Method for drawing a motion trajectory in a meteorological inspection process based on AR glasses

Abstract

The application discloses a method for drawing a motion trajectory in a meteorological inspection process based on AR glasses, and relates to the field of meteorological equipment inspection. The method comprises collecting inertial measurement unit data, GPS data and positioning beacon data of the AR glasses, and analyzing a signal strength ratio of an overlapping area of the positioning beacons to determine a beacon priority. In an indoor environment, the highest priority beacon data is used for positioning, and in an outdoor environment, GPS data is used for positioning. When the location environment changes, the inertial measurement unit data is subjected to hierarchical progressive filtering, and Kalman iteration is used to eliminate cumulative positioning errors caused by the change of the environment. A spatial attitude parameter is obtained through quaternion operation, and a distance measurement value between adjacent positioning beacons is adjusted according to the spatial attitude parameter, so that an accurate meteorological equipment area inspection trajectory is obtained. The application effectively solves the positioning accuracy problem in the process of indoor and outdoor environment conversion.

Description

Technical Field

[0001] This invention relates to the field of meteorological equipment inspection technology, specifically to a method for drawing motion trajectories during meteorological inspections based on AR glasses. Background Technology

[0002] With the increasing intelligence of meteorological equipment, inspection personnel need to conduct regular inspections of various meteorological devices distributed both indoors and outdoors. To improve inspection efficiency and recording accuracy, AR glasses are widely used in meteorological inspections. AR glasses can record inspection routes in real time, providing a basis for subsequent analysis and route optimization.

[0003] AR glasses primarily rely on GPS and inertial measurement units for positioning. Outdoors, GPS provides relatively accurate positioning information. However, indoors, GPS signal strength is significantly reduced due to building obstruction. While indoor positioning can be achieved by deploying positioning beacons, current technologies lack effective mechanisms for handling indoor-outdoor environment transitions, leading to positioning drift and accumulated errors during these transitions.

[0004] Existing AR glasses inspection trajectory mapping methods often employ a single data processing approach, failing to optimize for data characteristics under different environments. In beacon overlap areas, interference from multiple beacon signals can affect positioning accuracy. Furthermore, due to the zero-bias and scaling factor errors inherent in inertial measurement units, simple data fusion alone cannot effectively eliminate accumulated errors.

[0005] Current technologies commonly use Euler angles to calculate spatial attitude, which is prone to gimbal locking problems, affecting the accuracy of attitude calculation. Furthermore, the reliability of distance measurements between adjacent positioning beacons is low due to the lack of an effective attitude compensation mechanism. Summary of the Invention

[0006] The purpose of this invention is to provide a method for drawing motion trajectories during meteorological inspections based on AR glasses. This method solves the positioning drift problem caused by the transition between indoor and outdoor environments in the prior art, and provides a method that can effectively eliminate accumulated errors and improve trajectory drawing accuracy.

[0007] In a first aspect, an embodiment of the present invention provides a method for drawing motion trajectories based on AR glasses during weather inspection, characterized by comprising the following steps:

[0008] Collect inertial measurement unit data, GPS data, and positioning beacon data from AR glasses;

[0009] The system acquires overlapping areas of multiple beacons in the location beacon data, calculates the signal strength ratio of each beacon in the overlapping area, determines the beacon priority based on the signal strength ratio, uses the highest priority beacon data to determine the location when indoors, and uses GPS data to determine the location when outdoors, thus obtaining complete location data.

[0010] When the current location environment changes from indoor to outdoor or from outdoor to indoor based on the complete location data, the inertial measurement unit data is subjected to hierarchical progressive filtering. The filtered data is then processed using Kalman iteration to eliminate the cumulative positioning error caused by the environment change, resulting in corrected motion data.

[0011] Quaternion operations are performed on the corrected motion data to obtain spatial attitude parameters. The distance measurements between adjacent positioning beacons are adjusted based on the spatial attitude parameters to obtain the inspection trajectory of the meteorological equipment area.

[0012] Furthermore, the overlapping areas of multiple beacons in the positioning beacon data are obtained, the signal strength ratio of each beacon in the overlapping area is calculated, and the beacon priority is determined based on the signal strength ratio, including:

[0013] Real-time acquisition of location beacon data; acquisition of multiple location beacons with signal strength exceeding a preset strength threshold; sampling of the signal strength of location beacons in multiple consecutive sampling periods; and division of location beacons that meet the signal strength condition in consecutive sampling periods into multiple beacon overlapping areas.

[0014] The location beacon with the strongest signal in the overlapping area of ​​the multiple beacons is selected as the reference beacon. The signal strength ratio of other location beacons to the reference beacon is calculated to obtain the signal strength ratio characteristics of each location beacon.

[0015] The signal strength ratio feature is variance-calculated within a time window to obtain a signal stability index, and the handover cost is calculated in combination with the historical priority of the currently used positioning beacon.

[0016] Beacon priorities are determined based on signal stability metrics and handover costs.

[0017] Furthermore, when indoors, the highest priority beacon data is used to determine the location, and when outdoors, GPS data is used to determine the location, resulting in complete location data including:

[0018] The system collects signal continuity data from beacon data (the highest priority), GPS signal quantity variation data, illumination intensity data from the inertial measurement unit, and air pressure variation data, and obtains the velocity and acceleration distribution characteristics of the position trajectory.

[0019] The reliability of beacon and GPS data is assessed based on signal continuity data and GPS signal quantity variation data, and the scene is determined by combining light intensity data, air pressure variation data, and velocity and acceleration distribution characteristics.

[0020] When it is determined that the scene is transitioning from indoor to outdoor, the highest priority beacon data is used to determine the location and GPS data is collected. The number of GPS signals and their rate of change are calculated to obtain the GPS availability index. The weight coefficients of beacon data and GPS data are dynamically adjusted according to the GPS availability index to obtain the location data for the transition from indoor to outdoor.

[0021] When it is determined that the scene is transitioning from outdoor to indoor, the GPS data is maintained to determine the initial position and the surrounding beacon data is acquired. The initial position is corrected using the beacon data with the highest priority. The position data for the transition from outdoor to indoor is obtained through progressive weight adjustment.

[0022] The position data of the transition phase is time-aligned and coordinate-unified. Adaptive weights are designed based on the position accuracy evaluation index. Trajectory smoothing is performed in combination with inertial measurement unit data to obtain complete position data.

[0023] Furthermore, when determining from the complete location data whether the current location environment changes from indoor to outdoor or from outdoor to indoor, the hierarchical progressive filtering performed on the inertial measurement unit data includes:

[0024] The curvature change characteristics of the position trajectory are extracted from the complete position data, and the steering angle sequence is constructed by obtaining the gyroscope data of the inertial measurement unit.

[0025] Based on curvature change characteristics and turning angle sequence, determine whether the current location environment changes from indoor to outdoor or from outdoor to indoor;

[0026] Based on the judgment results, the acceleration data of the inertial measurement unit is decoupled by gravity component, and the gyroscope zero bias is compensated according to temperature data. Wavelet threshold filtering is used to remove high-frequency noise to obtain the preprocessed inertial measurement unit data.

[0027] When the location environment changes from indoor to outdoor, the heading angle is calibrated using the velocity vector in the GPS data; when the location environment changes from outdoor to indoor, the attitude estimation is corrected using the relative orientation information of the beacon; an adaptive attenuation factor is constructed to smooth the attitude change, and the calibrated inertial measurement unit data is obtained.

[0028] Based on the complete position data, the stationary and moving states are determined. In the stationary state, zero velocity updates are performed, and an error feedback loop is constructed using position constraint information. Integral drift suppression is performed on the preprocessed and calibrated inertial measurement unit (IMU) data to obtain filtered IMU data.

[0029] Furthermore, Kalman iteration is used to eliminate the cumulative positioning error caused by environmental changes on the filtered data, resulting in corrected motion data including:

[0030] The filtered data is decomposed into position, velocity and attitude components, and the rate of change of each component at adjacent time points is calculated to obtain the differences in data characteristics before and after the environmental transformation.

[0031] Iterative prediction rules are established based on differences in data characteristics. Position drift error is calculated based on the rate of change of position and velocity components, and cumulative error is calculated based on the rate of change of attitude components.

[0032] The filtered data is calculated according to the iterative prediction rule. The positioning drift error and cumulative error are added to the filtered data to obtain the state transformation relationship. The measurement relationship is constructed based on the state transformation relationship.

[0033] Kalman prediction is performed based on the filtered data and measurement relationship. The prediction results are compared with the actual measurement values ​​to obtain the residuals. The error covariance is then updated based on the residuals.

