Geotechnical engineering disaster risk dynamic monitoring method and related device

Abstract

The invention discloses a geotechnical engineering disaster risk dynamic monitoring method and a related device, and relates to the field of disaster monitoring, and the method comprises the steps: constructing a three-dimensional geologic model corresponding to urban infrastructure, obtaining multi-element monitoring data cooperatively collected by a plurality of perception sensors arranged at each monitoring point in the three-dimensional geologic model, and carrying out the multi-element monitoring data; the method comprises the following steps: acquiring multivariate monitoring data, processing the multivariate monitoring data through a data fusion algorithm to obtain fusion data, performing soil layer data analysis on the fusion data to obtain a multi-dimensional signal statistical value, and when a target signal value exceeding a corresponding early warning threshold interval exists in the multi-dimensional signal statistical value, judging that an engineering disaster risk exists in a region where the urban infrastructure is located. According to the invention, fusion and soil layer analysis are carried out through multi-element monitoring data acquired by multiple sensing sensors arranged in the three-dimensional geologic model in a cooperative manner, so that the problems of insufficient data measurement and low measurement precision of a single-point sensor are avoided, the situations of misinformation and missing report of disasters are reduced, and the accuracy and stability of dynamic monitoring of geotechnical engineering disaster risks are improved.

Description

Technical Field

[0001] The present invention relates to the field of disaster monitoring technology, and more particularly to a method and related device for dynamic monitoring of geotechnical engineering disaster risks. Background Art

[0002] As the urbanization process continues to accelerate, the construction scale and complexity of important urban infrastructure (such as underground tunnels, coal mine tunnels, dams and other key structures) are increasing. These geotechnical engineering facilities play an irreplaceable role in ensuring urban transportation, communication, evacuation and emergency rescue.

[0003] However, disasters such as earthquakes, heavy rainfall, and explosions are extremely destructive to geotechnical engineering facilities. Their suddenness and destructiveness make traditional surface monitoring methods inaccurate and difficult to meet current safety prevention and control and early warning needs. Summary of the Invention

[0004] In view of this, the present invention discloses a method and related devices for dynamic monitoring of geotechnical engineering disaster risks to achieve accuracy and stability in dynamic monitoring of geotechnical engineering disaster risks and meet current safety prevention and control and early warning needs.

[0005] A method for dynamic monitoring of geotechnical engineering disaster risks, comprising:

[0006] Construct a three-dimensional geological model corresponding to the city’s infrastructure;

[0007] Acquire multivariate monitoring data collaboratively collected by multiple sensing sensors deployed at each monitoring point in the three-dimensional geological model, and process the multivariate monitoring data using a data fusion algorithm to obtain fused data;

[0008] Performing soil layer data analysis on the fused data to obtain multi-dimensional signal statistics;

[0009] When there is a target signal value exceeding a corresponding warning threshold interval in the multi-dimensional signal statistics, it is determined that there is an engineering disaster risk in the area where the urban infrastructure is located.

[0010] Optionally, constructing a three-dimensional geological model corresponding to the urban infrastructure includes:

[0011] Taking key areas of urban infrastructure as monitoring points and deploying perception sensors at these monitoring points;

[0012] Acquiring soil layer data collected by the perception sensor;

[0013] Generating original geological survey data from the soil layer data through geotechnical exploration, and obtaining drilling data from the original geological survey data;

[0014] Converting the borehole data into three-dimensional coordinates of soil layer control points, and establishing a continuous soil layer boundary surface using a data interpolation algorithm for the borehole data;

[0015] The three-dimensional geological model is generated based on the three-dimensional coordinates of the soil layer control points and the soil layer boundary surface.

[0016] Optionally, after acquiring the multivariate monitoring data collaboratively collected by multiple sensing sensors deployed at each monitoring point in the three-dimensional geological model, the method further includes:

[0017] Inputting the multivariate monitoring data into a foundation bearing capacity prediction model to predict excess pore water pressure values;

[0018] The foundation bearing failure rate is obtained based on the excess pore water pressure and the effective stress, wherein the effective stress is determined based on the soil density.

[0019] Optionally, it also includes:

[0020] determining whether the foundation bearing failure rate is less than a warning limit;

[0021] If yes, it is determined that there is no engineering disaster risk in the area where the urban infrastructure is located;

[0022] If not, it is determined that there is an engineering disaster risk in the area where the urban infrastructure is located, and the warning level corresponding to the warning interval where the foundation bearing failure rate is located is used as the current warning level.

[0023] Optionally, after determining that the area where the urban infrastructure is located has an engineering disaster risk, the method further includes:

[0024] Calculating the percentage of the number of target signal values ​​exceeding the warning threshold interval in the total number of signal values ​​in the multidimensional signal statistics;

[0025] The warning level corresponding to the warning interval in which the percentage falls is used as the current warning level.

[0026] Optionally, performing soil layer data analysis on the fused data to obtain multidimensional signal statistics includes:

[0027] performing data preprocessing on the multivariate monitoring data to obtain target multivariate monitoring data;

[0028] The multidimensional signal statistics are obtained by performing soil layer data analysis on the target multivariate monitoring data.

[0029] Optionally, it also includes:

[0030] When the engineering disaster risk in the area where the urban infrastructure is located reaches the graded warning standard, the warning mechanism is triggered;

[0031] The multivariate monitoring data collected within a preset time period before and after the warning is stored, and / or a dynamic monitoring report on geotechnical engineering disaster risk is generated, which at least includes: disaster warning level, warning start time, warning triggering times and historical data storage.

