Horizontal displacement intelligent monitoring method and system based on reciprocating inclinometry robot

Abstract

The invention discloses a horizontal displacement intelligent monitoring method and system based on a reciprocating inclinometer robot, and relates to the technical field of engineering safety monitoring, and the method comprises the steps: dividing a monitoring region into a plurality of grid cells, and arranging and deploying the reciprocating inclinometer robot and an environment sensor in each grid cell; establishing a unified database, and storing the horizontal displacement data collected by the robot in each grid unit and the environmental data collected by the environmental sensor in the database in real time; performing statistical analysis on the multi-source data in the database, judging an association relationship between the horizontal displacement change and the environmental factors, and setting a dynamic environmental interference threshold value for each grid unit according to an association analysis result; for the grid units which are not interfered by the environment or are slightly interfered, directly carrying out quantitative analysis by adopting displacement data acquired by the robot; the problems that a traditional monitoring system is difficult to adapt to complex and changeable field working conditions, and false alarm, missing alarm or early warning delay easily occur are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering safety monitoring, and in particular to a horizontal displacement intelligent monitoring method and system based on a reciprocating inclinometer robot. Background Art

[0002] In recent years, with the development of intelligent sensors, robotic measurement platforms, and data analysis technologies, automated and intelligent displacement monitoring methods have gradually emerged. Reciprocating inclinometers, suitable for linear or distributed measurement in tunnels, slopes, foundation pits, and other areas, offer advantages such as controllable operating paths, high repeatability, and ease of network deployment. As a result, they are gradually being introduced into the field of horizontal displacement monitoring.

[0003] However, in practical applications, environmental factors (such as rainfall, temperature, wind speed, and groundwater level fluctuations) significantly interfere with measurement data, making it difficult for monitoring systems to accurately identify actual structural displacement changes. Furthermore, different grid areas are affected differently by geological structure, construction disturbances, or operational loads. Traditional methods often use uniform or static thresholds for judgment, which are difficult to adapt to complex and changing field conditions and are prone to false alarms, missed alarms, and delayed warnings.

[0004] Therefore, there is an urgent need to provide a horizontal displacement intelligent monitoring method and system that can integrate grid monitoring deployment, real-time data acquisition, multi-dimensional interference identification and dynamic threshold judgment mechanism to achieve a more accurate, stable and early warning geological displacement monitoring solution. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for intelligently monitoring horizontal displacement based on a reciprocating inclinometer robot to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method and system for intelligently monitoring horizontal displacement of a reciprocating inclinometer robot, comprising the following steps: Step S1: Divide the monitoring area into several grid units, and deploy a reciprocating inclinometer robot and environmental sensors in each grid unit; Step S2: Establish a unified database and store the horizontal displacement data collected by the robot in each grid unit and the environmental data collected by the environmental sensor in the database in real time; Step S3: Statistically analyze the multi-source data in the database to determine the correlation between horizontal displacement changes and environmental factors, and set a dynamic environmental interference threshold for each grid cell based on the correlation analysis results; Step S4: For grid cells that are not disturbed by the environment or are less disturbed, the displacement data collected by the robot is directly used for quantitative analysis; for grid cells that are disturbed by the environment, the displacement data is corrected according to the results of the environmental variable correlation analysis; Step S5: Based on the results of the quantitative analysis, a safety threshold for horizontal displacement is set for each grid unit. When the displacement change of a grid unit exceeds the safety threshold, the system automatically issues a warning message.

[0007] According to the above technical solution, step S3 further includes: Step S31: In each grid cell, historical horizontal displacement data within a preset time window and environmental monitoring data of a corresponding time period are selected, and their change trends are extracted to form a time series curve data set for comparison; Step S32: performing a horizontal comparison of the horizontal displacement changes of multiple grid cells in similar time periods, identifying grid cells that still exhibit abnormal displacement under similar environmental conditions, and marking the cells as interference-sensitive cells; Step S33: For the time series between each environmental factor and displacement change, a delay window is set to observe causal clues. When a certain environmental parameter jumps or fluctuates continuously, it is analyzed whether a displacement change is induced within the delay time, thereby determining the potential driving effect of the factor on the displacement change; Step S34: Calculate the impact score based on the frequency and amplitude of displacement fluctuations caused by each type of environmental factor in each grid cell, and use this as a reference for subsequent interference intensity assessment; Step S35: Preliminarily setting an environmental interference threshold for each grid cell, wherein the threshold is set based on a typical time period in the historical data of the cell where displacement fluctuations are frequent and highly consistent with changes in environmental factors; Step S36: During the monitoring process, when the environmental parameter monitored by a certain grid unit exceeds its set interference threshold, the displacement data in the time period is automatically marked as "potentially interfered data".

