Rail car positioning method and device, electronic equipment and readable storage medium

Abstract

The invention provides a rail car positioning method and device, electronic equipment and a readable storage medium, and belongs to the technical field of rail car positioning. Determining a driving track of the rail car on a rail map based on the acceleration data and the angular velocity data of the rail car; and determining the position of the rail car based on the driving track of the rail car on the rail map. According to the rail car positioning method and device, the electronic equipment and the readable storage medium, the positioning precision of the rail car in the tunnel can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of rail vehicle positioning, and more specifically, relates to a rail vehicle positioning method and device, electronic equipment, and a readable storage medium. Background Art

[0002] In the field of tunnel construction, accurate positioning of rail vehicles is crucial. Traditional rail vehicle positioning technology has exposed many shortcomings when facing the complex environment of multiple tracks and switches in tunnels. Some methods that rely on external signal sources (such as GPS) cannot work effectively due to the shielding and attenuation of signals by tunnels. Methods based on manual observation or landmark recognition are limited by light, field of view and human factors, and are inefficient and difficult to guarantee accuracy, making it difficult to meet the needs of real-time and accurate scheduling of rail vehicles during construction. As tunnel construction technology develops towards efficiency and automation, a new positioning method is urgently needed. Summary of the invention

[0003] The purpose of the present disclosure is to provide a rail vehicle positioning method and device, an electronic device, and a readable storage medium to improve the positioning accuracy of the rail vehicle in a tunnel.

[0004] A first aspect of an embodiment of the present disclosure provides a rail vehicle positioning method, comprising: Construct track maps; Determine a driving trajectory of the rail vehicle on the track map based on the acceleration data and the angular velocity data of the rail vehicle; The position of the rail vehicle is determined based on the travel trajectory of the rail vehicle on the track map.

[0005] A second aspect of the embodiments of the present disclosure provides a rail vehicle positioning device, comprising: Track map construction module, used to construct track map; A driving trajectory generating module, used for determining the driving trajectory of the rail vehicle on the track map based on the acceleration data and angular velocity data of the rail vehicle; The positioning module is used to determine the position of the rail vehicle based on the driving trajectory of the rail vehicle on the track map.

[0006] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned rail vehicle positioning method when executing the computer program.

[0007] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned rail vehicle positioning method are implemented.

[0008] The beneficial effects of the rail vehicle positioning method and device, electronic device, and readable storage medium provided by the embodiments of the present disclosure are: the embodiments of the present disclosure first provide an accurate reference benchmark for the real-time positioning of the rail vehicle by constructing a track map, making the positioning process more intuitive and reliable. Secondly, the acceleration data and angular velocity data are used to determine the driving trajectory of the rail vehicle, which not only has a high degree of dynamic adaptability, but also can accurately capture the changes in the motion state of the rail vehicle, thereby ensuring the accuracy of the driving trajectory. Finally, the driving trajectory is matched with the track map to achieve rapid and accurate determination of the position of the rail vehicle. This not only improves the positioning efficiency, but also enhances the accuracy and stability of the rail vehicle positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a flow chart of a rail vehicle positioning method provided by an embodiment of the present disclosure; Figure 2 A structural block diagram of a rail vehicle positioning device provided by an embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0011] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0012] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0013] During tunnel construction, multiple rails are often laid to meet the needs of material transportation, personnel deployment, and transfer of construction equipment. As an important means of transportation in tunnel construction, the precise positioning of rail vehicles is extremely critical to the efficient and safe advancement of construction. Due to the complex internal environment of the tunnel and poor communication conditions, traditional rail vehicle positioning methods have limitations in such a complex tunnel track environment. To this end, this embodiment discloses a rail vehicle positioning method and device, electronic equipment, and a readable storage medium.

[0014] Please refer to Figure 1 , Figure 1 A schematic diagram of a flow chart of a rail vehicle positioning method provided in an embodiment of the present disclosure, the method comprising: S101: Constructing a track map.

[0015] In this embodiment, the detailed spatial information of the rails and the surrounding environment in the entire tunnel can be obtained through three-dimensional laser scanning technology to construct a three-dimensional point cloud model; feature extraction is performed based on the three-dimensional point cloud model to obtain the geometric features of the rails and switches; and a track map containing the rail and switch information is generated based on the geometric features of the rails and switches.

[0016] In the track map, different rails can be represented by corresponding lines, and key nodes such as switches can be marked by specific symbols or logos. Each rail can be assigned a unique number for identification, and the switch can have a corresponding identification number and the indication information of its switchable track, making the entire track map an important reference for rail vehicle positioning, clearly and intuitively showing all the paths and key position relationships of the rail vehicle operation.

[0017] S102: Determine a driving track of the rail vehicle on the track map based on the acceleration data and angular velocity data of the rail vehicle.

[0018] In this embodiment, an acceleration sensor and an angular velocity sensor may be installed on the rail vehicle, wherein the acceleration data is data obtained by the acceleration sensor, and the angular velocity data is data obtained by the angular velocity sensor.

