A slope displacement monitoring and early warning method and system based on a first-point broken line method

Abstract

The application discloses a kind of based on first point broken line method's slope displacement monitoring early warning method and system, the method includes: S1: the displacement time series data of slope monitoring point is collected, and original data is filtered;S2: the data after filtering is handled to obtain key point and is connected to form broken line, to generate neat deformation monitoring grid, simultaneously according to the grid establishment with displacement increment trigger equal value interval early warning check mechanism;S3: using the deformation monitoring grid to calculate the deformation rate of monitoring point;S4: using the deformation monitoring grid to quantize acceleration;S5: according to the displacement, deformation rate, acceleration data obtained in the above steps to carry out multidimensional comprehensive judgment, to output corresponding early warning level and early warning information.The application solves the inherent defects of equal time interval early warning, achieves the effect of saving computing resources, reducing false alarm rate, avoiding repeated alarm, effectively improves the accuracy and reliability of slope monitoring early warning.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring and early warning technology, and more specifically, to a slope displacement monitoring and early warning method and system based on the first-point broken line method. Background Technology

[0002] Existing slope monitoring and early warning technologies have revealed many shortcomings in practical applications, and the overall operational effect is significantly different from the usage requirements.

[0003] Existing technologies generally employ fixed-time, equal-interval early warning modes, which are incompatible with the nonlinear and dynamically changing deformation patterns of slopes. Slope deformation can be stable at times and rapidly intensify at others, making it impossible to accurately capture critical deformation moments with fixed sampling intervals. If the interval is set too large, key data on accelerated slope deformation may be missed, resulting in delayed early warnings; if the interval is too small, massive amounts of redundant data will be generated, increasing the pressure on data storage and transmission, and invalid detections may occur during stable periods. Furthermore, early warning triggering relies solely on preset time nodes, which are disconnected from the actual deformation state of the slope, significantly reducing the effectiveness of the early warnings. For cluster monitoring platforms with a massive number of monitoring points, this mode requires periodic early warning calculations for all points. Even if most areas are stable, a full analysis is still required, consuming significant computational resources and limiting system scalability and response speed.

[0004] Meanwhile, the existing system suffers from significant false alarm problems. Monitoring equipment is susceptible to external environmental factors such as temperature, humidity, wind, vehicle vibration, and signal interference. Furthermore, the raw monitoring data itself contains fluctuations and noise, further exacerbating false alarms. Moreover, warning thresholds are mostly set based on human experience, without scientific optimization considering slope type, environmental conditions, and measured data, resulting in insufficient versatility and rationality. The system also lacks an intelligent management mechanism for warning status. When monitoring data continuously exceeds the threshold, it repeatedly pushes the same level of warning, creating information redundancy, increasing the workload of staff, and easily leading to warning fatigue, causing critical hazards to be overlooked.

[0005] At the core indicator calculation level, existing technologies also have shortcomings. Slope deformation rate and acceleration are important bases for assessing instability risk, but high-frequency monitoring data are highly oscillating and prone to error accumulation. Existing methods are unable to effectively smooth high-frequency data and extract the true deformation trend from oscillating data, causing the deformation rate calculation results to fluctuate significantly with changes in sampling frequency, making it difficult to form a stable criterion. Traditional deformation acceleration calculation requires differential operations on the rate, which further amplifies noise interference, and there is currently a lack of mature quantification methods, making it difficult to identify the continuous acceleration characteristics of slopes before they slide. In addition, most traditional early warning schemes rely only on single indicators such as displacement and rate to judge risk, failing to combine multiple dimensions of parameters such as displacement, rate, and acceleration for comprehensive judgment, and thus failing to fully reflect the complex instability process of slopes, ultimately resulting in low overall early warning accuracy and reliability.

[0006] Therefore, in order to overcome the shortcomings of the existing technology, this invention provides a slope displacement monitoring and early warning method and system based on the first-point broken line method, so as to realize an early warning mode that matches the physical process of slope deformation, and solve problems such as high false alarm rate, repeated alarms, fluctuation in rate calculation, difficulty in acceleration quantification, and waste of computing resources, thereby improving the accuracy and reliability of slope monitoring and early warning. Summary of the Invention

[0007] The present invention aims to overcome at least one of the defects (deficiencies) of the prior art and provide a slope displacement monitoring and early warning method and system based on the first-point broken line method. This method solves the inherent defects of equal time interval early warning and achieves the effects of saving computing resources, reducing false alarm rate, and avoiding repeated alarms, thereby effectively improving the accuracy and reliability of slope monitoring and early warning.

[0008] On the one hand, the technical solution adopted by the present invention is a slope displacement monitoring and early warning method based on the first-point broken line method, the method comprising the following steps: Step S1: Collect displacement time series data of slope monitoring points and filter the raw data; Step S2: Process the filtered data to obtain key points and connect them to form polylines, thereby generating a regular deformation monitoring grid. At the same time, establish an equal-value interval early warning inspection mechanism triggered by displacement increment based on the grid. Step S3: Calculate the deformation rate of the monitoring points using the deformation monitoring grid; Step S4: Quantify the acceleration using the deformation monitoring grid; Step S5: Based on the displacement, deformation rate, and acceleration data obtained in the above steps, a multi-dimensional comprehensive judgment is made to output the corresponding warning level and warning information.