[0008] According to the above technical solution, step S34 further includes: Step S341: Divide the data of each type of environmental factor in different time periods in each grid unit into preset time windows, extract the change amplitude, change frequency and duration of the environmental factor in each time window, and form an environmental change feature group; Step S342: Synchronously extract the displacement change data within the corresponding time window, and assign a change level identifier to the window based on the severity of the displacement fluctuation; Step S343: Count the matching degrees between the environmental feature groups and the displacement level identifiers in multiple time windows. The statistical indicators include: The proportion of significant displacement changes in the window with significant environmental changes; The incidence of abnormal displacement during the stable window of environmental changes; the approximate synchronization ratio between the magnitude of environmental changes and the magnitude of displacement responses; Step S344: performing weighted fusion on the above statistical indicators according to preset weights to obtain a reference coefficient of the impact of the environmental factor on the displacement in the grid unit; Step S345: normalize the reference coefficient to a percentage score value as the "impact score" of the environmental factor in the unit, and record it in the database for reference in subsequent interference judgment and threshold adjustment; Step S346: If multiple environmental factors are monitored, a set of scoring vectors is formed for each grid unit to evaluate the interference dominance of each factor, and when the score reaches a set limit value, a weight update procedure is triggered.

[0009] According to the above technical solution, step S4 further includes: Step S41: For each grid cell, after each monitoring cycle, the system automatically determines the impact level of the cell based on whether the current environmental parameters exceed the aforementioned dynamic environmental interference threshold, marking it as "negligible interference", "slight interference" or "significant interference"; Step S42: For a grid cell marked as “negligible interference”, the horizontal displacement raw data collected by the robot is directly adopted and enters the subsequent quantitative evaluation program to calculate the actual horizontal offset value and trend evolution; Step S43: When a grid cell is marked as “slightly disturbed”, a light correction process is started, which includes performing a linear proportional correction on the displacement data and the fluctuation amplitude of the current environmental factors to generate preliminary compensated displacement data; Step S44: When a grid cell is marked as "significant interference", the system calls the displacement change records under similar environmental conditions in its historical data, generates a background control curve, and performs an offset comparison with the current acquisition curve. The correction factor is used to repair the current displacement data, and the repair result is marked as "environmental correction value".

[0010] According to the above technical solution, step S44 further includes: Step S441: After determining that a grid cell is "significantly disturbed", the system automatically extracts historical time periods with environmental parameters similar to the current time period from the historical monitoring database of the grid cell to construct a candidate sample set; Step S442: Filter out historical sample segments whose horizontal displacement change trend is consistent with the displacement trend direction of the current time period from the candidate sample set, and generate a historical background displacement curve based on their number, duration, and change amplitude as the "background control curve"; Step S443: Synchronize the currently acquired displacement curve with the background control curve, and adjust the time axis starting point and data sampling interval to make the two comparable in the time domain; Step S444: Calculate the offset of the two curves at the corresponding time points, and determine a comprehensive offset weight factor based on the overall offset mean, fluctuation amplitude, and stage difference index; Step S445: using the offset weight factor to perform point-by-point correction on the data of each time node in the currently acquired displacement curve, the correction method is to perform amplitude compression, lifting or translation operation on each original displacement value according to the weight factor; Step S446: The entire corrected curve is marked as an "environmental correction value curve" and archived in the system database together with the original data, environmental data, and offset comparison information for subsequent analysis and call.

[0011] According to the above technical solution, step S5 further includes: Step S51: Based on the historical monitoring data of each grid unit, the displacement change range under different time periods and different environmental conditions is counted to form a typical displacement change curve family; Step S52: Based on the regional geological category, underground structure type and key risk level, different grid units are classified and managed, and benchmark safety threshold intervals are set respectively; Step S53: Compare the current displacement change value corrected by the environment with the historical steady-state threshold range of the corresponding grid unit. If the current value significantly deviates from the steady-state change range, enter the "warning trigger candidate area"; Step S54: The system makes a comprehensive judgment on whether to trigger an early warning based on the following three dimensions: (1) Whether the displacement change amplitude exceeds the safety upper limit; (2) Whether the displacement change rate exceeds the preset growth rate threshold; (3) Whether the duration of continuous abnormality exceeds the set time threshold; Step S55: When any two or more indicators are met, the system automatically generates a "warning event" for the grid unit, where the "warning event" information includes: grid unit number, abnormal start time, abnormal duration, maximum change value, current level, comparison background curve number and recommended response operation; Step S56: The warning information is encrypted by the system and pushed to the corresponding responsible person's terminal.

