Based on the slope construction safety risk assessment system

Abstract

The present invention discloses a slope construction safety risk assessment system, which relates to the field of slope construction technology. It constructs a complete system consisting of four major modules: data acquisition, dynamic disturbance identification, risk assessment, and intelligent early warning and decision-making. By high-frequency acquisition of multi-source data such as displacement, cracks, pore water pressure, seismic disturbance and blasting vibration, combined with time series analysis and disturbance feature extraction algorithm, it can realize rapid identification of sudden disturbance events; further, risk score prediction is performed based on a polynomial regression model, and multi-level warning is generated by comparison with set thresholds; finally, safety control suggestions are dynamically generated in combination with construction status, support parameters and disturbance intensity, effectively improving the system's response speed and prediction accuracy to sudden slope instability risks, and realizing real-time perception and intelligent control of risks during slope construction.

Description

Technical Field

[0001] The present invention relates to the technical field of slope construction, and in particular to a safety risk assessment system based on slope construction. Background Art

[0002] As infrastructure construction expands into complex geological regions such as mountainous and hilly areas, the safety risks of slope construction are becoming increasingly prominent. Traditional slope risk assessment methods rely on manual judgment or static analysis models. These methods suffer from subjectivity and poor real-time performance, making it difficult to effectively address the multi-source risk factors in dynamic construction environments. Currently, with the development of the Internet of Things, big data, and artificial intelligence technologies, building a slope construction safety risk assessment system that integrates data collection, intelligent analysis, and dynamic early warning has become a key approach to improving project safety management.

[0003] The existing technology has the following shortcomings:

[0004] Existing slope risk assessment models often suffer from response lags and misjudgments when faced with sudden geological disturbances. For example, under stress concentration conditions in specific areas, the slope structure can experience nonlinear deformation or even complete instability in a short period of time due to earthquake microtremors, blasting disturbances, or the transient infiltration of water from geological fissures. These sudden disturbances, due to their lack of precursory characteristics, are often overlooked by mainstream assessment models. Furthermore, dynamic modeling and accurate identification of these high-risk, low-probability events are crucial. Summary of the Invention

[0005] The purpose of the present invention is to provide a slope construction safety risk assessment system to address the shortcomings of the background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: based on the slope construction safety risk assessment system, it includes a data acquisition module, a dynamic disturbance identification module, a risk assessment module and an intelligent early warning and decision-making module;

[0007] The data acquisition module is used to obtain multi-source monitoring data from the slope construction site, including displacement, cracks, pore water pressure, seismic disturbance and blasting vibration data, and generate corresponding data sequences;

[0008] The dynamic disturbance identification module pre-processes the data sequence based on the time series analysis method and uses the disturbance feature extraction algorithm to obtain the disturbance feature sequence of the slope structure;

[0009] The risk assessment module analyzes the disturbance feature sequence based on the risk assessment model, generates a dynamic slope risk score, compares the dynamic slope risk score with a predetermined risk threshold, and generates different levels of warning levels based on the comparison results;

[0010] The risk assessment model is a pre-trained machine learning model, specifically including: converting the abnormal values of blasting vibration peak velocity and slope inclination angle change into comprehensive feature vectors, using the comprehensive feature vectors as input to the machine learning model, the machine learning model predicting the slope dynamic risk score label for each group of comprehensive feature vectors as a prediction target, minimizing the sum of prediction errors for all slope dynamic risk score labels as a training target, training the machine learning model until the sum of prediction errors reaches convergence, stopping the model training, and determining the slope dynamic risk score based on the model output results, wherein the machine learning model is a polynomial regression model;

[0011] The intelligent early warning and decision-making module generates a slope safety early warning sequence based on the dynamic risk score of the slope, and generates slope safety control recommendations based on the construction status, support parameters and disturbance intensity, which are used to provide construction adjustment or reinforcement plans.

[0012] Preferably, the data acquisition module includes a displacement monitoring sensor, a crack monitoring sensor, a pore water pressure gauge, a seismic disturbance monitoring device, a blasting vibration monitoring system and an edge computing terminal;

[0013] The displacement monitoring sensor is a GNSS high-precision displacement meter and / or an optical measuring instrument, the crack monitoring sensor is a crack meter and / or an optical fiber sensor, and the pore water pressure meter is a piezometer and / or an optical fiber water pressure sensor.

