Spanning frame positioning detection method based on Kalman filtering and related device
By combining Kalman filtering with global satellite navigation system and dual-mode differential technology, the problems of low efficiency, high risk and poor environmental adaptability of cross-bridge positioning and detection have been solved, achieving high-precision and rapid positioning, and optimizing construction progress and safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-25
- Publication Date
- 2026-04-03
AI Technical Summary
Existing cross-passage positioning and detection technologies are inefficient, lack dynamic monitoring capabilities, pose high risks for manual operations, and have poor environmental adaptability, thus affecting the safety and stability of the power system.
A positioning and detection method based on Kalman filtering is adopted, which combines the Global Navigation Satellite System and dual-mode differential technology. The positioning module acquires real-time geographical location and observation data, performs error analysis and initialization processing, and uses Kalman filtering to achieve high-precision positioning and detection.
It achieves high-precision and rapid positioning of the crossing frame, optimizes construction quality and progress, achieves positioning accuracy at the centimeter or even millimeter level, reduces construction waiting time, and avoids rework.
Smart Images
Figure CN121784791A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of positioning and detection technology, and in particular to a positioning and detection method and related apparatus for a gantry based on Kalman filtering. Background Technology
[0002] Crossing frames play a crucial role in power systems. As an important component for protecting live lines, their operation is directly related to the safety and stability of the power system. High-precision positioning and testing of crossing frames are necessary to prevent major safety accidents and ensure the quality of line erection. Currently, various technical means are widely used in the industry for positioning and testing of crossing frames, mainly including the following key technologies: (1) Theodolite positioning method: Theodolites are used to measure horizontal and vertical angles, and the plane position and elevation of the crossing frame are determined by combining the principle of triangulation. The accuracy is high, but it depends on manual operation and is greatly affected by weather. It is suitable for open terrain areas. (2) Level instrument elevation detection method: It is specifically used for elevation control of the crossing frame support points to ensure the levelness of the top surface of the crossing frame. However, it can only detect elevation and needs to be combined with other methods to locate the plane position. (3) Marker and string line positioning method: Mark the boundary points of the crossing frame on the design drawings, and bury markers at the corresponding positions on site; connect the markers with hemp rope or steel ruler to form the outline of the crossing frame and determine the position of the poles; suitable for crossing straight obstacles such as highways and railways, low cost, but accuracy depends on manual alignment, and has poor applicability in complex terrain. (4) Drawing comparison and on-site marking method: Bring the plan of the crossing frame layout from the design drawings to the site and determine the relative position by comparing it with the terrain features; mark the pole pit positions on the ground with lime or paint, or nail the boundary with wooden stakes. Suitable for small crossing frames or temporary emergency construction, depends on the experience of construction personnel, and has low accuracy.
[0003] In current cross-pass positioning and detection devices, a common method is to use a total station to measure and determine the position and orientation of the cross-pass. This method requires preliminary preparations such as setting up a control network, setting up the total station, and installing prisms. The positioning and detection process involves using the total station to aim at the backsight control point, measuring and storing the coordinates, and establishing a measurement coordinate system. Then, the initial coordinates are collected and the three-dimensional coordinates are recorded as reference values at each prism point. Periodic re-measurements are required to ensure accuracy.
[0004] Disadvantages of existing technologies: Low detection efficiency: Single-point measurement takes 30-60 seconds, and full detection of a 50-meter elevated bridge takes more than two hours; Lack of dynamic monitoring capability: There are blind spots in monitoring during manual re-measurement intervals, making it impossible to capture instantaneous displacement; High risk of manual operation: Inspectors need to climb a 50-meter elevated bridge to install prisms, working in dangerous environments such as high-voltage lines and highways, posing a high safety hazard; Poor environmental adaptability: Total station measurements rely on visible light, so the light cannot be too weak. Visibility is low in rainy and foggy weather, affecting the detection of the observed target and the accuracy of measurement. Summary of the Invention
[0005] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a method and related device for positioning and detecting crossing frames based on Kalman filtering, which can achieve high-precision and rapid positioning of crossing frames, thereby optimizing construction quality and progress.
