A GNSS-based deformation monitoring method, device, equipment and medium
By using edge computing and cloud-based collaborative GNSS deformation monitoring methods, the problems of strong dependence, frequency and cost contradictions, and noise confusion in existing technologies have been solved, achieving seamless deformation monitoring across the entire frequency band and improving the reliability and accuracy of monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2026-05-15
- Publication Date
- 2026-07-10
AI Technical Summary
Existing GNSS deformation monitoring technologies are highly dependent on real-time dynamic technologies, have a conflict between frequency and cost, are insensitive to slow deformation and are easily submerged by noise, and suffer from severe integral drift in single-station GNSS technology, making it impossible to achieve seamless monitoring across the entire frequency band.
By deploying monitoring stations and reference stations to collect GNSS observation data, using edge computing nodes to calculate real-time velocity vectors, and combining baseline calculation and Kalman filtering on cloud servers, full-band deformation monitoring is achieved, and independent early warning is provided when the network is interrupted.
It achieves sensitive capture of high-frequency sudden deformations and accurate monitoring of low-frequency slow trends, improving the reliability and robustness of monitoring, reducing bandwidth requirements, supporting lightweight node deployment, and possessing high accuracy and low false alarm rate.
Smart Images
Figure CN122362448A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of geodesy and geophysics, and in particular to a GNSS-based deformation monitoring method, device, equipment, and medium. Background Technology
[0002] Currently, deformation monitoring for scenarios such as landslides, dam instability, and bridge vibration mainly relies on contact sensors such as total stations and crack gauges, which suffer from problems such as low automation, limited monitoring range, and inability to capture sudden deformations.
[0003] Among related technologies, deformation monitoring can be performed using GNSS-based automated monitoring techniques, with Real-Time Kinematic (RTK) technology being the most widely used. RTK technology establishes a base station and a monitoring station, utilizing double-difference techniques to eliminate most common errors, achieving centimeter-level real-time displacement. However, this technology has the following inherent drawbacks: First, it is highly dependent; the monitoring station's calculations heavily rely on the real-time data communication link with the base station, and monitoring fails if communication is interrupted. Second, there is a conflict between frequency and cost; high-frequency RTK calculations require high data link bandwidth and computing resources, making large-scale deployment difficult. Third, slow deformation is confused with noise; for slow creep with a monthly settlement of only a few millimeters, the signal is weak and easily drowned out by RTK measurement noise.
[0004] In addition, some related technologies can also be monitored using a single-station GNSS receiver. Among them, the Time-Differenced Carrier Phase (TDCP) technique eliminates ambiguity parameters by differentiating the carrier phases of consecutive epochs, enabling direct calculation of the receiver's instantaneous velocity. This method requires no reference station and has extremely high independence and sensitivity to dynamic changes. However, the TDCP technique alone has fatal flaws in deformation monitoring applications: on the one hand, the integration drift problem is prominent, as millimeter-level white noise is always present in the TDCP velocimetry results, and long-term integration will produce huge cumulative errors; on the other hand, it is not sensitive to slow deformation, as the velocity corresponding to extremely slow deformation is much lower than the noise level of TDCP. Summary of the Invention
[0005] This application provides a GNSS-based deformation monitoring method, device, equipment, and medium to address the shortcomings of the aforementioned related technologies. The technical solution is as follows: Firstly, this application provides a GNSS-based deformation monitoring method, comprising: Raw GNSS observation data is collected by monitoring stations deployed on the target structure; reference GNSS observation data is collected by reference stations deployed at the reference point. The real-time velocity vector of the monitoring station at the corresponding epoch is calculated by the edge computing node based on the original GNSS observation data; The system receives raw GNSS observation data and reference GNSS observation data through a cloud server, performs baseline calculation to obtain the absolute three-dimensional coordinates of the monitoring station in the reference coordinate system, and sends the absolute three-dimensional coordinates to the edge computing nodes according to a preset cycle. The displacement change of the monitoring station within each consecutive sliding time window is calculated using edge computing nodes, including: If absolute three-dimensional coordinates are received within the sliding time window, Kalman filtering is performed based on the real-time velocity vector and absolute three-dimensional coordinates of each epoch within the sliding time window to obtain the displacement change of the corresponding sliding time window. Otherwise, the displacement change of the corresponding sliding time window is obtained by integrating the real-time velocity vector of each epoch within the sliding time window. The displacement sequence of the monitoring station is obtained based on the displacement change in each sliding time window, and deformation early warning is performed based on the displacement sequence.
