An automatic monitoring method for mountain slopes based on RTK sensors and related equipment

Abstract

An automated monitoring method for mountain slopes based on RTK sensors and related equipment, the present application belongs to the field of electronic digital data processing. In the method, a main monitoring point and each auxiliary monitoring point within a preset range of the main monitoring point are constructed as monitoring units; the auxiliary monitoring points are controlled to synchronously receive satellite signals according to preset time slices, and the signal data of the satellite signals are integrated in time sequence to obtain a signal matrix; the carrier phase observation values ​​and pseudo-range observation values ​​in the signal matrix are extracted, and their differences are calculated to obtain a double-difference observation value sequence; when any double-difference observation value sequence meets any one of several preset rules, the corresponding monitoring point is determined as an abnormal monitoring point, and the signal compensation value is calculated based on the double-difference observation value sequence of the two monitoring points adjacent to the abnormal monitoring point; the signal data received by the abnormal monitoring point is corrected according to the signal compensation value. The present application improves the integrity of mountain slope monitoring data, thereby improving the accuracy of mountain slope monitoring.

Description

Technical Field

[0001] The present application belongs to the field of electronic digital data processing, and in particular, relates to an automatic monitoring method for mountain slopes based on RTK sensors and related equipment. Background Art

[0002] Mountain slope deformation monitoring is an important research direction in the field of engineering geological disaster prevention and control. Accurately grasping the deformation characteristics of slopes is of great significance for preventing landslide disasters. Existing slope monitoring methods mainly include traditional monitoring methods such as inclinometers and displacement meters. These monitoring devices are subject to the limitations of the installation location and the influence of the external environment, making it difficult to achieve large-scale, high-precision dynamic monitoring of slopes. At the same time, foundation excavation and burial are required during the installation of the equipment, which can easily disturb the slope structure and may even cause new unstable factors.

[0003] With the development of satellite positioning technology, slope monitoring methods based on RTK (real-time dynamic carrier phase differential positioning) technology have gradually been applied. This method deploys RTK sensors on the slope surface and combines the differential positioning principle to obtain the three-dimensional coordinate changes of the monitoring points, thus realizing non-contact monitoring of slope deformation and reducing the damage to the slope structure caused by traditional monitoring methods.

[0004] However, in the complex terrain conditions of mountain slopes, dense vegetation or mountain obstruction can lead to unstable RTK signal reception, affecting positioning accuracy. Especially when the monitoring point is located on the shady side of the slope or in the canyon area, the insufficient number of visible satellites will cause interruption or distortion of monitoring data, reducing the integrity of the mountain slope monitoring data, and thus reducing the accuracy of mountain slope monitoring. Summary of the invention

[0005] The present application provides an RTK sensor-based automatic monitoring method for mountain slopes and related equipment, which are used to improve the integrity of mountain slope monitoring data and thereby improve the accuracy of mountain slope monitoring.

[0006] In a first aspect, the present application provides an automated monitoring method for a mountain slope based on an RTK sensor. When it is determined that a multi-layer monitoring network is set up on a mountain slope to be monitored, a main monitoring point in the multi-layer monitoring network and each auxiliary monitoring point within a preset range of the main monitoring point are constructed as monitoring units to obtain a plurality of monitoring units corresponding to the plurality of main monitoring points one by one. The multi-layer monitoring network includes a first monitoring layer and a second monitoring layer, each main monitoring point is located in the first monitoring layer, and each auxiliary monitoring point is located in the second monitoring layer;

[0007] Control the auxiliary monitoring points in each monitoring unit to synchronously receive satellite signals according to the preset time slice, and integrate the signal data of the satellite signals according to the time sequence to obtain the signal matrix corresponding to the monitoring unit;

[0008] Extract the carrier phase observation value and pseudorange observation value in the signal matrix, and calculate the difference between the carrier phase observation value and the pseudorange observation value to obtain a double difference observation value sequence for each monitoring point in the monitoring unit;

[0009] When any double difference observation value sequence meets any one of several preset rules, the corresponding monitoring point is determined as an abnormal monitoring point, and the signal compensation value of the abnormal monitoring point is calculated according to the double difference observation value sequences of the two monitoring points adjacent to the abnormal monitoring point;

[0010] The signal data received at the abnormal monitoring point is corrected according to the signal compensation value.

[0011] By adopting the above technical scheme, a multi-layer monitoring network and monitoring units are constructed to achieve hierarchical and multi-scale monitoring of mountain slopes. The main monitoring point is located in the first monitoring layer, and the auxiliary monitoring point is located in the second monitoring layer, so that the monitoring network has a clear spatial structure. The main monitoring point and its surrounding auxiliary monitoring points form a monitoring unit, and the monitoring results of the main monitoring point can be verified and supplemented by the observation data of the auxiliary monitoring point, thereby improving the reliability of the monitoring data. The synchronous observation of the auxiliary monitoring points is achieved by presetting the time slice, which not only reduces the signal interference between the monitoring points, but also improves the time consistency of the observation data. The use of double-difference observation value sequences for anomaly detection can reduce common errors such as satellite clock errors and receiver clock errors, and improve the sensitivity of anomaly detection. When an anomaly is detected, the compensation value is calculated and corrected using the observation data of the adjacent monitoring points, which improves the continuity and integrity of the monitoring data and the accuracy of mountain slope monitoring.

[0012] In combination with some embodiments of the first aspect, in some embodiments, controlling the auxiliary monitoring point in each monitoring unit to synchronously receive the satellite signal according to the preset time slice specifically includes:

[0013] Based on the spatial distribution characteristics of the auxiliary monitoring points in the monitoring unit, the preset collection period is divided into a preset number of time slices, where the preset number is the number of auxiliary monitoring points in the monitoring unit;

[0014] According to the spatial distribution order of the auxiliary monitoring points, a corresponding time slice is allocated to each auxiliary monitoring point;

[0015] In each time slice, the corresponding auxiliary monitoring points are controlled to synchronously receive multiple groups of signals from the satellite.