[0012] According to the above technical solution, in step S444, the method of determining a comprehensive offset weight factor specifically includes: Step S4441: Obtain the currently acquired horizontal displacement time series , the background control curve is , contains n sampling points in total, then the instantaneous offset is: ; Step S4442: Calculate the overall deviation mean, fluctuation range, and periodic difference index respectively. The corresponding calculation expressions are: Overall shift mean ; Fluctuation range ; Divide the entire data into m continuous time windows and calculate the local offset mean of each segment , and then find the maximum offset difference between adjacent segments: ; Step S4443: Comprehensive offset weight factor The calculation formula is: ; in, is a positive real constant that adjusts the sensitivity, the exponential function Used to amplify the impact of the overall offset, so that large long-term offsets can quickly cause a response; logarithmic function Control the influence of noise fluctuations so that slight changes caused by high-frequency noise have limited impact on the weight factor; hyperbolic tangent function Provide boundary enhancement response to phase differences so that sudden changes are highlighted but not over-amplified.

[0013] A horizontal displacement intelligent monitoring system based on a reciprocating inclinometer robot includes a network deployment module, a data acquisition module, a database, a data analysis module, a data correction module, and a safety warning module. The network deployment module, the data acquisition module, the database, the data analysis module, the data correction module, and the safety warning module are directly connected to each other; wherein, The grid deployment module is used to divide the monitoring area into a number of grid units and deploy a reciprocating inclinometer robot and an environmental sensor in each grid unit; The data acquisition module is used to collect displacement data and environmental data of each grid unit in real time; The database is used to store the displacement data and environmental data collected by the data acquisition module, and supports multi-dimensional association query operations based on time, location and parameter type; The data analysis module is used to perform statistical analysis on the multi-source time series data collected in the database, identify the correlation between horizontal displacement and environmental factors, and set the dynamic environmental interference threshold of each grid cell; The data correction module is used to analyze and process the displacement data of the disturbed grid cells, identify the interference level, and perform different levels of data correction according to the degree of influence of environmental variables; The safety warning module is used to determine whether the current state of the grid unit is within the safety threshold range based on the corrected displacement change data. When continuous anomalies meet the set warning conditions, the system automatically generates a warning event and pushes a notification.

[0014] According to the above technical solution, the data correction module includes an interference level determination unit, a historical data extraction unit and an offset factor calculation unit; wherein, The interference level determination unit is used to classify the grid unit into three states: "negligible interference", "slight interference" or "significant interference" according to the comparison result of the current environmental parameters and the historical interference threshold; The historical data extraction unit is adapted to extract historical time period samples similar to the current environmental conditions from the database under a significant interference state and construct a background control curve; The offset factor calculation unit is used to calculate a comprehensive offset weight factor according to the offset relationship between the current displacement curve and the background control curve.

[0015] According to the above technical solution, the security warning module includes a historical template construction unit, a multi-dimensional evaluation unit and a warning generation unit; wherein, The historical template construction unit is used to mine the typical horizontal displacement response characteristics of each grid unit under different environmental backgrounds based on long-term monitoring data, summarize its steady-state variation range, and construct a historical curve set containing representative fluctuation patterns; The multi-dimensional evaluation unit is used to perform status evaluation from multiple dimensions based on the displacement behavior in the current monitoring period; The warning generation unit is used to generate a warning record according to the evaluation result and send it to a designated management terminal through the information system.

