

[0040] Furthermore, S300 of the present application includes: Collect the size, shape, dimensions of each component, and connection structure of the tunneling equipment to obtain physical structure parameters; analyze the speed, power, operating characteristics, and morphological change characteristics of the tunneling equipment in the operating state to obtain operating state parameters; establish the correlation between the operating state parameters and the physical structure parameters; perform coordinate transformation according to the positional relationship between the position center coordinates and the physical structure parameters to determine the coordinate alignment relationship of the tunneling equipment, and fit the physical structure parameters of the tunneling equipment to the virtual model; according to the correlation between the operating state parameters and the physical structure parameters, fit the operating state parameters to the virtual model.

[0041] Specifically, static features such as the size, shape, dimensions of each component, and connection structure of the tunneling equipment are obtained to get the physical appearance of the tunneling equipment and the relative positional relationship of its components, that is, the physical structure parameters. Usually, the physical structure parameters can be obtained through the factory instructions of the tunneling equipment or laser measurement. During the operation of the tunneling equipment, monitor the speed, power, operation characteristics, and morphological change characteristics (such as the inclination and attitude change during tunneling) of the tunneling equipment, which reflect the working load and motion state of the equipment. For example, the behavior of the equipment when accelerating or decelerating, or the morphological changes caused by load changes during the working process.

[0042] Establish the correlation between the operation state parameters and the physical structure parameters, that is, understand how the physical structure of the equipment affects its operation state, and how the operation state in turn affects the physical structure of the equipment. Through the operation state data (such as power, speed, stress, etc.) collected during actual tunneling operations, combine these data with the physical structure model, and use regression analysis to find the mapping relationship between the physical structure and the operation state. Measure the relationship between the power consumption of the equipment and the physical structure parameters (such as size, shape, dimensions of each component, connection structure) under different working loads, so as to obtain the influence of the physical structure on the power consumption.

[0043] Use the physically structured data and operating status data collected historically, divide the data set into several subsets, and cyclically use each subset as the test set and the rest as the training set for model training and evaluation, and finally calculate the average performance of the model. The process of training the regression model is a process of model fitting. The goal is to optimize the model parameters (such as regression coefficients) by minimizing the error. Find a set of regression coefficients that minimize the gap between the model prediction value and the true value. Randomly initialize the regression coefficients. Usually, the regression coefficients will be randomly initialized or initialized to 0. Calculate the predicted value according to the input of the training set into the model, that is, predict the operation state parameters of the model according to the physical structure parameters. Calculate the error between the predicted value and the true value through the mean square error. Use the gradient descent algorithm to continuously adjust the regression coefficients to minimize the error. Repeat the above steps until the error reaches the minimum value or reaches the preset number of iterations, stop training, and obtain the correlation between the operation state parameters and the physical structure parameters.

[0044] Coordinate conversion is performed based on the positional relationship between the position center coordinates and the physical structure parameters, and the physical structure parameters of the equipment are converted into the standard coordinate system of the virtual model to ensure that the digital model of the equipment is aligned with other elements (such as geological structures) in the virtual scene. According to the geometric information in the physical structure parameters, the accurate position of the equipment in the virtual model is calculated to ensure that the spatial relationship between the equipment in the virtual model and the actual equipment is consistent. For example, the center point of the tunneling equipment in the virtual model needs to be aligned with the center point of the actual equipment to ensure that the position of the equipment in the virtual environment is the same as that of the real equipment. The physical structure parameters of the tunneling equipment (such as component sizes and connection methods) are mapped into the virtual model to ensure that the virtual model can reflect the external shape of the real equipment and the structural relationship of each component. For example, the tunneling equipment in the virtual model should be exactly the same as each component of the actual equipment (such as the cutter head and support arm) in terms of size and position.

[0045] According to the association relationship, the operating state parameters (such as speed, power, and morphological changes) are used to dynamically adjust the state of the equipment in the virtual model so that it can accurately reflect the dynamic behavior of the real equipment. For example, if the tunneling equipment encounters hard rock during operation, the virtual model will adjust its displayed power and speed according to the operating state parameters to reflect the behavior of the equipment under actual working conditions. By fitting the physical structure parameters and the operating state parameters into the virtual model, the actual working state of the tunneling equipment can be accurately simulated, providing a high-precision operation simulation.