[0009] First, this solution abandons the traditional equal-interval early warning mode. Instead, it relies on the first-point polyline method to extract key data points and construct a deformation monitoring grid. It also establishes a corresponding equal-interval early warning and inspection mechanism triggered by displacement increments. This fully adapts to the nonlinear deformation characteristics of slopes and can adaptively capture key nodes of rapid slope deformation. It avoids missing core data during the pre-slip stage due to improper sampling intervals, preventing early warning delays, and also eliminates the need for continuous high-frequency sampling that generates massive amounts of invalid data, significantly reducing data storage and transmission pressure. Furthermore, this mechanism eliminates the need for periodic full-scale scanning calculations of all monitoring points, effectively saving computing resources and improving the scalability and real-time response capabilities of large-scale cluster monitoring platforms.

[0010] Secondly, this scheme first filters the original displacement time series data, which can effectively remove noise caused by environmental interference and data fluctuations, reducing the probability of false alarms from the source. Combining deformation monitoring grids to calculate deformation rates allows for trend optimization of high-frequency oscillation data, weakening calculation biases caused by sampling frequency and accurately extracting the true deformation trend. The grids enable effective quantification of deformation acceleration, avoiding the noise amplification problem of traditional differential operations, and accurately identifying the continuous acceleration characteristics of slopes before slippage.

[0011] Finally, by integrating multiple dimensions of indicators such as displacement, deformation rate, and acceleration for comprehensive analysis, the limitations of single early warning indicators are overcome, and the true operating status of the slope is fully reflected. Coupled with a new early warning inspection mechanism, intelligent differentiation of early warning status can be achieved, avoiding repeated pushes of early warnings at the same level, reducing information redundancy, eliminating early warning fatigue, and significantly improving the effectiveness of early warning information and the accuracy and reliability of overall monitoring and early warning.

[0012] Preferably, in step S2, the process of processing the filtered data to obtain key points and connecting them to form polylines, thereby generating a regular deformation monitoring grid, specifically includes: Step S21: Determine multiple integer grid lines according to a preset displacement interval; Step S22: Based on the filtered data, identify the moment when the monitoring data first crosses each integer grid line and determine it as a key point; Step S23: Connect each key point in sequence to form a polyline, thereby obtaining a regular deformation monitoring grid.

[0013] By dividing the data into integer grid lines and identifying the moment when the data first crosses the grid line as the key point, and connecting these points to construct a deformation monitoring grid, many drawbacks of traditional equal-interval sampling are effectively solved. Slope deformation is non-linear. Dividing the grid lines at fixed displacement intervals and using equal-value intervals for early warning directly correlates the timing of early warning checks with the actual deformation, rather than being determined by a manually set time point. This aligns better with the physical nature of slope deformation and allows for adaptive adjustment of the early warning frequency. When the slope is stable, the frequency of checks automatically decreases to avoid invalid detections, while the frequency automatically increases when deformation accelerates, accurately capturing key changes in slope deformation and avoiding missing important data during the pre-slip phase. Furthermore, selecting the moment of the first grid line crossing as the key point eliminates invalid and redundant data, significantly reducing data volume and lowering storage, transmission, and computational loads. The regular grid formed by connecting key points provides a clear and intuitive data trend, reducing interference from oscillations in the original data. This lays a solid data foundation for accurate calculations of subsequent deformation rates and accelerations, and makes the early warning judgment logic more consistent with the physical laws of slope deformation, further improving the timeliness and accuracy of early warnings.

[0014] Preferably, in step S22, when the monitoring data crosses two or more grid lines consecutively between adjacent sampling points, only the crossing time corresponding to the highest level grid line is retained as a valid key point, and the next level integer grid line corresponding to the valid key point is automatically selected as the starting grid line.

[0015] The above rules can be used to address scenarios involving sudden and significant slope deformation and data spanning multiple grid levels. Only key points corresponding to the highest-level grid line are retained, reducing data volume and avoiding redundant nodes that could affect computational efficiency. Simultaneously, the next-level grid line is automatically selected as the new starting point, ensuring logical consistency between grid division and key point selection. This approach not only fully captures the characteristics of severe deformation but also maintains the continuity of monitoring and analysis, guaranteeing accurate and stable early warning judgments.

[0016] More preferably, step S22 further includes: using a continuous confirmation mechanism to verify the validity of the crossing; wherein, the continuous confirmation mechanism is: when the monitoring data is detected to cross the grid line, N consecutive sampling points need to exceed the grid line to confirm the crossing is valid, thereby reducing the false alarm rate.

[0017] By introducing a continuous confirmation mechanism during the identification of data crossing grid lines, a crossing is only considered valid when N consecutive sampling points cross the grid line. This method effectively distinguishes between actual slope displacement changes and instantaneous anomalies caused by environmental disturbances and fluctuations in raw data, filtering out accidentally generated false crossing signals and significantly reducing the false alarm probability of the monitoring and early warning system from the data source. Simultaneously, this mechanism, used in conjunction with equal-interval early warning logic, further improves the accuracy of key point identification without affecting normal deformation data capture or altering the original monitoring and early warning process. This ensures the reliability of subsequent deformation rate, acceleration calculations, and comprehensive early warning judgments, enhancing the stability and practicality of the entire monitoring and early warning method.