[0014] Preferably, the dynamic disturbance identification module is used to calculate the peak velocity of blasting vibration and evaluate the degree of disturbance of the construction blasting on the slope stability:

[0015] At the blasting construction site, a blasting vibrometer is used to collect vibration signal data at different monitoring points to record the ground vibration response during blasting, including the three-dimensional vibration velocity components. In the blasting vibration signal, the peak vibration velocity in each direction X, Y, and Z is expressed as: The blasting vibration peak velocity PPV is calculated by vector synthesis and the expression is: ; Where PPV is the composite peak vibration velocity, represents the peak vibration velocity in the X direction, represents the peak vibration velocity in the Y direction, Indicates the peak vibration velocity in the Z direction; 、 、 They represent the instantaneous vibration speeds in the X, Y, and Z directions at time t.

[0016] Preferably, the abnormal value of the slope inclination angle change is calculated to predict the slope instability trend in advance, wherein the abnormal value of the slope inclination angle change is calculated as follows:

[0017] Obtain the sequence of inclination angle change data within a period of time from the slope monitoring system: ; where: is the slope inclination angle of the i-th measurement, and n is the total number of sampling points; calculate the inclination angle change rate between each adjacent moment : ; where: represents the inclination angle change amount at the i-th time step; and are the inclination angle values at the current and previous moments respectively; calculate the first quartile Q1 and the third quartile Q3 of the inclination angle change amount Δθ: where: Q1 represents the value at the 25% position in the data; Q3 represents the value at the 75% position in the data, calculate the interquartile range IQR, and the expression is: IQR = Q3 - Q1; where: IQR is used to measure the distribution range of the data, set the range of normal data: lower boundary = Q1 - 1.5×IQR, upper boundary = Q3 + 1.5×IQR; for each data point , if it satisfies: < Q1 - 1.5×IQR or > Q3 + 1.5×IQR; then it is considered an outlier.

[0018] Preferably, compare the obtained slope dynamic risk score with a predetermined risk threshold, and generate different levels of early warning levels according to the comparison result, specifically including:

[0019] Compare the obtained slope dynamic risk score with the gradient risk threshold, the gradient risk threshold includes the first risk threshold and the second risk threshold, and the first risk threshold is less than the second risk threshold, and compare the slope dynamic risk score with the first risk threshold and the second risk threshold respectively;

[0020] If the slope dynamic risk score is greater than the second risk threshold, at this time, trigger a first-level early warning signal, mark it as a high-risk level, and require immediate emergency reinforcement measures;

[0021] If the slope dynamic risk score is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, at this time, trigger a second-level early warning signal, mark it as a medium-risk level, and it is recommended to monitor and adjust the construction plan;

[0022] If the slope dynamic risk score is less than the first risk threshold, at this time, no early warning signal is triggered and normal construction is carried out.

[0023] Preferably, the intelligent early warning and decision-making module further obtains construction status data, support parameters and disturbance intensity information, the construction status data including construction stage, blasting parameters and rainfall conditions, and the support parameters including support structure type and current bearing capacity; after generating the early warning level, combined with the support parameters and disturbance intensity, outputs control suggestions for adjusting the blasting charge, increasing support components, optimizing the drainage system or suspending construction.

[0024] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0025] By integrating data acquisition, dynamic disturbance identification, risk assessment, and intelligent early warning and decision-making modules, the present invention constructs a full-process risk management system from multi-source data perception to intelligent early warning response. The system not only acquires multidimensional monitoring data including displacement, cracks, pore water pressure, seismic disturbances, and blasting vibrations in real time, but also accurately identifies sudden disturbance events through time series analysis and feature extraction algorithms. It is particularly suitable for identifying high-risk, low-precursor nonlinear disturbances such as earthquake microseisms and blasting vibrations. By constructing a comprehensive feature vector based on blasting vibration peak velocity and inclination angle anomalies, the system introduces a polynomial regression model to score the dynamic risk of the slope and combines it with gradient risk thresholds to perform multi-level early warning classification, effectively improving the accuracy and response speed of the early warning. Furthermore, it generates targeted construction control recommendations based on construction status, support parameters, and disturbance intensity, achieving closed-loop management from risk identification and classification to decision support. This system significantly improves the intelligence level and safety control capabilities of slope construction and has broad engineering application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0027] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0029] For examples, see Figure 1 As shown, the slope construction safety risk assessment system described in this embodiment includes a data acquisition module, a dynamic disturbance identification module, a risk assessment module, and an intelligent early warning and decision-making module;