[0046] Furthermore, the present application further includes the following steps: Based on the monitoring data of the geomagnetic sensor, the geomagnetic interference characteristics are obtained; the influence relationship between the geomagnetic interference and the inertial navigation is established; according to the geomagnetic interference characteristics and the influence relationship, inertial navigation data interference compensation is performed; the inertial monitoring data corrected by the interference compensation is imported into the monitoring data network.

[0047] Specifically, monitoring data is collected through the geomagnetic sensor, including the intensity and direction of the geomagnetic field, which may vary with time and location. The magnetic field intensity vector (X, Y, Z) recorded by the geomagnetic sensor during tunneling is compared with the standard geomagnetic reference value to calculate the perturbation vector. The geomagnetic interference characteristics are the characteristics of the interference signal caused by the change of the geomagnetic field, and these changes may be caused by underground mineral deposits, geological structures or other factors.

[0048] Establish the influence relationship between geomagnetic interference and inertial navigation. Inertial navigation measures angular velocity and acceleration through gyroscopes and accelerometers, and realizes position and attitude estimation without relying on external signals. However, there are cumulative error drift problems, which are easily amplified by environmental interference, resulting in attitude angle drift and positioning errors. Establish the mathematical mapping between geomagnetic interference (input) and inertial navigation error (output) for error prediction and compensation. Geomagnetic monitoring data includes the magnetic field intensity of the X, Y, and Z axes. According to the magnetic field intensity in three directions, compare it with the standard geomagnetic reference value without interference to determine anomalies. Compare the attitude angle output by inertial navigation with the true reference value to obtain the navigation error. Use the geomagnetic interference amount as the input and the inertial navigation error as the output to construct a mapping model. Divide the training set and validation set through historical collected data for model training and evaluation, and use an additional test set for model validation under known geomagnetic disturbance conditions. Finally, this mapping model can be used to predict the possible errors of the inertial navigation system under the current geomagnetic interference.

[0049] According to the established influence relationship and geomagnetic interference characteristics, perform interference compensation on inertial navigation data, adjust or correct the output of the inertial navigation system to eliminate or reduce the influence of geomagnetic interference. Use the compensated inertial navigation output (position, attitude, speed, etc.) as effective monitoring data and import it into the constructed monitoring data network to jointly participate in virtual model reconstruction, path repositioning, and navigation control with other data sources such as vision and lidar. For example, the measured geomagnetic value is (35, 12, -25) μT, and the standard reference value is (30, 10, -30) μT. Calculate the modulus length from the measured value to be approximately 44.67 μT, and the modulus length from the reference value to be approximately 43.59 μT, with a difference of 1.08 μT, indicating that the geomagnetic vector intensity has changed significantly, indicating the existence of interference. Under this interference condition, the measured inertial navigation heading angle estimated by the inertial navigation system is 76.5°, while the true inertial navigation heading angle measured by a high-precision external system is 73.0°, and the deduced inertial navigation error is 3.5°, which is the target variable that needs to be predicted and compensated by establishing the relationship between geomagnetic interference and inertial navigation error. Through the trained geomagnetic interference → inertial navigation error mapping model, the input is this set of magnetic field fluctuation characteristics (such as the differences 5, 2, 5 in each direction or the modulus length fluctuation of 1.08), and the regression model predicts that the inertial navigation heading angle error at this time is 3.4°, indicating that the mapping model is effective. The original inertial navigation heading angle is 76.5°, and after compensation with the predicted error value, it is corrected to 73.1°, and the error is significantly reduced.

[0050] By real-time sensing geomagnetic disturbances, constructing the functional mapping between disturbances and navigation errors, and dynamically compensating inertial navigation data based on the predicted errors, the problem of error accumulation of inertial navigation systems in complex environments is effectively solved, and the positioning accuracy and stability of tunneling equipment under conditions without external signals are significantly improved.