[0034] The Kalman gain is calculated using the error covariance. The Kalman gain is multiplied by the residual to obtain the correction. The correction is applied to the prediction result to obtain the iterative result. The iterative result is compared with the previous iterative result. When the difference is less than the convergence threshold, the corrected motion data is output. When the difference is greater than the convergence threshold, the process returns to the Kalman prediction step to continue iterative calculation.

[0035] Furthermore, iterative prediction rules are established based on differences in data characteristics. Position drift error is calculated based on the rate of change of position and velocity components, and cumulative error is calculated based on the rate of change of attitude components, including:

[0036] Calculate the differences between the position, velocity, and attitude components at adjacent time points, and obtain data feature differences based on the changing trends of these differences before and after the environmental transition;

[0037] Based on the differences in data characteristics, iterative prediction rules for position components, velocity components, and attitude components in relation to time variations are established.

[0038] Substitute the rate of change of the position and velocity components into the iterative prediction rule, and combine the motion acceleration to calculate the positioning drift error.

[0039] The cumulative error is calculated based on the rate of change of the attitude components;

[0040] The state at the next moment is calculated according to the iterative prediction rule, and the positioning drift error and cumulative error are used as corrections to iteratively correct the state at the next moment.

[0041] Furthermore, quaternion operations are performed on the corrected motion data to obtain spatial attitude parameters. Based on these parameters, the distance measurements between adjacent positioning beacons are adjusted to obtain the inspection trajectory for the meteorological equipment area, including:

[0042] Obtain angular velocity information from the corrected motion data, determine the magnitude of the angular velocity value, calculate the rotation increment using series expansion when the angular velocity value is close to zero, and calculate the rotation increment using vector rotation when the angular velocity value is not zero, thus obtaining the quaternion attitude increment;

[0043] The attitude parameters at the previous moment are multiplied by the quaternion attitude increment to obtain the attitude parameters at the current moment. The attitude parameters are then normalized to eliminate accumulated errors, resulting in the corrected attitude parameters.

[0044] The roll angle, pitch angle, and yaw angle are obtained by cosine transformation using the corrected attitude parameters. The roll angle, pitch angle, and yaw angle are then combined to obtain the space attitude parameters.

[0045] The distance values ​​in the horizontal plane are corrected based on the roll angle in the spatial attitude parameters, the distance values ​​in the vertical direction are corrected based on the pitch angle, and the displacement direction is determined based on the heading angle to obtain the corrected distance values ​​between adjacent beacons.

[0046] A triangular grid is constructed using the locations of adjacent beacons as reference points. The corrected distance between adjacent beacons is used as the side length constraint. The spatial coordinates of the inspection location are obtained through adjustment calculations. The inspection trajectory of the meteorological equipment area is generated based on the spatial coordinates.

[0047] Secondly, an AR glasses system for mapping motion trajectories during weather inspections is provided in this embodiment of the invention, used to implement the method described in any one of claims 1-7, the system comprising:

[0048] The data acquisition module is used to collect data from the inertial measurement unit, GPS data, and positioning beacon data of the AR glasses;

[0049] The location determination module is used to acquire the overlapping area of ​​multiple beacons in the location beacon data, calculate the signal strength ratio of each beacon in the overlapping area, determine the beacon priority based on the signal strength ratio, use the highest priority beacon data to determine the location when indoors, and use GPS data to determine the location when outdoors, thus obtaining complete location data;

[0050] The data correction module is used to perform hierarchical progressive filtering on the inertial measurement unit data when the current location environment changes from indoor to outdoor or from outdoor to indoor based on the complete location data. The filtered data is then processed using Kalman iteration calculation to eliminate the cumulative positioning error caused by the environment change, and the corrected motion data is obtained.

[0051] The trajectory generation module is used to perform quaternion operations on the corrected motion data to obtain spatial attitude parameters, and adjust the distance measurement values ​​between adjacent positioning beacons according to the spatial attitude parameters to obtain the inspection trajectory of the meteorological equipment area.

[0052] Thirdly, one technical solution provided in this embodiment of the invention is an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the method for predicting greenhouse gas pump failures in the meteorological industry based on AR glasses as described in any one of claims 1 to 7.

[0053] Fourthly, one technical solution provided in this embodiment of the invention is a computer-readable storage medium storing a computer program, wherein when the processor executes the computer program, it implements the steps in the method for predicting greenhouse gas pump failures in the meteorological industry based on AR glasses as described in any one of claims 1 to 7.

[0054] The beneficial effects of this invention are:

[0055] By collecting multi-source positioning data through AR glasses, continuous and stable motion trajectory mapping can be achieved in both indoor and outdoor environments within the meteorological equipment area. Priority is determined by analyzing the signal strength ratio of overlapping beacon areas, improving indoor positioning accuracy. During environmental transitions, hierarchical progressive filtering and Kalman iteration are used to process inertial measurement unit data, effectively eliminating cumulative positioning errors caused by indoor-outdoor environment switching. Quaternion operations are used to obtain spatial attitude parameters, enabling precise description of the inspection personnel's motion posture. Based on these spatial attitude parameters, the distance measurements between adjacent positioning beacons are adjusted, improving the accuracy of the inspection trajectory mapping. This scheme overcomes the problems of jumps and drifts that traditional positioning methods easily produce during indoor-outdoor environment transitions, achieving a smooth transition of the inspection trajectory. Overall, it improves the reliability of trajectory recording during meteorological equipment inspections, providing accurate data support for subsequent analysis of inspection route rationality and evaluation of inspection work efficiency.

[0056] The above description of the invention is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0057] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0058] Figure 1 A flowchart illustrating a method for drawing motion trajectories during meteorological inspections based on AR glasses, provided in an embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of the structure of a motion trajectory drawing system based on AR glasses during meteorological inspection, provided in an embodiment of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0061] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it may also have additional steps not included in the figures; the process may correspond to a method, function, procedure, subroutine, subroutine, etc.

[0062] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. It should also be understood that in the various embodiments of the invention, the sequence number of each process does not imply a specific order of execution; the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the invention.

[0063] Example 1

[0064] like Figure 1 As shown, Figure 1 A flowchart illustrating a method for mapping motion trajectories during weather inspections using AR glasses, provided as an embodiment of the present invention. The method includes the following steps:

[0065] Collect inertial measurement unit data, GPS data, and positioning beacon data from AR glasses;

[0066] The system acquires overlapping areas of multiple beacons in the location beacon data, calculates the signal strength ratio of each beacon in the overlapping area, determines the beacon priority based on the signal strength ratio, uses the highest priority beacon data to determine the location when indoors, and uses GPS data to determine the location when outdoors, thus obtaining complete location data.

[0067] When the current location environment changes from indoor to outdoor or from outdoor to indoor based on the complete location data, the inertial measurement unit data is subjected to hierarchical progressive filtering. The filtered data is then processed using Kalman iteration to eliminate the cumulative positioning error caused by the environment change, resulting in corrected motion data.

[0068] Quaternion operations are performed on the corrected motion data to obtain spatial attitude parameters. The distance measurements between adjacent positioning beacons are adjusted based on the spatial attitude parameters to obtain the inspection trajectory of the meteorological equipment area.

[0069] In one alternative embodiment,

[0070] Obtain the overlapping area of ​​multiple beacons in the positioning beacon data, calculate the signal strength ratio of each beacon in the overlapping area, and determine the beacon priority based on the signal strength ratio, including:

[0071] Real-time acquisition of location beacon data; acquisition of multiple location beacons with signal strength exceeding a preset strength threshold; sampling of the signal strength of location beacons in multiple consecutive sampling periods; and division of location beacons that meet the signal strength condition in consecutive sampling periods into multiple beacon overlapping areas.

[0072] The location beacon with the strongest signal in the overlapping area of ​​the multiple beacons is selected as the reference beacon. The signal strength ratio of other location beacons to the reference beacon is calculated to obtain the signal strength ratio characteristics of each location beacon.

[0073] The signal strength ratio feature is variance-calculated within a time window to obtain a signal stability index, and the handover cost is calculated in combination with the historical priority of the currently used positioning beacon.

[0074] Beacon priorities are determined based on signal stability metrics and handover costs.

