Debris flow geological disaster monitoring and emergency response method and system

Abstract

The embodiment of the invention relates to the technical field of debris flow monitoring, and particularly discloses a debris flow geological disaster monitoring and emergency response method and system. According to the embodiment of the invention, a debris flow monitoring area is determined and divided into a plurality of monitoring sub-areas; carrying out multi-source sensing monitoring; performing data preprocessing on the plurality of pieces of sensing monitoring data; performing data synchronization fusion on the plurality of standard monitoring data; and when the debris flow danger exists, danger grading is carried out, area grading information is generated, and unmanned aerial vehicle emergency early warning control is carried out by taking the key risk sub-area as a starting point. A debris flow monitoring area can be divided into a plurality of monitoring sub-areas, multi-source sensing monitoring of different sub-areas is carried out, data preprocessing, synchronous fusion and danger grading are carried out, area grading information is generated, then corresponding unmanned aerial vehicle emergency early warning control is carried out, different danger grading and emergency response are achieved, and the safety of debris flow monitoring is improved. Therefore, the defects of insufficient data acquisition, low early warning precision and poor early warning timeliness are overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of debris flow monitoring, and in particular relates to a debris flow geological disaster monitoring and emergency response method and system. Background Art

[0002] A debris flow is a natural disaster phenomenon in which a mixture of water, mud, stones and other materials flows rapidly downhill along hillsides, river valleys and other terrains at a high speed.

[0003] Debris flows usually occur under conditions such as heavy rainfall, melting ice and snow, or earthquakes. They can quickly carry large amounts of mud, sand, and gravel, posing a serious threat to human life and property.

[0004] In the existing technology, since debris flows usually occur in areas with complex terrain, the monitoring and early warning of debris flow geological disasters often have the defects of insufficient data collection, low warning accuracy and poor warning timeliness, making it impossible to carry out different hazard classifications and emergency responses. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a debris flow geological disaster monitoring and emergency response method and system, aiming to solve the problems raised in the background technology.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A debris flow geological disaster monitoring and emergency response method, the method specifically comprising the following steps: Determine a debris flow monitoring area, obtain geological and topographic data of the debris flow monitoring area, divide the debris flow monitoring area into multiple monitoring sub-areas, and select key risk sub-areas; Performing multi-source sensor monitoring on the plurality of monitoring sub-areas to obtain a plurality of sensor monitoring data; Performing data preprocessing on the plurality of sensor monitoring data to obtain a plurality of standard monitoring data; Synchronously fusing a plurality of the standard monitoring data to generate fused monitoring data; Based on the fused monitoring data, when there is a risk of debris flow, multiple monitoring sub-areas are classified into risk categories, regional classification information is generated, and drone emergency warning control is carried out based on key risk sub-areas.

[0007] A debris flow geological disaster monitoring and emergency response system, the system includes a monitoring area division unit, a multi-source sensor monitoring unit, a monitoring data preprocessing unit, a data synchronization fusion unit and an emergency warning control unit, wherein: A monitoring area division unit is used to determine a debris flow monitoring area, obtain geological and topographic data of the debris flow monitoring area, divide the debris flow monitoring area into multiple monitoring sub-areas, and select key risk sub-areas; A multi-source sensing monitoring unit, configured to perform multi-source sensing monitoring on the plurality of monitoring sub-areas and obtain a plurality of sensing monitoring data; A monitoring data preprocessing unit, configured to perform data preprocessing on the plurality of sensor monitoring data to obtain a plurality of standard monitoring data; a data synchronization fusion unit, configured to perform data synchronization fusion on a plurality of the standard monitoring data to generate fused monitoring data; The emergency warning control unit is used to classify the dangers of multiple monitoring sub-areas based on the fused monitoring data when there is a risk of debris flow, generate regional classification information, and perform drone emergency warning control based on key risk sub-areas.

[0008] Compared with the prior art, the present invention has the following beneficial effects: The embodiment of the present invention determines a debris flow monitoring area and divides it into multiple monitoring sub-areas; performs multi-source sensor monitoring; performs data preprocessing on multiple sensor monitoring data; performs data synchronization fusion on multiple standard monitoring data; and performs hazard classification when there is a debris flow hazard, generates regional classification information, and performs drone emergency warning control based on key risk sub-areas. The debris flow monitoring area can be divided into multiple monitoring sub-areas, multi-source sensor monitoring of different sub-areas can be performed, and data preprocessing, synchronization fusion and hazard classification can be performed to generate regional classification information, and then corresponding drone emergency warning control can be performed to achieve different hazard classifications and emergency responses, thereby solving the defects of insufficient data collection, low warning accuracy and poor warning timeliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0010] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0011] Figure 2 A flow chart of dividing multiple monitoring sub-areas in the method provided by an embodiment of the present invention is shown.

[0012] Figure 3 A flow chart of multi-source sensing monitoring in the method provided by an embodiment of the present invention is shown.

[0013] Figure 4A flow chart of obtaining multiple standard monitoring data in the method provided by an embodiment of the present invention is shown.

[0014] Figure 5 A flow chart of data synchronization fusion in the method provided by an embodiment of the present invention is shown.

[0015] Figure 6 The flowchart of the emergency warning control of the UAV in the method provided by the embodiment of the present invention is shown.

[0016] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0017] Figure 8 The figure shows a structural block diagram of a monitoring area division unit in a system provided by an embodiment of the present invention.

[0018] Figure 9 The figure shows a structural block diagram of a multi-source sensing monitoring unit in a system provided by an embodiment of the present invention.

