Slope monitoring method and system based on multi-parameter fusion

Abstract

The invention relates to the technical field of slope monitoring, in particular to a slope monitoring method and system based on multi-parameter fusion, and the system comprises a multi-parameter slope monitoring module, a basic instability fusion evaluation module, an evolution instability fusion evaluation module and a comprehensive instability early warning module. A multi-parameter slope monitoring module and a basic instability fusion evaluation module are set, the current instability risk of a slope is periodically analyzed based on a multi-parameter fusion mode, various monitoring parameters of the slope are predicted in combination with an LSTM model, and a slope analysis model is constructed in combination with finite element analysis software. The evolution influence of each prediction parameter on other monitoring parameters is deeply simulated and analyzed, the key action point and conduction path of each parameter in the slope instability process can be revealed, the comprehensive instability early warning level of the slope is further comprehensively determined, the slope monitoring and stability evaluation process is optimized from multiple aspects, and the slope stability early warning level is improved. And a decision maker can comprehensively and deeply know the instability risk of the side slope.

Description

Technical Field

[0001] The present invention relates to the technical field of slope monitoring, and more specifically, to a slope monitoring method and system based on multi-parameter fusion. Background Art

[0002] In many engineering construction and natural slope maintenance scenarios, slope stability plays a vital role in ensuring the safety of surrounding personnel, protecting infrastructure, and maintaining the ecological environment. With the continuous expansion of slope engineering scale and increasing complexity, accurate, comprehensive and timely monitoring and evaluation of slope stability has become a key issue that needs to be solved urgently.

[0003] Traditional slope monitoring methods often focus on monitoring and analyzing a single or a few parameters. For example, a common method is to focus only on surface displacement parameters and measure the displacement of the slope surface through total stations, GPS receivers and other equipment to determine whether the slope is stable. However, the stability of the slope is actually the result of the interaction of multiple complex factors. It is difficult to fully reflect the true stability of the slope by relying solely on a single parameter. For example, even if the surface displacement does not change significantly in the short term, the abnormal rise in groundwater levels may be gradually weakening the shear strength of the slope soil, or the concentrated accumulation of stress inside the rock and soil may also quietly change the mechanical balance of the slope. These potential unstable factors cannot be detected from the surface displacement parameters alone, which can easily lead to misjudgment of slope stability and miss the best opportunity for prevention and control.

[0004] In addition, some traditional methods that use multi-parameter monitoring also have many shortcomings. Most of these methods simply list the various monitoring parameters, lack in-depth analysis of the internal correlation and interaction between the parameters, and the data of each parameter are relatively isolated, which cannot accurately reveal the complex physical and mechanical changes in the slope and the dynamic influence mechanism between the various factors. For example, when multiple parameters such as groundwater level, stress and vibration change at the same time, it is difficult to determine how they affect each other and work together on the stability of the slope, and it is impossible to accurately assess the actual instability risk of the slope under different working conditions.

[0005] At the same time, traditional stability assessments are mostly static, only analyzing the state of the slope at a specific point in time, and lack the ability to predict the trend of slope stability changes in the future. This means that when decision makers formulate slope maintenance and management strategies, they can only make decisions based on the current situation, and it is difficult to plan response measures in advance, making it relatively passive to deal with the potential risk of slope instability.

[0006] In view of the many deficiencies in the above-mentioned traditional slope monitoring methods, stability assessment means, and early warning mechanisms, there is an urgent need for an innovative slope monitoring method and system that can comprehensively consider multiple parameters and their interaction relationships to achieve accurate and dynamic assessment of slope stability and scientific and reasonable early warning. Summary of the Invention

[0007] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a slope monitoring method and system based on multi-parameter fusion.

[0008] To achieve the above purpose, the present invention provides the following technical solutions:

[0009] A slope monitoring system based on multi-parameter fusion includes a multi-parameter slope monitoring module, a basic instability fusion evaluation module, an evolutionary instability fusion evaluation module, and a comprehensive instability early warning module;

[0010] The multi-parameter slope monitoring module collects various monitoring parameters related to the slope in real time;

[0011] The basic instability fusion evaluation module regularly determines the parameter characteristics of various monitoring parameters, and then determines the basic instability evaluation index of the slope;

[0012] After determining the basic instability evaluation index of the slope, the evolutionary instability fusion evaluation module synchronously determines the time-series prediction characteristics of various monitoring parameters, constructs a slope analysis model, obtains the parameter linkage influence index of various monitoring parameters, and then determines the evolutionary instability evaluation index of the slope;

[0013] The comprehensive instability early warning module determines the comprehensive instability early warning level of the slope based on the basic instability evaluation index and the evolutionary instability evaluation index of the slope.