[0075] For example, in a location beacon network environment, a mobile terminal determines its own location by collecting data from surrounding location beacons in real time. The terminal device is equipped with a beacon signal receiving module to receive wireless signals transmitted by each location beacon. The signal receiving module acquires the beacon identifier and its corresponding signal strength value and transmits the data to a signal processing unit for processing. This method first sets a signal strength threshold, for example, -85dBm. When the received location beacon signal strength exceeds this threshold, the beacon is considered usable for location calculation. Within each sampling period (e.g., 100 milliseconds), the terminal device samples the surrounding location beacon signals and records the beacon identifier and its signal strength. To ensure the reliability of the beacon signals, the terminal device continuously monitors the beacon signals over multiple consecutive sampling periods (e.g., 10 consecutive sampling periods). When certain location beacons maintain a signal strength higher than a preset threshold throughout these consecutive sampling periods, these beacons are classified as components of a multi-beacon overlapping area.

[0076] For identified overlapping areas of multiple beacons, the terminal device selects the positioning beacon with the highest signal strength as the reference beacon. Assuming three positioning beacons A, B, and C are identified, with signal strengths of -60dBm, -70dBm, and -75dBm respectively, beacon A is selected as the reference beacon. The signal strength ratios of the other beacons to the reference beacon are then calculated. Signal strength is usually expressed in dBm; to facilitate ratio calculation, dBm is first converted to milliwatts (mW) before calculating the ratios. For example, -60dBm is converted to 0.000001mW, -70dBm to 0.0000001mW, and -75dBm to 0.00000003162mW. Using beacon A as the reference, the signal strength ratio of B to A is calculated to be 0.1, and the signal strength ratio of C to A is 0.03162. These ratios constitute the signal strength ratio characteristics of each positioning beacon.

[0077] Signal strength ratios fluctuate over time. To assess the stability of beacon signals, this method collects multiple sets of signal strength ratio data within a specific time window (e.g., 5 seconds) and calculates the variance of these data. A smaller variance indicates a more stable signal. Assuming beacon B collects 50 sets of signal strength ratio data within a 5-second time window, the calculated variance is 0.002, while the variance of beacon C's signal strength ratio is 0.015, indicating that beacon B's signal is more stable than beacon C's. Furthermore, considering that frequent switching of positioning beacons can cause positioning result jitter, this method also calculates the cost of switching positioning beacons. The switching cost is related to the historical priority of the currently used positioning beacon; the higher the historical priority, the greater the switching cost. Assuming beacon A is currently being used, with a historical priority of 0.8 (ranging from 0 to 1), the base cost of switching to another beacon is 0.8.

[0078] The determination of beacon priorities comprehensively considers signal strength, signal stability indicators, and handover costs. The priority calculation formula can be expressed as: beacon priority equals the normalized value of signal strength multiplied by a weighting coefficient (e.g., 0.5), plus the normalized value of signal stability indicators multiplied by a weighting coefficient (e.g., 0.3), minus the handover cost multiplied by a weighting coefficient (e.g., 0.2). For the baseline beacon, since no handover is required, its handover cost is 0. The priority of each beacon is calculated using this formula, and the beacon with the highest priority is selected for subsequent positioning calculations. For example, if the calculated priorities of beacons A, B, and C are 0.85, 0.76, and 0.62 respectively, then beacon A is selected as the primary positioning beacon. When environmental changes cause changes in beacon priorities, the system will reselect the best beacon based on the new priorities.

[0079] This method effectively solves the positioning instability problem caused by frequent switching in multi-beacon environments by evaluating the signal strength ratio and stability of each positioning beacon in the overlapping area of ​​multiple beacons in real time, and taking into account the cost of beacon switching. This technology not only improves positioning accuracy, but also enhances the robustness of the positioning system in complex electromagnetic environments.

[0080] In one optional embodiment, the location is determined using the highest priority beacon data when indoors and using GPS data when outdoors, resulting in complete location data including:

[0081] The system collects signal continuity data from beacon data (the highest priority), GPS signal quantity variation data, illumination intensity data from the inertial measurement unit, and air pressure variation data, and obtains the velocity and acceleration distribution characteristics of the position trajectory.

[0082] The reliability of beacon and GPS data is assessed based on signal continuity data and GPS signal quantity variation data, and the scene is determined by combining light intensity data, air pressure variation data, and velocity and acceleration distribution characteristics.

[0083] When it is determined that the scene is transitioning from indoor to outdoor, the highest priority beacon data is used to determine the location and GPS data is collected. The number of GPS signals and their rate of change are calculated to obtain the GPS availability index. The weight coefficients of beacon data and GPS data are dynamically adjusted according to the GPS availability index to obtain the location data for the transition from indoor to outdoor.

[0084] When it is determined that the scene is transitioning from outdoor to indoor, the GPS data is maintained to determine the initial position and the surrounding beacon data is acquired. The initial position is corrected using the beacon data with the highest priority. The position data for the transition from outdoor to indoor is obtained through progressive weight adjustment.

[0085] The position data of the transition phase is time-aligned and coordinate-unified, and the trajectory is smoothed by combining the inertial measurement unit data to obtain complete position data.

[0086] For example, when meteorological inspectors wear AR glasses for inspections, the AR glasses have a built-in positioning module. This positioning module includes a beacon receiver, a GPS receiver, an inertial measurement unit (IMU), a light sensor, and a barometric pressure sensor. The beacon receiver receives signals from positioning beacons placed indoors, the GPS receiver receives satellite navigation signals, the IMU measures acceleration and angular velocity, the light sensor measures ambient light intensity, and the barometric pressure sensor measures changes in air pressure. During meteorological equipment inspections, the positioning module collects data every 100 milliseconds, including the highest priority beacon data, beacon signal continuity data, GPS signal quantity and changes, light intensity data, barometric pressure data, and IMU data.

[0087] Signal continuity data is obtained by recording the stability of beacon signal reception over a 5-second window. For example, if the beacon signal reception rate exceeds 95% within a 5-second window, the signal continuity score is 0.95. GPS signal quantity change data is obtained by recording the change in the number of visible satellites over a 10-second window. For example, an increase from 0 to 4 satellites represents a change rate of 0.4 satellites per second. Light intensity data is collected by a light sensor. Normal indoor light intensity ranges from 50 to 500 lux, while outdoor sunlight can reach 5000 to 10000 lux. Barometric pressure change data is obtained by recording the magnitude of barometric pressure changes over a 30-second window. For example, a short-term pressure change exceeding 2 hPa may indicate a change in the spatial environment. The velocity and acceleration distribution characteristics of the location trajectory are calculated from IMU data. Indoor walking speed for inspection personnel is typically 0.5-1.5 m / s, while outdoor walking speed may reach 1.0-2.5 m / s, with significant differences in acceleration distribution.

[0088] Based on the collected data, the positioning module assesses the reliability of beacon and GPS data. Beacon data reliability is calculated using a signal continuity score; for example, a continuity score of 0.95 indicates 95% reliability. GPS data reliability is assessed using the number of visible satellites and signal strength; for example, a GPS reliability score of 0.9 is achieved when the number of visible satellites is greater than 4 and the signal strength is greater than -130 dBm. Combining light intensity, air pressure changes, and velocity and acceleration distribution characteristics, the positioning module determines the current scene. For example, if light intensity suddenly increases from 100 lux to 5000 lux, air pressure drops by 2.5 hPa within 10 seconds, and velocity increases from 1.2 m / s to 1.8 m / s, it can be determined that the scene is transitioning from indoors to outdoors.

[0089] When the system determines a transition from indoor to outdoor conditions, the positioning module continues to use the highest-priority beacon data to determine location, while simultaneously starting to collect GPS data. The number of GPS signals and its rate of change are calculated to obtain the GPS availability index. For example, when the number of visible satellites increases from 1 to 6, with a change rate of 0.5 satellites / second, the GPS availability index can be calculated as 0.6. The weighting coefficients of beacon and GPS data are dynamically adjusted based on the GPS availability index. For instance, initially, the beacon data weight is 0.9 and the GPS data weight is 0.1. As the GPS availability index increases to 0.6, the beacon data weight is adjusted to 0.4 and the GPS data weight to 0.6. By weighted fusion of the two types of data, the location data for the indoor-to-outdoor transition is obtained, achieving a smooth transition.

[0090] When a transition from outdoor to indoor scenes is detected, the positioning module first maintains GPS data to determine an initial location while simultaneously acquiring surrounding beacon data. For example, if the light intensity drops from 6000 lux to 300 lux, the air pressure rises by 3 hPa within 15 seconds, and the number of visible satellites decreases from 8 to 3, a transition from outdoor to indoor scenes is identified. At this point, the initial GPS-determined location is corrected using the highest-priority beacon data. For instance, if the GPS positioning accuracy is 5 meters and the beacon positioning accuracy is 1.5 meters, beacon data correction can improve the positioning accuracy to 2 meters. A progressive weighting method is used, with GPS data having a weight of 0.8 and beacon data having a weight of 0.2 initially. As the depth inside the building increases, the GPS data weight gradually decreases to 0.1, while the beacon data weight increases to 0.9, thus obtaining the location data for the transition from outdoor to indoor.

