GNSS and IMU-based meteorological station pose monitoring system and method

Abstract

The invention discloses a meteorological station pose monitoring system and method based on a GNSS and an IMU. The system comprises a positioning time service module GNSS, a pose sensing unit IMU and a main control unit. The IMU serves as a low-power-consumption trigger at the same time, and when the system is in a dormant state, only the IMU is in operation; when the inclination angle is detected to exceed a preset threshold value, the IMU wakes up other units; the main control unit integrated edge calculation module constructs a space-time constraint error model of the GNSS, the IMU and the barometer by setting a sliding window which is not less than three times of the maximum sampling period of the sensor, iteratively corrects the IMU accumulated drift and the barometer temperature drift through error feedback, and then determines the fusion confidence coefficient based on the error reciprocal and weights the fusion confidence coefficient. And finally, fusing multi-source data in the window to generate a pose result. And the pose monitoring precision is improved.

Description

Technical Field

[0001] This invention belongs to the field of measurement and control technology, and more specifically, relates to a meteorological station pose monitoring system and method based on GNSS and IMU. Background Technology

[0002] Against the backdrop of global warming and frequent extreme weather events, the location information (latitude, longitude, and altitude) of meteorological stations is a crucial foundation for spatial positioning of various meteorological data and climate analysis, directly impacting the accuracy of core operations such as refined forecasting services, meteorological disaster early warning, and climate change research. Firstly, the China Meteorological Administration has clear requirements for the accuracy of meteorological station location information, mandating the use of the CGCS2000 national geodetic coordinate system with accuracy down to the second to ensure the uniformity and comparability of meteorological data nationwide. However, current methods for acquiring meteorological station location information in my country still have serious deficiencies, primarily relying on manual surveying or observation using GPS positioning devices and manual reading and recording. This is not only costly and inefficient but also suffers from problems such as inconsistent benchmarks, delayed updates, large data errors, and manual input errors, severely affecting the quality of meteorological data and the reliability of operational applications.

[0003] Currently, meteorological station location information is collected in a bottom-up, hierarchical manner. Station construction units fill out station information and report it to the superior meteorological department. Provincial meteorological departments review, verify, and report the information to their respective provincial meteorological stations, which is then compiled by the China Meteorological Administration. Location information for regional stations (unmanned stations) relies on smartphones or civilian GPS locators, which suffer from significant latitude and longitude accuracy deviations (over ±10 meters), mixed coordinate systems (WGS84 and CGCS2000 national coordinate systems coexist), inconsistent elevation datums (geodetic height and 1985 elevation are used interchangeably), and non-standard data formats (decimal units and degrees, minutes, and seconds coexist).

[0004] Furthermore, existing technologies lack the ability to dynamically monitor and automatically alarm for the position and orientation of weather stations. This makes it impossible to promptly detect anomalies such as station relocation or theft. Even under extreme weather conditions, invalid data may continue to be collected after the station has shifted or tilted, leading to delayed position and orientation information updates. This hinders rapid response to anomalies and impacts the accuracy of meteorological data and operational judgment. Simultaneously, the risks associated with manual operation cannot be ignored, including high data entry error rates and low efficiency in updating location information, further reducing the overall system reliability. Summary of the Invention

[0005] This invention aims to address the systemic deficiencies in the acquisition and monitoring of meteorological station position and attitude information, particularly the issues of accuracy and real-time performance in meteorological station position and attitude monitoring.

[0006] To address the above-mentioned deficiencies or improvement needs of existing technologies, this invention provides a weather station attitude monitoring system based on GNSS and IMU, comprising: a GNSS module configured for positioning and timing and outputting weather station location information, an attitude sensing unit (IMU) configured for monitoring the tilt angle of the weather station, and a main control unit. The IMU also functions as a low-power trigger. When the system is in sleep mode, only the IMU is running, monitoring the tilt angle changes of the weather station. When the tilt angle exceeds a preset threshold, the IMU will wake up other unit modules to complete the location information transmission and abnormal alarm. The main control unit integrates an edge computing module, which is configured to fuse and correct the collected data and diagnose anomalies in the meteorological station pose data. The data fusion and correction are achieved by setting a sliding window with a duration of no less than three times the maximum sampling period of the sensor, constructing a spatiotemporal constraint error model for GNSS, IMU, and barometer, using error feedback to iteratively correct the cumulative drift of the IMU and the temperature drift of the barometer, then determining the fusion confidence based on the reciprocal of the error and weighting it, and finally fusing multi-source data within this window to generate pose results.

[0007] Furthermore, the location information consists of the latitude and longitude data of the meteorological station based on the CGCS2000 national coordinate system and the elevation data that conforms to the 1985 national elevation datum after conversion by combining the EGM2008 gravity field model. Together, they constitute the complete spatial location parameters of the meteorological station.