[0051] Furthermore, the present application further includes the following steps: Based on the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters, a fitness evaluation function is constructed. According to the fitness evaluation function and relying on the optimization algorithm processing logic, a path optimization search module is established, and the path optimization search module is added to the virtual model; the tunneling task requirement parameters and the positioning data are used as input parameters to activate the path optimization search module to identify passable areas and obstacle characteristics in the virtual model according to the geological structure and the monitoring data network, and the path with the highest evaluation of the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters is obtained through iterative search, and the planned path is output.

[0052] Specifically, the tunneling task requirement parameters include tunneling objectives, tunneling speeds, tunneling efficiencies, etc., which are used to guide the operation of tunneling equipment; the tunneling execution obstacle penalty parameters include obstacles, geological conditions, safety restrictions, etc., which are used to evaluate the feasibility and safety of the tunneling path. The tunneling execution obstacle penalty parameters are used to measure the degree of influence of adverse environments encountered in the path and are penalty-type parameters.

[0053] A fitness evaluation function is constructed according to the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters, comprehensively considering factors such as tunneling objectives, speeds, efficiencies, as well as obstacles, geological conditions, safety restrictions, etc., to evaluate the quality of the tunneling path. Specifically, task requirement objectives (such as target points, shortest time consumption, lowest risk) are set, combined with the penalty coefficients of the obstacle areas, the function form is designed and preliminary parameter tuning is carried out to obtain the fitness evaluation function. For example, the fitness evaluation function , where a and b are weight coefficients, determined according to actual requirements or user confirmation at the client side, f(a) is a parameter composed of tunneling task requirement parameters such as tunneling objectives, tunneling speeds, tunneling efficiencies, etc., usually determined according to different tunneling operations and subject to the actual situation. f(b) is a penalty-type parameter, usually determined according to the faults, water-rich layers, and high-position areas corresponding to the tunneling operation to evaluate the quality of the tunneling path.

[0054] A path optimization search module is established to search for and optimize the tunneling path in the virtual model using an optimization algorithm. The path optimization search module is embedded as a plug-in into the virtual model architecture. According to the tunneling task requirement parameters and positioning data, the path optimization search module is activated to search for the planned path in the virtual model. Based on the geological structure and the monitoring data network, the passability and obstacle characteristics are identified, and a batch of paths (individuals) are randomly generated. Each path is a set of continuous three-dimensional coordinate points. These paths usually need to be judged for the existence of obstacles. If there are obstacles, the path is corrected to ensure bypassing the obstacles. For the corrected paths, according to the geological data, the passing cost of each path is determined, and all paths with relatively high passing costs and approximately impassable paths are eliminated to obtain the final feasible paths. Specifically, the geological structure can be clearly defined in the aforementioned virtual model, so as to determine the path passability and obstacle identification, and thus obtain the feasible paths. These paths are represented as a sequence of points and encoded as node numbers + directions.

[0055] Using the fitness evaluation function in the path optimization search module, the fitness of a batch of paths (individuals) is calculated to obtain the fitness score of each path. The paths with high fitness are retained for the next generation. The middle segments of every two paths are exchanged to generate new paths. A certain node of some paths is slightly adjusted or bypassed to obtain more new paths, and the original feasible paths are updated using these new paths. The fitness evaluation function is used again to calculate the fitness of the updated multiple feasible paths to obtain the fitness value of each path. This process is repeated until the maximum number of iterations is reached or the fitness converges and remains unchanged. In the optimization process of each generation, the poor paths are gradually eliminated, and the excellent paths are retained and replicated. Among the finally determined multiple paths, the path with the highest fitness value is selected as the planned path, that is, the path with the highest evaluation of the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters.

[0056] By constructing the fitness evaluation function and the path optimization search module, the optimization and search of the tunneling path are realized, which helps to improve the safety and efficiency of the tunneling operation because it comprehensively considers the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters to search for the optimal tunneling path.

[0057] S400: Based on the planned path, attitude and inertia analysis are performed in the virtual model to obtain a path attitude strategy, which is used to describe the attitude control parameters of the tunneling equipment in the planned path.

[0058] Furthermore, S400 of the present application includes: Identify the path tunneling environment characteristics in the virtual model according to the planned path; perform constraint analysis on the path tunneling environment characteristics based on the physical structure parameters and operating state parameters to obtain tunneling constraint conditions; perform corresponding analysis of path postures according to the tunneling task requirement parameters and the tunneling constraint conditions to obtain the attitude control parameters of each path node; smoothly connect the attitude control parameters of each path node according to the planned path to obtain the path attitude strategy.