[0006] To address the aforementioned technical problems, embodiments of the present invention provide a method for gantry positioning and detection based on Kalman filtering, the method comprising: Positioning is performed based on the positioning module installed on the crossing frame to obtain the real-time geographical location of the crossing frame and the raw observation data from two independent global satellite navigation systems; Based on the real-time geographic location and the original observation data, error analysis processing is performed to obtain the original measurement error data; The Kalman filter is initialized to form an initialized Kalman filter; The initial Kalman filter is used to perform positioning detection processing on the crossing frame using the original measurement error data to obtain the positioning detection result of the crossing frame.
[0007] Optionally, the positioning processing based on the positioning module installed on the crossing frame to obtain the real-time geographical location of the crossing frame and the raw observation data from two independent global satellite navigation systems includes: The positioning module is installed on the crossing frame. The positioning module has the function of communicating with the global satellite navigation system and performs positioning in a dual-mode differential manner. The positioning module obtains the real-time geographical location of the crossing frame, and simultaneously receives signals from two independent global navigation satellite systems, and obtains the raw observation data based on the signals from the two independent global navigation satellite systems.
[0008] Optionally, the step of performing error analysis processing based on the real-time geographical location and the original observation data to obtain the original measurement error data includes: Based on the dual-mode differential positioning in the positioning module, error analysis is performed using the real-time geographic location and the original observation data to obtain the original measurement error data.
[0009] Optionally, the initialization process for the Kalman filter to form an initialized Kalman filter includes: The Kalman filter is initialized based on the different crossing frames, different installation locations, and the requirements and allowable tolerances for the installation position, thus forming an initialized Kalman filter.
[0010] Optionally, the process of using the initialization-based Kalman filter to perform positioning detection processing on the crossing frame using the original measurement error data is as follows: A state-space model is constructed, and the state vector is defined for the detection of the strut structure as follows: ; in, for Three-dimensional position at any moment; for 3D velocity at any given moment; for Three-dimensional acceleration at any given moment; Assuming uniform acceleration motion, the state transition equations are as follows: ; in, Here is the state transition matrix. This is the process noise gain matrix; Global Navigation Satellite System positioning is based on pseudorange observations, and the observation equations are as follows: ; in, The observation vector contains pseudoranges from m visible satellites; The elements of the observation matrix are calculated as follows: For the i-th satellite, the pseudorange observation equation, after linearization, is: ; The real-time geographical location of the positioning module is as follows: ; The speed of light; For the positioning module clock deviation; This is due to satellite clock deviation; For satellite position; linearized observation matrix The row vectors are: ; in, This represents the geometric distance from the satellite to the positioning module.
[0011] Optionally, the state transition matrix is as follows: ; The process noise gain matrix is as follows: ; in, The sampling time interval; It is a 3x3 identity matrix; It is a 3x3 zero matrix.
[0012] Optionally, the positioning detection process for the crossing frame includes: The crossing frame is subjected to positioning detection processing in a static scenario or in a dynamic vibration scenario.
[0013] In addition, embodiments of the present invention also provide a Kalman filter-based strut positioning and detection device, the device comprising: Positioning module: used to perform positioning processing based on the positioning module set on the crossing frame, to obtain the real-time geographical location of the crossing frame and the raw observation data of two independent global satellite navigation systems; Error analysis module: used to perform error analysis processing based on the real-time geographical location and the original observation data to obtain the original measurement error data; Initialization module: Used to initialize the Kalman filter, forming an initialized Kalman filter; Positioning detection module: used to perform positioning detection processing on the crossing frame based on the initial Kalman filter and the original measurement error data, and obtain the positioning detection result of the crossing frame.
[0014] In addition, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the processor runs a computer program or code stored in the memory to implement the straddle positioning and detection method as described in any of the above embodiments.
[0015] In addition, embodiments of the present invention also provide a computer-readable storage medium for storing a computer program or code, which, when executed by a processor, implements the straddle positioning detection method as described above.