[0019] The acceleration sensor and angular velocity sensor can obtain the acceleration change of the rail vehicle during operation and the rotational angular velocity of the vehicle body in real time.

[0020] In this embodiment, first raw data of the acceleration sensor is acquired, and second raw data of the angular velocity sensor is acquired; the first raw data and the second raw data are preprocessed respectively to obtain acceleration data and angular velocity data.

[0021] The first and second original data obtained contain certain noise and errors, and data preprocessing is required first, such as using a filtering algorithm to remove high-frequency noise interference, identifying and correcting outliers, and ensuring that the data used for subsequent analysis is relatively accurate and reliable.

[0022] In this embodiment, the displacement information of the rail vehicle can be obtained based on the acceleration data, and the steering characteristic information of the rail vehicle (such as the steering angle and steering time of the rail vehicle) can be obtained based on the angular velocity data.

[0023] Taking the initial known position of the rail vehicle (such as the known coordinate position starting from a specific track starting point) as the starting point, the trajectory of the rail vehicle in the time series is gradually drawn according to the continuously updated speed, displacement and steering information.

[0024] During the drawing process, it is necessary to constantly refer to the constructed track map and determine which specific track the rail vehicle is currently traveling along based on the direction, curvature and other characteristics of the track. When passing the switch position, it is possible to determine whether the rail vehicle has switched to another connected track through the sudden changes in acceleration and angular velocity and the matching with the switch switching logic, thereby accurately constructing a complete and realistic driving trajectory within the framework of the track map.

[0025] S103: Determine the position of the rail vehicle based on the driving track of the rail vehicle on the rail map.

[0026] In this embodiment, the driving track of the rail vehicle is matched and compared with the track map to determine the position of the rail vehicle.

[0027] The track map can be used to filter out matching rail lines based on the overall direction of the driving track and the key nodes it passes through (such as switches with specific numbers, intersections of different rails, etc.). For example, if the track shows that the railcar has passed through several switches with specific markings, and the direction matches the direction of a track in the map and the order in which it passes through these switches, the track where the railcar is located and its specific position on the track can be locked.

[0028] For example, suppose that in a tunnel construction, the rail vehicle starts from the track starting point A, and the starting point coordinates are (0,0). The track map is constructed by three-dimensional laser scanning, where track 1 is numbered T1, track 2 is numbered T2, and switch 1 is numbered S1, connecting T1 and T2.

[0029] After the rail car starts, the acceleration sensor and angular velocity sensor start working, and the raw data are obtained and pre-processed to obtain acceleration data and angular velocity data. The speed is calculated based on the acceleration data, and the displacement is obtained by integrating again, and the direction is determined by combining the angular velocity. For example, the rail car first travels a straight distance, the displacement reaches 100 meters and there is no turn. According to the track map, it is still on the T1 track.

[0030] When driving near the switch S1, the sensor data suddenly changes, and the changes in acceleration and angular velocity are consistent with the logic of switching from T1 to T2, so it is determined that the railcar switches to the T2 track. Continue to draw the trajectory based on the subsequent displacement and steering information. When the trajectory shows that the railcar has passed a specific marked switch and the direction is consistent with the subsequent branch track of T2, it is finally determined that the railcar is at a certain position on the T2 track, such as 50 meters away from a certain intersection. In this way, the railcar is accurately positioned during the entire construction process to ensure the orderly progress of tunnel construction.

[0031] It can be concluded from the above that this embodiment first provides an accurate reference benchmark for the real-time positioning of the rail vehicle by constructing a track map, making the positioning process more intuitive and reliable. Secondly, the acceleration data and angular velocity data are used to determine the driving trajectory of the rail vehicle, which not only has a high degree of dynamic adaptability, but also can accurately capture the changes in the motion state of the rail vehicle, thereby ensuring the accuracy of the driving trajectory. Finally, the driving trajectory is matched with the track map to achieve rapid and accurate determination of the position of the rail vehicle. This not only improves the positioning efficiency, but also enhances the accuracy and stability of the rail vehicle positioning.

[0032] In one embodiment of the present disclosure, determining a driving track of a rail vehicle on a track map based on acceleration data and angular velocity data of the rail vehicle includes: Processing the acceleration data to obtain vibration characteristic data of the rail vehicle; Processing the angular velocity data to obtain the turning characteristic data of the rail vehicle; determining a travel trajectory of the rail vehicle based on the vibration characteristic data and the steering characteristic data; The driving trajectory of the rail vehicle is matched with the track map to obtain the driving trajectory of the rail vehicle on the track map.

[0033] In this embodiment, the acceleration data may be subjected to a fast Fourier transform to obtain vibration characteristic data of the rail vehicle.

[0034] Fast Fourier transform can convert the acceleration signal in the time domain into a frequency domain signal, thereby obtaining the intensity distribution of different frequency components. The vibration of the rail vehicle during driving may come from a variety of factors, such as track unevenness, wheel out-of-roundness, and the vehicle's own mechanical vibration. These vibrations show specific frequency peaks or frequency bands in the frequency domain. For example, the periodic unevenness of the track may cause a strong vibration signal at a fixed frequency. By identifying these characteristic frequencies and the corresponding vibration amplitudes, the vibration characteristic data of the rail vehicle can be obtained.