[0030] The data acquisition module is used to obtain multi-source monitoring data from the slope construction site, including displacement, cracks, pore water pressure, seismic disturbance and blasting vibration data, and generate corresponding data sequences;

[0031] The dynamic disturbance identification module pre-processes the data sequence based on the time series analysis method and uses the disturbance feature extraction algorithm to obtain the disturbance feature sequence of the slope structure;

[0032] The risk assessment module analyzes the disturbance feature sequence based on the risk assessment model, generates a dynamic slope risk score, compares the dynamic slope risk score with a predetermined risk threshold, and generates different levels of warning levels based on the comparison results;

[0033] The intelligent early warning and decision-making module generates a slope safety early warning sequence based on the dynamic risk score of the slope, and generates slope safety control recommendations based on the construction status, support parameters and disturbance intensity, which are used to provide construction adjustment or reinforcement plans.

[0034] The data acquisition module of the present invention is applied to slope construction sites, and uses Internet of Things (IoT) sensors, wireless data transmission networks, and edge computing terminals to achieve real-time collection and processing of multi-source slope monitoring data. Specifically, the hardware configuration of the data acquisition module includes:

[0035] Displacement monitoring sensors (GNSS high-precision displacement meters, optical measuring instruments): used to monitor displacement changes on the slope surface or deep soil and obtain three-dimensional coordinate data;

[0036] Crack monitoring sensors (crack meters, fiber optic sensors): used to measure the width change and expansion rate of slope cracks and obtain crack dynamic data;

[0037] Pore water pressure meter (osmometer, fiber optic water pressure sensor): used to monitor groundwater level changes and soil pore water pressure, and provide hydrological condition information;

[0038] Seismic disturbance monitoring equipment (accelerometers, strong motion meters): used to record the effects of earthquake waves and ground vibrations on slopes;

[0039] Blasting vibration monitoring system (blasting vibration meter): used to monitor the vibration impact of construction blasting on the slope in real time and analyze the propagation and attenuation characteristics of vibration waves;

[0040] Edge computing terminal: includes data storage, preprocessing and wireless communication modules to realize local storage and remote transmission of monitoring data.

[0041] Before slope construction, various sensors are deployed at key locations based on the geological characteristics of the construction area and risk assessment requirements. For example, displacement meters and crack meters are placed on the slope surface, pore water pressure gauges are buried underground, and vibration meters are installed in the blasting area. All sensors are connected to the data acquisition system and undergo initial calibration, including zero point adjustment, sensitivity adjustment, and data synchronization.

[0042] The data acquisition module collects real-time data from each monitoring point at fixed time intervals (such as 1s, 10s, 1min) or event triggering mode (such as earthquake, blast triggering). The collected data includes:

[0043] Displacement data (3D coordinate changes and timestamps); crack growth data (crack width change, length change and timestamp); pore water pressure data (water pressure change value and timestamp); earthquake disturbance data (XYZ acceleration and timestamp); blasting vibration data (Vibration peak velocity, frequency and timestamp).

[0044] All data points form a multidimensional monitoring data sequence: .

[0045] Due to the complex field monitoring environment, the data may contain noise, loss or outliers. Therefore, the data acquisition module performs the following preprocessing on the raw data:

[0046] Outlier elimination: 3σ criterion or boxplot method is used to detect and eliminate abnormal data points; data smoothing: sliding average filtering or wavelet transform is used to reduce data jitter and improve signal stability; data completion: interpolation algorithms (such as linear interpolation and spline interpolation) are used to fill missing data to ensure time series integrity; time synchronization: all data sources are time-calibrated based on GPS timing to ensure synchronization of data from different sensors. The processed data is stored in the edge computing terminal and generates a high-quality monitoring data set: .