[0016] In this embodiment of the invention, after obtaining the real-time geographical location raw observation data using the positioning module, error analysis processing is performed to obtain raw measurement error data. Kalman filtering is then used to perform positioning detection processing on the crossing frame using the raw measurement error data to obtain the positioning detection result of the crossing frame. This achieves high-precision and rapid positioning of the crossing frame, thereby optimizing construction quality and progress. GNSS high-precision rapid differential positioning technology is adopted, correcting for atmospheric errors, multipath effects, and other interference factors through differences in data from multiple positioning points, achieving centimeter-level or even millimeter-level positioning accuracy. Furthermore, high-precision positioning can be completed within 1-2 minutes, avoiding construction delays caused by excessive positioning time and accelerating the installation progress of the crossing frame. The combination of high-precision positioning and intelligent judgment avoids construction rework caused by positioning errors from the source. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart illustrating the Kalman filter-based strut positioning and detection method in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structural composition of the cross-train positioning and detection device based on Kalman filtering in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structural composition of the electronic device in an embodiment of the present invention. Detailed Implementation
[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0020] Example 1, please refer to Figure 1 , Figure 1 This is a flowchart illustrating the Kalman filter-based strut positioning and detection method in an embodiment of the present invention.
[0021] like Figure 1 As shown, a method for locating and detecting a gantry based on Kalman filtering is described, the method comprising: S101: Based on the positioning module installed on the crossing frame, perform positioning processing to obtain the real-time geographical location of the crossing frame and the raw observation data of two independent global satellite navigation systems; In the specific implementation of this invention, the positioning processing based on the positioning module installed on the crossing frame to obtain the real-time geographical location of the crossing frame and the raw observation data of two independent global satellite navigation systems includes: setting the positioning module on the crossing frame, the positioning module having the function of communicating with the global satellite navigation system and performing positioning in a dual-mode differential manner; obtaining the real-time geographical location of the crossing frame based on the positioning module, while the positioning module receives signals from two independent global satellite navigation systems, and obtaining the raw observation data based on the signals from the two independent global satellite navigation systems.
[0022] Specifically, by setting up a positioning module on the gantry, which employs dual-mode differential positioning, the device can accurately determine its real-time geographical location on Earth. It can simultaneously receive signals from two independent global satellite navigation systems and eliminate common errors by using a fixed reference station with a known precise location. This allows for the comprehensive processing of raw observation data and differential correction data from multiple systems and satellites.
[0023] S102: Perform error analysis processing based on the real-time geographical location and the original observation data to obtain the original measurement error data; In the specific implementation of this invention, the step of performing error analysis processing based on the real-time geographical location and the original observation data to obtain the original measurement error data includes: performing error analysis processing based on the dual-mode differential positioning in the positioning module using the real-time geographical location and the original observation data to obtain the original measurement error data.
[0024] Specifically, at this point, the dual-mode differential positioning is invoked within the positioning module to perform error analysis processing using real-time geographic location and raw observation data, thereby obtaining raw measurement error data.
[0025] S103: Initialize the Kalman filter to form an initialized Kalman filter; In the specific implementation of this invention, the initialization process of the Kalman filter to form an initialized Kalman filter includes: initializing the Kalman filter according to the requirements and allowable tolerances of the installation position for different crossing frames and different installation locations to form an initialized Kalman filter.
[0026] Specifically, different crossing frames and different installation points may have different position requirements and allowable tolerances. That is, it is necessary to receive on-site input or selection from users. Therefore, it is necessary to provide users with a corresponding input interface and input buttons so that operators can intuitively specify the detection target and requirements. Finally, the Kalman filter can be initialized according to the requirements and allowable tolerances of the installation position for different crossing frames and different installation locations to form an initialized Kalman filter.
[0027] S104: Based on the initialization of the Kalman filter, the original measurement error data is used to perform positioning detection processing on the crossing frame to obtain the positioning detection result of the crossing frame.
[0028] In the specific implementation of this invention, the process of using the initialization-based Kalman filter to perform positioning and detection processing on the crossing frame using the original measurement error data is as follows: A state-space model is constructed, and the state vector is defined for the crossing frame structure detection as follows: ; in, for Three-dimensional position at any moment; for 3D velocity at any given moment; for Three-dimensional acceleration at any given moment; Assuming uniform acceleration motion, the state transition equations are as follows: ; in, Here is the state transition matrix. This is the process noise gain matrix; Global Navigation Satellite System positioning is based on pseudorange observations, and the observation equations are as follows: ; in, The observation vector contains pseudoranges from m visible satellites; The elements of the observation matrix are calculated as follows: For the i-th satellite, the pseudorange observation equation, after linearization, is: ; The real-time geographical location of the positioning module is as follows: ; The speed of light; For the positioning module clock deviation; This is due to satellite clock deviation; For satellite position; linearized observation matrix The row vectors are: ; in, This represents the geometric distance from the satellite to the positioning module.