[0035] In this embodiment, the angular velocity data may be integrated to obtain the attitude angle data of the rail vehicle, and the steering feature data may be extracted by analyzing the amplitude and frequency of the attitude angle change.

[0036] When the railcar turns, the angular velocity of the railcar will change significantly, and the attitude angle obtained by integration will also change accordingly. By observing the rate of change of the attitude angle, the duration of the turn, and the size of the turn angle, the turning feature data can be constructed. For example, a large angular velocity for a period of time means that the railcar is making a large angle turn.

[0037] In this embodiment, the vibration characteristic data can provide some position-related information of the railcar during its travel. For example, if a strong vibration of a specific frequency occurs at a certain position, the railcar may have passed through a track joint or a specific defective position on the track. Combined with the steering characteristic data, when the railcar is turning, the approximate path of the railcar on the track can be inferred based on the changes in the steering angle and speed. Thus, the initial driving trajectory of the railcar is constructed.

[0038] In this embodiment, matching the driving trajectory of the rail vehicle with the track map to obtain the driving trajectory of the rail vehicle on the track map includes: comparing the geometric features of the driving trajectory (such as curve shape, length, etc.) and the track segments in the track map, and combining the correlation between the vibration feature data and the steering feature data and the key nodes in the track map.

[0039] For example, if the vibration characteristics in the driving trajectory match a section of uneven track marked in the track map, and the turning characteristics also match the track direction in that area (such as passing through a known curve), the specific driving trajectory of the railcar on the track map can be determined.

[0040] For example, suppose there is a rail car running on a complex track. After the acceleration data collected by its accelerometer is fast Fourier transformed, it is found that there is an obvious peak at 50Hz in the frequency domain with a high amplitude. It is judged that this is consistent with the periodic unevenness of a specific position of the track, indicating that the rail car is traveling on this special track section. This is the position information brought by the vibration characteristic data. At the same time, the attitude angle change obtained by integrating the gyroscope data shows that the angular velocity increases rapidly in a certain period of time and lasts for 5 seconds, and the turning angle reaches 45 degrees. It is determined that the rail car has made a large angle turning operation and extracted the steering characteristic data. Combining these vibration and steering characteristic data, the driving trajectory of the rail car is preliminarily outlined, such as passing through a straight track first and then entering a curve. When matching this trajectory with the track map, it is found that its geometric shape is similar to a certain section of track in the map, and the vibration characteristics correspond to the uneven track marked in the map, and the steering characteristics also fit the known curve. Finally, the driving trajectory of the rail car on the track map is accurately determined, and precise positioning and path tracking are achieved.

[0041] From the above, it can be concluded that this embodiment obtains vibration characteristic data by processing acceleration data, and can use vibration information to reflect the track condition and the relationship between the rail vehicle position, such as determining the location of track defects based on specific vibrations to supplement details for positioning. The steering characteristic data obtained by processing angular velocity data can accurately capture the turning situation of the rail vehicle and effectively track changes in the driving path. The driving trajectory is determined based on the two, without relying on external complex positioning facilities, and has strong adaptability in tunnel environments. Finally, it is matched with the track map to further improve the positioning accuracy, and the rail vehicle position can be accurately locked in complex scenes with multiple tracks and switches to ensure efficient and safe operation.

[0042] In one embodiment of the present disclosure, the acceleration data is processed to obtain vibration characteristic data of the rail vehicle, including: Decompose the acceleration data to obtain the acceleration component along the track tangent direction and the centrifugal component perpendicular to the track direction; The acceleration component along the track tangent direction and the centrifugal component perpendicular to the track direction are integrated to obtain the speed change curve of the rail vehicle; The speed variation curve of the rail car is subjected to feature extraction to obtain the vibration characteristic data of the rail car.

[0043] In this embodiment, the acceleration component reflects the change in the speed of the rail vehicle, such as the acceleration and deceleration process; the centrifugal component is related to the centripetal acceleration generated by the change of direction when the rail vehicle is traveling on a curve, and can reflect the turning situation of the rail vehicle.

[0044] To establish a track coordinate system, the forward direction of the rail vehicle can be set as the track tangent direction, and the track tangent direction can be set as the x-axis; the z-axis is perpendicular to the track plane; the y-axis is determined according to the right-hand rule; the total acceleration vector is obtained based on the acceleration data, and the total acceleration vector is decomposed into the corresponding coordinate axis direction of the track coordinate system to obtain the acceleration component along the track tangent direction and the centrifugal component perpendicular to the track direction.

[0045] In this embodiment, the acceleration component is integrated over time to obtain the speed information of the rail vehicle along the tangent direction of the track; the centrifugal component is integrated to obtain the change of the speed component related to the direction change caused by the turn over time. Based on the speed information that changes over time and the change of the speed component related to the direction change over time, the speed change curve of the rail vehicle is obtained, which can intuitively show the speed of the rail vehicle at different times and the direction change trend.