[0018] Preferably, the equal-value interval early warning check mechanism triggered by displacement increment uses the displacement increment of the monitoring point as the trigger condition. The early warning rule calculation is only triggered when the deformation reaches the preset displacement interval. Stable monitoring points that have not reached the displacement interval do not execute the early warning analysis process, thereby reducing the consumption of computing resources.

[0019] This invention employs displacement increment-triggered early warning calculations, initiating analysis only when a monitoring point experiences a valid displacement change. Stable points do not require repeated calculations, significantly reducing resource consumption. This is particularly suitable for provincial cluster monitoring platforms supporting tens of thousands of monitoring points, effectively enhancing system scalability and real-time response capabilities. Furthermore, the displacement interval parameter can be flexibly adjusted according to the slope risk level. Smaller intervals are used for high-risk slopes to ensure early warning accuracy, while larger intervals are selected for stable slopes to improve operational efficiency, achieving a reasonable balance between early warning accuracy and computational efficiency.

[0020] Preferably, step S3 includes: calculating the average rate of deformation of the monitoring point based on the displacement difference and time difference between two adjacent key points in the deformation monitoring grid.

[0021] This scheme calculates the average deformation rate by using the displacement and time differences between adjacent key points in the deformation monitoring grid. Calculations are performed using selected, valid key points, which avoids the problems of severe oscillations and error accumulation in the original high-frequency data. The calculation results are less affected by the sampling frequency, resulting in more stable and accurate values. Compared to directly using the original time-series data to calculate the rate, this method can accurately remove data noise, truly restore the overall deformation trend of the slope, and provide a reliable rate indicator for subsequent acceleration quantification and multi-dimensional early warning assessment, effectively improving the scientific rigor and stability of slope risk assessment.

[0022] Preferably, step S4 includes: calculating the rate difference between two adjacent broken line segments based on the deformation monitoring grid to determine the acceleration state; wherein, if the difference is greater than a preset threshold δ, it is determined to be acceleration, and the continuous acceleration count is incremented by 1; if the difference is less than -δ, it is determined to be deceleration, and the continuous acceleration count is reset to zero; otherwise, it is determined to be uniform speed, and the continuous acceleration count is reset to zero; thereby quantifying the acceleration and using the number of continuous accelerations to determine the slope deformation trend.

[0023] This scheme relies on a deformation monitoring grid to determine and quantify the acceleration state by analyzing the rate difference between adjacent segment segments, abandoning traditional differential calculation methods and avoiding further amplification of noise. It distinguishes between acceleration, deceleration, and uniform velocity states using preset thresholds and incorporates a continuous acceleration counting mechanism to accurately identify continuous acceleration characteristics before slope slippage and intuitively quantify deformation trends. Based on regularized key point data, this calculation method exhibits strong stability and is less affected by data oscillations, providing reliable acceleration criteria for multi-dimensional comprehensive early warning, improving the accuracy of slope instability risk prediction, and ensuring that early warning results align with the actual deformation patterns of the slope.

[0024] Preferably, in step S5, the displacement warning is triggered only when the displacement warning threshold is reached for the first time, thereby avoiding repeated alarms; while the deformation rate warning and acceleration warning can be triggered repeatedly to ensure that no alarms are missed.

[0025] This solution employs differentiated triggering logic for different early warning indicators. Displacement warnings are only activated upon the first occurrence of a threshold, fundamentally preventing repeated alarms caused by continuously exceeding data limits. This reduces redundancy in early warning information, avoids warning fatigue for management personnel, and ensures efficient and orderly transmission of early warning information. Simultaneously, deformation rate and acceleration warnings allow for repeated triggering, enabling real-time tracking of dynamic changes in slope deformation and providing continuous alerts for escalating instability risks, effectively preventing missed reporting of hazards. This differentiated triggering strategy balances the need for information simplification with the need for continuous risk monitoring, optimizing the management experience while comprehensively ensuring the rigor and security of slope monitoring and early warning systems.

[0026] On the other hand, the present invention also provides a slope displacement monitoring and early warning system based on the first-point broken line method according to the above-described monitoring and early warning method, wherein the system includes: Data parsing module: used to collect and extract displacement time-series data from slope monitoring points; The data filtering module is used to filter the acquired displacement time series data. The early warning analysis module is used to process the filtered data using the first-point polyline method algorithm, extract key points, use displacement to determine the timing of early warning checks, calculate deformation rate and acceleration, and thus make multi-dimensional comprehensive early warning judgments based on displacement, deformation rate and acceleration, and generate corresponding early warning information. The visualization module is used to display the original data curve, filtered data curve, first point polyline, and early warning information.

[0027] This system utilizes a modular architecture to automate the entire process of slope displacement monitoring and early warning. Each module has a clear division of labor and works collaboratively. The data analysis and filtering modules work together to acquire complete on-site displacement time-series data while effectively filtering out environmental interference and data noise, improving data quality from the source. The early warning analysis module employs the first-point polyline method to achieve equal-interval early warnings and accurate calculation of velocity and acceleration, avoiding the resource waste and calculation distortion problems of traditional technologies. Furthermore, it combines multi-dimensional indicators for comprehensive judgment, ensuring the scientific reliability of the early warning results. The visualization module can intuitively present various data curves and early warning information, facilitating real-time monitoring of slope deformation status by staff. The entire system has a clear structure, stable operation, and comprehensively considers monitoring accuracy, operational efficiency, and ease of use, significantly improving the level of slope safety management.

