A downhole vehicle active safety early warning positioning method based on scene signal correction

Abstract

The application discloses a kind of active safety early warning positioning methods of underground vehicle based on scene signal correction, comprising the following steps: step S1, scene partition is carried out to underground roadway, and the UWB non-line-of-sight signal transmission loss correction model of multiple scenes is established;Step S2, the roadway where vehicle is currently located is judged in combination with operation scene, and personnel position data is adaptively corrected according to operation scene;Step S3, the continuous inertial positioning prediction data of vehicle is solved by vehicle-mounted IMU inertial measurement unit;Step S4, the signal signal-to-noise ratio of vehicle-mounted UWB base station is collected in real time, and the fusion weight of UWB man-car position data and IMU inertial positioning prediction data is dynamically distributed.The application provides a kind of active safety early warning positioning methods of underground vehicle based on scene signal correction, the UWB signal of multiple scenes in underground is differentiated and corrected, while using adaptive switching weight fusion strategy, meet the high-precision requirement of safety warning.

Description

Technical Field

[0001] This invention relates to an active safety early warning and positioning method for underground vehicles based on scene signal correction, belonging to the field of coal mine safety monitoring technology. Background Technology

[0002] Currently, underground transportation systems are a core component of mine safety production systems, and the safe operation capability of underground transportation vehicles directly determines the level of intelligent and inherently safe management in mines. With the continuous advancement of mechanization, automation, and intelligent transformation in domestic mines, trackless transportation equipment such as trackless rubber-tired vehicles, monorails, and railcars are becoming widely used. Scenarios involving mixed traffic of personnel and vehicles, vehicle intersections, and equipment and material handling occupying roadways are becoming increasingly frequent underground, making underground transportation a key area for high-incidence mine safety accidents and requiring strict control.

[0003] The working environment in underground mine roadways is harsh, with common problems such as narrow roadways, numerous bends, many blind spots, and insufficient underground lighting. At the same time, the surrounding rock, mining equipment, and accumulated materials generate severe dust and water mist interference, significantly weakening the stability of wireless sensing and communication systems. Traditional underground vehicle safety protection relies on manual observation, on-site experience judgment, and simple audible and visual alarms, resulting in extremely high warning delays. In close-range mixed operations involving personnel and vehicles, collisions, personnel crushing, and vehicles accidentally entering dangerous mining faces are highly likely to occur, causing not only casualties and equipment damage but also interrupting continuous mine production, leading to economic losses and safety management risks. The industry urgently needs high-precision, high-reliability proactive safety control technology solutions.

[0004] Ultra-wideband (UWB) positioning technology, with its nanosecond-level high temporal resolution, decimeter-level positioning accuracy, and anti-interference advantages in enclosed spaces, is ideally suited for the confined spaces underground where GPS satellite signals cannot penetrate. It has extremely high application value in personnel positioning, vehicle ranging, hazardous area management, and proximity warning, making it the mainstream technology for underground positioning. However, underground, non-line-of-sight propagation and multipath reflection interference are significant problems. The surrounding rock of the tunnel and large mining equipment can directly block the direct signal between the vehicle-mounted UWB base station and the personnel tag. The signal relies on multipath propagation through wall reflections, resulting in huge random errors in ranging. General fixed algorithms cannot adapt to the signal loss differences in different tunnel scenarios, and dynamic positioning errors can reach several meters, failing to meet the high-precision requirements for safety warnings. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide an active safety warning and positioning method for underground vehicles based on scene signal correction. The method performs differentiated correction on UWB signals in multiple underground scenes and adopts an adaptive switching weight fusion strategy to meet the high precision requirements of safety warning.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A method for active safety early warning and positioning of underground vehicles based on scene signal correction includes the following steps: Step S1: Divide the underground roadway into scene zones and establish a multi-scene UWB non-line-of-sight signal transmission loss correction model; Step S2: Jointly determine the work scenario of the roadway where the vehicle is currently located, and adaptively correct the personnel location data according to the work scenario; Step S3: Solve the vehicle's continuous inertial positioning prediction data using the onboard IMU inertial measurement unit; Step S4: Real-time acquisition of the signal-to-noise ratio of the vehicle-mounted UWB base station, and dynamic allocation of the fusion weights of UWB vehicle and person position data and IMU inertial positioning prediction data; Step S5: Based on the fusion of the relative positions of people and vehicles, perform multi-level active safety warnings for people and vehicles approaching each other and execute corresponding vehicle control.