[0032] Optionally, storing the multivariate monitoring data collected within a preset time period before and after the warning includes:

[0033] In the multivariate monitoring data collection process, when the absolute value of the difference between adjacent sampling points exceeds the set trigger fluctuation limit, the dynamic storage mechanism is triggered to store the fluctuation signal of the collection process;

[0034] or,

[0035] In the multivariate monitoring data collection process, when the current signal value is greater than the upper warning threshold, the upper warning is triggered to store the signal greater than the upper warning threshold; when the current signal value is less than the lower warning threshold, the lower warning is triggered to store the signal less than the lower warning threshold.

[0036] A geotechnical engineering disaster risk dynamic monitoring device, comprising:

[0037] A model building unit, used to build a three-dimensional geological model corresponding to the urban infrastructure;

[0038] a monitoring data acquisition unit, configured to acquire multivariate monitoring data collaboratively collected by a plurality of sensing sensors deployed at each monitoring point in the three-dimensional geological model, and to process the multivariate monitoring data using a data fusion algorithm to obtain fused data;

[0039] A data analysis unit, configured to perform soil layer data analysis on the fused data to obtain multi-dimensional signal statistics;

[0040] The disaster risk determination unit is used to determine that there is an engineering disaster risk in the area where the urban infrastructure is located when there is a target signal value exceeding the corresponding warning threshold interval in the multidimensional signal statistics.

[0041] An electronic device, comprising: a memory and a processor;

[0042] The memory is used to store at least one instruction;

[0043] The processor is used to execute the at least one instruction to implement any one of the geotechnical engineering disaster risk dynamic monitoring methods.

[0044] As can be seen from the above technical solutions, the present invention discloses a method and related device for dynamic monitoring of geotechnical engineering disaster risks, which constructs a three-dimensional geological model corresponding to urban infrastructure, obtains multivariate monitoring data collected by multiple sensing sensors deployed at each monitoring point in the three-dimensional geological model, processes the multivariate monitoring data through a data fusion algorithm to obtain fused data, performs soil layer data analysis on the fused data to obtain multidimensional signal statistics, and determines that the area where the urban infrastructure is located has an engineering disaster risk when there is a target signal value exceeding the corresponding warning threshold interval in the multidimensional signal statistics. By constructing a three-dimensional geological model and deploying sensing sensors at multiple monitoring points in the three-dimensional geological model, the present invention can improve the monitoring coverage area and the positioning accuracy of the measuring points, effectively avoid the generation of monitoring blind spots, and provide a basis for long-term dynamic monitoring; by fusing and analyzing the multivariate monitoring data collected by multiple sensing sensors, it can effectively avoid the problem of insufficient measurement data and low measurement accuracy of single-point sensors, reduce the situation of disaster false alarms and missed alarms, thereby improving the accuracy and stability of dynamic monitoring of geotechnical engineering disaster risks and meeting the current needs of safety prevention and control and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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 merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the disclosed drawings without any creative work.

[0046] Figure 1 This is a flow chart of a method for dynamic monitoring of geotechnical engineering disaster risks disclosed in an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of the layout of sensing sensors in a three-dimensional geological model disclosed in an embodiment of the present invention;

[0048] Figure 3 A flowchart for constructing a three-dimensional geological model disclosed in an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of a process for data preprocessing and data analysis of multivariate monitoring data disclosed in an embodiment of the present invention;

[0050] Figure 5 A schematic diagram of a data storage strategy disclosed in an embodiment of the present invention;

[0051] Figure 6 A dynamic storage flow chart disclosed in an embodiment of the present invention;

[0052] Figure 7This is a flowchart of an early warning storage disclosed in an embodiment of the present invention;

[0053] Figure 8 A flow chart for generating a dynamic monitoring report on geotechnical engineering disaster risks disclosed in the present invention;

[0054] Figure 9 This is a schematic structural diagram of a device for dynamic monitoring of geotechnical engineering disaster risks disclosed in an embodiment of the present invention;

[0055] Figure 10 The figure is a schematic structural diagram of an electronic device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] Explanation of related terms

[0058] Frequency domain analysis refers to the process of converting a signal from the time domain to the frequency domain. While time domain signals represent signals that vary over time, frequency domain signals represent the components of a signal at different frequencies. Frequency domain analysis helps analyze the frequency distribution, periodicity, and characteristics of a signal and is often used to analyze periodicity, harmonic content, and filtering.

[0059] Fourier transform: refers to converting a time domain signal into a frequency domain signal. By integrating the signal, it is decomposed into sine waves or cosine waves of different frequencies.

[0060] Signal filtering: refers to the removal of noise or unwanted frequency components from a signal through specific algorithms or circuits. Filtering is divided into different types based on the design of the filter: Low-pass filter: allows low-frequency signals to pass through, suppresses high-frequency signals, and is often used to remove high-frequency noise. High-pass filter: allows high-frequency signals to pass through, suppresses low-frequency signals, and is often used to remove low-frequency drift. Band-pass filter: only allows signals in a specific frequency band to pass through, and is usually used to extract signals in a specific frequency range. Band-stop filter: blocks signals in a specific frequency band from passing through, and is used to suppress noise at certain specific frequencies.

[0061] Ground failure rate: This represents the relationship between excess pore water pressure (dynamic pore pressure change) and effective stress (earth pressure). Ground failure rate is often used to evaluate the relative magnitude of pore water pressure in soil and is a key parameter in soil strength and stability analysis.

[0062] join Figure 1, a flow chart of a method for dynamic monitoring of geotechnical engineering disaster risks disclosed in an embodiment of the present invention, the method comprising:

[0063] Step S101: construct a three-dimensional geological model corresponding to urban infrastructure.

