Positioning method and device of anti-slip iron shoe, computer equipment, readable storage medium and program product
By integrating data from the inertial measurement unit and the satellite navigation system, the problem of low positioning accuracy of the iron shoe was solved, enabling accurate positioning even under conditions of signal obstruction or poor environmental conditions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-25
- Publication Date
- 2026-05-05
AI Technical Summary
In existing technologies, the accuracy of iron shoe positioning is low, especially in situations where train wheels obstruct the view or in closed environments, it is difficult to accurately determine whether the shoe has been correctly placed on the designated track.
The acceleration and angular velocity data of the iron shoe are obtained by inertial measurement unit. Combined with the position data of satellite navigation system, the data is processed by de-averaging, data fusion and correction to determine the position of the iron shoe.
It improves the accuracy of track shoe positioning, ensuring that the track shoe can be accurately determined whether it is placed on the correct track even when the signal is blocked or the environment is poor.
Smart Images
Figure CN121977540A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of railway technology, and in particular to a positioning method, device, computer equipment, computer-readable storage medium, and computer program product for anti-slipping iron shoes. Background Technology
[0002] A brake shoe is a braking device used to prevent trains from slipping. It is usually placed under the train's wheels to ensure the train remains stable when stopped. Since multiple tracks exist in a train stopping scenario, accurately determining whether the brake shoe is correctly placed on the designated track is crucial.
[0003] In related technologies, the Global Navigation Satellite System (GNSS) is frequently used for positioning. GNSS is a positioning system based on artificial Earth satellites, encompassing multiple navigation satellite systems, and features high accuracy, strong real-time performance, and wide coverage. Furthermore, through mathematical algorithms, GNSS can calculate the three-dimensional coordinates (longitude, latitude, and elevation) of the object to be positioned.
[0004] However, when the iron shoes are placed on the tracks, they will be obstructed by the wheels, and during the process of placing the iron shoes from the storage cabinet under the train wheels, they will pass through non-open environments such as carriages. Currently, the accuracy of the relevant technology in locating the iron shoes is low. Summary of the Invention
[0005] Therefore, it is necessary to provide a positioning method, device, computer equipment, computer-readable storage medium, and computer program product for anti-slip iron shoes that can improve positioning accuracy in response to the above-mentioned technical problems.
[0006] Firstly, this application provides a positioning method for anti-slip track shoes, including:
[0007] Based on an inertial measurement unit that is relatively fixed to the iron shoe, the acceleration and angular velocity data of the iron shoe are acquired, and based on a satellite navigation system, the first position data of the iron shoe is acquired.
[0008] The acceleration data for each axis are subjected to mean removal processing, and based on the acceleration data and the angular velocity data, the first velocity data and the first heading data of the iron shoe are determined;
[0009] Based on the first position data, the first speed data is corrected to obtain the second speed data, and the first heading data is corrected to obtain the second heading data;
[0010] Based on the second speed data and the second heading data, the second position data of the iron shoe is determined.
[0011] In one embodiment, the acceleration data for each axis includes acceleration data for a first axis, acceleration data for a second axis, and acceleration data for a third axis. The first axis, the second axis, and the third axis are perpendicular to each other, and the first axis extends from the toe of the shoe to the handhold of the shoe. The step of performing mean-reduction processing on the acceleration data for each axis includes:
[0012] Based on the acceleration data of each axis obtained within a historical time period, the average acceleration value corresponding to each axis is determined respectively; the historical time period includes at least one swing cycle of the iron shoe;
[0013] In response to the fact that the mean acceleration value corresponding to the second axis and / or the mean acceleration value corresponding to the third axis are not zero, the acceleration data for each axis are subjected to mean-reduction processing based on the mean acceleration value corresponding to each axis.
[0014] In one embodiment, the step of correcting the first velocity data based on the first position data to obtain the second velocity data includes:
[0015] Based on the first position data at the current moment and the first position data at historical moments, determine the reference speed data;
[0016] The reference speed data and the first speed data are fused to obtain the second speed data.
[0017] In one embodiment, after determining the second position data of the iron shoe based on the second speed data and the second heading data, the process includes:
[0018] If, based on the first position data at the current moment and the first position data at a historical moment, the heading change of the iron shoe is determined to be greater than a preset angle threshold, the second position data is smoothed and then output.