[0019] Figure 10 The structure block diagram of the emergency warning control unit in the system provided by the embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] It is understandable that in the existing technology, since debris flows usually occur in areas with complex terrain, the monitoring and early warning of debris flow geological disasters often have the defects of insufficient data collection, low warning accuracy and poor warning timeliness, making it impossible to carry out different hazard classifications and emergency responses.

[0022] To solve the above problems, the embodiments of the present invention determine a debris flow monitoring area, obtain geological and topographic data of the debris flow monitoring area, divide the debris flow monitoring area into multiple monitoring sub-areas, and select key risk sub-areas; perform multi-source sensor monitoring on the multiple monitoring sub-areas to obtain multiple sensor monitoring data; perform data preprocessing on the multiple sensor monitoring data to obtain multiple standard monitoring data; perform data synchronization fusion on the multiple standard monitoring data to generate fused monitoring data; and based on the fused monitoring data, when there is a debris flow hazard, perform hazard classification on the multiple monitoring sub-areas to generate regional classification information, and perform UAV emergency warning control based on the key risk sub-areas. The debris flow monitoring area can be divided into multiple monitoring sub-areas, multi-source sensor monitoring is performed on different sub-areas, and data preprocessing, synchronization fusion and hazard classification are performed to generate regional classification information, and then corresponding UAV emergency warning control is performed to achieve different hazard classifications and emergency responses, thereby solving the defects of insufficient data collection, low warning accuracy and poor warning timeliness.

[0023] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0024] Specifically, a debris flow geological disaster monitoring and emergency response method comprises the following steps: Step S101 : determining a debris flow monitoring area, obtaining geological and topographic data of the debris flow monitoring area, dividing the debris flow monitoring area into a plurality of monitoring sub-areas, and selecting key risk sub-areas.

[0025] In an embodiment of the present invention, by determining a debris flow monitoring area with debris flow geological disaster monitoring and emergency response needs, obtaining geological and topographic data of the debris flow monitoring area, and performing topographic analysis on the geological and topographic data, the debris flow monitoring area is divided into multiple monitoring sub-areas, and then from the geological and topographic data, multiple geological and topographic sub-area data corresponding to the multiple monitoring sub-areas are matched, and risk initialization analysis is performed on the multiple geological and topographic sub-area data, and a key risk sub-area is selected from the multiple monitoring sub-areas.

[0026] It is understandable that the key risk sub-area can be the monitoring sub-area where debris flow geological disasters occur most frequently, determined by analyzing the historical records corresponding to multiple monitoring sub-areas.

[0027] Specifically, Figure 2 A flow chart of dividing multiple monitoring sub-areas in the method provided by an embodiment of the present invention is shown.

[0028] In a preferred embodiment of the present invention, determining a debris flow monitoring area, obtaining geological and topographic data of the debris flow monitoring area, dividing the debris flow monitoring area into a plurality of monitoring sub-areas, and selecting key risk sub-areas specifically include the following steps: Step S1011, determining a debris flow monitoring area; Step S1012, obtaining geological and topographic data of the debris flow monitoring area; Step S1013: dividing the debris flow monitoring area into a plurality of monitoring sub-areas according to the geological and topographic data; Step S1014: Based on the geological and topographic data, perform risk initialization analysis on the plurality of monitoring sub-areas and select key risk sub-areas.

[0029] In a preferred embodiment of the present invention, the risk initialization analysis of the plurality of monitoring sub-areas is performed based on the geological and topographic data, and the selection of key risk sub-areas specifically includes the following steps: According to the geological and topographic data, the terrain characteristic triplet data of each monitoring sub-area is extracted. The terrain characteristic triplet data includes the slope angle, rock and soil density and coverage index of the center point; The topographic feature triplet data of each monitoring sub-region is input into the principal component analysis model to calculate the variance contribution rate of the first principal component. The variance contribution rate of the first principal component is used as the comprehensive topographic index to obtain the principal component score of each monitoring sub-region. The membership function threshold is set according to the regional geomechanical parameters, and the historical disaster critical value is obtained. When the principal component score of each monitoring sub-region exceeds the historical disaster critical value, the membership degree of the principal component score of each monitoring sub-region is calculated using the S-type function to obtain the geological risk membership degree of each monitoring sub-region; The terrain surface curvature gradient of each monitoring sub-area is analyzed to obtain the terrain mutation characteristic index; Obtain the historical risk time records and terrain data of each monitoring sub-area at the corresponding time; The neural network model is trained using the historical risk time records of each monitoring sub-area and the terrain data at the corresponding time. Through the correlation analysis between the frequency of disasters and terrain parameters, the weight coefficients of each parameter are iteratively optimized to obtain the dynamic weight of the terrain characteristics. The geological risk membership is weighted with the dynamic weight of the corresponding terrain characteristics to obtain the weighted sum of the geological risk membership of each monitoring sub-area; The terrain mutation characteristic index is amplified and integrated with the weighted sum of the geological risk membership of each monitoring sub-region to obtain the comprehensive risk value of each monitoring sub-region; The comprehensive risk values ​​of each monitoring sub-area are sorted in descending order, and the top 20% are classified as first-level risk areas, the middle 30% as second-level risk areas, and the rest as third-level risk areas, so as to obtain a regional risk distribution map with graded labels.

[0030] In this approach, the present invention combines principal component analysis of geological parameters with topographic surface curvature gradient analysis to simultaneously capture macroscopic topographic features and microscopic landform mutation information. Compared to traditional single slope analysis, this method can identify potential sliding surfaces. The dual nonlinear processing of fuzzy membership and gradient exponential amplification achieves higher accuracy in identifying gully zones than linear models.