[0008] Furthermore, the meteorological station position and posture monitoring system also includes an external unit, which is a barometer configured to acquire altitude data. The altitude data is used to verify the 1985 National Elevation Data. The barometer is also used to monitor air pressure changes. When the barometer detects abnormal pressure changes, the system will use these changes as inputs for abnormal response analysis and perform hierarchical classification processing.

[0009] Furthermore, the specific method for the hierarchical classification process is as follows: Assume the barometer continuously collects data for a period of time of 100 minutes. Time interval , The data acquisition window length corresponds to the air pressure value at that moment. Define the energy value of the variable pressure gradient. Quantitative abnormal transformer strength: , in, and Representing time respectively and The corresponding air pressure value; Extracting the area near the weather station Window length in the same period of the previous year Historical extreme value sequence of minute-by-minute pressure change gradient energy ,in, The historical sample size was used, and samples were filtered according to a preset time granularity, with the finest granularity being the daily level; the quartiles of the sequences were calculated: 25th percentile. and 75th percentile The level is determined by combining real-time EP (Electronic Performance Scale) results. when When it is a Level 1 anomaly, that is, an extreme voltage change; when When it is a level two anomaly, that is, a significant pressure change; when The time is classified as Level 3 abnormality, which means slight pressure change; Combined with IMU tilt angle change GNSS displacement Define abnormal coupling degree Implementing categorization: , when It is classified into the first category, namely, the equipment attitude abnormality category; when It is classified into the second category, namely the environmental air pressure anomaly category.

[0010] Furthermore, the IMU collects triaxial acceleration and gyroscope data from the weather station, and simultaneously analyzes the vibration energy amplitude of the weather station based on this data. When the vibration energy amplitude is detected to exceed a preset threshold, the IMU will also wake up other unit modules to complete the location information transmission and abnormal alarm.

[0011] Furthermore, the specific method for fusing and correcting the collected data is as follows: Suppose that GNSS is at time... The position is ,in, For GNSS at time Collected geographical longitude coordinates; For GNSS at time The collected geographic latitude coordinates; For a moment Collected elevation coordinates; Let the acceleration output by the IMU be... ,in, The IMU at time respectively Collected Axial acceleration components; barometer altitude: ;Accumulated drift of IMU and barometer temperature drift Error feedback iterative correction is employed: , in, They are time points The IMU cumulative drift vector; For gradient operators; They are time points The barometer temperature drift vector; It is a symbolic function; The corrected data is as follows:

[0012] , Define the spatiotemporal constraint error quantity : , Define fusion confidence The reciprocal of the spatiotemporal constraint error: , , in, For normalized , To indicate within a time window Within, the fusion confidence level at all times The sum; Not less than 3 times the maximum sampling period of the sensor; The final fused pose P(t) is: , in, This is the corrected IMU tilt angle.

[0013] Furthermore, the specific calculation method for the change in tilt angle is as follows: Let the sampling time of the IMU during the sleep cycle be . ,interval The triaxial acceleration data at the corresponding time are , ,in, express Real-time data collection Axial acceleration components; correspondingly, express Real-time collection The axial acceleration component; then the change in tilt angle: , in, It is the acceleration due to gravity. For vector cross product, For modulo length calculation.

[0014] Furthermore, the specific process for analyzing the vibration energy amplitude is as follows: Suppose that the triaxial acceleration sequence acquired by the IMU during its sleep cycle is as follows: Sampling interval Extracting the horizontal vibration component: , in, The average acceleration over the period, This refers to the pure vibration acceleration after removing static gravity. Calculate vibration energy amplitude : , in, This represents the number of sampling points in the vibration acceleration sequence. They are time points The pure vibrational acceleration vector; The time interval between two adjacent samples; This is the index variable for the sampling points, taking values ​​from 2 to n sequentially, used to traverse the vibration acceleration sequence.

[0015] Furthermore, the main control unit also includes: a dynamic sampling and control module, a multi-protocol data adaptation module, a communication module, and a system status self-monitoring module; The dynamic sampling and control module is configured to adaptively adjust the sampling frequency and runtime of each sensor based on the attitude anomaly level output by the IMU: when attitude changes are detected, the sampling density of the positioning and timing module and the barometer is increased to obtain high-frequency data; when the system maintains a stable attitude, the operating frequency of other modules is reduced, balancing monitoring performance and energy consumption in accordance with functional requirements. The multi-protocol data adaptation module is configured to support the access of sensor data of different interface types. Through a standardized data conversion mechanism, it converts the heterogeneous format data output by the positioning and timing module, attitude sensing unit, and barometer into a system-compatible general data format and outputs it to the edge computing module for direct use. The communication module is configured to dynamically select the transmission mode and transmission frequency according to the data priority: when receiving the abnormal diagnosis result output by the edge computing module, it transmits the abnormal data and alarm information back in real time through the wireless communication link with the highest priority; when transmitting regular pose data, it adopts the batch packet transmission mode; it also supports communication status monitoring and link switching, and automatically switches to the backup communication channel when the main communication link is interrupted. The system status self-monitoring module is configured to periodically detect the operating status of each functional unit, and trigger a preset abnormality handling mechanism when an abnormal status is detected.

