High-precision sensing GNSS (Global Navigation Satellite System) monitoring equipment applied to slope monitoring

Abstract

The invention relates to the technical field of geological monitoring, and discloses a high-precision sensing GNSS monitoring device applied to slope monitoring, and the monitoring device comprises a base, a supporting assembly is installed on the top of the base, a fixing assembly is installed in the middle of the base, a main rod is installed on the top of the supporting assembly, a spherical cage cover is installed on the outer side of the main rod, and the spherical cage cover is installed on the outer side of the base. The top of the main rod is fixedly connected with a data acquisition module, a wind power generation assembly is installed in the spherical cage cover, the fixing assembly comprises a fixing rod, the fixing rod is arranged in the middle of the base in a penetrating mode, the periphery of the fixing rod is fixedly connected with multiple sets of spiral blades, and the outer sides of the spiral blades are rotationally connected with rotating pieces; a limiting groove is formed in the middle of the rotating piece. The equipment is rapidly installed by rotating the fixing rod, meanwhile, the rotating piece is additionally arranged to prevent the fixing rod from rotating reversely, so that the stability of the installed equipment is guaranteed, meanwhile, the equipment can directly act on the ground, additional ground treatment is not needed, and the installation efficiency is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological monitoring, and specifically to a GNSS monitoring device with high-precision perception applied in slope monitoring. Background Art

[0002] Slope stability monitoring is an important link in geological disaster prevention and control, and is widely applied in fields such as mining areas, mountainous areas, and transportation infrastructure. In these environments, geological disasters such as slope landslides and collapses not only damage engineering facilities, but also pose serious threats to personal safety and property. Therefore, it is of great engineering significance and social value to monitor the deformation and displacement of slopes in real time and accurately, and to timely warn of potential risks.

[0003] Currently, the commonly used GNSS monitoring devices achieve high-precision monitoring of slopes by receiving satellite signals. However, in actual applications, there are many inconveniences in the installation and operation of the devices. On the one hand, during the installation process, it is necessary to pre-treat the ground, such as excavating foundation pits, laying foundation structures, etc., and fix them by embedding. This method not only has a cumbersome construction process, is time-consuming and laborious, but also increases the installation cost. Especially in areas with complex or harsh geological conditions, the implementation difficulty is further increased. In addition, this fixed installation method also limits the rapid deployment and flexible application of the devices at other monitoring points, affecting the efficiency of the monitoring work.

[0004] On the other hand, in order to improve the autonomy of power supply, some GNSS monitoring devices are equipped with a wind power generation module as an auxiliary power source. However, the existing wind power generation modules usually adopt a fixed structure and cannot be automatically adjusted according to the real-time change of the wind direction, resulting in low wind energy utilization rate. In an environment with variable wind directions, it is difficult for the fixed wind power generation module to capture the maximum wind energy, and the power generation efficiency is significantly reduced, affecting the stability of the device power supply, and further affecting the continuity and reliability of the monitoring work.

[0005] Therefore, in view of the problems in the prior art such as complex installation process, large construction difficulty, and low wind power generation efficiency, there is an urgent need for a high-precision GNSS monitoring device with a simpler structure, stronger adaptability, and the function of automatically adjusting wind energy to achieve rapid installation and deployment and efficient power supply, and meet the high-precision, long-term and stable operation requirements of slope monitoring. This can not only improve the efficiency of the monitoring work, but also reduce the project cost and broaden the application scope of GNSS technology in complex environments. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a GNSS monitoring device with high-precision perception applied in slope monitoring, which solves the problems of complex installation process, large construction difficulty of the existing GNSS monitoring devices, and low power generation efficiency caused by the inability of the wind power generation module to be dynamically adjusted according to the wind direction.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A GNSS monitoring device for high-precision perception in slope monitoring, including a base, a support component is installed on the top of the base, a fixing component is installed in the middle of the base, a main rod is installed on the top of the support component, a spherical cover is installed outside the main rod, a data acquisition module is fixedly connected to the top of the main rod, and a wind power generation component is installed inside the spherical cover.

[0008] Preferably, the fixing component includes a fixing rod, the fixing rod is inserted through the middle of the base, multiple groups of spiral blades are fixedly connected to the outer periphery of the fixing rod, a rotating piece is rotatably connected to the outside of the spiral blades, a limiting groove is opened in the middle of the rotating piece, a limiting rod is fixedly connected to the top of the spiral blade, and the limiting rod is located in the middle of the limiting groove.

[0009] Preferably, the support component includes a support frame, the support frame is fixedly connected to the top of the base, a ferrule is installed on the top of the support frame, the main rod is inserted through the middle of the ferrule, and the ferrule and the main rod are connected by a screw rod.

[0010] Preferably, the wind power generation component includes a rotating ring, the rotating ring is rotatably connected to the top of the main rod, a semi-ring is rotatably connected to the outside of the rotating ring, a generator is fixedly connected to the middle of the semi-ring, an impeller is installed at the input end of the generator, and a tail fin is installed at the tail end of the generator.

[0011] Preferably, a positioning rod is fixedly connected to the outside of the main rod, the rotating ring is located between the positioning rods, a group of positioning rods are installed on the outside of the rotating ring, and the semi-ring is located in the middle of the positioning rods.