[0016] Compared with the existing technology, the beneficial effects achieved by the present invention are as follows: the present invention significantly improves the anti-interference ability and data reliability of displacement monitoring in complex environments through a dynamic environmental interference threshold mechanism and a hierarchical data correction strategy; further sets safety thresholds based on multi-dimensional indicators such as displacement amplitude, change rate and abnormality duration, realizes intelligent risk assessment and automatic early warning push, effectively solves the problems of high false alarm and missed alarm rates and poor environmental adaptability of traditional methods, and provides high-precision, adaptive, and low-manpower-dependent displacement monitoring solutions for projects such as slopes and tunnels. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a schematic diagram of the overall process of a method for intelligently monitoring horizontal displacement based on a reciprocating inclinometer robot provided in the first embodiment of the present invention; Figure 2 This is a framework diagram of a horizontal displacement intelligent monitoring system based on a reciprocating inclinometer robot provided in the second embodiment of the present invention; Figure 3 A schematic diagram of the composition of a data correction module provided in the second embodiment of the present invention; Figure 4 This is a schematic diagram of the composition of the security warning module provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] Example 1 Figure 1 This is a schematic diagram of the overall process of a method for intelligently monitoring horizontal displacement based on a reciprocating inclinometer robot provided in the first embodiment of the present invention; In this embodiment, the method includes the following steps: Step S1: Divide the monitoring area into several grid cells, and deploy a reciprocating inclinometer robot and environmental sensors in each grid cell. By dividing the monitoring area into multiple grid cells and deploying a reciprocating inclinometer robot and environmental sensors in each cell, comprehensive coverage of complex terrain or engineering areas is achieved, effectively improving the spatial resolution and deployment flexibility of displacement monitoring. Step S2: Establish a unified database to store the horizontal displacement data collected by the robot in each grid cell and the environmental data collected by the environmental sensor in real time in the database. By building a unified database, the real-time horizontal displacement data and environmental factor monitoring data are integrated, and through multidimensional statistics and causal analysis, the specific source and impact intensity of environmental interference are identified, thereby providing a basis for displacement data correction and significantly improving the reliability and interpretability of the data. Step S3: Statistically analyze the multi-source data in the database to determine the correlation between horizontal displacement changes and environmental factors. Based on the correlation analysis results, a dynamic environmental interference threshold is set for each grid cell. This breaks through the limitations of traditional static judgment criteria and implements a more adaptive anomaly detection mechanism, effectively reducing false positives and missed negatives. Step S4: For grid cells that are not disturbed by the environment or are less disturbed, the displacement data collected by the robot is directly used for quantitative analysis; for grid cells that are disturbed by the environment, the displacement data is corrected according to the results of the environmental variable correlation analysis; Step S5: Based on the results of the quantitative analysis, a safety threshold for horizontal displacement is set for each grid unit. When the displacement change of a grid unit exceeds the safety threshold, the system automatically issues a warning message.

[0020] Step S3 further comprises: Step S31: In each grid cell, historical horizontal displacement data within a preset time window and environmental monitoring data of a corresponding time period are selected, and their change trends are extracted to form a time series curve data set for comparison; Step S32: performing a horizontal comparison of the horizontal displacement changes of multiple grid cells in similar time periods, identifying grid cells that still exhibit abnormal displacement under similar environmental conditions, and marking the cells as interference-sensitive cells; Step S33: For the time series between each environmental factor and displacement change, a delay window is set to observe causal clues. When a certain environmental parameter jumps or fluctuates continuously, it is analyzed whether a displacement change is induced within the delay time, thereby determining the potential driving effect of the factor on the displacement change; Step S34: Calculate the impact score based on the frequency and amplitude of displacement fluctuations caused by each type of environmental factor in each grid cell, and use this as a reference for subsequent interference intensity assessment; Step S35: Preliminarily setting the environmental interference threshold for each grid cell, the threshold being set based on a typical time period in the historical data of the cell where displacement fluctuations are frequent and highly consistent with changes in environmental factors; Step S36: During the monitoring process, when the environmental parameter monitored by a certain grid unit exceeds its set interference threshold, the displacement data in the time period is automatically marked as "potentially interfered data".

[0021] Step S34 further includes: Step S341: Divide the data of each type of environmental factor in different time periods in each grid unit into preset time windows, extract the change amplitude, change frequency and duration of the environmental factor in each time window, and form an environmental change feature group; Step S342: synchronously extracting displacement change data within a corresponding time window, and assigning a change level identifier to the window based on the severity of the displacement fluctuation (e.g., change rate or jump amplitude); Step S343: Count the matching degrees between the environmental feature groups and the displacement level identifiers in multiple time windows. The statistical indicators include: The proportion of significant displacement changes in the window with significant environmental changes; The incidence of abnormal displacement during the stable window of environmental changes; the approximate synchronization ratio between the magnitude of environmental changes and the magnitude of displacement responses; Step S344: performing weighted fusion on the above statistical indicators according to preset weights to obtain a reference coefficient of the impact of the environmental factor on the displacement in the grid unit; Step S345: Normalize the reference coefficient to a percentage score, which serves as the "impact score" of the environmental factor within the unit, and record it in the database for reference in subsequent interference judgment and threshold adjustment. By statistically analyzing the degree of match between the environmental factor and the displacement response, and combining them with preset weights for weighted fusion, the degree of influence of different environmental variables on the displacement data can be effectively quantified. The multidimensional indicators used not only consider the direct drive of displacement by significant environmental events, but also encompass atypical causes of displacement anomalies, helping to establish a more stable and comprehensive interference discrimination model, thereby achieving a quantitative assessment of the credibility of displacement data under complex interference backgrounds, effectively improving the system's automatic correction capability and anti-interference performance in non-ideal environments, and reducing the false alarm rate. Step S346: If multiple environmental factors are monitored, a set of scoring vectors is formed for each grid unit to evaluate the interference dominance of each factor, and when the score reaches a set limit value, a weight update procedure is triggered.