[0059] Further, the present application further includes the following steps: Simulate the operating state parameters according to the control range of the attitude control parameters to obtain the morphological change characteristics; simulate the influence effect of the physical structure parameters and the morphological change characteristics on the path tunneling environment characteristics; set the environmental influence effect threshold based on the tunneling task requirement parameters and the geological influence characteristics; constrain the influence effect according to the environmental influence effect threshold to obtain the constrained control range of the attitude control parameters and obtain the tunneling constraint conditions.

[0060] Specifically, according to the finally determined planned path, load the corresponding path nodes into the virtual model, identify the environmental characteristics around each node, and obtain the path tunneling environment characteristics. The path tunneling environment characteristics include environmental characteristics such as the geological conditions, obstacles, and space limitations of each node in the planned path. Obtain the attitude control parameters of the tunneling equipment, that is, the allowable range of attitude adjustment of the tunneling equipment, such as angles, speeds, etc.

[0061] Input the control range of the attitude control parameters into the virtual model to simulate the operating state parameters. Simulate the changes in operating state parameters such as speed, acceleration, and steering angle caused by the changes in the attitude control parameters during the operation of the tunneling equipment to obtain the morphological change characteristics, that is, the change characteristics of the shape, position, etc. of the tunneling equipment during the operation.

[0062] Combine the physical structure parameters and the morphological change characteristics to simulate the influence effect of the tunneling equipment on the path environment, and evaluate the influence of the physical structure parameters such as the size, shape, and strength of the tunneling equipment, and the morphological change characteristics caused by the changes in the attitude control parameters on the underground environment such as rocks, faults, water bodies, etc.

[0063] The tunneling task requirement parameters include tunneling targets, tunneling speeds, tunneling efficiencies, etc., which are used to guide the operation of tunneling equipment; the geological influence characteristics are the influences of geological conditions on tunneling operations, such as rock hardness, faults, etc. According to the tunneling task requirement parameters and the geological influence characteristics, an environmental impact effect threshold is set, that is, the maximum allowable value of the set environmental impact, which is used to ensure construction safety. The set threshold is used to limit the simulated environmental impact effect, and the range of attitude control parameters under the condition of meeting the environmental impact effect threshold is determined, so as to obtain the tunneling constraint conditions, ensuring that the impact of the tunneling equipment on the environment during operation remains within a safe range and meeting the task requirements at the same time. The tunneling constraint conditions are the final upper and lower limit intervals after further tightening of the attitude control parameters under the environmental threshold constraint, and are used as the operation boundary for safe and efficient tunneling.

[0064] According to the tunneling task requirement parameters and the tunneling constraint conditions, attitude corresponding analysis is carried out on each path node of the planned path to obtain the attitude control parameters of each path node. That is to say, for each node, the guiding vector is calculated along two adjacent nodes of the path, and the theoretical attitude parameters are decomposed. Compare the theoretical attitude with the tunneling constraint conditions: if it exceeds the upper limit, intercept it according to the boundary value; and determine the final attitude control parameters in combination with the operating state (such as switching to the low-speed mode when the current torque is less than the required torque). Collect the attitude control parameters of all nodes, and smoothly transition the attitude control parameters of each path node to avoid sudden changes. Using the interpolation algorithm or smoothing filtering technology, smooth the attitude control parameters of each path node to obtain the overall strategy for the attitude control of the tunneling equipment in the planned path. Select a suitable interpolation algorithm according to the data characteristics and requirements, such as spline interpolation, to interpolate the attitude control parameters of the path nodes. Select a suitable smoothing filtering technology according to the noise level and the data change trend to smooth the interpolated data. Plot the smoothed attitude control parameters into a graph to visually display the smoothing effect. Calculate the error before and after smoothing processing to evaluate the smoothing effect. According to the error analysis and actual requirements, adjust the parameters of the interpolation algorithm or smoothing filter to optimize the smoothing effect.