[0046] For example, assume that the acceleration component along the track tangent direction is , It represents the function of time. The speed of the rail car in this direction at the initial moment is , after time t, the velocity in the tangential direction It can be calculated by integration:

[0047] Similarly, for the velocity component corresponding to the centrifugal component perpendicular to the track direction A similar integral calculation method can also be used to finally combine the speed components in each direction to obtain the speed change curve of the rail vehicle.

[0048] In this embodiment, during the running of the rail vehicle, vibration will occur due to many factors such as the condition of the track itself, the interaction between the wheels and the track, and the mechanical structure characteristics of the vehicle itself. The vibration phenomenon can reflect certain regular characteristics on the speed change curve, such as periodic fluctuations in speed at certain frequencies, or sudden changes at specific times.

[0049] In this embodiment, the speed change curve can be converted from the time domain to the frequency domain through fast Fourier transform, and the vibration characteristic data can be obtained based on the intensity distribution information of different frequency components in the frequency domain.

[0050] For example, an obvious peak is found at a specific frequency f in the frequency domain, and the amplitude corresponding to the peak is large, indicating that the rail car is affected by a fixed-period interference source during driving. These characteristic data are used to reflect the vibration condition of the rail car.

[0051] From the above, it can be concluded that this embodiment can accurately analyze the motion state of the rail vehicle by processing the acceleration data. Decomposing the acceleration can distinguish the tangent and vertical components, and clarify the influence of the speed magnitude and the acceleration related to the steering. The speed change curve is obtained by integration, which intuitively presents the evolution of the speed over time, covering acceleration, deceleration and changes in the speed vector caused by turning. The vibration feature data extracted by the feature can effectively reveal the vibration of the rail vehicle caused by factors such as the flatness of the track and the state of the wheels, and provide a key basis for accurately determining the driving trajectory of the rail vehicle on the track map. In addition, this embodiment can also judge the track condition and predict the loss of rail vehicle components in this way.

[0052] In one embodiment of the present disclosure, the angular velocity data is processed to obtain the attitude angle characteristic data of the rail vehicle, including: The angular velocity data are decomposed to obtain the rotation component around the axis perpendicular to the orbital plane; Integrate the rotation component around the axis perpendicular to the track plane to obtain the attitude angle change curve of the track vehicle; The characteristics of the attitude angle change curve of the rail vehicle are extracted to obtain the steering characteristic data of the rail vehicle.

[0053] In this embodiment, the rotation component reflects the key information of the turning and posture change of the rail vehicle, eliminating the interference caused by other minor rotational movements.

[0054] The angular velocity vector is obtained based on the angular velocity data, and the angular velocity vector is decomposed into the direction of the corresponding coordinate axis of the orbital coordinate system to obtain the rotation component around the axis perpendicular to the orbital plane (z axis).

[0055] The rotation component is integrated over time to obtain the attitude angle change curve, that is, the change of the angle of the rail vehicle around the axis perpendicular to the rail plane over time.

[0056] For example, the rotation component about the axis perpendicular to the orbital plane is set to , It represents the function of time. Assume that the attitude angle of the railcar around the axis perpendicular to the rail plane at the initial moment is , after time t, the attitude angle It can be calculated by integration:

[0057] Through continuous integral calculation, a complete attitude angle change curve can be drawn, clearly showing the dynamic changes of the railcar's attitude. The attitude angle change curve reflects how the railcar's attitude angle changes over time during driving, and intuitively shows the process of the railcar's attitude changes such as turning and maintaining straight driving.

[0058] In this embodiment, the turning characteristic data of the rail vehicle can be obtained based on the slope of the attitude angle change curve.

[0059] Determine the slope of the attitude angle change curve (i.e. the rate of attitude angle change). If the slope in a certain period of time is greater than the set value, it indicates that the rail vehicle is passing through the turnout at the corresponding moment. By determining the slope of the attitude angle change curve, features are extracted and then summarized to form steering feature data, so as to accurately grasp the steering characteristics of the rail vehicle.

[0060] From the above, it can be concluded that this embodiment accurately focuses on the rotation component around the axis perpendicular to the track plane by decomposing the angular velocity data, eliminating the interference of irrelevant information. The attitude angle change curve is obtained by integration, which intuitively presents the dynamic process of the rail vehicle turning and reflects the details of its direction change on the track. The turning feature data is then obtained through feature extraction, which provides a basis for accurately judging the turning timing, angle and speed of the rail vehicle, thereby effectively improving the accuracy of determining the driving trajectory and helping the rail vehicle to accurately position itself in a complex environment.

[0061] In one embodiment of the present disclosure, it also includes: Determine the motion state of the rail vehicle based on the acceleration data, the motion state of the rail vehicle including uniform linear motion and non-uniform linear motion; Determining weights of the acceleration data and the angular velocity data based on the motion state of the rail vehicle; The acceleration data and angular velocity data are weightedly fused to obtain the acceleration vector and angular velocity vector respectively.