[0064] A 3D geological model is a digital, three-dimensional visualization constructed from geological data. It describes the geological structure, lithologic distribution, stratigraphic interfaces, structural characteristics, and physical properties (such as porosity, permeability, and mineral composition) of the underground space. Its core purpose is to integrate discrete geological observation data (such as drilling logs, geophysical exploration data, and geological maps) into a continuous 3D spatial model through mathematical algorithms and computer graphics techniques. This provides decision support for resource exploration, environmental engineering, geological hazard prediction, and other fields.

[0065] Step S102: acquiring multivariate monitoring data collaboratively collected by a plurality of sensing sensors deployed at each monitoring point in the three-dimensional geological model, and processing the multivariate monitoring data by a data fusion algorithm to obtain fused data.

[0066] Among them, the sensing sensors include but are not limited to soil pressure sensors, pore water pressure sensors and acceleration sensors.

[0067] Earth pressure sensors are used to measure the vertical or lateral pressure exerted by soil or rock on the surface of a structure (such as a tunnel lining, retaining wall, or pile foundation).

[0068] Pore ​​water pressure sensors are used to measure the pressure of water in soil or rock pores, reflecting changes in groundwater levels and permeability.

[0069] Accelerometers are used to measure the acceleration of an object in the X / Y / Z axis direction, reflecting vibration intensity or motion state, and can be used for permanent structural health monitoring and disaster warning.

[0070] In practical applications, three types of sensors, namely soil pressure sensors, pore water pressure sensors and acceleration sensors, can be deployed at each detection point. Each sensing sensor collects one type of monitoring data, thereby obtaining multivariate monitoring data.

[0071] This embodiment uses a data fusion algorithm to fuse monitoring data collected by different types of sensors at various monitoring points within a 3D geological model. Different fusion weights can be assigned to the monitoring data collected by different sensors during the data fusion process. For example, higher fusion weights can be assigned to the more important pore water pressure sensing under low-frequency heavy rainfall loads and the more significant soil pressure sensing under high-frequency traffic loads. This multi-dimensional monitoring data fusion effectively addresses issues such as insufficient single-point sensor measurement data and low measurement accuracy, thereby improving data accuracy and stability.

[0072] In practical applications, the data fusion algorithm used to fuse multivariate monitoring data can be Kalman filtering. Kalman filtering is an optimal estimation method based on the state space model. It recursively combines the observed data with the predicted value of the system model to estimate the optimal estimate of the system state.

[0073] The following equation is used to describe the principle of Kalman filtering to achieve data fusion process: (1)

[0074] (1);

[0075] Where, express The system status at the moment, express The system status at the moment, express The amount of control over the system at any given moment, express The process noise at the moment, A represents the first system matrix parameter, B represents the second system matrix parameter, and A and B represent different system matrix parameters.

[0076] The measurement update equation mainly obtains the current sensor measurement value, as shown in formula (2):

[0077] (2);

[0078] Where, express The measured value at the moment, represents the matrix parameters of the measurement system, express The measurement noise at the moment.

[0079] Based on formula (1) and formula (2), the next state vector estimate can be obtained, as shown in formula (3) and formula (4):

[0080] (3);

[0081] (4);

[0082] Where, Represents the estimated value of the next state vector predicted by the previous state, Indicates the optimal result of the previous state, Indicates the control quantity of the current state. is the covariance of , yes The corresponding covariance, for is the transposed matrix of , and Q is the covariance of the system process.

[0083] Step S103: performing soil layer data analysis on the fused data to obtain multi-dimensional signal statistics.

[0084] Among them, the multidimensional signal statistical values ​​include but are not limited to root mean square, standard deviation, variance, kurtosis, skewness, maximum value, minimum value, average value, and arithmetic mean.

[0085] Step S104: When there is a target signal value exceeding a corresponding warning threshold value interval in the multi-dimensional signal statistics, it is determined that there is an engineering disaster risk in the area where the urban infrastructure is located.

[0086] The warning threshold interval refers to the interval consisting of the warning upper threshold and the warning lower threshold.

[0087] This embodiment sets corresponding warning threshold intervals for different signal statistics in the multidimensional signal statistics. By comparing the signal statistics with the corresponding warning threshold intervals, it is determined whether each signal statistic exceeds the corresponding upper warning threshold or lower warning threshold. This embodiment determines the signal statistic value that exceeds the corresponding upper warning threshold or lower warning threshold as the target signal value. When the target signal value is present in the multidimensional signal statistics, it is determined that the area where the urban infrastructure is located is at risk of engineering disasters.

[0088] In summary, the present invention discloses a method for dynamic monitoring of geotechnical engineering disaster risk, which constructs a three-dimensional geological model corresponding to urban infrastructure, obtains multivariate monitoring data collected by multiple sensing sensors deployed at each monitoring point in the three-dimensional geological model, processes the multivariate monitoring data through a data fusion algorithm to obtain fused data, performs soil layer data analysis on the fused data to obtain multidimensional signal statistics, and determines that there is an engineering disaster risk in the area where the urban infrastructure is located when there is a target signal value exceeding the corresponding warning threshold interval in the multidimensional signal statistics. By constructing a three-dimensional geological model and deploying sensing sensors at multiple monitoring points in the three-dimensional geological model, the present invention can improve the monitoring coverage area and the positioning accuracy of the measuring points, effectively avoid the generation of monitoring blind spots, and provide a basis for long-term dynamic monitoring; by fusing and analyzing the multivariate monitoring data collected by multiple sensing sensors, the problem of insufficient measurement data and low measurement accuracy of single-point sensor can be effectively avoided, and the situation of disaster false alarms and missed alarms can be reduced, thereby improving the accuracy and stability of dynamic monitoring of geotechnical engineering disaster risk and meeting the current needs of safety prevention and control and early warning.