[0014] Furthermore, the process of determining the parameter characteristics of the monitoring parameters is as follows: Select a type of monitoring parameter, obtain all the data of this type of monitoring parameter within a period, preprocess and extract the features of the data of this monitoring parameter, and the extracted parameter characteristics of this monitoring parameter are obtained.

[0015] Furthermore, the process of determining the basic instability evaluation index of the slope is as follows: Combine the parameter characteristics of various monitoring parameters into a parameter feature set in the form of a feature set, select a basic instability evaluation model, input the parameter feature set into the basic instability evaluation model, and the basic instability evaluation model outputs the basic instability evaluation index of the slope.

[0016] Further, for the time series prediction feature of the monitoring parameter, the determination process is as follows: Select a type of monitoring parameter, obtain the parameter features of this monitoring parameter in the previous j cycles, combine the parameter features of the j cycles into a parameter time series feature set in the form of a feature set, select the parameter time series prediction model corresponding to this type of monitoring parameter, input the parameter time series feature set into the parameter time series prediction model, and the parameter time series prediction model outputs the time series prediction feature of this monitoring parameter.

[0017] Further, for the parameter linkage influence index of the monitoring parameter, the obtaining process is as follows: Select a type of monitoring parameter, obtain the time series prediction feature of this type of monitoring parameter, mark the remaining types of monitoring parameters as evolution influence parameters, obtain the parameter features of each evolution influence parameter, synchronously input the time series prediction feature of this type of monitoring parameter and the parameter features of each evolution influence parameter into the slope analysis model, the slope analysis model conducts a cycle of simulation, after the simulation ends, obtain the simulated parameter features of each evolution influence parameter in the slope analysis model, combine the simulated parameter features and the parameter features of the same evolution influence parameter into a feature comparison group, obtain the feature evolution comparison model corresponding to each evolution influence parameter, input the feature comparison group of each evolution influence parameter into the corresponding feature evolution comparison model, obtain the evolution anomaly index of each evolution influence parameter, obtain the number of times of evolution anomaly intensification, and obtain the parameter linkage influence index of the monitoring parameter based on the evolution anomaly index and the number of times of evolution anomaly intensification of each evolution influence parameter.

[0018] Further, for the number of times of evolution anomaly intensification, the obtaining process is as follows: Set the evolution anomaly threshold index, when the evolution anomaly index of the evolution influence parameter is greater than the evolution anomaly threshold index, increase the number of times of evolution anomaly intensification by one.

[0019] Further, for the evolution instability evaluation index of the slope, the determination process is as follows: Perform a sum mean calculation on the parameter linkage influence indexes of various types of monitoring parameters to obtain the average parameter linkage influence index, obtain the number of times of linkage promotion, and determine the evolution instability evaluation index of the slope based on the average parameter linkage influence index and the number of times of linkage promotion.

[0020] Further, for the number of times of linkage promotion, the obtaining process is as follows: Compare various types of monitoring parameters pairwise, calculate the sum value of the parameter linkage influence indexes of the two compared types of monitoring parameters to obtain the linkage promotion sum index, set the linkage promotion sum threshold index, when the linkage promotion sum index is greater than the linkage promotion sum threshold index, increase the number of times of linkage promotion by one.

[0021] Further, the slope monitoring method based on multi-parameter fusion includes the following steps:

[0022] Step 1: Regularly determine the parameter features of various types of monitoring parameters, and then determine the basic instability evaluation index of the slope;

[0023] Step 2: Determine the time series prediction characteristics of various monitoring parameters, construct a slope analysis model, obtain the parameter linkage influence index of various monitoring parameters, and then determine the evolution instability evaluation index of the slope;

[0024] Step 3: Determine the comprehensive instability warning level of the slope based on the basic instability evaluation index and the evolution instability evaluation index of the slope.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The method of the present invention can reveal the key action points and conduction paths of each parameter in the process of slope instability, and then comprehensively determine the comprehensive instability warning level of the slope, optimizing the slope monitoring and stability evaluation process from multiple levels, enabling decision-makers to more comprehensively and deeply understand the instability risk of the slope;

[0027] 2. The system of the present invention sets a multi-parameter slope monitoring module and a basic instability fusion evaluation module, regularly analyzes the current instability risk of the slope based on the multi-parameter fusion method, predicts each monitoring parameter of the slope in combination with the LSTM model, combines with finite element analysis software, constructs a slope analysis model, deeply simulates and analyzes the evolution influence of each predicted parameter on the remaining monitoring parameters, intuitively presents the dynamic action mechanism between the monitoring parameters, and more comprehensively understands the actual influence of each predicted parameter on the slope stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a flow chart of the slope monitoring method based on multi-parameter fusion;