[0047] Wavelet transforms are used to reduce noise in monitoring data, removing high-frequency noise. The Z-score algorithm is also used to detect and eliminate outliers: a data point is considered an outlier if it deviates from the mean μ by more than three times the standard deviation σ. A sliding window approach is used to analyze short-term trends in monitoring data. The window size W is dynamically adjusted based on the sensor sampling frequency and geological characteristics. For each time window W, statistical characteristics such as mean, variance, kurtosis, and skewness are calculated to determine whether abnormal disturbances have occurred.

[0048] Different detection methods are used for different types of disturbance signals:

[0049] Displacement anomaly detection: Use cumulative sum control charts to analyze the sudden changes in displacement time series;

[0050] Crack propagation detection: Use the autoregressive moving average model to predict crack change trends and detect abnormal deviations;

[0051] Pore water pressure sudden change detection: Use the exponentially weighted moving average method to monitor sudden changes in water pressure;

[0052] Earthquake and blast vibration detection: Short-time Fourier transform is used to analyze the spectral characteristics of vibration signals, and historical disturbance patterns are matched based on dynamic time warping.

[0053] The peak velocity of blasting vibration is calculated to evaluate the degree of disturbance of construction blasting on slope stability. The calculation method of the peak velocity of blasting vibration is as follows:

[0054] At the blasting construction site, a blasting vibration meter (such as a seismometer or accelerometer) is used to collect vibration signal data at different monitoring points to record the ground vibration response during blasting, including the three-dimensional vibration velocity components (X, Y, and Z directions). In the blasting vibration signal, the peak vibration velocity in each direction (X, Y, and Z) is expressed as: The blasting vibration peak velocity PPV is calculated by vector synthesis and the expression is: ; Where PPV is the composite peak vibration velocity, represents the peak vibration velocity in the X direction, represents the peak vibration velocity in the Y direction, Indicates the peak vibration velocity in the Z direction; 、 、 They represent the instantaneous vibration speeds in the X, Y, and Z directions at time t.

[0055] The calculated PPV value is compared with relevant standards. For example: PPV < 10 mm / s: has little impact on slope stability; 10 mm / s ≤ PPV < 25 mm / s: may cause disturbance of loose rock and soil, and displacement changes need to be monitored; PPV ≥ 25 mm / s: may cause local slope instability or crack expansion, and support measures or adjustment of blasting parameters are needed.

[0056] Calculating the abnormal value of slope inclination angle change can monitor the subtle changes in slope inclination angle and predict the slope instability trend in advance. The calculation method of the abnormal value of slope inclination angle change is:

[0057] Obtain the sequence of tilt angle change data within a period of time from the slope monitoring system: ; where: is the slope tilt angle of the i-th measurement (unit: °); n is the total number of sampling points.

[0058] Calculate the tilt angle change rate between each adjacent moment : ; where: represents the tilt angle change amount at the i-th time step; and are the tilt angle values at the current and previous moments respectively; Calculate the first quartile Q1 and the third quartile Q3 of the tilt angle change amount Δθ: Q1 = the 25th percentile, Q3 = the 75th percentile; where: Q1 represents the value at the 25% position in the data, indicating the boundary of smaller changes; Q3 represents the value at the 75% position in the data, indicating the boundary of larger changes, Calculate the interquartile range IQR, and the expression is: IQR = Q3 - Q1; where: IQR is used to measure the distribution range of the data, indicating the 50% change range in the data set.

[0059] Set the range of normal data: lower boundary = Q1 - 1.5×IQR, upper boundary = Q3 + 1.5×IQR; where: 1.5 × IQR is used as the standard for anomaly detection and can usually effectively detect most outliers.

[0060] For each data point , if it satisfies: < Q1 - 1.5×IQR or > Q3 + 1.5×IQR; then this point is considered an outlier, indicating that there may be abnormal slope movement.

[0061] Convert the peak blasting vibration velocity and the outliers of the slope tilt angle change into a comprehensive feature vector, use the comprehensive feature vector as the input of the machine learning model, take predicting the slope dynamic risk score label for each set of comprehensive feature vectors as the prediction target, and take minimizing the sum of the prediction errors for all slope dynamic risk score labels as the training target, train the machine learning model until the sum of the prediction errors reaches convergence and then stop the model training, and determine the slope dynamic risk score according to the model output result, where the machine learning model is a polynomial regression model.