[0022] Step S4 further comprises: Step S41: For each grid cell, after each monitoring cycle, the system automatically determines the impact level of the cell based on whether the current environmental parameters exceed the aforementioned dynamic environmental interference threshold, marking it as "negligible interference", "slight interference" or "significant interference"; Step S42: For a grid cell marked as “negligible interference”, the horizontal displacement raw data collected by the robot is directly adopted and enters the subsequent quantitative evaluation program to calculate the actual horizontal offset value and trend evolution; Step S43: When a grid cell is marked as “slightly disturbed”, a light correction process is started, which includes performing a linear proportional correction on the displacement data and the fluctuation amplitude of the current environmental factors to generate preliminary compensated displacement data; Step S44: When a grid unit is marked as "significant interference", the system calls the displacement change records under similar environmental conditions in its historical data, generates a background control curve, and performs offset comparison with the current acquisition curve. The correction factor is used to repair the current displacement data, and the repair result is marked as "environmental correction value"; for different interference levels, the system adopts mild linear correction and deep repair strategies based on historical control curves, combined with the offset factor calculation model, to reasonably correct the interfered data, so that the monitoring data can still maintain stability and representativeness under the background of strong environmental fluctuations.

[0023] Step S44 further includes: Step S441: After determining that a grid cell is "significantly disturbed", the system automatically extracts historical time periods with environmental parameters similar to the current time period from the historical monitoring database of the grid cell to construct a candidate sample set; Step S442: Filter out historical sample segments whose horizontal displacement change trend is consistent with the displacement trend direction of the current time period from the candidate sample set, and generate a historical background displacement curve based on their number, duration, and change amplitude as the "background control curve"; Step S443: Synchronize the currently acquired displacement curve with the background control curve, and adjust the time axis starting point and data sampling interval to make the two comparable in the time domain; Step S444: Calculate the offset of the two curves at the corresponding time points, and determine a comprehensive offset weight factor based on the overall offset mean, fluctuation amplitude, and stage difference index; Step S445: using the offset weight factor to perform point-by-point correction on the data of each time node in the currently acquired displacement curve, the correction method is to perform amplitude compression, lifting or translation operation on each original displacement value according to the weight factor; Step S446: The entire corrected curve is marked as an "environmental correction value curve" and archived in the system database together with the original data, environmental data, and offset comparison information for subsequent analysis and call.

[0024] Step S5 further comprises: Step S51: Based on the historical monitoring data of each grid unit, the displacement change range under different time periods and different environmental conditions is counted to form a typical displacement change curve family; Step S52: Based on the regional geological category, underground structure type and key risk level, different grid units are classified and managed, and benchmark safety threshold intervals are set respectively; Step S53: Compare the current displacement change value corrected by the environment with the historical steady-state threshold range of the corresponding grid unit. If the current value significantly deviates from the steady-state change range, enter the "warning trigger candidate area"; Step S54: The system makes a comprehensive judgment on whether to trigger an early warning based on the following three dimensions: (1) Whether the displacement change amplitude exceeds the safety upper limit; (2) Whether the displacement change rate exceeds the preset growth rate threshold; (3) Whether the duration of continuous abnormality exceeds the set time threshold; Step S55: When any two or more indicators are met, the system automatically generates a "warning event" for the grid unit, where the "warning event" information includes: grid unit number, abnormal start time, abnormal duration, maximum change value, current level, comparison background curve number and recommended response operation; Step S56: The warning information is encrypted and pushed to the responsible terminal. A multidimensional assessment model is constructed based on the three dimensions of displacement amplitude, change rate, and abnormality duration, forming a more comprehensive abnormality determination standard. When multiple indicators exceed the limit simultaneously, detailed warning event information is automatically generated and pushed to the responsible terminal, achieving closed-loop control from monitoring to response, and improving the automation and intelligence level of the system.

[0025] In step S444, the method for determining a comprehensive offset weight factor specifically includes: Step S4441: Obtain the currently acquired horizontal displacement time series , the background control curve is , contains n sampling points in total, then the instantaneous offset is: ; Step S4442: Calculate the overall deviation mean, fluctuation range, and stage difference index respectively. The corresponding calculation expressions are: Overall shift mean ; Fluctuation range ; Divide the entire data into m continuous time windows and calculate the local offset mean of each segment , and then find the maximum offset difference between adjacent segments: ; Step S4443: Comprehensive offset weight factor The calculation formula is: ; in, A positive real constant to adjust the sensitivity can be obtained through offline training or expert calibration, and the exponential function Used to amplify the impact of the overall offset, so that large long-term offsets can quickly cause a response; logarithmic function Control the influence of noise fluctuations so that slight changes caused by high-frequency noise have limited impact on the weight factor; hyperbolic tangent function Provide boundary enhancement response to phase differences so that sudden changes are highlighted but not over-amplified.