[0089] In one embodiment, step S101 may specifically include:

[0090] (1) Key areas of urban infrastructure are used as monitoring points, and perception sensors are deployed at the monitoring points.

[0091] The sensing sensors deployed at each monitoring point in this application include at least: soil pressure sensor, pore water pressure sensor and acceleration sensor. The specific deployment points of the sensing sensors are as follows: Figure 2 As shown in the figure, five monitoring points (i.e., monitoring point 1, monitoring point 2, monitoring point 3, monitoring point 4, and monitoring point 5) are established with rainfall load, traffic load, wave load, blasting load, and earthquake load as the main layout conditions. The sensing sensors are arranged horizontally, vertically, and vertically. The buried depth of the sensors covers the entire soil layer from shallow to deep to reduce the generation of monitoring blind spots.

[0092] (2) Obtaining soil layer data collected by the sensing sensor.

[0093] See also Figure 3 The three-dimensional geological model construction flow chart shown first uses the deployed perception sensors to collect soil layer data to obtain preliminary coating information.

[0094] (3) The soil layer data is subjected to geotechnical exploration to form original geological survey data, and drilling data is obtained from the original geological survey data.

[0095] (4) Converting the borehole data into three-dimensional coordinates of soil layer control points, and establishing a continuous soil layer boundary surface using a data interpolation algorithm for the borehole data.

[0096] The role of data interpolation in building a 3D geological model is to convert discrete geological, geophysical, and borehole observation data into a spatially continuous 3D attribute field. In practical applications, the data interpolation algorithm can use Kriging interpolation to fully utilize spatial correlation. The expression of Kriging interpolation is as follows:

[0097] (5);

[0098] (6);

[0099] Where, MAE represents the mean absolute error, RMSE represents the root mean square error, M is the modeling interpolation result, and N is the number of verification samples. For the The number of samples, n is the total number of samples.

[0100] (5) Generating a three-dimensional geological model based on the three-dimensional coordinates of the soil layer control points and the soil layer boundary surface.

[0101] The 3D geological model can display information such as underground soil structure and stress distribution. Real-time updates to the 3D geological model based on soil data dynamically collected by sensing sensors can provide data support for subsequent earthquake disaster risk monitoring.

[0102] In one embodiment, after step S102 acquires multivariate monitoring data collaboratively collected by multiple sensing sensors deployed at each monitoring point in the three-dimensional geological model, the following steps may also be included:

[0103] (1) The multivariate monitoring data are input into the foundation bearing capacity prediction model to predict the excess pore water pressure value.

[0104] Among them, the foundation bearing capacity prediction model adopts the peak data picking method.

[0105] Combining the basic theory of multiplication of independent variables, the expression of the foundation bearing capacity prediction model is as follows:

[0106] (7);

[0107] Where, Indicates the excess pore water pressure at the end of the Nth week of cyclic loading. represents the excess pore water pressure at the end of the N-1th cycle load, and , unit kPa; represents the initial effective stress, in kPa; represents the soil characteristic parameters, Indicates soil density, unit ; Indicates the maximum acceleration amplitude, unit ; represents the thickness of the effective shear layer, in m; N represents the Nth cycle load; represents the consolidation ratio, 、 、 、 represents different undetermined constant coefficients.

[0108] (2) The foundation bearing failure rate is obtained based on the excess pore water pressure and effective stress.

[0109] The effective stress is determined based on the soil density.

[0110] The expression of foundation bearing failure rate is as follows:

[0111] (8);

[0112] In the formula, it represents represents the foundation bearing failure rate, represents the excess pore water pressure, represents the effective stress, ,in, is the soil density, which is determined by sampling in practical applications. is the acceleration due to gravity, and h is the depth.

[0113] When the foundation bearing failure rate approaches 100%, that is, the excess pore water pressure is equal to the effective stress, the soil loses its bearing capacity and manifests itself as a liquefied state. In actual monitoring, when the pore pressure ratio exceeds 80%, it indicates that there is a risk of soil liquefaction at that location.

[0114] In summary, the present invention can effectively solve the problems of insufficient measurement data information and low measurement accuracy of single-point sensor by fusing multivariate monitoring data and constructing a foundation bearing capacity prediction model. It can improve the accuracy and stability of the data while realizing the prediction of liquefaction risks of different soil sites, which can effectively support the assessment of engineering earthquake disaster risks.

[0115] In one embodiment, the method for dynamic monitoring of geotechnical engineering disaster risks may further include:

[0116] Determine whether the foundation bearing failure rate is less than the warning limit;

[0117] If yes, it is determined that there is no engineering disaster risk in the area where the urban infrastructure is located;

[0118] If not, it is determined that there is an engineering disaster risk in the area where the urban infrastructure is located, and the warning level corresponding to the warning interval where the foundation bearing failure rate is located is used as the current warning level.

[0119] The value of the warning limit is determined according to actual needs. For example, the warning limit is 60%.

[0120] Specifically, different warning intervals (i.e., upper and lower warning thresholds) can be pre-set for different warning levels. When the foundation bearing failure rate is not less than the warning limit, the warning level corresponding to the warning interval in which the foundation bearing failure rate falls is used as the current warning level. Assuming the upper and lower warning thresholds of the warning interval are a and b, the upper warning is triggered when the foundation bearing failure rate is greater than a, and the lower warning is triggered when the foundation bearing failure rate is less than b.

[0121] In summary, the present invention monitors potential disaster risks in real time by constructing a foundation bearing capacity prediction model, and can combine monitoring data from single points at different depths (for example, 2 to 3 single points) with an early warning model to achieve early warning of the entire disaster risk area.