[0065] The path attitude strategy is the attitude control parameters of each path node determined according to the planned path and the tunneling task requirement parameters, such as speed, acceleration, steering angle, etc., ensuring that when the tunneling equipment operates along the planned path, it can smoothly transition between various postures, avoiding sudden acceleration, deceleration or steering, thereby improving the safety and efficiency of the operation. By identifying the path tunneling environment characteristics, analyzing the tunneling constraint conditions, and obtaining the path attitude strategy, the precise control of the tunneling equipment is realized, which helps to improve the safety and efficiency of the tunneling operation.

[0066] S500: Perform the operation navigation control of the tunneling equipment according to the said path attitude strategy.

[0067] Specifically, according to the path attitude strategy, determine the attitude control parameters that the tunneling equipment should adopt during operation, including parameters such as speed, acceleration, and steering angle at each path node. Input these attitude control parameters into the control center of the tunneling equipment to control its operating state. According to the input attitude control parameters, adjust the operating state of the tunneling equipment, such as speed, acceleration, and steering angle, so that it operates according to the predetermined path and attitude strategy. By real-time monitoring the operating state and position of the tunneling equipment, feedback and adjustment are made to the control center to ensure that it always operates according to the predetermined path and attitude strategy. By performing operating navigation control on the tunneling equipment according to the path attitude strategy, precise control of the tunneling equipment can be achieved, guiding the tunneling equipment to complete the tunneling task safely and efficiently.

[0068] In summary, the autonomous navigation and positioning method for tunneling equipment based on multi-source data provided by this application has the following beneficial effects: By connecting multi-source monitoring devices, obtaining a multi-source data set, aligning the multi-source data set in space and time, and establishing a monitoring data network; docking a transparent geological model, obtaining geological information such as thickness, faults, the scope of water-rich areas, and roadway surrounding rock stress, and fusing it with the monitoring data network to construct a virtual model, which is used to simulate the tunneling scenario; fitting the physical structure parameters and operating state parameters of the tunneling equipment to the virtual model, positioning the tunneling equipment through the virtual model, and performing an optimized search for the tunneling path in combination with the real-time data of the monitoring data network according to the positioning data to obtain a planned path; performing attitude and inertial analysis in the virtual model based on the planned path to obtain a path attitude strategy, which is used to describe the attitude control parameters of the tunneling equipment in the planned path; performing operating navigation control on the tunneling equipment according to the path attitude strategy. That is to say, by integrating multi-source navigation data and combining a transparent geological model, including formation thickness, fault location, the scope of water-rich areas, and roadway surrounding rock stress, a virtual model is constructed. The physical structure parameters and operating state parameters of the tunneling equipment are input into the virtual model to achieve the positioning of the tunneling equipment. According to the positioning data and real-time monitoring data, an optimized search for the tunneling path is carried out. According to the obtained planned path, attitude and inertial analysis are performed in the virtual model to obtain a path attitude strategy, guiding the tunneling equipment to complete the tunneling task safely and efficiently, improving the accuracy of navigation and positioning of the tunneling equipment in complex environments, and thus improving the tunneling efficiency and safety.

[0069] Embodiment 2, based on the same inventive concept as the autonomous navigation and positioning method for tunneling equipment based on multi-source data in the foregoing Embodiment 1, this application also provides an autonomous navigation and positioning system for tunneling equipment based on multi-source data. Please refer to the appendix Figure 2 , the autonomous navigation and positioning system for tunneling equipment based on multi-source data includes: The data acquisition and alignment module 11 is used to connect multi-source monitoring devices, obtain multi-source data sets, perform spatio-temporal alignment on the multi-source data sets, and establish a monitoring data network; the virtual model construction module 12 is used to dock with a transparent geological model, obtain geological information such as thickness, faults, water-rich area ranges, and roadway surrounding rock stresses, and fuse with the monitoring data network to construct a virtual model, which is used to simulate the tunneling scenario; the excavation path planning module 13 is used to fit the physical structure parameters and operating state parameters of the tunneling equipment to the virtual model, position the tunneling equipment through the virtual model, and perform real-time data-based excavation path optimization search according to the positioning data combined with the monitoring data network to obtain a planned path; the attitude strategy determination module 14 is used to perform attitude and inertia analysis in the virtual model based on the planned path to obtain a path attitude strategy, which is used to describe the attitude control parameters of the tunneling equipment in the planned path; the operation navigation control module 15 is used to perform operation navigation control of the tunneling equipment according to the path attitude strategy.

