Positioning and navigation device for pipe jacking construction

Abstract

The invention provides a positioning and navigation device for pipe jacking construction, and relates to the technical field of pipe jacking construction. The positioning and navigation device for pipe jacking construction comprises a cooperative work framework, a data acquisition and transmission structure, a calculation and processing structure, a control and execution structure, an environment sensing and monitoring structure, a safety guarantee structure and a man-machine interaction and remote monitoring structure, and the data acquisition and transmission structure comprises a sensor network. The sensor network comprises a laser radar; and the calculation and processing structure comprises an inertial navigation system based on quantum sensing and a self-adaptive navigation system based on machine learning. The two systems are combined with a Kalman filtering algorithm to give full play to respective advantages, Kalman filtering is used for fusing quantum I MU and sensor network data and providing high-precision navigation data, the quantum I MU provides high-precision inertial measurement data, a machine learning model is used for environmental perception, adaptive adjustment and prediction, and the Kalman filtering algorithm is used for providing high-precision inertial measurement data. And the environmental adaptability and the intelligent level of the system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of pipe jacking construction, in particular to a positioning and navigation device for pipe jacking construction. Background Art

[0002] Pipe jacking construction is a trenchless construction method and a pipeline burial construction technology that requires no excavation or less excavation. Pipe jacking construction is to use the jacking force generated by the jacking equipment in the working pit to overcome the friction between the pipeline and the surrounding soil, jack the pipeline into the soil according to the designed slope, and transport the soil away. After one section of the pipe is pushed into the soil layer, the second section of the pipe is pushed in. The principle is to use the thrust of the main jacking cylinder and the pipeline room, relay room, etc. to push the tool pipe or tunneling machine from the working pit through the soil layer and lift it into the receiving pit. The pipeline follows the tool pipe or tunneling machine and is buried between the two pits. During the pipe jacking construction process, the tunneling equipment needs to be positioned and navigated to ensure that the tunneling angle will not deviate.

[0003] There are still some problems in the use of existing positioning and navigation devices for pipe jacking construction. Traditional inertial navigation systems rely on IMU data, but IMU has cumulative errors, and long-term use will lead to a decrease in navigation accuracy. Traditional IMUs have drift and cumulative errors, and the high precision of quantum IMUs can significantly reduce these errors and provide more stable attitude and position estimates. Therefore, technical personnel in this field provide a positioning and navigation device for pipe jacking construction to solve the problems raised in the above background technology. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the shortcomings of the prior art, the present invention provides a positioning and navigation device for pipe jacking construction, which solves the problem that the traditional inertial navigation system relies on IMU data, but the IMU has cumulative errors, and long-term use will lead to a decrease in navigation accuracy. The traditional IMU has drift and cumulative errors, and the high precision of the quantum IMU can significantly reduce these errors and provide more stable attitude and position estimation.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a positioning and navigation device for pipe jacking construction, comprising:

[0008] Collaborative work framework, data acquisition and transmission structure, computing and processing structure, control and execution structure, environmental perception and monitoring structure, security structure, and human-computer interaction and remote monitoring structure;

[0009] The data collection and transmission structure includes a sensor network, and the sensor network includes a laser radar;

[0010] The computing and processing structure includes an inertial navigation system based on quantum sensing and an adaptive navigation system based on machine learning. The inertial navigation system based on quantum sensing includes a software system, a quantum navigation algorithm, which processes IMU data, performs attitude estimation, position calculation and motion compensation, a data fusion and filtering algorithm, which fuses data from different sensors, performs filtering and error compensation, and the adaptive navigation system based on machine learning includes a software system, data preprocessing and feature extraction, which preprocesses sensor data and extracts useful feature information, machine learning model training and reasoning, which uses historical data to train the machine learning model and performs real-time data analysis and prediction at the construction site, and an adaptive navigation algorithm, which automatically adjusts navigation parameters and construction parameters according to the output of the machine learning model. The quantum inertial measurement unit uses quantum effects to measure acceleration and angular velocity, and its accuracy is several orders of magnitude higher than that of traditional MEMS or fiber optic IMU. The quantum IMU can provide sub-microradian angular velocity and sub-micron acceleration measurement accuracy. The measurement of the quantum IMU does not depend on the physical properties of mechanical components or optical devices, so its drift characteristics are much lower than traditional IMU, which makes the quantum IMU MU can maintain higher accuracy and stability during long-term operation. The machine learning-based environmental perception system can automatically learn and adapt to different construction environments. The machine learning model can learn the characteristics of different types of obstacles through training data and detect and identify obstacles in real time at the construction site. The machine learning model can use historical data and real-time data to make predictions, predict the movement trend of the pipe jacking machine, changes in the surrounding environment, etc. This can help the system make decisions in advance and improve the safety and efficiency of navigation and control.

