GNSS and imu based weather station pose monitoring system and method

Abstract

The application discloses a weather station pose monitoring system and method based on GNSS and IMU, and the system comprises a positioning and timing module GNSS, an attitude sensing unit IMU and a main control unit; the IMU simultaneously serves as a low-power trigger, and only the IMU is running when the system is in a dormant state; when it is detected that the inclination exceeds a preset threshold, the IMU wakes up other units; the main control unit integrates an edge computing module, a sliding window with a time length not less than 3 times of a maximum sampling period of a sensor is set, a space-time constraint error model of the GNSS, the IMU and a barometer is constructed, the IMU cumulative drift and the barometer temperature drift are iteratively corrected through error feedback, fusion confidence is determined based on error reciprocals and is weighted, and finally multi-source data is fused to generate a pose result in the window. The precision of pose monitoring is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of measurement and control, and more particularly relates to a GNSS and IMU-based weather station pose monitoring system and method. BACKGROUND

[0002] Under the background of global warming and frequent extreme weather, the position information (latitude, longitude, and altitude) of a weather station is a key basis for spatial positioning and climate analysis of various meteorological data, directly affecting the accuracy of core services such as fine forecasting services, meteorological disaster warnings, and climate change research. First, the China Meteorological Administration has clear requirements for the accuracy of weather station position information, which must use the CGCS2000 national geodetic coordinate system and be accurate to the second level to ensure the uniformity and comparability of national meteorological data. However, the current method of obtaining position information for weather stations in China still has serious defects, mainly relying on manual surveying or using GPS positioning instruments to observe data and read by eye, and recording manually, which not only has high costs and low efficiency, but also has problems such as non-uniformity of the reference, lag in updating, large data errors, and manual input errors, seriously affecting the quality of meteorological data and the reliability of business applications.

[0003] Currently, the position information of weather stations is collected in a bottom-up, step-by-step manner. The station information is filled in by the station establishment unit and reported to the superior meteorological department, which is audited, inspected, and reported by the provincial meteorological department, and is then uniformly summarized by the China Meteorological Administration. The position information of regional stations (unattended stations) is obtained by using a smart phone or a civilian GPS positioning instrument, which has a large deviation in latitude and longitude accuracy (±10 meters or more), a mixed coordinate system (WGS84 and CGCS2000 national coordinate system coexist), a non-uniform height reference (geodetic height and 1985 height are mixed), and an unstandardized data format (decimal system and degree-minute-second coexist).

[0004] In addition, the existing technical means lack dynamic monitoring and automatic alarm capability for the pose of the weather station, and cannot timely discover abnormal situations such as migration or theft of the weather station, and even continue to collect invalid data after the weather station is displaced or tilted in extreme weather, resulting in lag in updating the position information, difficulty in quickly responding to and processing the abnormal situation of the weather station pose, and affecting the accuracy of meteorological data and business judgment. At the same time, the risks brought by manual operation cannot be ignored, including high error rate of data entry, low efficiency of updating position information, and further reducing the reliability of the overall system. SUMMARY

[0005] The present application aims to solve the systematic defects in obtaining and monitoring the position and attitude information of weather stations, especially the accuracy and real-time problems of weather station pose monitoring.

[0006] In view of the above defects or improvement needs of the prior art, the present application provides a GNSS and IMU-based weather station pose monitoring system, comprising: a positioning and timing module GNSS configured to position and time and output weather station position information, an attitude sensing unit IMU configured to monitor the tilt angle of the weather station, and a master control unit;

[0007] The IMU simultaneously acts as a low-power trigger. When the system is in a sleep state, only the IMU is running to monitor the tilt angle change of the weather station. When it is detected that the tilt angle exceeds a preset threshold, the IMU wakes up other unit modules to complete the positioning and feedback of information and abnormal alarm;

[0008] The master control unit integrates an edge computing module, which is configured to fuse and correct the collected data and diagnose the abnormality of the weather station pose data.

[0009] The fusion and correction of the data are performed by setting a sliding window with a length not less than 3 times the maximum sampling period of the sensor, constructing a spatio-temporal constraint error model of the GNSS, IMU and barometer, iteratively correcting the IMU cumulative drift and barometer temperature drift based on error feedback, determining the fusion confidence based on the error inverse and performing weighting, and finally fusing the multi-source data within the window to generate the pose result.