[0029] Furthermore, the state transition matrix is as follows: ; The process noise gain matrix is as follows: ; in, The sampling time interval; It is a 3x3 identity matrix; It is a 3x3 zero matrix.
[0030] Furthermore, the positioning detection process for the crossing frame includes: performing positioning detection on the crossing frame in a static scenario, or performing positioning detection on the crossing frame in a dynamic vibration scenario.
[0031] Specifically, the Kalman filter recursive state estimation algorithm is applied within the positioning module. By fusing the system dynamic model and observation data, it optimally estimates the hidden state of the system under uncertainties, and its application in a dual-mode differential positioning system is explained. First, a state-space model is constructed. For monitoring and detecting the strut structure, the state vector is defined as follows: ; in, for Three-dimensional position at any moment; for 3D velocity at any given moment; for Three-dimensional acceleration at any given moment; Assuming uniform acceleration motion, the state transition equations are as follows: ; in, Here is the state transition matrix. This is the process noise gain matrix; Global Navigation Satellite System positioning is based on pseudorange observations, and the observation equations are as follows: ; in, The observation vector contains pseudoranges from m visible satellites; The elements of the observation matrix are calculated as follows: For the i-th satellite, the pseudorange observation equation, after linearization, is: ; The real-time geographical location of the positioning module is as follows: ; The speed of light; For the positioning module clock deviation; This is due to satellite clock deviation; For satellite position; linearized observation matrix The row vectors are: ; in, This represents the geometric distance from the satellite to the positioning module.
[0032] The state transition matrix is as follows: ; The process noise gain matrix is as follows: ; in, The sampling time interval; It is a 3x3 identity matrix; It is a 3x3 zero matrix.
[0033] Kalman filtering in GNSS straddle positioning: case study and conclusions; verification of multipath error suppression in static scene: simulation conditions are fixed position (100, 100, 50) m, no acceleration or velocity; multipath error and Gaussian white noise are added to GNSS, sampling interval is 1S.
[0034] Filter parameters: Process noise covariance Q: location noise speed noise ;Observation noise covariance R: pseudorange error .
[0035] Calculation process: Initial state: The iterative formula is as follows: predict: ; ; renew: ; ; Results analysis: Table 1 Comparison of Positioning Errors
[0036] Convergence characteristics: It converges quickly in the first 5 iterations, and the error stabilizes within 0.05m after 10 iterations.
[0037] Tracking structural displacement under dynamic vibration scenario: Simulation conditions: The actual trajectory is The y and z directions are similar; the GNSS observation results show a pseudorange noise standard deviation of 1m, with random loss of lock (interrupted for 5 seconds every 100 seconds).
[0038] The filter parameters are as follows: State vector: ; The process noise dynamic factor α = 1.0, and the sampling time T = 0.1s; Observation matrix H: Consider 4 satellites, all with elevation angles > 15°; The key calculation formula is as follows: State transition matrix: ; Process noise covariance: ; Results verification: Dynamic tracking accuracy: The positioning error at the vibration peak (maximum speed) was ±3.2cm before filtering and ±0.8cm after filtering; Displacement drift during unlocking: 12.5cm before filtering and 1.3cm after filtering; Error spectrum analysis: The 0.5Hz vibration signal was submerged by noise before filtering, and the SNR was improved to 25dB after filtering, making the vibration characteristics clearly distinguishable.
[0039] A computational example in a multi-satellite obstruction scenario verifies the resilience against loss of lock. The simulation conditions are as follows: Satellite visibility: 4 satellites for the first 200 seconds, 2 satellites for 200-250 seconds (PDOP=8), and 4 satellites restored after 250 seconds; Filtering strategy: Adaptive forgetting factor enabled. Increase the observation noise weight when PDOP > 6; Key parameters: ; The performance indicators are as follows: Positioning maintenance when satellites are insufficient: During the period of 200-250 seconds, the rate of increase of the filtered position error is 0.2 mm / s, which is significantly lower than the 1.5 mm / s before filtering; Recovery time: After the number of satellites is restored, the filtering result converges again within 3 iterations (0.3 seconds), while the traditional method requires more than 10 seconds.