[0019] In one embodiment, before determining the second position data of the iron shoe based on the second speed data and the second heading data, the following steps are included:
[0020] If the first speed data and / or the second speed data are less than a preset speed threshold, the updated first position data is obtained by clustering the first position data.
[0021] In one embodiment, before performing mean-reduction processing on the acceleration data for each axis and determining the first velocity data and first heading data of the track shoe based on the acceleration data and the angular velocity data, the process includes:
[0022] The swing period of the iron shoe is determined based on the zero point of the target angular velocity data; the target angular velocity data includes angular velocity data of pitch angle, and the swing period is used to determine the first velocity data of the iron shoe.
[0023] Secondly, this application also provides a positioning device for anti-slip iron shoes, comprising:
[0024] The data acquisition module is used to acquire the acceleration and angular velocity data of the iron shoe based on the inertial measurement unit that is relatively fixed to the iron shoe, and to acquire the first position data of the iron shoe based on the satellite navigation system;
[0025] The first data processing module is used to perform mean-removal processing on the acceleration data of each axis, and to determine the first velocity data and the first heading data of the iron shoe based on the acceleration data and the angular velocity data.
[0026] The second data processing module is used to correct the first speed data based on the first position data to obtain second speed data, and to correct the first heading data to obtain second heading data.
[0027] The positioning determination module is used to determine the second position data of the iron shoe based on the second speed data and the second heading data.
[0028] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the above-mentioned embodiments.
[0029] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the preceding claims.
[0030] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the above claims.
[0031] The aforementioned positioning method, device, computer equipment, computer-readable storage medium, and computer program product for anti-slip track shoes can effectively eliminate the influence of gravity on acceleration data by performing mean-reduction processing on acceleration data of each axis. This reduces acceleration data errors caused by the mismatch between the inertial measurement unit and the direction of travel during the movement of the track shoes from the storage cabinet to the placement location. By fusing positioning data determined by the inertial measurement unit and the satellite navigation system, positioning errors caused by a single data source can be reduced, ensuring positioning accuracy under conditions of signal obstruction or poor environmental conditions (such as inside the carriage or under the wheels). This improves the accuracy of positioning the track shoes and, consequently, enables accurate determination of whether the track shoes are placed on the correct track. Attached Figure Description
[0032] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0033] Figure 1 This is a flowchart illustrating the positioning method of the anti-slip iron shoe in one embodiment;
[0034] Figure 2 This is a schematic diagram illustrating the process of removing the mean from the acceleration data along each axis in one embodiment.
[0035] Figure 3 This is a waveform diagram of the acceleration data before the mean-removing processing is performed on the acceleration data of each axis in one embodiment;
[0036] Figure 4 This is a schematic diagram of the acceleration data waveforms after the mean was removed from the acceleration data along each axis in one embodiment.
[0037] Figure 5 This is a schematic diagram of a process in one embodiment where a first velocity data is corrected based on a first position data to obtain a second velocity data;
[0038] Figure 6 This is a flowchart illustrating the positioning method of the anti-slip iron shoe in another embodiment;
[0039] Figure 7 This is a schematic diagram of the positioning points corresponding to the first position data at the current moment and the first position data at a historical moment in the relevant technology.
[0040] Figure 8This is a schematic diagram of the positioning points corresponding to the first position data at the current moment and the first position data at a historical moment in one embodiment;
[0041] Figure 9 This is a flowchart illustrating the positioning method of the anti-slip iron shoe in another embodiment;
[0042] Figure 10 This is a schematic diagram of the positioning points corresponding to the first position data at the current moment and the first position data at a historical moment in another embodiment;
[0043] Figure 11 This is a flowchart illustrating the positioning method of the anti-slip iron shoe in yet another embodiment;
[0044] Figure 12 This is a waveform diagram of angular velocity data during one oscillation cycle of the iron shoe in related technologies.
[0045] Figure 13 This is a waveform diagram of angular velocity data during one swing cycle of the iron shoe in one embodiment.