[0010] Further, the position information is weather station longitude and latitude data based on the CGCS2000 national coordinate system and elevation data converted from the EGM2008 gravity field model and conforming to the 1985 national elevation datum, which together constitute the complete spatial position parameters of the weather station.

[0011] Further, the weather station pose monitoring system further comprises an external unit, which is a barometer configured to acquire altitude data, the altitude data being used to verify the 1985 national elevation data, and the barometer being simultaneously used to monitor pressure changes. When the barometer detects abnormal pressure changes, the system takes these changes as input for abnormal response analysis and performs hierarchical classification processing.

[0012] Further, the specific method of the hierarchical classification processing is as follows:

[0013] Let the continuous acquisition time of the barometer be , the time interval , is the acquisition window length, the pressure value at the corresponding time is , and the pressure gradient energy value is defined as The abnormal pressure intensity is quantified as

[0014] ,

[0015] wherein, With respectively represent the time and corresponding air pressure values;

[0016] extract the historical extreme value sequence of the minute air pressure change gradient energy of the meteorological station in the region where the meteorological station is located The length of the same period window is minute air pressure change gradient energy , wherein is the number of historical samples, and the same period is filtered according to a preset time granularity, and the finest granularity is a daily level; the quartiles of the calculation sequence are calculated: the 25% quartile and the 75% quartile , combined with the real-time EP determination level:

[0017] When , it is a first-level anomaly, that is, an extreme pressure change; when , it is a second-level anomaly, that is, a significant pressure change; and when , it is a third-level anomaly, that is, a slight pressure change.

[0018] combined with the IMU inclination change , the GNSS displacement , the abnormal coupling degree is defined to realize classification:

[0019] ,

[0020] When , it is classified into the first classification, that is, the device posture anomaly classification; and when , it is classified into the second classification, that is, the environmental pressure anomaly classification.

[0021] Further, the IMU collects three-axis acceleration and gyro data of the meteorological station, and simultaneously completes analysis of the vibration energy amplitude of the meteorological station based on the data, and when it is detected that the vibration energy amplitude exceeds a preset threshold, the IMU simultaneously wakes up other unit modules to complete information positioning and return and abnormal alarm.

[0022] Further, the specific method for fusion and correction of the collected data is:

[0023] the position of the GNSS at time is , wherein is the geographic longitude coordinate collected by the GNSS at time ; is the geographic latitude coordinate collected by the GNSS at time ; is the elevation coordinate collected at time ;

[0024] Let the acceleration outputted by the IMU be where, are the IMU accumulated drift vectors at time axis acceleration components collected by the IMU; the barometer altitude is ; the IMU accumulated drift and the barometer temperature drift , are iteratively corrected by error feedback:

[0025] ,

[0026] where, are the IMU accumulated drift vectors at time is the gradient operator; are the barometer temperature drift vectors at time is the sign function;

[0027] The corrected data are:

[0028]

[0029] ,

[0030] The spatio-temporal constraint error is defined as

[0031] ,

[0032] The fusion confidence is defined as the inverse of the spatio-temporal constraint error:

[0033] ,

[0034] ,

[0035] where, is the normalized , is the sum of the fusion confidence at all times within the time window ; the time window is not less than 3 times the maximum sampling period of the sensor;

[0036] The final fused pose P(t) is:

[0037] ,

[0038] where, is the corrected IMU tilt angle.

[0039] ​​​​​Further, the specific calculation method of the inclination change is:

[0040] Let the sampling time of the IMU in the sleep period be , the interval , and the three-axis acceleration data at the corresponding time be , , wherein represents the axis acceleration component collected at the time; correspondingly, represents the axis acceleration component collected at the time; and the inclination change is:

[0041] ,

[0042] , wherein is the gravitational acceleration, is the vector cross product, and is the modulus operation.

[0043] Further, the specific process of the analysis of the vibration energy amplitude is:

[0044] Let the three-axis acceleration sequence collected by the IMU in the sleep period be , the sampling interval be , and the horizontal vibration component be extracted:

[0045] ,

[0046] , wherein is the average acceleration in the period, is the pure vibration acceleration after the static gravity is removed;

[0047] The vibration energy amplitude is calculated as :

[0048] ,

[0049] , wherein is the number of sampling points of the vibration acceleration sequence; are the pure vibration acceleration vectors at the times ; is the time interval between adjacent two samplings; is an index variable of the sampling points, which is sequentially taken from 2 to n, and is used to traverse the vibration acceleration sequence.