[0007] Furthermore, in step S1, the underground roadway is divided into scene zones, and a multi-scene UWB non-line-of-sight signal transmission loss correction model is established, specifically including the following steps: Based on the underground tunnel drawings, the underground tunnels are divided into three types of operation scenarios: straight tunnels, curved tunnels, and mining faces. Three UWB non-line-of-sight signal transmission loss correction models were fitted and generated for three different operational scenarios.

[0008] Furthermore, the calculation formula for the UWB non-line-of-sight signal transmission loss correction model is as follows: ; in, To correct the distance measurement values ​​for people and vehicles; This represents the original measured ranging value of the vehicle-mounted UWB base station; , and These are the fitting correction coefficients.

[0009] Furthermore, in step S2, the operation scenario of the current lane where the vehicle is located is jointly determined, and the personnel location data is adaptively corrected according to the operation scenario. Specifically, this includes the following steps: By combining the vehicle motion characteristic data output by the vehicle-mounted IMU inertial measurement unit with the signal quality received by the UWB base station, the current working scenario of the roadway where the vehicle is located is jointly determined. The corresponding UWB non-line-of-sight signal transmission loss correction model is called to compensate for the error of the original ranging position data of personnel tags collected by the vehicle-mounted UWB base station, and the corrected relative position data of people and vehicles is output.

[0010] Furthermore, the method of jointly determining the current operating scenario of the roadway by combining the vehicle motion characteristic data output by the onboard IMU inertial measurement unit with the signal quality received by the UWB base station specifically includes the following steps: If the angular velocity of the vehicle motion characteristic data is zero and the ranging signal received by the UWB base station is free of jitter, it is determined to be a straight roadway. If the angular velocity of the vehicle's motion characteristic data changes continuously and regularly, and the ranging signal received by the UWB base station jitters periodically, then it is determined to be a curved roadway. If the acceleration of the vehicle motion characteristic data changes continuously, and the ranging signal received by the UWB base station is periodically lost, then it is determined to be a mining face.

[0011] Furthermore, in step S3, the vehicle's continuous inertial positioning prediction data is calculated using the onboard IMU inertial measurement unit, specifically including the following steps: The onboard IMU (Inertial Measurement Unit) collects the vehicle's acceleration and angular velocity motion data; Vehicle displacement, heading, and position increment are calculated using dead reckoning. Output continuous inertial positioning prediction data for vehicles.

[0012] Furthermore, the weighting rule for the fusion of the UWB vehicle and pedestrian location data and the IMU inertial positioning prediction data is as follows: When the signal-to-noise ratio of the personnel tag signal received by the vehicle-mounted UWB base station is higher than the preset signal-to-noise ratio threshold, the weight of the UWB personnel and vehicle position data is set to 0.7~0.9, and the weight of the IMU inertial positioning prediction data is set to 0.1~0.3. The corrected personnel and vehicle relative position data is used to determine the personnel and vehicle proximity risk. When the signal-to-noise ratio (SNR) of the personnel tag signal received by the vehicle-mounted UWB base station is lower than the preset SNR threshold, it indicates that the vehicle-mounted UWB base station detection is unstable. The weight of the UWB personnel and vehicle location data is reduced to 0.1~0.3, and the weight of the IMU inertial positioning prediction data is increased to 0.7~0.9. The vehicle trajectory calculated by the vehicle-mounted IMU inertial measurement unit is used to determine the risk of personnel and vehicle approach.

[0013] Furthermore, in step S5, based on the fused relative positions of the people and vehicles, a multi-level active safety warning for approaching people and vehicles is performed, and corresponding vehicle control is executed. Specifically, this includes the following steps: When the relative distance between a person and a vehicle is 8 to 15 meters, it is determined to be a Level 1 warning, and the driver should remind people in the vicinity to avoid the area. When the relative distance between a person and a vehicle is 3 to 8 meters, it is determined to be a Level 2 hazard warning, and the driver should reduce the vehicle speed. When the relative distance between a person and a vehicle is less than 3 meters, it is determined to be a Level 3 collision warning, and the driver shall perform emergency braking of the vehicle.