[0006] In one alternative of the first aspect, the raw GNSS observation data includes carrier phase observations, Doppler shift observations, and pseudorange observations for each observation satellite; The process of calculating the real-time velocity vector of the monitoring station at the corresponding epoch using edge computing nodes based on raw GNSS observation data includes: Edge computing nodes perform differential calculations based on the carrier phase observations of the current epoch and the adjacent previous epoch, and establish a set of observation equations with the receiver three-dimensional velocity and receiver clock speed as unknowns. The real-time velocity vector of the monitoring station in the current epoch is obtained by solving the observation equations using the weighted least squares method.
[0007] In one alternative of the first aspect, before establishing the set of observation equations with the receiver's three-dimensional velocity and receiver clock speed as unknowns, cycle slip detection is performed on each observation satellite, including: A geometrically independent combination term is constructed based on carrier phase observations and pseudorange observations at the same epoch. The difference between the geometrically independent combination terms of two adjacent epochs is calculated. If the absolute value of the difference between the geometrically independent combination terms is greater than the preset geometrically independent combination threshold, then a cycle slip is determined to have occurred. Alternatively, calculate the actual change in carrier phase observation between two adjacent epochs, and obtain the predicted change in carrier phase observation based on the Doppler frequency shift observation of the corresponding two epochs. If the absolute value of the difference between the actual change and the predicted change is greater than the preset change threshold, then a cycle slip is determined to have occurred. All observations from satellites that experienced cycle slips were removed.
[0008] In one alternative embodiment of the first aspect, the method further includes: In the process of solving the weighted least squares method, the weights of each observation value of the corresponding observation satellite are determined according to the elevation angle and / or the signal-to-noise ratio of each observation satellite, and a weight matrix is constructed.
[0009] In one alternative embodiment of the first aspect, the method further includes: In the process of solving the weighted least squares method, the observation residuals and standardized residuals of each observation value of each observation satellite at the current epoch are calculated; The median absolute deviation of the observation residuals corresponding to each class of observations is calculated, and the standardized residual threshold is determined based on a preset multiple of the median absolute deviation. The weights of observations whose standardized residuals are greater than the standardized residual threshold are reduced to obtain the updated weight matrix.
[0010] In one alternative embodiment of the first aspect, the method further includes: If the difference between the carrier phase observations of two adjacent epochs is less than the preset noise reference in multiple consecutive epochs, then the monitoring station is determined to be in a static state. When establishing a set of observation equations with the receiver's three-dimensional velocity and clock speed as unknowns, zero-velocity constraint equations in the east, north, and top directions are added to the set of observation equations, and the weights of the zero-velocity constraint equations are dynamically adjusted according to the static confidence level of the monitoring station.
[0011] In one alternative embodiment of the first aspect, the method further includes: If the values of the real-time velocity vectors in multiple consecutive epochs are all less than the first velocity threshold, then the monitoring status label of the monitoring station is set to a safe state. If the values of the real-time velocity vectors in multiple consecutive epochs are all greater than or equal to the first velocity threshold and less than the second velocity threshold, then the monitoring status label of the monitoring station is set to the alert state, and the sampling frequency of the monitoring station in collecting raw GNSS observation data is increased. If the values of the real-time velocity vectors in multiple consecutive epochs are all greater than or equal to the second velocity threshold, or if the displacement change determined based on the displacement sequence is greater than or equal to the preset displacement threshold, then a deformation warning message is issued.
[0012] Secondly, this application also provides a GNSS-based deformation monitoring device, comprising: The data acquisition unit is used to acquire raw GNSS observation data through monitoring stations deployed on the target structure, and also to acquire reference GNSS observation data through reference stations deployed at the reference point; Edge computing nodes are used to calculate the real-time velocity vector of the monitoring station at the corresponding epoch based on the raw GNSS observation data; The cloud server is used to receive raw GNSS observation data and reference GNSS observation data, perform baseline calculations to obtain the absolute three-dimensional coordinates of the monitoring station in the reference coordinate system, and send the absolute three-dimensional coordinates to the edge computing nodes according to a preset cycle. The edge computing node is also used to calculate the displacement change of the monitoring station within each consecutive sliding time window, including: If absolute three-dimensional coordinates are received within the sliding time window, Kalman filtering is performed based on the real-time velocity vector and absolute three-dimensional coordinates of each epoch within the sliding time window to obtain the displacement change of the corresponding sliding time window. Otherwise, the displacement change of the corresponding sliding time window is obtained by integrating the real-time velocity vector of each epoch within the sliding time window. The deformation early warning unit is used to obtain the displacement sequence of the monitoring station based on the displacement change in each sliding time window, and to perform deformation early warning based on the displacement sequence.