[0062] In this embodiment, the accelerometer data is integrated to obtain the instantaneous speed information of the rail vehicle. Assume that the acceleration components measured by the accelerometer in the three coordinate axis directions of the above rail coordinate system are , , , the initial velocity is , the speed after time t In each axis direction, it can be calculated by the following integral formula:

[0063]

[0064]

[0065] In response to the set time window, the motion state of the rail vehicle is determined based on the acceleration change of the rail vehicle.

[0066] For example, a time window length is set to T, and the change of the velocity component within the time period is checked every T time interval. , , If satisfied , , (in, A very small speed change threshold is set, which can be determined according to the actual rail vehicle running speed accuracy requirement), then it is preliminarily judged that the rail vehicle is in a uniform motion state.

[0067] Further combined with the position information of the rail vehicle, if the position change in the uniform motion state is basically along the straight line direction of the track, that is, the component of the displacement in the direction perpendicular to the track changes very little, then the rail vehicle is finally determined to be in a uniform linear motion state; otherwise, it is judged to be a non-uniform linear motion state, and the non-uniform linear motion state can be a motion state such as acceleration, deceleration or turning.

[0068] In this embodiment, when the motion state is a uniform linear motion state, the weight of the acceleration data is set to , accordingly, the weight of the angular velocity data is set to ,in, .

[0069] For example, when the motion state of the rail car is uniform linear motion, , , so the attitude of the railcar can be determined mainly by the data detected by the angular velocity sensor. Because in uniform linear motion, most of what the acceleration sensor measures is noise or slight interference, reducing its weight can reduce the adverse effects on attitude determination.

[0070] When the motion state of the rail vehicle is non-uniform linear motion, it is further determined whether it is acceleration or deceleration. This can be determined by observing the change trend of the velocity component. For example, if the velocity component Continue to increase and accelerate in several consecutive time windows The value is greater than a set acceleration threshold , it is judged as the acceleration stage; if the velocity component continues to decrease and the acceleration value is less than the set deceleration threshold, it is judged as the deceleration stage. In the acceleration or deceleration stage, the weight of the accelerometer data can be increased, such as setting ,but , making it possible to make better use of accelerometer data to accurately capture changes in the speed of the railcar, assisting in more accurately determining the motion state of the railcar and subsequent trajectory calculation.

[0071] In this embodiment, the initial acceleration vector measured by the acceleration sensor is assumed to be , the angular velocity vector measured by the angular velocity sensor is .

[0072] The obtained acceleration data weight is , the angular velocity data weight is , then the fused data vector used to describe the motion state of the rail vehicle can be calculated by the following weighted average method:

[0073] in, is the fused acceleration vector, is the acceleration vector after fusion at the previous moment. This takes into account the continuity of historical data and avoids sudden changes in fusion results.

[0074]

[0075] in, is the fused angular velocity vector, is the angular velocity vector after fusion at the previous moment.

[0076] From the above, it can be concluded that this embodiment determines the weights of acceleration and angular velocity data and weighted fusion by judging the motion state of the rail vehicle based on acceleration data, which can effectively reduce the noise interference of acceleration data during uniform linear motion and accurately determine the posture; in non-uniform linear motion, the weight of acceleration data is reasonably increased to capture speed changes, improve the accuracy of motion state and trajectory calculation, make rail vehicle positioning and motion analysis more reliable, and ensure its safety and efficiency in rail operation.

[0077] In one embodiment of the present disclosure, it also includes: Calculating the distance traveled by the railcar based on the rotation of the railcar's wheels; The position of the railcar is calibrated based on the distance the railcar has traveled.

[0078] In this embodiment, the acceleration sensor may have certain measurement deviations when measuring acceleration. When calculating the velocity and displacement by integration, these deviations will accumulate over time, resulting in a large deviation between the calculated railcar position and the actual position. In the long-term detection process, these deviations will accumulate, thereby affecting the positioning accuracy of the railcar. To this end, this embodiment can calculate the travel distance of the railcar based on the wheel rotation, calibrate the determined railcar position based on the travel distance of the railcar, and improve the accuracy of position determination.

[0079] In this embodiment, the rotation angle can be determined by detecting the change of the light signal when the wheel rotates through the photoelectric encoder, and the travel distance of the rail vehicle can be determined based on the rotation angle.

[0080] For example, the wheels of a railcar roll on the track. For each rotation of the wheel, the railcar will move forward a fixed distance on the track, which is directly related to the circumference of the wheel. If the radius of the wheel is known to be r, according to the circumference formula of a circle C=2πr, for each rotation of the wheel, the railcar will move a distance of 2πr along the track. By installing a photoelectric encoder on the wheel to record the number of rotations of the wheel n, the travel distance s of the railcar can be calculated using the formula s=2πrn.

[0081] In this embodiment, the initial known position of the railcar is used as a reference. As the railcar runs, after the railcar reaches a key node, the position of the railcar is updated according to the calculated wheel rotation distance.

[0082] It can be concluded from the above that this embodiment can directly obtain relatively stable displacement information of the rail vehicle by calculating the travel distance based on wheel rotation. Calibration of the position in this way can effectively compensate for the position deviation problem caused by error accumulation when positioning only based on acceleration data and angular velocity data. It not only greatly improves the accuracy of rail vehicle position determination, but also provides reliable guarantee for the safe driving, precise scheduling and accurate positioning of rail vehicles on track maps.