[0091] The location data during the transition phase is processed first. Time alignment is performed to ensure consistency in timestamps across different sources; for example, GPS and beacon data are interpolated to the same sampling frequency (e.g., 10Hz). Next, coordinate unification is performed, converting the GPS WGS-84 coordinate system to the same local coordinate system as the beacon. Finally, trajectory smoothing is performed using IMU data, employing methods such as sliding window averaging or Kalman filtering to eliminate trajectory jitter. The window size can be set to 5 data points. Through these processes, complete and consistent location data for meteorological inspectors both indoors and outdoors is obtained, enabling the plotting of accurate movement trajectories.

[0092] This method, through multi-source data fusion technology, solves the technical problem of location data fragmentation when meteorological inspectors switch between indoor and outdoor scenes, achieving seamless connection of location information. It reliably records the complete movement trajectory of meteorological inspectors, effectively supporting information enhancement display on AR glasses and improving inspection efficiency and accuracy. Furthermore, this method is highly adaptable, automatically adjusting its positioning strategy according to environmental changes, providing accurate location data in various complex environments, and offering strong technical support for meteorological inspection work.

[0093] In one optional embodiment, when determining from complete location data whether the current location environment changes from indoor to outdoor or from outdoor to indoor, performing hierarchical progressive filtering on the inertial measurement unit data includes:

[0094] The curvature change characteristics of the position trajectory are extracted from the complete position data, and the steering angle sequence is constructed by obtaining the gyroscope data of the inertial measurement unit.

[0095] Based on curvature change characteristics and turning angle sequence, determine whether the current location environment changes from indoor to outdoor or from outdoor to indoor;

[0096] Based on the judgment results, the acceleration data of the inertial measurement unit is decoupled by gravity component, and the gyroscope zero bias is compensated according to temperature data. Wavelet threshold filtering is used to remove high-frequency noise to obtain the preprocessed inertial measurement unit data.

[0097] When the location environment changes from indoor to outdoor, the heading angle is calibrated using the velocity vector in the GPS data; when the location environment changes from outdoor to indoor, the attitude estimation is corrected using the relative orientation information of the beacon; an adaptive attenuation factor is constructed to smooth the attitude change, and the calibrated inertial measurement unit data is obtained.

[0098] Based on the complete position data, the stationary and moving states are determined. In the stationary state, zero velocity updates are performed, and an error feedback loop is constructed using position constraint information. Integral drift suppression is performed on the preprocessed and calibrated inertial measurement unit (IMU) data to obtain filtered IMU data.

[0099] For example, when meteorological inspectors wear AR glasses to inspect equipment, they need to draw movement trajectories in real time to assist navigation and record inspection paths. After the AR glasses' built-in positioning module acquires complete location data, it extracts the curvature change features of the location trajectory to determine environmental transitions. The location trajectory curvature is calculated using three consecutive location points. Location data is collected at a sampling frequency of 10Hz. For each location point Pi, the angle formed by it and its two preceding and following points Pi-1 and Pi+1 is calculated to obtain the curvature value. Indoor environments, due to space limitations, the trajectory curvature changes are usually larger, with an average curvature value of approximately 0.15-0.25; while outdoor environments have relatively smoother trajectories, with an average curvature value of approximately 0.05-0.12. By setting a threshold of 0.14, when the average curvature of 10 consecutive location points changes from above the threshold to below the threshold, it can be preliminarily determined that it is a transition from indoors to outdoors; conversely, it can be preliminarily determined that it is a transition from outdoors to indoors.

[0100] Simultaneously, the inertial measurement unit (IMU) in the AR glasses collects gyroscope data to construct a turning angle sequence. The gyroscope collects angular velocity data at a frequency of 100Hz, calculating the cumulative angle change every 100ms to form the turning angle sequence. Meteorological inspectors turn frequently indoors, resulting in significant fluctuations in the turning angle sequence, with an average cumulative turning angle of approximately 120-180 degrees per minute; while in outdoor environments, they turn relatively less, averaging approximately 30-90 degrees per minute. By combining the statistical characteristics of the turning angle sequence with the curvature change characteristics, a valid environmental transition is determined when both simultaneously meet the environmental transition conditions. For example, if the average curvature decreases from 0.18 to 0.08 within 60 consecutive seconds, and the cumulative turning angle per minute decreases from 150 degrees to 60 degrees, then an indoor-to-outdoor transition is determined.

[0101] After the environmental transition judgment is completed, the acceleration data from the IMU is decoupled for gravity components. The triaxial accelerometer in the AR glasses has a measurement range of ±8g and a resolution of 0.002g. Gravity component decoupling is achieved by estimating the device's attitude angles (roll, pitch, and yaw), thus decoupling the gravitational acceleration component (approximately 9.81 m / s²). 2 This is separated from the raw acceleration data. For example, when the AR glasses' attitude angles are 15 degrees roll and 10 degrees pitch, the gravitational component measured by the triaxial accelerometer is approximately 2.54 m / s². 2 1.70m / s 2 and 9.41m / s 2These components are subtracted from the original data to obtain the pure motion acceleration. Simultaneously, the gyroscope bias is compensated based on temperature data from the IMU's built-in temperature sensor. When the temperature changes from 25°C to 35°C, the gyroscope bias may increase from 0.2° / s to 0.35° / s. The bias correction value is calculated using a temperature compensation coefficient (approximately 0.015° / s / °C). Subsequently, wavelet threshold filtering is used to remove high-frequency noise. Using the db4 wavelet basis function, a decomposition layer of 4 is used, and a threshold of 0.05 is selected, effectively removing noise components exceeding 20Hz from the acceleration data, resulting in preprocessed IMU data.

[0102] When the location environment changes from indoors to outdoors, the IMU's heading angle is calibrated using the velocity vector from GPS data. The velocity vector accuracy provided by the GPS receiver is typically 0.1 m / s, and the direction accuracy is approximately 2 degrees, which can be used as a reference for the heading angle. For example, if the GPS measures a velocity vector of 1.5 m / s at 30 degrees east of north, while the heading angle calculated by the IMU integration is 35 degrees east of north, the IMU's heading angle is corrected to 30 degrees east of north to eliminate accumulated errors. When the location environment changes from outdoors to indoors, the attitude estimation is corrected using the relative azimuth information of beacons. Since the beacon's position is known, the relative azimuth is calculated using the rate of change of signal strength. For example, if the beacon signal strength is detected to increase from -75 dBm to -65 dBm within 0.5 seconds, it indicates that the beacon is approaching. Combining the azimuth information of multiple beacons, the attitude angle calculated by the IMU is corrected, improving accuracy by approximately 40%. To smooth attitude changes, an adaptive attenuation factor is constructed, with a value ranging from 0.05 to 0.95, dynamically adjusted according to the rate of attitude change. For example, when the attitude change rate is less than 5° / s, the attenuation factor is 0.9; when the change rate is greater than 30° / s, the attenuation factor is 0.3, so as to achieve a smooth attitude transition and obtain calibrated IMU data.

[0103] The stationary and moving states are determined based on complete location data by calculating the displacement changes of five consecutive location points. A stationary state is defined as a displacement change less than 0.1 meters that lasts for more than 2 seconds. In the stationary state, a zero-velocity update is performed, forcing the velocity vector to zero to eliminate velocity estimation errors. Simultaneously, an error feedback loop is constructed using position constraint information. When the deviation between the IMU integrated position and the GPS or beacon positioning result exceeds a threshold (2 meters outdoors, 0.8 meters indoors), the integrated position is corrected by a scaling factor of 0.2 to prevent position drift. Different strengths of integration drift suppression strategies are applied to the preprocessed and calibrated IMU data. Strong constraints (correction factor 0.3) are used for preprocessed data, while weak constraints (correction factor 0.1) are used for calibrated data. The resulting filtered IMU data effectively suppresses drift caused by long-term integration.

[0104] By employing the aforementioned hierarchical progressive filtering method, the quality of IMU data during meteorological inspections is significantly improved, providing a reliable data foundation for AR glasses to accurately map motion trajectories. This method adaptively adjusts the filtering strategy based on environmental changes, solving the problem of integral drift in traditional inertial navigation.

[0105] This method enables high-precision mapping of the movement trajectories of meteorological inspectors, providing accurate position and posture references for AR glasses, significantly improving the efficiency and accuracy of meteorological equipment inspections, while reducing the cognitive burden on inspectors and increasing their work comfort.