[0046] Figure 14 This is a structural block diagram of the positioning device for the anti-slip iron shoe in one embodiment;
[0047] Figure 15 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0049] In one exemplary embodiment, such as Figure 1 As shown, a positioning method for anti-slip track shoes is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:
[0050] Step S102: Based on the inertial measurement unit that is relatively fixed to the iron shoe, acquire the acceleration data and angular velocity data of the iron shoe, and based on the satellite navigation system, acquire the first position data of the iron shoe.
[0051] The anti-slip wheel shoe can be equipped with an Inertial Measurement Unit (IMU) and a satellite positioning module. The IMU may include a set of accelerometers and gyroscopes to measure the wheel shoe's acceleration and angular velocity, respectively. Since the IMU is fixed relative to the wheel shoe, during the installation process where the installer moves the wheel shoe from the storage cabinet to the placement location, the swinging of the installer's arm and the rotation of their wrist cannot guarantee a strong binding relationship between the IMU's various axes and the direction of travel, leading to errors in acceleration data. Similarly, errors in angular velocity data will also occur. Specifically, due to the swinging of the installer's arm and the rotation of their wrist, the gyroscope's heading angle data for the corresponding axis will include the amplitude of wrist rotation, thus resulting in errors in the obtained heading angle change. Furthermore, due to the influence of the IMU's sampling frequency, some larger angular changes may not be captured during this process. The satellite positioning module is used to receive signals from a satellite navigation system (e.g., GNSS, GPS, etc.) to provide the wheel shoe's initial position data. This initial position data may include longitude, latitude, altitude, and a timestamp. When passing through non-open spaces such as carriages or placed under wheels, the accuracy and reliability of the first position data are poor.
[0052] For example, during the process of placing the iron shoe from the storage cabinet under the train wheels, the terminal can continuously acquire the acceleration data, angular velocity data, and first position data of the iron shoe, and then determine the first velocity data and first heading data of the iron shoe at each current moment in real time.
[0053] Step S104: The acceleration data of each axis are processed to remove the mean, and the first velocity data and first heading data of the iron shoe are determined based on the acceleration data and angular velocity data.
[0054] For example, the accelerometer can have three acceleration axes, used to measure the linear acceleration of the wheeled shoe in three directions. The method of averaging the acceleration data for each axis can include: averaging the acceleration data for each axis based on statistically obtained compensation values corresponding to each axis. The compensation values for each axis can be determined by comparing the acceleration data for each axis with the data where gravity acts only on one axis versus the data where gravity acts on multiple axes after the wheeled shoe is tilted at a certain angle, thus eliminating the influence of gravity on the acceleration data for each axis when the IMU's axes do not match the direction of travel. The method of determining the first velocity data and first heading data of the wheeled shoe based on the acceleration and angular velocity data can include: obtaining the first velocity data and first heading data of the wheeled shoe by integrating the acceleration and angular velocity data.
[0055] Step S106: Based on the first position data, the first velocity data is corrected to obtain the second velocity data, and the first heading data is corrected to obtain the second heading data.
[0056] For example, the correction of the first speed data and the second heading data can be based on data fusion or on model.
[0057] Step S108: Based on the second speed data and the second heading data, determine the second position data of the iron shoe.
[0058] For example, the second position data of the iron shoe at the current moment can be determined based on the second position data of the iron shoe determined at the previous moment, the second velocity data and the second heading data corrected at the current moment, until the iron shoe is placed under the wheel, so as to obtain the positioning trajectory and positioning endpoint of the iron shoe.
[0059] Alternatively, the second position data of the iron shoe can be determined based on the first velocity data and the first heading data; and the second position data can be corrected based on the first position data.
[0060] In the aforementioned positioning method for anti-slip track shoes, by performing mean-reduction processing on the acceleration data of each axis, the influence of gravity on the acceleration data can be effectively eliminated. This reduces the acceleration data error caused by the mismatch between the inertial measurement unit and the direction of travel due to the swinging of the track shoes during the movement from the storage cabinet to the placement location. By fusing positioning data determined by the inertial measurement unit and the satellite navigation system, the positioning error caused by a single data source can be reduced, ensuring the accuracy of positioning under conditions of signal obstruction or poor environment (such as inside the carriage or under the wheels). This improves the accuracy of positioning the track shoes and, consequently, enables accurate determination of whether the track shoes are placed on the correct track.