[0016] By adopting the above technical solution, the number of time slices is determined according to the number of auxiliary monitoring points to ensure that each monitoring point has an independent observation period. Time slices are allocated based on the spatial distribution order of the monitoring points, taking into account the spatial relationship between the monitoring points, so that the observation times of adjacent monitoring points are staggered, reducing signal interference. In each time slice, the corresponding auxiliary monitoring points are controlled to synchronously receive multiple groups of satellite signals, thereby improving the redundancy of a single observation. Time slicing reduces the signal interference that may be caused by simultaneous observations of multiple monitoring points. Time slices are allocated based on the spatial distribution order, so that the observation data has clear spatial continuity. Receiving multiple groups of signals in a single time slice improves the observation accuracy.

[0017] In conjunction with some embodiments of the first aspect, in some embodiments, extracting carrier phase observation values ​​and pseudorange observation values ​​in the signal matrix specifically includes:

[0018] Perform noise filtering on the signal data of each time slice in the signal matrix to obtain filtered signal data;

[0019] Detect the cycle slip phenomenon in the filtered signal data, and repair the filtered signal with the cycle slip phenomenon to obtain qualified signal data;

[0020] Extract carrier phase observations and pseudorange observations from qualified signal data.

[0021] By adopting the above technical solution, the original signal is filtered for noise, reducing the impact of random errors. The cycle slip phenomenon is detected and repaired, improving the continuity of the carrier phase observation value. The observation value in the qualified signal data is extracted to ensure the data quality used in subsequent processing.

[0022] In combination with some embodiments of the first aspect, in some embodiments, after extracting the carrier phase observation value and the pseudorange observation value in the signal matrix, the method further includes:

[0023] Calculate the spatiotemporal correlation coefficient matrix between each monitoring point in the monitoring unit;

[0024] Based on the time-space correlation coefficient matrix, the signal propagation characteristic equation of the monitoring unit is established;

[0025] The periodic component and trend component are extracted according to the signal propagation characteristic equation.

[0026] By adopting the above technical solution, the spatiotemporal correlation coefficient matrix between the monitoring points is calculated, the degree of correlation between the monitoring points is quantified, and the signal propagation characteristic equation is established based on the correlation coefficient matrix to describe the signal propagation law in the monitoring area. The periodic component and trend component of the signal are extracted, and the decomposition analysis of the deformation characteristics is realized.

[0027] In conjunction with some embodiments of the first aspect, in some embodiments, the plurality of preset rules specifically include:

[0028] In the preset time window, the double-difference observation sequence has a data interruption exceeding the first preset time length;

[0029] The standard deviation of the double-difference observations exceeds a first preset threshold;

[0030] The jump of carrier phase observation value between adjacent epochs exceeds the preset number of cycles;

[0031] The difference between the double difference observation value of the monitoring point and the observation values ​​of its two adjacent monitoring points exceeds a second preset threshold, and the duration of the difference exceeds a second preset duration.

[0032] By adopting the above technical solution, setting rules for data interruption duration can timely detect signal loss or equipment failure, improve the continuity and integrity of monitoring data, and reduce abnormal data caused by environmental noise or equipment errors by controlling the standard deviation of double-difference observations not to exceed a certain threshold, so as to improve data reliability. Monitoring the jump of carrier phase observations helps to identify abnormal displacements caused by sudden geological events (such as landslides or earthquakes), thereby quickly responding to possible disasters. Strict control of the difference and duration of observations between monitoring points can accurately identify uneven movement or deformation of local slopes, improve the sensitivity and response speed of the slope monitoring system, and enable small changes in the slope to be captured and analyzed in time, thereby improving the early warning capability of slope disasters.

[0033] In conjunction with some embodiments of the first aspect, in some embodiments, after calculating the signal compensation value of the abnormal monitoring point according to the double difference observation value sequence of two monitoring points adjacent to the abnormal monitoring point, the method further includes:

[0034] Based on the periodic component of the signal propagation characteristic equation, the theoretical signal value that the abnormal monitoring point should obtain in the current time slice is predicted;

[0035] Calculate signal attenuation compensation coefficients according to the spatiotemporal correlation coefficient matrix;

[0036] The theoretical signal value is multiplied by the signal attenuation compensation coefficient to obtain a final signal compensation value, and the final signal compensation value is replaced by the signal compensation value.

[0037] By adopting the above technical solution and using the periodic component of the signal propagation characteristic equation for prediction, the theoretical signal value received by the abnormal monitoring point in the current time slice can be inferred based on the historical data pattern, taking into account the periodic change characteristics of the signal, enhancing the predictability and accuracy of signal processing, and calculating the signal attenuation compensation coefficient based on the time-space correlation coefficient matrix, so that the system can adjust the signal compensation value according to the relative position and environmental factors between the monitoring points, and improve the accuracy of the compensation measures. By multiplying the theoretical signal value with the compensation coefficient, the final signal compensation value obtained can more truly reflect the actual situation of the monitoring point, reduce errors, and improve the stability and reliability of the signal, especially in a complex and changeable external environment, it can reduce false alarms and missed alarms, and improve the overall performance of the mountain slope monitoring system.

[0038] In combination with some embodiments of the first aspect, in some embodiments, after correcting the signal data received at the abnormal monitoring point according to the signal compensation value, the method further includes:

[0039] The signal data is used to perform carrier phase difference analysis to obtain the three-dimensional coordinate values ​​of each monitoring point in the monitoring unit;

[0040] A polar coordinate system is established with the main monitoring point as the center, and the radial displacement and tangential displacement of each auxiliary monitoring point relative to the main monitoring point are calculated;

[0041] Calculate the strain tensor of the monitoring unit based on the radial displacement and the tangential displacement;

[0042] When the principal strain value of the strain tensor exceeds a third preset threshold, an early warning signal is triggered.