[0013] Thirdly, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method provided by the first aspect of this application or any implementation thereof.
[0014] Fourthly, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of this application or any implementation thereof.
[0015] This application can not only use inter-epoch carrier phase differential technology to highly sensitively capture high-frequency sudden deformations such as bridge vibration and landslides, but also use baseline-derived absolute coordinates to accurately monitor low-frequency slow trends such as dam settlement and slope creep, achieving seamless monitoring across the entire frequency band. Even when communication is interrupted, edge computing nodes can still independently provide early warnings of sudden deformations based on velocity vector integration, solving the problem of traditional real-time dynamic technologies failing upon network outages and significantly improving reliability and robustness in harsh field environments. By fusing real-time velocity with absolute three-dimensional coordinates from the cloud server using Kalman filtering, the integral drift of single-station velocity measurement is effectively eliminated, ensuring long-term accuracy. Simultaneously, the asynchronous low-frequency communication mode between the cloud server and edge computing nodes significantly reduces bandwidth requirements, supports flexible deployment of lightweight nodes, and can still successfully provide early warnings of sudden deformations as small as 5 mm / s even during network interruptions, combining high precision, low false alarm rate, economy, and environmental adaptability. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating a GNSS-based deformation monitoring method provided in an embodiment of this application. Figure 2 This is one of the schematic diagrams illustrating the effect of a GNSS-based deformation monitoring method provided in the embodiments of this application; Figure 3 This is the second schematic diagram illustrating the effect of a GNSS-based deformation monitoring method provided in this application embodiment; Figure 4 This is the third schematic diagram illustrating the effect of a GNSS-based deformation monitoring method provided in this application embodiment; Figure 5 This is a schematic diagram of the structure of a GNSS-based deformation monitoring device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0019] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or apparatus.
[0020] It should be noted that the terms "first" and "second" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in an order other than those described or illustrated herein.
[0021] The present application will now be described in detail with reference to specific embodiments.
[0022] Next, combine Figure 1 This paper introduces a GNSS-based deformation monitoring method provided by an embodiment of this application. For details, please refer to... Figure 1 , Figure 1 This diagram illustrates a flowchart of a GNSS-based deformation monitoring method provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps: S101: Raw GNSS observation data is collected through monitoring stations deployed on the target structure; reference GNSS observation data is collected through reference stations deployed at the reference point.
[0023] It should be noted that the target structure can include various targets requiring deformation monitoring, such as bridges, buildings, and mountains. Several points on the target structure can be selected as monitoring points (such as the mid-span of the main beam of a bridge or the top of the bridge tower), and monitoring stations can be set up at the corresponding monitoring points. For reference points, reference points can be set up in structurally stable areas outside the area where the target structure is located, such as stable bedrock.
[0024] Specifically, the monitoring station can collect raw GNSS observation data through its GNSS receiver, while the reference station can collect reference GNSS observation data through its GNSS receiver.
[0025] Specifically, based on real-time acquired GNSS observation data, various observation values from multiple navigation systems (such as GPS and BDS) and multiple frequencies of observation satellites can be decoded, including carrier phase observation values, Doppler frequency shift observation values, and pseudorange observation values of the observation satellites.
[0026] S102 calculates the real-time velocity vector of the monitoring station at the corresponding epoch by using edge computing nodes based on the original GNSS observation data.
[0027] It should be noted that edge computing nodes can be deployed at any monitoring station of the target structure, or edge computing nodes can be configured separately at each monitoring point, and the raw GNSS observation data of the monitoring station can be processed directly through the edge control nodes.
[0028] In some embodiments, S102 can use an inter-epoch carrier phase difference algorithm to calculate the real-time velocity vector of the monitoring point, specifically including the following steps: S1021, the edge computing node performs differential calculations based on the carrier phase observations of the current epoch and the adjacent previous epoch, and establishes a set of observation equations with the receiver three-dimensional velocity and the receiver clock speed as unknowns.
[0029] Before establishing the observation equation set, the observations of each observation satellite can be corrected first, including: Satellite velocity calculation: The satellite position sequence in the precise ephemeris is interpolated using an Nth-order Lagrange polynomial. The interpolation formula is as follows: ; The instantaneous velocity of the satellite is obtained by taking the analytic derivative of the above Lagrange interpolation polynomial: ; in, This represents the estimated position of the observed satellite at time t, calculated using Lagrange interpolation. This indicates the known position of the observed satellite at time t, specifically the satellite position data at discrete time points provided in the precise ephemeris. and This represents a discrete time point provided in the precise ephemeris, where i and j represent the time point indices used for interpolation. This represents the instantaneous velocity of the observed satellite at time t. This represents the basis function for Lagrange interpolation.
