Target optimization positioning method and system for railway platform canopy inspection

Abstract

The invention discloses a target optimization positioning method and system for railway platform canopy inspection, and the method comprises the steps: obtaining the longitude and latitude coordinate values of a platform, and obtaining the starting point coordinate, terminal point coordinate and central axis length of the central axis of the platform; gNSS positioning of the inspection personnel is obtained in real time, and a projection point of a positioning point on the central axis in the along-track direction is obtained; calculating the distance between the positioning point and the projection points of different platforms, and taking the projection point with the absolute minimum value as real-time positioning of the inspection personnel; calculating the distance between adjacent positioning information, judging the state of the current inspection personnel according to the value of the acceleration sensor and the distance difference between the adjacent positioning information, and returning real-time positioning information; and converting the calculated positioning information into longitude and latitude coordinate values, driving the figure model to move, and driving the figure model to rotate towards and at a visual angle. According to the invention, low-cost positioning of an indoor scene is realized, the problems of large positioning deviation, positioning information loss and the like caused by canopy shielding and electromagnetic interference are solved, and the real-time positioning precision is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of navigation and positioning, and in particular to a target optimization positioning method and system for railway platform canopy inspection. Background Art

[0002] Platform canopies are the key to the operation, maintenance and management of railway building equipment. Their safe operation is related to the safety of society and the lives and property of the people. In order to ensure the safe operation of the railway, the railway building management department needs to conduct daily inspections of platform canopies. With the proposal and development of digital railways, railway building operation and maintenance have gradually adopted electronic inspection systems to replace traditional manual inspections.

[0003] Real-time positioning technology is the key technology for the informatization of daily patrol inspections. Commonly used wireless positioning technologies include GNSS positioning (Global Navigation Satellite System), network positioning, UWB positioning technology, ZigBee positioning technology, Bluetooth positioning technology, etc.

[0004] GNSS positioning includes GPS, Beidou, etc. The positioning accuracy is greatly affected by occlusion. The advantage is that it does not rely on the network and does not require supporting hardware support.

[0005] Network positioning includes WiFi positioning and base station positioning. It depends on the number of networks and base stations, and has low positioning accuracy. It is not suitable for scenarios where there is no WiFi on the platform canopy and the operator's signal is weak.

[0006] UWB positioning technology has the dual advantages of real-time positioning and precise positioning. It requires the deployment of base stations and is easily affected by obstructions.

[0007] ZigBee positioning technology has high positioning accuracy, requires supporting base stations and receivers, and has poor anti-interference capabilities. It has unstable positioning in the railway high-voltage contact network environment.

[0008] Bluetooth positioning technology requires a large number of Bluetooth beacons as positioning terminals, which is high in cost and high in positioning accuracy.

[0009] Platform canopy inspection requires visual tracking of inspection routes and real-time risk warning, which places high demands on positioning accuracy. The platform canopy covers the entire platform, which is an indoor positioning scenario. There are many buildings and train stops around the scene that block wireless signals. The railway high-voltage contact network interferes with the stability and transmission quality of wireless signals. The magnetic field around the scene is complex, the GNSS signal is weak, the positioning accuracy is low, and the delay is large.

[0010] The scene positioning space of the platform canopy is long and narrow, with a width of 450 meters or 550 meters along the track, but a smaller width perpendicular to the track. Routine inspections are usually carried out along the track, so higher accuracy is required along the track. The vertical direction needs to ensure that the positioning does not deviate from the platform range.

[0011] The characteristics of platform canopy inspection are that the inspection personnel are relatively fixed and the inspection involves multiple platforms. The hardware layout within the yard has great safety hazards under the repeated action of various dynamic loads such as train vibration and train wind. Therefore, considering the safety and economic practicality requirements, base stations, beacons, smart handheld terminals and other hardware are not suitable for platform canopy positioning scenarios.

[0012] The problems and defects of the prior art are: In view of the indoor scene characteristics of platform canopies and the scene positioning accuracy requirements, the existing positioning technology has low network positioning accuracy and is not suitable for scenes where there is no WIFI on the platform canopy and the operator signal is weak. ZigBee positioning technology has poor anti-interference ability and is not suitable for scenes such as railway high-voltage contact networks. UWB positioning technology and Bluetooth positioning technology require the deployment of base stations, beacons, etc., and have high hardware deployment requirements and high costs. They are not suitable for scenes with multiple platforms and relatively fixed inspection personnel.