[0026] Example 2 Figure 2 This is a framework diagram of a horizontal displacement intelligent monitoring system based on a reciprocating inclinometer robot provided in the second embodiment of the present invention; In this embodiment, the system includes: a network deployment module, a data acquisition module, a database, a data analysis module, a data correction module and a security warning module, and the network deployment module, the data acquisition module, the database, the data analysis module, the data correction module and the security warning module are directly connected to each other; wherein, The grid deployment module is used to divide the monitoring area into several grid units and deploy a reciprocating inclinometer robot and environmental sensors in each grid unit; The data acquisition module is used to collect displacement data and environmental data of each grid unit in real time; The database is used to store the displacement data and environmental data collected by the data acquisition module, and supports multi-dimensional correlation query operations based on time, location and parameter type; The data analysis module is used to perform statistical analysis on the multi-source time series data collected in the database, identify the correlation between horizontal displacement and environmental factors, and set the dynamic environmental interference threshold for each grid cell; The data correction module is used to analyze and process the displacement data of the disturbed grid cells, identify the interference level, and perform different levels of data correction according to the degree of influence of environmental variables; The safety warning module is used to determine whether the current state of the grid unit is within the safety threshold range based on the corrected displacement change data. When continuous anomalies meet the set warning conditions, the system automatically generates a warning event and pushes a notification.

[0027] Figure 3 The data correction module provided in the second embodiment of the present invention is shown in FIG. Figure 3 As shown, the data correction module includes an interference level determination unit, a historical data extraction unit and an offset factor calculation unit; wherein, The interference level determination unit is used to classify grid cells into three states: "negligible interference", "slight interference" or "significant interference" based on the comparison results of current environmental parameters and historical interference thresholds; The historical data extraction unit is suitable for extracting samples of historical time periods similar to the current environmental conditions from the database under significant interference conditions to construct a background control curve; The offset factor calculation unit is used to calculate a comprehensive offset weight factor according to the offset relationship between the current displacement curve and the background control curve.

[0028] Figure 4The schematic diagram of the security warning module provided in the second embodiment of the present invention is as follows: Figure 4 As shown, the security warning module includes a historical template construction unit, a multi-dimensional evaluation unit and a warning generation unit; wherein, The historical template construction unit is used to mine the typical horizontal displacement response characteristics of each grid unit under different environmental backgrounds based on long-term monitoring data, summarize its steady-state variation range, and construct a historical curve set containing representative fluctuation patterns; The multi-dimensional evaluation unit is used to evaluate the status from multiple dimensions based on the displacement behavior within the current monitoring period; The warning generation unit is used to generate a warning record based on the evaluation results and send it to the designated management terminal through the information system; This application significantly improves the anti-interference ability and data reliability of displacement monitoring in complex environments through a dynamic environmental interference threshold mechanism and a hierarchical data correction strategy; further sets safety thresholds based on multi-dimensional indicators such as displacement amplitude, change rate and abnormality duration, realizes intelligent risk assessment and automatic early warning push, and effectively solves the problems of high false alarm and missed alarm rates and poor environmental adaptability of traditional methods, providing high-precision, adaptive, and low-manpower-dependent displacement monitoring solutions for slope, tunnel and other projects.

[0029] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0030] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0031] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0032] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A horizontal displacement intelligent monitoring method based on a reciprocating inclinometer robot, characterized in that: The following steps are involved: Step S1: Divide the monitoring area into several grid units, and deploy a reciprocating inclinometer robot and environmental sensors in each grid unit; Step S2: Establish a unified database and store the horizontal displacement data collected by the robot in each grid unit and the environmental data collected by the environmental sensor in the database in real time; Step S3: Statistically analyze the multi-source data in the database to determine the correlation between horizontal displacement changes and environmental factors, and set a dynamic environmental interference threshold for each grid cell based on the correlation analysis results; Step S4: For grid cells that are not disturbed by the environment or are less disturbed, the displacement data collected by the robot is directly used for quantitative analysis; for grid cells that are disturbed by the environment, the displacement data is corrected according to the results of the environmental variable correlation analysis; Step S5: Based on the results of the quantitative analysis, a safety threshold for horizontal displacement is set for each grid unit. When the displacement change of a grid unit exceeds the safety threshold, the system automatically issues a warning message.