[0030] Spatial geometric correction: A Sagnac effect correction term is introduced. Based on the time it takes for the signal to travel from the satellite to the receiver, the Earth's rotation angle is calculated, and the satellite's coordinates in the geocentric Earth-fixed coordinate system are rotated to compensate for the satellite coordinate offset caused by the Earth's rotation during signal propagation. The correction formula is as follows: ; ; Where, ω e The Earth's rotational angular velocity is (7.2921151467 × 10⁻⁶). -5 (rad / s), τ is the signal propagation time, , This indicates the corrected satellite coordinates. , This indicates the satellite coordinates before correction, and s represents the satellite's serial number.
[0031] Atmospheric residual correction: Calculate the tropospheric delay of each satellite in the current epoch and the previous epoch respectively, take the difference as the tropospheric delay change between epochs, and subtract it from the observation equation.
[0032] The tropospheric delay was calculated using a simplified Saastamoinen model: ; The tropospheric delay variation between epochs is: ; in, The zenith dry delay is approximately 2.3 meters. The mapping function related to the satellite elevation angle E, Indicates tropospheric delay, This represents the tropospheric delay of epoch k. This represents the tropospheric delay of epoch k-1.
[0033] Furthermore, embodiments of this application can also perform cycle slip detection on each observation satellite, including: A geometrically independent combination term is constructed based on carrier phase observations and pseudorange observations at the same epoch. The difference between the geometrically independent combination terms of two adjacent epochs is calculated. If the absolute value of the difference between the geometrically independent combination terms is greater than the preset geometrically independent combination threshold, then a cycle slip is determined to have occurred. Alternatively, calculate the actual change in carrier phase observation between two adjacent epochs, and obtain the predicted change in carrier phase observation based on the Doppler frequency shift observation of the corresponding two epochs. If the absolute value of the difference between the actual change and the predicted change is greater than the preset change threshold, then a cycle slip is determined to have occurred. All observations from satellites that experienced cycle slips were removed.
[0034] Then, a set of observation equations can be constructed based on the observations from the retained observation satellites.
[0035] In some embodiments, if the difference between the carrier phase observation values of two adjacent epochs is less than a preset noise reference within a series of consecutive epochs, then the monitoring station is determined to be in a static state. When establishing the observation equation set with the receiver's three-dimensional velocity and receiver clock speed as unknowns, zero-velocity constraint equations in the east, north, and top directions are added to the observation equation set. The weights of the zero-velocity constraint equations are dynamically adjusted according to the confidence level of the monitoring station in a static state to suppress background noise and improve the accuracy of velocity measurement in a static state.
[0036] The extended set of observation equations is expressed as follows: ; Where A is the original design matrix, and I is the identity matrix. denoted as static constraint weights, x is the parameter vector to be determined (specifically, the three-dimensional velocity vector of the monitoring point, including east, north, and top directions), and b is the observation vector.
[0037] The observation equation obtained by interepoch carrier phase difference is expressed as: ; in, The difference in geometric distance between the satellite and the receiver. For carrier phase difference, For tropospheric delay difference, For epoch intervals, At the speed of light, For satellite clock speed, , , For unit line-of-sight vector components, , , For the receiver's three-dimensional velocity, This refers to the receiver clock speed.
[0038] S1022, the observation equations are solved by weighted least squares method to obtain the real-time velocity vector of the monitoring station in the current epoch.
[0039] Specifically, in the solution process of the weighted least squares method, based on the elevation angle of each observed satellite... And / or, based on the signal-to-noise ratio of each observation satellite, determine the weights of each observation value of the corresponding observation satellite, and construct a weight matrix.
[0040] In this way, weights can be assigned to the observations of each satellite, prioritizing the trust of high-quality signals to offset differences in antenna environment and obstruction between different sites, thus ensuring that the algorithm maintains consistent velocity measurement accuracy in different deployment environments.
[0041] Specifically, in the process of solving the weighted least squares method, the weight matrix can also be adjusted, including: Calculate the observation residuals and standardized residuals of each observation from each observation satellite at the current epoch; The median absolute deviation of the observation residuals corresponding to each class of observations is calculated, and the standardized residual threshold is determined based on a preset multiple of the median absolute deviation. The weights of observations whose standardized residuals are greater than the standardized residual threshold are reduced to obtain the updated weight matrix.