[0013] Although GNSS positioning meets the inspection and positioning requirements of platform canopies, the vertical positioning accuracy is blocked by the canopy cover, the positioning is offset out of the platform range and is located on the track, the along-track positioning accuracy error is about 2-5 meters, and the positioning delay is large. Therefore, the GNSS positioning accuracy does not meet the requirements of platform canopy inspection, and there are no mature algorithms and supporting systems on the market that can be used to improve the GNSS positioning accuracy. It is necessary to develop corresponding algorithms to assist positioning and improve positioning accuracy for platform canopy inspection scenarios.

[0014] In summary, the significance of the present invention is: According to the characteristics of indoor positioning scenarios for railway platform canopy inspections, based on different positioning accuracy requirements in the along-track and perpendicular-track directions, in order to achieve low-cost positioning based on GNSS, the present invention proposes a positioning optimization algorithm based on the target scenario to solve the problems of GNSS positioning offset out of the platform in the perpendicular-track direction, large positioning accuracy error in the along-track direction, and GNSS signal loss due to shading by the canopy roof, thereby improving the positioning accuracy of inspection personnel and meeting the positioning requirements for daily inspections of railway platform canopies.

[0015] This positioning method has higher robustness and positioning accuracy, does not require the installation of a large number of additional hardware equipment, is easy to promote, and has great advantages and commercial prospects in platform canopy inspection and positioning application scenarios in complex environments. Summary of the invention

[0016] In view of the above problems, the present invention proposes a target optimization positioning method and system for railway platform canopy inspection, and the specific scheme is as follows: A target optimization positioning method for railway platform canopy inspection includes the following steps: S1: Initialize the GNSS positioning, gyro sensor, and acceleration sensor data of the mobile device, obtain the longitude and latitude coordinates of the four corner points of each platform of the platform canopy, perform UTM plane rectangular coordinate conversion, and obtain the starting point coordinates, end point coordinates, and length of the central axis of the platform along the track; S2: Obtain the GNSS positioning of the inspection personnel in real time, convert the longitude and latitude coordinates obtained into UTM plane rectangular coordinates, and obtain the projection point of the positioning point on the central axis along the track through vector dot product operation; S3: Calculate the distance between the positioning point and the projection points of different platforms, take the absolute minimum value to determine the platform where the current patrol officer is located, and use the projection point along the track to the central axis as the real-time positioning of the patrol officer, so that the patrol officer can move on the central axis of the platform without deviating from the platform range; S4: Obtain data information of the acceleration sensor, as well as the current GNSS positioning information and the previous GNSS positioning information, calculate the distance between adjacent positioning information, and determine whether the current inspection personnel is in a stationary state or a moving state through the difference between the acceleration sensor value and the distance of the adjacent positioning; S5: When it is determined that the inspection personnel is in a stationary state, the average value of multiple GNSS positioning information obtained with delay is calculated and the positioning information is returned; S6: When it is determined that the inspection personnel are in motion, the GNSS positioning information of the calibration point and its projection point on the central axis are obtained, the offset value of the inspection personnel in motion and the calibration point on the central axis is calculated, the sum of the inspection personnel's real-time GNSS positioning information and the offset value is used as the input value, the Kalman filter prediction model is introduced, the state value is updated in real time after multiple predictions, and the real-time positioning information is returned; S7: Convert the calculated positioning information into longitude and latitude coordinate values, drive the movement of the character model, obtain data information from the gyroscope sensor, and drive the direction and viewing angle of the character model to rotate.

[0017] Preferably, in step S2, the vector dot product operation refers to: It is known that vector A=(A(lat),A(lng)) is the vector from the starting point of the central axis to the positioning point, and vector B=(B(lat),B(lng)) is the vector from the starting point of the central axis to the end point along the track; A(lat) and A(lng) are the components of vector A in longitude and latitude, B(lat) and B(lng) are the components of vector B in longitude and latitude; Through the vector dot product operation , |A|cos(θ) is the projection length of A on vector B, and the projection point and projection length of the inspector on the central axis are obtained. Through linear interpolation, the longitude and latitude coordinate values ​​of the current positioning point are calculated.

[0018] Preferably, in step S3, the vertical distance between the positioning point and the projection points of different platforms is a positive value or a negative value, and in order to determine the platform where the inspection personnel is located, the absolute minimum value of the vertical distance is taken.

[0019] Preferably, in step S4, determining whether the current inspection personnel is in a stationary state or a moving state includes the following methods: S41: When the acceleration obtained by the acceleration sensor is less than the normal walking acceleration of an adult, the current state is static; S42: When the difference between the positioning coordinate values ​​of adjacent GNSS is less than the distance moved per second by a normal adult, the current state is static; S43: When the corresponding positioning distance is greater than the normal adult's moving distance per second, and the acceleration obtained by the acceleration sensor is greater than the normal adult's walking acceleration, the current state is motion.