[0016] As a second aspect of the present invention, a method for monitoring the pose of a weather station based on GNSS and IMU is also provided, which utilizes the implementation of a weather station pose monitoring system based on GNSS and IMU as described in any of the preceding claims, including: S1. After the system is powered on, the main control unit configures the IMU to low-power sampling mode and controls the GNSS and barometer to enter sleep mode; the IMU collects the three-axis acceleration data of the weather station in real time and continuously monitors the tilt angle change; The S2.IMU calculates the real-time tilt change through a preset algorithm. When the change exceeds a preset threshold, the IMU outputs a wake-up signal to the main control unit, which then triggers the GNSS and barometer to switch from sleep mode to working mode. S3.GNSS initiates positioning and timing functions, outputting the location information of the weather station; the barometer collects the current environmental air pressure data and converts it into elevation data; the IMU synchronously outputs triaxial acceleration and gyroscope data; each unit transmits the collected data to the main control unit; S4. The edge computing module of the main control unit receives multi-source data, completes data processing, and generates the pose data of the weather station; S5. The edge computing module compares the fused pose data with the preset normal range. If an anomaly is detected, an anomaly alarm is generated. The main control unit controls the communication module to send the anomaly information and pose data back to the remote data center and records the anomaly log. S6. After completing data transmission and alarm, the main control unit determines whether the tilt angle has returned to normal based on the IMU monitoring results. If it has returned to normal, the GNSS and barometer are controlled to re-enter sleep mode, while only the low-power monitoring of the IMU is retained. If it has not returned to normal, the working status of each unit is maintained, and data acquisition and transmission continue.

[0017] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The GNSS and IMU-based meteorological station attitude monitoring system of the present invention achieves continuous monitoring through a low-power IMU, avoiding energy waste caused by the high-frequency operation of GNSS, barometers, etc. At the same time, once an abnormal tilt angle occurs, the IMU can quickly trigger the wake-up mechanism to ensure that the system starts positioning transmission and abnormal alarm in a timely manner, which not only ensures the continuity of monitoring, but also takes into account the timeliness of abnormal response, effectively balancing system energy consumption and monitoring efficiency.

[0018] 2. The GNSS and IMU-based meteorological station pose monitoring system of the present invention integrates an edge computing module into the main control unit. This module fuses and corrects the position information output by GNSS, the acceleration and tilt data output by IMU, and the altitude data output by barometer, and performs anomaly diagnosis of the meteorological station pose data. It can complete the local processing of multi-source data without relying on a remote data center, reducing latency and loss during data transmission and improving the real-time performance of pose data. Simultaneously, the fusion and correction function of the edge computing module can eliminate errors such as IMU cumulative drift and barometer temperature drift, and the anomaly diagnosis function can directly identify pose anomalies, significantly improving the accuracy of pose monitoring data and the efficiency of anomaly judgment, ensuring reliable monitoring results.

[0019] 3. The GNSS and IMU-based meteorological station pose monitoring system of the present invention improves the accuracy of raw data by specifically correcting the inherent drift error of the sensors; the spatiotemporal constraint error model realizes the inherent consistency verification of GNSS, IMU and barometer data, avoiding the dominant influence of single sensor error on the results; the weighted fusion within the sliding window combines historical data to smooth fluctuations, so that the output pose data has both real-time performance and stability, effectively solving the problems of heterogeneity and error accumulation of multi-source sensor data, and significantly improving the accuracy of pose monitoring. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the system unit structure according to an embodiment of the present invention; Figure 2 This is a specific implementation architecture of an embodiment of the present invention; Figure 3 This is a schematic diagram of the main control unit according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the unit data flow guidance in an embodiment of the present invention; Figure 5 This is a flowchart of a weather station pose monitoring method based on GNSS and IMU according to an embodiment of the present invention. Detailed Implementation

[0021] 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 merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] Example 1 Please refer to Figure 1This embodiment 1 provides a weather station pose monitoring system based on GNSS and IMU, including: a GNSS module configured for positioning and timing and outputting weather station location information, an attitude sensing unit (IMU) configured for monitoring the tilt angle of the weather station, and a main control unit.