[0042] The formula for calculating the absolute deviation of the median is as follows: ; The formula for calculating the standardized residual threshold can be expressed as: ; in, This represents the residual of the i-th observation. Represents all observed values. This indicates the calculation of the median.
[0043] The solution is obtained using the updated weight matrix, specifically through the QR decomposition method. ; The velocity in the ECEF coordinate system can be obtained by solving. Then, it is transformed into the ENU local coordinate system using the rotation matrix E: ; Horizontal velocity scalar for: ; Heading angle for: ; in, Indicates the eastward velocity component, This represents the northbound velocity component.
[0044] Therefore, the real-time velocity vector of the monitoring station at the corresponding epoch can be calculated.
[0045] S103 receives raw GNSS observation data and reference GNSS observation data through a cloud server, performs baseline calculation to obtain the absolute three-dimensional coordinates of the monitoring station in the reference coordinate system, and sends the absolute three-dimensional coordinates to the edge computing nodes according to a preset cycle.
[0046] It should be noted that the cloud server can receive observation data from the base station and monitoring station via 4G network communication. Based on long-term observation data, baseline calculation can be performed using a static relative positioning baseline calculation strategy (such as calculating once per hour or per day). This allows for the acquisition of high-precision absolute three-dimensional coordinates of the monitoring station within a global reference frame. Because static calculation uses long-term observation data, its results can effectively eliminate the effects of atmospheric delay and orbital errors, achieving an absolute positioning accuracy of 1 to 2 millimeters in the horizontal direction and 2 to 4 millimeters in the vertical direction. This enables the precise capture of the long-term, slow creep process at the monitoring station's location.
[0047] Understandably, the cloud server sends the calculated absolute three-dimensional coordinates to the edge computing nodes via 4G network communication according to a preset period, and the edge computing nodes can perform subsequent calculations based on the absolute three-dimensional coordinates.
[0048] S104 calculates the displacement change of the monitoring station within each consecutive sliding time window through edge computing nodes.
[0049] Specifically, edge computing nodes can calculate based on the real-time velocity vector of each epoch within each sliding time window. For sliding time windows that do not receive the absolute three-dimensional coordinates from the cloud server, the displacement change of the corresponding sliding time window is obtained by integrating the real-time velocity vector of each epoch within the sliding time window. If absolute three-dimensional coordinates are received within the sliding time window, Kalman filtering is performed based on the real-time velocity vector and absolute three-dimensional coordinates of each epoch within the sliding time window to obtain the displacement change of the corresponding sliding time window.
[0050] Specifically, in the Kalman filtering process, during the state prediction stage, the real-time velocity vector of the edge computing node is used for position prediction. During the measurement update stage, the absolute three-dimensional coordinates generated by the cloud server are used as the measurement value to update the filter, thereby correcting the cumulative drift error caused by velocity integration and ensuring the long-term accuracy of the monitoring results.
[0051] S105, the displacement sequence of the monitoring station is obtained based on the displacement change in each sliding time window, and deformation early warning is performed based on the displacement sequence.
[0052] In some embodiments, if the values of the real-time velocity vectors for multiple consecutive epochs (e.g., 3 epochs) are all less than the first velocity threshold, the monitoring status label of the monitoring station is set to a safe state. If the values of the real-time velocity vectors in multiple consecutive epochs are all greater than or equal to the first velocity threshold and less than the second velocity threshold, then the monitoring status label of the monitoring station is set to the alert state, and the sampling frequency of the monitoring station for collecting raw GNSS observation data is increased, for example, from 1Hz to 10Hz. This application embodiment does not limit this. If the values of the real-time velocity vectors in multiple consecutive epochs are all greater than or equal to the second velocity threshold, or if the displacement change determined based on the displacement sequence is greater than or equal to the preset displacement threshold, then a deformation warning message is issued. The value of the second speed threshold is greater than that of the first speed threshold.
[0053] In this way, even if the network connection between the cloud server and the edge computing node is unstable or disconnected, the edge computing node can independently issue early warnings based on the real-time velocity vectors of each epoch within the sliding time window. It has the core capability to independently complete emergency early warnings without relying on real-time network communication. After the network is restored, it uploads the GNSS observation data during the network failure to the cloud server to calculate the displacement change more accurately.