[0020] Preferably, in step S5, the average value is calculated by directly summing up the number of GNSS positioning information obtained and then taking the average value, or by assigning weights according to the order in which the GNSS positioning information is obtained and then calculating the weighted average value.

[0021] Preferably, in step S6, the Kalman filter algorithm inputs the measurement value, calculates the Kalman gain through the predicted state evaluation value and covariance matrix, and then updates the state evaluation value and covariance matrix according to the measurement value, and inputs the sum of the current GNSS positioning and offset values ​​as the measurement value, and performs prediction updates by obtaining uninterrupted positioning information input in real time.

[0022] Preferably, in step S6, the offset value is the offset value between the current GNSS positioning of the inspection personnel and the current calibration point on the central axis, there is one or more calibration points, and the number of corresponding offset values ​​is consistent with the number of calibration points.

[0023] Further preferably, the calibration point is a reference point whose positioning information is unique and easily identifiable.

[0024] The second purpose of the present invention is: A target optimization positioning system for railway platform canopy inspection, which applies a target optimization positioning method for railway platform canopy inspection, includes the following modules: Data acquisition and storage module, used to collect and store GNSS positioning data, acceleration sensor data, gyroscope sensor information and other related information; Platform data processing module, used to realize the UTM conversion of platform coordinates and the calculation of the platform centerline coordinates; The positioning data processing module projects the real-time GNSS positioning data of the inspectors onto the central axis of the platform along the track after calculation to obtain the coordinate value of the projection point; The platform positioning and identification module is used to calculate the distance between the inspection personnel and the projection points of different platforms and identify the current platform; Inspection status judgment module, used to calculate the value of the acceleration sensor and the distance difference between adjacent positioning, and identify whether the current inspection personnel is in a stationary state or in a moving state; Inspection positioning optimization module, used to adopt different algorithms according to different states of inspection personnel to return optimized positioning information; The model-driven control module is used to drive the character model and adjust the viewing angle according to the optimized positioning information.

[0025] Preferably, the target optimization positioning system for railway platform canopy inspection is mounted on a mobile terminal with GNSS positioning, gyroscope sensor and acceleration sensor.

[0026] The beneficial effects of the present invention are: The present invention obtains the projection point of the positioning point along the track to the central axis through vector dot product operation, and determines the positioning algorithm of the platform where it is located, which solves the problem that GNSS positioning cannot be used for indoor positioning scenarios with full coverage of platform canopies, and ensures that the positioning of patrol personnel does not deviate from the platform range.

[0027] By calculating the difference between the acceleration sensor value and the distance to the corresponding positioning point, the real-time status of the inspection personnel is judged, and the along-track positioning calibration is realized through the average value method and Kalman filter principle, which solves the problem of large delays caused by GNSS positioning interference from the platform magnetic field. At the same time, it reduces the risk of large positioning deviations of inspection personnel, effectively improves the positioning accuracy of inspection personnel, and ensures the inspection efficiency and real-time performance.

[0028] Compared with the prior art, the advantages of the present invention further include: Through the targeted optimization of the along-track and perpendicular-track positioning algorithms, the problems of low GNSS positioning accuracy and failure to meet the positioning scenarios for railway platform canopy inspections were solved, and the real-time positioning accuracy of inspection personnel was improved, thereby improving the visual tracking level of the inspection trajectory and the real-time early warning capability of the inspection, which is beneficial to improving the structural safety of the platform canopy and ensuring the operational safety of the railway and the personal safety of passengers.

[0029] The technical solution of the present invention is based on the scene positioning needs of railway platform canopy inspections in complex surrounding environments. It meets the existing railway building operation and maintenance management and operation modes, and does not require additional hardware equipment. It has good economic applicability and is easy to promote. It has great advantages and commercial prospects.

[0030] The technical solution of the present invention is conducive to improving the work efficiency of platform canopy inspectors, improving the ability to detect diseases during inspections and provide real-time warnings, which is conducive to reducing the overall risk of platform canopies and improving the efficiency and level of operation and maintenance management.

[0031] The technical solution of the present invention improves the positioning accuracy of indoor positioning scenarios without increasing hardware equipment, and can be extended to narrow and long indoor positioning scenarios with different positioning accuracy requirements in two directions. The technical solution has strong versatility and great promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments, but it should be understood that these drawings are designed only for explanation purposes and are not intended to limit the scope of the present invention. In addition, unless otherwise specified, these drawings are intended only to conceptually illustrate the structural configurations described herein and are not necessarily drawn to scale.