[0040] In this embodiment of the invention, after obtaining the real-time geographical location raw observation data using the positioning module, error analysis processing is performed to obtain raw measurement error data. Kalman filtering is then used to perform positioning detection processing on the crossing frame using the raw measurement error data to obtain the positioning detection result of the crossing frame. This achieves high-precision and rapid positioning of the crossing frame, thereby optimizing construction quality and progress. GNSS high-precision rapid differential positioning technology is adopted, correcting for atmospheric errors, multipath effects, and other interference factors through differences in data from multiple positioning points, achieving centimeter-level or even millimeter-level positioning accuracy. Furthermore, high-precision positioning can be completed within 1-2 minutes, avoiding construction delays caused by excessive positioning time and accelerating the installation progress of the crossing frame. The combination of high-precision positioning and intelligent judgment avoids construction rework caused by positioning errors from the source.
[0041] Example 2, please refer to Figure 2 , Figure 2 This is a schematic diagram of the structural composition of the gantry positioning and detection device based on Kalman filtering in an embodiment of the present invention.
[0042] like Figure 2 As shown, a Kalman filter-based strut positioning and detection device includes: Positioning module 201: used to perform positioning processing based on the positioning module set on the crossing frame, to obtain the real-time geographical location of the crossing frame and the raw observation data of two independent global satellite navigation systems; In the specific implementation of this invention, the positioning processing based on the positioning module installed on the crossing frame to obtain the real-time geographical location of the crossing frame and the raw observation data of two independent global satellite navigation systems includes: setting the positioning module on the crossing frame, the positioning module having the function of communicating with the global satellite navigation system and performing positioning in a dual-mode differential manner; obtaining the real-time geographical location of the crossing frame based on the positioning module, while the positioning module receives signals from two independent global satellite navigation systems, and obtaining the raw observation data based on the signals from the two independent global satellite navigation systems.
[0043] Specifically, by setting up a positioning module on the gantry, which employs dual-mode differential positioning, the device can accurately determine its real-time geographical location on Earth. It can simultaneously receive signals from two independent global satellite navigation systems and eliminate common errors by using a fixed reference station with a known precise location. This allows for the comprehensive processing of raw observation data and differential correction data from multiple systems and satellites.
[0044] Error analysis module 202: used to perform error analysis processing based on the real-time geographical location and the original observation data to obtain the original measurement error data; In the specific implementation of this invention, the step of performing error analysis processing based on the real-time geographical location and the original observation data to obtain the original measurement error data includes: performing error analysis processing based on the dual-mode differential positioning in the positioning module using the real-time geographical location and the original observation data to obtain the original measurement error data.
[0045] Specifically, at this point, the dual-mode differential positioning is invoked within the positioning module to perform error analysis processing using real-time geographic location and raw observation data, thereby obtaining raw measurement error data.
[0046] Initialization module 203: Used to initialize the Kalman filter and form an initialized Kalman filter; In the specific implementation of this invention, the initialization process of the Kalman filter to form an initialized Kalman filter includes: initializing the Kalman filter according to the requirements and allowable tolerances of the installation position for different crossing frames and different installation locations to form an initialized Kalman filter.
[0047] Specifically, different crossing frames and different installation points may have different position requirements and allowable tolerances. That is, it is necessary to receive on-site input or selection from users. Therefore, it is necessary to provide users with a corresponding input interface and input buttons so that operators can intuitively specify the detection target and requirements. Finally, the Kalman filter can be initialized according to the requirements and allowable tolerances of the installation position for different crossing frames and different installation locations to form an initialized Kalman filter.
[0048] Positioning detection module 204: used to perform positioning detection processing on the crossing frame based on the initial Kalman filter and the original measurement error data, and obtain the positioning detection result of the crossing frame.
[0049] In the specific implementation of this invention, the process of using the initialization-based Kalman filter to perform positioning and detection processing on the crossing frame using the original measurement error data is as follows: A state-space model is constructed, and the state vector is defined for the crossing frame structure detection as follows: ; in, for Three-dimensional position at any moment; for 3D velocity at any given moment; for Three-dimensional acceleration at any given moment; Assuming uniform acceleration motion, the state transition equations are as follows: ; in, Here is the state transition matrix. This is the process noise gain matrix; Global Navigation Satellite System positioning is based on pseudorange observations, and the observation equations are as follows: ; in, The observation vector contains pseudoranges from m visible satellites; The elements of the observation matrix are calculated as follows: For the i-th satellite, the pseudorange observation equation, after linearization, is: ; The real-time geographical location of the positioning module is as follows: ; The speed of light; For the positioning module clock deviation; This is due to satellite clock deviation; For satellite position; linearized observation matrix The row vectors are: ; in, This represents the geometric distance from the satellite to the positioning module.