[0011] The navigation and control system receives navigation data and control instructions from the quantum sensing inertial navigation system and the machine learning adaptive navigation system, controls the movement direction, speed and thrust of the pipe jacking machine, and realizes precise navigation and construction control;

[0012] The environmental perception and monitoring structure includes an environmental perception system, which uses equipment such as laser radar, ultrasonic sensors and cameras to perceive the environment around the pipe jacking machine in real time, build a three-dimensional map of the construction area, and detect and identify obstacles.

[0013] Preferably, the human-computer interaction and remote monitoring structure includes a visualization and monitoring interface and a remote control and command center. The visualization and monitoring interface provides a visual display and real-time monitoring of the construction status, displays the motion status of the pipe jacking machine, environmental perception data and construction parameters. The remote control and command center realizes remote monitoring and control, supports remote expert guidance and decision-making, and provides remote access to and management of construction data.

[0014] Preferably, the safety assurance structure includes a redundancy and backup system, and a redundant system and backup mechanism are designed to ensure safe shutdown or switching to a backup system in the event of a system failure, monitor the system operating status, and detect and diagnose faults.

[0015] Preferably, the collaborative framework effectively integrates the inertial navigation system based on quantum sensing and the adaptive navigation system based on machine learning and applies them to pipe jacking construction. It is necessary to design a collaborative work framework so that the two can complement each other and achieve high-precision, high-reliability and high-adaptability navigation and control. The following is a detailed collaborative work plan:

[0016] S1. Data collection and transmission. The quantum sensing inertial navigation system uses quantum IMU to collect high-precision inertial data such as acceleration and angular velocity. The sensor calibration system calibrates the IMU data in real time to eliminate bias and noise. LiDAR and other sensor networks provide environmental perception data for attitude estimation and position calculation. The machine learning adaptive navigation system collects sensor network data, including LiDAR, ultrasonic sensor and camera data, performs data preprocessing and feature extraction, and provides high-quality input for machine learning models.

[0017] S2. Data processing and fusion: Quantum navigation algorithm processes IMU data to perform attitude estimation, position calculation and motion compensation. Data fusion and filtering algorithms are used to fuse data from different sensors, perform filtering and error compensation, and output high-precision navigation data. Machine learning models train and reason about preprocessed sensor data, use historical data to train models, perform real-time data analysis and prediction at the construction site, and output prediction results.

[0018] S3. Model reasoning, machine learning models for real-time data analysis and prediction;

[0019] S4. Navigation command generation, integrating navigation data and prediction results to generate navigation commands and control parameters;

[0020] S5. Navigation and control: integrating quantum navigation data with the prediction results of the machine learning model to form the final navigation instructions and control parameters, controlling the movement direction, speed and thrust of the pipe jacking machine, and achieving precise navigation and construction control;

[0021] S6. Environmental perception: using laser radar and other equipment to perceive the environment, build a three-dimensional map, and detect obstacles;

[0022] S7. Safety assurance, monitoring system operation status, redundant and backup systems ensure safety. Combining these two systems with the Kalman filter algorithm can give full play to their respective advantages. Kalman filtering is used to fuse quantum IMU and sensor network data to provide high-precision navigation data. Quantum IMU provides high-precision inertial measurement data. Machine learning models are used for environmental perception, adaptive adjustment and prediction to improve the environmental adaptability and intelligence level of the system.

[0023] Preferably, the collaborative framework also includes a collaborative algorithm, a data fusion algorithm, a Kalman filter, which fuses quantum IMU data and sensor network data, estimates system status, and improves navigation accuracy. The algorithm formula is as follows:

[0024]

[0025] in, is the state estimation, P k is the covariance matrix, K k is the Kalman gain, Z k The Kalman filter is an observation value. It can effectively fuse data from different sensors, eliminate sensor noise and deviation, and improve the accuracy of navigation data. The algorithm can process data in real time and quickly update the system state estimate to meet the requirements of real-time navigation and control in pipe jacking construction. The Kalman filter has a certain robustness to system noise and model uncertainty, and can provide reliable navigation data even in complex construction environments. By adjusting the parameters of the Kalman filter, it can adapt to different construction environments and sensor configurations, and improve the system's adaptability.