[0050] Further, the main control unit further comprises a dynamic sampling regulation module, a multi-protocol data adaptation module, a communication module, and a system state self-monitoring module.

[0051] ​​The dynamic sampling regulation module is configured to adaptively adjust the sampling frequency and running time of each sensor based on the attitude anomaly level of the IMU output: when the attitude changes are monitored, 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 running frequency of other modules is reduced, and the monitoring performance and energy consumption are balanced according to the functional requirements;

[0052] The multi-protocol data adaptation module is configured to support different interface types of sensor data access, and convert the heterogeneous format data output by the positioning and timing module, the attitude sensing unit and the barometer into a general data format compatible with the system through a standardized data conversion mechanism, and output the data to the edge computing module for direct calling;

[0053] 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, the abnormal data and alarm information are transmitted in real time through the wireless communication link with the highest priority; when the conventional position data is transmitted, the batch packaging transmission mode is adopted; and the communication state monitoring and link switching are supported, and the system is automatically switched to the standby communication channel when the main communication link is interrupted;

[0054] The system state self-monitoring module is configured to periodically detect the running state of each functional unit, and trigger the preset abnormal handling mechanism when an abnormal state is identified.

[0055] As a second aspect of the application, a weather station posture monitoring method based on GNSS and IMU is also provided, which is implemented by using the weather station posture monitoring system based on GNSS and IMU as claimed in any one of the preceding aspects, and the method comprises the following steps:

[0056] S1. After the system is powered on, the main control unit configures the IMU to a low-power sampling mode, and controls the GNSS and the barometer to enter a sleep state; the IMU collects the three-axis acceleration data of the weather station in real time, and continuously monitors the inclination change;

[0057] S2. The IMU calculates the real-time inclination change amount through a preset algorithm, and when the change amount exceeds a preset threshold, the IMU outputs a wake-up signal to the main control unit, and the main control unit triggers the GNSS and the barometer to switch from the sleep state to the working state;

[0058] S3. The GNSS starts the positioning and timing function, and outputs the position information of the weather station; the barometer collects the pressure data of the current environment and converts it into elevation data; the IMU synchronously outputs the three-axis acceleration and gyro data; and each unit transmits the collected data to the main control unit;

[0059] S4. The edge computing module of the main control unit receives the multi-source data, completes the data processing, and generates the posture data of the weather station;

[0060] S5. The edge computing module compares the fused pose data with the preset normal range, and if it is determined that there is an abnormality, an abnormal alarm information is generated; the main control unit controls the communication module to return the abnormal information and the pose data to the remote data center, and records the abnormal log;

[0061] S6. After completing data return and alarm, the main control unit judges whether the inclination angle has returned to normal according to the IMU monitoring result, if yes, controls the GNSS and barometer to re-enter the sleep state, only the low-power monitoring of the IMU is reserved; if not, the working state of each unit is maintained, and data acquisition and return are continuously carried out.

[0062] Overall, compared with the prior art, the above technical solutions conceived by the present application can achieve the following beneficial effects:

[0063] 1. The GNSS and IMU-based weather station pose monitoring system of the present application realizes continuous monitoring through the low-power IMU, avoiding energy waste caused by high-frequency operation of GNSS, barometer and the like; at the same time, once the inclination angle is abnormal, the IMU can quickly trigger the wake-up mechanism to ensure that the system starts positioning return and abnormal alarm in time, which not only guarantees the continuity of monitoring, but also takes into account the timeliness of abnormal response, effectively balancing system energy consumption and monitoring efficiency.

[0064] 2. The GNSS and IMU-based weather station pose monitoring system of the present application integrates an edge computing module in the main control unit, which fuses and corrects the position information output by the GNSS, the acceleration and inclination angle data output by the IMU, and the altitude data output by the barometer, and carries out abnormal diagnosis of the weather station pose data. Without relying on the remote data center, the localization processing of multi-source data can be completed, reducing the delay and loss in the data transmission process and improving the real-time performance of the pose data; at the same time, the fusion and correction function of the edge computing module can eliminate the errors such as IMU cumulative drift and barometer temperature drift, and the abnormal diagnosis function can directly identify the pose abnormality, significantly improving the accuracy of the pose monitoring data and the efficiency of the abnormal judgment, ensuring the reliability of the monitoring results.