2. The horizontal displacement intelligent monitoring method based on a reciprocating inclinometer robot according to claim 1 is characterized in that: The step S3 further comprises: Step S31: In each grid cell, historical horizontal displacement data within a preset time window and environmental monitoring data of a corresponding time period are selected, and their change trends are extracted to form a time series curve data set for comparison; Step S32: performing a horizontal comparison of the horizontal displacement changes of multiple grid cells in similar time periods, identifying grid cells that still exhibit abnormal displacement under similar environmental conditions, and marking the cells as interference-sensitive cells; Step S33: For the time series between each environmental factor and displacement change, a delay window is set to observe causal clues. When a certain environmental parameter jumps or fluctuates continuously, it is analyzed whether a displacement change is induced within the delay time, thereby determining the potential driving effect of the factor on the displacement change; Step S34: Calculate the impact score based on the frequency and amplitude of displacement fluctuations caused by each type of environmental factor in each grid cell, and use this as a reference for subsequent interference intensity assessment; Step S35: Preliminarily setting an environmental interference threshold for each grid cell, wherein the threshold is set based on a typical time period in the historical data of the cell where displacement fluctuations are frequent and highly consistent with changes in environmental factors; Step S36: During the monitoring process, when the environmental parameter monitored by a certain grid unit exceeds its set interference threshold, the displacement data in the time period is automatically marked as "potentially interfered data".

3. The method for intelligently monitoring horizontal displacement based on a reciprocating inclinometer robot according to claim 2, characterized in that: Step S34 further includes: Step S341: Divide the data of each type of environmental factor in different time periods in each grid unit into preset time windows, extract the change amplitude, change frequency and duration of the environmental factor in each time window, and form an environmental change feature group; Step S342: Synchronously extract the displacement change data within the corresponding time window, and assign a change level identifier to the window based on the severity of the displacement fluctuation; Step S343: Count the matching degrees between the environmental feature groups and the displacement level identifiers in multiple time windows. The statistical indicators include: The proportion of significant displacement changes in the window with significant environmental changes; The incidence of abnormal displacement during the stable window of environmental changes; the approximate synchronization ratio between the magnitude of environmental changes and the magnitude of displacement responses; Step S344: performing weighted fusion on the above statistical indicators according to preset weights to obtain a reference coefficient of the impact of the environmental factor on the displacement in the grid unit; Step S345: Normalize the reference coefficient to a percentage score value as the "impact score" of the environmental factor in the unit, and record it in the database for reference in subsequent interference judgment and threshold adjustment; Step S346: If multiple environmental factors are monitored, a set of scoring vectors is formed for each grid unit to evaluate the interference dominance of each factor, and when the score reaches a set limit value, a weight update procedure is triggered.

4. The method for intelligently monitoring horizontal displacement based on a reciprocating inclinometer robot according to claim 1, characterized in that: The step S4 further comprises: Step S41: For each grid cell, after each monitoring cycle, the system automatically determines the impact level of the cell based on whether the current environmental parameters exceed the previously set dynamic environmental interference threshold, marking it as "negligible interference", "slight interference", or "significant interference". Step S42: For grid cells marked as "negligible interference," the raw horizontal displacement data collected by the robot is directly used and enters the subsequent quantitative evaluation process to calculate the actual horizontal offset value and trend evolution; Step S43: When a grid cell is marked as "slightly disturbed", a light correction process is started, which includes performing a linear proportional correction on the displacement data and the fluctuation amplitude of the current environmental factors to generate preliminary compensated displacement data; Step S44: When a grid cell is marked as "significant interference", the system calls its historical data to record displacement changes under similar environmental conditions, generates a background control curve, and performs an offset comparison with the current acquisition curve. The correction factor is used to repair the current displacement data, and the repair result is marked as "environmental correction value".

5. The method for intelligently monitoring horizontal displacement based on a reciprocating inclinometer robot according to claim 4, characterized in that: The step S44 further comprises: Step S441: After determining that a grid cell is "significantly disturbed", the system automatically extracts historical time periods with environmental parameters similar to the current time period from the historical monitoring database of the grid cell to construct a candidate sample set; Step S442: Filter out historical sample segments from the candidate sample set whose horizontal displacement change trend matches the displacement trend direction of the current time period, and generate a historical background displacement curve based on their number, duration, and change amplitude as the "background control curve"; Step S443: Synchronize the currently acquired displacement curve with the background control curve, and adjust the time axis starting point and data sampling interval to make the two comparable in the time domain; Step S444: Calculate the offset of the two curves at the corresponding time points, and determine a comprehensive offset weight factor based on the overall offset mean, fluctuation amplitude, and stage difference index; Step S445: using the offset weight factor to perform point-by-point correction on the data of each time node in the currently acquired displacement curve, the correction method is to perform amplitude compression, lifting or translation operation on each original displacement value according to the weight factor; Step S446: The entire corrected curve is marked as "environmental correction value curve" and archived in the system database together with the original data, environmental data, and offset comparison information for subsequent analysis and call.