[0083] In one embodiment of the present disclosure, calibrating the position of a rail vehicle based on the travel distance of the rail vehicle includes: The acceleration data and angular velocity data of the rail vehicle are integrated with the travel distance of the rail vehicle to obtain a position correction value of the rail vehicle; The position of the railcar is calibrated based on the position correction value.

[0084] In this embodiment, the acceleration data and angular velocity data of the rail vehicle are mainly obtained through the acceleration sensor and the angular acceleration sensor. Based on these data, the speed, displacement, and attitude change of the rail vehicle can be obtained, and then the position of the rail vehicle can be determined. However, in the calculation process, due to the measurement error of the sensor itself, coupled with the integral operation, these errors accumulate over time, resulting in a deviation between the calculated position and the actual position. The travel distance calculated by wheel rotation is relatively more direct and accurate. Fusion of it with the acceleration and angular velocity data can combine the advantages of various aspects to obtain a more accurate position correction value.

[0085] In this embodiment, a state equation can be constructed based on the acceleration data and the angular velocity data to obtain the state of the rail vehicle at the current moment.

[0086] The position, speed, attitude angle, etc. of the rail vehicle are taken as state variables, and the acceleration data and angular velocity data are taken as input quantities and included in the equation to reflect the impact on state changes.

[0087] The travel distance calculated based on the wheel rotation is used as the observation value to establish the observation equation.

[0088] The observation equation describes the relationship between the observed value (travel distance) and the state variables (such as the position, speed, attitude angle, etc. of the rail vehicle).

[0089] Based on the state of the rail vehicle at the previous moment, the state at the current moment, the observation value and the observation equation, the predicted state is corrected according to the set weight to obtain the fused position correction value.

[0090] For example, the state vector X is defined to include the position coordinates x and y of the rail vehicle, and the velocity components along the x-axis and y-axis directions. , And attitude angle ,Right now .

[0091] When the rail car starts, the initial position coordinates are known to be The initial velocities in the x-axis and y-axis directions are , , the initial attitude angle is , the initial state vector .

[0092] Considering the measurement errors of acceleration sensors and angular velocity sensors, the noise covariance matrix Q is set to describe the uncertainty of the sensor itself. At the same time, since there may be certain errors in calculating the driving distance based on wheel rotation, the observation noise covariance matrix R is set to characterize the uncertainty of observation. The specific element values ​​in the covariance matrix Q and the covariance matrix R can be determined by performing precision tests and experimental analysis on each sensor in advance.

[0093] The state equation is:

[0094] in, is The state vector at the moment; is the state transfer matrix, which represents the state variables from time k to The changing relationship of moments; It is the control input matrix. The acceleration data and angular velocity data are used as control inputs. The element values ​​are determined according to their interaction with the state variables. is the control input vector, which contains the acceleration components measured by the accelerometer in the x and y axis directions , and angular velocity ; is the system noise vector, whose mean is zero and the covariance matrix is ​​Q set above, which is used to simulate the influence of various uncertain factors in the system on state changes.

[0095] The observation equation is:

[0096] in, is the observation vector at the time, which refers to the travel distance s here; is the observation matrix, which represents the relationship between state variables and observations.

[0097] According to the last moment ( The state estimation at time , use the state equation to predict the state at the current time (time k) :

[0098] At the same time, the covariance matrix of the predicted state is calculated :

[0099] in, It is the covariance matrix of the state estimate at the previous moment. Through these calculations, we can get a priori estimate of the state at the current moment when it is not combined with the current observation value.

[0100] Calculate Kalman gain , Kalman gain Determines the weight given to the observed value and the predicted value during fusion. The calculation formula is:

[0101] Use Kalman gain to combine current observations Forecast status Update to get the fused current state estimate :

[0102] Finally, the covariance matrix of the state estimate is updated :

[0103] in, is the identity matrix.

[0104] After the above-mentioned repeated prediction and update steps, the state of the rail vehicle is dynamically fused and calculated each time according to the newly acquired acceleration, angular velocity data and the travel distance calculated by wheel rotation, and the final result is The position coordinate component in is the fused position correction value, which can be used for subsequent calibration of the rail vehicle position to make its position estimate closer to the actual real position situation.

[0105] For example, at a certain moment, after calculation, the predicted rail vehicle position coordinates are (10.2, 5.1) (unit: meter). The position correction value obtained after Kalman filter fusion is +0.3 meter in the x direction and -0.1 meter in the y direction. The calibrated position coordinates become (10.5, 5.0) meters. The accuracy of the rail vehicle position is ensured through continuous fusion calibration.

[0106] From the above, it can be concluded that this embodiment integrates the acceleration, angular velocity data of the rail vehicle with the travel distance to calibrate the position. It effectively compensates for the deviation caused by the accumulation of sensor errors when the position is calculated solely by relying on acceleration and angular velocity, and integrates the advantages of multi-source data. Through Kalman filtering, the position is accurately fused and dynamically corrected to improve positioning accuracy and reliability.