[0122] In one embodiment, after determining that an area where urban infrastructure is located has an engineering disaster risk, the following steps may also be performed:

[0123] Calculate the percentage of target signal values ​​exceeding the warning threshold interval in the total number of signal values ​​in the multi-dimensional signal statistics;

[0124] The warning level corresponding to the warning interval where the percentage is located is used as the current warning level.

[0125] In practical applications, after analyzing soil layer data, the monitoring data can be converted into a time-domain map. In addition, the foundation bearing capacity prediction model and the percentage of target signal values ​​exceeding the warning threshold in the multi-dimensional signal statistics in the time-domain map can be combined to establish a multi-level warning mechanism. The specific warning standard methods are as follows:

[0126] No warning: The signals at each point in the time domain map do not exceed the upper and lower warning thresholds, and the foundation bearing failure rate is less than 60%;

[0127] Level 3 warning: less than 10% of the point signals in the time domain map exceed the upper and lower warning thresholds, or the foundation bearing failure rate is greater than 60% and less than 70%;

[0128] Level 2 warning: 10% to 20% of the point signals in the time domain map exceed the upper and lower warning thresholds, or the foundation bearing failure rate is greater than 70% and less than 80%;

[0129] Level 1 warning: More than 20% of the point signals in the time domain map exceed the upper and lower warning thresholds or the foundation bearing failure rate is greater than 80%.

[0130] It should be noted that the upper and lower warning thresholds, that is, the two critical values ​​in the warning threshold range, are: the upper warning threshold and the lower warning threshold.

[0131] In one embodiment, step S103 may specifically include:

[0132] Perform data preprocessing on the multivariate monitoring data to obtain target multivariate monitoring data;

[0133] A soil layer data analysis is performed on the target multivariate monitoring data to obtain multidimensional signal statistics.

[0134] The process of data preprocessing and data analysis for multivariate monitoring data can be found in Figure 4 As shown, data preprocessing includes signal filtering and calculus processing to reduce noise and remove power frequency interference on time domain signals.

[0135] Signal filtering includes low-pass filtering, high-pass filtering, band-pass filtering and band-stop filtering.

[0136] Calculus includes first / second order differentials and first / second order integrals.

[0137] In practical applications, either of the two data preprocessing methods, signal filtering or calculus processing, can be selected.

[0138] When performing data analysis on the pre-processed target multivariate monitoring data, it includes frequency domain / time-frequency domain analysis, windowing function, averaging, superposition and numerical statistics to achieve real-time analysis of the time domain and frequency domain signals of the target multivariate monitoring data.

[0139] Frequency domain analysis (via Fourier transform) includes power and amplitude spectra. Windowing provides 18 window functions (rectangular, Hanning, Hamming, Blackman-Harris, Exact Blackman, Blackman, Flat Top, 4th-order Blackman-Harris, Low Sidelobe, Blackman Nuttall, triangular, Bartlett-Hanning, Bohamn, Parzen, Welch, Kaiser, Dolph-Chebyshev, and Gaussian) to process signals in various frequency domains.

[0140] Take five commonly used window functions as an example to introduce them in detail, as shown in Table 1. represents the index of the window function, represents the window function, Indicates the window length, Represented as a parameter greater than 0.

[0141] Table 1. Five commonly used window function formulas and their advantages

[0142]

[0143] Averaging types include vector averaging, RMS averaging, and peak hold. Averaging is primarily used to reduce the impact of random noise on signals and improve signal observability. By averaging multiple sampling or measurement results, you can improve the signal-to-noise ratio and obtain a more stable spectrum. The overlay function overlaps signal power spectral densities to improve spectral resolution and maintain signal continuity.

[0144] In one embodiment, the method for dynamic monitoring of geotechnical engineering disaster risks may further include:

[0145] When the engineering disaster risk in the area where the urban infrastructure is located reaches the graded warning standard, the warning mechanism is triggered;

[0146] The multivariate monitoring data collected within a preset time period before and after the warning is stored, and / or a dynamic monitoring report on geotechnical engineering disaster risks is generated.

[0147] Among them, the dynamic monitoring report of geotechnical engineering disaster risk includes at least: disaster warning level, warning start time, warning triggering times and historical data storage.

[0148] Specifically, when the engineering disaster risk in the area where urban infrastructure is located reaches the graded warning standard, the system automatically triggers the warning mechanism and stores the multivariate monitoring data collected within a preset time period (for example, 12 seconds) before and after the warning. In addition, historical data after the disaster can also be stored to provide data basis for subsequent disaster analysis.

[0149] This application provides a variety of data storage strategies, see Figure 5 The data storage strategy shown in the figure includes dynamic storage and early warning storage. Dynamic storage saves only fluctuation signals during the acquisition process, while early warning storage saves only signals exceeding the threshold during the acquisition process. Dynamic storage and early warning storage are designed to meet the storage needs of different engineering experiments.

[0150] In one embodiment, the process of storing the multivariate monitoring data collected within a preset time period before and after the warning may include:

[0151] In the multivariate monitoring data collection process, when the absolute value of the difference between adjacent sampling points exceeds the set trigger fluctuation limit, the dynamic storage mechanism is triggered to store the fluctuation signal of the collection process;

[0152] or,

[0153] In the multivariate monitoring data collection process, when the current signal value is greater than the upper warning threshold, the upper warning is triggered to store the signal greater than the upper warning threshold; when the current signal value is less than the lower warning threshold, the lower warning is triggered to store the signal less than the lower warning threshold.