[0065] 3. The GNSS and IMU-based weather station pose monitoring system of the present application improves the accuracy of the original data by correcting the inherent drift error of the sensor; the spatio-temporal constraint error model realizes the internal consistency verification of the GNSS, IMU and barometer data, avoiding the dominant influence of single sensor error on the result; the weighted fusion in the sliding window combines the historical data to smooth the fluctuations, so that the output pose data has both real-time performance and stability, effectively solving the problems of multi-source sensor data heterogeneity and error accumulation, and significantly improving the accuracy of pose monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 A schematic diagram of a system unit structure of an embodiment of the present application;

[0067] Figure 2 A specific implementation architecture of an embodiment of the present application;

[0068] Figure 3 A schematic diagram of a master control unit of an embodiment of the present application;

[0069] Figure 4 A schematic diagram of unit data flow direction of an embodiment of the present application;

[0070] Figure 5 A flow chart of a GNSS and IMU-based weather station pose monitoring method of an embodiment of the present application. DETAILED DESCRIPTION

[0071] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application 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 used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0072] Embodiment 1

[0073] Referring to Figure 1 , the present embodiment 1 provides a GNSS and IMU-based weather station pose monitoring system, which comprises a positioning and timing module GNSS configured to position and time and output weather station position information, an attitude sensing unit IMU configured to monitor the tilt angle of the weather station, and a master control unit.

[0074] Referring to Figure 2 , Figure 2 A specific application hardware architecture setting of the present embodiment 1 is shown, which comprises:

[0075] Beidou high-precision positioning and timing subsystem: after the antenna receives the satellite signal, it is amplified by LNA (low noise amplifier) and filtered by SAW (surface acoustic wave filter), and then input into the Beidou high-precision positioning and timing chip; the chip combines TCXO (temperature compensated crystal oscillator) and Crystal (crystal) to ensure the time and frequency accuracy, and outputs the positioning information, and realizes accurate time synchronization through PPS timing.

[0076] Master and data processing subsystem: MCU (ARM Cortex-M4) as the core controller, through UART (serial port) and Beidou chip communication, get the positioning data; At the same time, through the I2C interface connection IMU (inertial measurement unit) to collect acceleration, angular velocity and other attitude data, through the SDIO interface and SD card interface (external SD card seat) to realize data storage; MCU also external Crystal (crystal) to guarantee the stability of its own time base.

[0077] Power and interface subsystem: 3.3V power interface from external power supply, power supply for each module; MCU through the serial port, switch and RS232 / RS485 interface circuit to realize the communication interaction with external equipment; IO interface connection LED indicator light, for system state visualization.

[0078] The hardware architecture provides physical support for low-power wake-up, multi-source data fusion, and abnormal diagnosis functions at the software level, ensuring that the system can efficiently complete the pose monitoring task. In a preferred embodiment, the weather station pose monitoring system further comprises an external unit, which is a barometer.

[0079] Specifically, embodiment 1 further expands the above system units.

[0080] (1) Positioning and timing module GNSS

[0081] In the traditional method, the national observation station relies on the high cost and long cycle of surveying and mapping, and the regional station is limited by the low accuracy of mobile phone positioning and the mixed coordinate system, resulting in insufficient data reliability. However, embodiment 1 uses GNSS positioning and timing chip to achieve high-precision positioning and timing. The GNSS module can provide position information with second-level accuracy, ensuring the unity of the position data of the weather station and the CGCS2000 national geodetic coordinate system. By combining the EGM2008 gravity field model, the system can convert elevation data in real time and output unified position information based on the CGCS2000 national coordinate system and the 1985 national height datum, thereby solving the problem of non-uniform and outdated weather station pose information reference.

[0082] In addition, based on the GNSS module, the system also has data uploading function. When an abnormal event is detected, the system will automatically wake up the GNSS module for positioning and data uploading, and send the position information and attitude information to the data center through the wireless communication module. The data center can analyze and process these data in real time, and timely discover and handle the abnormal situation of the weather station. This function improves the real-time and accuracy of the weather station pose monitoring, and ensures the reliability of the weather data.