[0026] (III) Beneficial effects

[0027] The present invention provides a positioning and navigation device for pipe jacking construction. It has the following beneficial effects:

[0028] 1. In the present invention, Kalman filtering can effectively fuse data from different sensors, eliminate sensor noise and deviation, and improve the accuracy of navigation data. The algorithm can process data in real time and quickly update system state estimation to meet the requirements of pipe jacking construction for real-time navigation and control. Kalman filtering has a certain robustness to system noise and model uncertainty, and can provide reliable navigation data even in complex construction environments. By adjusting the parameters of the Kalman filter, it can adapt to different construction environments and sensor configurations, thereby improving the system's adaptability.

[0029] 2. In the present invention, the quantum inertial measurement unit uses quantum effects to measure acceleration and angular velocity, and its accuracy is several orders of magnitude higher than that of traditional MEMS or fiber optic IMU. The quantum IMU can provide sub-microradian angular velocity and sub-micron acceleration measurement accuracy. The measurement of the quantum IMU does not depend on the physical properties of mechanical parts or optical devices, so its drift characteristics are much lower than traditional IMU, which enables the quantum IMU to maintain higher accuracy and stability during long-term operation.

[0030] 3. In the present invention, the environment perception system based on machine learning can automatically learn and adapt to different construction environments. The machine learning model can learn the characteristics of different types of obstacles through training data, and detect and identify obstacles in real time at the construction site. The machine learning model can use historical data and real-time data to make predictions, predict the movement trend of the pipe jacking machine, changes in the surrounding environment, etc., which can help the system make decisions in advance and improve the safety and efficiency of navigation and control.

[0031] 4. In the present invention, the two systems are combined with the Kalman filter algorithm to give full play to their respective advantages. The Kalman filter is used to fuse the quantum IMU and sensor network data to provide high-precision navigation data, the quantum IMU provides high-precision inertial measurement data, and the machine learning model is used for environmental perception, adaptive adjustment and prediction, so as to improve the environmental adaptability and intelligence level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of the process of effectively integrating the quantum sensing-based inertial navigation system and the machine learning-based adaptive navigation system of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Embodiment 1:

[0035] like Figure 1 As shown, an embodiment of the present invention provides a positioning and navigation device for pipe jacking construction, comprising:

[0036] Collaborative work framework, data acquisition and transmission structure, computing and processing structure, control and execution structure, environmental perception and monitoring structure, security structure, and human-computer interaction and remote monitoring structure;

[0037] The data collection and transmission structure includes a sensor network, and the sensor network includes a lidar;

[0038] The computing and processing structure includes an inertial navigation system based on quantum sensing and an adaptive navigation system based on machine learning. The inertial navigation system based on quantum sensing includes a software system, a quantum navigation algorithm, which processes IMU data, performs attitude estimation, position calculation and motion compensation, and a data fusion and filtering algorithm, which fuses data from different sensors, performs filtering and error compensation. The adaptive navigation system based on machine learning includes a software system, data preprocessing and feature extraction, which preprocesses sensor data and extracts useful feature information, machine learning model training and reasoning, which uses historical data to train machine learning models and conducts real-time data analysis and prediction at the construction site, and an adaptive navigation algorithm, which automatically adjusts navigation parameters and construction parameters according to the output of the machine learning model. The quantum inertial measurement unit uses quantum effects to measure acceleration and angular velocity, and its accuracy is several orders of magnitude higher than that of traditional MEMS or fiber optic IMUs. The quantum IMU can provide sub-microradian angular velocity and sub-micron acceleration measurement accuracy. The measurement of the quantum IMU does not depend on the physical properties of mechanical components or optical devices, so its drift characteristics are much lower than traditional IMUs, which makes the quantum IMU MU can maintain higher accuracy and stability during long-term operation. The machine learning-based environmental perception system can automatically learn and adapt to different construction environments. The machine learning model can learn the characteristics of different types of obstacles through training data and detect and identify obstacles in real time at the construction site. The machine learning model can use historical data and real-time data to make predictions, predict the movement trend of the pipe jacking machine, changes in the surrounding environment, etc. This can help the system make decisions in advance and improve the safety and efficiency of navigation and control.