[0054] In some embodiments, the mean and standard deviation of the real-time velocity vector values at the monitoring station location can be statistically analyzed based on historical observation data. The first speed threshold is the mean plus N times the standard deviation, where N is a preset confidence coefficient; If the value of the real-time velocity vector in multiple consecutive epochs exceeds the first velocity threshold, and the total duration of the epochs in which the real-time velocity vector exceeds the first velocity threshold is greater than the duration threshold, then the monitoring status label of the monitoring station is set to the alert state, and the sampling frequency of the monitoring station in collecting raw GNSS observation data is increased.
[0055] In some embodiments, taking a sudden landslide early warning scenario as an example, multiple monitoring stations can be deployed at key locations on the landslide body, and a benchmark station can be established on the stable bedrock outside the landslide body.
[0056] Under normal circumstances, the system operates in a low-power monitoring mode. Each monitoring station runs a high-frequency early warning engine at 1Hz through an edge node, enabling static constraint mode. The output velocity scalar value fluctuates within the background noise level (less than 5mm / s). The cloud server can perform a static calculation once a day based on the observation data from the monitoring and base stations, recording the calculated absolute coordinates of each monitoring station to analyze long-term creep trends.
[0057] When the mountain enters the accelerated sliding phase, the rock mass's speed instantly exceeds the noise threshold. The high-frequency early warning engine on the edge node detects that the speed scalar value continuously exceeds the dynamic trigger threshold. The system immediately switches from a safe state to an alert state, automatically increasing the sampling frequency to 10Hz and beginning to retain high-frequency raw data. If the speed continues to increase, and the cumulative displacement exceeds 30 millimeters within a 10-second sliding window, the system switches to an alarm state, triggering a local audible and visual alarm and sending the highest-level alarm to the cloud via the 4G network. Even if the 4G network is interrupted during this process, the edge node can still independently complete the early warning and upload the cached data to the cloud for post-event analysis after the network is restored.
[0058] In one specific embodiment, to further verify the performance and robustness of the method provided in the embodiments of this application under different real-world environments, four representative large bridges (labeled DSCZ, JXZF, QKHX, and YJWZ, respectively) were selected, monitoring stations were deployed at key vibration points of the bridges, and a continuous test was conducted for one week.
[0059] The data sampling rate of the monitoring station was set to 10Hz, and only edge computing nodes were used for real-time speed measurement of a single station to simulate the most severe independent working conditions without network access. The evaluation indicators included the solution success rate, average speed measurement accuracy (RMS error), and accuracy interval distribution. The results are shown in Tables 1 and 2.
[0060] Table 1: Comparison of Success Rate and Accuracy of Monitoring Stations for Four Bridges
[0061] Table 2: Distribution of Speed Measurement Accuracy Ranges at Monitoring Stations on the Four Bridges
[0062] Based on the above experimental data, the following conclusions can be drawn: First, high precision and high reliability. The average velocity measurement accuracy of all four bridge monitoring stations remained stable in the range of 4.5 to 5.6 mm / s, achieving the millimeter-level standard for TDCP velocity measurement. In terms of accuracy distribution, the proportion of epochs rated "excellent + good" (i.e., accuracy better than 10 mm / s) exceeded 95% for all stations, proving that the method of this invention can provide highly reliable data support for real-time early warning of minor structural deformations.
[0063] Second, excellent environmental consistency. The monitoring stations on the four bridges faced different antenna environments, electromagnetic interference, and satellite obstruction, but the range of their average velocities was only 1.09 mm / s, demonstrating extremely high consistency. This strongly proves that the depth-weighted model based on signal-to-noise ratio (SNR) in this embodiment played a core role, effectively offsetting environmental differences and ensuring the universality of the algorithm. Figure 2 As shown.
[0064] Third, a strong gross error suppression effect. The proportion of gross errors (>20 mm / s) at all stations was strictly controlled to within 1.3%, far lower than the unprocessed original TDCP solution results (usually 5% to 10%). This proves the effectiveness of the combined quality control strategy of "multi-criteria cycle slip detection + MAD robust estimation" adopted in this invention. Figure 3 and Figure 4 As shown.
[0065] Fourth, high-quality data self-discipline. The success rate of 81% to 84% is not due to a lack of algorithmic capability, but rather a manifestation of "high-quality data self-discipline." The approximately 16% to 19% of epochs that are discarded are usually accompanied by severe multipath effects or satellite lock-offs; forcibly solving them would only contaminate the final deformation trend analysis. This invention ensures the high purity of the final output velocity sequence by actively removing these questionable epochs.
[0066] The following are apparatus embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the method embodiments of this application.