[0028] Preferably, the early warning analysis module includes: The deformation monitoring mesh generation unit is used to determine multiple integer grid lines according to a preset displacement interval; then, based on the filtered data, it identifies the moment when the monitoring data first crosses each integer grid line and determines it as a key point; finally, based on the generated key points, it connects each key point in sequence to form a polyline, thereby obtaining a regular deformation monitoring mesh.

[0029] Deformation rate calculation unit, used to calculate the deformation rate based on the deformation monitoring grid; The acceleration determination unit is used to quantify the acceleration based on the deformation monitoring grid, thereby determining the acceleration state.

[0030] By subdividing the early warning analysis module into functional units, the core operations of the first-point polyline method are broken down and implemented, resulting in clear logic and efficient operation. The deformation monitoring grid generation unit delineates grid lines according to displacement intervals, extracts the first crossing moment as a key point, and constructs a grid to realize an equal-value interval early warning mechanism. This adapts to the nonlinear deformation law of slopes, reduces invalid calculations and data redundancy, and the deformation rate calculation unit relies on regular grids and key point calculations to effectively suppress the oscillation interference of the original data, ensuring stable and accurate rate calculation results. The acceleration judgment unit completes acceleration quantification and state identification based on grid data, avoiding the defects of traditional differential operations that amplify noise, and can accurately capture continuous acceleration characteristics. This provides solid and reliable core data support for comprehensive early warning judgment, further improving the monitoring accuracy and analysis capabilities of the entire system.

[0031] Preferably, the deformation monitoring grid generation unit further includes a continuous confirmation mechanism to verify the validity of the crossing; wherein, the continuous confirmation mechanism is: when monitoring data is detected to cross the grid line, N consecutive sampling points need to exceed the grid line to confirm the crossing is valid, thereby reducing the false alarm rate.

[0032] In the deformation monitoring grid generation unit, a continuous confirmation mechanism is added. Only when N consecutive sampling points cross the grid line is the crossing behavior deemed valid and marked as a key point. This effectively filters out false crossing signals caused by environmental disturbances and instantaneous data fluctuations, avoids the generation of invalid key points, and reduces the probability of false alarms from the source. At the same time, it ensures the accuracy of grid construction, making the subsequent calculation results of deformation rate and acceleration more realistic and reliable. It also ensures the stable operation of the equal-value interval early warning mechanism, and improves the anti-interference capability and operational reliability of the grid generation unit and even the entire monitoring system.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Equal interval warning: The warning timing is directly related to the actual deformation of the slope, and the warning frequency is adaptively adjusted, which fundamentally solves the inherent defects of equal interval warning.

[0034] 2. Saves computing resources: The use of displacement increment to trigger early warning calculations means that stable monitoring points do not trigger complex early warning rule calculations, which is of great significance for large platforms such as provincial cluster monitoring systems.

[0035] 3. Reduce false alarm rate: Data filtering combined with a continuous confirmation mechanism effectively filters out environmental interference and data noise, reducing the false alarm rate.

[0036] 4. Avoid repeated alarms: Displacement warnings are triggered only when the threshold is reached for the first time, avoiding repeated alarms at the same level and reducing warning fatigue.

[0037] 5. Stable rate calculation: The rate is calculated based on key points with equal intervals, and the results are stable and reliable, unaffected by fluctuations in the sampling frequency.

[0038] 6. Effective Quantification of Acceleration: The acceleration is quantified by the difference in the rate of the broken line segments, providing a more scientific basis for early warning.

[0039] 7. Multi-dimensional comprehensive early warning: Taking into account three dimensions—displacement, deformation rate, and acceleration—makes the early warning more comprehensive and accurate. Attached Figure Description

[0040] Figure 1 The flowchart of the slope displacement monitoring and early warning method based on the first-point broken line method provided by the present invention is shown.

[0041] Figure 2 This is a schematic diagram of data filtering provided by the present invention.

[0042] Figure 3 This is a schematic diagram of the deformation monitoring grid provided by the present invention.

[0043] Figure 4 This is a comparative diagram of equal-value interval warning (left) and equal-time interval warning (right) provided by the present invention.

[0044] Figure 5 The flowchart for early warning judgment provided by the present invention.

[0045] Figure 6 The overall system architecture diagram provided by this invention.

[0046] Figure 7 A schematic diagram of the data visualization interface provided by the present invention. Detailed Implementation

[0047] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the invention. To better illustrate the following embodiments, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions; it is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0048] Example 1 like Figure 1 As shown in the figure, this embodiment provides a slope displacement monitoring and early warning method based on the first-point polyline method. The method includes the following steps: Step S1: Collect displacement time series data of slope monitoring points and filter the raw data; Preferably, in this embodiment, data acquisition is performed using a highway slope monitoring point as an example. GNSS data of the monitoring point is exported from the slope monitoring system, including two columns of data: time and displacement in the free direction. Then, the raw displacement data is filtered. In this embodiment, a sliding window moving average method is used to filter the data, with a window length of 16 data points. The calculation formula is: Let the original sequence be y1, y2, ..., y n … 16-period moving average (MA) 16n for: MA 16n =(y n 15 +y n 14 + +y n 1+y n ) / 16, n≥16 Where n represents the current data node; MA16n represents the 16-period moving average of the nth data point; Thus, the above filtering method yields the following result: Figure 2 The filtering results shown effectively eliminate measurement noise while preserving the deformation trend.