[0033] Figure 1 This is a flow chart of a target optimization positioning method for railway platform canopy inspection provided by Embodiment 1 of the present invention; Figure 2 is a flow chart of the central axis positioning algorithm mentioned in steps S1 to S3 provided in embodiment 1 of the present invention; Figure 3 is a logic flow chart of determining the current state of step S4 provided in embodiment 1 of the present invention; Figure 4 is a flow chart of the average value algorithm and the Kalman filter algorithm in steps S5 to S7 provided in embodiment 1 of the present invention; Figure 5 is a schematic diagram of obtaining the projection point of the positioning point on the along-track central axis through vector dot product calculation provided by Embodiment 1 of the present invention; Figure 6 It is a schematic diagram of the system modules provided in Example 2 of the present invention.

[0034] In the figure: 1-Data acquisition and storage module; 2-Platform data processing module; 3-Positioning data processing module; 4-Platform positioning identification module; 5-Inspection status judgment module; 6-Inspection positioning optimization module; 7-Model driven control module. DETAILED DESCRIPTION

[0035] First of all, it should be noted that the specific structure, features and advantages of the present invention will be specifically described below by way of example, but all descriptions are only for illustration and should not be understood as limiting the present invention in any way. In addition, any single technical feature described or implied in the embodiments mentioned herein, or any single technical feature displayed or implied in the drawings, can still be combined or deleted between these technical features to obtain more other embodiments of the present invention that may not be directly mentioned in this document. In addition, in order to simplify the drawings, the same or similar technical features may be marked in only one place in the same drawing.

[0036] In the present invention, unless otherwise clearly stipulated and limited, the terms such as "installation", "setting", "connection", "fixation" and "screw-on" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. Unless otherwise clearly defined, ordinary technicians in this field can understand the specific meanings of the above terms in the present invention according to the specific circumstances.

[0037] The following is combined with Figure 1 -Attached Figure 6 The present invention will be described in detail. Example 1

[0038] A target optimization positioning method for railway platform canopy inspection includes the following steps: S1: Initialize the GNSS positioning, gyro sensor, and acceleration sensor data of the mobile device, obtain the longitude and latitude coordinates of the four corner points of each platform of the platform canopy, perform UTM plane rectangular coordinate conversion, and obtain the starting point coordinates, end point coordinates, and length of the central axis of the platform along the track; S2: Obtain the GNSS positioning of the inspection personnel in real time, convert the longitude and latitude coordinates obtained into UTM plane rectangular coordinates, and obtain the projection point of the positioning point on the central axis along the track through vector dot product operation; S3: Calculate the distance between the positioning point and the projection points of different platforms, take the absolute minimum value to determine the platform where the current patrol officer is located, and use the projection point along the track to the central axis as the real-time positioning of the patrol officer, so that the patrol officer can move on the central axis of the platform without deviating from the platform range; This step is used to solve the problem of excessive GNSS positioning deviation and out-of-station range; S4: Obtain data information of the acceleration sensor, as well as the current GNSS positioning information and the previous GNSS positioning information, calculate the distance between adjacent positioning information, and determine whether the current inspection personnel is in a stationary state or a moving state through the difference between the acceleration sensor value and the distance of the adjacent positioning; S5: When it is determined that the inspection personnel is in a stationary state, the average value of multiple GNSS positioning information obtained with delay is calculated and the positioning information is returned; This step is used to solve the problem of large delays in GNSS positioning due to interference; S6: When it is determined that the inspection personnel are in motion, the GNSS positioning information of the calibration point and its projection point on the central axis are obtained, the offset value of the inspection personnel in motion and the calibration point on the central axis is calculated, the sum of the inspection personnel's real-time GNSS positioning information and the offset value is used as the input value, the Kalman filter prediction model is introduced, the state value is updated in real time after multiple predictions, and the real-time positioning information is returned; This step is used to reduce the risk of large deviations in positioning along the track by the inspection personnel; S7: Convert the calculated positioning information into longitude and latitude coordinate values, drive the movement of the character model, obtain data information from the gyroscope sensor, and drive the direction and viewing angle of the character model to rotate.

[0039] The innovative point of the target optimization positioning method for railway platform canopy inspection provided by an embodiment of the present invention is that: without adding hardware equipment, the present invention solves the problems of low GNSS positioning accuracy and failure to meet the positioning scenarios of railway platform canopy inspection through target optimization of along-track and perpendicular-track positioning algorithms, thereby improving the positioning accuracy of indoor positioning scenarios. The technical solution has strong versatility and great promotion value.