[0014] By adopting the above technical solution, the present invention has the following beneficial effects: 1. This invention provides a dedicated correction model for three types of downhole working conditions, effectively reducing ranging errors in non-line-of-sight environments, resulting in more accurate personnel location data and a significant decrease in false alarm rates.

[0015] 2. When the UWB signal is working normally, the present invention directly uses the position of personnel tags to determine the proximity risk. When the UWB signal is blocked and fails, it seamlessly switches to IMU to calculate the vehicle trajectory to maintain the early warning, thus making up for the shortcomings of using a single UWB signal.

[0016] 3. This invention does not require complex coordinate calculations and directly uses UWB personnel location as the basis for early warning. The algorithm is simple, the hardware modification cost is low, and it is easy to promote and apply it on a large scale in underground mines.

[0017] 4. This invention adopts a graded early warning strategy to avoid collisions between people and vehicles while maximizing the efficiency of underground transportation operations. Attached Figure Description

[0018] Figure 1 This is a flowchart of the active safety early warning and positioning method for underground vehicles based on scene signal correction according to the present invention. Detailed Implementation

[0019] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0020] like Figure 1 As shown, this embodiment provides a method for active safety warning and positioning of underground vehicles based on scene signal correction, including the following steps: Step S1: Divide the underground roadway into scene zones and establish a multi-scene UWB non-line-of-sight signal transmission loss correction model. Specifically: Based on the underground tunnel drawings, the underground tunnels are divided into three typical operating scenarios: straight tunnels, curved tunnels, and mining faces. UWB non-line-of-sight signal transmission loss correction models are then fitted and generated for each of the three operating scenarios.

[0021] The calculation formula for the UWB non-line-of-sight signal transmission loss correction model is as follows: ; in, To correct the distance measurement values ​​for people and vehicles; This represents the original measured ranging value of the vehicle-mounted UWB base station; , and To fit the correction coefficients, different correction coefficients are configured for straight roadways, curved roadways, and mining faces, forming three independent correction models. During runtime, the model matching the current work scenario is called to perform error compensation.

[0022] In the scenario of working in a straight tunnel =0.0012、 0.025 =0.15.

[0023] In the scenario of working in curved tunnels =0.0035、 0.068 =0.42.

[0024] In the context of mining operations... =0.0058、 =0.092、 =0.75.

[0025] Step S2: Perform joint judgment on the work scenario of the current roadway where the vehicle is located, and adaptively correct the personnel location data according to the work scenario. Specifically: By combining the vehicle motion characteristic data output by the vehicle-mounted IMU inertial measurement unit with the signal quality received by the UWB base station, the current working scenario of the roadway where the vehicle is located is jointly determined. The corresponding UWB non-line-of-sight signal transmission loss correction model is called to compensate for the error of the original ranging position data of personnel tags collected by the vehicle-mounted UWB base station, and the corrected relative position data of people and vehicles is output as the basis for judging the risk of people and vehicles approaching each other.

[0026] The process of jointly determining the current operating scenario of the roadway by combining vehicle motion characteristic data output by the onboard IMU inertial measurement unit with the signal quality received by the UWB base station includes the following steps: If the angular velocity of the vehicle's motion characteristic data is zero, it indicates that the vehicle is traveling at a constant speed in a straight line for a long time, and the ranging signal received by the UWB base station is free of jitter, then it is determined to be a straight roadway.

[0027] If the angular velocity of the vehicle's motion characteristic data changes continuously and regularly, it indicates that the vehicle is continuously turning, and the ranging signal received by the UWB base station fluctuates periodically, then it is determined to be a curved roadway.

[0028] If the acceleration of the vehicle motion characteristic data changes continuously, it indicates that the vehicle starts and stops frequently, the vehicle body vibrates violently, and the ranging signal received by the UWB base station is periodically lost, then it is determined to be a mining face.