[0031] Furthermore, the debris flow geological disaster monitoring and emergency response method further includes the following steps: Step S102: performing multi-source sensor monitoring on the plurality of monitoring sub-areas to obtain a plurality of sensor monitoring data.

[0032] In an embodiment of the present invention, soil moisture sensing monitoring is performed on multiple monitoring sub-areas to obtain soil monitoring data, and rainfall sensing monitoring is performed on multiple monitoring sub-areas to obtain rainfall monitoring data. Displacement sensing monitoring of the surface and / or rock formations is performed on multiple monitoring sub-areas to obtain displacement monitoring data. At the same time, vibration sensing monitoring is performed on multiple monitoring sub-areas to obtain vibration monitoring data. The soil monitoring data, rainfall monitoring data, displacement monitoring data, and vibration monitoring data together constitute multiple sensor monitoring data.

[0033] Specifically, Figure 3 A flow chart of multi-source sensing monitoring in the method provided by an embodiment of the present invention is shown.

[0034] In a preferred embodiment of the present invention, performing multi-source sensor monitoring on the plurality of monitoring sub-areas and obtaining a plurality of sensor monitoring data specifically includes the following steps: Step S1021, performing soil moisture sensing monitoring on the plurality of monitoring sub-areas to obtain soil monitoring data; Step S1022: performing rainfall sensing monitoring on the plurality of monitoring sub-areas to obtain rainfall monitoring data; Step S1023, performing displacement sensing monitoring on the plurality of monitoring sub-areas to obtain displacement monitoring data; Step S1024: Perform vibration sensing monitoring on the plurality of monitoring sub-areas to obtain vibration monitoring data.

[0035] Furthermore, the debris flow geological disaster monitoring and emergency response method further includes the following steps: Step S103 , performing data preprocessing on the plurality of sensor monitoring data to obtain a plurality of standard monitoring data.

[0036] In an embodiment of the present invention, multiple valid monitoring data are obtained by identifying and removing noise, redundancy and abnormal data from multiple sensor monitoring data, and then the multiple valid monitoring data are standardized according to preset data standards to obtain multiple standard monitoring data.

[0037] It is understandable that abnormal data may be data caused by sensor failure or excessive errors. Abnormal data can be identified through machine learning.

[0038] Specifically, Figure 4 A flow chart of obtaining multiple standard monitoring data in the method provided by an embodiment of the present invention is shown.

[0039] In a preferred embodiment of the present invention, the step of preprocessing the plurality of sensor monitoring data to obtain the plurality of standard monitoring data specifically includes the following steps: Step S1031, identifying and removing noise, redundancy and abnormal data from the plurality of sensor monitoring data to obtain a plurality of valid monitoring data; Step S1032: performing standardization processing on the plurality of valid monitoring data to obtain a plurality of standard monitoring data.

[0040] In a preferred embodiment of the present invention, the step of standardizing the plurality of valid monitoring data to obtain a plurality of standard monitoring data specifically includes the following steps: Obtain the sensor number corresponding to the valid monitoring data, and obtain the corresponding sensor factory accuracy identification code based on the sensor number; Parse the factory accuracy identification code of the sensor to obtain the benchmark mean and standard deviation of each type of sensor; Obtain the sensor monitoring data sequence from the valid monitoring data within T minutes before the current moment; Sort the same type of data in the sensor monitoring data sequence by time to obtain a sliding window data sequence, and take the middle position value in the sorting to obtain the sliding window median value of each type of sensor; The interquartile range is calculated based on the values ​​of the 25% and 75% positions in the sliding window data sequence; Generate an adaptive adjustment factor according to the number and interquartile range of data in the sliding window data sequence; Calculate the difference between the current data timestamp and the data reception time in the sliding window data sequence, and substitute the difference into the S-type function for nonlinear transformation to obtain the time-sensitive enhancement coefficient; Standardize each type of current sensor monitoring data in the effective monitoring data using the sensor's baseline mean and standard deviation to generate several initial standardized results; Calculate the absolute deviation between the current sensor monitoring data in the effective monitoring data and the median value of the sliding window, and use the hyperbolic tangent function to perform nonlinear mapping on the absolute deviation according to the adaptive adjustment factor to obtain the generated anti-error adjustment amount; Several initial standardized results are fused with the generated anti-error adjustment amount to obtain a fusion result, and then the fusion result is time-strengthened using the time-strengthening coefficient to obtain the final multiple standard monitoring data.

[0041] In this solution, the present invention analyzes the sensor's factory code and uses a binary bit parsing algorithm to convert hardware parameters into mathematical features, automatically matching accuracy levels with range parameters to achieve adaptive sensor characteristics. To ensure optimal timeliness, the timeliness coefficient uses an S-shaped decay function, with newly collected data weighted 3.2 times as heavily as data from 10 minutes ago, thereby enhancing data freshness.

[0042] Furthermore, the debris flow geological disaster monitoring and emergency response method further includes the following steps: Step S104: synchronously fuse the plurality of standard monitoring data to generate fused monitoring data.

[0043] In an embodiment of the present invention, by obtaining the sampling periods and / or timestamps of multiple standard monitoring data, and then using Kalman filtering to align the multiple sampling periods and / or multiple timestamps, multiple alignment time points are generated to ensure data time synchronization, and then according to the multiple alignment time points, the multiple standard monitoring data are fused to generate fused monitoring data.