[0029] Figure 2 is a principle block diagram of the slope monitoring system based on multi-parameter fusion;

[0030] Figure 3 is a determination flow chart of the time series prediction characteristics of the monitoring parameters;

[0031] Figure 4 is a determination flow chart of the basic instability evaluation index of the slope. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] Example 1: Refer to Figure 1 , the slope monitoring method based on multi-parameter fusion includes the following steps:

[0033] Step 1: Regularly determine the parameter characteristics of various monitoring parameters, and then determine the basic instability evaluation index of the slope;

[0034] Step 2: Determine the time series prediction characteristics of various monitoring parameters, construct a slope analysis model, obtain the parameter linkage influence index of various monitoring parameters, and then determine the evolution instability evaluation index of the slope;

[0035] Step 3: Determine the comprehensive instability warning level of the slope based on the foundation instability evaluation index and the evolution instability evaluation index of the slope.

[0036] Example 2: Refer to Figures 2 to 4 , a slope monitoring system based on multi-parameter fusion, including a multi-parameter slope monitoring module, a foundation instability fusion evaluation module, an evolution instability fusion evaluation module, and a comprehensive instability warning module.

[0037] The above method can reveal the key action points and conduction paths of each parameter in the process of slope instability, and then comprehensively determine the comprehensive instability warning level of the slope, optimizing the slope monitoring and stability assessment process from multiple levels, enabling decision-makers to more comprehensively and deeply understand the instability risk of the slope.

[0038] The multi-parameter slope monitoring module collects various monitoring parameters associated with the slope in real time (the types of monitoring parameters include surface displacement parameters, stress parameters, groundwater level parameters, vibration parameters, etc.).

[0039] The foundation instability fusion evaluation module regularly determines the parameter characteristics of various monitoring parameters, and then determines the foundation instability evaluation index of the slope .

[0040] The determination process of the parameter characteristics of the monitoring parameters is as follows: Select a type of monitoring parameter, obtain all the data of this type of monitoring parameter within a period, perform preprocessing and feature extraction on the data of this monitoring parameter (the preprocessing methods include outlier processing, missing value processing, denoising processing, normalization processing, etc., and the feature extraction methods include time-domain feature extraction, frequency-domain feature extraction, etc.), and extract the parameter characteristics of this monitoring parameter.

[0041] The determination process of the foundation instability evaluation index of the slope is as follows: Combine the parameter characteristics of various monitoring parameters into a parameter feature set in the form of a feature set, select a foundation instability evaluation model, input the parameter feature set into the foundation instability evaluation model, and the foundation instability evaluation model outputs the foundation instability evaluation index of the slope.

[0042] The construction process of the foundation instability evaluation model is as follows: Construct a neural network model, collect i parameter feature sets, train the neural network model through the parameter feature sets, assign a foundation instability evaluation index to each parameter feature set, and the index range of the foundation instability evaluation index is (1.0~15.0). The larger the foundation instability evaluation index, the greater the current instability hidden danger of the slope. Divide the training data into a training set, a validation set, and a test set, with a ratio of 60%:20%:20%, and perform neural network iterative training on the training set, validation set, and test set. Finally, construct the foundation instability evaluation model.

[0043] The evolution instability fusion evaluation module, after determining the basic instability evaluation index of the slope, synchronously determines the time-series prediction characteristics of various monitoring parameters, constructs a slope analysis model, obtains the parameter linkage influence index of various monitoring parameters, and then determines the evolution instability evaluation index of the slope.

[0044] The process of determining the time-series prediction characteristics of monitoring parameters is as follows: Select a type of monitoring parameter, obtain the parameter characteristics of this monitoring parameter in the previous j cycles, combine the parameter characteristics of the j cycles into a parameter time-series feature set in the form of a feature set, select the parameter time-series prediction model corresponding to this type of monitoring parameter, input the parameter time-series feature set into the parameter time-series prediction model, and the parameter time-series prediction model outputs the time-series prediction characteristics of this monitoring parameter.