[0043] By adopting the above technical solution, more accurate three-dimensional coordinate values ​​of monitoring points can be obtained by using signal data for carrier phase differential analysis. The polar coordinate system established with the main monitoring point as the center can more accurately obtain the displacement direction and distance of each auxiliary monitoring point relative to the main monitoring point. The strain tensor calculated based on radial displacement and tangential displacement can intuitively present the strain state of the slope. When the principal strain value of the strain tensor exceeds the preset threshold, the system will automatically trigger an early warning signal, providing a time window for taking countermeasures. The utilization efficiency and analysis dimension of monitoring data are improved, and the practicality and disaster prevention capabilities of the early warning system are enhanced.

[0044] The embodiment of the present application also provides a related device for automatic monitoring of mountain slopes based on RTK sensors, and the related device includes a system, a computer-readable storage medium and a computer program product.

[0045] In the second aspect, an embodiment of the present application provides an automated monitoring system for mountain slopes based on RTK sensors, the automated monitoring system for mountain slopes based on RTK sensors comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and one or more processors call the computer instructions so that the system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0046] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0047] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0048] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0049] 1. The present application provides a method for constructing a multi-layer monitoring network and monitoring units for an automated monitoring method for mountain slopes based on RTK sensors, thereby realizing hierarchical and multi-scale monitoring of mountain slopes. The main monitoring point is located in the first monitoring layer, and the auxiliary monitoring point is located in the second monitoring layer, so that the monitoring network has a clear spatial structure. The main monitoring point and its surrounding auxiliary monitoring points form a monitoring unit, and the monitoring results of the main monitoring point can be verified and supplemented by the observation data of the auxiliary monitoring point, thereby improving the reliability of the monitoring data. The synchronous observation of the auxiliary monitoring points is achieved by presetting time slices, which not only reduces the signal interference between the monitoring points, but also improves the time consistency of the observation data. The use of double-difference observation value sequences for anomaly detection can reduce common errors such as satellite clock errors and receiver clock errors, and improve the sensitivity of anomaly detection. When an anomaly is detected, the compensation value is calculated and corrected using the observation data of adjacent monitoring points, thereby improving the continuity and integrity of the monitoring data and the accuracy of mountain slope monitoring.

[0050] 2. The present application provides an automated monitoring method for mountain slopes based on RTK sensors. The rules for setting data interruption duration can timely detect signal loss or equipment failure, improve the continuity and integrity of monitoring data, and reduce abnormal data caused by environmental noise or equipment errors by controlling the standard deviation of double-difference observations not to exceed a certain threshold, so as to improve data reliability. Monitoring the jumps in carrier phase observations helps to identify abnormal displacements caused by sudden geological events (such as landslides or earthquakes), thereby quickly responding to possible disasters. Strict control of the differences and duration of observations between monitoring points can accurately identify uneven movement or deformation of local slopes, thereby improving the sensitivity and response speed of the slope monitoring system, so that even small changes in the slope can be captured and analyzed in a timely manner, thereby improving the early warning capability of slope disasters.

[0051] 3. The present application provides an automated monitoring method for mountain slopes based on RTK sensors. The periodic component of the signal propagation characteristic equation is used for prediction. The theoretical signal value received by the abnormal monitoring point in the current time slice can be inferred based on the historical data pattern. The periodic change characteristics of the signal are taken into account, and the predictability and accuracy of signal processing are enhanced. The signal attenuation compensation coefficient is calculated based on the time-space correlation coefficient matrix, so that the system can adjust the signal compensation value according to the relative position and environmental factors between the monitoring points, thereby improving the accuracy of the compensation measures. By multiplying the theoretical signal value by the compensation coefficient, the final signal compensation value obtained can more truly reflect the actual situation of the monitoring point, reduce errors, and improve the stability and reliability of the signal. Especially in a complex and changeable external environment, it can reduce false alarms and missed alarms, and improve the overall performance of the mountain slope monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of an automated monitoring method for mountain slopes based on RTK sensors in an embodiment of the present application.

[0053] Figure 2 It is a flow chart of a method for correcting a signal compensation value in an embodiment of the present application.

[0054] Figure 3 It is a schematic diagram of the physical device structure of an RTK sensor-based mountain slope automatic monitoring system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0056] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0057] Combine the following Figure 1 , an automatic monitoring method for mountain slopes based on RTK sensors in an embodiment of the present application is described:

[0058] See also Figure 1 , which is a flow chart of an automated mountain slope monitoring method based on RTK sensor in an embodiment of the present application.

[0059] S101, when it is determined that a multi-layer monitoring network is set up on the mountain slope to be monitored, a main monitoring point in the multi-layer monitoring network and each auxiliary monitoring point within a preset range of the main monitoring point are constructed as monitoring units to obtain a plurality of monitoring units corresponding to the plurality of main monitoring points one by one;

[0060] When it is determined that a multi-layer monitoring network is set up on the mountain slope to be monitored, the system constructs the main monitoring point in the multi-layer monitoring network and each auxiliary monitoring point within a preset range of the main monitoring point as a monitoring unit, and obtains a number of monitoring units corresponding one-to-one to the main monitoring points. The multi-layer monitoring network includes a first monitoring layer and a second monitoring layer, each main monitoring point is located in the first monitoring layer, and each auxiliary monitoring point is located in the second monitoring layer.

[0061] The system first needs to determine that a multi-layer monitoring network has been set up on the mountain slope to be monitored. The multi-layer monitoring network usually includes multiple monitoring points distributed at different heights and locations to achieve comprehensive monitoring of the mountain slope. In this embodiment, the multi-layer monitoring network specifically includes a first monitoring layer and a second monitoring layer, wherein each main monitoring point is located in the first monitoring layer, and each auxiliary monitoring point is located in the second monitoring layer.

[0062] After the multi-layer monitoring network is set up, the system constructs the main monitoring point in the network and each auxiliary monitoring point within the preset range of the main monitoring point into a monitoring unit. The preset range can be determined according to the actual monitoring needs and the terrain characteristics of the mountain slope. For example, it can be within a certain radius centered on the main monitoring point. Through this construction method, the system can obtain several monitoring units corresponding to several main monitoring points. Each monitoring unit consists of a main monitoring point and multiple auxiliary monitoring points around it, which can perform refined monitoring of local areas of the mountain slope.