[0061] In an exemplary embodiment, the acceleration data for each axis may include acceleration data for a first axis, acceleration data for a second axis, and acceleration data for a third axis. The first axis, second axis, and third axis are perpendicular to each other, with the first axis pointing from the toe of the shoe to the handhold of the shoe. Figure 2 As shown, the steps described above for performing mean-removal processing on the acceleration data for each axis may include:
[0062] Step A1: Based on the acceleration data of each axis obtained within the historical time period, determine the average acceleration value corresponding to each axis; the historical time period includes at least one swing cycle of the iron shoe.
[0063] Step A2: In response to the fact that the mean acceleration value corresponding to the second axis and / or the mean acceleration value corresponding to the third axis are not zero, the acceleration data of each axis are subjected to mean-reduction processing based on the mean acceleration value corresponding to each axis.
[0064] For example, when the metal shoe is placed on the ground, the various axes of the IMU may include an x-axis (first axis) pointing from the toe of the metal shoe to the handheld part of the metal shoe, a y-axis (second axis) perpendicular to the ground, and a z-axis (third axis) perpendicular to both the x-axis and y-axis. When the installer picks up the metal shoe using the handheld part, in a stationary state, the x-axis, y-axis, and z-axis of the accelerometer correspond to the vertical, forward / backward, and horizontal acceleration directions, respectively. The x-axis, y-axis, and z-axis of the gyroscope correspond to the yaw angle, roll angle, and pitch angle, respectively. Please refer to... Figure 3 , Figure 3 This is a waveform diagram of the acceleration data before mean removal processing is performed on the acceleration data along each axis in one embodiment. L1, L2, L3, L4, L5, and L6 correspond to the data along the x-axis of the accelerometer, the x-axis of the gyroscope, the z-axis of the accelerometer, the y-axis of the gyroscope, the y-axis of the accelerometer, and the z-axis of the gyroscope, respectively, during the process of the iron shoe being placed under the train wheels from the storage cabinet. It is understood that, under normal circumstances, when the x-axis of the accelerometer is perpendicular to the ground, the mean values of the y-axis and z-axis accelerations should approach 0. However, during the installation process, the x-axis is not always perpendicular to the ground. For example, as... Figure 2 As shown, the metal shoe tilts due to the swinging of the installer's arm or the rotation of their wrist, causing gravitational acceleration to act on the z-axis of the accelerometer, resulting in a mean z-axis acceleration greater than 0. It should be noted that when the metal shoe is tilted, the y-axis acceleration can also be greater than 0, or both the y-axis and z-axis accelerations can be greater than 0 simultaneously.
[0065] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the acceleration data waveforms after mean-removal processing of the acceleration data along each axis in one embodiment. L1', L3', and L5' represent the mean acceleration values based on the x-axis, y-axis, and z-axis of the accelerometer, respectively. Figure 3 The values of L1, L3, and L5 were obtained after removing the mean. Figure 3 and Figure 4The waveform diagram contains multiple swing cycles of the wheeled shoe. For example, the average acceleration can be determined based on acceleration data acquired over a historical time period, thus eliminating the influence of gravity and centrifugal force generated by the swing on the acceleration data. The historical time period can include at least one swing cycle of the wheeled shoe; for example, the historical time period can include the previous swing cycle. It is understood that the historical time period can also include all swing cycles prior to the current moment.
[0066] In this embodiment, by performing mean-reduction processing on the acceleration data of each axis based on the average acceleration value determined by the historical swing cycle when the iron shoe is tilted, the influence of gravity and centrifugal force generated by swing on the acceleration data can be eliminated at the same time, and the real-time performance of the acceleration average value is good. Therefore, the error of the acceleration data is further reduced.
[0067] In one exemplary embodiment, such as Figure 5 As shown, the step of correcting the first velocity data based on the first position data to obtain the second velocity data may include:
[0068] Step B1: Determine reference speed data based on the first position data at the current moment and the first position data at historical moments.
[0069] Step B2 involves fusing the reference speed data and the first speed data to obtain the second speed data.
[0070] In one possible implementation, a weighted average can be taken between the reference velocity data and the first velocity data. In another possible implementation, a Kalman filter can be used to fuse the reference velocity data and the first velocity data.