[0040] Furthermore, it can also be considered in the embodiment that in step S2, the vector dot product operation refers to: It is known that vector A=(A(lat),A(lng)) is the vector from the starting point of the central axis to the positioning point, and vector B=(B(lat),B(lng)) is the vector from the starting point of the central axis to the end point along the track; A(lat) and A(lng) are the components of vector A in longitude and latitude, B(lat) and B(lng) are the components of vector B in longitude and latitude; Through the vector dot product operation , |A|cos(θ) is the projection length of A on vector B, and the projection point and projection length of the inspector on the central axis are obtained. Through linear interpolation, the longitude and latitude coordinate values ​​of the current positioning point are calculated.

[0041] Furthermore, in the embodiment, it can also be considered that in step S3, the vertical distance between the positioning point and the projection points of different platforms is a positive value or a negative value, and in order to determine the platform where the inspection personnel is located, the absolute minimum value of the vertical distance is taken.

[0042] In this embodiment, the vertical distance between the positioning point and the projection points of different platforms may be positive or negative, so the absolute minimum value is taken to determine the platform where the inspection personnel are located. At the same time, the display positioning of the inspection personnel can only appear on the platform, and will not appear in other locations such as the station building and track.

[0043] Furthermore, in the embodiment, it can also be considered that in step S4, judging whether the current inspection personnel is in a stationary state or a moving state includes the following methods: S41: When the acceleration obtained by the acceleration sensor is less than the normal walking acceleration of an adult, the current state is static; S42: When the difference between the positioning coordinate values ​​of adjacent GNSS is less than the distance moved per second by a normal adult, the current state is static; S43: When the corresponding positioning distance is greater than the normal adult's moving distance per second, and the acceleration obtained by the acceleration sensor is greater than the normal adult's walking acceleration, the current state is motion.

[0044] In this embodiment, the walking acceleration of a normal adult is 1-1.5 m / s, and the distance a normal adult moves per second is 1-1.5 m.

[0045] Furthermore, in the embodiment, it can be considered that in step S5, the average value is calculated by directly summing up the number of GNSS positioning information obtained, or by assigning weights according to the order in which the GNSS positioning information is obtained, and then calculating a weighted average value.

[0046] Furthermore, in the embodiment, it can also be considered that in step S6, the Kalman filter algorithm inputs the measurement value, calculates the Kalman gain through the predicted state evaluation value and the covariance matrix, and then updates the state evaluation value and the covariance matrix according to the measurement value, and inputs the sum of the current GNSS positioning and offset values ​​as the measurement value, and performs prediction updates by obtaining uninterrupted positioning information input in real time.

[0047] Furthermore, in the embodiment, it can also be considered that in step S6, the offset value is the offset value between the current GNSS positioning of the inspection personnel and the current calibration point on the central axis. The calibration point can be one or more, and the number of corresponding offset values ​​is consistent with the number of calibration points.

[0048] Furthermore, in the embodiments, it can also be considered that the calibration point is a reference point whose positioning information is unique and easily identifiable.

[0049] In this embodiment, the calibration points are reference points with unique positioning information such as platform corners, awning columns, and station buildings, which are easily identifiable.

[0050] Furthermore, it can also be considered in the embodiment that in step S1, the mobile device should have the functions of GNSS positioning, gyroscope sensor and acceleration sensor, including mobile phones, tablet computers, smart terminals, handheld terminals, etc. The mobile device system can be IOS, Android, Hongmeng, etc.

[0051] Furthermore, in the embodiment, it can be considered that in step S1, the platform canopy can be a canopy with platform columns or a canopy without platform columns at stations of high-speed railways, conventional railways, heavy-load railways, intercity railways, etc. The platform canopy can be a concrete canopy, a steel structure canopy, a composite structure canopy, etc. in terms of structure.

[0052] Furthermore, in the embodiment, it can also be considered that in step S1, the longitude and latitude coordinate values ​​of the platform corner points can be obtained through map data such as Baidu and Amap, or can be obtained through surveying and mapping.

[0053] Furthermore, it can also be considered in the embodiment that in step S1, the length of the central axis of the platform generally includes 450 meters, 550 meters, etc. The width of the platform includes 8 meters, 10.5 meters, 12 meters, etc.