[0029] Step S3: Calculate the vehicle's continuous inertial positioning prediction data using the onboard IMU (Inertial Measurement Unit). Specifically: The onboard IMU (Inertial Measurement Unit) collects the vehicle's acceleration and angular velocity motion data, calculates the vehicle's displacement, heading, and position increment using dead reckoning, and outputs continuous inertial positioning prediction data. In this embodiment, the continuous inertial positioning prediction data can autonomously and continuously output the vehicle's motion trajectory without requiring external UWB wireless signals, and is mainly used for: 1. Extract vehicle motion features and combine them with UWB signal features to complete the recognition of three types of alleyway scenes, providing a basis for switching the UWB non-line-of-sight signal transmission loss correction model.

[0030] 2. When the UWB signal is blocked or interrupted, the vehicle movement trajectory is continuously output. Combined with historical vehicle and pedestrian location data, the risk of people approaching is predicted to avoid a gap in the early warning system.

[0031] Step S4: Real-time acquisition of the signal-to-noise ratio of the vehicle-mounted UWB base station and the status of detectable personnel tags; dynamic allocation of fusion weights between UWB vehicle and personnel location data and IMU inertial positioning prediction data. The allocation rule for fusion weights is as follows: When the signal-to-noise ratio (SNR) of the personnel tag signal received by the vehicle-mounted UWB base station is higher than the preset SNR threshold, the SNR threshold in this embodiment is set to 35dB. The weight of the UWB personnel and vehicle position data is set to 0.7~0.9, and the weight of the IMU inertial positioning prediction data is set to 0.1~0.3. The corrected personnel and vehicle relative position data is used to determine the personnel and vehicle proximity risk.

[0032] When the signal-to-noise ratio (SNR) of the personnel tag signal received by the vehicle-mounted UWB base station is lower than the preset SNR threshold, it indicates that the vehicle-mounted UWB base station detection is unstable. The weight of the UWB personnel and vehicle location data is reduced to 0.1~0.3, and the weight of the IMU inertial positioning prediction data is increased to 0.7~0.9. The vehicle trajectory inferred by the vehicle-mounted IMU inertial measurement unit is used to determine the risk of personnel and vehicle approach.

[0033] Step S5: Based on the fused relative positions of people and vehicles, perform multi-level active safety warnings for pedestrian-vehicle proximity and execute corresponding vehicle control measures. Specifically: When the relative distance between a person and a vehicle is 8 to 15 meters, it is determined to be a Level 1 long-distance warning, and the driver should remind people around to avoid the area.

[0034] When the relative distance between a person and a vehicle is 3 to 8 meters, it is determined to be a Level 2 medium-distance hazard warning, and the driver should reduce the vehicle speed.

[0035] When the relative distance between a person and a vehicle is less than 3 meters, it is determined to be a Level 3 close-range collision warning. The driver should apply emergency braking to avoid a crushing collision.

[0036] The specific embodiments described above further illustrate the technical problems, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for active safety early warning and positioning of underground vehicles based on scene signal correction, characterized in that, Includes the following steps: Step S1: Divide the underground roadway into scene zones and establish a multi-scene UWB non-line-of-sight signal transmission loss correction model; Step S2: Jointly determine the work scenario of the roadway where the vehicle is currently located, and adaptively correct the personnel location data according to the work scenario; Step S3: Solve the vehicle's continuous inertial positioning prediction data using the onboard IMU inertial measurement unit; Step S4: Real-time acquisition of the signal-to-noise ratio of the vehicle-mounted UWB base station, and dynamic allocation of the fusion weights of UWB vehicle and person position data and IMU inertial positioning prediction data; Step S5: Based on the fusion of the relative positions of people and vehicles, perform multi-level active safety warnings for people and vehicles approaching each other and execute corresponding vehicle control.

2. The active safety early warning and positioning method for underground vehicles based on scene signal correction according to claim 1, characterized in that, In step S1, the underground roadway is divided into scene zones, and a multi-scene UWB non-line-of-sight signal transmission loss correction model is established, which specifically includes the following steps: Based on the underground tunnel drawings, the underground tunnels are divided into three types of operation scenarios: straight tunnels, curved tunnels, and mining faces. Three UWB non-line-of-sight signal transmission loss correction models were fitted and generated for three different operational scenarios.