[0083] (2) Attitude sensing unit IMU

[0084] Secondly, the embodiment 1 realizes dynamic monitoring and abnormality perception of the meteorological observation station. The traditional system cannot update the position information in time after the station is moved, and cannot perceive the abnormality such as equipment dumping and theft in real time, especially when the equipment displacement or inclination caused by extreme weather leads to invalid data, it is difficult to find in time. The IMU of the embodiment 1 is used as a posture perception unit, which is used to monitor the inclination angle of the meteorological station in real time. The IMU can provide three-axis acceleration and gyro data of the meteorological station, and through processing and analysis of the data, the system can master whether the meteorological station has displacement or inclination in real time. The IMU also serves as a low-power trigger. When the system is in a sleep state, only the IMU is running, monitoring the inclination change of the meteorological station. When the inclination is detected to exceed the preset threshold, the IMU wakes up the main control unit and the GNSS module, realizes rapid response and positioning, thereby effectively reducing the power consumption of the system and improving the response speed to abnormal conditions.

[0085] In a preferred embodiment, the specific calculation method of the inclination change is as follows:

[0086] Let the sampling time of the IMU in the sleep period be , the interval , and the three-axis acceleration data at the corresponding time be , , wherein represents the axis acceleration component collected at the time ; the corresponding represents the axis acceleration component collected at the time ; and the inclination change is

[0087] ,

[0088] , wherein is the gravitational acceleration, is the vector cross product, is the module length operation.

[0089] At the same time, the IMU collects three-axis acceleration and gyro data of the meteorological station, and simultaneously completes analysis of the vibration energy amplitude of the meteorological station based on the data. When the vibration energy amplitude is detected to exceed the preset threshold, the IMU will also wake up other unit modules to complete the positioning of the information, return and abnormality alarm.

[0090] In a preferred embodiment, the specific process of the analysis of the vibration energy amplitude is as follows:

[0091] Let the three-axis acceleration sequence collected by the IMU in the sleep period be , the sampling interval be , and the horizontal vibration component be extracted:

[0092] ,

[0093] wherein, is the average acceleration in a period, is the pure vibration acceleration after removing static gravity;

[0094] Calculate the vibration energy amplitude :

[0095] ,

[0096] wherein, is the number of sampling points of the vibration acceleration sequence; are the pure vibration acceleration vectors at time ; is the time interval between adjacent two samplings; is an index variable of the sampling points, taking values from 2 to n in turn, for traversing the vibration acceleration sequence.

[0097] At the same time based on the IMU unit, the system supports the dual-mode triggering mechanism of hibernation and abnormal wake-up, and is provided with three power consumption modes: normal mode, hibernation mode and deep hibernation mode. In the normal mode, all modules work normally, providing real-time position and attitude information. In the hibernation mode, only the IMU runs, and the remaining modules are in a hibernation state, and only wake up when there is an abnormal situation. In the deep hibernation mode, most of the modules of the system are closed, and only the necessary monitoring functions are in working state, which maximally reduces the power consumption. This design significantly reduces the energy consumption of the system without affecting the monitoring accuracy, prolonging the service life of the device.

[0098] (3) Barometer

[0099] The barometer is used to obtain altitude data, which is used to verify the 1985 national elevation to ensure the accuracy of the elevation data. The barometer is also used to monitor 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. This mechanism can further improve the system's ability to detect abnormal events and ensure that the position and attitude of the weather station can be detected and responded to in a timely manner under various conditions. In a preferred embodiment, the specific method of hierarchical classification processing is as follows:

[0100] Let the barometer continuously collect time , the time interval , is the length of the collection window, and the pressure value at time is defined as the pressure gradient energy value quantifies the intensity of abnormal pressure changes:

[0101] ,

[0102] wherein, with respectively represent the time and corresponding to the air pressure value;

[0103] extract the historical extreme value sequence of the minute pressure change gradient energy in the same period window length of the same period of the meteorological station in the region where the meteorological station is located , wherein, is the number of historical samples, and the same period is filtered according to a preset time granularity, and the finest granularity is a daily level; the quartiles of the calculation sequence are: the 25% quartile and the 75% quartile , combined with the real-time EP determination level: When , it is a first-level anomaly, that is, an extreme pressure change; when

[0104] , it is a second-level anomaly, that is, a significant pressure change; and when , it is a third-level anomaly, that is, a slight pressure change. combined with the IMU inclination change , the GNSS displacement

[0105] , define the abnormal coupling degree to realize classification:

[0106] ,

[0107] When , it is classified into the first classification, that is, the device posture anomaly classification; and when , it is classified into the second classification, that is, the environmental pressure anomaly classification.