[0106] In one optional embodiment, the filtered data is processed using Kalman iteration to eliminate the cumulative positioning error caused by environmental transitions, resulting in corrected motion data including:

[0107] The filtered data is decomposed into position, velocity and attitude components, and the rate of change of each component at adjacent time points is calculated to obtain the differences in data characteristics before and after the environmental transformation.

[0108] Iterative prediction rules are established based on differences in data characteristics. Position drift error is calculated based on the rate of change of position and velocity components, and cumulative error is calculated based on the rate of change of attitude components.

[0109] The filtered data is calculated according to the iterative prediction rule. The positioning drift error and cumulative error are added to the filtered data to obtain the state transformation relationship. The measurement relationship is constructed based on the state transformation relationship.

[0110] Kalman prediction is performed based on the filtered data and measurement relationship. The prediction results are compared with the actual measurement values ​​to obtain the residuals. The error covariance is then updated based on the residuals.

[0111] The Kalman gain is calculated using the error covariance. The Kalman gain is multiplied by the residual to obtain the correction. The correction is applied to the prediction result to obtain the iterative result. The iterative result is compared with the previous iterative result. When the difference is less than the convergence threshold, the corrected motion data is output. When the difference is greater than the convergence threshold, the process returns to the Kalman prediction step to continue iterative calculation.

[0112] For example, when meteorological inspectors wear AR glasses to inspect equipment, the filtered data needs to be decomposed into position, velocity, and attitude components for refined processing. The position component contains three-dimensional spatial coordinates (x, y, z) in meters; the velocity component contains velocity values ​​in three directions (vx, vy, vz) in meters per second; and the attitude component contains roll, pitch, and yaw angles in degrees. By calculating the rate of change of each component at adjacent moments, the differences in data characteristics before and after environmental transformation are obtained. For example, in an indoor environment, the typical rate of change for the position component is (0.5 m / s, 0.5 m / s, 0.1 m / s), and the typical rate of change for the velocity component is (0.2 m / s). 2 0.2m / s 2 0.05m / s 2 The typical rate of change for the attitude component is (10° / s, 8° / s, 15° / s); while in outdoor environments, the typical rate of change for the position component is (1.2 m / s, 1.2 m / s, 0.2 m / s), and the typical rate of change for the velocity component is (0.4 m / s). 2 0.4m / s 2 0.1m / s 2 The typical rates of change for attitude components are (5° / s, 3° / s, 8° / s). By comparing these differences in data characteristics, environmental transition points can be identified, and subsequent processing strategies can be adjusted accordingly.

[0113] Iterative prediction rules are established based on differences in data characteristics. For position and velocity components, positioning drift error is calculated based on their rate of change. For example, when a weather inspector moves from indoors to outdoors, assuming the AR glasses' internal positioning algorithm has a drift rate of 0.2 m / 10 s indoors and 0.5 m / 10 s outdoors, this change in drift rate leads to cumulative positioning error. By analyzing historical data, a prediction model is established to estimate the drift error that may occur at environmental transition points. Specifically, if the inspector stays indoors for 120 seconds, the cumulative drift error is approximately 2.4 meters; upon transitioning to the outdoor environment, an additional 0.5 meters of drift will occur every 10 seconds. These drift errors need to be corrected using Kalman filtering. The rate of change of attitude components also directly affects positioning accuracy. For example, indoors, due to geomagnetic interference, the cumulative error rate of the heading angle is approximately 2° / minute; while outdoors, this error rate decreases to 0.5° / minute. By analyzing the rate of change of attitude components, the cumulative error can be estimated.

[0114] The filtered data is calculated based on iterative prediction rules, and the positioning drift error and cumulative error are added to the filtered data to obtain the state transformation relationship. For example, for the position state, the predicted position can be obtained by adding the current position to the velocity multiplied by the time interval and then adding the positioning drift error; for the velocity state, the predicted velocity can be obtained by adding the current velocity to the acceleration multiplied by the time interval; for the attitude state, the predicted attitude can be obtained by adding the current attitude to the angular velocity multiplied by the time interval and then adding the cumulative error. In this way, a complete state transformation relationship is constructed. Based on this relationship, a measurement relationship is further constructed to establish a correspondence between the predicted state and the actual observed sensor data. In the meteorological inspection scenario, the measurement data mainly comes from GPS receivers, beacon receivers, and IMUs. For example, in an outdoor environment, a GPS receiver provides position measurements with an accuracy of approximately 3 meters; in an indoor environment, a beacon receiver provides position measurements with an accuracy of approximately 1.5 meters; while the IMU provides velocity and attitude measurements in both environments.

[0115] Kalman prediction is performed based on filtered data and measurement relationships. Taking a sampling period of 50 milliseconds as an example, the state transition matrix and process noise covariance matrix need to be updated before each prediction. The state transition matrix reflects the relationship between state variables, such as the relationship between position and velocity; the process noise covariance matrix represents the uncertainty in the prediction process. At environmental transition points, the value of this matrix will increase accordingly, for example, from 0.01 to 0.05 for the diagonal elements, indicating an increase in prediction uncertainty. After the prediction is completed, the prediction result is compared with the actual measurement value to obtain the residual. For example, if the predicted position is (102.5m, 85.3m, 1.2m), and the GPS measurement position is (104.0m, 84.8m, 1.5m), then the residual is (1.5m, -0.5m, 0.3m). The error covariance is updated based on the residual, reflecting the reliability of the prediction result.

[0116] The Kalman gain is calculated using the error covariance, which determines the weighting of the predicted and measured values. For example, when the error covariance is large, the Kalman gain will favor the measured value (e.g., a gain of 0.7); when the error covariance is small, the Kalman gain will favor the predicted value (e.g., a gain of 0.3). The correction is obtained by multiplying the Kalman gain by the residual. For example, with a gain of 0.7 and a residual of (1.5m, -0.5m, 0.3m), the correction is (1.05m, -0.35m, 0.21m). This correction is then applied to the prediction result to obtain the iterative result. For example, adding the correction (1.05m, -0.35m, 0.21m) to the predicted position (102.5m, 85.3m, 1.2m) yields the iterative result (103.55m, 84.95m, 1.41m). The iteration result is compared with the previous iteration result. When the difference is less than the convergence threshold, the corrected motion data is output. For example, if the convergence threshold is set to 0.1 meters, if the position difference between two consecutive iterations is less than 0.1 meters, the iteration is considered to have converged, and the final result is output; otherwise, the Kalman prediction step is returned to continue iterative calculation until the convergence condition or the maximum number of iterations (usually set to 5) is reached.

[0117] This Kalman iterative computation method effectively eliminates the cumulative positioning error caused by environmental changes during meteorological inspections, providing a technical guarantee for AR glasses to draw accurate motion trajectories. In practical applications, this method significantly improves the accuracy and continuity of motion trajectories, especially performing exceptionally well in complex inspection scenarios with frequent switching between indoor and outdoor environments. Comparative experimental data shows that these improvements directly enhance the efficiency and accuracy of meteorological inspections, enabling inspectors to more intuitively identify abnormal equipment conditions while reducing their cognitive burden and improving their work comfort. This method is applicable to equipment inspections at various meteorological stations, providing strong support for intelligent meteorological monitoring.

[0118] In one optional embodiment, an iterative prediction rule is established based on differences in data characteristics. The positioning drift error is calculated based on the rate of change of the position and velocity components, and the cumulative error is calculated based on the rate of change of the attitude components, including:

[0119] The differences in position, velocity, and attitude components between adjacent time points are calculated respectively, and the differences in data characteristics are obtained based on the changing trends of each difference before and after the environmental transition.

[0120] Based on the differences in data characteristics, iterative prediction rules for position components, velocity components, and attitude components in relation to time variations are established.

[0121] Substitute the rate of change of the position and velocity components into the iterative prediction rule, and combine the motion acceleration to calculate the positioning drift error.

[0122] The cumulative error is calculated based on the rate of change of the attitude components;

[0123] The state at the next moment is calculated according to the iterative prediction rule, and the positioning drift error and cumulative error are used as corrections to iteratively correct the state at the next moment.