[0012] Preferably, a solar panel and an electrical box are installed on the outside of the main rod, and the solar panel and the generator are both connected to the storage battery inside the electrical box through a charge and discharge controller.

[0013] A GNSS monitoring system for high-precision perception in slope monitoring, including, A data acquisition module for collecting GNSS observation data, including pseudorange, carrier phase, and signal strength; A multipath error inversion module for modeling and inversely estimating the multipath error in GNSS observation data; An adaptive extended Kalman filter module for real-time state estimation and multipath error correction of GNSS observation data, and optimizing the filtering effect by dynamically adjusting the process noise covariance and the observation noise covariance; An error feedback closed-loop correction module for feeding back the inversely estimated multipath error to the observation equation, dynamically correcting the GNSS observation data, and forming a closed-loop optimization; A high-precision data output module for outputting the corrected GNSS three-dimensional position information; Among them, the adaptive extended Kalman filter module dynamically filters the GNSS data through state prediction and observation update steps, and combines error feedback to achieve error closed-loop optimization, and outputs high-precision monitoring results.

[0014] Preferably, the multipath error inversion module analyzes the pseudorange, signal strength, and phase data of the GNSS signal, establishes an error model including parameters such as the amplitude of the reflected signal, phase difference, incident angle, and path delay, estimates the multipath error, and the estimation result is used for error correction of the subsequent filter module.

[0015] Preferably, the adaptive extended Kalman filter module includes: A state space modeling unit for uniformly modeling the GNSS three-dimensional position information and the multipath error as a dynamic state vector; A prediction unit for predicting the state and its covariance of the system based on the state equation; An update unit for updating the predicted state by combining the Kalman gain through the observation equation; A noise covariance adaptive estimation unit for dynamically adjusting the process noise covariance and the observation noise covariance according to the residuals of the observation data to improve the adaptability and accuracy of the filtering.

[0016] Preferably, the error feedback closed-loop correction module dynamically optimizes the original observation value by introducing the inversely estimated multipath error into the correction process of the observation data, and outputs the GNSS data after error correction, thereby forming a closed-loop working mode of error estimation and optimization iteration.

[0017] Working principle: During installation, first place the base at the installation position, then pass multiple fixing rods through the prefabricated holes in the middle of the base, so that the bottom of the fixing rod contacts the ground, and then rotate the fixing rod from the top of the fixing rod with tools. At this time, under the action of the spiral blades, the fixing rod will gradually insert into the ground, thus completing the installation. When the fixing rod reverses and becomes loose, the rotating piece will be affected by the surrounding soil and rotate in the reverse direction and expand outward. Then, under the influence of the limiting rod and the limiting groove, the rotating piece will rotate to a specific angle. At this time, when the fixing rod reverses, the rotating piece will be inserted into the surrounding soil, thereby increasing the grasping force on the soil and ensuring the stability of the equipment installation.

[0018] Secondly, a wind power generation component is installed in this device. When affected by air flow, the impeller rotates and cooperates with the generator to generate electricity. When the direction of the air flow changes, it acts on the tail fin. At this time, the overall center of gravity of the generator shifts, driving the semi-ring to rotate outside the rotating ring to change the orientation of the impeller. Secondly, the rotating ring also rotates outside the main rod to adjust the orientation of the impeller left and right. In this way, the impeller can better fit the air flow direction, thereby improving the power generation efficiency to adapt to the changes in the air flow on the slope.

[0019] The present invention provides a GNSS monitoring device with high-precision perception applied in slope monitoring. It has the following beneficial effects: 1. The present invention quickly installs the device by rotating the fixing rod, and at the same time prevents the fixing rod from reversing by adding a rotating piece, so as to ensure the stability of the installed device. At the same time, it can directly act on the ground without additional ground treatment, greatly improving the installation efficiency.

[0020] 2. The present invention installs the generator in the form of a gyroscope, and changes the orientation of the generator through the semi-ring and the rotating ring, so that the impeller can better face the wind direction, thereby improving the power generation efficiency and ensuring the stability of the device during operation.

[0021] 3. Through the collaborative work of multi-path error inversion modeling, adaptive extended Kalman filtering and error closed-loop correction module, the present invention can effectively eliminate the errors caused by multi-path effects in complex environments and perform real-time correction on dynamic and changing non-linear noises. The accuracy of the corrected GNSS monitoring data can reach the millimeter level, which is significantly better than the traditional single GNSS observation method. This high precision provides technical guarantee for the monitoring of subtle changes in slope deformation and is applicable to geological disaster early warning and long-term monitoring scenarios.

[0022] 4. The data acquisition module of the present invention supports the reception of multi-constellation and multi-band GNSS signals, and combines high-gain antennas and anti-multi-path designs to effectively improve the signal reception ability in complex environments. The system can maintain the stability and continuity of observation data in scenarios such as mountains, dense forests, and building blockages. In addition, the adaptive extended Kalman filtering module further enhances the robustness of the system to noise characteristics in different environments by dynamically adjusting the noise covariance matrix. Description of the Drawings

[0023] Figure 1 is a three-dimensional view of the present invention; Figure 2 is a structural schematic diagram of the spherical cover in the present invention; Figure 3 is a schematic diagram of the wind power generation component in the present invention; Figure 4 is a structural schematic diagram of the fixing component in the present invention; Figure 5 for Figure 4 The enlarged view of point A in the middle; Figure 6 It is a schematic diagram of the module framework of the system in the present invention.