[0071] Similarly, the steps described above for correcting the first heading data based on the first position data to obtain the second heading data may include: determining reference heading data based on the first position data at the current time and the first position data at historical times; and performing data fusion on the reference heading data and the first heading data to obtain the second heading data.
[0072] In this embodiment, the speed and heading determined by the inertial measurement unit are corrected in real time by using positioning data determined by the satellite navigation system, which can improve the continuity and accuracy of the positioning trajectory of the iron shoe.
[0073] In one exemplary embodiment, such as Figure 6 As shown, the above positioning method may further include:
[0074] Step S109: Based on the first position data at the current moment and the first position data at historical moments, if it is determined that the change in the heading of the iron shoe is greater than a preset angle threshold, the second position data is smoothed and then output.
[0075] Specifically, determining that the heading change of the track shoe exceeds a preset angle threshold based on the current and historical position data can be used to characterize a potentially large error in the initial position data. This could be because the track shoe is located in a non-open environment. Alternatively, please refer to... Figure 7 In the case that the first position data includes RTK (Real-time kinematic) data (including: fixed-solution RTK, floating-point solution RTK, and unsolvable RTK), it can also be a solution state switch of the first position data.
[0076] For example, reference heading data can be determined based on the first position data at the current moment and the first position data at a historical moment; the heading change of the wheel shoe at the current moment can be determined based on the reference heading data at the current moment and the reference heading data at a historical moment. Furthermore, in response to the heading change of the wheel shoe exceeding a preset angle threshold (e.g., 45°), the second position data can be smoothed and then output. In one possible implementation, please refer to... Figure 8 Smoothing the data at the second position can be achieved by adjusting the weighting of the data at the first position. Alternatively, smoothing the data at the second position can be achieved by applying a moving average to the data at the second position over a period of time.
[0077] Optionally, if the change in the heading of the wheel shoe is greater than a preset angle threshold based on the second position data at the current moment and the second position data at historical moments, the second position data can be smoothed before output. Specifically, determining that the change in the heading of the wheel shoe is greater than the preset angle threshold based on the second position data at the current moment and the second position data at historical moments can be used to characterize the potential for large errors in the acceleration and angular velocity data.
[0078] In this embodiment, by smoothing the positioning trajectory when there are large changes in heading between positioning points, the accuracy of positioning the iron shoe can be further improved.
[0079] In one exemplary embodiment, such as Figure 9 As shown, the above positioning method may further include:
[0080] Step S107: If the first speed data and / or the second speed data are less than a preset speed threshold, the updated first position data is obtained by performing clustering processing on the first position data.
[0081] Among them, the fact that the first speed data and / or the second speed data are less than the preset speed threshold can be used to indicate that the iron shoe has been placed under the wheel. At this time, the satellite navigation is blocked by the wheel, and the first position data is divergent.
[0082] For example, clustering the first location data can be performed by continuously calculating the average of the first location data at the current moment and the first location data at historical moments, using this average as the updated first location data. For example, please refer to... Figure 10 When the first location data includes RTK data, the positioning points provided by the first location data continue to diverge. In this case, the endpoint of the positioning point at an adjacent time can be taken as the updated first location data.
[0083] Furthermore, the second position data can be corrected based on the updated first position data. The process of correcting the second position data can refer to the process of correcting the first velocity data, and will not be elaborated upon here.
[0084] In this embodiment, by performing clustering processing on the positioning data determined by the divergent satellite navigation system when the iron shoe is almost stationary, the stability of the output positioning endpoint can be guaranteed.
[0085] In one exemplary embodiment, such as Figure 11 As shown, the above positioning method may further include:
[0086] Step S107: Determine the swing period of the iron shoe based on the zero point of the target angular velocity data; the target angular velocity data includes the angular velocity data of the pitch angle, and the swing period is used to determine the first velocity data of the iron shoe.
[0087] The target angular velocity data may include angular velocity data of pitch angle or angular velocity data of other axes that change periodically.
[0088] As the installer moves the metal shoe from the storage cabinet to the placement location, their arm swings back and forth with each step, causing the metal shoe to swing periodically (two steps correspond to one swing cycle). Figure 3 As shown in L6, the change in pitch angle follows a certain regularity.