[0049] Step S2: Process the filtered data to obtain key points and connect them to form polylines, thereby generating a regular deformation monitoring grid. At the same time, establish an equal-value interval early warning inspection mechanism triggered by displacement increment based on the grid. Specifically, step S2 involves processing the filtered data to obtain key points and connecting them to form polylines, thereby generating a regular deformation monitoring grid. Step S21: Determine multiple integer grid lines according to a preset displacement interval; In this embodiment, the number of integer grid lines is determined based on the maximum displacement of the filtered data. The grid interval can be adjusted according to the slope characteristics. In this embodiment, a fixed displacement threshold method with 10mm intervals is used to pick up marker points. Using 10mm as the graded displacement boundary, progressively defining 20mm, 30mm, 40mm, etc., judgment standards, thereby determining a number of integer grid lines, such as... Figure 3 As shown.

[0050] Step S22: Based on the filtered data, identify the moment when the monitoring data first crosses each integer grid line and determine it as a key point; In step S22, the specific steps are as follows: when the monitoring data crosses two or more grid lines consecutively between adjacent sampling points, only the crossing time corresponding to the highest level grid line is retained as a valid key point, and the next level integer grid line corresponding to the valid key point is automatically selected as the starting grid line.

[0051] Based on the filtered measured data, marking is only completed when the data first touches the corresponding displacement boundary. No key points are generated when the boundary is not reached or when the boundary is not touched for the first time. For example, if the data first reaches the 30mm displacement boundary at time t2, the point is marked immediately; t 2-1 If the target is not met for the first time in a similar period, it will not be marked as a record. Figure 3 As shown. If the monitoring data crosses two or more displacement boundaries consecutively within a short period of time, only the highest displacement boundary that is ultimately crossed is retained as the valid key point. The starting boundary is then determined by automatically selecting the next ten-millimeter value of the next valid boundary.

[0052] Further preferably, in order to ensure the reliability of the crossing judgment in this embodiment, step S22 further includes: using a continuous confirmation mechanism to verify the validity of the crossing; wherein, the continuous confirmation mechanism is: when the monitoring data is detected to cross the grid line, it is necessary for N consecutive sampling points to exceed the grid line to confirm the crossing is valid. In this embodiment, N is set to 3, and it is confirmed to be valid only when 3 consecutive sampling points exceed the grid line, thereby avoiding false judgments caused by data fluctuations and reducing the false alarm rate.

[0053] Step S23: Record the time, displacement value, and corresponding grid line position of each key point, and connect the key points sequentially to form a polyline, as shown below. Figure 3 The well-organized deformation monitoring grid shown is illustrated.

[0054] This method effectively solves many drawbacks of traditional equal-interval sampling by dividing the grid into integer grid lines, identifying the moment when data first crosses the grid line as a key point, and connecting these points to construct a deformation monitoring grid. Since slope deformation is non-linear, dividing the grid with fixed displacement intervals and using equal-value interval early warnings directly correlates the timing of early warning checks with the actual deformation, rather than being determined by a manually set time point. Figure 4 As shown, Figure 4The diagram illustrates a comparison between equal-interval early warning (left) and equal-time interval early warning (right). It is clear from the diagram that equal-interval early warning is more consistent with the non-linear, gradual process of slope deformation, which can deform rapidly in a short time or remain stable for a long time. The equal-time interval early warning method contradicts the physical nature of slope deformation and cannot adaptively capture critical deformation moments. The equal-interval early warning judgment logic described in this embodiment is more in line with the physical laws of slope deformation, further improving the timeliness and accuracy of the early warning.

[0055] More preferably, the equal-value interval early warning check mechanism triggered by displacement increment uses the displacement increment of the monitoring point as the trigger condition. The early warning rule calculation is only triggered when the deformation reaches the preset displacement interval. Stable monitoring points that have not reached the displacement interval do not execute the early warning analysis process, thereby reducing the consumption of computing resources.

[0056] In this embodiment, the displacement interval for triggering early warning calculations can be adjusted according to slope characteristics. For high-risk slopes sensitive to deformation, a smaller displacement interval (e.g., 5mm) can be set; for slopes with relatively stable deformation, a larger displacement interval (e.g., 15mm or 20mm) can be set, thus balancing early warning accuracy and calculation efficiency. Furthermore, the early warning check frequency can be adaptively adjusted to match the slope deformation state. For example, in the early monitoring stage, when the slope is stable and deformation is slow, taking approximately 30 days to cross the first grid line (10mm), the early warning check frequency automatically decreases to avoid invalid early warnings; in the later monitoring stage, when the slope deforms rapidly, crossing a grid line in approximately 5 days, the early warning check frequency automatically increases to promptly capture key changes. Moreover, early warning rule calculations are only triggered when a monitoring point generates sufficient displacement increments. For monitoring points in a stable state, there is no need to execute complex early warning analysis procedures, thus significantly reducing computational resource consumption. This feature is of great significance for large platforms like provincial cluster monitoring systems that need to process tens of thousands of monitoring points simultaneously, effectively improving the system's scalability and real-time response capabilities.