[0063] In addition to the two-layer layout scheme of the first monitoring layer and the second monitoring layer, the system can also set up a multi-layer monitoring network of three or more layers according to the scale and complexity of the mountain slope, which is not limited here. The monitoring points in different monitoring layers can adopt different layout densities and spacings to balance monitoring accuracy and cost. At the same time, when constructing monitoring units, the system can also comprehensively consider factors such as the accessibility and stability of the monitoring points, optimize the spatial distribution of the monitoring units, and improve the reliability of the monitoring data.

[0064] S102, controlling the auxiliary monitoring points in each monitoring unit to synchronously receive satellite signals according to a preset time slice, and integrating the signal data of the satellite signals according to the time sequence to obtain a signal matrix corresponding to the monitoring unit;

[0065] The system controls the auxiliary monitoring points in each monitoring unit to synchronously receive satellite signals according to the preset time slices, including:

[0066] Based on the spatial distribution characteristics of the auxiliary monitoring points in the monitoring unit, the preset collection period is divided into a preset number of time slices, where the preset number is the number of auxiliary monitoring points in the monitoring unit;

[0067] According to the spatial distribution order of the auxiliary monitoring points, a corresponding time slice is allocated to each auxiliary monitoring point;

[0068] In each time slice, the corresponding auxiliary monitoring point is controlled to synchronously receive multiple groups of signals from the satellite;

[0069] The signal data of the satellite signal are integrated in time sequence to obtain the signal matrix corresponding to the monitoring unit.

[0070] After the monitoring unit is built, the system needs to control the auxiliary monitoring points in each monitoring unit to synchronously receive satellite signals to obtain monitoring data. Specifically, the system first divides the preset acquisition period into a preset number of time slices equal to the number of auxiliary monitoring points based on the spatial distribution characteristics of the auxiliary monitoring points in the monitoring unit. Then, the system allocates corresponding time slices to each auxiliary monitoring point according to the spatial distribution order of the auxiliary monitoring points. In each time slice, the system controls the corresponding auxiliary monitoring point to synchronously receive multiple groups of signals from the satellite.

[0071] Through the above-mentioned time division multiple access method, the system can ensure that all auxiliary monitoring points in the monitoring unit complete data collection in the same collection cycle, thereby improving data synchronization. At the same time, since different auxiliary monitoring points work in different time slices, signal interference between adjacent monitoring points can be avoided, thereby improving data quality.

[0072] After receiving the signal, the system integrates the signal data of the satellite signal in time sequence to obtain the signal matrix corresponding to the monitoring unit. The signal matrix contains the satellite signal data received by all monitoring points in the monitoring unit during the entire acquisition cycle, which is the basis for subsequent data processing and analysis.

[0073] S103, extracting the carrier phase observation value and the pseudorange observation value in the signal matrix, and calculating the difference between the carrier phase observation value and the pseudorange observation value to obtain a double difference observation value sequence for each monitoring point in the monitoring unit;

[0074] The system extracts the carrier phase observation value and pseudorange observation value in the signal matrix, including:

[0075] Perform noise filtering on the signal data of each time slice in the signal matrix to obtain filtered signal data;

[0076] Detect the cycle slip phenomenon in the filtered signal data, and repair the filtered signal with the cycle slip phenomenon to obtain qualified signal data;

[0077] The carrier phase observation value and pseudorange observation value in the qualified signal data are extracted, and the difference between the carrier phase observation value and the pseudorange observation value is calculated to obtain a double difference observation value sequence for each monitoring point in the monitoring unit.

[0078] After obtaining the signal matrix corresponding to the monitoring unit, the system needs to extract valid observation data from it for subsequent displacement calculation and deformation analysis. In this step, the system focuses on extracting the carrier phase observation value and pseudorange observation value in the signal matrix, and obtains the double difference observation value sequence by differentiating the two types of observation values.

[0079] Specifically, the system first performs noise filtering on the signal data of each time slice in the signal matrix to remove high-frequency noise interference and obtain the filtered signal data. Then, the system detects whether there is a cycle slip phenomenon in the filtered signal data, and repairs the signal data with the cycle slip phenomenon to obtain qualified signal data. The cycle slip phenomenon is caused by the change of the integer ambiguity of the satellite signal, which will cause the carrier phase observation value to have a sudden change in the integer cycle, which needs to be detected and repaired.

[0080] After completing data preprocessing, the system extracts carrier phase observations and pseudorange observations from qualified signal data, and calculates the difference between the two types of observations to obtain a double-difference observation sequence for each monitoring point in the monitoring unit. Double-difference observations are obtained by performing inter-satellite differentials on the difference between the observations of two monitoring points, which can eliminate the influence of multiple error sources such as receiver clock errors and tropospheric delays, and improve the accuracy of observation data.

[0081] S104, when any double difference observation value sequence satisfies any one of several preset rules, the corresponding monitoring point is determined as an abnormal monitoring point, and the signal compensation value of the abnormal monitoring point is calculated according to the double difference observation value sequences of two monitoring points adjacent to the abnormal monitoring point;

[0082] When any double difference observation value sequence satisfies any one of several preset rules, the system determines the corresponding monitoring point as an abnormal monitoring point, and calculates the signal compensation value of the abnormal monitoring point based on the double difference observation value sequences of the two monitoring points adjacent to the abnormal monitoring point, wherein the several preset rules are specifically:

[0083] In the preset time window, the double-difference observation sequence has a data interruption exceeding the first preset time length;

[0084] The standard deviation of the double-difference observations exceeds a first preset threshold;

[0085] The jump of carrier phase observation value between adjacent epochs exceeds the preset number of cycles;

[0086] The difference between the double difference observation value of the monitoring point and the observation values ​​of its two adjacent monitoring points exceeds a second preset threshold, and the duration of the difference exceeds a second preset duration.

[0087] After obtaining the double difference observation value sequence of each monitoring point in the monitoring unit, the system needs to detect and process the observation data. In this step, the system identifies the abnormal monitoring points in the observation sequence based on the preset abnormal judgment rules and calculates the signal compensation value of the abnormal points.