[0044] Specifically, Figure 5 A flow chart of data synchronization fusion in the method provided by an embodiment of the present invention is shown.

[0045] In a preferred embodiment of the present invention, the step of synchronously fusing the plurality of standard monitoring data to generate fused monitoring data specifically includes the following steps: Step S1041, obtaining a plurality of sampling periods and / or timestamps of the standard monitoring data; Step S1042: Using Kalman filtering, align the multiple sampling periods and / or the multiple timestamps to generate multiple aligned time points; Step S1043 : performing data fusion on the plurality of standard monitoring data according to the plurality of aligned time points to generate fused monitoring data.

[0046] In a preferred embodiment of the present invention, the step of fusing the plurality of standard monitoring data according to the plurality of aligned time points to generate fused monitoring data specifically includes the following steps: Obtain the frequency of occurrence of each sensor data value in the standard monitoring data to obtain the data frequency distribution; Calculating a first information entropy value according to the data frequency distribution, and converting the first information entropy value into a second weight coefficient; Normalizing the second weight coefficient to obtain a dynamic weight coefficient of each sensor; Obtain the timestamp of each sensor data value in the standard monitoring data, and calculate the time difference between the current moment and the timestamp of the sensor data value; Obtain the number of historical warnings, perform logarithmic smoothing on the time difference based on the number of historical warnings, and obtain the smoothed time difference; Compare the data value of each sensor in the standard monitoring data with the reference range of the corresponding sensor. Based on the comparison result and the smoothed time difference, apply exponential decay suppression to the data that deviates from the reference range to obtain the data value adjusted in the spatiotemporal dual domain for each sensor. Obtain the surface curvature change rate of the monitoring sub-area, calculate the ratio of the surface curvature of the current monitoring sub-area to the maximum surface curvature value of all monitoring sub-areas, and perform nonlinear amplification on the ratio to generate the terrain feature amplification coefficient; Obtaining the comprehensive data distribution of all sensors in the monitoring sub-area, and calculating the second information entropy value of the entire monitoring sub-area based on the comprehensive data distribution of all sensors in the monitoring sub-area; The second information entropy value is mapped to a smooth interval of 0-1 using a hyperbolic function to generate a regional conflict adjustment coefficient; The data values ​​of each sensor after time-space dual-domain adjustment are weighted by the dynamic weight coefficient of each sensor, and the terrain feature amplification coefficient is used to amplify the weighted results to obtain the data processing results of each sensor. The regional conflict adjustment coefficient is applied to the data processing results of each sensor, and then the results are accumulated and synthesized to obtain the fused monitoring data.

[0047] In the above-mentioned solution, the present invention dynamically adjusts the weight of each sensor by calculating the information entropy of sensor data in real time. This mechanism automatically identifies faulty sensors. When a sensor's data is abnormal, its weight is automatically reduced to below the baseline value. Furthermore, by combining a time decay factor with a spatial benchmark, the weight of outdated historical data is reduced to reduce errors. Furthermore, by incorporating a terrain surface curvature gradient parameter, the system deeply integrates terrain features, effectively improving monitoring sensitivity in areas with steep slopes (≥25°).

[0048] Furthermore, the debris flow geological disaster monitoring and emergency response method further includes the following steps: Step S105: Based on the fused monitoring data, when there is a risk of debris flow, multiple monitoring sub-areas are classified into risk categories, regional classification information is generated, and emergency warning control of drones is performed based on key risk sub-areas.

[0049] In an embodiment of the present invention, a comparative analysis of the dangers of the fused monitoring data is performed according to the preset danger threshold data to determine whether there is a mudslide hazard. If it is determined that there is a mudslide hazard, the multiple monitoring sub-areas are classified into danger levels, and the regional classification information is recorded. Then, based on the key risk sub-area as the starting point, the flight warning route is planned according to the regional classification information, and then the UAV is controlled for emergency warning according to the flight warning route.

[0050] It is understandable that after completing the selection of key risk sub-areas, it is necessary to control the drone to fly to the key risk sub-areas in advance for emergency warning standby.

[0051] Specifically, Figure 6 The flowchart of the emergency warning control of the UAV in the method provided by the embodiment of the present invention is shown.

[0052] In a preferred embodiment of the present invention, when there is a risk of debris flow, the fusion monitoring data is used to classify the risk of multiple monitoring sub-areas, generate regional classification information, and perform drone emergency warning control based on the key risk sub-areas. Specifically, the following steps are included: Step S1051, performing a risk analysis on the fused monitoring data to determine whether there is a debris flow risk; Step S1052: When there is a risk of debris flow, the plurality of monitoring sub-areas are classified into risk categories to generate regional classification information; Step S1053: Taking the key risk sub-area as a starting point and according to the regional classification information, a flight warning route is planned; Step S1054: Perform emergency warning control of the UAV according to the flight warning route.