[0045] Each type of monitoring parameter corresponds to a parameter time-series prediction model. All parameter time-series prediction models are constructed based on the LSTM model. The construction of all parameter time-series prediction models only differs in the training data. Hereinafter, taking the surface displacement parameter as an example, the construction process of the parameter time-series prediction model of the surface displacement parameter will be disclosed: Construct an LSTM model, collect the parameter time-series feature sets of k surface displacement parameters, train the LSTM model through the parameter time-series feature sets of the surface displacement parameters, assign a time-series prediction characteristic to each parameter time-series feature set of the surface displacement parameter. The time-series prediction characteristic is the parameter characteristic predicting the surface displacement parameter in the next cycle. Divide the training data into a training set, a validation set, and a test set, with a ratio of 70%:15%:15%. Use the training set to train the model, and by continuously adjusting the parameters of the model, minimize the value of the loss function. During the training process, use the validation set to monitor the performance of the model to avoid overfitting, and use the test set to evaluate the trained model. Finally, construct the parameter time-series prediction model of the surface displacement parameter.

[0046] The slope analysis model is constructed as follows: Determine the type of rock and soil mass of the slope (through on-site geological exploration, taking rock and soil samples for indoor test analysis, and combining the geological exploration report and the experience of on-site engineers to determine the specific types of slope rock and soil, such as sandy soil, clay, silty soil, rock, or their mixtures, etc.). Determine the stratification of rock and soil (using means such as drilling and exploratory pits to obtain the distribution of rock and soil masses at different depths of the slope, and record information such as the thickness of each layer of rock and soil, the elevation of the top and bottom surfaces, and the position of the interface between layers. At the same time, collect rock and soil samples at different stratifications, test the physical and mechanical parameters of each layer, and clarify the differences in strength, permeability, etc. of each layer of rock and soil masses to provide an accurate stratification basis for subsequent modeling). Determine the geometric shape (using surveying equipment such as total stations and GPS receivers to accurately measure geometric dimension parameters such as the slope surface length, slope height, and slope angle of the slope, and determine the overall shape and spatial scope of the slope). Select a finite element analysis software (such as ABAQUS, ANSYS, etc.). In the finite element analysis software, draw the three-dimensional geometric contour of the slope according to the geometric shape of the slope. Based on the collected rock and soil types and the physical and mechanical parameters of each layer, define the corresponding material models and parameters for different rock and soil parts in the material property setting module of the software. For example, for sandy soil rock and soil mass, select a suitable constitutive model (such as the Mohr-Coulomb constitutive model), and input parameters such as the density, internal friction angle, cohesion, elastic modulus, and Poisson's ratio of the sandy soil. For the rock and soil masses of different layers, assign values according to the actual measured parameters of each layer to ensure that the material properties in the model are consistent with the characteristics of the rock and soil masses of the actual slope. In the mesh generation module of the software, according to the complexity of the model and the requirements of calculation accuracy, select a suitable mesh type (such as triangular elements, quadrilateral elements, etc. for two-dimensional models, tetrahedral elements, hexahedral elements, etc. for three-dimensional models) and mesh size to divide the geometric model of the slope. At the same time, at key parts such as the interfaces between different rock and soil layers and stress concentration areas, set the corresponding boundary conditions in the software according to the actual boundary conditions of the slope. For example, for the bottom of the slope, it is usually set as a fixed constraint boundary to restrict its displacement in the horizontal and vertical directions to simulate the relatively fixed relationship between the bottom rock and soil and the foundation in reality. For the side of the slope, set appropriate normal constraint or frictional contact conditions according to the actual constraint conditions of the surrounding rock and soil. If the slope is subjected to external loads (such as the load of buildings at the slope top, traffic loads, etc.), set the load boundary conditions at the corresponding positions by applying pressure, concentrated force, etc. For the groundwater boundary, if it is close to a water source, set the corresponding water head boundary conditions to simulate the recharge or discharge of groundwater to ensure that the boundary conditions of the model can truly reflect the actual stress and constraint state of the slope, and construct the slope analysis model.

[0047] The parameter linkage influence index of the monitoring parameter is obtained as follows: Select a category of monitoring parameters, obtain the time series prediction features of this category of monitoring parameters, mark the remaining categories of monitoring parameters as evolution influence parameters (for example, if the surface displacement parameter is selected, obtain the time series prediction features of the surface displacement parameter, and mark the monitoring parameters other than the surface displacement parameter as evolution influence parameters), obtain the parameter features of each evolution influence parameter, synchronously input the time series prediction features of this category of monitoring parameters and the parameter features of each evolution influence parameter into the slope analysis model (the slope analysis model can perform evolution simulation based on the time series prediction features and parameter features), the slope analysis model conducts a cycle of simulation, after the simulation ends, obtain the simulated parameter features of each evolution influence parameter in the slope analysis model, combine the simulated parameter features and parameter features of the same evolution influence parameter into a feature comparison group, obtain the feature evolution comparison model corresponding to each evolution influence parameter, input the feature comparison group of each evolution influence parameter into the corresponding feature evolution comparison model, and obtain the evolution anomaly index of each evolution influence parameter , c = 1, 2, …, C - 1, C, where c represents the serial number of the corresponding evolution influence parameter, and C is the total number of evolution influence parameters. Set the evolution anomaly coefficient as , y = 1, 2, …, Y - 1, Y, < <…< < , each evolution anomaly coefficient corresponds to an evolution anomaly index within a range. The value range of the evolution anomaly index includes (0, , ( , , …, ( , . When ∈(0, , the evolution anomaly coefficient is . Set the evolution anomaly threshold index (the evolution anomaly threshold index is a preset index used to compare with the evolution anomaly index). When the evolution anomaly index of the evolution influence parameter is greater than the evolution anomaly threshold index, increase the number of times of evolution anomaly intensification by one (when the evolution anomaly index of the evolution influence parameter is less than or equal to the evolution anomaly threshold index, no processing is performed), mark the number of times of evolution anomaly intensification as , and obtain the parameter linkage influence index of this monitoring parameter through .