[0089] Currently, the periodicity of data is mainly determined based on the poles of periodically changing data. However, as... Figure 12 As shown, the angular velocity data of the pitch angle of the iron shoe fluctuates near the pole, making it difficult for related technologies to determine the oscillation period of the iron shoe. Therefore, as Figure 13 As shown, the swing period of the iron shoe can be determined based on the zero point of the target angular velocity data.
[0090] Furthermore, for each oscillation cycle, calculating the average of the acceleration data yields the change in velocity per unit time within the current cycle. Then, by using the data timestamps to calculate the duration of the current cycle, we can obtain the first velocity data and travel distance corresponding to the current cycle. Similarly, for each oscillation cycle, calculating the average of the angular velocity data yields the change in attitude per unit time within the current cycle. Then, by using the data timestamps to calculate the duration of the current cycle, we can obtain the degree of attitude change corresponding to the current cycle.
[0091] In this embodiment, the swing period of the iron shoe is determined by the zero point of the periodically changing angular velocity data, which has high accuracy and can thus more accurately correct the positioning data.
[0092] In summary, the above-mentioned positioning method for anti-slip track shoes effectively eliminates the influence of gravity on acceleration data by averaging the acceleration data along each axis. This reduces acceleration data errors caused by the mismatch between the inertial measurement unit and the direction of travel during the movement of the track shoes from the storage cabinet to the placement location. By fusing positioning data determined by the inertial measurement unit and the satellite navigation system, positioning errors caused by a single data source are reduced, ensuring positioning accuracy even under conditions of signal obstruction or poor environmental conditions (such as inside the carriage or under the wheels). This improves the accuracy of track shoe positioning and, consequently, allows for accurate determination of whether the track shoes are placed on the correct track.
[0093] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0094] Based on the same inventive concept, this application also provides a positioning device for implementing the positioning method for anti-slip iron shoes described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more positioning device embodiments provided below can be found in the limitations of the positioning method for anti-slip iron shoes described above, and will not be repeated here.
[0095] In one exemplary embodiment, such as Figure 14As shown, a positioning device 300 for anti-slip iron shoes is provided, including: a data acquisition module 301, a first data processing module 302, a second data processing module 303, and a positioning determination module 304, wherein:
[0096] The data acquisition module 301 is used to acquire the acceleration and angular velocity data of the iron shoe based on the inertial measurement unit that is relatively fixed to the iron shoe, and to acquire the first position data of the iron shoe based on the satellite navigation system.
[0097] The first data processing module 302 is used to perform mean-reduction processing on the acceleration data of each axis, and to determine the first velocity data and first heading data of the iron shoe based on the acceleration data and angular velocity data.
[0098] The second data processing module 303 is used to correct the first speed data based on the first position data to obtain the second speed data, and to correct the first heading data to obtain the second heading data.
[0099] The positioning determination module 304 is used to determine the second position data of the iron shoe based on the second speed data and the second heading data.
[0100] In an exemplary embodiment, the acceleration data for each axis includes acceleration data for a first axis, acceleration data for a second axis, and acceleration data for a third axis. The first axis, second axis, and third axis are perpendicular to each other, with the first axis pointing from the toe of the shoe to the handhold of the shoe. The aforementioned first data processing module 302 is further configured to:
[0101] Based on the acceleration data of each axis obtained within a historical time period, the average acceleration value corresponding to each axis is determined; the historical time period includes at least one swing cycle of the iron shoe.
[0102] In response to the fact that the mean acceleration value corresponding to the second axis and / or the mean acceleration value corresponding to the third axis are not zero, the acceleration data for each axis are subjected to mean-reduction processing based on the mean acceleration value corresponding to each axis.
[0103] In an exemplary embodiment, the second data processing module 303 described above is further configured to:
[0104] Based on the first position data at the current moment and the first position data at historical moments, determine the reference speed data;
[0105] The reference speed data and the first speed data are fused to obtain the second speed data.
[0106] In one exemplary embodiment, the positioning device 300 for the anti-slip track shoe further includes:
[0107] The third data processing module is used to smooth the second position data and output it after determining that the change in the heading of the iron shoe is greater than a preset angle threshold based on the first position data at the current time and the first position data at historical times.