[0023] Please refer to Figure 2 , Figure 2 This embodiment 1 illustrates a hardware architecture setup for a specific application, including: The BeiDou high-precision positioning and timing subsystem receives satellite signals, which are then amplified by an LNA (low-noise amplifier) ​​and filtered by a SAW (surface acoustic wave filter) before being input into the BeiDou high-precision positioning and timing chip. This chip combines a TCXO (temperature compensated crystal oscillator) and a Crystal to ensure time and frequency accuracy. While outputting positioning information, it also achieves precise time synchronization through PPS timing.

[0024] Main control and data processing subsystem: The MCU (ARM Cortex-M4) serves as the core controller, communicating with the Beidou chip via UART (serial port) to acquire positioning data; at the same time, it connects to the IMU (inertial measurement unit) via the I2C interface to collect attitude data such as acceleration and angular velocity, and realizes data storage via the SDIO interface and SD card interface (external SD card slot); the MCU also connects to an external Crystal to ensure its own time base stability.

[0025] Power supply and interface subsystem: The 3.3V power interface draws power from an external power source to power each module; the MCU communicates with external devices through serial port, switch and RS232 / RS485 interface circuits; the IO interface connects to LED indicators for system status visualization.

[0026] This hardware architecture provides physical support for software-level functions such as low-power wake-up, multi-source data fusion, and anomaly diagnosis, ensuring that the system can efficiently complete the pose monitoring task. In a preferred embodiment, the meteorological station pose monitoring system also includes an external unit, which is a barometer.

[0027] Specifically, this embodiment 1 further elaborates on the above system units.

[0028] (1) Positioning and Timing Module GNSS Traditional methods rely on costly and time-consuming surveying for national-level observation stations, while regional stations suffer from low mobile phone positioning accuracy and inconsistent coordinate systems, resulting in insufficient data reliability. This embodiment 1 utilizes a GNSS positioning and timing chip to achieve high-precision positioning and timing. The GNSS module provides second-level accuracy in location information, ensuring the consistency of meteorological station location data with the CGCS2000 national geodetic coordinate system. By combining the EGM2008 gravity field model, the system can convert elevation data in real time, outputting unified location information based on the CGCS2000 national coordinate system and the 1985 national elevation datum, thus solving the problems of inconsistent and delayed updates of existing meteorological station pose information.

[0029] Furthermore, based on the GNSS module, the system also features data uploading capabilities. When an abnormal event is detected, the system automatically activates the GNSS module for positioning and data upload, transmitting the location and attitude information to the data center via the wireless communication module. The data center can then perform real-time analysis and processing of this data, promptly identifying and addressing any anomalies at the weather station. This functionality enhances the real-time performance and accuracy of weather station attitude monitoring, ensuring the reliability of meteorological data.

[0030] (2) Attitude sensing unit (IMU) Secondly, this embodiment 1 achieves dynamic monitoring and anomaly detection of the meteorological observation station. Traditional systems cannot update location information in a timely manner after station relocation, and cannot detect anomalies such as equipment tilting or theft in real time, especially when equipment displacement or tilting causes data loss under extreme weather conditions. In this embodiment 1, the IMU serves as an attitude sensing unit to monitor the tilt angle of the meteorological station in real time. The IMU can provide three-axis acceleration and gyroscope data of the meteorological station. By processing and analyzing this data, the system can monitor whether the meteorological station has shifted or tilted in real time. The IMU also functions as a low-power trigger. When the system is in sleep mode, only the IMU is running, monitoring changes in the tilt angle of the meteorological station. When the tilt angle exceeds a preset threshold, the IMU wakes up the main control unit and GNSS module to achieve rapid response and positioning, thereby effectively reducing system power consumption and improving the response speed to anomalies.

[0031] In a preferred embodiment, the specific calculation method for the tilt angle change is as follows: Let the sampling time of the IMU during the sleep cycle be . ,interval The triaxial acceleration data at the corresponding time are , ,in, express Real-time collection Axial acceleration components; correspondingly, express Real-time data collection The axial acceleration component; then the change in tilt angle: , in, It is the acceleration due to gravity. For vector cross product, For modulo length calculation.

[0032] Meanwhile, the IMU collects triaxial acceleration and gyroscope data from the weather station, and analyzes the vibration energy amplitude of the weather station based on this data. When the vibration energy amplitude exceeds a preset threshold, the IMU will wake up other unit modules to complete the location information transmission and abnormal alarm.

[0033] In a preferred embodiment, the specific process for analyzing the vibration energy amplitude is as follows: Suppose that the triaxial acceleration sequence acquired by the IMU during its sleep cycle is as follows: Sampling interval Extracting the horizontal vibration component: , in, The average acceleration over the period, This refers to the pure vibration acceleration after removing static gravity. Calculate vibration energy amplitude : , in, This represents the number of sampling points in the vibration acceleration sequence. They are time points The pure vibrational acceleration vector; The time interval between two adjacent samples; This is the index variable for the sampling points, taking values ​​from 2 to n sequentially, used to traverse the vibration acceleration sequence.