[0067] Please see below. Figure 5 A GNSS-based deformation monitoring device, provided as an exemplary embodiment of this application, includes: The data acquisition unit is used to acquire raw GNSS observation data through monitoring stations deployed on the target structure, and also to acquire reference GNSS observation data through reference stations deployed at the reference point; Edge computing nodes are used to calculate the real-time velocity vector of the monitoring station at the corresponding epoch based on the raw GNSS observation data; The cloud server is used to receive raw GNSS observation data and reference GNSS observation data, perform baseline calculations to obtain the absolute three-dimensional coordinates of the monitoring station in the reference coordinate system, and send the absolute three-dimensional coordinates to the edge computing nodes according to a preset cycle. The edge computing node is also used to calculate the displacement change of the monitoring station within each consecutive sliding time window, including: If absolute three-dimensional coordinates are received within the sliding time window, Kalman filtering is performed based on the real-time velocity vector and absolute three-dimensional coordinates of each epoch within the sliding time window to obtain the displacement change of the corresponding sliding time window. Otherwise, the displacement change of the corresponding sliding time window is obtained by integrating the real-time velocity vector of each epoch within the sliding time window. The deformation early warning unit is used to obtain the displacement sequence of the monitoring station based on the displacement change in each sliding time window, and to perform deformation early warning based on the displacement sequence.
[0068] It should be noted that the apparatus provided in the above embodiments, when executing a GNSS-based deformation monitoring method, is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their implementation process is detailed in the method embodiments, which will not be repeated here.
[0069] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of any of the methods described above.
[0070] Please see Figure 6 This is a structural block diagram of an electronic device provided in an embodiment of this application.
[0071] like Figure 6 As shown, the electronic device includes a processor and a memory.
[0072] In this embodiment, the processor is the control center of the computer system, and can be a processor of a physical machine or a processor of a virtual machine. The processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor can be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array).
[0073] A processor can also include a main processor and a coprocessor. The main processor is used to process data in the wake-up state and is also called the CPU (Central Processing Unit). The coprocessor is a low-power processor used to process data in the standby state.
[0074] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments of this application, the non-transitory computer-readable storage media in the memory are used to store at least one instruction, which is executed by a processor to implement the methods in the embodiments of this application.
[0075] In some embodiments, the electronic device further includes a peripheral device interface and at least one peripheral device. The processor, memory, and peripheral device interface are connected via a bus or signal line. Each peripheral device is connected to the peripheral device interface via a bus, signal line, or circuit board. Specifically, the peripheral device includes: a display screen, a camera, and audio circuitry. The peripheral device interface can be used to connect at least one I / O (Input / Output) related peripheral device to the processor and memory.
[0076] In some embodiments of this application, the processor, memory, and peripheral device interfaces are integrated on the same chip or circuit board; in other embodiments of this application, any one or two of the processor, memory, and peripheral device interfaces can be implemented on separate chips or circuit boards. This application does not specifically limit the implementation in this regard.
[0077] The electronic device structural block diagrams shown in the embodiments of this application do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or use different component arrangements.
[0078] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the methods in any of the foregoing embodiments. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.
[0079] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A GNSS-based deformation monitoring method, characterized in that, include: Raw GNSS observation data is collected by monitoring stations deployed on the target structure; Reference GNSS observation data are collected by reference stations deployed at benchmark points; The real-time velocity vector of the monitoring station at the corresponding epoch is calculated by the edge computing node based on the original GNSS observation data; The system receives raw GNSS observation data and reference GNSS observation data through a cloud server, performs baseline calculation to obtain the absolute three-dimensional coordinates of the monitoring station in the reference coordinate system, and sends the absolute three-dimensional coordinates to the edge computing nodes according to a preset cycle. The displacement change of the monitoring station within each consecutive sliding time window is calculated using edge computing nodes, including: If absolute three-dimensional coordinates are received within the sliding time window, Kalman filtering is performed based on the real-time velocity vector and absolute three-dimensional coordinates of each epoch within the sliding time window to obtain the displacement change of the corresponding sliding time window. Otherwise, the displacement change of the corresponding sliding time window is obtained by integrating the real-time velocity vector of each epoch within the sliding time window. The displacement sequence of the monitoring station is obtained based on the displacement change in each sliding time window, and deformation early warning is performed based on the displacement sequence.