[0154] For easier understanding, see Figure 6 The dynamic storage flow chart shown in the figure, this application automatically triggers dynamic storage only when the signal changes. The dynamic storage principle is as follows Figure 8 As shown, a trigger fluctuation limit a is set, the average of the sampling points in the previous second and the average of the sampling points in the next second are taken, and the absolute value b is subtracted from the average of the sampling points in the previous second and the next second. If b>a, dynamic storage is triggered; otherwise, dynamic storage is not triggered. When the dynamic storage function is triggered, the system automatically records the data and the current storage time.

[0155] See also Figure 7The warning storage flow chart shown in the figure sets upper and lower warning thresholds a and b, obtains the current signal value m, and compares it with a and b, respectively. If m > a, an upper limit warning is triggered and the data is stored. If m ≤ a, the current signal value m and b are compared. If m < b, a lower limit warning is triggered and the data is stored. If m ≥ b, the warning storage process ends. The stored data is for a preset period of time (for example, 12 seconds) before and after the warning, and the warning time is recorded.

[0156] In order to facilitate data visualization, data archiving and traceability, this application has designed a function to export geotechnical engineering disaster risk dynamic monitoring report, such as Figure 8 After a monitoring task is completed, you can select points (including: above-ground building points and underground tunnel points) and check data options (including: root mean square, standard deviation, variance, kurtosis, skewness, maximum value, minimum value, average value, arithmetic mean) to generate a dynamic monitoring report on geotechnical engineering disaster risks.

[0157] The geotechnical engineering disaster risk dynamic monitoring report includes the disaster warning level, warning start time, warning trigger counts, and historical data storage. By analyzing post-disaster data in this report format and summarizing the monitoring characteristics and warning effectiveness at the time of the disaster, it can facilitate further optimization of the warning system and monitoring strategy.

[0158] In summary, the present invention, by constructing a three-dimensional geological model, combining multiple sensing sensors, and fusing the multivariate monitoring data collected by these sensors, can reduce misjudgments of disaster warnings caused by abnormal signals. In other words, it reduces the occurrence of false alarms and missed disaster reports. By establishing a foundation bearing capacity prediction model, it can monitor soil liquefaction risks. Through a multi-level disaster warning mechanism, the stability and reliability of dynamic monitoring of geotechnical engineering disaster risks can be effectively improved, and dynamic monitoring of urban infrastructure can be achieved.

[0159] Corresponding to the above method embodiment, the present invention also discloses a dynamic monitoring device for geotechnical engineering disaster risks.

[0160] See also Figure 9 , a schematic structural diagram of a geotechnical engineering disaster risk dynamic monitoring device disclosed in an embodiment of the present invention, the device may include:

[0161] The model building unit 201 is used to build a three-dimensional geological model corresponding to the urban infrastructure.

[0162] A 3D geological model is a digital, three-dimensional visualization constructed from geological data. It describes the geological structure, lithologic distribution, stratigraphic interfaces, structural characteristics, and physical properties (such as porosity, permeability, and mineral composition) of the underground space. Its core purpose is to integrate discrete geological observation data (such as drilling logs, geophysical exploration data, and geological maps) into a continuous 3D spatial model through mathematical algorithms and computer graphics techniques. This provides decision support for resource exploration, environmental engineering, geological hazard prediction, and other fields.

[0163] The monitoring data acquisition unit 202 is used to acquire multivariate monitoring data collaboratively collected by multiple sensing sensors deployed at each monitoring point in the three-dimensional geological model, and process the multivariate monitoring data through a data fusion algorithm to obtain fused data.

[0164] Among them, the sensing sensors include but are not limited to soil pressure sensors, pore water pressure sensors and acceleration sensors.

[0165] Earth pressure sensors are used to measure the vertical or lateral pressure exerted by soil or rock on the surface of a structure (such as a tunnel lining, retaining wall, or pile foundation).

[0166] Pore ​​water pressure sensors are used to measure the pressure of water in soil or rock pores, reflecting changes in groundwater levels and permeability.

[0167] Accelerometers are used to measure the acceleration of an object in the X / Y / Z axis direction, reflecting vibration intensity or motion state, and can be used for permanent structural health monitoring and disaster warning.

[0168] In practical applications, three types of sensors, namely soil pressure sensors, pore water pressure sensors and acceleration sensors, can be deployed at each detection point. Each sensing sensor collects one type of monitoring data, thereby obtaining multivariate monitoring data.

[0169] This embodiment uses a data fusion algorithm to fuse monitoring data collected by different types of sensors at various monitoring points within a 3D geological model. Different fusion weights can be assigned to the monitoring data collected by different sensors during the data fusion process. For example, higher fusion weights can be assigned to the more important pore water pressure sensing under low-frequency heavy rainfall loads and the more significant soil pressure sensing under high-frequency traffic loads. This multi-dimensional monitoring data fusion effectively addresses issues such as insufficient single-point sensor measurement data and low measurement accuracy, thereby improving data accuracy and stability.

[0170] In practical applications, the data fusion algorithm used to fuse multivariate monitoring data can be Kalman filtering. Kalman filtering is an optimal estimation method based on the state space model. It recursively combines the observed data with the predicted value of the system model to estimate the optimal estimate of the system state.

[0171] The data analysis unit 203 is configured to perform soil layer data analysis on the fused data to obtain multi-dimensional signal statistics.

[0172] Among them, the multidimensional signal statistical values ​​include but are not limited to root mean square, standard deviation, variance, kurtosis, skewness, maximum value, minimum value, average value, and arithmetic mean.

[0173] The disaster risk determination unit 204 is configured to determine that an engineering disaster risk exists in the area where the urban infrastructure is located when a target signal value exceeding a corresponding warning threshold interval exists in the multi-dimensional signal statistics.