[0024] Among them, 1. base; 2. support frame; 3. hoop; 4. main rod; 5. electrical box; 6. solar panel; 7. ball cage; 8. data acquisition module; 9. rotating ring; 10. half ring; 11. generator; 12. impeller; 13. positioning rod; 14. tail wing; 15. fixing rod; 16. spiral blade; 17. rotating piece; 18. limit groove; 19. limit rod. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Example: Please see attached Figure 1 , Attachment Figure 4 and attached Figure 5 The embodiment of the present invention provides a GNSS monitoring device with high-precision perception used in slope monitoring, including a base 1, a fixing component installed in the middle of the base 1, and the fixing component including a fixing rod 15, the fixing rod 15 is passed through the middle of the base 1, and a plurality of sets of spiral leaves 16 are fixedly connected to the outer periphery of the fixing rod 15. When installing the monitoring device, the base 1 is first placed on the ground to be installed, and then the fixing rod 15 is passed through the hole in the middle of the base 1, and the fixing rod 15 is rotated by a tool. At this time, under the action of the spiral leaves 16, the fixing rod 15 will gradually insert into the ground, and the installation is completed in this way, without the need for additional pre-embedded treatment of the ground, which greatly improves the installation efficiency. The outer side of the spiral blade 16 is rotatably connected with a rotating piece 17, and a limiting groove 18 is provided in the middle of the rotating piece 17. The top of the spiral blade 16 is fixedly connected to a limiting rod 19, and the limiting rod 19 is located in the middle of the limiting groove 18. When the equipment is subjected to external force and the fixed rod 15 generates a reverse force, the rotating piece 17 will first be affected by the surrounding soil and rotate in the opposite direction and expand outward, and then under the influence of the limiting rod 19 and the limiting groove 18, the rotating piece 17 will rotate to a specific angle. At this time, when the fixed rod 15 reverses, the rotating piece 17 will be inserted into the surrounding soil, thereby increasing the grip with the soil, thereby ensuring the stability of the equipment installation. Please see attached Figure 1, a support component is installed on the top of the base 1, and a main rod 4 is installed on the top of the support component. The support component includes a support frame 2, the support frame 2 is fixedly connected to the top of the base 1, a ferrule 3 is installed on the top of the support frame 2, the main rod 4 passes through the middle of the ferrule 3, and the ferrule 3 and the main rod 4 are connected by a screw. The base 1 and the ferrule 3 are connected by the support frame 2, and then after the main rod 4 passes through the middle of the ferrule 3, it is connected by a screw, and the assembly of this monitoring device can be completed.

[0027] Please refer to the appendix Figure 1 - appendix Figure 3 , a spherical cage 7 is installed on the outside of the main rod 4, a data acquisition module 8 is fixedly connected to the top of the main rod 4, and the data information of the slope is collected through the data acquisition module 8. A wind power generation component is installed inside the spherical cage 7. The wind power generation component includes a rotating ring 9, the rotating ring 9 is rotatably connected to the outside of the main rod 4, a half ring 10 is rotatably connected to the outside of the rotating ring 9, a generator 11 is fixedly connected to the middle of the half ring 10, an impeller 12 is installed at the input end of the generator 11, and a tail fin 14 is installed at the end of the generator 11. When affected by the air flow, it will drive the impeller 12 to rotate. At this time, electric power can be generated through the generator 11 to complete power generation. At the same time, when the air flow direction changes, it will cause the center of gravity of the generator 11 to shift. At this time, the generator 11 will drive the rotating ring 9 and the half ring 10 to rotate, so as to horizontally and vertically adjust the direction to ensure that the impeller 12 better fits the air flow direction, thereby improving the power generation efficiency.

[0028] Please refer to the appendix Figure 3 , a positioning rod 13 is fixedly connected to the outside of the main rod 4, the rotating ring 9 is located between the positioning rods 13, a group of positioning rods 13 are installed on the outside of the rotating ring 9, and the half ring 10 is located in the middle of the positioning rods 13. The rotation angle of the generator 11 is limited by multiple groups of positioning rods 13 to prevent the generator 11 from rotating excessively.

[0029] Please refer to the appendix Figure 1 - appendix Figure 3 , a solar panel 6 and an electrical box 5 are installed on the outside of the main rod 4. The solar panel 6 and the generator 11 are both connected to the battery inside the electrical box 5 through a charge and discharge controller. The electric energy generated by the solar panel 6 and the generator 11 is transmitted towards the battery through the charge and discharge controller for power storage.

[0030] Please refer to the appendix Figure 6 , as a part of this application, the present invention also provides an embodiment, a GNSS monitoring system for high-precision perception applied in slope monitoring, including, A data acquisition module for collecting GNSS observation data, including pseudorange, carrier phase, and signal strength; The data acquisition module includes a GNSS receiver, a GNSS antenna, a communication module, and a data acquisition and transmission unit, which is used to collect GNSS raw observation data in real time and transmit it to the data processing center of the system.