[0053] In a preferred embodiment of the present invention, when there is a risk of debris flow, the plurality of monitoring sub-areas are classified into risk categories, and the generation of regional classification information specifically includes the following steps: Extract the total number of warning events that occurred in the same monitoring sub-area within 3 months from the current time; Obtaining a second information entropy value within a current detection period and a historical maximum entropy value, and normalizing the second information entropy value within the current detection period using the historical maximum entropy value to obtain a normalized entropy value; The normalized entropy value is weighted and superimposed with the total number of warning events to obtain the dynamic entropy weight coefficient; Extract soil moisture time series data and rainfall monitoring data from the fused monitoring data, input the rainfall monitoring data into the S-type function converter, and output a 0-1 soil risk index; Calculate the percentage of the slope of the current monitoring sub-area to the maximum slope of all monitoring sub-areas to obtain the slope value; The corresponding slope weight is set according to the soil moisture time series data, and the slope value is weighted with the slope weight to obtain the enhanced slope value; The enhanced slope value is fused with the soil risk index to obtain the terrain coupling weight; Calculate the covariance relationship value of the soil moisture time series data and the rainfall monitoring data extracted in the past hour, and standardize the variance relationship value to obtain the standardized covariance relationship value; The standardized covariance relationship value is introduced into the time attenuation factor for attenuation, and the attenuation result is constrained to the interval [-1,1] through the hyperbolic tangent function to obtain the covariance weight coefficient; Establish a surface curvature-attenuation rate mapping table, substitute the surface curvature change rate of the monitoring sub-area into the surface curvature-attenuation rate mapping table to obtain the surface curvature adjustment coefficient; Calculate the time difference between the current moment and the most recent warning, and use the surface curvature adjustment coefficient to adjust the time between the current moment and the most recent warning to obtain the terrain-corrected time difference; Asymmetric Gaussian attenuation calculation is performed based on the terrain-corrected time difference to obtain the primary attenuation coefficient; According to the surface curvature change rate of the monitoring sub-area, linear attenuation compensation is applied to the corresponding flat area part of the primary attenuation coefficient to obtain the spatiotemporal attenuation coefficient; The dynamic entropy weight coefficient is integrated with the terrain coupling weight to construct the hierarchical decision-making numerator; Obtain the topographic surface curvature benchmark value, fuse the ratio of the surface curvature change rate of the monitored sub-area to the topographic surface curvature benchmark value with the covariance weight coefficient, and construct the denominator term of the hierarchical decision; The numerator of the grading decision is divided by the denominator of the grading decision, and the spatiotemporal attenuation coefficient is applied to generate the final grading value of 0-10 to obtain the regional grading information.

[0054] In the above-mentioned scheme, the present invention develops adaptive risk perception capabilities by calculating the information entropy (a quantitative indicator of data chaos) of monitoring data in real time and combining it with historical disaster event frequencies. This significantly improves the grading stability under data anomalies compared to traditional fixed-weight models. A dual-factor enhancement model based on surface curvature and soil moisture automatically triggers an exponential multiplication of risk weights when the slope is greater than 25° and the soil moisture content is greater than 28%, improving disaster identification and response speed in steep slope areas. Furthermore, a multi-source data collaborative analysis method analyzes the covariance time series between soil moisture and rainfall to achieve dynamic quantitative assessment of parameter correlation strength, which can improve the identification of hidden landslide points in heavy rain scenarios and reduce false alarm rates. Furthermore, a spatiotemporal attenuation model is designed to integrate a terrain surface curvature compensation mechanism. This applies linear attenuation to flat areas (surface curvature <0.05) while retaining 60% data validity in complex terrain (surface curvature >0.1), effectively improving the model's robustness to interference.

[0055] Further, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0056] Among them, in another preferred embodiment provided by the present invention, a debris flow geological disaster monitoring and emergency response system includes: The monitoring area division unit 101 is used to determine a debris flow monitoring area, obtain geological and topographic data of the debris flow monitoring area, divide the debris flow monitoring area into multiple monitoring sub-areas, and select key risk sub-areas.

[0057] In an embodiment of the present invention, the monitoring area division unit 101 determines a debris flow monitoring area with debris flow geological disaster monitoring and emergency response needs, obtains geological and topographic data of the debris flow monitoring area, divides the debris flow monitoring area into multiple monitoring sub-areas by performing topographic analysis on the geological and topographic data, and then matches multiple geological and topographic sub-area data corresponding to the multiple monitoring sub-areas from the geological and topographic data, performs risk initialization analysis on the multiple geological and topographic sub-area data, and selects a key risk sub-area from the multiple monitoring sub-areas.

[0058] Specifically, Figure 8 FIG. 1 shows a structural block diagram of the monitoring area division unit 101 in the system provided by an embodiment of the present invention.

[0059] In a preferred embodiment of the present invention, the monitoring area division unit 101 specifically includes: The area determination module 1011 is used to determine the debris flow monitoring area; The data acquisition module 1012 is used to acquire geological and topographic data of the debris flow monitoring area; A region division module 1013 is configured to divide the debris flow monitoring area into a plurality of monitoring sub-areas according to the geological and topographic data; The risk initialization analysis module 1014 is configured to perform risk initialization analysis on the plurality of monitoring sub-areas based on the geological and topographic data, and select key risk sub-areas.

[0060] Furthermore, the debris flow geological disaster monitoring and emergency response system also includes: The multi-source sensing monitoring unit 102 is configured to perform multi-source sensing monitoring on the plurality of monitoring sub-areas to obtain a plurality of sensing monitoring data.

[0061] In an embodiment of the present invention, the multi-source sensing monitoring unit 102 performs soil moisture sensing monitoring on multiple monitoring sub-areas to obtain soil monitoring data, performs rainfall sensing monitoring on multiple monitoring sub-areas to obtain rainfall monitoring data, performs displacement sensing monitoring on the surface and / or rock formations on multiple monitoring sub-areas to obtain displacement monitoring data, and simultaneously performs vibration sensing monitoring on multiple monitoring sub-areas to obtain vibration monitoring data. The soil monitoring data, rainfall monitoring data, displacement monitoring data, and vibration monitoring data together constitute multiple sensing monitoring data.