[0174] The warning threshold interval refers to the interval consisting of the warning upper threshold and the warning lower threshold.

[0175] This embodiment sets corresponding warning threshold intervals for different signal statistics in the multidimensional signal statistics. By comparing the signal statistics with the corresponding warning threshold intervals, it is determined whether each signal statistic exceeds the corresponding upper warning threshold or lower warning threshold. This embodiment determines the signal statistic value that exceeds the corresponding upper warning threshold or lower warning threshold as the target signal value. When the target signal value is present in the multidimensional signal statistics, it is determined that the area where the urban infrastructure is located is at risk of engineering disasters.

[0176] In summary, the present invention discloses a dynamic monitoring device for geotechnical engineering disaster risk, which constructs a three-dimensional geological model corresponding to urban infrastructure, obtains multivariate monitoring data collected by multiple sensing sensors deployed at each monitoring point in the three-dimensional geological model, processes the multivariate monitoring data through a data fusion algorithm to obtain fused data, performs soil layer data analysis on the fused data to obtain a multidimensional signal statistic, and determines that the area where the urban infrastructure is located has an engineering disaster risk when there is a target signal value exceeding the corresponding warning threshold interval in the multidimensional signal statistic. By constructing a three-dimensional geological model and deploying sensing sensors at multiple monitoring points in the three-dimensional geological model, the present invention can improve the monitoring coverage area and the positioning accuracy of the measuring points, effectively avoid the generation of monitoring blind spots, and provide a basis for long-term dynamic monitoring; by fusing and analyzing the multivariate monitoring data collected by multiple sensing sensors, it can effectively avoid the problem of insufficient measurement data and low measurement accuracy of single-point sensors, reduce the situation of disaster false alarms and missed reports, thereby improving the accuracy and stability of dynamic monitoring of geotechnical engineering disaster risks and meeting the current needs of safety prevention and control and early warning.

[0177] In one embodiment, the model building unit 201 may be specifically configured to:

[0178] Taking key areas of urban infrastructure as monitoring points and deploying perception sensors at these monitoring points;

[0179] Acquiring soil layer data collected by the perception sensor;

[0180] Generating original geological survey data from the soil layer data through geotechnical exploration, and obtaining drilling data from the original geological survey data;

[0181] Converting the borehole data into three-dimensional coordinates of soil layer control points, and establishing a continuous soil layer boundary surface using a data interpolation algorithm for the borehole data;

[0182] The three-dimensional geological model is generated based on the three-dimensional coordinates of the soil layer control points and the soil layer boundary surface.

[0183] In one embodiment, the monitoring data acquisition unit 202 may further be used to:

[0184] Inputting the multivariate monitoring data into a foundation bearing capacity prediction model to predict excess pore water pressure values;

[0185] The foundation bearing failure rate is obtained based on the excess pore water pressure and the effective stress, wherein the effective stress is determined based on the soil density.

[0186] In one embodiment, the monitoring data acquisition unit 202 may further be used to:

[0187] determining whether the foundation bearing failure rate is less than a warning limit;

[0188] If yes, it is determined that there is no engineering disaster risk in the area where the urban infrastructure is located;

[0189] If not, it is determined that there is an engineering disaster risk in the area where the urban infrastructure is located, and the warning level corresponding to the warning interval where the foundation bearing failure rate is located is used as the current warning level.

[0190] In one embodiment, the geotechnical engineering disaster risk dynamic monitoring device may further include:

[0191] a calculation unit, configured to calculate a percentage of the number of target signal values ​​exceeding the warning threshold interval in the multidimensional signal statistics to the total number of signal values;

[0192] The warning level determination unit is configured to take the warning level corresponding to the warning interval in which the percentage lies as the current warning level.

[0193] In one embodiment, the data analysis unit 203 may be specifically configured to:

[0194] performing data preprocessing on the multivariate monitoring data to obtain target multivariate monitoring data;

[0195] The multidimensional signal statistics are obtained by performing soil layer data analysis on the target multivariate monitoring data.

[0196] In one embodiment, the geotechnical engineering disaster risk dynamic monitoring device may further include:

[0197] An early warning mechanism triggering unit, configured to trigger an early warning mechanism when the engineering disaster risk in the area where the urban infrastructure is located reaches a graded early warning standard;

[0198] The storage unit is used to store the multivariate monitoring data collected within a preset time period before and after the warning, and / or to generate a dynamic monitoring report on geotechnical engineering disaster risks, which at least includes: the disaster warning level, the warning start time, the number of warning triggers and historical data storage.

[0199] In one embodiment, the storage unit may be specifically used for:

[0200] In the multivariate monitoring data collection process, when the absolute value of the difference between adjacent sampling points exceeds the set trigger fluctuation limit, the dynamic storage mechanism is triggered to store the fluctuation signal of the collection process;

[0201] or,

[0202] In the multivariate monitoring data collection process, when the current signal value is greater than the upper warning threshold, the upper warning is triggered to store the signal greater than the upper warning threshold; when the current signal value is less than the lower warning threshold, the lower warning is triggered to store the signal less than the lower warning threshold.

[0203] It should be noted that, for the specific working principles of each component in the device embodiment, please refer to the corresponding part of the method embodiment, which will not be repeated here.

[0204] Corresponding to the above embodiment, the present invention also discloses a computer storage medium, which stores at least one instruction. When the at least one instruction is executed by a processor, the steps shown in the embodiment of the method for dynamic monitoring of geotechnical engineering disaster risks are implemented.