[0031] In a possible implementation, the GNSS receiver can support signal reception of multiple constellations (such as GPS, GLONASS, Beidou, Galileo, etc.) and multiple frequency bands (such as L1, L2, L5, B1, B2, etc.). Specifically, by supporting signal reception of multiple constellations, the data acquisition module can effectively improve the availability of satellite signals in complex environments and use multi-frequency band signals to eliminate ionospheric errors.

[0032] It should be noted that the observation data obtained by the GNSS receiver includes pseudorange, carrier phase, and signal strength (C / N0). Among them: Pseudorange data: It is the distance observation value between each satellite and the receiver, usually in meters, with an accuracy reaching sub-meter level, providing a basis for subsequent positioning solutions.

[0033] Carrier phase data: Records the phase difference between the satellite signal carrier and the receiver carrier, which can be used for subsequent double-difference solutions of carrier phases to improve positioning accuracy.

[0034] Signal strength (C / N0): Represents the ratio of the signal power received by the receiver to the noise power, with the unit of dB-Hz, and is used to analyze the stability of the signal and the significance of the multipath effect.

[0035] In a possible implementation, in order to ensure the signal reception quality in harsh environments, the GNSS antenna selects a high-gain antenna and has good anti-interference performance. Exemplarily, the GNSS antenna can be configured with a design for anti-multipath effect, including a protective cover for shielding backward signals to reduce the interference of reflected signals on the observation data.

[0036] As an option, the GNSS receiver and the antenna can be combined and installed at key points on the slope (such as crack positions, the top and bottom of the slope), and the antenna can be ensured to be stable through a reinforced base to avoid data errors caused by displacement or vibration.

[0037] The implementation methods of the communication module include the following: In one implementation, the communication module uses 4G / 5G networks or LoRa wireless communication technology to achieve stable transmission of large-capacity data. Specifically, the communication module transmits GNSS observation data to the data processing center in real time to ensure the real-time and continuous nature of the system.

[0038] It should be noted that in some embodiments, the communication module can select different transmission methods according to the actual application scenario. For example, in areas with good signal coverage, the 4G / 5G communication method can be preferentially selected, while in remote areas, low-power wide-area network technologies such as LoRa can be used to ensure the stability of data transmission.

[0039] To improve the adaptability and stability of data collection, the design of the data collection module also includes the following: In a possible implementation, the data collection module can synchronously collect environmental data in combination with external sensors (such as inclinometers, temperature and humidity sensors, etc.). These environmental data can be used to assist in analyzing the change trend of GNSS signals and provide references for external factors in slope monitoring.

[0040] Exemplarily, the inclinometer can real-time monitor the change of the inclination angle of the slope and judge the overall deformation trend of the slope in combination with GNSS data; while the temperature and humidity sensor is used to record the changes in the external environment and provide a basis for the analysis of the geological conditions of the slope.

[0041] It should be particularly noted that the implementation of GNSS data collection also involves the preliminary processing of raw data, including the elimination of invalid data and the screening of signal strength: Elimination of invalid data: During the data collection process, if the signal strength (C / N0) is lower than a certain threshold (such as 30 dB-Hz), it is considered that the signal is severely interfered and should be eliminated.

[0042] Signal strength screening: Priority is given to selecting satellite data with higher signal strength for subsequent processing to improve the reliability of the positioning result.

[0043] In a possible extended application, the data collection module can implement differential processing in combination with reference station data. Specifically, a GNSS reference station is set at a stable location in the slope monitoring area (such as on a rock mass that is not easily deformed), the reference observation data is collected and transmitted to the data processing center, and differential calculation is performed with the GNSS data of the monitoring point to further improve the positioning accuracy.

[0044] It should be noted that the core of the data collection module in the present invention lies in high precision, real-time performance and multi-scenario adaptability: High precision is achieved through the reception and screening of signals in multiple constellations and multiple frequency bands; Real-time performance is guaranteed by the stable transmission of the communication module; Multi-scenario adaptability is enhanced through methods such as antenna anti-interference design, external sensor fusion, and reference station differential application.

[0045] It can be understood that the data acquisition module of the present invention can not only provide basic support for GNSS data processing in complex environments, but also combine external sensor data to provide high-quality input for subsequent error correction and filtering algorithms of the system, thereby ensuring the performance of the overall system.

[0046] A multipath error inversion module for modeling and inversely estimating the multipath error in GNSS observation data; the multipath error inversion module is a key technical module for realizing high-precision GNSS monitoring. Its main function is to model and analyze the multipath effect in GNSS signals, and inversely estimate the multipath error through mathematical methods, so as to provide error correction parameters for the subsequent filtering module. The multipath effect is the main error source in GNSS monitoring in complex terrains and environments (such as mountains, building blockages, and densely vegetated areas). Therefore, establishing an accurate multipath error model and realizing efficient parameter inversion are the technical basis for ensuring the high-precision monitoring performance of the system of the present invention.

[0047] It should be noted that the multipath error inversion module takes GNSS observation data as input, combines signal pseudorange, carrier phase, and signal strength (C / N0) characteristics to establish a nonlinear error model, and dynamically inversely estimates the model parameters through an optimization algorithm. Through the processing of this module, the multipath error can be accurately quantified, providing an important basis for subsequent filtering processing.