[0108] In an exemplary embodiment, the third data processing module described above is further configured to:
[0109] If the first speed data and / or the second speed data are less than a preset speed threshold, the updated first position data is obtained by clustering the first position data.
[0110] In one exemplary embodiment, the positioning device 300 for the anti-slip track shoe further includes:
[0111] The period determination module is used to determine the swing period of the iron shoe based on the zero point of the target angular velocity data; the target angular velocity data includes the angular velocity data of the pitch angle, and the swing period is used to determine the first velocity data of the iron shoe.
[0112] The various modules in the positioning device of the aforementioned anti-slip iron shoe can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.
[0113] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 15As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a positioning method for anti-slip shoe sensors. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.
[0114] Those skilled in the art will understand that Figure 15 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0115] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0116] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.
[0117] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.
[0118] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.
[0119] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0120] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A positioning method for anti-slip iron shoes, characterized in that, The method includes: Based on an inertial measurement unit that is relatively fixed to the iron shoe, the acceleration and angular velocity data of the iron shoe are acquired, and based on a satellite navigation system, the first position data of the iron shoe is acquired. The acceleration data for each axis are subjected to mean removal processing, and based on the acceleration data and the angular velocity data, the first velocity data and the first heading data of the iron shoe are determined; Based on the first position data, the first speed data is corrected to obtain the second speed data, and the first heading data is corrected to obtain the second heading data; Based on the second speed data and the second heading data, the second position data of the iron shoe is determined.
2. The method according to claim 1, characterized in that, The acceleration data for each axis includes acceleration data for a first axis, acceleration data for a second axis, and acceleration data for a third axis. The first axis, the second axis, and the third axis are perpendicular to each other. The first axis extends from the toe of the iron shoe to the handhold of the iron shoe. The process of removing the mean from the acceleration data for each axis includes: Based on the acceleration data of each axis obtained within a historical time period, the average acceleration value corresponding to each axis is determined respectively; the historical time period includes at least one swing cycle of the iron shoe; In response to the fact that the mean acceleration value corresponding to the second axis and / or the mean acceleration value corresponding to the third axis are not zero, the acceleration data for each axis are subjected to mean-reduction processing based on the mean acceleration value corresponding to each axis.
3. The method according to claim 1, characterized in that, The step of correcting the first velocity data based on the first position data to obtain the second velocity data includes: Based on the first position data at the current moment and the first position data at historical moments, determine the reference speed data; The reference speed data and the first speed data are fused to obtain the second speed data.
4. The method according to claim 1, characterized in that, After determining the second position data of the iron shoe based on the second speed data and the second heading data, the process includes: If, based on the first position data at the current moment and the first position data at a historical moment, the heading change of the iron shoe is determined to be greater than a preset angle threshold, the second position data is smoothed and then output.
5. The method according to claim 1, characterized in that, Before determining the second position data of the iron shoe based on the second speed data and the second heading data, the following steps are included: If the first speed data and / or the second speed data are less than a preset speed threshold, the updated first position data is obtained by clustering the first position data.
6. The method according to claim 1, characterized in that, Before performing mean-reduction processing on the acceleration data for each axis, and determining the first velocity data and first heading data of the track shoe based on the acceleration data and the angular velocity data, the following steps are included: The swing period of the iron shoe is determined based on the zero point of the target angular velocity data; the target angular velocity data includes angular velocity data of pitch angle, and the swing period is used to determine the first velocity data of the iron shoe.
7. A positioning device for anti-slip iron shoes, characterized in that, The device includes: The data acquisition module is used to acquire the acceleration and angular velocity data of the iron shoe based on the inertial measurement unit that is relatively fixed to the iron shoe, and to acquire the first position data of the iron shoe based on the satellite navigation system; The first data processing module is used to perform mean-removal processing on the acceleration data of each axis, and to determine the first velocity data and the first heading data of the iron shoe based on the acceleration data and the angular velocity data. The second data processing module is used to correct the first speed data based on the first position data to obtain second speed data, and to correct the first heading data to obtain second heading data. The positioning determination module is used to determine the second position data of the iron shoe based on the second speed data and the second heading data.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.