[0057] Step S3: Calculate the deformation rate of the monitoring points using the deformation monitoring grid; Preferably, step S3 includes: calculating the average rate of deformation of the monitoring point based on the displacement difference and time difference between two adjacent key points in the deformation monitoring grid, wherein the calculation formula is: rate = (current key point displacement - previous key point displacement) / time interval (days).

[0058] In this embodiment, as Figure 3 As shown, after acquiring the second or more valid key feature points, the calculation of the interval average deformation rate is initiated. Let the i-th time and the i-th time be... One valid marker point corresponds to a 16-period moving average displacement value of MA.16i MA 16i 1, corresponding to time t i t i 1. Then the average speed V between the two points. i,i-1 =(MA 16i -MA 16i-1 ) / (t i -t i-1 Connecting the two sets of key feature points forms a polygonal line representing the first-point rate. The slope of this line represents the average deformation rate for the corresponding time period. The resulting average deformation rate better reflects the true deformation trend, is unaffected by short-term fluctuations, and the calculation results are stable and reliable, providing a reliable criterion for early warning.

[0059] This method calculates the average deformation rate by utilizing the displacement and time differences between adjacent key points in the deformation monitoring grid. By relying on selected effective key points for calculation, it avoids the problems of severe oscillations and error accumulation in the original high-frequency data. The calculation results are less affected by the sampling frequency, resulting in more stable and accurate values. Compared to directly using raw time-series data to calculate the rate, this method can accurately remove data noise, truly restore the overall deformation trend of the slope, and provide a reliable rate indicator for subsequent acceleration quantification and multi-dimensional early warning assessment, effectively improving the scientific rigor and stability of slope risk assessment.

[0060] Step S4: Quantify the acceleration using the deformation monitoring grid; Preferably, step S4 includes: calculating the rate difference between two adjacent broken line segments based on the deformation monitoring grid, that is, calculating the difference between the rate of the current broken line segment and the rate of the previous segment to determine the acceleration state; wherein, in this embodiment, the preset threshold δ is set to 2mm / d². If the difference is greater than the preset threshold δ, it is determined to be acceleration, and the continuous acceleration count is incremented by 1; if the difference is less than -δ, it is determined to be deceleration, and the continuous acceleration count is reset to zero; otherwise, it is determined to be uniform speed, and the continuous acceleration count is reset to zero; thus, the acceleration state is determined and quantified by the rate difference between adjacent broken line segments, abandoning the traditional differential calculation method and avoiding the problem of further amplification of noise. Combining the preset threshold to distinguish between the three states of acceleration, deceleration, and uniform speed, and setting a continuous acceleration counting mechanism, the continuous acceleration characteristics before slope slippage can be accurately identified, and the deformation development trend can be intuitively quantified. This calculation method is based on the normalized key point data, and the results are highly stable and less affected by data oscillations. It can provide reliable acceleration criteria for multi-dimensional comprehensive early warning, improve the accuracy of slope instability risk prediction, and ensure that the early warning results are consistent with the actual deformation law of the slope.

[0061] Step S5: Based on the displacement, deformation rate, and acceleration data obtained in the above steps, a multi-dimensional comprehensive judgment is made to output the corresponding warning level and warning information.

[0062] Specifically, such as Figure 5 As shown, in this embodiment, a comprehensive early warning judgment is performed each time a new grid line is crossed: Displacement warning: Triggered only upon first reaching each warning threshold to avoid repeated warnings. The system maintains the set of triggered warning levels to ensure that each level triggers only once.

[0063] Deformation rate warning: It is judged every time the grid line is crossed. It can be triggered repeatedly to ensure that no dynamic changes are missed.

[0064] Acceleration warning: Based on the number of consecutive accelerations, it can be triggered repeatedly to reflect changes in deformation trends in a timely manner.

[0065] Comprehensive Early Warning: The highest level among all triggering conditions is taken as the final early warning level. The early warning information includes a detailed description of all triggering conditions, and risk alerts are automatically pushed. The comprehensive early warning threshold setting table is shown in Table 1.

[0066] Table 1 For example, if the cumulative displacement of the monitoring point reaches 45mm, when it crosses the 4th grid line (40mm): When the displacement reaches the 40mm threshold for the first time, a yellow displacement warning is triggered (this is the first trigger and will not be repeated).

[0067] If the average velocity during this period is 6 mm / d, a yellow velocity warning will be triggered.

[0068] If the first acceleration is detected, a yellow acceleration warning will be triggered.

[0069] Overall assessment result: Yellow alert. The alert is based on detailed information from three dimensions: displacement, velocity, and acceleration.