[0048] In this embodiment, the specific implementation manner of the multipath error inversion module includes the following content: In a possible implementation manner, the multipath error inversion module establishes an error model based on the physical characteristics of the multipath effect to describe the influence of the path difference between the reflected signal and the direct signal on the pseudorange observation data. Specifically, the mathematical model of the multipath error can be expressed in the following form: It should be noted that the physical meanings of the parameters in the above formula are as follows: : The magnitude of the multipath error, in meters; : The amplitude of the reflected signal, which determines the amplitude of the error and specifically depends on the material of the reflecting surface and the reflection conditions; : The phase difference between the reflected signal and the direct signal, determined by the path difference between the two signals; : The incident angle of the reflected signal, related to the geometric relationship of the reflection path; : The delay distance of the reflection path, that is, the additional propagation path length of the reflected signal; :Path attenuation factor, which represents the characteristic that the reflected signal gradually attenuates as the propagation path increases.

[0049] In this embodiment, in order to accurately estimate the multipath error, the parameter inversion technology is used to optimize and solve the unknown parameters (such as , , ) in the above model.

[0050] As an option, the parameter inversion adopts the nonlinear least squares method to estimate the parameters by minimizing the difference between the observed values and the model calculated values. The optimization objective function can be expressed as: where, represents the number of sampling data, is the set of parameters to be optimized.

[0051] Specifically, the implementation method of the parameter inversion includes the following steps: First, collect the pseudorange observation data, carrier phase data, and signal strength (C / N0) data of the GNSS signal, and use them as the inversion input.

[0052] Then, set the initial values of the model parameters based on the initial estimates. For example, the initial estimate of the amplitude can be normalized according to the signal strength (C / N0); the path delay can be estimated by geometric analysis; the path attenuation factor is set to the default value according to the conventional wireless channel propagation model.

[0053] After setting the initial values, an iterative optimization algorithm is used to solve the objective function. In a possible implementation, the optimization steps are iteratively updated by the following formula: It should be noted that in the above formula and represent the first-order gradient and the second-order Hessian matrix of the objective function respectively. Through this method, the optimal parameter value can be gradually approximated until the change of the objective function satisfies the convergence condition (such as ） .

[0054] In a possible implementation, in order to further improve the accuracy of the multipath error estimation, the parameter estimation range can be dynamically adjusted in combination with the change trend of the signal strength (C / N0). For example, when the signal strength is low (such as below 30 dB-Hz), the influence of the multipath effect is usually more significant. At this time, the sensitivity of the error modeling can be enhanced by increasing the initial estimate value of the amplitude .

[0055] In this embodiment, the extended functions of the multipath error inversion module further include data screening and invalid data elimination: As an option, before inverting the parameters, low-quality observation data can be screened and eliminated through the threshold of signal strength (C / N0). For example, when the signal strength is lower than 30 dB-Hz, it is considered that the observed value is greatly interfered and should be eliminated.

[0056] Specifically, a data screening rule can be defined: If the signal strength , the corresponding pseudorange observation data will be eliminated; If the signal strength meets the threshold, the observation data will be retained and input into the inversion model for processing.

[0057] It should be noted that the output results of the multipath error inversion module include the multipath error value estimated by inversion and the model parameters . These results will be used as important inputs for the subsequent filtering module to dynamically correct GNSS observation data.

[0058] It can be understood that the multipath error inversion module of the present invention effectively solves the error problem caused by the multipath effect in complex environments by combining physical modeling and mathematical optimization methods, significantly improves the quality of GNSS observation data, and provides technical support for high-precision slope monitoring.

[0059] An adaptive extended Kalman filtering module, which is used to perform real-time state estimation and multipath error correction on GNSS observation data, and optimizes the filtering effect by dynamically adjusting the process noise covariance and the observation noise covariance; The adaptive extended Kalman filtering module is a key technical module for realizing high-precision GNSS monitoring. Its main task is to combine GNSS signal observations with a state model to perform dynamic estimation and correction of multipath errors. By introducing an adaptive mechanism, the module adjusts the noise covariance matrix in the filtering process in real time to improve the robustness and adaptability of the system in complex environments. Through the joint modeling of GNSS three-dimensional position information and multipath errors, and by combining prediction and update steps, the adaptive extended Kalman filtering module can efficiently handle nonlinear and non-Gaussian error problems in complex environments.

[0060] In the present invention, the adaptive extended Kalman filter module is a key technical module for realizing high-precision GNSS monitoring. Its main task is to combine GNSS signal observations with the state model to dynamically estimate and correct multipath errors. By introducing an adaptive mechanism, this module can adjust the noise covariance matrix in the filtering process in real time to improve the robustness and adaptability of the system in complex environments. Through the joint modeling of GNSS three-dimensional position information and multipath errors, and combining prediction and update steps, the adaptive extended Kalman filter module can efficiently handle non-linear and non-Gaussian error problems in complex environments.

[0061] It should be noted that the adaptive extended Kalman filter module is based on the output of the multipath error inversion module and combines the GNSS raw observation data to form a closed-loop working mechanism for state prediction, observation update, and error correction, thus significantly improving the accuracy and reliability of GNSS monitoring data.