[0054] Furthermore, it can also be considered in the embodiment that in step S2, the GNSS positioning includes Beidou, GPS, GLONASS, Galileo, etc. Example 2

[0055] A target optimization positioning system for railway platform canopy inspection, using a target optimization positioning method for railway platform canopy inspection, includes the following modules: Data acquisition and storage module 1, used to collect and store GNSS positioning data, acceleration sensor data, gyroscope sensor information and other related information; Platform data processing module 2, used to realize the UTM conversion of platform coordinates and the calculation of the coordinates of the platform centerline; Positioning data processing module 3 projects the real-time GNSS positioning data of the inspectors onto the central axis of the platform along the track after calculation to obtain the coordinate value of the projection point; Platform positioning and identification module 4, used to calculate the distance between the inspection personnel and the projection points of different platforms and identify the current platform; Inspection status judgment module 5, used to calculate the value of the acceleration sensor and the distance difference between adjacent positioning, and identify whether the current inspection personnel is in a stationary state or a moving state; Inspection positioning optimization module 6, used to adopt different algorithms according to different states of inspection personnel to return optimized positioning information; The model driving control module 7 is used to drive the character model and adjust the viewing angle according to the optimized positioning information.

[0056] Furthermore, it can also be considered in the embodiments that the target optimization positioning system for the railway platform canopy inspection is mounted on a mobile terminal with GNSS positioning, a gyroscope sensor and an acceleration sensor.

[0057] Application Example 1: To further illustrate the present invention, a high-speed railway passenger station cross-line steel structure canopy is taken as an example, and the present invention is described in detail in conjunction with the accompanying drawings: Step S1: Initialize the GNSS positioning, gyroscope sensor, and acceleration sensor data information of an Android phone, obtain the longitude and latitude coordinates of the four corner points of each platform of a platform canopy, perform UTM plane rectangular coordinate conversion, and obtain the starting point coordinates, end point coordinates, and central axis length of the platform along the track to the central axis.

[0058] In step S1, a high-speed railway passenger station cross-line steel structure canopy includes two platforms, the platform length is 450 meters, and the platform width is 12 meters.

[0059] In step S1, data information initialization is to obtain the GNSS positioning information of the current patrol personnel, obtain the horizontal rotation angle information and pitch angle information of the gyroscope sensor, determine the current holding state of the mobile phone, obtain the acceleration of the acceleration sensor, and determine the motion state of the current patrol personnel.

[0060] In step S1, the topography around a platform canopy is obtained through surveying and as-built drawings, and the longitude and latitude coordinate values ​​of a total of 8 corner points of the two platforms are extracted. The starting and end points of the two central axes of the two platforms are calculated, and the calculated length of the central axis is 450 meters, which is consistent with the actual value.

[0061] Step S2: Obtain the GPS location of the patrol personnel in real time, convert the longitude and latitude coordinates obtained into UTM plane rectangular coordinates, and obtain the projection point of the positioning point on the central axis along the track through vector dot product operation, such as Figure 5 shown.

[0062] In step S2, vector A=(A(lat), A(lng)) is the vector from the bottom midpoint of the central axis to the positioning point. A(lat)=longitude coordinate value of the positioning point-longitude coordinate value of the bottom midpoint, A(lng)=latitude coordinate value of the positioning point-latitude coordinate value of the bottom midpoint.

[0063] Vector B = (B (lat), B (lng)) is the vector from the bottom midpoint to the top midpoint along the central axis. B (lat) = the longitude coordinate value of the bottom midpoint - the longitude coordinate value of the top midpoint, B (lng) = the latitude coordinate value of the bottom midpoint - the latitude coordinate value of the top midpoint.

[0064] Vector dot product operation . , |A| is the length of vector A, |B| is the length of vector B, so |A|cos(θ) is the length of the projection of vector A on vector B, ,The projection length of the current inspection personnel on the central axis can be obtained, and the longitude and latitude coordinates of the projection point can be calculated through linear interpolation.

[0065] Step S3, calculate the distance between the current GNSS positioning point of the patrol personnel and the projection points of different platforms, take the absolute minimum value to determine the platform where the current patrol personnel is located, and use the projection point along the track to the central axis as the real-time positioning of the patrol personnel, so that the patrol personnel can move on the central axis of the platform without deviating from the platform range.

[0066] In step S3: the actual position of the inspection personnel should be on the platform, but the current GNSS positioning may be on the platform due to awning cover, signal interference, etc., or it may appear on the track, station building and other locations. By calculating the distance between the current GNSS positioning point and the projection point of the central axis of Platform 1 and Platform 2, taking the absolute minimum value of the distance, it is determined that the current inspection personnel is located at Platform 1, and the longitude and latitude coordinate values ​​of the projection point of Platform 1 are returned to drive the movement of the character model on the central axis.