[0062] Specifically, Figure 9 FIG. 1 shows a structural block diagram of the multi-source sensing monitoring unit 102 in the system provided by an embodiment of the present invention.

[0063] In a preferred embodiment of the present invention, the multi-source sensing monitoring unit 102 specifically includes: A soil moisture sensor 1021 is configured to perform soil moisture sensing monitoring on the plurality of monitoring sub-areas to obtain soil monitoring data; A rain sensor 1022 is used to perform rain sensing monitoring on the plurality of monitoring sub-areas and obtain rain monitoring data; The displacement sensor 1023 is used to perform displacement sensing monitoring on the plurality of monitoring sub-areas and obtain displacement monitoring data; The vibration sensor 1024 is used to perform vibration sensing monitoring on the multiple monitoring sub-areas to obtain vibration monitoring data.

[0064] Furthermore, the debris flow geological disaster monitoring and emergency response system also includes: The monitoring data preprocessing unit 103 is used to perform data preprocessing on the plurality of sensor monitoring data to obtain a plurality of standard monitoring data.

[0065] In an embodiment of the present invention, the monitoring data preprocessing unit 103 obtains multiple valid monitoring data by identifying and removing noise, redundancy and abnormal data from multiple sensor monitoring data, and then standardizes the multiple valid monitoring data according to preset data standards to obtain multiple standard monitoring data.

[0066] The data synchronization fusion unit 104 is used to perform data synchronization fusion on the plurality of standard monitoring data to generate fused monitoring data.

[0067] In an embodiment of the present invention, the data synchronization fusion unit 104 obtains the sampling periods and / or timestamps of multiple standard monitoring data, and then uses Kalman filtering to align the multiple sampling periods and / or multiple timestamps to generate multiple aligned time points to ensure data time synchronization, and then fuses the multiple standard monitoring data according to the multiple aligned time points to generate fused monitoring data.

[0068] The emergency warning control unit 105 is used to classify the dangers of multiple monitoring sub-areas based on the fused monitoring data when there is a risk of debris flow, generate regional classification information, and perform drone emergency warning control based on key risk sub-areas.

[0069] In an embodiment of the present invention, the emergency warning control unit 105 performs a comparative analysis of the danger of the fused monitoring data according to the preset danger threshold data to determine whether there is a mudslide hazard. If it is determined that there is a mudslide hazard, multiple monitoring sub-areas are classified into danger levels, and the regional classification information is recorded. Then, based on the key risk sub-area as the starting point, the flight warning route is planned according to the regional classification information, and then the emergency warning flight control of the UAV is performed according to the flight warning route.

[0070] Specifically, Figure 10 It shows a structural block diagram of the emergency warning control unit 105 in the system provided by an embodiment of the present invention.

[0071] Among them, in the preferred embodiment provided by the present invention, the emergency warning control unit 105 specifically includes: The danger judgment module 1051 is used to perform a danger analysis on the fused monitoring data to determine whether there is a debris flow danger; The hazard classification module 1052 is configured to classify the hazard of the plurality of monitoring sub-areas when there is a risk of debris flow and generate regional classification information; Route planning module 1053, configured to plan a flight warning route based on the key risk sub-area and the regional classification information; The warning control module 1054 is used to perform emergency warning control of the UAV according to the flight warning route.

[0072] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0073] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0074] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0075] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A debris flow geological disaster monitoring and emergency response method, characterized in that: The method specifically comprises the following steps: Determine a debris flow monitoring area, obtain geological and topographic data of the debris flow monitoring area, divide the debris flow monitoring area into multiple monitoring sub-areas, and select key risk sub-areas; Performing multi-source sensor monitoring on the plurality of monitoring sub-areas to obtain a plurality of sensor monitoring data; Performing data preprocessing on the plurality of sensor monitoring data to obtain a plurality of standard monitoring data; Synchronously fusing a plurality of the standard monitoring data to generate fused monitoring data; According to the fused monitoring data, when there is a risk of debris flow, multiple monitoring sub-areas are classified into risk categories, regional classification information is generated, and emergency warning control of drones is performed based on key risk sub-areas; The determining of the debris flow monitoring area, obtaining geological and topographic data of the debris flow monitoring area, dividing the debris flow monitoring area into a plurality of monitoring sub-areas, and selecting key risk sub-areas specifically comprises the following steps: Determine debris flow monitoring areas; Acquiring geological and topographic data of the debris flow monitoring area; According to the geological and topographic data, the debris flow monitoring area is divided into a plurality of monitoring sub-areas; Based on the geological and topographic data, a risk initialization analysis is performed on the plurality of monitoring sub-areas, and key risk sub-areas are selected.