[0062] In this embodiment, the specific implementation method of the adaptive extended Kalman filter module includes the following content: In a possible implementation, the adaptive extended Kalman filter module first performs state space modeling on the three-dimensional position information and multipath errors of GNSS. Specifically, the state vector of the system can be defined as: The above state vector includes the GNSS three-dimensional displacement components ( , , ) and the multipath error components ( ) .

[0063] It should be noted that the state space model includes two parts: the state transition equation and the observation equation.

[0064] As an option, the state transition equation can be expressed as: where, is the state transition function, which describes the dynamic evolution of the system state over time; is the process noise, which follows a Gaussian distribution .

[0065] Exemplarily, the observation equation can be expressed as: where, represents the observation value; is a non - linear observation function that describes the relationship between the state and the observation; is the observation noise, which follows a Gaussian distribution .

[0066] In this embodiment, the calculation steps of the adaptive extended Kalman filter include the following aspects: As a possible implementation, the calculation of the adaptive extended Kalman filter is divided into two main steps: state prediction and observation update.

[0067] In the state prediction step, the state of the system at the next moment is predicted based on the state transition equation. The prediction formula is as follows: It should be noted that is the Jacobian matrix of the state transition function, which is used to describe the local linearization characteristics of the system; is the predicted state covariance matrix; is the process noise covariance matrix.

[0068] In the observation update step, the state prediction value is corrected by combining the observation equation. The update formula includes the following parts: Calculate the Kalman gain: Update the state vector: Update the state covariance: Where is the Jacobian matrix of the observation function, which is used to describe the local linearization characteristics of the observation equation; is the Kalman gain, which is used to adjust the fusion ratio of the observation information and the prediction information.

[0069] In this embodiment, the implementation method of the adaptive mechanism is as follows: As an option, the adaptive extended Kalman filter module improves the adaptability of the filtering algorithm to the non - stationary noise environment by dynamically adjusting the process noise covariance and the observation noise covariance .

[0070] Specifically, the estimation of the observation noise covariance can be based on the statistical characteristics of the observation residual: Where represents the observation residual.

[0071] Process noise covariance The dynamic adjustment can be achieved through the following formula: where is the smoothing factor, and its usual value range is 0.8 - 0.95; is the covariance of the state prediction error.

[0072] It should be noted that by dynamically adjusting the noise covariance matrix, the adaptive extended Kalman filter module can effectively cope with the noise changes in complex environments and further improve the filtering effect.

[0073] The error feedback closed-loop correction module is used to feedback the multipath error estimated by inversion to the observation equation, dynamically correct the GNSS observation data, and form a closed-loop optimization; The error feedback closed-loop correction module is a key technical module for realizing high-precision GNSS monitoring. Its main function is to perform real-time dynamic correction on GNSS observation data to form a closed-loop working mechanism for error estimation and optimization. By feeding back the processing results of the multipath error inversion module and the adaptive extended Kalman filter module to the observation equation, the error feedback closed-loop correction module can perform real-time correction on the multipath error and other non-linear errors in the original GNSS data, thereby ensuring the accuracy and reliability of the subsequent data output.

[0074] It should be noted that as a bridge between multipath error inversion and filtering correction, the core of the error feedback closed-loop correction module lies in constructing a dynamic correction strategy, using the error estimation result to optimize the observation data, and providing more accurate input for the filtering module to further enhance the overall performance of the system.

[0075] In this embodiment, the specific implementation manner of the error feedback closed-loop correction module includes the following content: In a possible implementation manner, the error feedback closed-loop correction module corrects the original GNSS observation data based on the output result of the multipath error inversion module. Specifically, for each satellite signal, the multipath error value provided by the inversion module is directly introduced into the correction formula of the observation data: where is the original pseudorange observation value, with the unit of meter; is the multipath error estimated by inversion; is the corrected pseudorange data for subsequent filtering and positioning solution.

[0076] As an option, in the correction formula, the correction strategy can be further optimized according to the variation law of the multipath error. For example, when the multipath error inversion module identifies that the error has a periodic characteristic (such as caused by periodic reflection signals), the error components in the correction formula can be weighted to assign a higher weight to enhance the correction effect.

[0077] In this embodiment, the specific working steps of the error feedback closed-loop correction module include the following: First, receive the error value output by the multipath error inversion module , and judge the significance of the error according to the time series trend of the error. When the error amplitude exceeds a preset threshold (such as 1 meter), start the correction process.

[0078] Second, dynamically introduce the error value into the observation equation to perform real-time correction on the original observation value to obtain the corrected observation data . In a possible implementation, the corrected data will be directly input into the adaptive extended Kalman filter module to further improve the filtering effect.

[0079] It should be noted that the corrected observation data is not only used for filtering processing, but can also be stored as a historical record for subsequent analysis and verification.

[0080] Specifically, to ensure the stability and robustness of the closed-loop correction, this module also designs an invalid data rejection and noise suppression mechanism: In a possible implementation, when the signal strength (C / N0) is lower than a specific threshold (such as 30 dB-Hz), it is considered that the error inversion result of the signal is unreliable. At this time, the correction step can be skipped and the observation data can be directly marked as invalid data.

[0081] Exemplarily, for an environment with significant multipath effects in slope monitoring, this module smooths the error by continuous sampling. For example, the sliding average method is used to perform smoothing to reduce the impact of sudden errors on the correction result. The formula is as follows: where is the smoothing window size, and its value range is usually 3 to 5.