[0070] Further, the data acquisition and alignment module 11 in the tunneling equipment autonomous navigation and positioning system based on multi-source data is further used for: The multi-source monitoring devices include: inertial navigation, lidar, visual navigation, and geomagnetic sensors.

[0071] Further, the data acquisition and alignment module 11 in the tunneling equipment autonomous navigation and positioning system based on multi-source data is further used for: Identify the acquisition timestamps, acquisition position centers, acquisition ranges, and acquisition parameters of the multi-source monitoring devices; perform time alignment on the multi-source data sets according to the acquisition timestamps; determine the position center coordinates, acquisition directions, and acquisition distance relationships according to the acquisition position centers, acquisition ranges, and acquisition parameters; establish a standard coordinate system, project each monitoring data in the multi-source data sets according to the position center coordinates, acquisition directions, and acquisition distance relationships, and perform data spatial alignment according to the coordinate relationships; construct the monitoring data network according to the time alignment and data spatial alignment relationships.

[0072] Further, the data acquisition and alignment module 11 in the tunneling equipment autonomous navigation and positioning system based on multi-source data is further used for: Align the position center coordinates with the center point coordinates of the standard coordinate system, and determine the acquisition coordinates according to the acquisition directions and acquisition distances; project the monitoring data into the standard coordinate system according to the acquisition coordinates of the monitoring data, and position the coordinate relationships of the monitoring data.

[0073] Further, the excavation path planning module 13 in the tunneling equipment autonomous navigation and positioning system based on multi-source data is further used for: Collect the size, shape, dimensions of each component, and connection structure of the tunneling equipment to obtain physical structure parameters; analyze the speed, power, operating characteristics, and morphological change characteristics of the tunneling equipment in the operating state to obtain operating state parameters; establish the correlation between the operating state parameters and the physical structure parameters; perform coordinate transformation according to the positional relationship between the position center coordinates and the physical structure parameters to determine the coordinate alignment relationship of the tunneling equipment, and fit the physical structure parameters of the tunneling equipment into the virtual model; according to the correlation between the operating state parameters and the physical structure parameters, fit the operating state parameters into the virtual model.

[0074] Further, the excavation path planning module 13 in the autonomous navigation and positioning system for tunneling equipment based on multi-source data is further configured to: Obtain the geomagnetic interference characteristics based on the monitoring data of the geomagnetic sensor; establish the influence relationship between geomagnetic interference and inertial navigation; perform interference compensation for inertial navigation data according to the geomagnetic interference characteristics and the influence relationship; import the inertial monitoring data corrected by interference compensation into the monitoring data network.

[0075] Further, the excavation path planning module 13 in the autonomous navigation and positioning system for tunneling equipment based on multi-source data is further configured to: Construct a fitness evaluation function based on the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters, establish a path optimization search module according to the fitness evaluation function relying on the optimization algorithm processing logic, and add the path optimization search module to the virtual model; use the tunneling task requirement parameters and the positioning data as input parameters to activate the path optimization search module to identify passable and obstacle characteristics in the virtual model according to the geological structure and the monitoring data network, and obtain the path with the highest evaluation of the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters through iterative search, and output the planned path.

[0076] Further, the attitude strategy determination module 14 in the autonomous navigation and positioning system for tunneling equipment based on multi-source data is further configured to: Identify the path tunneling environment characteristics in the virtual model according to the planned path; perform constraint analysis on the path tunneling environment characteristics according to the physical structure parameters and the operating state parameters to obtain tunneling constraint conditions; perform path attitude correspondence analysis according to the tunneling task requirement parameters and the tunneling constraint conditions to obtain the attitude control parameters of each path node; smoothly connect the attitude control parameters of each path node according to the planned path to obtain the path attitude strategy.