[0050] Furthermore, the state transition matrix is as follows: ; The process noise gain matrix is as follows: ; in, The sampling time interval; It is a 3x3 identity matrix; It is a 3x3 zero matrix.
[0051] Furthermore, the positioning detection process for the crossing frame includes: performing positioning detection on the crossing frame in a static scenario, or performing positioning detection on the crossing frame in a dynamic vibration scenario.
[0052] Specifically, the Kalman filter recursive state estimation algorithm is applied within the positioning module. By fusing the system dynamic model and observation data, it optimally estimates the hidden state of the system under uncertainties, and its application in a dual-mode differential positioning system is explained. First, a state-space model is constructed. For monitoring and detecting the strut structure, the state vector is defined as follows: ; in, for Three-dimensional position at any moment; for 3D velocity at any given moment; for Three-dimensional acceleration at any given moment; Assuming uniform acceleration motion, the state transition equations are as follows: ; in, Here is the state transition matrix. This is the process noise gain matrix; Global Navigation Satellite System positioning is based on pseudorange observations, and the observation equations are as follows: ; in, The observation vector contains pseudoranges from m visible satellites; The elements of the observation matrix are calculated as follows: For the i-th satellite, the pseudorange observation equation, after linearization, is: ; The real-time geographic location of the positioning module is as follows: ; The speed of light; For the positioning module clock deviation; This is due to satellite clock deviation; For satellite position; linearized observation matrix The row vectors are: ; in, This represents the geometric distance from the satellite to the positioning module.
[0053] The state transition matrix is as follows: ; The process noise gain matrix is as follows: ; in, The sampling time interval; It is a 3x3 identity matrix; It is a 3x3 zero matrix.
[0054] Kalman filtering in GNSS straddle positioning: case study and conclusions; verification of multipath error suppression in static scene: simulation conditions are fixed position (100, 100, 50) m, no acceleration or velocity; multipath error and Gaussian white noise are added to GNSS, sampling interval is 1S.
[0055] Filter parameters: Process noise covariance Q: location noise speed noise ;Observation noise covariance R: pseudorange error .
[0056] Calculation process: Initial state: The iterative formula is as follows: predict: ; ; renew: ; ; Results analysis: Table 1 Comparison of Positioning Errors
[0057] Convergence characteristics: It converges quickly in the first 5 iterations, and the error stabilizes within 0.05m after 10 iterations.
[0058] Tracking structural displacement under dynamic vibration scenario: Simulation conditions: The actual trajectory is The y and z directions are similar; the GNSS observation results show a pseudorange noise standard deviation of 1m, with random loss of lock (interrupted for 5 seconds every 100 seconds).
[0059] The filter parameters are as follows: State vector: ; The process noise dynamic factor α = 1.0, and the sampling time T = 0.1s; Observation matrix H: Consider 4 satellites, all with elevation angles > 15°; The key calculation formula is as follows: State transition matrix: ; Process noise covariance: ; Results verification: Dynamic tracking accuracy: The positioning error at the vibration peak (maximum speed) was ±3.2cm before filtering and ±0.8cm after filtering; Displacement drift during unlocking: 12.5cm before filtering and 1.3cm after filtering; Error spectrum analysis: The 0.5Hz vibration signal was submerged by noise before filtering, and the SNR was improved to 25dB after filtering, making the vibration characteristics clearly distinguishable.
[0060] A computational example in a multi-satellite obstruction scenario verifies the resilience against loss of lock. The simulation conditions are as follows: Satellite visibility: 4 satellites for the first 200 seconds, 2 satellites for 200-250 seconds (PDOP=8), and 4 satellites restored after 250 seconds; Filtering strategy: Adaptive forgetting factor enabled. Increase the observation noise weight when PDOP > 6; Key parameters: ; The performance indicators are as follows: Positioning maintenance when satellites are insufficient: During the period of 200-250 seconds, the rate of increase of the filtered position error is 0.2 mm / s, which is significantly lower than the 1.5 mm / s before filtering; Recovery time: After the number of satellites is restored, the filtering result converges again within 3 iterations (0.3 seconds), while the traditional method requires more than 10 seconds.