[0082] In a possible extended implementation, the error feedback closed-loop correction module can also combine external auxiliary data (such as inclination angle, temperature and humidity, etc.) to further optimize the correction result: For example, when the inclination sensor detects a significant change in the local inclination angle of the slope, the weight parameter in the correction formula can be dynamically adjusted to enhance the sensitivity of the correction to environmental changes.

[0083] Specifically, assuming the change in inclination angle is , a correction coefficient related to the inclination angle can be introduced, and the improved correction formula is as follows: Among them, , is an empirical coefficient used to quantify the impact of the inclination angle change on error correction.

[0084] In this embodiment, the output results of the error feedback closed-loop correction module include: The corrected observation data and the historical record of the correction parameters. It should be particularly noted that these correction data are not only used for real-time filtering but also can serve as the basic data for system evaluation, providing a basis for subsequent optimization.

[0085] It can be understood that the error feedback closed-loop correction module forms a real-time optimized closed-loop working mechanism by dynamically introducing the multi-path error inversion result into the observation data correction process. This mechanism significantly enhances the reliability of GNSS observation data and provides an important guarantee for subsequent high-precision filtering processing.

[0086] The high-precision data output module is used to output the corrected GNSS three-dimensional position information; Among them, the adaptive extended Kalman filter module dynamically filters the GNSS data through state prediction and observation update steps, and combines error feedback to achieve error closed-loop optimization, outputting high-precision monitoring results.

[0087] The high-precision data output module is the final link to realize the presentation and application of the system monitoring results. Its main function is to integrate and output the high-precision GNSS observation data processed by the multi-path error inversion, adaptive extended Kalman filter, and error closed-loop correction module. This module can provide the corrected GNSS three-dimensional displacement data in real time, support the analysis and visualization display of the monitoring results, and generate early warning signals and data reports for slope monitoring.

[0088] It should be noted that the high-precision data output module is based on the corrected GNSS data and combines the operating status and environmental parameters of the system, and can output the monitoring data in various forms to meet the different needs of users, while supporting subsequent decision-making and safety management.

[0089] In this embodiment, the specific implementation method of the high-precision data output module includes the following content: In a possible implementation, the high-precision data output module receives the corrected GNSS observation data output from the error closed-loop correction module, including three-dimensional displacement data ( , , ), and related error correction records (such as multipath error values ). After these data are integrated and analyzed, they can be output in the forms of numerical values, charts, three-dimensional models, etc.

[0090] As an option, the output of this module includes the following: The corrected three-dimensional displacement amount, which is used to reflect the displacement change of the monitoring point in the time series; The error correction data record, including the multipath error value and related parameters applied in each correction; The time series data of environmental parameters (such as signal strength, inclination angle change, etc.), which is used to assist in the analysis of monitoring results.

[0091] Specifically, the calculation result of the three-dimensional displacement amount is based on the following formula: wherein, ( , , ) is the corrected GNSS coordinate value; ( , , ) is the initial coordinate value of the reference point.

[0092] It should be noted that this module supports the dynamic update of three-dimensional displacement data to meet the requirements of real-time monitoring.

[0093] In a possible implementation, the high-precision data output module further includes a data visualization function for visually presenting the corrected monitoring data.

[0094] Exemplarily, the visualization content includes: The real-time monitoring point displacement curve, which shows the change trend of three-dimensional displacement over time in the form of a chart; The three-dimensional dynamic model of the overall slope, which generates a three-dimensional visualization image of slope deformation by integrating the displacement data of multiple monitoring points; The displacement distribution map, which uses colors or arrows to identify the deformation amount and direction of different regions of the slope.

[0095] It should be noted that the visualization function supports users to view the deformation of the slope in real time and provides the ability to quickly identify abnormal data. For example, when the displacement of a certain monitoring point exceeds the preset threshold, the system will prompt the user in the form of color highlighting or flashing.

[0096] In this embodiment, to enhance the applicability of the module, the data output supports multiple formats and communication methods: As an option, this module can output data to the user terminal, supporting common data formats (such as CSV, JSON, XML, etc.) for the convenience of users for offline analysis and secondary processing.

[0097] In a possible implementation, the module also supports real-time transmission of data to the remote monitoring center via 4G / 5G or LoRa network to achieve cross-regional monitoring and management.

[0098] It should be noted that in specific application scenarios, such as remote slope monitoring areas, this module also supports sending data to on-site terminal devices via low-power Bluetooth to improve the flexibility of data transmission.

[0099] In the specific implementation of the present invention, the high-precision data output module also has a warning function: As an implementation, the module will calculate the cumulative displacement and displacement rate of the monitoring point based on the corrected monitoring data and compare them with the preset threshold. When the displacement or rate of any monitoring point exceeds the threshold, the module will automatically generate a warning signal.

[0100] Specifically, the warning signal can be conveyed to relevant personnel in the form of an audible and visual alarm, a text message notification, or an APP push. For example, when the cumulative displacement reaches a certain value, the system will trigger a secondary warning.

[0101] In some embodiments, the module also supports generating a complete monitoring data report, including the following content: The three-dimensional displacement change of each monitoring point and its time series graph; Statistical data on multipath error correction, including the average correction value and the error range; The correlation analysis results of environmental parameters, such as the influence of inclination change on the displacement of the monitoring point.