3. The active safety early warning and positioning method for underground vehicles based on scene signal correction according to claim 2, characterized in that, The calculation formula for the UWB non-line-of-sight signal transmission loss correction model is as follows: ； in, To correct the distance measurement values ​​for people and vehicles; This represents the original measured ranging value of the vehicle-mounted UWB base station; , and These are the fitting correction coefficients.

4. The active safety early warning and positioning method for underground vehicles based on scene signal correction according to claim 1, characterized in that, In step S2, the current lane location of the vehicle is jointly determined for the work scenario, and the personnel location data is adaptively corrected according to the work scenario. Specifically, this includes the following steps: By combining the vehicle motion characteristic data output by the vehicle-mounted IMU inertial measurement unit with the signal quality received by the UWB base station, the current working scenario of the roadway where the vehicle is located is jointly determined. The corresponding UWB non-line-of-sight signal transmission loss correction model is called to compensate for the error of the original ranging position data of personnel tags collected by the vehicle-mounted UWB base station, and the corrected relative position data of people and vehicles is output.

5. The active safety early warning and positioning method for underground vehicles based on scene signal correction according to claim 4, characterized in that, The process of jointly determining the current operating scenario of the roadway by combining the vehicle motion characteristic data output by the onboard IMU inertial measurement unit with the signal quality received by the UWB base station includes the following steps: If the angular velocity of the vehicle motion characteristic data is zero and the ranging signal received by the UWB base station is free of jitter, it is determined to be a straight roadway. If the angular velocity of the vehicle's motion characteristic data changes continuously and regularly, and the ranging signal received by the UWB base station jitters periodically, then it is determined to be a curved roadway. If the acceleration of the vehicle motion characteristic data changes continuously, and the ranging signal received by the UWB base station is periodically lost, then it is determined to be a mining face.

6. The active safety early warning and positioning method for underground vehicles based on scene signal correction according to claim 1, characterized in that, In step S3, the vehicle continuous inertial positioning prediction data is calculated using the onboard IMU inertial measurement unit, specifically including the following steps: The onboard IMU (Inertial Measurement Unit) collects the vehicle's acceleration and angular velocity motion data; Vehicle displacement, heading, and position increment are calculated using dead reckoning. Output continuous inertial positioning prediction data for vehicles.

7. The active safety early warning and positioning method for underground vehicles based on scene signal correction according to claim 1, characterized in that, The weighting rules for the fusion of UWB vehicle and pedestrian location data and IMU inertial positioning prediction data are as follows: When the signal-to-noise ratio of the personnel tag signal received by the vehicle-mounted UWB base station is higher than the preset signal-to-noise ratio threshold, the weight of the UWB personnel and vehicle position data is set to 0.7~0.9, and the weight of the IMU inertial positioning prediction data is set to 0.1~0.

3. The corrected personnel and vehicle relative position data is used to determine the personnel and vehicle proximity risk. When the signal-to-noise ratio (SNR) of the personnel tag signal received by the vehicle-mounted UWB base station is lower than the preset SNR threshold, it indicates that the vehicle-mounted UWB base station detection is unstable. The weight of the UWB personnel and vehicle location data is reduced to 0.1~0.3, and the weight of the IMU inertial positioning prediction data is increased to 0.7~0.

9. The vehicle trajectory calculated by the vehicle-mounted IMU inertial measurement unit is used to determine the risk of personnel and vehicle approach.

8. The active safety early warning and positioning method for underground vehicles based on scene signal correction according to claim 1, characterized in that, In step S5, based on the fused relative positions of the people and vehicles, a multi-level active safety warning for approaching people and vehicles is performed, and corresponding vehicle control is executed. This specifically includes the following steps: When the relative distance between a person and a vehicle is 8 to 15 meters, it is determined to be a Level 1 warning, and the driver should remind people in the vicinity to avoid the area. When the relative distance between a person and a vehicle is 3 to 8 meters, it is determined to be a Level 2 hazard warning, and the driver should reduce the vehicle speed. When the relative distance between a person and a vehicle is less than 3 meters, it is determined to be a Level 3 collision warning, and the driver shall perform emergency braking of the vehicle.

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