2. The debris flow geological disaster monitoring and emergency response method according to claim 1 is characterized in that: The step of performing risk initialization analysis on the plurality of monitoring sub-areas based on the geological and topographic data and selecting key risk sub-areas specifically comprises the following steps: According to the geological and topographic data, the terrain characteristic triplet data of each monitoring sub-area is extracted, and the terrain characteristic triplet data includes the slope angle, rock and soil density and coverage index of the center point; The terrain feature triplet data of each monitoring sub-area is input into the principal component analysis model, the variance contribution rate of the first principal component is calculated, and the variance contribution rate of the first principal component is used as the comprehensive terrain index to obtain the principal component score of each monitoring sub-area; The membership function threshold is set according to the regional geomechanical parameters, and the historical disaster critical value is obtained. When the principal component score of each monitoring sub-region exceeds the historical disaster critical value, the membership degree of the principal component score of each monitoring sub-region is calculated using the S-type function to obtain the geological risk membership degree of each monitoring sub-region; The terrain surface curvature gradient of each monitoring sub-area is analyzed to obtain the terrain mutation characteristic index; Obtain the historical risk time records of each monitoring sub-area and the terrain data at the corresponding time; The neural network model is trained with the historical risk time records of each monitoring sub-area and the terrain data at the corresponding time. Through the correlation analysis between the frequency of disasters and terrain parameters, the weight coefficients of each parameter are iteratively optimized to obtain the dynamic weight of the terrain characteristics. The geological risk membership is weighted with the dynamic weight of the corresponding terrain features to obtain the weighted sum of the geological risk membership of each monitoring sub-area; The terrain mutation characteristic index is amplified and integrated with the weighted sum of the geological risk membership of each monitoring sub-area to obtain the comprehensive risk value of each monitoring sub-area; The comprehensive risk values ​​of each monitoring sub-area are sorted in descending order, and the first 20% are classified as level 1 risk areas, the middle 30% as level 2 risk areas, and the rest as level 3 risk areas, so as to obtain a regional risk distribution map with graded labels.

3. The debris flow geological disaster monitoring and emergency response method according to claim 2 is characterized in that: The performing multi-source sensor monitoring on the plurality of monitoring sub-areas and obtaining a plurality of sensor monitoring data specifically comprises the following steps: Perform soil moisture sensing monitoring on the plurality of monitoring sub-areas to obtain soil monitoring data; Performing rainfall sensing monitoring on the plurality of monitoring sub-areas to obtain rainfall monitoring data; Performing displacement sensing monitoring on the plurality of monitoring sub-areas to obtain displacement monitoring data; Vibration sensing monitoring is performed on the plurality of monitoring sub-areas to obtain vibration monitoring data.

4. The debris flow geological disaster monitoring and emergency response method according to claim 3 is characterized in that: The data preprocessing of the plurality of sensor monitoring data to obtain a plurality of standard monitoring data specifically comprises the following steps: Identify and remove noise, redundancy and abnormal data from the plurality of sensor monitoring data to obtain a plurality of valid monitoring data; The plurality of effective monitoring data are standardized to obtain a plurality of standard monitoring data.

5. The debris flow geological disaster monitoring and emergency response method according to claim 4 is characterized in that: The standardization of the plurality of valid monitoring data to obtain a plurality of standard monitoring data specifically comprises the following steps: Obtain the sensor number corresponding to the valid monitoring data, and obtain the corresponding sensor factory accuracy identification code based on the sensor number; Parse the factory accuracy identification code of the sensor to obtain the benchmark mean and standard deviation of each type of sensor; Obtain the sensor monitoring data sequence from the valid monitoring data within T minutes before the current moment; The same type of data in the sensor monitoring data sequence is sorted by time to obtain a sliding window data sequence, and the middle position value in the sorting is taken to obtain the sliding window median value of each type of sensor; The interquartile range is calculated based on the values ​​at the 25% and 75% positions in the sliding window data sequence; Generate an adaptive adjustment factor according to the number and interquartile range of data in the sliding window data sequence; Calculate the difference between the current data timestamp and the data receiving time in the sliding window data sequence, and substitute the difference into the S-type function for nonlinear transformation to obtain the time-effectiveness enhancement coefficient; Standardize each type of current sensor monitoring data in the effective monitoring data using the sensor's baseline mean and standard deviation to generate a number of initial standardized results; Calculate the absolute deviation between the current sensor monitoring data in the effective monitoring data and the median value of the sliding window, and according to the adaptive adjustment factor, use the hyperbolic tangent function to nonlinearly map the absolute deviation to obtain the generated anti-error adjustment amount; A number of initial standardized results are fused with the generated robustness adjustment amount to obtain a fused result, and then the fused result is time-strengthened using the time-strengthening coefficient to obtain the final multiple standard monitoring data.

6. The debris flow geological disaster monitoring and emergency response method according to claim 5 is characterized in that: The synchronous fusion of the plurality of standard monitoring data to generate fused monitoring data specifically comprises the following steps: Obtaining a sampling period and / or timestamp of a plurality of the standard monitoring data; Using Kalman filtering, aligning the plurality of sampling periods and / or the plurality of time stamps to generate a plurality of aligned time points; According to the plurality of aligned time points, data fusion is performed on the plurality of standard monitoring data to generate fused monitoring data.

7. The debris flow geological disaster monitoring and emergency response method according to claim 6 is characterized in that: The step of fusing the plurality of standard monitoring data according to the plurality of aligned time points to generate fused monitoring data specifically comprises the following steps: Obtain the frequency of occurrence of each sensor data value in the standard monitoring data to obtain the data frequency distribution; Calculate a first information entropy value according to the data frequency distribution, and convert the first information entropy value into a second weight coefficient; Normalizing the second weight coefficient to obtain a dynamic weight coefficient of each sensor; Obtain the timestamp of each sensor data value in the standard monitoring data, and calculate the time difference between the current moment and the timestamp of the sensor data value; Obtain the number of historical warnings, perform logarithmic smoothing on the time difference according to the number of historical warnings, and obtain the smoothed time difference; The data value of each sensor in the standard monitoring data is compared with the reference range of the corresponding sensor. According to the comparison result and the smoothed time difference, exponential attenuation suppression is applied to the data deviating from the reference range to obtain the data value adjusted in the time and space dual domains of each sensor. Obtain the surface curvature change rate of the monitoring sub-area, calculate the ratio of the surface curvature of the current monitoring sub-area to the maximum surface curvature value of all monitoring sub-areas, and amplify the ratio nonlinearly to generate the terrain feature amplification coefficient; Obtaining the comprehensive data distribution of all sensors in the monitoring sub-area, and calculating the second information entropy value of the entire monitoring sub-area according to the comprehensive data distribution of all sensors in the monitoring sub-area; The second information entropy value is mapped to a smooth interval of 0-1 using a hyperbolic function to generate a regional conflict adjustment coefficient; The data values ​​adjusted in the time and space dual domains of each sensor are weighted by the dynamic weight coefficient of each sensor, and then the terrain feature amplification coefficient is used to amplify the weighted result to obtain the data processing result of each sensor. The regional conflict adjustment coefficient is added to the data processing result of each sensor, and then accumulated and synthesized to obtain the fused monitoring data.