[0102] It should be noted that the report content can be customized according to user needs, supporting both comprehensive analysis of the entire area and detailed reports of single monitoring points. It can be understood that through diverse data integration and output forms, the high-precision data output module not only meets the requirements of real-time monitoring but also enhances the ability of data analysis and anomaly detection, providing solid technical support for the scientific management of slope deformation.

[0103] In an extended implementation, the module can also be docked with a geological disaster prediction system to combine the monitoring data with external data (such as rainfall and earthquake monitoring data), further enhancing the interpretability and application value of the monitoring results.

[0104] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A GNSS monitoring device for high-precision perception in slope monitoring, including a base (1), characterized in that, A support component is installed on the top of the base (1), a fixing component is installed in the middle of the base (1), a main rod (4) is installed on the top of the support component, a spherical cage (7) is installed on the outer side of the main rod (4), a data acquisition module (8) is fixedly connected to the top of the main rod (4), and a wind power generation component is installed inside the spherical cage (7). The fixing component includes a fixing rod (15), the fixing rod (15) is arranged through the middle of the base (1), a plurality of groups of spiral blades (16) are fixedly connected to the outer periphery of the fixing rod (15), a rotating piece (17) is rotatably connected to the outer side of the spiral blades (16), a limiting groove (18) is formed in the middle of the rotating piece (17), a limiting rod (19) is fixedly connected to the top of the spiral blades (16), and the limiting rod (19) is located in the middle of the limiting groove (18). The wind power generation component includes a rotating ring (9), the rotating ring (9) is rotatably connected to the outer side of the main rod (4), a half ring (10) is rotatably connected to the outer side of the rotating ring (9), a generator (11) is fixedly connected to the middle of the half ring (10), an impeller (12) is installed at the input end of the generator (11), and a tail fin (14) is installed at the tail end of the generator (11).

2. The GNSS monitoring device for high-precision perception applied in slope monitoring according to claim 1, wherein The support component includes a support frame (2), the support frame (2) is fixedly connected to the top of the base (1), a ferrule (3) is installed on the top of the support frame (2), the main rod (4) is arranged through the middle of the ferrule (3), and the ferrule (3) and the main rod (4) are connected by a screw.

3. The GNSS monitoring device for high-precision perception applied in slope monitoring according to claim 1, characterized in that, A positioning rod (13) is fixedly connected to the outer side of the main rod (4), the rotating ring (9) is located between the positioning rods (13), a group of positioning rods (13) are installed on the outer side of the rotating ring (9), and the half ring (10) is located in the middle of the positioning rods (13).

4. The GNSS monitoring device for high-precision perception applied in slope monitoring according to claim 1, characterized in that, A solar panel (6) and an electrical box (5) are installed on the outer side of the main rod (4), and both the solar panel (6) and the generator (11) are connected to the storage battery inside the electrical box (5) through a charge and discharge controller.

5. A GNSS monitoring system for high-precision perception applied in slope monitoring, characterized in that, Using a GNSS monitoring device for high-precision perception applied in slope monitoring according to any one of claims 1-4, including A data acquisition module for collecting GNSS observation data, including pseudorange, carrier phase, and signal strength; A multipath error inversion module for modeling and inversely estimating the multipath error in GNSS observation data; An adaptive extended Kalman filter module for performing real-time state estimation and multipath error correction on GNSS observation data, and optimizing the filtering effect by dynamically adjusting the process noise covariance and the observation noise covariance; An error feedback closed-loop correction module for feeding back the inversely estimated multipath error to the observation equation, dynamically correcting the GNSS observation data, and forming a closed-loop optimization; A high-precision data output module for outputting the corrected GNSS three-dimensional position information; wherein, the adaptive extended Kalman filtering module performs dynamic filtering processing on GNSS data through state prediction and observation update steps, and combines error feedback to achieve error closed-loop optimization, and outputs high-precision monitoring results.

6. The GNSS monitoring system for high-precision perception applied in slope monitoring according to claim 5, characterized in that, The multipath error inversion module analyzes the pseudorange, signal strength, and phase data of GNSS signals to establish an error model including parameters such as the amplitude of the reflected signal, phase difference, incident angle, and path delay, estimates the multipath error, and the estimation result is used for error correction of the subsequent filtering module.

7. The GNSS monitoring system for high-precision perception applied in slope monitoring according to claim 5, characterized in that, The adaptive extended Kalman filtering module includes: A state space modeling unit for uniformly modeling the GNSS three-dimensional position information and the multipath error as a dynamic state vector; A prediction unit for predicting the state and its covariance of the system based on the state equation; An update unit for updating the predicted state by combining the Kalman gain through the observation equation; A noise covariance adaptive estimation unit for dynamically adjusting the process noise covariance and the observation noise covariance according to the residuals of the observation data to improve the adaptability and accuracy of filtering.

8. The GNSS monitoring system for high-precision perception applied in slope monitoring according to claim 5, characterized in that, The error feedback closed-loop correction module dynamically optimizes the original observation value by introducing the inversely estimated multipath error into the correction process of the observation data, outputs the GNSS data after error correction, thereby forming a closed-loop working mode of error estimation and optimization iteration.

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