[0067] Step S4: Obtain data information from the acceleration sensor, as well as the current GNSS positioning information and the previous GNSS positioning information, calculate the distance between adjacent positioning information, and determine whether the current inspection personnel is in a stationary state or a moving state through the acceleration sensor value and the distance difference between adjacent positioning information.

[0068] In step S4, the acceleration sensor is first initialized, and the returned acceleration sensor data information is monitored in real time. If the acceleration sensor does not return data information, it is determined that the current state is static. If the acceleration sensor returns data information, the acceleration at this time is less than 0.8m / s 2 , it is determined that the current state is static.

[0069] In step S4, the returned GNSS positioning information is monitored in real time, and the current GNSS positioning information is compared with the previous positioning information to obtain the difference. If the difference is 0, the current GNSS positioning information is invalid. If the difference between the two positioning information is less than 0.5 meters, it is determined that the current state is static.

[0070] In step S4, when the difference between the two positioning information is not less than 0.5 meters, and the acceleration at this time is not less than 0.8m / s 2 , it is judged that the current state is motion.

[0071] Step S5: When it is determined that the inspection personnel are stationary, multiple GNSS positioning information acquired with delay is collected, and a weighted average is calculated in the order of return. The positioning information acquired later is closer to the real information and has a larger weight. If there are more collected positioning information, the positioning information can also be returned every 3 seconds or every 3 collected.

[0072] In step S5, the returned positioning information is converted into UTM coordinates, and the vector dot product is performed according to step S2, and finally the longitude and latitude coordinate values ​​of the projection point on the platform centerline are returned to drive the movement of the character model.

[0073] Step S6: When it is determined that the inspection personnel is in motion, take a certain awning column as the current positioning calibration point, obtain the GNSS positioning information of the awning column, obtain its projection point information on the central axis according to step S2, and calculate the distance difference between the inspection personnel in motion and the projection point of the awning column calibration point on the central axis as the offset value.

[0074] In step S6, the Kalman filter algorithm inputs the measured value, calculates the Kalman gain through the predicted state evaluation value and covariance matrix, and then updates the state evaluation value and covariance matrix according to the measured value. Therefore, the sum of the current GNSS positioning and offset value is used as the measured value input, and the prediction update is performed by obtaining uninterrupted positioning information input in real time.

[0075] In step S6, the Kalman filter prediction model is introduced, and the coordinate value of the inspection personnel's projection point on the central axis plus the offset value are used as input variables. Iterative prediction is performed at a frequency of 100 times per second. After the Kalman filter prediction iteration, it can be close to the actual state. The 100th result after the iteration is returned, and the returned longitude and latitude coordinate values ​​drive the movement of the character model.

[0076] In step S6, the positioning calibration can be repeated multiple times, and multiple reference points with unique positioning information and easy to identify, such as awning columns, station building corners, platform corners, etc., can be selected as calibration points to obtain GNSS positioning information.

[0077] Step S7: convert the positioning information returned in the static or moving state into longitude and latitude coordinate values ​​to drive the movement of the character model. Use the horizontal rotation angle information and pitch angle information obtained by the gyroscope sensor to drive the movement of the character model's viewing angle.

[0078] The above embodiments describe the present invention in detail, but the contents are only preferred embodiments of the present invention and cannot be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A target optimization positioning method for railway platform canopy inspection, characterized in that: The following steps are involved: S1: Initialize the GNSS positioning, gyro sensor, and acceleration sensor data of the mobile device, obtain the longitude and latitude coordinates of the four corner points of each platform of the platform canopy, perform UTM plane rectangular coordinate conversion, and obtain the starting point coordinates, end point coordinates, and length of the central axis of the platform along the track; S2: Obtain the GNSS positioning of the inspection personnel in real time, convert the longitude and latitude coordinates obtained into UTM plane rectangular coordinates, and obtain the projection point of the positioning point on the central axis along the track through vector dot product operation; S3: Calculate the distance between the positioning point and the projection points of different platforms, take the absolute minimum value to determine the platform where the current patrol officer is located, and use the projection point along the track to the central axis as the real-time positioning of the patrol officer, so that the patrol officer can move on the central axis of the platform without deviating from the platform range; S4: Obtain data information of the acceleration sensor, as well as the current GNSS positioning information and the previous GNSS positioning information, calculate the distance between adjacent positioning information, and determine whether the current inspection personnel is in a stationary state or a moving state through the difference between the acceleration sensor value and the distance of the adjacent positioning; S5: When it is determined that the inspection personnel is in a stationary state, the average value of multiple GNSS positioning information obtained with delay is calculated and the positioning information is returned; S6: When it is determined that the inspection personnel are in motion, the GNSS positioning information of the calibration point and its projection point on the central axis are obtained, the offset value of the inspection personnel in motion and the calibration point on the central axis is calculated, the sum of the inspection personnel's real-time GNSS positioning information and the offset value is used as the input value, the Kalman filter prediction model is introduced, the state value is updated in real time after multiple predictions, and the real-time positioning information is returned; S7: Convert the calculated positioning information into longitude and latitude coordinate values, drive the movement of the character model, obtain data information from the gyroscope sensor, and drive the direction and viewing angle of the character model to rotate.