[0124] For example, when meteorological inspectors wear AR glasses to perform inspection tasks, the filtered motion data contains three components: position, velocity, and attitude. The differences in position, velocity, and attitude components between adjacent time points are calculated to identify environmental transition characteristics. In practical applications, the AR glasses collect data at a frequency of 50Hz, with a time interval of 20 milliseconds between adjacent frames. The position component difference is calculated by subtracting the position of the previous frame from the current frame position. For example, if the current frame position is (105.36m, 78.92m, 1.45m) and the previous frame position is (105.32m, 78.89m, 1.45m), then the position component difference is (0.04m, 0.03m, 0.00m). The velocity component difference is calculated by subtracting the velocity of the previous frame from the velocity of the current frame. For example, if the current frame velocity is (1.2 m / s, 0.9 m / s, 0.0 m / s) and the previous frame velocity is (1.15 m / s, 0.85 m / s, 0.0 m / s), then the velocity component difference is (0.05 m / s, 0.05 m / s, 0.0 m / s). The attitude component difference is calculated by subtracting the attitude angle of the previous frame from the attitude angle of the current frame. For example, if the current frame attitude angle is (5.2°, 1.8°, 78.5°) and the previous frame attitude angle is (5.0°, 1.7°, 78.0°), then the attitude component difference is (0.2°, 0.1°, 0.5°).

[0125] When meteorological inspectors move from the indoor meteorological station to the outdoor observation site, the characteristics of the position component difference change. In an indoor environment, due to space constraints, the inspectors move slowly, and the average value of the position component difference is typically (0.02m, 0.02m, 0.00m), with a standard deviation of approximately 0.01m. In an outdoor environment, the inspectors' movement speed increases, and the average value of the position component difference increases to (0.05m, 0.05m, 0.01m), with a standard deviation of approximately 0.02m. The change in the velocity component difference before and after the environmental transition is as follows: indoor environment, the average value is (0.03m / s, 0.03m / s, 0.01m / s), with a standard deviation of approximately 0.02m / s; outdoors, the average value increases to (0.08m / s, 0.08m / s, 0.02m / s), with a standard deviation of approximately 0.04m / s. The changes in attitude component differences before and after environmental transitions are as follows: In indoor environments, the heading angle (the third component) changes frequently, with average differences of (0.1°, 0.1°, 0.8°) and a standard deviation of approximately 0.4°; in outdoor environments, the heading angle changes less, with average differences of (0.2°, 0.1°, 0.3°) and a standard deviation of approximately 0.2°. By continuously monitoring these characteristic changes in 50 frames (approximately 1 second) of data, environmental transition points can be identified.

[0126] Based on the aforementioned differences in data characteristics, iterative prediction rules are established for the position, velocity, and attitude components in relation to time variations. For the position component, the iterative prediction rule is: the position at the next moment equals the current position plus the velocity multiplied by the time interval, plus half of that multiplied by the acceleration multiplied by the square of the time interval. In an indoor environment, the time interval is 20 milliseconds, and the average acceleration is 0.2 m / s². 2 0.2m / s 2 0.05m / s 2 In outdoor environments, the average acceleration is adjusted to (0.4 m / s²). 2 0.4m / s 2 0.1m / s 2 For the velocity component, the iterative prediction rule is: the velocity at the next moment equals the current velocity plus the acceleration multiplied by the time interval. For the attitude component, the iterative prediction rule is: the attitude angle at the next moment equals the current attitude angle plus the angular velocity multiplied by the time interval. In an indoor environment, the average angular velocity is (5° / s, 3° / s, 10° / s); in an outdoor environment, the average angular velocity is adjusted to (3° / s, 2° / s, 5° / s).

[0127] The positioning drift error is calculated by substituting the rates of change of position and velocity components into the iterative prediction rule and combining them with motion acceleration. The positioning drift error mainly consists of two parts: error caused by sensor noise and systematic error caused by environmental changes. Error caused by sensor noise can be estimated using statistical methods; for example, in a stationary state, the standard deviation of position drift is approximately 0.01 m / 20 ms, and the standard deviation of velocity drift is approximately 0.005 m / s / 20 ms. Systematic error caused by environmental changes requires modeling based on historical data. For example, when a weather inspector moves from indoors to outdoors, the positional drift rate decreases from 0.01 m / s to 0.005 m / s due to improved GPS signal; however, due to increased walking speed, the integral error may increase from 0.02 m / s to 0.03 m / s. Combining these two factors, the positioning drift error at the environmental transition point can be calculated. Specifically, for the position component, the positioning drift error is calculated as 20 times the rate of change of position multiplied by the rate of change of environmental characteristics. For example, when the rate of change of position is 0.05m / 20ms and the rate of change of environmental characteristics is 0.5, the positioning drift error is 0.5m. For the velocity component, the positioning drift error is calculated as 10 times the rate of change of velocity multiplied by the rate of change of environmental characteristics. For example, when the rate of change of velocity is 0.08m / s / 20ms and the rate of change of environmental characteristics is 0.5, the positioning drift error is 0.4m / s.

[0128] The cumulative error is calculated based on the rate of change of attitude components. The cumulative attitude error mainly originates from gyroscope drift and changes in the ambient magnetic field. In indoor environments, due to the influence of reinforced concrete structures and electronic equipment, magnetic field disturbances are significant, resulting in a cumulative heading angle error rate of approximately 0.5° / s. In outdoor environments, the magnetic field is relatively stable, and the cumulative heading angle error rate decreases to 0.1° / s. Roll and pitch angles are mainly affected by gyroscope drift, and their cumulative error rates do not change significantly before and after environmental transitions, remaining at approximately 0.05° / s. Based on these characteristics, the cumulative attitude error at the environmental transition point can be calculated. Specifically, the cumulative attitude error is calculated as 30 times the rate of change of attitude multiplied by the rate of change of environmental characteristics. For example, when the rate of change of heading angle is 0.5° / 20ms and the rate of change of environmental characteristics is 0.6, the cumulative heading angle error is 9°.

[0129] The state at the next moment is calculated according to the iterative prediction rule, and the positioning drift error and cumulative error are used as correction factors to iteratively correct the state at the next moment. For example, the current position is (105.36m, 78.92m, 1.45m), the velocity is (1.2m / s, 0.9m / s, 0.0m / s), and the acceleration is (0.3m / s²). 2 0.3m / s 2 0.0m / s 2The time interval is 20 milliseconds. The next position calculated according to the iterative prediction rule is (105.386m, 78.938m, 1.45m). If the positioning drift error is (0.01m, 0.01m, 0.002m), the corrected next position is (105.396m, 78.948m, 1.452m). The current attitude angle is (5.2°, 1.8°, 78.5°), and the angular velocity is (3° / s, 2° / s, 5° / s). The next attitude angle calculated according to the iterative prediction rule is (5.26°, 1.84°, 78.6°). If the cumulative attitude error is (0.05°, 0.03°, 0.2°), the corrected next attitude angle is (5.31°, 1.87°, 78.8°). This iterative correction method can effectively compensate for positioning and attitude errors caused by environmental changes.

[0130] The method for mapping motion trajectories during meteorological inspections using AR glasses, implemented with the aforementioned technology, significantly improves trajectory accuracy and continuity, particularly during transitions between indoor and outdoor environments. This method, through detailed analysis of data feature differences before and after environmental changes, establishes targeted iterative prediction rules, effectively compensating for cumulative errors caused by sensor drift and environmental variations. This allows meteorological inspectors to see accurate and smooth inspection trajectories on AR glasses, greatly improving inspection efficiency and accuracy, reducing error rates and missed detection rates, and providing a solid technical foundation for intelligent inspection of meteorological equipment.

[0131] In one optional embodiment, quaternion operations are performed on the corrected motion data to obtain spatial attitude parameters. The distance measurements between adjacent positioning beacons are adjusted based on these spatial attitude parameters to obtain the inspection trajectory for the meteorological equipment area, including:

[0132] Obtain angular velocity information from the corrected motion data, determine the magnitude of the angular velocity value, calculate the rotation increment using series expansion when the angular velocity value is close to zero, and calculate the rotation increment using vector rotation when the angular velocity value is not zero, thus obtaining the quaternion attitude increment;

[0133] The attitude parameters at the previous moment are multiplied by the quaternion attitude increment to obtain the attitude parameters at the current moment. The attitude parameters are then normalized to eliminate accumulated errors, resulting in the corrected attitude parameters.

[0134] The roll angle, pitch angle, and yaw angle are obtained by cosine transformation using the corrected attitude parameters. The roll angle, pitch angle, and yaw angle are then combined to obtain the space attitude parameters.

[0135] The distance values ​​in the horizontal plane are corrected based on the roll angle in the spatial attitude parameters, the distance values ​​in the vertical direction are corrected based on the pitch angle, and the displacement direction is determined based on the heading angle to obtain the corrected distance values ​​between adjacent beacons.

[0136] A triangular grid is constructed using the locations of adjacent beacons as reference points. The corrected distance between adjacent beacons is used as the side length constraint. The spatial coordinates of the inspection location are obtained through adjustment calculations. The inspection trajectory of the meteorological equipment area is generated based on the spatial coordinates.