8. The debris flow geological disaster monitoring and emergency response method according to claim 7 is characterized in that: The method of performing risk classification on multiple monitoring sub-areas according to the fused monitoring data when there is a risk of debris flow, generating regional classification information, and performing emergency warning control of drones based on key risk sub-areas specifically includes the following steps: Performing risk analysis on the fused monitoring data to determine whether there is a risk of debris flow; When there is a risk of debris flow, the plurality of monitoring sub-areas are classified into risk categories to generate regional classification information; Taking the key risk sub-areas as the starting point and according to the regional classification information, plan the flight warning route; Carry out emergency warning control of the drone according to the flight warning route.

9. The debris flow geological disaster monitoring and emergency response method according to claim 8, characterized in that: When there is a risk of debris flow, the plurality of monitoring sub-areas are classified into risk categories, and the generation of regional classification information specifically comprises the following steps: Extract the total number of warning events that occurred in the same monitoring sub-area within 3 months from the current time; Obtaining a second information entropy value in a current detection cycle and a historical maximum entropy value, and normalizing the second information entropy value in the current detection cycle using the historical maximum entropy value to obtain a normalized entropy value; The normalized entropy value and the total number of warning events are weighted and superimposed to obtain the dynamic entropy weight coefficient; Extract soil moisture time series data and rainfall monitoring data from the fused monitoring data, input the rainfall monitoring data into the S-type function converter, and output a soil risk index of 0-1; Calculate the percentage of the slope of the current monitoring sub-area and the maximum slope of all monitoring sub-areas to obtain the slope value; The corresponding slope weight is set according to the soil moisture time series data, and the slope value and the slope weight are weighted to obtain the enhanced slope value; The enhanced slope value is fused with the soil risk index to obtain the terrain coupling weight; Calculate the covariance relationship value of the soil moisture time series data and the rainfall monitoring data extracted from the data in the past hour, and standardize the variance relationship value to obtain the standardized covariance relationship value; The standardized covariance relationship value is introduced into the time decay factor for decay, and the decay result is constrained to the interval [-1,1] through the hyperbolic tangent function to obtain the covariance weight coefficient; Establish a surface curvature-attenuation rate mapping table, substitute the surface curvature change rate of the monitored sub-area into the surface curvature-attenuation rate mapping table, and obtain the surface curvature adjustment coefficient; Calculate the time difference between the current moment and the most recent warning, and use the surface curvature adjustment coefficient to adjust the time between the current moment and the most recent warning to obtain the terrain-corrected time difference; Asymmetric Gaussian attenuation calculation is performed based on the terrain-corrected time difference to obtain the primary attenuation coefficient; According to the surface curvature change rate of the monitoring sub-area, linear attenuation compensation is applied to the corresponding flat area part of the primary attenuation coefficient to obtain the spatiotemporal attenuation coefficient; The dynamic entropy weight coefficient is integrated with the terrain coupling weight to construct the molecular term of hierarchical decision-making; Obtain the topographic surface curvature reference value, fuse the ratio of the surface curvature change rate of the monitored sub-area to the topographic surface curvature reference value with the covariance weight coefficient, and construct the denominator term of the hierarchical decision; The numerator of the grading decision is divided by the denominator of the grading decision, and the spatiotemporal attenuation coefficient is applied to generate a final grading value of 0-10 to obtain the regional grading information.

10. A debris flow geological disaster monitoring and emergency response system, the system is applied to the debris flow geological disaster monitoring and emergency response method according to any one of claims 1 to 9, characterized in that: The system includes a monitoring area division unit, a multi-source sensor monitoring unit, a monitoring data preprocessing unit, a data synchronization fusion unit and an emergency warning control unit, wherein: A monitoring area division unit is used to determine a debris flow monitoring area, obtain geological and topographic data of the debris flow monitoring area, divide the debris flow monitoring area into a plurality of monitoring sub-areas, and select key risk sub-areas; A multi-source sensor monitoring unit, used to perform multi-source sensor monitoring on the plurality of monitoring sub-areas to obtain a plurality of sensor monitoring data; A monitoring data preprocessing unit, used for performing data preprocessing on the plurality of sensor monitoring data to obtain a plurality of standard monitoring data; A data synchronization fusion unit, used for performing data synchronization fusion on a plurality of the standard monitoring data to generate fused monitoring data; The emergency warning control unit is used to classify the dangers of multiple monitoring sub-areas according to the fused monitoring data when there is a risk of debris flow, generate regional classification information, and perform emergency warning control of drones based on key risk sub-areas.

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