[0061] In this embodiment of the invention, after obtaining the real-time geographical location raw observation data using the positioning module, error analysis processing is performed to obtain raw measurement error data. Kalman filtering is then used to perform positioning detection processing on the crossing frame using the raw measurement error data to obtain the positioning detection result of the crossing frame. This achieves high-precision and rapid positioning of the crossing frame, thereby optimizing construction quality and progress. GNSS high-precision rapid differential positioning technology is adopted, correcting for atmospheric errors, multipath effects, and other interference factors through differences in data from multiple positioning points, achieving centimeter-level or even millimeter-level positioning accuracy. Furthermore, high-precision positioning can be completed within 1-2 minutes, avoiding construction delays caused by excessive positioning time and accelerating the installation progress of the crossing frame. The combination of high-precision positioning and intelligent judgment avoids construction rework caused by positioning errors from the source.
[0062] This invention provides a computer-readable storage medium storing a computer program. When executed by a processor, this program implements the straddle positioning and detection method of any of the above embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, the storage device includes any medium that stores or transmits information in a readable form by a device (e.g., a computer, a mobile phone), and can be a read-only memory, a disk, or an optical disk, etc.
[0063] This invention also provides a computer application running on a computer, which is used to execute the straddle positioning and detection method of any of the above embodiments.
[0064] also, Figure 3 This is a schematic diagram of the structural composition of the electronic device in an embodiment of the present invention.
[0065] This invention also provides an electronic device, such as... Figure 3 As shown. The electronic device includes a processor 302, a memory 303, an input unit 304, and a display unit 305, among other devices. Those skilled in the art will understand that... Figure 3The structural components of the illustrated electronic device do not constitute a limitation on all devices and may include more or fewer components than illustrated, or combine certain components. Memory 303 can be used to store application program 301 and various functional modules. Processor 302 runs application program 301 stored in memory 303, thereby performing various functional applications and data processing of the device. Memory can be internal memory or external memory, or both. Internal memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. External memory may include hard disks, floppy disks, ZIP disks, USB flash drives, magnetic tapes, etc. The memory disclosed in this invention includes, but is not limited to, these types of memory. The memory disclosed in this invention is only an example and not a limitation.
[0066] Input unit 304 is used to receive signal input and user-input keywords. Input unit 304 may include a touch panel and other input devices. The touch panel can collect user touch operations on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel) and drive the corresponding connection device according to a pre-set program; other input devices may include, but are not limited to, one or more of physical keyboards, function keys (such as play control buttons, power buttons, etc.), trackballs, mice, joysticks, etc. Display unit 305 can be used to display user-input information or information provided to the user, as well as various menus of the terminal device. Display unit 305 may be in the form of a liquid crystal display, organic light-emitting diode, etc. Processor 302 is the control center of the terminal device, connecting various parts of the entire device through various interfaces and lines, and performing various functions and processing data by running or executing software programs and / or modules stored in memory 303, and calling data stored in memory.
[0067] As one embodiment, the electronic device includes: one or more processors 302, a memory 303, and one or more application programs 301, wherein the one or more application programs 301 are stored in the memory 303 and configured to be executed by the one or more processors 302, and the one or more application programs 301 are configured to perform the cross-train positioning and detection method corresponding to any of the above embodiments.
[0068] In this embodiment of the invention, after obtaining the real-time geographical location raw observation data using the positioning module, error analysis processing is performed to obtain raw measurement error data. Kalman filtering is then used to perform positioning detection processing on the crossing frame using the raw measurement error data to obtain the positioning detection result of the crossing frame. This achieves high-precision and rapid positioning of the crossing frame, thereby optimizing construction quality and progress. GNSS high-precision rapid differential positioning technology is adopted, correcting for atmospheric errors, multipath effects, and other interference factors through differences in data from multiple positioning points, achieving centimeter-level or even millimeter-level positioning accuracy. Furthermore, high-precision positioning can be completed within 1-2 minutes, avoiding construction delays caused by excessive positioning time and accelerating the installation progress of the crossing frame. The combination of high-precision positioning and intelligent judgment avoids construction rework caused by positioning errors from the source.