[0034] Simultaneously, based on the IMU unit, the system supports a dual-mode triggering mechanism for sleep and abnormal wake-up, and features three power consumption modes: normal mode, sleep mode, and deep sleep mode. In normal mode, all modules operate normally, providing real-time position and attitude information. In sleep mode, only the IMU operates, while the remaining modules are in sleep mode, only waking up in case of abnormal situations. In deep sleep mode, most system modules are shut down, with only essential monitoring functions active, minimizing power consumption. This design significantly reduces energy consumption and extends the equipment's lifespan without compromising monitoring accuracy.

[0035] (3) Barometer The barometer is used to acquire altitude data, which is used to verify the 1985 National Height Standard and ensure the accuracy of the elevation data. The barometer is also used to monitor air pressure changes. When the barometer detects abnormal pressure changes, the system uses these changes as input for anomaly response analysis, performing hierarchical classification processing. This mechanism further improves the system's ability to detect abnormal events, ensuring timely detection and response to changes in the position and orientation of the weather station under various conditions. In a preferred embodiment, the specific method for hierarchical classification processing is as follows: Assume the barometer continuously collects data for a period of time of 100 minutes. Time interval , The data acquisition window length corresponds to the air pressure value at that moment. Define the energy value of the variable pressure gradient. Quantitative abnormal transformer strength: , in, and Representing time respectively and The corresponding air pressure value; Extracting the area near the weather station Window length in the same period of the previous year Historical extreme value sequence of minute-by-minute pressure change gradient energy ,in, The historical sample size was used, and samples were filtered according to a preset time granularity, with the finest granularity being the daily level; the quartiles of the sequences were calculated: 25th percentile. and 75th percentile The level is determined by combining real-time EP (Electronic Performance Scale) results. when When it is a Level 1 anomaly, that is, an extreme voltage change; when When it is a level two anomaly, that is, a significant pressure change; when The time is classified as Level 3 abnormality, which means slight pressure change; Combined with IMU tilt angle change GNSS displacement Define abnormal coupling degree Implementing categorization: , when It is classified into the first category, namely, the equipment attitude abnormality category; when It is classified into the second category, namely the environmental air pressure anomaly category.

[0036] (4) Main control unit Please refer to Figure 3In this embodiment, the main control unit is the core control and data processing hub of the system, forming a close collaborative relationship with each unit module: it receives positioning and timing information from the GNSS positioning and timing module through the interface, collects attitude data from the attitude sensing unit IMU, integrates the altitude data from the barometer, and completes the fusion correction and anomaly diagnosis of multi-source data through the built-in edge computing function; at the same time, according to the monitoring results of the IMU, it controls the switching of the working status of the Beidou positioning and timing subsystem and the barometer, drives the communication module to realize the remote transmission of data and alarm information, and performs local data archiving, and displays the system operating status through indicator lights, and is powered by the power supply subsystem, coordinating the efficient operation of each unit module to ensure the accurate implementation of the attitude monitoring task.

[0037] The main control unit integrates an edge computing module, which is configured for the fusion and correction of collected data and the anomaly diagnosis of meteorological station pose data. The specific method for the fusion and correction of collected data is as follows: Suppose that GNSS is at time... The position is ,in, For GNSS at time Collected geographical longitude coordinates; For GNSS at time The collected geographic latitude coordinates; For a moment Collected elevation coordinates; Let the acceleration output by the IMU be... ,in, The IMU at time respectively Collected Axial acceleration components; barometer altitude: ;Accumulated drift of IMU and barometer temperature drift Error feedback iterative correction is employed: , in, They are time points The IMU cumulative drift vector; For gradient operators; They are time points The barometer temperature drift vector; It is a symbolic function; The corrected data is as follows:

[0038] , Define the spatiotemporal constraint error quantity : , Define fusion confidence The reciprocal of the spatiotemporal constraint error: , , in, For normalized , To indicate within a time window Within, the fusion confidence level at all times The sum; Not less than 3 times the maximum sampling period of the sensor; The final fused pose P(t) is: , in, This is the corrected IMU tilt angle.