[0062] Compare the obtained slope dynamic risk score with a predetermined risk threshold, and generate different levels of early warning levels according to the comparison result, specifically including:

[0063] Comparing the obtained slope dynamic risk score with the gradient risk threshold, the gradient risk threshold includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold, and comparing the slope dynamic risk score with the first risk threshold and the second risk threshold respectively;

[0064] If the slope dynamic risk score is greater than the second risk threshold, a first-level warning signal is triggered, marking it as a high-risk level and requiring immediate emergency reinforcement measures;

[0065] If the slope dynamic risk score is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, a secondary warning signal is triggered, marking it as a medium risk level and recommending monitoring and adjusting the construction plan;

[0066] If the dynamic risk score of the slope is less than the first risk threshold, no warning signal will be triggered and construction will proceed normally.

[0067] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and construction personnel can make corresponding handling according to the level of the warning signal.

[0068] After comparing the dynamic risk score of the slope generated within a fixed time period with the predetermined risk threshold, a warning sequence P of different levels is generated. After the warning level is generated, the slope stability needs to be comprehensively evaluated in combination with the construction status, support parameters and disturbance intensity, and a construction adjustment or reinforcement plan is provided.

[0069] Construction status data includes: construction stage (such as excavation, blasting, support, backfill, etc.); blasting parameters (charge quantity, number of blasts, interval time); rainfall conditions (heavy rain, continuous precipitation, dry period); support method (anchor rods, slope protection nets, anti-slip piles, etc.) and current bearing capacity.

[0070] If the current support method (such as anchor rods) has reached its bearing limit, it is necessary to add anchor rods or replace the reinforcement scheme (such as anti-slip piles); if the pore water pressure inside the slope continues to rise, the drainage system should be optimized to avoid instability caused by rising water levels; if construction blasting causes a significant increase in PPV, it is necessary to reduce the single charge or adjust the blasting sequence.

[0071] When the slope is in a safe state, it means that the disturbance intensity is low and the current construction will not have a significant impact on the slope stability. Therefore, the construction can proceed as planned without additional adjustments and without the need for additional support measures.

[0072] When a slope enters a medium-risk state, it indicates that the disturbance intensity is at a moderate level. While the slope may face a certain risk of instability, it has not yet reached a dangerous level. At this point, a series of adjustments are necessary to mitigate the impact of construction on slope stability. For example, blasting charges can be reduced to minimize the impact of blasting vibration on the slope; drainage systems can be optimized to reduce the impact of pore water pressure; and construction loads can be reduced to minimize additional stress on the slope. Furthermore, to further enhance slope stability, anchor density can be increased to improve the slope's resistance to sliding, or shotcrete can be used to reinforce the slope surface to prevent loosening and spalling of the rock and soil.

[0073] When a slope enters a high-risk state, it indicates a high disturbance intensity and a significant risk of instability, requiring immediate action. First, construction should be halted immediately to avoid further exacerbating slope instability. At the same time, a comprehensive inspection of the support system is necessary to ensure the effectiveness of the existing support structure and to implement appropriate reinforcement measures based on actual conditions. For example, anti-slip piles can be installed to improve the slope's overall anti-slip capacity; retaining walls can be reinforced to increase the bearing capacity of the slope foot and prevent slope sliding; and drainage systems can be optimized to further minimize the impact of groundwater and reduce the threat of pore water pressure to slope stability. These measures will help restore slope stability and prevent geological disasters such as landslides.