[0077] Further, the attitude strategy determination module 14 in the autonomous navigation and positioning system for tunneling equipment based on multi-source data is further configured to: Simulate the operating state parameters according to the control range of the attitude control parameters to obtain the morphological change characteristics; simulate the influence effect on the path tunneling environment characteristics according to the physical structure parameters and the morphological change characteristics; set the environmental influence effect threshold based on the tunneling task requirement parameters and the geological influence characteristics; constrain the influence effect according to the environmental influence effect threshold to obtain the constrained control range of the attitude control parameters and obtain the tunneling constraint conditions.

[0078] The various embodiments in this specification are described in a progressive manner. The key point of each embodiment is the difference from other embodiments. The Figure 1 The tunneling equipment autonomous navigation and positioning method and specific examples based on multi-source data in the first embodiment are equally applicable to the tunneling equipment autonomous navigation and positioning system based on multi-source data in this embodiment. Through the detailed description of the tunneling equipment autonomous navigation and positioning method based on multi-source data above, those skilled in the art can clearly know the tunneling equipment autonomous navigation and positioning system based on multi-source data in this embodiment. Therefore, for the sake of brevity of the specification, it will not be elaborated here.

[0079] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0080] Obviously, for those skilled in the art, without departing from the principle of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the protection scope of the present application.

Claims

1. A self-navigation and positioning method for tunneling equipment based on multi-source data, characterized in that Including: Connecting multi-source monitoring devices, obtaining multi-source data sets, performing spatio-temporal alignment on the multi-source data sets, and establishing a monitoring data network; Docking with a transparent geological model, obtaining geological information such as thickness, faults, the range of water-rich areas, and roadway surrounding rock stress, and fusing with the monitoring data network to construct a virtual model, which is used to simulate the tunneling scenario; Fitting the physical structure parameters and operating state parameters of the tunneling equipment to the virtual model, positioning the tunneling equipment through the virtual model, and performing an optimized search for the tunneling path according to the positioning data combined with the real-time data of the monitoring data network to obtain a planned path; Performing attitude and inertia analysis in the virtual model based on the planned path to obtain a path attitude strategy, which is used to describe the attitude control parameters of the tunneling equipment in the planned path; Performing operating navigation control on the tunneling equipment according to the path attitude strategy.

2. The autonomous navigation and positioning method for tunneling equipment based on multi-source data according to claim 1, wherein The multi-source monitoring devices include: inertial navigation, lidar, visual navigation, and geomagnetic sensors.

3. The autonomous navigation and positioning method of tunneling equipment based on multi-source data according to claim 2, characterized in that, Performing spatio-temporal alignment on the multi-source data sets and establishing a monitoring data network, including: Identifying the acquisition timestamps, acquisition position centers, acquisition ranges, and acquisition parameters of the multi-source monitoring devices; Performing time alignment on the multi-source data sets according to the acquisition timestamps; Determining the position center coordinates, acquisition directions, and acquisition distance relationships according to the acquisition position centers, acquisition ranges, and acquisition parameters; Establishing a standard coordinate system, projecting the monitoring data in the multi-source data sets according to the position center coordinates, acquisition directions, and acquisition distance relationships, and performing data spatial alignment according to the coordinate relationships; Constructing the monitoring data network according to the time alignment and data spatial alignment relationships.

4. The autonomous navigation and positioning method for tunneling equipment based on multi-source data according to claim 3, characterized in that, Projecting the monitoring data in the multi-source data sets according to the position center coordinates, acquisition directions, and acquisition distance relationships, including: Aligning the position center coordinates with the center point coordinates of the standard coordinate system, and determining the acquisition coordinates according to the acquisition directions and acquisition distances; Projecting the monitoring data into the standard coordinate system according to the acquisition coordinates of the monitoring data to locate the coordinate relationships of the monitoring data.

5. The autonomous navigation and positioning method of tunneling equipment based on multi-source data according to claim 4, characterized in that, Fitting the physical structure parameters and operating state parameters of the tunneling equipment to the virtual model, including: Collecting the size, shape, dimensions of each component, and connection structure of the tunneling equipment to obtain physical structure parameters; Analyzing the speed, power, operating characteristics, and morphological change characteristics of the tunneling equipment in the operating state to obtain operating state parameters; Establishing the correlation relationship between the operating state parameters and the physical structure parameters; Performing coordinate transformation according to the position relationship between the position center coordinates and the physical structure parameters to determine the coordinate alignment relationship of the tunneling equipment, and fitting the physical structure parameters of the tunneling equipment to the virtual model; Fitting the operating state parameters to the virtual model according to the correlation relationship between the operating state parameters and the physical structure parameters.