[0039] The navigation and control system receives navigation data and control instructions from the quantum sensing inertial navigation system and the machine learning adaptive navigation system to control the movement direction, speed and thrust of the pipe jacking machine, achieving precise navigation and construction control;

[0040] The environmental perception and monitoring structure includes an environmental perception system, which uses equipment such as lidar, ultrasonic sensors and cameras to perceive the environment around the pipe jacking machine in real time, build a three-dimensional map of the construction area, and detect and identify obstacles.

[0041] The human-computer interaction and remote monitoring structure includes a visualization and monitoring interface and a remote control and command center. The visualization and monitoring interface provides a visual display and real-time monitoring of the construction status, displays the movement status of the pipe jacking machine, environmental perception data and construction parameters. The remote control and command center realizes remote monitoring and control, supports remote expert guidance and decision-making, and provides remote access to and management of construction data.

[0042] The safety assurance structure includes redundancy and backup systems. Redundancy systems and backup mechanisms are designed to ensure safe shutdown or switching to backup systems in the event of system failure, monitor system operating status, and detect and diagnose faults.

[0043] The collaborative framework effectively integrates the inertial navigation system based on quantum sensing and the adaptive navigation system based on machine learning and applies them to pipe jacking construction. It is necessary to design a collaborative work framework so that the two can complement each other and achieve high-precision, high-reliability and high-adaptability navigation and control. The following is a detailed collaborative work plan:

[0044] S1. Data collection and transmission. The quantum sensing inertial navigation system uses quantum IMU to collect high-precision inertial data such as acceleration and angular velocity. The sensor calibration system calibrates the IMU data in real time to eliminate bias and noise. LiDAR and other sensor networks provide environmental perception data for attitude estimation and position calculation. The machine learning adaptive navigation system collects sensor network data, including LiDAR, ultrasonic sensor and camera data, performs data preprocessing and feature extraction, and provides high-quality input for machine learning models.

[0045] S2. Data processing and fusion: Quantum navigation algorithm processes IMU data to perform attitude estimation, position calculation and motion compensation. Data fusion and filtering algorithms are used to fuse data from different sensors, perform filtering and error compensation, and output high-precision navigation data. Machine learning models train and reason about preprocessed sensor data, use historical data to train models, perform real-time data analysis and prediction at the construction site, and output prediction results.

[0046] S3. Model reasoning, machine learning models for real-time data analysis and prediction;

[0047] S4. Navigation command generation, integrating navigation data and prediction results to generate navigation commands and control parameters;

[0048] S5. Navigation and control: integrating quantum navigation data with the prediction results of the machine learning model to form the final navigation instructions and control parameters, controlling the movement direction, speed and thrust of the pipe jacking machine, and achieving precise navigation and construction control;

[0049] S6. Environmental perception: using laser radar and other equipment to perceive the environment, build a three-dimensional map, and detect obstacles;

[0050] S7. Safety assurance, monitoring system operation status, redundant and backup systems ensure safety. Combining these two systems with the Kalman filter algorithm can give full play to their respective advantages. Kalman filtering is used to fuse quantum IMU and sensor network data to provide high-precision navigation data. Quantum IMU provides high-precision inertial measurement data. Machine learning models are used for environmental perception, adaptive adjustment and prediction to improve the environmental adaptability and intelligence level of the system.

[0051] The collaborative framework also includes collaborative algorithms, data fusion algorithms, Kalman filtering, fusing quantum IMU data and sensor network data, estimating system status, and improving navigation accuracy. The algorithm formula is as follows:

[0052]

[0053] in, is the state estimation, P k is the covariance matrix, K k is the Kalman gain, Z k The Kalman filter is an observation value. It can effectively fuse data from different sensors, eliminate sensor noise and deviation, and improve the accuracy of navigation data. The algorithm can process data in real time and quickly update the system state estimate to meet the requirements of real-time navigation and control in pipe jacking construction. The Kalman filter has a certain robustness to system noise and model uncertainty, and can provide reliable navigation data even in complex construction environments. By adjusting the parameters of the Kalman filter, it can adapt to different construction environments and sensor configurations, and improve the system's adaptability.