[0069] Furthermore, the above provides a detailed description of the cross-train positioning and detection method and related devices based on Kalman filtering provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for locating and detecting a gantry based on Kalman filtering, characterized in that, The method includes: Positioning is performed based on the positioning module installed on the crossing frame to obtain the real-time geographical location of the crossing frame and the raw observation data from two independent global satellite navigation systems; Based on the real-time geographic location and the original observation data, error analysis processing is performed to obtain the original measurement error data; The Kalman filter is initialized to form an initialized Kalman filter; The initialization-based Kalman filter uses the original measurement error data to perform positioning detection processing on the crossing frame, thereby obtaining the positioning detection result of the crossing frame.
2. The method for positioning and detecting a gantry according to claim 1, characterized in that, The positioning process, based on the positioning module installed on the crossing frame, obtains the real-time geographical location of the crossing frame and raw observation data from two independent global satellite navigation systems, including: The positioning module is installed on the crossing frame. The positioning module has the function of communicating with the global satellite navigation system and performs positioning in a dual-mode differential manner. The positioning module obtains the real-time geographical location of the crossing frame, and simultaneously receives signals from two independent global navigation satellite systems, and obtains the raw observation data based on the signals from the two independent global navigation satellite systems.
3. The method for positioning and detecting a gantry according to claim 1, characterized in that, The step of performing error analysis processing based on the real-time geographical location and the original observation data to obtain original measurement error data includes: Based on the dual-mode differential positioning in the positioning module, error analysis is performed using the real-time geographic location and the original observation data to obtain the original measurement error data.
4. The method for positioning and detecting a gantry according to claim 1, characterized in that, The initialization process for the Kalman filter, forming an initialized Kalman filter, includes: The Kalman filter is initialized based on the different crossing frames, different installation locations, and the requirements and allowable tolerances for the installation position, thus forming an initialized Kalman filter.
5. The method for positioning and detecting a gantry according to claim 1, characterized in that, The process of using the initialization-based Kalman filter to perform positioning and detection processing on the crossing frame using the original measurement error data is as follows: A state-space model is constructed, and the state vector is defined for the detection of the strut structure as follows: ； in, for Three-dimensional position at any moment; for 3D velocity at any given moment; for Three-dimensional acceleration at any given moment; Assuming uniform acceleration motion, the state transition equations are as follows: ； in, Here is the state transition matrix. This is the process noise gain matrix; Global Navigation Satellite System positioning is based on pseudorange observations, and the observation equations are as follows: ； in, The observation vector contains pseudoranges from m visible satellites; The elements of the observation matrix are calculated as follows: For the i-th satellite, the pseudorange observation equation, after linearization, is: ； The real-time geographic location of the positioning module is as follows: ; The speed of light; For the positioning module clock deviation; This is due to satellite clock deviation; For satellite position; linearized observation matrix The row vectors are: ； in, This represents the geometric distance from the satellite to the positioning module.
6. The method for positioning and detecting a gantry according to claim 5, characterized in that, The state transition matrix is as follows: ； The process noise gain matrix is as follows: ； in, The sampling time interval; It is a 3x3 identity matrix; It is a 3x3 zero matrix.
7. The method for positioning and detecting a gantry according to claim 1, characterized in that, The process of positioning and detecting the crossing frame includes: The crossing frame is subjected to positioning detection processing in a static scenario or in a dynamic vibration scenario.
8. A positioning and detection device for a gantry crane based on Kalman filtering, characterized in that, The device includes: Positioning module: used to perform positioning processing based on the positioning module set on the crossing frame, to obtain the real-time geographical location of the crossing frame and the raw observation data of two independent global satellite navigation systems; Error analysis module: used to perform error analysis processing based on the real-time geographical location and the original observation data to obtain the original measurement error data; Initialization module: Used to initialize the Kalman filter, forming an initialized Kalman filter; Positioning detection module: used to perform positioning detection processing on the crossing frame based on the initial Kalman filter and the original measurement error data, and obtain the positioning detection result of the crossing frame.
9. An electronic device comprising a processor and a memory, characterized in that, The processor runs a computer program or code stored in the memory to implement the straddle positioning and detection method as described in any one of claims 1 to 7.
10. A computer-readable storage medium for storing computer programs or code, characterized in that, When the computer program or code is executed by a processor, the gantry positioning and detection method as described in any one of claims 1 to 7 is implemented.