2. The GNSS-based deformation monitoring method according to claim 1, characterized in that, The raw GNSS observation data includes carrier phase observations, Doppler shift observations, and pseudorange observations for each observation satellite; The process of calculating the real-time velocity vector of the monitoring station at the corresponding epoch using edge computing nodes based on raw GNSS observation data includes: Edge computing nodes perform differential calculations based on the carrier phase observations of the current epoch and the adjacent previous epoch, and establish a set of observation equations with the receiver three-dimensional velocity and receiver clock speed as unknowns. The real-time velocity vector of the monitoring station in the current epoch is obtained by solving the observation equations using the weighted least squares method.
3. The GNSS-based deformation monitoring method according to claim 2, characterized in that, Before establishing the observation equations with the receiver's three-dimensional velocity and clock speed as unknowns, cycle slip detection is performed on each observation satellite, including: A geometrically independent combination term is constructed based on carrier phase observations and pseudorange observations at the same epoch. The difference between the geometrically independent combination terms of two adjacent epochs is calculated. If the absolute value of the difference between the geometrically independent combination terms is greater than the preset geometrically independent combination threshold, then a cycle slip is determined to have occurred. Alternatively, calculate the actual change in carrier phase observation between two adjacent epochs, and obtain the predicted change in carrier phase observation based on the Doppler frequency shift observation of the corresponding two epochs. If the absolute value of the difference between the actual change and the predicted change is greater than the preset change threshold, then a cycle slip is determined to have occurred. All observations from satellites that experienced cycle slips were removed.
4. The GNSS-based deformation monitoring method according to claim 2, characterized in that, The method further includes: In the process of solving the weighted least squares method, the weights of each observation value of the corresponding observation satellite are determined according to the elevation angle and / or the signal-to-noise ratio of each observation satellite, and a weight matrix is constructed.
5. The GNSS-based deformation monitoring method according to claim 4, characterized in that, The method further includes: In the process of solving the weighted least squares method, the observation residuals and standardized residuals of each observation value of each observation satellite at the current epoch are calculated; The median absolute deviation of the observation residuals corresponding to each class of observations is calculated, and the standardized residual threshold is determined based on a preset multiple of the median absolute deviation. The weights of observations whose standardized residuals are greater than the standardized residual threshold are reduced to obtain the updated weight matrix.
6. The GNSS-based deformation monitoring method according to claim 1, characterized in that, The method further includes: If the difference between the carrier phase observations of two adjacent epochs is less than the preset noise reference in multiple consecutive epochs, then the monitoring station is determined to be in a static state. When establishing a set of observation equations with the receiver's three-dimensional velocity and clock speed as unknowns, zero-velocity constraint equations in the east, north, and top directions are added to the set of observation equations, and the weights of the zero-velocity constraint equations are dynamically adjusted according to the static confidence level of the monitoring station.
7. The GNSS-based deformation monitoring method according to claim 1, characterized in that, The method further includes: If the values of the real-time velocity vectors in multiple consecutive epochs are all less than the first velocity threshold, then the monitoring status label of the monitoring station is set to a safe state. If the values of the real-time velocity vectors in multiple consecutive epochs are all greater than or equal to the first velocity threshold and less than the second velocity threshold, then the monitoring status label of the monitoring station is set to the alert state, and the sampling frequency of the monitoring station in collecting raw GNSS observation data is increased. If the values of the real-time velocity vectors in multiple consecutive epochs are all greater than or equal to the second velocity threshold, or if the displacement change determined based on the displacement sequence is greater than or equal to the preset displacement threshold, then a deformation warning message is issued.
8. A GNSS-based deformation monitoring device, characterized in that, include: The data acquisition unit is used to acquire raw GNSS observation data through monitoring stations deployed on the target structure, and also to acquire reference GNSS observation data through reference stations deployed at the reference point; Edge computing nodes are used to calculate the real-time velocity vector of the monitoring station at the corresponding epoch based on the raw GNSS observation data; The cloud server is used to receive raw GNSS observation data and reference GNSS observation data, perform baseline calculations to obtain the absolute three-dimensional coordinates of the monitoring station in the reference coordinate system, and send the absolute three-dimensional coordinates to the edge computing nodes according to a preset cycle. The edge computing node is also used to calculate the displacement change of the monitoring station within each consecutive sliding time window, including: If absolute three-dimensional coordinates are received within the sliding time window, Kalman filtering is performed based on the real-time velocity vector and absolute three-dimensional coordinates of each epoch within the sliding time window to obtain the displacement change of the corresponding sliding time window. Otherwise, the displacement change of the corresponding sliding time window is obtained by integrating the real-time velocity vector of each epoch within the sliding time window. The deformation early warning unit is used to obtain the displacement sequence of the monitoring station based on the displacement change in each sliding time window, and to perform deformation early warning based on the displacement sequence.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.