[0108] (4) The main control unit

[0109] Please refer to Figure 3 , the main control unit of the embodiment 1 is the core control and data processing center of the system, and forms a close cooperative relationship with each unit module: through the interface, the positioning and timing information of the GNSS positioning and timing module is received, the attitude data of the attitude sensing unit IMU is collected, the altitude data of the barometer is integrated, the fusion correction and abnormal diagnosis of multiple source data are completed through the built-in edge computing function; at the same time, according to the monitoring result of the IMU, the working state switching of the Beidou positioning and timing subsystem and the barometer is regulated and controlled, the communication module is driven to realize the remote transmission of data and alarm information, and local data archiving is carried out, and the system running state is presented through the indicator light, and the power subsystem provides power support, and coordinates the efficient operation of each unit module, and guarantees the accurate implementation of the position and posture monitoring task.

[0110] ​​The main control unit integrates an edge computing module, and the edge computing module is configured to fuse and correct the collected data and diagnose the pose data of the weather station.

[0111] Let the position of GNSS at time be , wherein is the geographic longitude coordinate collected by GNSS at time ; is the geographic latitude coordinate collected by GNSS at time ; is the elevation coordinate collected at time ;

[0112] Let the acceleration output by the IMU be , wherein are the axis acceleration components collected by the IMU at time ; is the barometer altitude; is the cumulative drift of the IMU, and is the temperature drift of the barometer, and the error feedback iterative correction is adopted:

[0113] ,

[0114] wherein are the IMU cumulative drift vectors at time ; is the gradient operator; are the barometer temperature drift vectors at time ; is the sign function;

[0115] The corrected data are:

[0116]

[0117] ,

[0118] The space-time constraint error amount is defined as:

[0119] ,

[0120] The fusion confidence is defined as the reciprocal of the space-time constraint error:

[0121] ,

[0122] ,

[0123] wherein, is the normalized , is the sum of fusion confidence over all time instants within the time window ; is not less than 3 times of the maximum sampling period of the sensor;

[0124] The final fused pose P(t) is:

[0125] ,

[0126] wherein, is the corrected IMU roll angle.

[0127] Meanwhile, the main control unit further comprises a dynamic sampling regulation module, a multi-protocol data adaptation module, a communication module and a system state self-monitoring module.

[0128] The dynamic sampling regulation module is configured to adaptively adjust the sampling frequency and running time of each sensor based on the attitude anomaly level output by the IMU: when attitude changes are monitored, 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 running frequency of other modules is reduced, and the monitoring performance and energy consumption are balanced according to the functional requirements;

[0129] The multi-protocol data adaptation module is configured to support sensor data access of different interface types, and through a standardized data conversion mechanism, the heterogeneous format data output by the positioning and timing module, the attitude sensing unit and the barometer are uniformly converted into a general data format compatible with the system and output to the edge computing module for direct calling;

[0130] 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, the abnormal data and alarm information are real-time returned through the wireless communication link with the highest priority; when transmitting the normal pose data, the batch packaging transmission mode is adopted; and the communication state monitoring and link switching are supported, and the system automatically switches to the standby communication channel when the main communication link is interrupted.

[0131] The system state self-monitoring module is configured to periodically detect the running state of each functional unit, and when an abnormal state is identified, a preset abnormal handling mechanism is triggered.

[0132] Further, please refer to Figure 4The various functional modules of the posture monitoring system of this embodiment 1 are closely linked to form a complete closed loop of "dynamic scheduling - multi-source collection - fusion processing - remote transmission". Among them, the dynamic sampling regulation and control module is the "intelligent scheduling center" of the system, responsible for controlling the sampling state of the positioning and timing module GNSS, the attitude sensing unit IMU and the barometer: only maintaining the low-power operation of the IMU in daily life, continuously monitoring the inclination change of the weather station, and triggering the GNSS and barometer to switch from sleep mode to working state as soon as the inclination exceeds the preset threshold, which not only avoids unnecessary energy consumption, but also quickly responds when an anomaly occurs.