[0070] In addition, this plan proposes early warning response requirements to better accomplish the early warning work, as shown in Table 2:

[0071] Table 2 This approach allows for differentiated triggering logic for different early warning indicators. Displacement warnings are only activated upon the first time the threshold is reached, fundamentally preventing repeated alarms caused by continuously exceeding data limits. This reduces redundancy in early warning information, avoids warning fatigue for management personnel, and ensures efficient and orderly transmission of early warning information. Simultaneously, deformation rate and acceleration warnings can be triggered repeatedly, allowing real-time tracking of dynamic changes in slope deformation and providing continuous alerts for escalating instability risks, effectively preventing missed reporting of hazards. This differentiated triggering strategy balances the need for information simplification with the need for continuous risk monitoring, optimizing the management experience while comprehensively ensuring the rigor and security of slope monitoring and early warning systems.

[0072] Example 2 Based on the monitoring and early warning method described in Example 1, this example provides a slope displacement monitoring and early warning system based on the first-point broken line method, wherein... Figure 6 The system shown includes: Data parsing module: Used to collect and extract displacement time series data from slope monitoring points; specifically, this module is used to read monitoring data files in Excel format, parse the time series and displacement data, and thus extract the corresponding displacement time series data.

[0073] The data filtering module is used to filter the acquired displacement time-series data. The filtering method can be selected based on the data characteristics, such as Savitzky-Golay filtering, moving average filtering, or Kalman filtering. This embodiment uses a Savitzky-Golay filter to filter the raw data, thereby suppressing high-frequency noise and preserving deformation trend characteristics.

[0074] The early warning analysis module is used to process the filtered data using the first-point polyline method algorithm, extract key points, use displacement to determine the timing of early warning checks, calculate deformation rate and acceleration, and thus make multi-dimensional comprehensive early warning judgments based on displacement, deformation rate and acceleration, and generate corresponding early warning information. Preferably, the early warning analysis module includes: The deformation monitoring mesh generation unit is used to determine multiple integer grid lines according to a preset displacement interval; then, based on the filtered data, it identifies the moment when the monitoring data first crosses each integer grid line and determines it as a key point; finally, based on the generated key points, it connects each key point in sequence to form a polyline, thereby obtaining a regular deformation monitoring mesh.

[0075] More preferably, the deformation monitoring grid generation unit further includes a continuous confirmation mechanism to verify the validity of the crossing; wherein, the continuous confirmation mechanism is as follows: when monitoring data is detected crossing the grid line, N consecutive sampling points must all exceed the grid line to confirm the crossing as valid. This can effectively filter out false crossing signals caused by environmental disturbances and instantaneous data fluctuations, avoid the generation of invalid key points, and thus reduce the false alarm rate; at the same time, it ensures the accuracy of grid construction, making the subsequent calculation results of deformation rate and acceleration more realistic and reliable, and also ensures the stable operation of the equal value interval early warning mechanism, thereby improving the anti-interference capability and operational reliability of the grid generation unit and even the entire monitoring system.

[0076] Deformation rate calculation unit, used to calculate the deformation rate based on the deformation monitoring grid; The acceleration determination unit is used to quantify the acceleration based on the deformation monitoring grid, thereby determining the acceleration state.

[0077] Preferably, the acceleration judgment unit includes: calculating the rate difference between two adjacent broken line segments based on the deformation monitoring grid to determine the acceleration state; wherein, if the difference is greater than a preset threshold δ, it is determined to be acceleration, and the continuous acceleration count is incremented by 1; if the difference is less than -δ, it is determined to be deceleration, and the continuous acceleration count is reset to zero; otherwise, it is determined to be uniform speed, and the continuous acceleration count is reset to zero; thereby quantifying the acceleration, it can accurately identify the continuous acceleration characteristics of the slope before it slides, intuitively quantify the deformation development trend, and improve the accuracy of predicting the slope instability risk.

[0078] The visualization module is used to display the original data curve, filtered data curve, first point polyline, and early warning information.

[0079] like Figure 7 As shown, this system uses ECharts to render a data visualization interface, which simultaneously displays information such as the original data curve, filtered data curve, and first-point polyline. The location and warning level of each warning point are marked on the chart, and the table below lists the detailed information of all warning events. At the same time, it standardizes the data prompts and the reporting templates for warning information at all levels, simplifies the information description, highlights the core risk elements, and makes it easier for staff to quickly grasp key monitoring information and improve the efficiency of emergency response.

[0080] Therefore, in this embodiment, the system relies on a modular architecture to achieve fully automated operation of the slope displacement monitoring and early warning process, with each module having a clear division of labor and working in concert. The data acquisition and filtering modules work together to not only acquire complete on-site displacement time-series data but also effectively filter out environmental interference and data noise, improving data quality from the source. The early warning analysis module uses the first-point broken-line method to achieve equal-value interval early warning and accurate calculation of velocity and acceleration, avoiding the problems of resource waste and calculation distortion in traditional technologies. At the same time, it combines multi-dimensional indicators for comprehensive judgment to ensure that the early warning results are scientific and reliable. The visualization module can intuitively present various data curves and early warning information, making it easy for staff to grasp the slope deformation status in real time. The entire system has a clear structure, stable operation, and comprehensively considers monitoring accuracy, operational efficiency, and ease of use, significantly improving the level of slope safety management.