[0205] Corresponding to the above embodiment, Figure 10 As shown, the present invention also provides a structural diagram of an electronic device, which may include: a processor 1 and a memory 2;

[0206] The processor 1 and the memory 2 communicate with each other via a communication bus 3.

[0207] Processor 1, configured to execute at least one instruction;

[0208] Memory 2, used to store at least one instruction;

[0209] The processor 1 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0210] The memory 2 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0211] The processor executes at least one instruction to implement the steps shown in the embodiment of the method for dynamic monitoring of geotechnical engineering disaster risks.

[0212] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0213] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0214] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for dynamic monitoring of geotechnical engineering disaster risk, characterized in that: include: Construct a three-dimensional geological model corresponding to the city’s infrastructure; Acquire multivariate monitoring data collaboratively collected by multiple sensing sensors deployed at each monitoring point in the three-dimensional geological model, and process the multivariate monitoring data using a data fusion algorithm to obtain fused data; Performing soil layer data analysis on the fused data to obtain multi-dimensional signal statistics; When there is a target signal value exceeding a corresponding warning threshold interval in the multi-dimensional signal statistics, it is determined that there is an engineering disaster risk in the area where the urban infrastructure is located.

2. The method for dynamic monitoring of geotechnical engineering disaster risk according to claim 1, characterized in that: The construction of a three-dimensional geological model corresponding to the urban infrastructure includes: Taking key areas of urban infrastructure as monitoring points and deploying perception sensors at these monitoring points; Acquiring soil layer data collected by the perception sensor; Generating original geological survey data from the soil layer data through geotechnical exploration, and obtaining drilling data from the original geological survey data; Converting the borehole data into three-dimensional coordinates of soil layer control points, and establishing a continuous soil layer boundary surface using a data interpolation algorithm for the borehole data; The three-dimensional geological model is generated based on the three-dimensional coordinates of the soil layer control points and the soil layer boundary surface.

3. The method for dynamic monitoring of geotechnical engineering disaster risk according to claim 1 or 2, characterized in that: After acquiring the multivariate monitoring data collaboratively collected by multiple sensing sensors deployed at each monitoring point in the three-dimensional geological model, the method further includes: Inputting the multivariate monitoring data into a foundation bearing capacity prediction model to predict excess pore water pressure values; The foundation bearing failure rate is obtained based on the excess pore water pressure and the effective stress, wherein the effective stress is determined based on the soil density.

4. The method for dynamic monitoring of geotechnical engineering disaster risk according to claim 3, characterized in that: Also includes: determining whether the foundation bearing failure rate is less than a warning limit; If yes, it is determined that there is no engineering disaster risk in the area where the urban infrastructure is located; If not, it is determined that there is an engineering disaster risk in the area where the urban infrastructure is located, and the warning level corresponding to the warning interval where the foundation bearing failure rate is located is used as the current warning level.

5. The method for dynamic monitoring of geotechnical engineering disaster risk according to claim 1 or 2, characterized in that: After determining that the area where the urban infrastructure is located has an engineering disaster risk, the method further includes: Calculating the percentage of the number of target signal values ​​exceeding the warning threshold interval in the total number of signal values ​​in the multidimensional signal statistics; The warning level corresponding to the warning interval in which the percentage falls is used as the current warning level.

6. The method for dynamic monitoring of geotechnical engineering disaster risk according to claim 1 or 2, characterized in that: The performing soil layer data analysis on the fused data to obtain multi-dimensional signal statistics includes: performing data preprocessing on the multivariate monitoring data to obtain target multivariate monitoring data; The multidimensional signal statistics are obtained by performing soil layer data analysis on the target multivariate monitoring data.

7. The method for dynamic monitoring of geotechnical engineering disaster risk according to claim 1, characterized in that: Also includes: When the engineering disaster risk in the area where the urban infrastructure is located reaches the graded warning standard, the warning mechanism is triggered; The multivariate monitoring data collected within a preset time period before and after the warning is stored, and / or a dynamic monitoring report on geotechnical engineering disaster risk is generated, which at least includes: disaster warning level, warning start time, warning triggering times and historical data storage.

8. The method for dynamic monitoring of geotechnical engineering disaster risk according to claim 7, characterized in that: The storing of the multivariate monitoring data collected within a preset time period before and after the warning includes: In the multivariate monitoring data collection process, when the absolute value of the difference between adjacent sampling points exceeds the set trigger fluctuation limit, the dynamic storage mechanism is triggered to store the fluctuation signal of the collection process; or, In the multivariate monitoring data collection process, when the current signal value is greater than the upper warning threshold, the upper warning is triggered to store the signal greater than the upper warning threshold; when the current signal value is less than the lower warning threshold, the lower warning is triggered to store the signal less than the lower warning threshold.

9. A dynamic monitoring device for geotechnical engineering disaster risk, characterized in that: include: A model building unit, used to build a three-dimensional geological model corresponding to the urban infrastructure; a monitoring data acquisition unit, configured to acquire multivariate monitoring data collaboratively collected by a plurality of sensing sensors deployed at each monitoring point in the three-dimensional geological model, and to process the multivariate monitoring data using a data fusion algorithm to obtain fused data; A data analysis unit, configured to perform soil layer data analysis on the fused data to obtain multi-dimensional signal statistics; The disaster risk determination unit is used to determine that there is an engineering disaster risk in the area where the urban infrastructure is located when there is a target signal value exceeding the corresponding warning threshold interval in the multidimensional signal statistics.

10. An electronic device, characterized in that: The electronic device includes: a memory and a processor; The memory is used to store at least one instruction; The processor is used to execute the at least one instruction to implement the dynamic monitoring method for geotechnical engineering disaster risks according to any one of claims 1 to 8.

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