[0088] Specifically, the system sets several preset rules to determine whether the double-difference observation sequence is abnormal. These rules include: within the preset time window, the double-difference observation sequence has a data interruption that exceeds the first preset duration; the standard deviation of the double-difference observation exceeds the first preset threshold; the jump of the carrier phase observation between adjacent epochs exceeds the preset number of cycles; and the difference between the double-difference observation of the monitoring point and the observations of its two adjacent monitoring points exceeds the second preset threshold, and the duration of the difference exceeds the second preset duration. When any double-difference observation sequence meets any of the above rules, the system will determine the corresponding monitoring point as an abnormal monitoring point.

[0089] For the identified abnormal monitoring point, the system calculates the signal compensation value of the abnormal point based on the double difference observation value sequence of its two adjacent normal monitoring points. Specifically, the system can estimate the double difference observation value that the abnormal point should have under normal circumstances by interpolation or extrapolation, and use the difference between it and the actual observation value as the signal compensation value.

[0090] S105, correcting the signal data received at the abnormal monitoring point according to the signal compensation value;

[0091] After calculating the signal compensation value of the abnormal monitoring point, the system needs to correct the signal data received at the abnormal point to eliminate the influence of abnormal factors and improve data quality. In this step, the system corrects the observation data of the abnormal monitoring point according to the signal compensation value obtained in the previous step.

[0092] Specifically, the system algebraically superimposes the original carrier phase observation value and pseudorange observation value of the abnormal monitoring point with the corresponding signal compensation value to obtain the corrected observation value. The corrected observation value can better reflect the observation results that the abnormal monitoring point should have under normal working conditions, effectively reducing the error introduced by abnormal factors.

[0093] In the process of signal correction, the system needs to pay attention to maintaining the integrity and continuity of the observed values. For long-term abnormal conditions, the system can adopt a segmented correction strategy to avoid introducing new errors. At the same time, the system can also combine the historical observation data of the abnormal monitoring point to smooth the signal compensation value and improve the reliability of the correction result.

[0094] S106, performing carrier phase difference calculation using the signal data to obtain the three-dimensional coordinate value of each monitoring point in the monitoring unit;

[0095] After completing the data correction of the abnormal monitoring point, the system uses the corrected signal data to perform carrier phase differential analysis to obtain the three-dimensional coordinate values ​​of each monitoring point in the monitoring unit. This step is the key link in realizing the displacement monitoring of the mountain slope. Through the high-precision carrier phase differential positioning technology, the spatial position of each monitoring point can be accurately determined.

[0096] Specifically, the system builds a carrier phase differential positioning model based on the double-difference observation sequence of all monitoring points in the monitoring unit. The model comprehensively considers the influence of various error sources such as satellite clock error, receiver clock error, ionospheric delay, tropospheric delay, and introduces ambiguity parameters for solution. The system can use a variety of optimization algorithms such as least squares method and Kalman filtering to achieve efficient estimation of model parameters and obtain the three-dimensional coordinate value of each monitoring point.

[0097] In the process of carrier phase differential analysis, the system can flexibly select different differential strategies, such as single baseline analysis, network analysis, etc., to adapt to different monitoring scenarios and accuracy requirements. At the same time, the system can also introduce a variety of quality control measures, such as cycle slip detection, ambiguity fixation, coordinate constraints, etc., to further improve the reliability of positioning results.

[0098] S107, establishing a polar coordinate system with the main monitoring point as the center, and calculating the radial displacement and tangential displacement of each auxiliary monitoring point relative to the main monitoring point;

[0099] After obtaining the three-dimensional coordinate values ​​of each monitoring point in the monitoring unit, the system needs to further calculate the displacement of each auxiliary monitoring point relative to the main monitoring point to reflect the deformation state of the mountain slope. In this step, the system establishes a local polar coordinate system with the main monitoring point as the center, and calculates the radial displacement and tangential displacement of the auxiliary monitoring point in this coordinate system.

[0100] Specifically, the system first establishes a local rectangular coordinate system with the main monitoring point as the origin based on the three-dimensional coordinate value of the main monitoring point. Then, the system converts the rectangular coordinate system into a polar coordinate system, and establishes radial and tangential directions with the line connecting the main monitoring point and the auxiliary monitoring point as the radial direction. In the polar coordinate system, the system calculates the radial distance change and tangential angle change of each auxiliary monitoring point relative to the main monitoring point, as the radial displacement and tangential displacement of the point, respectively.

[0101] The radial displacement reflects the movement trend of the auxiliary monitoring point along the connecting line, and can be used to evaluate the overall stability of the mountain slope. The tangential displacement reflects the torsion or slip trend of the auxiliary monitoring point in the tangential direction, and can be used to analyze the local deformation characteristics of the mountain slope. Through the comprehensive analysis of radial displacement and tangential displacement, the system can comprehensively evaluate the deformation state and instability risk of the mountain slope.

[0102] In the process of calculating the displacement, the system can use the coordinate values ​​of different periods to extract the displacement change trend and periodic characteristics through time series analysis, and predict the future deformation trend. At the same time, the system can also combine the displacement distribution characteristics of different auxiliary monitoring points in the monitoring unit to perform cluster analysis and division of the deformation area, and identify the key parts and weak links of the deformation.

[0103] S108. Calculate the strain tensor of the monitoring unit based on the radial displacement and the tangential displacement, and trigger a warning signal when the principal strain value of the strain tensor exceeds a third preset threshold.

[0104] After obtaining the radial displacement and tangential displacement of each auxiliary monitoring point, the system needs to further calculate the strain state of the monitoring unit to evaluate the overall stability of the mountain slope. In this step, the system calculates the strain tensor of the unit based on the displacement of all monitoring points in the monitoring unit, and compares the principal strain value with the warning threshold to promptly detect the risk of slope instability.

[0105] Specifically, the system uses the radial displacement and tangential displacement of each monitoring point in the monitoring unit as discrete sampling points of the displacement field, and constructs the continuous displacement field function of the unit through interpolation or fitting methods. Then, the system calculates the gradient of the displacement field function to obtain the strain tensor of any point in the unit. The strain tensor is a three-dimensional matrix that describes the strain state and principal strain direction of each point in the unit.