[0074] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0075] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0076] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. Based on the slope construction safety risk assessment system, the following features are featured: It includes data acquisition module, dynamic disturbance identification module, risk assessment module and intelligent early warning and decision-making module; The data acquisition module is used to obtain multi-source monitoring data from the slope construction site, including displacement, cracks, pore water pressure, seismic disturbance and blasting vibration data, and generate corresponding data sequences; The dynamic disturbance identification module pre-processes the data sequence based on the time series analysis method and uses the disturbance feature extraction algorithm to obtain the disturbance feature sequence of the slope structure; The risk assessment module analyzes the disturbance feature sequence based on the risk assessment model, generates a dynamic slope risk score, compares the dynamic slope risk score with a predetermined risk threshold, and generates different levels of warning levels based on the comparison results; The risk assessment model is a pre-trained machine learning model, specifically including: converting the abnormal values of blasting vibration peak velocity and slope inclination angle change into comprehensive feature vectors, using the comprehensive feature vectors as input to the machine learning model, the machine learning model predicting the slope dynamic risk score label for each group of comprehensive feature vectors as a prediction target, minimizing the sum of prediction errors for all slope dynamic risk score labels as a training target, training the machine learning model until the sum of prediction errors reaches convergence, stopping the model training, and determining the slope dynamic risk score based on the model output results, wherein the machine learning model is a polynomial regression model; The calculation method for blasting vibration peak velocity is as follows: At the blasting construction site, use a blasting vibrometer to collect vibration signal data at different monitoring points and record the ground vibration response when the blasting occurs, including the three-dimensional vibration velocity components. In the blasting vibration signal, the peak vibration velocity in each direction X, Y, and Z is expressed as: The blasting vibration peak velocity PPV is calculated by vector synthesis and the expression is: ; Where PPV is the composite peak vibration velocity, represents the peak vibration velocity in the X direction, represents the peak vibration velocity in the Y direction, Indicates the peak vibration velocity in the Z direction; 、 、 Respectively represent the instantaneous vibration speed in the X, Y, and Z directions at time t; The calculation method for the abnormal value of the slope inclination angle change is as follows: Obtain the sequence of inclination angle change data within a period of time from the slope monitoring system: ; where: is the slope inclination angle measured at the i-th time, and n is the total number of sampling points; Calculate the inclination angle change rate between every two adjacent moments : ; where: represents the inclination angle change amount at the i-th time step; and are the inclination angle values at the current and previous moments respectively; Calculate the first quartile Q1 and the third quartile Q3 of the inclination angle change amount Δθ: where: Q1 represents the value at the 25% position in the data; Q3 represents the value at the 75% position in the data, calculate the interquartile range IQR, and the expression is: IQR = Q3 - Q1; where: IQR is used to measure the distribution range of the data, set the range of normal data: lower boundary = Q1 - 1.5×IQR, upper boundary = Q3 + 1.5×IQR; For each data point , if it satisfies: < Q1 - 1.5×IQR or > Q3 + 1.5×IQR; then it is considered an abnormal value; The intelligent early warning and decision-making module generates a slope safety early warning sequence based on the dynamic risk score of the slope, and generates slope safety control recommendations based on the construction status, support parameters and disturbance intensity, which are used to provide construction adjustment or reinforcement plans.

2. The slope construction safety risk assessment system according to claim 1 is characterized in that: The data acquisition module includes a displacement monitoring sensor, a crack monitoring sensor, a pore water pressure gauge, a seismic disturbance monitoring device, a blasting vibration monitoring system, and an edge computing terminal; The displacement monitoring sensor is a GNSS high-precision displacement meter and / or an optical measuring instrument, the crack monitoring sensor is a crack meter and / or an optical fiber sensor, and the pore water pressure meter is a piezometer and / or an optical fiber water pressure sensor.

3. The slope construction safety risk assessment system according to claim 1 is characterized in that: The obtained slope dynamic risk score is compared with the predetermined risk threshold, and different levels of warning are generated based on the comparison results, including: Comparing the obtained slope dynamic risk score with the gradient risk threshold, the gradient risk threshold includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold, and comparing the slope dynamic risk score with the first risk threshold and the second risk threshold respectively; If the slope dynamic risk score is greater than the second risk threshold, a first-level warning signal is triggered, marking it as a high-risk level and requiring immediate emergency reinforcement measures; If the slope dynamic risk score is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, a secondary warning signal is triggered, marking it as a medium risk level and recommending monitoring and adjusting the construction plan; If the dynamic risk score of the slope is less than the first risk threshold, no warning signal will be triggered and construction will proceed normally.

4. The slope construction safety risk assessment system according to claim 3 is characterized in that: The intelligent early warning and decision-making module further obtains construction status data, support parameters and disturbance intensity information. The construction status data includes the construction stage, blasting parameters and rainfall conditions, and the support parameters include the support structure type and current bearing capacity. After generating the early warning level, the intelligent early warning and decision-making module combines the support parameters and disturbance intensity to output control suggestions for adjusting the blasting charge, adding support components, optimizing the drainage system or suspending construction.

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