[0081] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the technical solution of the present invention, and are not intended to limit the specific implementation of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the claims of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A slope displacement monitoring and early warning method based on the first-point polyline method, characterized in that, The method includes the following steps: S1: Collect displacement time series data of slope monitoring points and filter the raw data; S2: The filtered data is processed to obtain key points and connected to form polylines, thereby generating a regular deformation monitoring grid. At the same time, an equal-value interval early warning inspection mechanism triggered by displacement increment is established based on the grid. S3: Calculate the deformation rate of the monitoring points using the deformation monitoring grid; S4: Use the aforementioned deformation monitoring grid to quantify the acceleration; S5: Based on the displacement, deformation rate, and acceleration data obtained from the above steps, a multi-dimensional comprehensive judgment is made to output the corresponding warning level and warning information.

2. The slope displacement monitoring and early warning method based on the first-point broken line method according to claim 1, characterized in that, In step S2, the process of processing the filtered data to obtain key points and connecting them to form polylines, thereby generating a regular deformation monitoring grid, specifically includes: S21: Determine multiple integer grid lines according to a preset displacement interval; S22: Based on the filtered data, identify the moment when the monitoring data first crosses each integer grid line and determine it as a key point; S23: Connect the key points in sequence to form a polygonal line, thereby obtaining a regular deformation monitoring grid.

3. A slope displacement monitoring and early warning method based on the first-point polyline method according to claim 2, characterized in that, In step S22, when the monitoring data crosses two or more grid lines consecutively between adjacent sampling points, only the crossing time corresponding to the highest level grid line is retained as a valid key point, and the next level integer grid line corresponding to the valid key point is automatically selected as the starting grid line.

4. The slope displacement monitoring and early warning method based on the first-point broken line method according to claim 2, characterized in that, Step S22 further includes: using a continuous confirmation mechanism to verify the validity of the crossing; wherein, the continuous confirmation mechanism is: when the monitoring data is detected to cross the grid line, N consecutive sampling points need to exceed the grid line to confirm the crossing is valid, thereby reducing the false alarm rate.

5. The slope displacement monitoring and early warning method based on the first-point polyline method according to claim 1, characterized in that, The equal-value interval early warning check mechanism triggered by displacement increment uses the displacement increment of the monitoring point as the trigger condition. The early warning rule calculation is only triggered when the deformation reaches the preset displacement interval. Stable monitoring points that have not reached the displacement interval do not execute the early warning analysis process, thereby reducing the consumption of computing resources.

6. A slope displacement monitoring and early warning method based on the first-point polyline method according to claim 2, characterized in that, Step S3 includes: calculating the average rate of deformation of the monitoring point based on the displacement difference and time difference between two adjacent key points in the deformation monitoring grid.

7. A slope displacement monitoring and early warning method based on the first-point polyline method according to claim 5, characterized in that, Step S4 includes: calculating the rate difference between two adjacent broken line segments based on the deformation monitoring grid to determine the acceleration state; wherein, if the difference is greater than a preset threshold δ, it is determined to be acceleration, and the continuous acceleration count is incremented by 1; if the difference is less than -δ, it is determined to be deceleration, and the continuous acceleration count is reset to zero; otherwise, it is determined to be uniform speed, and the continuous acceleration count is reset to zero; thereby quantifying the acceleration and using the number of continuous accelerations to determine the slope deformation trend.

8. A slope displacement monitoring and early warning method based on the first-point polyline method according to claim 1, characterized in that, In step S5, a displacement warning is triggered only when the displacement warning threshold is reached for the first time, thereby avoiding repeated alarms; while deformation rate warning and acceleration warning can be triggered repeatedly to ensure that no alarms are missed.

9. A slope displacement monitoring and early warning system based on the first-point broken line method according to any one of claims 1 to 8, characterized in that, The system includes: Data parsing module: used to collect and extract displacement time-series data from slope monitoring points; The data filtering module is used to filter the acquired displacement time series data. The early warning analysis module is used to process the filtered data using the first-point polyline method algorithm, extract key points, use displacement to determine the timing of early warning checks, calculate deformation rate and acceleration, and thus make multi-dimensional comprehensive early warning judgments based on displacement, deformation rate and acceleration, and generate corresponding early warning information. The visualization module is used to display the original data curve, filtered data curve, first point polyline, and early warning information.

10. A slope displacement monitoring and early warning system based on the first-point polyline method according to claim 9, characterized in that, The early warning analysis module includes: The deformation monitoring mesh generation unit is used to determine multiple integer grid lines according to a preset displacement interval; then, based on the filtered data, it identifies the moment when the monitoring data first crosses each integer grid line and determines it as a key point; finally, based on the generated key points, it connects each key point in sequence to form a polyline, thereby obtaining a regular deformation monitoring mesh. Deformation rate calculation unit, used to calculate the deformation rate based on the deformation monitoring grid; The acceleration determination unit is used to quantify the acceleration based on the deformation monitoring grid, thereby determining the acceleration state.

11. A slope displacement monitoring and early warning system based on the first-point polyline method according to claim 10, characterized in that, The deformation monitoring grid generation unit also includes a continuous confirmation mechanism to verify the validity of the crossing; wherein, the continuous confirmation mechanism is: when the monitoring data is detected to cross the grid line, N consecutive sampling points need to exceed the grid line to confirm the crossing is valid, thereby reducing the false alarm rate.

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