[0106] After obtaining the strain tensor of the unit, the system performs characteristic decomposition on it and obtains three principal strain values ​​and principal strain directions. The principal strain value represents the maximum degree of unit deformation, while the principal strain direction represents the main trend of deformation. The system focuses on the maximum principal strain value and compares it with the preset third warning threshold. When the maximum principal strain value exceeds the threshold, the system determines that the monitoring unit has a large deformation and the stability of the mountain slope is threatened, thereby triggering a warning signal.

[0107] In the process of strain calculation and stability assessment, the system can make full use of the high-density observation data of the monitoring unit, and use numerical simulation methods such as finite element analysis and discrete element analysis to carry out refined modeling and stability calculation of mountain slopes, thereby improving the reliability and accuracy of early warning. At the same time, the system can also combine prior information such as the geological structure and physical and mechanical properties of the mountain to inversely identify key parameters and optimize the applicability of the model.

[0108] In addition, the RTK sensor can be initialized after continuous observation for a period of time, for example, it can be turned off for ten minutes before 0:00 every day, or it can be turned off once a month. Each RTK sensor can be turned off at different times and in batches, which is not limited here.

[0109] In the above embodiments, the method of constructing a multi-layer monitoring network and monitoring units realizes hierarchical and multi-scale monitoring of mountain slopes. The main monitoring point is located in the first monitoring layer, and the auxiliary monitoring point is located in the second monitoring layer, so that the monitoring network has a clear spatial structure. The main monitoring point and its surrounding auxiliary monitoring points form a monitoring unit, and the monitoring results of the main monitoring point can be verified and supplemented by the observation data of the auxiliary monitoring point, thereby improving the reliability of the monitoring data. The synchronous observation of the auxiliary monitoring points is achieved by presetting the time slice, which not only reduces the signal interference between the monitoring points, but also improves the time consistency of the observation data. The use of double-difference observation value sequences for anomaly detection can reduce common errors such as satellite clock errors and receiver clock errors, and improve the sensitivity of anomaly detection. When an anomaly is detected, the compensation value is calculated and corrected using the observation data of adjacent monitoring points, which improves the continuity and integrity of the monitoring data and the accuracy of mountain slope monitoring. And by using the corrected signal data for carrier phase differential analysis, more accurate three-dimensional coordinate values ​​of the monitoring points can be obtained. The polar coordinate system established with the main monitoring point as the center can more accurately obtain the displacement direction and distance of each auxiliary monitoring point relative to the main monitoring point. The strain tensor calculated based on radial displacement and tangential displacement can intuitively present the strain state of the slope. When the principal strain value of the strain tensor exceeds the preset threshold, the system will automatically trigger an early warning signal, providing a time window for taking countermeasures. It improves the utilization efficiency and analysis dimension of monitoring data, and enhances the practicality and disaster prevention capabilities of the early warning system.

[0110] After the signal compensation value of the abnormal monitoring point is calculated according to the double difference observation value sequence of two monitoring points adjacent to the abnormal monitoring point in the previous embodiment, the signal compensation value may have errors in some cases, so it is necessary to further correct the signal compensation value. Figure 2 , a method for correcting a signal compensation value in an embodiment of the present application is described:

[0111] See also Figure 2 , which is a flow chart of a method for correcting a signal compensation value in an embodiment of the present application.

[0112] S201, calculating the spatiotemporal correlation coefficient matrix between each monitoring point in the monitoring unit;

[0113] The system first obtains the location coordinates and observation time series of each monitoring point in the monitoring unit, then calculates the spatial distance between each monitoring point based on the location coordinates, and calculates the time correlation between the observation values ​​of each monitoring point based on the observation time series. Then, the system uses the spatial distance and time correlation to calculate the spatiotemporal correlation coefficient between each monitoring point through methods such as the Pearson correlation coefficient, the Spearman rank correlation coefficient, or the mutual information, and constructs the spatiotemporal correlation coefficient matrix.

[0114] Furthermore, when calculating spatial distance, the system can use a variety of distance measurement methods such as Euclidean distance, Manhattan distance, and Chebyshev distance. At the same time, when calculating temporal correlation, the system can first perform preprocessing such as interpolation and smoothing on the observed time series to improve the calculation accuracy of temporal correlation. In addition, the spatiotemporal correlation coefficient matrix constructed by the system is a symmetric matrix, and only the upper or lower triangular elements need to be calculated, which can reduce the amount of calculation. The spatiotemporal correlation coefficient matrix describes the spatiotemporal correlation characteristics between the monitoring points in the monitoring unit and is an important basis for subsequent analysis.

[0115] S202, establishing a signal propagation characteristic equation of the monitoring unit based on the spatiotemporal correlation coefficient matrix;

[0116] The system uses the spatiotemporal correlation coefficient matrix and the network topology of the monitoring unit to establish an equation that reflects the signal propagation characteristics within the monitoring unit. Specifically, the system can use the spatiotemporal correlation coefficient matrix as an adjacency matrix and the monitoring points as network nodes to construct an undirected weighted network. Then, based on relevant theories of graph theory and network science, such as the shortest path and random walk, the system derives the propagation law of the signal in the network and establishes the signal propagation characteristic equation.

[0117] Furthermore, the signal propagation characteristic equation established by the system can be in various forms such as linear equations, differential equations, convolution equations, etc., and can be solved using various mathematical tools such as Green's function, wavelet transform, Fourier transform, etc. At the same time, the system can also consider factors such as signal attenuation and interference during the propagation process, introduce corresponding correction terms into the signal propagation characteristic equation, and improve the accuracy of the equation. The signal propagation characteristic equation quantitatively describes the propagation law of the signal within the monitoring unit, reveals the mutual influence of the signals between the monitoring points, and is the key to signal analysis and prediction.