[0107] In one embodiment of the present disclosure, constructing a track map includes: Based on the spatial distribution of the point cloud data corresponding to the track, the center line of the track is obtained; A track map is constructed based on the centerline of the track and the geometric characteristics of the track.

[0108] In this embodiment, the laser radar tunnel environment is scanned to obtain point cloud data of the tunnel environment; the point cloud data corresponding to the track contains the three-dimensional spatial information of the track, reflecting the actual shape of the track and its relative position relationship with the surrounding environment.

[0109] The point cloud data of the tunnel environment is clustered to distinguish the points belonging to the track from the points in the surrounding environment, and the point cloud data corresponding to the track is obtained.

[0110] By identifying the geometric characteristics of the point cloud data corresponding to the track in space, such as the direction and curvature change, the extension direction of the track can be obtained and the key points reflecting the trend of the central axis of the track can be found.

[0111] Based on the identified key points, a curve fitting algorithm is used to construct a smooth curve, which is the center line of the track. The smooth curve can abstractly represent the main direction of the track in space, ignoring the tiny bumps and other details on the track surface, and provide a core baseline for the subsequent construction of the track map.

[0112] In this embodiment, the curvature of each section of the track is marked at the corresponding position of the center line to indicate where the curve is and the degree of curvature of the curve; the switch position is clearly marked to clarify the branching and connection relationship of the track. The track map constructed in this way can fully present the overall picture of the track, provide an accurate reference basis for applications such as rail vehicle positioning, navigation, and matching of driving trajectories, and help accurately grasp the position and driving status of the rail vehicle in the entire rail network.

[0113] In one embodiment of the present disclosure, first reference data is obtained based on the distance between the rail vehicle and a target key node, where the target key node is a key node that is closest to the rail vehicle during the travel of the rail vehicle; The position of the railcar is calibrated based on the second reference data.

[0114] In actual applications, due to the complex environment of tunnel construction, power outages may occur. When a railcar encounters a power outage in a tunnel, the acceleration data and angular velocity data of the railcar will not be obtained normally, and the railcar will stop operating. However, due to uncontrollable factors, the railcar may deviate on the track when it stops operating, which may cause errors in the positioning of the railcar when the power supply is normal. To this end, this embodiment can calibrate the deviation caused by power outages.

[0115] In this embodiment, first, when the rail vehicle is running normally, the distance information between it and the nearest key node (such as a specific track identification point, a switch point, etc.) is continuously monitored and recorded, and the obtained distance information is stored in real time. Once a power outage occurs, the distance between the rail vehicle and the target key node at this time can be immediately recorded, that is, the first reference data is obtained. During the power outage, the rail vehicle may deviate on the track due to various factors.

[0116] When the power supply is restored, the acceleration sensor and angular velocity sensor start working again. At this time, the distance between the rail vehicle and the previously recorded target key node is obtained again to form the second reference data. If the position of the rail vehicle is offset, there will be a deviation in the distance data from the target key node obtained twice.

[0117] By calculating the deviation between the first reference data and the second reference data, the displacement change of the rail vehicle during the power outage can be inferred. Based on this deviation, the current position of the rail vehicle is calibrated to correct the positioning error caused by the power outage offset.

[0118] For example, if the first reference data shows that the distance to the critical node was 10 meters when the power was cut off, and the second reference data is 10.5 meters after the power was restored, it means that the rail vehicle has shifted 0.5 meters away from the critical node. The position coordinates of the rail vehicle can be adjusted accordingly to ensure the accuracy of the rail vehicle's positioning, so that it can continue to operate safely and efficiently based on the correct position information after the power is restored.

[0119] It can be concluded from the above that this embodiment obtains the first reference data by recording the distance between the rail vehicle and the target key node, obtains the second reference data after power failure is restored, and calibrates the position accordingly, which effectively solves the positioning error problem caused by the deviation of the rail vehicle due to power failure in complex environments such as tunnel construction. There is no need to rely on complex sensor fusion or map matching algorithms. Only the distance information of the key nodes can be used to accurately correct the position, ensure the continuity and safety of the rail vehicle operation, and improve the efficiency of operation management.

[0120] Corresponding to the rail vehicle positioning method of the above embodiment, Figure 2 This is a structural block diagram of a rail vehicle positioning device provided by an embodiment of the present disclosure. For ease of description, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The rail vehicle positioning device 20 includes: a rail map construction module 21, a driving trajectory generation module 22, and a positioning module 23.

[0121] Wherein, the track map construction module 21 is used to construct the track map; A driving trajectory generating module 22, for determining a driving trajectory of the rail vehicle on the track map based on the acceleration data and angular velocity data of the rail vehicle; The positioning module 23 is used to determine the position of the rail vehicle based on the driving track of the rail vehicle on the rail map.