[0133] The positioning and timing module GNSS outputs latitude, longitude, elevation and accurate timing information, the attitude sensing unit IMU collects attitude data such as three-axis acceleration and angular velocity, and the barometer provides altitude data, and the three synchronously input multi-source heterogeneous data into the edge computing module. This module is the "data processing core" of the system, which has built-in fusion correction algorithm (iterative correction of IMU cumulative drift and barometer temperature drift) and abnormal diagnosis logic, which analyzes the spatio-temporal constraint error of multi-source data, and finally generates high-precision posture monitoring results.

[0134] Finally, the communication module stably transmits the posture 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 low-power wake-up, multi-source data fusion and abnormal diagnosis in this invention, ensuring that the system achieves an optimal balance between energy consumption control and monitoring accuracy, and efficiently completes the whole process of weather station posture monitoring.

[0135] The application prospect of this embodiment 1 is extremely wide. First of all, the system of this embodiment 1 provides a high-precision, low-power and automated solution for the posture monitoring of weather stations, which can effectively solve the problems of lagging position information acquisition, large data error and manual input error of weather stations. By using the combination of GNSS / IMU / barometer, the system can monitor the posture change of the weather station in real time and accurately, and output unified geographic coordinates and elevation information, which meets the requirements of the China Meteorological Administration for weather station position information, and helps to improve the uniformity and comparability of national meteorological data. In addition, the system has the ability of abnormal monitoring and rapid response, which can issue an alarm in time when the weather station has an abnormal situation, avoid the collection of invalid data, and ensure the accuracy of meteorological data and the reliability of business judgment.

[0136] In addition, the application of this patent is not limited to weather stations, but can be extended to other industries and application scenarios that require pose monitoring and low-power operation, such as earthquake monitoring stations, hydrological monitoring stations, etc. The low-power design makes this system particularly suitable for remote, unattended environmental monitoring sites, significantly reducing operating costs and maintenance requirements. At the same time, the system supports multiple power consumption modes, making it flexible to apply in various environments, thereby enhancing the applicability and reliability of the system.

[0137] In summary, the technology of Embodiment 1 has great application potential and prospects in future meteorological monitoring and cross-field applications, which can significantly improve the quality and real-time of monitoring data, provide more accurate data support for weather forecasting and disaster warning, while reducing operating costs and improving the automation and intelligence level of the system.

[0138] Embodiment 2

[0139] For reference Figure 5 , Embodiment 2 provides a meteorological station pose monitoring method based on GNSS and IMU, which is implemented by applying a meteorological station pose monitoring system based on GNSS and IMU as described above, including:

[0140] S1. After the system is powered on, the main control unit configures the IMU to a low-power sampling mode, and controls the GNSS and barometer to enter a sleep state; the IMU collects real-time three-axis acceleration data of the meteorological station, continuously monitoring the inclination change;

[0141] S2. The IMU calculates the real-time inclination change amount through a preset algorithm, and when the change amount exceeds a preset threshold, the IMU outputs a wake-up signal to the main control unit, which immediately triggers the GNSS and barometer to switch from the sleep state to the working state;

[0142] S3. The GNSS starts the positioning and timing function, and outputs the position information of the meteorological station; the barometer collects the current environmental pressure data and converts it into elevation data; the IMU synchronously outputs three-axis acceleration and gyro data; each unit transmits the collected data to the main control unit;

[0143] S4. The edge computing module of the main control unit receives multi-source data, completes data processing and generates the pose data of the meteorological station;

[0144] S5. The edge computing module compares the fused pose data with the preset normal range, and if it is determined that there is an abnormality, it generates an abnormal alarm information; the main control unit controls the communication module to return the abnormal information and the pose data to the remote data center, and records the abnormal log;

[0145] S6. After the data backhaul and alarm are completed, the main control unit determines whether the tilt angle has returned to normal according to the IMU monitoring result. If the tilt angle has returned to normal, the GNSS and the barometer are controlled to re-enter the sleep state, and only the low-power monitoring of the IMU is retained. If the tilt angle has not returned to normal, the working states of the units are maintained, and data collection and backhaul are continuously performed.

[0146] Those skilled in the art will easily understand that the above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement within the spirit and principle of the present application shall be included in the protection scope of the present application.

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. 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 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.

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 category of abnormal ambient air pressure.

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 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.

7. A weather 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.

8. The 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.

9. A method for monitoring the position and attitude of a meteorological 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 1-8 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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