[0039] Meanwhile, the main control unit also includes: a dynamic sampling and control module, a multi-protocol data adaptation module, a communication module, and a system status self-monitoring module; The dynamic sampling and control module is configured to adaptively adjust the sampling frequency and runtime of each sensor based on the attitude anomaly level output by the IMU: when attitude changes are detected, the sampling density of the positioning and timing module and the barometer is increased to obtain high-frequency data; when the system maintains a stable attitude, the operating frequency of other modules is reduced, balancing monitoring performance and energy consumption in accordance with functional requirements. The multi-protocol data adaptation module is configured to support the access of sensor data of different interface types. Through a standardized data conversion mechanism, it converts the heterogeneous format data output by the positioning and timing module, attitude sensing unit, and barometer into a system-compatible general data format and outputs it to the edge computing module for direct use. The communication module is configured to dynamically select the transmission mode and transmission frequency according to the data priority: when receiving the abnormal diagnosis result output by the edge computing module, it transmits the abnormal data and alarm information back in real time through the wireless communication link with the highest priority; when transmitting regular pose data, it adopts the batch packet transmission mode; it also supports communication status monitoring and link switching, and automatically switches to the backup communication channel when the main communication link is interrupted. The system status self-monitoring module is configured to periodically detect the operating status of each functional unit, and trigger a preset abnormality handling mechanism when an abnormal status is detected.

[0040] Further, please refer to Figure 4In this embodiment 1, the various functional modules of the pose monitoring system are interconnected, forming a complete closed loop of "dynamic scheduling - multi-source acquisition - fusion processing - remote transmission". Among them, the dynamic sampling and control module is the "intelligent scheduling center" of the system, which is responsible for controlling the sampling status of the GNSS positioning and timing module, the IMU attitude sensing unit, and the barometer: normally, it only maintains the IMU in low-power operation and continuously monitors the tilt angle changes of the weather station. Once the tilt angle is detected to exceed the preset threshold, it immediately triggers the GNSS and barometer to switch from sleep mode to working state, which avoids unnecessary energy consumption and can respond quickly when anomalies occur.

[0041] The GNSS positioning and timing module outputs latitude, longitude, elevation, and precise time information; the attitude sensing unit (IMU) collects attitude data such as three-axis acceleration and angular velocity; and the barometer provides altitude data. These three components synchronously input multi-source heterogeneous data into the edge computing module. This module is the system's "data processing core," incorporating a fusion correction algorithm (iteratively correcting IMU cumulative drift and barometer temperature drift) and anomaly diagnosis logic. It performs spatiotemporal constraint error analysis on the multi-source data, ultimately generating high-precision pose monitoring results.

[0042] Finally, the communication module stably transmits the pose data or abnormal alarm information output by the edge computing module to the remote data center, realizing remote interaction and centralized management of monitoring information. The entire module architecture is fully compatible with the core technical features of this invention, such as low-power wake-up, multi-source data fusion, and anomaly diagnosis, ensuring that the system achieves an optimal balance between energy consumption control and monitoring accuracy, and efficiently completes the entire process of meteorological station pose monitoring.

[0043] The application prospects of Embodiment 1 are extremely broad. Firstly, the system in Embodiment 1 provides a high-precision, low-power, and automated solution for meteorological station pose monitoring, effectively addressing current problems such as delayed updates to meteorological station location information, large data errors, and manual data entry errors. By employing a combination of GNSS / IMU / barometer, the system can monitor meteorological station pose changes in real time and accurately, outputting unified geographic coordinates and elevation information. This meets the requirements of the China Meteorological Administration for meteorological station location information, contributing to improved uniformity and comparability of national meteorological data. Furthermore, the system possesses anomaly monitoring and rapid response capabilities, promptly issuing alarms when anomalies occur at the meteorological station, avoiding the collection of invalid data, and ensuring the accuracy of meteorological data and the reliability of operational judgments.

[0044] Furthermore, the application of this patent is not limited to weather stations; it can be extended to other industries and application scenarios requiring position monitoring and low-power operation, such as earthquake monitoring stations and hydrological monitoring stations. Its low-power design makes the system particularly suitable for remote, unattended environmental monitoring sites, significantly reducing operating costs and maintenance requirements. Simultaneously, the system supports multiple power consumption modes, enabling flexible application in various environments, thereby enhancing the system's applicability and reliability.

[0045] In summary, the technology of Embodiment 1 has great application potential and prospects in future meteorological monitoring and cross-domain applications. It can significantly improve the quality and real-time performance of monitoring data, provide more accurate data support for weather forecasting and disaster early warning, and at the same time reduce operating costs and improve the automation and intelligence level of the system.