6. The autonomous navigation and positioning method for tunneling equipment based on multi-source data according to claim 2, characterized in that, Before performing an optimized search for the tunneling path according to the positioning data combined with the real-time data of the monitoring data network to obtain a planned path, it also includes: Obtaining geomagnetic interference characteristics based on the monitoring data of the geomagnetic sensors; Establish the influence relationship between geomagnetic interference and inertial navigation; According to the geomagnetic interference characteristics and the influence relationship, perform interference compensation on inertial navigation data; Import the inertial monitoring data corrected by interference compensation into the monitoring data network.

7. The autonomous navigation and positioning method for tunneling equipment based on multi-source data according to claim 6, characterized in that, Optimize and search the tunneling path in real time according to the positioning data and the real-time data of the monitoring data network to obtain a planned path, including: Based on the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters, construct a fitness evaluation function. According to the fitness evaluation function and relying on the optimization algorithm processing logic, establish a path optimization search module, and add the path optimization search module to the virtual model; Use the tunneling task requirement parameters and the positioning data as input parameters to activate the path optimization search module to identify passable and obstacle characteristics in the virtual model according to the geological structure and the monitoring data network, and obtain the path with the highest evaluation of the tunneling task requirement parameters and the tunneling execution obstacle penalty parameters through iterative search, and output the planned path.

8. The autonomous navigation and positioning method for tunneling equipment based on multi-source data according to claim 5, wherein, Perform attitude and inertial analysis on the virtual model based on the planned path to obtain a path attitude strategy, including: According to the planned path, identify the path tunneling environment characteristics in the virtual model; Perform constraint analysis on the path tunneling environment characteristics according to the physical structure parameters and the operating state parameters to obtain tunneling constraint conditions; Perform corresponding path attitude analysis according to the tunneling task requirement parameters and the tunneling constraint conditions to obtain the attitude control parameters of each path node; Smoothly connect the attitude control parameters of each path node according to the planned path to obtain the path attitude strategy.

9. The autonomous navigation and positioning method for tunneling equipment based on multi-source data according to claim 8, characterized in that Perform constraint analysis on the path tunneling environment characteristics according to the physical structure parameters and the operating state parameters to obtain tunneling constraint conditions, including: Simulate the operating state parameters according to the control range of the attitude control parameters to obtain the morphological change characteristics; Simulate the influence effect of the physical structure parameters and the morphological change characteristics on the path tunneling environment characteristics; Set the environmental influence effect threshold based on the tunneling task requirement parameters and the geological influence characteristics; Constrain the influence effect according to the environmental influence effect threshold to obtain the constraint control range of the attitude control parameters, and obtain the tunneling constraint conditions.

10. An autonomous navigation and positioning system for tunneling equipment based on multi-source data, characterized in that, For implementing the steps of the multi-source data-based tunneling equipment autonomous navigation and positioning method according to any one of claims 1 to 9, the multi-source data-based tunneling equipment autonomous navigation and positioning system includes: A data acquisition alignment module, which is used to connect multi-source monitoring devices, obtain a multi-source data set, perform spatio-temporal alignment on the multi-source data set, and establish a monitoring data network; A virtual model construction module, which is used to dock with a transparent geological model, obtain geological information such as thickness, fault, water-rich area range, and roadway surrounding rock stress, and fuse with the monitoring data network to construct a virtual model, and the virtual model is used to simulate the tunneling scenario; A mining path planning module, which is used to fit the physical structure parameters and operating state parameters of the tunneling equipment to the virtual model, perform positioning of the tunneling equipment through the virtual model, and optimize and search the tunneling path in real time according to the positioning data and the real-time data of the monitoring data network to obtain a planned path; The attitude strategy determination module is used to perform attitude and inertia analysis in the virtual model based on the planned path to obtain a path attitude strategy, and the path attitude strategy is used to describe the attitude control parameters of the tunneling equipment in the planned path; The operation navigation control module is used to perform operation navigation control of the tunneling equipment according to the path attitude strategy.

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