[0137] For example, when meteorological inspectors wear AR glasses to inspect areas with meteorological equipment, they extract angular velocity information from the corrected motion data, including angular velocity values ​​along the X, Y, and Z axes, in radians per second. The inertial measurement unit (IMU) collects angular velocity data at a frequency of 200Hz; for example, the angular velocity value collected at a certain moment might be (0.05, 0.02, 0.1) rad / s. The magnitude of the angular velocity value is determined, with a threshold of 0.001 rad / s. When the magnitude of the angular velocity value is less than this threshold, it is considered that the angular velocity is approaching zero, and the rotational increment is calculated using a series expansion. In the series expansion, the real part of the attitude increment is 1, and each component of the imaginary part is the angular velocity of each axis multiplied by half the sampling period. For example, when the angular velocity is (0.0005, 0.0003, 0.0008) rad / s and the sampling period is 5 milliseconds, the quaternion attitude increment is (1, 0.00000125, 0.00000075, 0.000002). When the angular velocity value is not zero, the rotation increment is calculated using a vector rotation method. In the vector rotation method, the magnitude of the angular velocity is first calculated, then the rotation angle per unit time is calculated, followed by the unit vector of the rotation axis, and finally the quaternion attitude increment is calculated based on the rotation angle and rotation axis. For example, when the angular velocity is (0.05, 0.02, 0.1) rad / s, the angular velocity magnitude is 0.112 rad / s, the sampling period is 5 milliseconds, the rotation angle is 0.00056 rad, the rotation axis unit vector is (0.446, 0.179, 0.893), and the quaternion attitude increment is (0.99999, 0.000125, 0.00005, 0.00025).

[0138] The attitude parameters at the current moment are obtained by performing a quaternion product operation on the attitude parameters from the previous moment and the quaternion attitude increment. The quaternion product operation involves multiplying and adding four components, calculated according to the rules of quaternion multiplication. For example, if the attitude parameters at the previous moment are (0.9962, 0.0349, 0.0523, 0.0610) and the quaternion attitude increment is (0.99999, 0.000125, 0.00005, 0.00025), the preliminary attitude parameters at the current moment obtained through quaternion product operation are (0.9961, 0.0350, 0.0524, 0.0612). Because rounding errors may occur during numerical calculation, causing the quaternion modulus to no longer equal to 1, it is necessary to normalize the attitude parameters to eliminate accumulated errors. Normalization is achieved by calculating the quaternion modulus and then dividing each of the four components by the modulus. For example, the initial attitude parameters (0.9961, 0.0350, 0.0524, 0.0612) have a modulus of 1.0001, and after normalization, the corrected attitude parameters are (0.9960, 0.0350, 0.0524, 0.0612).

[0139] The roll, pitch, and yaw angles are obtained using cosine transform on the corrected attitude parameters. The cosine transform is based on the quaternion-to-Euler angle conversion relationship, and singularity handling must be considered during the calculation. For example, when the corrected attitude parameters are (0.9960, 0.0350, 0.0524, 0.0612), the calculated roll angle is 5.82 degrees, the pitch angle is 6.15 degrees, and the yaw angle is 7.85 degrees. Combining these three angles yields the spatial attitude parameters, represented as (5.82°, 6.15°, 7.85°). These parameters describe the AR glasses' attitude in space, providing a basis for subsequent distance correction.

[0140] The horizontal distance value is corrected based on the roll angle from the spatial attitude parameters. When the AR glasses have a roll angle, the horizontal displacement obtained by inertial measurement will have an error. The correction method is to scale the horizontal distance based on the cosine of the roll angle. For example, when the roll angle is 5.82 degrees, the cosine value is 0.9949, and if the original horizontal distance is 10.5 meters, the corrected horizontal distance is 10.45 meters. The vertical distance value is corrected based on the pitch angle, and the correction method is similar, adjusting the vertical distance using the cosine of the pitch angle. For example, when the pitch angle is 6.15 degrees, the cosine value is 0.9942, and if the original vertical distance is 2.8 meters, the corrected vertical distance is 2.78 meters. The displacement direction is determined based on the heading angle. The heading angle defines the angle between the direction of motion and true north. The heading angle can be used to decompose the distance value into east-west and north-south components. For example, when the heading angle is 7.85 degrees, if the horizontal displacement is 5.3 meters, then the east-west component is 0.72 meters and the north-south component is 5.25 meters. Through the above corrections, the corrected distance values ​​between adjacent beacons are obtained.

[0141] A triangular grid is constructed using adjacent beacon positions as reference points. Meteorological equipment areas typically have multiple positioning beacons; for example, four beacons are placed inside a meteorological station, and six beacons are placed at the observation site. The distance between adjacent beacons ranges from 5 to 20 meters. Three non-collinear beacons are selected to form a triangular grid cell; for example, beacon A (0, 0, 0), beacon B (10, 0, 0), and beacon C (5, 8, 0) form a triangle. When the inspection personnel are located within this triangle, their position can be determined using the distance values ​​to the three beacons. The corrected distance values ​​between adjacent beacons are used as side length constraints; for example, the measured distances between the inspection personnel and beacons A, B, and C are 8.5 meters, 5.2 meters, and 4.8 meters, respectively. The spatial coordinates of the inspection position are obtained through adjustment calculations based on the least squares principle, iteratively solving for the optimal position that satisfies the distance constraints. For example, the calculated spatial coordinates of the inspection position are (6.2, 4.5, 1.2) meters. As the inspection personnel move, their position is calculated every 100 milliseconds, and the position points are continuously recorded to form a spatial trajectory. The inspection trajectory for the meteorological equipment area is generated based on the spatial coordinate sequence, and the trajectory points are smoothly connected by cubic spline interpolation, displaying the inspection path in real time on the AR glasses.

[0142] This method for mapping motion trajectories during meteorological inspections using AR glasses effectively solves the integral drift and attitude error problems of traditional inertial navigation systems by employing quaternion attitude calculation and distance correction techniques, achieving high-precision trajectory mapping. The method utilizes quaternions to represent attitude, avoiding the gimbal locking problem in Euler angle representation and ensuring the stability and continuity of attitude calculation. Through a meticulous angular velocity judgment strategy and targeted calculation method selection, a good balance is achieved between computational efficiency and accuracy, enabling AR glasses to process high-frequency inertial data in real time. This significantly improves the intelligent management level of meteorological stations.

[0143] Example 2

[0144] like Figure 2 As shown, Figure 2 To illustrate the structure of a motion trajectory system based on AR glasses during weather inspection, including:

[0145] The data acquisition module is used to collect data from the inertial measurement unit, GPS data, and positioning beacon data of the AR glasses;

[0146] The location determination module is used to acquire the overlapping area of ​​multiple beacons in the location beacon data, calculate the signal strength ratio of each beacon in the overlapping area, determine the beacon priority based on the signal strength ratio, use the highest priority beacon data to determine the location when indoors, and use GPS data to determine the location when outdoors, thus obtaining complete location data;

[0147] The data correction module is used to perform hierarchical progressive filtering on the inertial measurement unit data when the current location environment changes from indoor to outdoor or from outdoor to indoor based on the complete location data. The filtered data is then processed using Kalman iteration calculation to eliminate the cumulative positioning error caused by the environment change, and the corrected motion data is obtained.

[0148] The trajectory generation module is used to perform quaternion operations on the corrected motion data to obtain spatial attitude parameters, and adjust the distance measurement values ​​between adjacent positioning beacons according to the spatial attitude parameters to obtain the inspection trajectory of the meteorological equipment area.

[0149] This invention also provides a schematic diagram of the structure of an electronic device, including: a memory, a processor, and a computer program for a method stored in the memory and executable on the processor.

[0150] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the method provided in this invention and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0151] Those skilled in the art will understand that implementing all or part of the processes in the above embodiments can be accomplished by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the invention. The scope of the invention includes, but is not limited to, these specific embodiments. All equivalent changes made according to the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for mapping motion trajectories during weather inspections using AR glasses, characterized in that, Includes the following steps: Collect inertial measurement unit data, GPS data, and positioning beacon data from AR glasses; The system acquires overlapping areas of multiple beacons in the location beacon data, calculates the signal strength ratio of each beacon in the overlapping area, determines the beacon priority based on the signal strength ratio, uses the highest priority beacon data to determine the location when indoors, and uses GPS data to determine the location when outdoors, thus obtaining complete location data. When the current location environment changes from indoor to outdoor or from outdoor to indoor based on the complete location data, the inertial measurement unit data is subjected to hierarchical progressive filtering. The filtered data is then processed using Kalman iteration to eliminate the cumulative positioning error caused by the environment change, resulting in corrected motion data. Quaternion operations are performed on the corrected motion data to obtain spatial attitude parameters. The distance measurements between adjacent positioning beacons are adjusted based on the spatial attitude parameters to obtain the inspection trajectory of the meteorological equipment area.