[0118] S203, extracting the periodic component and the trend component according to the signal propagation characteristic equation;

[0119] The system uses the signal propagation characteristic equation to decompose the signal in the monitoring unit and extract the periodic component and trend component of the signal. The periodic component reflects the periodic change law of the signal, such as daily cycle, seasonal cycle, etc.; the trend component reflects the long-term change trend of the signal, such as upward trend, downward trend, etc. Extracting the periodic component and trend component can help the system better understand the change pattern of the signal and achieve a fine characterization of the signal.

[0120] Specifically, the system can use Fourier transform, wavelet transform and other signal processing tools to solve the signal propagation characteristic equation and obtain the signal representation in the time-frequency domain. Then, the system analyzes the energy distribution of the signal at different frequencies and scales, identifies the main frequency corresponding to the periodic component, and the low-frequency or approximate DC component corresponding to the trend component, and realizes the extraction of the periodic component and trend component.

[0121] Furthermore, when the system extracts the periodic component, it can use an adaptive, sparse representation-based method to automatically determine the main frequency and amplitude of the periodic component. When the system extracts the trend component, it can use a kernel function-based, regularization-based method to smooth the trend component curve and suppress noise interference. The periodic component describes the periodic pattern of the signal, and the trend component describes the long-term trend of the signal, which is of great significance for predicting abnormal monitoring point signals.

[0122] S204, calculating the difference between the carrier phase observation value and the pseudorange observation value to obtain a double difference observation value sequence for each monitoring point in the monitoring unit;

[0123] The step S204 has been described in detail in the previous embodiment and will not be repeated here. After executing this step, continue to execute step S104, when any double difference observation value sequence meets any one of several preset rules, the corresponding monitoring point is determined as an abnormal monitoring point, and the signal compensation value of the abnormal monitoring point is calculated based on the double difference observation value sequences of the two monitoring points adjacent to the abnormal monitoring point.

[0124] S205, predicting the theoretical signal value that the abnormal monitoring point should obtain in the current time slice based on the periodic component of the signal propagation characteristic equation;

[0125] When a monitoring point in the monitoring unit is identified as an abnormal monitoring point, the system uses the periodic component of the previously extracted signal propagation characteristic equation to predict the theoretical signal value that should be observed at the abnormal monitoring point in the current time slice. Specifically, the system substitutes the current time into the expression of the periodic component, calculates the function value of the periodic component at the current moment, and uses it as the theoretical signal value of the abnormal monitoring point. This theoretical signal value reflects the periodic changes that the abnormal monitoring point should have under normal circumstances, and provides a reference for signal compensation.

[0126] Furthermore, when predicting the theoretical signal value, the system can use methods based on historical data and machine learning to dynamically adjust and optimize the parameters of the periodic component to improve the prediction accuracy. At the same time, the system can also consider factors such as the duration of the anomaly and the type of anomaly, introduce corresponding correction models, and correct the theoretical signal value. The difference between the theoretical signal value and the actual observed value of the abnormal monitoring point reflects the signal deviation caused by the anomaly and is the basis for calculating the signal compensation value.

[0127] S206, calculating the signal attenuation compensation coefficient according to the time-space correlation coefficient matrix;

[0128] The signal received by the abnormal monitoring point is usually attenuated due to factors such as the propagation path and environmental interference. In order to compensate for the abnormal signal more accurately, the system needs to consider the signal attenuation effect and introduce a signal attenuation compensation coefficient. The system uses the previously calculated spatiotemporal correlation coefficient matrix to evaluate the correlation between the signal of the abnormal monitoring point and other monitoring points, and calculates the signal attenuation compensation coefficient accordingly.

[0129] Specifically, the system takes the abnormal monitoring point as the center and selects the neighborhood monitoring points within a certain range. The system calculates the mean value of the correlation coefficient between the abnormal monitoring point and these neighborhood monitoring points in the spatiotemporal correlation coefficient matrix as an indicator to measure the degree of signal attenuation at the abnormal monitoring point. Then, the system establishes a mapping relationship between the signal attenuation compensation coefficient and the mean value of the correlation coefficient, and calculates the signal attenuation compensation coefficient from the mean value of the correlation coefficient. The larger the mean value of the correlation coefficient, the stronger the signal correlation between the abnormal monitoring point and the neighborhood monitoring point, the smaller the signal attenuation, and the closer the compensation coefficient is to 1; conversely, the smaller the mean value of the correlation coefficient, the greater the signal attenuation, and the greater the compensation coefficient is than 1.

[0130] Furthermore, when the system calculates the signal attenuation compensation coefficient, it can use similarity measurement-based and machine learning-based methods to optimize the mapping model from the mean of the correlation coefficient to the compensation coefficient, thereby improving the calculation accuracy of the compensation coefficient. In addition, the system can also introduce prior knowledge such as environmental factors and abnormal types to adaptively adjust the signal attenuation compensation coefficient. The signal attenuation compensation coefficient quantifies the attenuation degree of the signal at the abnormal monitoring point, and is used to correct the theoretical signal value to obtain the final signal compensation value.

[0131] S207: Multiply the theoretical signal value by the signal attenuation compensation coefficient to obtain a final signal compensation value, and replace the final signal compensation value with the signal compensation value.

[0132] The system multiplies the theoretical signal value of the abnormal monitoring point by the corresponding signal attenuation compensation coefficient to calculate the final signal compensation value of the abnormal monitoring point. The final signal compensation value not only takes into account the periodic change law of the abnormal monitoring point signal, but also takes into account the attenuation effect during the signal propagation process, and more accurately restores the signal value that should be observed at the abnormal monitoring point. Then, the system replaces the signal compensation value previously calculated based on the double difference observation value sequence of adjacent monitoring points with the final signal compensation value to complete the correction of the abnormal monitoring point signal.

[0133] Furthermore, when calculating the final signal compensation value, the system can also compare the difference between the theoretical signal value and the signal compensation value calculated by the double difference observation sequence of adjacent monitoring points to evaluate the compensation effect of the two methods. When the difference is large, the system can use weighted average, Bayesian fusion and other methods to combine the two signal compensation values ​​to obtain a more reliable final signal compensation value. The corrected abnormal monitoring point signal can better participate in subsequent positioning, navigation and other applications, and improve the service quality of the system.