[0046] Example 2 Please refer to Figure 5 This embodiment 2 provides a method for monitoring the pose of a weather station based on GNSS and IMU, which applies the previously described implementation of a weather station pose monitoring system based on GNSS and IMU, including: S1. After the system is powered on, the main control unit configures the IMU to low-power sampling mode and controls the GNSS and barometer to enter sleep mode; the IMU collects the three-axis acceleration data of the weather station in real time and continuously monitors the tilt angle change; The S2.IMU calculates the real-time tilt change through a preset algorithm. When the change exceeds a preset threshold, the IMU outputs a wake-up signal to the main control unit, which then triggers the GNSS and barometer to switch from sleep mode to working mode. S3.GNSS initiates positioning and timing functions, outputting the location information of the weather station; the barometer collects the current environmental air pressure data and converts it into elevation data; the IMU synchronously outputs triaxial acceleration and gyroscope data; each unit transmits the collected data to the main control unit; S4. The edge computing module of the main control unit receives multi-source data, completes data processing, and generates the pose data of the weather station; S5. The edge computing module compares the fused pose data with the preset normal range. If an anomaly is detected, an anomaly alarm is generated. The main control unit controls the communication module to send the anomaly information and pose data back to the remote data center and records the anomaly log. S6. After completing data transmission and alarm, the main control unit determines whether the tilt angle has returned to normal based on the IMU monitoring results. If it has returned to normal, the GNSS and barometer are controlled to re-enter sleep mode, while only the low-power monitoring of the IMU is retained. If it has not returned to normal, the working status of each unit is maintained, and data acquisition and transmission continue.

[0047] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A weather station pose monitoring system based on GNSS and IMU, characterized in that, include: The system includes a GNSS (Positioning and Timing Module) configured for positioning and timing and outputting meteorological station location information, an IMU (Index Unit) configured for monitoring the tilt angle of the meteorological station, and a main control unit. The IMU also functions as a low-power trigger. When the system is in sleep mode, only the IMU is running, monitoring the tilt angle changes of the weather station. When the tilt angle exceeds a preset threshold, the IMU will wake up other unit modules to complete the location information transmission and abnormal alarm. The main control unit integrates an edge computing module, which is configured to fuse and correct the collected data and diagnose anomalies in the meteorological station pose data. The data fusion and correction are achieved by setting a sliding window with a duration of no less than three times the maximum sampling period of the sensor, constructing a spatiotemporal constraint error model for GNSS, IMU, and barometer, using error feedback to iteratively correct the cumulative drift of the IMU and the temperature drift of the barometer, then determining the fusion confidence based on the reciprocal of the error and weighting it, and finally fusing multi-source data within this window to generate pose results.

2. The weather station pose monitoring system based on GNSS and IMU according to claim 1, characterized in that, The location information consists of latitude and longitude data of the meteorological station based on the CGCS2000 national coordinate system and elevation data that conforms to the 1985 national elevation datum after conversion using the EGM2008 gravity field model. Together, they constitute the complete spatial location parameters of the meteorological station.

3. The meteorological station pose monitoring system based on GNSS and IMU according to claim 1, characterized in that, The meteorological station position and orientation monitoring system also includes an external unit, which is a barometer configured to acquire altitude data. The altitude data is used to verify the 1985 National Elevation Data. The barometer is also used to monitor air pressure changes. When the barometer detects abnormal pressure changes, the system will use these changes as input for abnormal response analysis and perform hierarchical classification processing.

4. The meteorological station pose monitoring system based on GNSS and IMU according to claim 3, characterized in that, The specific method for the hierarchical classification process is as follows: Assume the barometer continuously collects data for a period of time of 100 minutes. Time interval , The data acquisition window length corresponds to the air pressure value at that moment. Define the energy value of the variable pressure gradient. Quantitative abnormal transformer strength: ， in, and Representing time respectively and The corresponding air pressure value; Extracting the area near the weather station Window length in the same period of the previous year Historical extreme value sequence of minute-by-minute pressure change gradient energy ,in, The historical sample size was used, and samples were filtered according to a preset time granularity, with the finest granularity being the daily level; the quartiles of the sequences were calculated: 25th percentile. and 75th percentile The level is determined by combining real-time EP (Electronic Performance Scale) results. when When it is a Level 1 anomaly, that is, an extreme voltage change; when When it is a level two anomaly, that is, a significant pressure change; when The time is classified as Level 3 abnormality, which means slight pressure change; Combined with IMU tilt angle change GNSS displacement Define abnormal coupling degree Implementing categorization: ， when It is classified into the first category, namely, the equipment attitude abnormality category; when It is classified into the second category, namely the environmental air pressure anomaly category.

5. A meteorological station pose monitoring system based on GNSS and IMU according to claim 1, characterized in that, The IMU collects triaxial acceleration and gyroscope data from the weather station and analyzes the vibration energy amplitude of the weather station based on this data. When the vibration energy amplitude exceeds a preset threshold, the IMU will wake up other unit modules to complete the location information transmission and abnormal alarm.