2. The method according to claim 1, characterized in that, Obtain the overlapping area of ​​multiple beacons in the positioning beacon data, calculate the signal strength ratio of each beacon in the overlapping area, and determine the beacon priority based on the signal strength ratio, including: Real-time acquisition of location beacon data; acquisition of multiple location beacons with signal strength exceeding a preset strength threshold; sampling of the signal strength of location beacons within multiple consecutive sampling periods; and division of location beacons that meet the signal strength condition within consecutive sampling periods into multiple beacon overlapping regions. The location beacon with the strongest signal in the overlapping area of ​​the multiple beacons is selected as the reference beacon. The signal strength ratio of other location beacons to the reference beacon is calculated to obtain the signal strength ratio characteristics of each location beacon. The signal strength ratio feature is variance-calculated within a time window to obtain a signal stability index, and the handover cost is calculated in combination with the historical priority of the currently used positioning beacon. Beacon priorities are determined based on signal stability metrics and handover costs.

3. The method according to claim 1, characterized in that, When indoors, the highest priority beacon data is used to determine the location; when outdoors, GPS data is used to determine the location. Complete location data includes: The system collects signal continuity data from beacon data (the highest priority), GPS signal quantity variation data, illumination intensity data from the inertial measurement unit, and air pressure variation data, and obtains the velocity and acceleration distribution characteristics of the position trajectory. The reliability of beacon and GPS data is assessed based on signal continuity data and GPS signal quantity variation data, and the scene is determined by combining light intensity data, air pressure variation data, and velocity and acceleration distribution characteristics. When it is determined that the scene is transitioning from indoor to outdoor, the highest priority beacon data is used to determine the location and GPS data is collected. The number of GPS signals and their rate of change are calculated to obtain the GPS availability index. The weight coefficients of beacon data and GPS data are dynamically adjusted according to the GPS availability index to obtain the location data for the transition from indoor to outdoor. When it is determined that the scene is transitioning from outdoor to indoor, the GPS data is maintained to determine the initial position and the surrounding beacon data is acquired. The initial position is corrected using the beacon data with the highest priority. The position data for the transition from outdoor to indoor is obtained through progressive weight adjustment. The position data of the transition phase is time-aligned and coordinate-unified, and the trajectory is smoothed by combining the inertial measurement unit data to obtain complete position data.

4. The method according to claim 1, characterized in that, When determining whether the current location environment changes from indoors to outdoors or from outdoors to indoors based on complete location data, the inertial measurement unit data undergoes hierarchical progressive filtering, including: The curvature change characteristics of the position trajectory are extracted from the complete position data, and the steering angle sequence is constructed by obtaining the gyroscope data of the inertial measurement unit. Based on curvature change characteristics and turning angle sequence, determine whether the current location environment changes from indoor to outdoor or from outdoor to indoor; Based on the judgment results, the acceleration data of the inertial measurement unit is decoupled by gravity component, and the gyroscope zero bias is compensated according to temperature data. Wavelet threshold filtering is used to remove high-frequency noise to obtain the preprocessed inertial measurement unit data. When the location environment changes from indoor to outdoor, the heading angle is calibrated using the velocity vector in the GPS data; when the location environment changes from outdoor to indoor, the attitude estimation is corrected using the relative orientation information of the beacon; an adaptive attenuation factor is constructed to smooth the attitude change, and the calibrated inertial measurement unit data is obtained. Based on the complete position data, the stationary and moving states are determined. In the stationary state, zero velocity updates are performed, and an error feedback loop is constructed using position constraint information. Integral drift suppression is performed on the preprocessed and calibrated inertial measurement unit (IMU) data to obtain filtered IMU data.

5. The method according to claim 1, characterized in that, The filtered data is processed using Kalman iteration to eliminate the cumulative positioning error caused by environmental changes, resulting in corrected motion data including: The filtered data is decomposed into position, velocity and attitude components, and the rate of change of each component at adjacent time points is calculated to obtain the differences in data characteristics before and after the environmental transformation. Iterative prediction rules are established based on differences in data characteristics. Position drift error is calculated based on the rate of change of position and velocity components, and cumulative error is calculated based on the rate of change of attitude components. The filtered data is calculated according to the iterative prediction rule. The positioning drift error and cumulative error are added to the filtered data to obtain the state transformation relationship. The measurement relationship is constructed based on the state transformation relationship. Kalman prediction is performed based on the filtered data and measurement relationship. The prediction results are compared with the actual measurement values ​​to obtain the residuals. The error covariance is then updated based on the residuals. The Kalman gain is calculated using the error covariance. The Kalman gain is multiplied by the residual to obtain the correction. The correction is applied to the prediction result to obtain the iterative result. The iterative result is compared with the previous iterative result. When the difference is less than the convergence threshold, the corrected motion data is output. When the difference is greater than the convergence threshold, the process returns to the Kalman prediction step to continue iterative calculation.

6. The method according to claim 5, characterized in that, Iterative prediction rules are established based on differences in data characteristics. Position drift error is calculated based on the rate of change of position and velocity components, and cumulative error is calculated based on the rate of change of attitude components, including: The position component difference, velocity component difference, and attitude component difference between adjacent time points are calculated respectively, and the data feature differences are obtained based on the changing trends of each difference before and after the environment change. Based on the differences in data characteristics, iterative prediction rules for position components, velocity components, and attitude components in relation to time variations are established. Substitute the rate of change of the position and velocity components into the iterative prediction rule, and combine the motion acceleration to calculate the positioning drift error. The cumulative error is calculated based on the rate of change of the attitude components; The state at the next moment is calculated according to the iterative prediction rule, and the positioning drift error and cumulative error are used as corrections to iteratively correct the state at the next moment.

7. The method according to claim 1, characterized in that, Quaternion operations are performed on the corrected motion data to obtain spatial attitude parameters. Based on these parameters, the distance measurements between adjacent positioning beacons are adjusted to obtain the inspection trajectory for the meteorological equipment area, including: Obtain angular velocity information from the corrected motion data, determine the magnitude of the angular velocity value, calculate the rotation increment using series expansion when the angular velocity value is close to zero, and calculate the rotation increment using vector rotation when the angular velocity value is not zero, thus obtaining the quaternion attitude increment; The attitude parameters at the previous moment are multiplied by the quaternion attitude increment to obtain the attitude parameters at the current moment. The attitude parameters are then normalized to eliminate accumulated errors, resulting in the corrected attitude parameters. The roll angle, pitch angle, and yaw angle are obtained by cosine transformation using the corrected attitude parameters. The roll angle, pitch angle, and yaw angle are then combined to obtain the space attitude parameters. The distance values ​​in the horizontal plane are corrected based on the roll angle in the spatial attitude parameters, the distance values ​​in the vertical direction are corrected based on the pitch angle, and the displacement direction is determined based on the heading angle to obtain the corrected distance values ​​between adjacent beacons. A triangular grid is constructed using the locations of adjacent beacons as reference points. The corrected distance between adjacent beacons is used as the side length constraint. The spatial coordinates of the inspection location are obtained through adjustment calculations. The inspection trajectory of the meteorological equipment area is generated based on the spatial coordinates.

8. A system for mapping motion trajectories during weather inspections using AR glasses, for implementing the method described in any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to collect data from the inertial measurement unit, GPS data, and positioning beacon data of the AR glasses; The location determination module is used to acquire the overlapping area of ​​multiple beacons in the location beacon data, calculate the signal strength ratio of each beacon in the overlapping area, determine the beacon priority based on the signal strength ratio, use the highest priority beacon data to determine the location when indoors, and use GPS data to determine the location when outdoors, thus obtaining complete location data; The data correction module is used to perform hierarchical progressive filtering on the inertial measurement unit data when the current location environment changes from indoor to outdoor or from outdoor to indoor based on the complete location data. The filtered data is then processed using Kalman iteration calculation to eliminate the cumulative positioning error caused by the environment change, and the corrected motion data is obtained. The trajectory generation module is used to perform quaternion operations on the corrected motion data to obtain spatial attitude parameters, and adjust the distance measurement values ​​between adjacent positioning beacons according to the spatial attitude parameters to obtain the inspection trajectory of the meteorological equipment area.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of a method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the processor executes the computer program, it implements the steps of a method as described in any one of claims 1 to 7.

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