[0122] In one embodiment of the present disclosure, the driving trajectory generation module 22 is further configured to: Processing the acceleration data to obtain vibration characteristic data of the rail vehicle; Processing the angular velocity data to obtain the turning characteristic data of the rail vehicle; determining a travel trajectory of the rail vehicle based on the vibration characteristic data and the steering characteristic data; The driving trajectory of the rail vehicle is matched with the track map to obtain the driving trajectory of the rail vehicle on the track map.

[0123] In one embodiment of the present disclosure, the driving trajectory generation module 22 is further configured to: Decompose the acceleration data to obtain the acceleration component along the track tangent direction and the centrifugal component perpendicular to the track direction; The acceleration component along the track tangent direction and the centrifugal component perpendicular to the track direction are integrated to obtain the speed change curve of the rail vehicle; The speed variation curve of the rail car is subjected to feature extraction to obtain the vibration characteristic data of the rail car.

[0124] In one embodiment of the present disclosure, the driving trajectory generation module 22 is further configured to: The angular velocity data are decomposed to obtain the rotation component around the axis perpendicular to the orbital plane; Integrate the rotation component around the axis perpendicular to the track plane to obtain the attitude angle change curve of the track vehicle; The characteristics of the attitude angle change curve of the rail vehicle are extracted to obtain the steering characteristic data of the rail vehicle.

[0125] In one embodiment of the present disclosure, the railcar positioning device 20 further includes: a calibration module; The calibration module is specifically used for: Calculating the distance traveled by the railcar based on the rotation of the railcar's wheels; The position of the railcar is calibrated based on the distance the railcar has traveled.

[0126] In one embodiment of the present disclosure, the calibration module is further configured to: The acceleration data and angular velocity data of the rail vehicle are integrated with the travel distance of the rail vehicle to obtain a position correction value of the rail vehicle; The position of the railcar is calibrated based on the position correction value.

[0127] In one embodiment of the present disclosure, the track map construction module 21 is specifically used to: Based on the spatial distribution of the point cloud data corresponding to the track, the center line of the track is obtained; A track map is constructed based on the centerline of the track and the geometric characteristics of the track.

[0128] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 22 are shown.

[0129] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0130] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0131] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0132] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the rail vehicle positioning method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0133] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0134] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0135] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0136] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0139] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0140] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A rail vehicle positioning method, characterized in that: include: Construct track maps; Determining a driving trajectory of the rail vehicle on a track map based on the acceleration data and the angular velocity data of the rail vehicle; The position of the rail vehicle is determined based on the travel trajectory of the rail vehicle on the rail map.

2. The rail vehicle positioning method according to claim 1, characterized in that: The determining of the driving track of the rail vehicle on the track map based on the acceleration data and the angular velocity data of the rail vehicle comprises: Processing the acceleration data to obtain vibration characteristic data of the rail vehicle; Processing the angular velocity data to obtain the turning characteristic data of the rail vehicle; determining a travel trajectory of the rail vehicle based on the vibration characteristic data and the steering characteristic data; The driving track of the rail vehicle is matched with the rail map to obtain the driving track of the rail vehicle on the rail map.

3. The rail vehicle positioning method according to claim 2, characterized in that: The processing of the acceleration data to obtain vibration characteristic data of the rail vehicle includes: Decompose the acceleration data to obtain the acceleration component along the track tangent direction and the centrifugal component perpendicular to the track direction; Integrating the acceleration component along the track tangent direction and the centrifugal component perpendicular to the track direction to obtain a speed change curve of the rail vehicle; Feature extraction is performed on the speed variation curve of the rail vehicle to obtain vibration feature data of the rail vehicle.

4. The rail vehicle positioning method according to claim 2, characterized in that: The processing of the angular velocity data to obtain the attitude angle characteristic data of the rail vehicle includes: The angular velocity data are decomposed to obtain the rotation component around the axis perpendicular to the orbital plane; Integrate the rotation component around the axis perpendicular to the track plane to obtain the attitude angle change curve of the track vehicle; Feature extraction is performed on the attitude angle change curve of the rail vehicle to obtain steering feature data of the rail vehicle.

5. The rail vehicle positioning method according to claim 1, characterized in that: Also includes: calculating a travel distance of the railcar based on wheel rotations of the railcar; The position of the railcar is calibrated based on the distance traveled by the railcar.

6. The rail vehicle positioning method according to claim 5, characterized in that: The calibrating the position of the rail vehicle based on the travel distance of the rail vehicle comprises: The acceleration data and angular velocity data of the rail vehicle are integrated with the travel distance of the rail vehicle to obtain a position correction value of the rail vehicle; The position of the railcar is calibrated based on the position correction value.

7. The rail vehicle positioning method according to claim 1, characterized in that: The constructing of the track map comprises: Based on the spatial distribution of the point cloud data corresponding to the track, the center line of the track is obtained; A track map is constructed based on the centerline of the track and geometric features of the track.

8. A rail vehicle positioning device, characterized in that: include: Track map construction module, used to construct track map; A driving trajectory generating module, used for determining the driving trajectory of the rail vehicle on the track map based on the acceleration data and angular velocity data of the rail vehicle; A positioning module is used to determine the position of the rail vehicle based on the driving track of the rail vehicle on the rail map.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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