6. The method for intelligently monitoring horizontal displacement based on a reciprocating inclinometer robot according to claim 1, characterized in that: The step S5 further comprises: Step S51: Based on the historical monitoring data of each grid unit, the displacement change range under different time periods and different environmental conditions is counted to form a typical displacement change curve family; Step S52: Based on the regional geological category, underground structure type and key risk level, different grid units are classified and managed, and benchmark safety threshold intervals are set respectively; Step S53: Compare the current displacement change value corrected by the environment with the historical steady-state threshold range of the corresponding grid unit. If the current value significantly deviates from the steady-state change range, enter the "warning trigger candidate area"; Step S54: The system makes a comprehensive judgment on whether to trigger an early warning based on the following three dimensions: (1) Whether the displacement change amplitude exceeds the safety upper limit; (2) Whether the displacement change rate exceeds the preset growth rate threshold; (3) Whether the duration of continuous abnormality exceeds the set time threshold; Step S55: When any two or more indicators are met, the system automatically generates a "warning event" for the grid unit. The "warning event" information includes: grid unit number, abnormal start time, abnormal duration, maximum change value, current level, comparison background curve number, and recommended response operation; Step S56: The warning information is encrypted by the system and pushed to the corresponding responsible person's terminal.

7. The method for intelligently monitoring horizontal displacement based on a reciprocating inclinometer robot according to claim 5, characterized in that: In step S444, the method for determining a comprehensive offset weight factor specifically includes: Step S4441: Obtain the currently acquired horizontal displacement time series , the background control curve is , contains n sampling points in total, then the instantaneous offset is: ; Step S4442: Calculate the overall deviation mean, fluctuation range, and stage difference index respectively. The corresponding calculation expressions are: Overall shift mean ; Fluctuation range ; Divide the entire data into m continuous time windows and calculate the local offset mean of each segment , and then find the maximum offset difference between adjacent segments: ; Step S4443: Comprehensive offset weight factor The calculation formula is: ； in, A positive real constant that adjusts the sensitivity.

8. An intelligent horizontal displacement monitoring system based on a reciprocating inclinometer robot, characterized by: The horizontal displacement intelligent monitoring system includes a network deployment module, a data acquisition module, a database, a data analysis module, a data correction module and a safety warning module. The network deployment module, the data acquisition module, the database, the data analysis module, the data correction module and the safety warning module are directly connected to each other; wherein, The grid deployment module is used to divide the monitoring area into a number of grid units and deploy a reciprocating inclinometer robot and an environmental sensor in each grid unit; The data acquisition module is used to collect displacement data and environmental data of each grid unit in real time; The database is used to store the displacement data and environmental data collected by the data acquisition module, and supports multi-dimensional association query operations based on time, location and parameter type; The data analysis module is used to perform statistical analysis on the multi-source time series data collected in the database, identify the correlation between horizontal displacement and environmental factors, and set the dynamic environmental interference threshold of each grid cell; The data correction module is used to analyze and process the displacement data of the disturbed grid cells, identify the interference level, and perform different levels of data correction according to the degree of influence of environmental variables; The safety warning module is used to determine whether the current state of the grid unit is within the safety threshold range based on the corrected displacement change data. When continuous anomalies meet the set warning conditions, the system automatically generates a warning event and pushes a notification.

9. The horizontal displacement intelligent monitoring system based on the reciprocating inclinometer robot according to claim 8, characterized in that: The data correction module includes an interference level determination unit, a historical data extraction unit and an offset factor calculation unit; wherein, The interference level determination unit is used to classify the grid unit into three states: "negligible interference", "slight interference" or "significant interference" based on the comparison result of the current environmental parameters and the historical interference threshold; The historical data extraction unit is adapted to extract historical time period samples similar to the current environmental conditions from the database under a significant interference state and construct a background control curve; The offset factor calculation unit is used to calculate a comprehensive offset weight factor according to the offset relationship between the current displacement curve and the background control curve.

10. The horizontal displacement intelligent monitoring system based on the reciprocating inclinometer robot according to claim 8, characterized in that: The security warning module includes a history template construction unit, a multi-dimensional evaluation unit and a warning generation unit; wherein, The historical template construction unit is used to mine the typical horizontal displacement response characteristics of each grid unit under different environmental backgrounds based on long-term monitoring data, summarize its steady-state variation range, and construct a historical curve set containing representative fluctuation patterns; The multi-dimensional evaluation unit is used to perform status evaluation from multiple dimensions based on the displacement behavior in the current monitoring period; The warning generation unit is used to generate a warning record according to the evaluation result and send it to a designated management terminal through the information system.

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