6. The meteorological station pose monitoring system based on GNSS and IMU according to claim 1, characterized in that, The specific method for fusing and correcting the collected data is as follows: Suppose that GNSS is at time... The position is ,in, For GNSS at time Collected geographical longitude coordinates; For GNSS at time The collected geographic latitude coordinates; For a moment Collected elevation coordinates; Let the acceleration output by the IMU be... ,in, The IMU at time respectively Collected Axial acceleration components; barometer altitude: ;Accumulated drift of IMU and barometer temperature drift Error feedback iterative correction is employed: ， in, They are time points The IMU cumulative drift vector; For gradient operators; They are time points The barometer temperature drift vector; It is a symbolic function; The corrected data is as follows: ， ， Define the spatiotemporal constraint error quantity : ， Define fusion confidence The reciprocal of the spatiotemporal constraint error: ， ， in, For normalized , To indicate within a time window Within, the fusion confidence level at all moments The sum; Not less than 3 times the maximum sampling period of the sensor; The final fused pose P(t) is: ， in, This is the corrected IMU tilt angle.

7. The meteorological station pose monitoring system based on GNSS and IMU according to claim 1, characterized in that, The specific calculation method for the change in tilt angle is as follows: Let the sampling time of the IMU during the sleep cycle be . ,interval The triaxial acceleration data at the corresponding time are , ,in, express Real-time data collection Axial acceleration components; correspondingly, express Real-time data collection The axial acceleration component; then the change in tilt angle: ， in, It is the acceleration due to gravity. For vector cross product, For modulo length calculation.

8. A meteorological station pose monitoring system based on GNSS and IMU according to claim 5, characterized in that, The specific process for analyzing the vibration energy amplitude is as follows: Suppose that the triaxial acceleration sequence acquired by the IMU during its sleep cycle is as follows: Sampling interval Extracting the horizontal vibration component: ， in, The average acceleration over the period, This refers to the pure vibration acceleration after removing static gravity. Calculate vibration energy amplitude : ， in, This represents the number of sampling points in the vibration acceleration sequence. They are time points The pure vibrational acceleration vector; The time interval between two adjacent samples; This is the index variable for the sampling points, taking values ​​from 2 to n sequentially, used to traverse the vibration acceleration sequence.

9. A meteorological station pose monitoring system based on GNSS and IMU according to claim 1, characterized in that, The main control unit also includes: a dynamic sampling and control module, a multi-protocol data adaptation module, a communication module, and a system status self-monitoring module; The dynamic sampling and control module is configured to adaptively adjust the sampling frequency and runtime of each sensor based on the attitude anomaly level output by the IMU: when attitude changes are detected, the sampling density of the positioning and timing module and the barometer is increased to obtain high-frequency data; when the system maintains a stable attitude, the operating frequency of other modules is reduced, balancing monitoring performance and energy consumption in accordance with functional requirements. The multi-protocol data adaptation module is configured to support the access of sensor data of different interface types. Through a standardized data conversion mechanism, it converts the heterogeneous format data output by the positioning and timing module, attitude sensing unit, and barometer into a system-compatible general data format and outputs it to the edge computing module for direct use. The communication module is configured to dynamically select the transmission mode and transmission frequency according to the data priority: when receiving the abnormal diagnosis result output by the edge computing module, it transmits the abnormal data and alarm information back in real time through the wireless communication link with the highest priority; when transmitting regular pose data, it adopts the batch packet transmission mode; it also supports communication status monitoring and link switching, and automatically switches to the backup communication channel when the main communication link is interrupted. The system status self-monitoring module is configured to periodically detect the operating status of each functional unit, and trigger a preset abnormality handling mechanism when an abnormal status is detected.

10. A method for monitoring the position and attitude of a weather station based on GNSS and IMU, characterized in that, The implementation of a GNSS and IMU-based meteorological station pose monitoring system as described in any one of claims 1-9 includes: S1. After the system is powered on, the main control unit configures the IMU to low-power sampling mode and controls the GNSS and barometer to enter sleep mode; the IMU collects the three-axis acceleration data of the weather station in real time and continuously monitors the tilt angle change; The S2.IMU calculates the real-time tilt change through a preset algorithm. When the change exceeds a preset threshold, the IMU outputs a wake-up signal to the main control unit, which then triggers the GNSS and barometer to switch from sleep mode to working mode. S3.GNSS initiates positioning and timing functions, outputting the location information of the weather station; the barometer collects the current environmental air pressure data and converts it into elevation data; the IMU synchronously outputs triaxial acceleration and gyroscope data; each unit transmits the collected data to the main control unit; S4. The edge computing module of the main control unit receives multi-source data, completes data processing, and generates the pose data of the weather station; S5. The edge computing module compares the fused pose data with the preset normal range. If an anomaly is detected, an anomaly alarm is generated. The main control unit controls the communication module to send the anomaly information and pose data back to the remote data center and records the anomaly log. S6. After completing data transmission and alarm, the main control unit determines whether the tilt angle has returned to normal based on the IMU monitoring results. If it has returned to normal, the GNSS and barometer are controlled to re-enter sleep mode, while only the low-power monitoring of the IMU is retained. If it has not returned to normal, the working status of each unit is maintained, and data acquisition and transmission continue.

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