[0134] In the above embodiment, the periodic component of the signal propagation characteristic equation is used for prediction, and the theoretical signal value that should be received by the abnormal monitoring point in the current time slice can be inferred based on the historical data pattern, taking into account the periodic change characteristics of the signal, enhancing the predictability and accuracy of signal processing, and calculating the signal attenuation compensation coefficient based on the time-space correlation coefficient matrix, so that the system can adjust the signal compensation value according to the relative position and environmental factors between the monitoring points, thereby improving the accuracy of the compensation measures. By multiplying the theoretical signal value by the compensation coefficient, the final signal compensation value obtained can more truly reflect the actual situation of the monitoring point, reduce errors, and improve the stability and reliability of the signal, especially in a complex and changeable external environment, it can reduce false alarms and missed alarms, and improve the overall performance of the mountain slope monitoring system.

[0135] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of an RTK sensor-based mountain slope automatic monitoring system provided in an embodiment of the present application.

[0136] It should be noted that Figure 3 The structure of the system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0137] like Figure 3 As shown, the system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0138] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0139] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0140] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0141] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0142] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of a system, the system implements the method provided in the above embodiment.

[0143] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0144] As used in the above embodiments, the term "when..." may be interpreted as "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted as "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0145] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0146] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. An automatic monitoring method for mountain slopes based on RTK sensors, characterized in that: include: In the case where it is determined that a multi-layer monitoring network is set up on the mountain slope to be monitored, a main monitoring point in the multi-layer monitoring network and each auxiliary monitoring point within a preset range of the main monitoring point are constructed as monitoring units to obtain a plurality of monitoring units corresponding to the plurality of main monitoring points one by one, wherein the multi-layer monitoring network includes a first monitoring layer and a second monitoring layer, each of the main monitoring points is located in the first monitoring layer, and each of the auxiliary monitoring points is located in the second monitoring layer; Controlling the auxiliary monitoring point in each monitoring unit to synchronously receive satellite signals according to a preset time slice, and integrating the signal data of the satellite signals according to a time sequence to obtain a signal matrix corresponding to the monitoring unit; Extracting carrier phase observations and pseudorange observations in the signal matrix, and calculating the difference between the carrier phase observations and the pseudorange observations to obtain a double difference observation sequence for each monitoring point in the monitoring unit; Calculating the spatiotemporal correlation coefficient matrix between the monitoring points in the monitoring unit; Based on the spatiotemporal correlation coefficient matrix, establishing a signal propagation characteristic equation of the monitoring unit; Extracting the periodic component and the trend component according to the signal propagation characteristic equation; When any of the double difference observation value sequences satisfies any one of several preset rules, the corresponding monitoring point is determined as an abnormal monitoring point, and the signal compensation value of the abnormal monitoring point is calculated according to the double difference observation value sequences of two monitoring points adjacent to the abnormal monitoring point; The signal data received by the abnormal monitoring point is corrected according to the signal compensation value.

2. The method according to claim 1, characterized in that The controlling the auxiliary monitoring point in each monitoring unit to synchronously receive satellite signals according to a preset time slice specifically includes: Based on the spatial distribution characteristics of the auxiliary monitoring points in the monitoring unit, the preset acquisition period is divided into a preset number of time slices, where the preset number is the number of the auxiliary monitoring points in the monitoring unit; According to the spatial distribution order of the auxiliary monitoring points, a corresponding time slice is allocated to each of the auxiliary monitoring points; In each of the time slices, the corresponding auxiliary monitoring points are controlled to synchronously receive multiple groups of signals from satellites.

3. The method according to claim 1, characterized in that The extracting of the carrier phase observation value and the pseudorange observation value in the signal matrix specifically includes: Performing noise filtering on the signal data of each time slice in the signal matrix to obtain filtered signal data; Detecting a cycle slip phenomenon in the filtered signal data, and repairing the filtered signal having the cycle slip phenomenon to obtain qualified signal data; The carrier phase observation value and the pseudorange observation value in the qualified signal data are extracted.

4. The method according to claim 1, characterized in that The preset rules specifically include: Within the preset time window, the double-difference observation value sequence has a data interruption exceeding a first preset time length; The standard deviation of the double difference observation value exceeds a first preset threshold; The carrier phase observation value jumps between adjacent epochs exceeding a preset number of cycles; The difference between the double difference observation value of the monitoring point and the observation values ​​of its two adjacent monitoring points exceeds a second preset threshold, and the duration of the difference exceeds a second preset duration.

5. The method according to claim 1, characterized in that After calculating the signal compensation value of the abnormal monitoring point according to the double difference observation value sequence of two monitoring points adjacent to the abnormal monitoring point, the method further includes: Based on the periodic component of the signal propagation characteristic equation, predict the theoretical signal value that the abnormal monitoring point should obtain in the current time slice; Calculating a signal attenuation compensation coefficient according to the spatiotemporal correlation coefficient matrix; The theoretical signal value is multiplied by the signal attenuation compensation coefficient to obtain a final signal compensation value, and the final signal compensation value is replaced by the signal compensation value.

6. The method according to claim 1, characterized in that After correcting the signal data received by the abnormal monitoring point according to the signal compensation value, the method further includes: Using the signal data to perform carrier phase difference analysis to obtain the three-dimensional coordinate values ​​of each monitoring point in the monitoring unit; Establishing a polar coordinate system with the main monitoring point as the center, and calculating the radial displacement and tangential displacement of each of the auxiliary monitoring points relative to the main monitoring point; calculating a strain tensor of the monitoring unit based on the radial displacement and the tangential displacement; When the principal strain value of the strain tensor exceeds a third preset threshold, an early warning signal is triggered.

7. An automatic monitoring system for mountain slopes based on RTK sensors, characterized in that: The system comprises: One or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a system, the system is caused to execute the method according to any one of claims 1 to 6.

9. A computer program product, characterized in that When the computer program product is executed on a system, the system is caused to execute the method according to any one of claims 1 to 6.

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