2. The target optimization positioning method for railway platform canopy inspection according to claim 1 is characterized in that: In step S2, the vector dot product operation refers to: It is known that vector A=(A(lat),A(lng)) is the vector from the starting point of the central axis to the positioning point, and vector B=(B(lat),B(lng)) is the vector from the starting point of the central axis to the end point along the track; A(lat) and A(lng) are the components of vector A in longitude and latitude, B(lat) and B(lng) are the components of vector B in longitude and latitude; Through the vector dot product operation , |A|cos(θ) is the projection length of A on vector B, and the projection point and projection length of the inspector on the central axis are obtained. Through linear interpolation, the longitude and latitude coordinate values ​​of the current positioning point are calculated.

3. The target optimization positioning method for railway platform canopy inspection according to claim 1 is characterized by: In step S3, the vertical distance between the positioning point and the projection points of different platforms is a positive value or a negative value. To determine the platform where the inspection personnel are located, the absolute minimum value of the vertical distance is taken.

4. The target optimization positioning method for railway platform canopy inspection according to claim 1 is characterized in that: In step S4, it is determined whether the current patrol personnel is in a stationary state or a moving state, including the following methods: S41: When the acceleration obtained by the acceleration sensor is less than the normal walking acceleration of an adult, the current state is static; S42: When the difference between the positioning coordinate values ​​of adjacent GNSS is less than the distance moved per second by a normal adult, the current state is static; S43: When the corresponding positioning distance is greater than the normal adult's moving distance per second, and the acceleration obtained by the acceleration sensor is greater than the normal adult's walking acceleration, the current state is motion.

5. The target optimization positioning method for railway platform canopy inspection according to claim 1 is characterized by: In step S5, the average value is calculated by directly summing up the number of GNSS positioning information obtained, or by assigning weights according to the order in which the GNSS positioning information is obtained, and then calculating a weighted average value.

6. The target optimization positioning method for railway platform canopy inspection according to claim 1 is characterized by: In step S6, the Kalman filter algorithm inputs the measurement value, calculates the Kalman gain through the predicted state evaluation value and covariance matrix, and then updates the state evaluation value and covariance matrix according to the measurement value, and inputs the sum of the current GNSS positioning and offset values ​​as the measurement value. The prediction update is performed by obtaining uninterrupted positioning information input in real time.

7. The target optimization positioning method for railway platform canopy inspection according to claim 1 is characterized by: In step S6, the offset value is the offset value between the current GNSS positioning of the inspection personnel and the current calibration point on the central axis. There is one or more calibration points, and the number of corresponding offset values ​​is consistent with the number of calibration points.

8. The target optimization positioning method for railway platform canopy inspection according to claim 7 is characterized by: Calibration points are unique and easily identifiable reference points for positioning information.

9. A target optimization positioning system for railway platform canopy inspection, using the target optimization positioning method for railway platform canopy inspection according to any one of claims 1 to 8, characterized in that: Includes the following modules: A data collection and storage module (1) is used to collect and store GNSS positioning data, acceleration sensor data, gyroscope sensor information and other related information; A platform data processing module (2), used to realize the UTM conversion of platform coordinates and the calculation of the coordinates of the platform centerline; The positioning data processing module (3) projects the acquired real-time GNSS positioning data of the inspection personnel onto the central axis of the platform along the track after calculation to obtain the coordinate value of the projection point; The platform positioning and identification module (4) is used to calculate the distance between the inspection personnel and the projection points of different platforms and identify the current platform; The inspection state judgment module (5) is used to calculate the value of the acceleration sensor and the distance difference between adjacent positionings to identify whether the current inspection personnel is in a stationary state or a moving state; The inspection positioning optimization module (6) is used to adopt different algorithms according to different states of the inspection personnel to return optimized positioning information; The model driving control module (7) is used to drive the character model and adjust the viewing angle according to the optimized positioning information.

10. The target optimization positioning system for railway platform canopy inspection according to claim 9, characterized in that: The target optimization positioning system for railway platform canopy inspection is mounted on a mobile terminal with GNSS positioning, gyroscope sensor and acceleration sensor.

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