[0054] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A positioning and navigation device for pipe jacking construction, characterized in that: include: Collaborative work framework, data acquisition and transmission structure, computing and processing structure, control and execution structure, environmental perception and monitoring structure, security structure, and human-computer interaction and remote monitoring structure; The data collection and transmission structure includes a sensor network, and the sensor network includes a laser radar; The computing and processing structure includes an inertial navigation system based on quantum sensing and an adaptive navigation system based on machine learning. The inertial navigation system based on quantum sensing includes a software system, a quantum navigation algorithm, which processes IMU data, performs attitude estimation, position calculation and motion compensation, a data fusion and filtering algorithm, which fuses data from different sensors, performs filtering and error compensation, and the adaptive navigation system based on machine learning includes a software system, data preprocessing and feature extraction, which preprocesses sensor data and extracts useful feature information, machine learning model training and reasoning, which uses historical data to train machine learning models and performs real-time data analysis and prediction at the construction site, and an adaptive navigation algorithm, which automatically adjusts navigation parameters and construction parameters according to the output of the machine learning model; The navigation and control system receives navigation data and control instructions from the quantum sensing inertial navigation system and the machine learning adaptive navigation system, controls the movement direction, speed and thrust of the pipe jacking machine, and realizes precise navigation and construction control; The environmental perception and monitoring structure includes an environmental perception system, which uses equipment such as laser radar, ultrasonic sensors and cameras to perceive the environment around the pipe jacking machine in real time, build a three-dimensional map of the construction area, and detect and identify obstacles.

2. A positioning and navigation device for pipe jacking construction according to claim 1, characterized in that: The human-computer interaction and remote monitoring structure includes a visualization and monitoring interface and a remote control and command center. The visualization and monitoring interface provides a visual display and real-time monitoring of the construction status, displays the movement status of the pipe jacking machine, environmental perception data and construction parameters. The remote control and command center realizes remote monitoring and control, supports remote expert guidance and decision-making, and provides remote access and management of construction data.

3. A positioning and navigation device for pipe jacking construction according to claim 1, characterized in that: The safety assurance structure includes redundancy and backup systems, and the redundant systems and backup mechanisms are designed to ensure safe shutdown or switching to backup systems in the event of system failure, monitor system operating status, and detect and diagnose faults.

4. A positioning and navigation device for pipe jacking construction according to claim 1, characterized in that: The collaborative framework effectively integrates the inertial navigation system based on quantum sensing and the adaptive navigation system based on machine learning and applies them to pipe jacking construction. It is necessary to design a collaborative work framework so that the two can complement each other and achieve high-precision, high-reliability and high-adaptability navigation and control. The following is a detailed collaborative work plan: S1. Data collection and transmission. The quantum sensing inertial navigation system uses quantum IMU to collect high-precision inertial data such as acceleration and angular velocity. The sensor calibration system calibrates the IMU data in real time to eliminate bias and noise. LiDAR and other sensor networks provide environmental perception data for attitude estimation and position calculation. The machine learning adaptive navigation system collects sensor network data, including LiDAR, ultrasonic sensor and camera data, performs data preprocessing and feature extraction, and provides high-quality input for machine learning models. S2. Data processing and fusion: Quantum navigation algorithm processes IMU data to perform attitude estimation, position calculation and motion compensation. Data fusion and filtering algorithms are used to fuse data from different sensors, perform filtering and error compensation, and output high-precision navigation data. Machine learning models train and reason about preprocessed sensor data, use historical data to train models, perform real-time data analysis and prediction at the construction site, and output prediction results. S3. Model reasoning, machine learning models for real-time data analysis and prediction; S4. Navigation command generation, integrating navigation data and prediction results to generate navigation commands and control parameters; S5. Navigation and control: integrating quantum navigation data with the prediction results of the machine learning model to form the final navigation instructions and control parameters, controlling the movement direction, speed and thrust of the pipe jacking machine, and achieving precise navigation and construction control; S6. Environmental perception: using laser radar and other equipment to perceive the environment, build a three-dimensional map, and detect obstacles; S7. Security assurance, monitoring system operation status, redundancy and backup systems to ensure safety.

5. A positioning and navigation device for pipe jacking construction according to claim 1, characterized in that: The collaborative framework also includes collaborative algorithms, data fusion algorithms, Kalman filtering, fusing quantum IMU data and sensor network data, estimating system status, and improving navigation accuracy. The algorithm formula is as follows: in, is the state estimation, P k is the covariance matrix, K k is the Kalman gain, Z k is the observed value.

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