[0048] ​Each type of monitoring parameter corresponds to a characteristic evolution comparison model. All characteristic evolution comparison models are constructed based on deep learning models. The construction of all characteristic evolution comparison models only differs in the training data. Taking the stress parameter as an example, the construction process of the characteristic evolution comparison model for the stress parameter is disclosed as follows: construct a deep learning model, collect L characteristic comparison groups of stress parameters. Each characteristic comparison group includes the simulated parameter characteristics and parameter characteristics of the stress parameter. Train the deep learning model with the characteristic comparison groups of the stress parameter, assign an evolution anomaly index to each characteristic comparison group of the stress parameter. The index range of the evolution anomaly index is (1.0 - 5.0). The larger the evolution anomaly index, the more abnormal the simulated parameter characteristics of the stress parameter are compared with the parameter characteristics after evolution. Divide the training data into a training set and a validation set with a ratio of 70%:30%. Train the training set and the validation set. Finally, construct the characteristic evolution comparison model of the stress parameter.

[0049] The determination process of the evolution instability evaluation index of the slope is as follows: calculate the sum mean of the parameter linkage influence indexes of various monitoring parameters to obtain the average parameter linkage influence index. , compare various monitoring parameters pairwise, calculate the sum value of the parameter linkage influence indexes of the two compared monitoring parameters to obtain the linkage promotion sum index. Set the linkage promotion sum threshold index (the linkage promotion sum threshold index is a preset index used to compare with the linkage promotion sum index). When the linkage promotion sum index is greater than the linkage promotion sum threshold index, increase the linkage promotion count by one (when the linkage promotion sum index is less than or equal to the linkage promotion sum threshold index, no processing is done). Mark the linkage promotion count as , through calculate to obtain the evolution instability evaluation index of the slope. .

[0050] The comprehensive instability warning module determines the comprehensive instability warning level of the slope based on the basic instability evaluation index and the evolution instability evaluation index of the slope.

[0051] Determine the comprehensive instability warning level of the slope based on the basic instability evaluation index and the evolution instability evaluation index of the slope: calculate to obtain the comprehensive instability warning index through . Set the range corresponding to each comprehensive instability warning index to a comprehensive instability warning level. The range of the comprehensive instability warning index is [0, , ([[]], , …, ([[]], , , …, ([[]], , , the comprehensive instability warning levels include comprehensive instability warning level 1, comprehensive instability warning level 2, …, comprehensive instability warning level S - 1, and comprehensive instability warning level S. The slope instability risk of comprehensive instability warning level 2 is higher than that of comprehensive instability warning level 1, and so on.

[0052] Set up a multi-parameter slope monitoring module and a basic instability fusion evaluation module. Regularly analyze the current instability risk of the slope based on the multi-parameter fusion method, and combine with the LSTM model to predict the monitoring parameters of the slope. Combine with the finite element analysis software to construct a slope analysis model, deeply simulate and analyze the evolution impact of each prediction parameter on the other monitoring parameters, visually present the dynamic interaction mechanism between the monitoring parameters, and more comprehensively understand the actual impact of each prediction parameter on the slope stability.

[0053] The above formulas are all dimensionless and take their numerical values for calculation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0054] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on the computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0055] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0056] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0057] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0058] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings, direct couplings, or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0059] If the described functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0060] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application and should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. The slope monitoring system based on multi-parameter fusion is characterized by: It includes multi-parameter slope monitoring module, foundation instability fusion evaluation module, evolution instability fusion evaluation module, and comprehensive instability warning module; The multi-parameter slope monitoring module collects various monitoring parameters associated with the slope in real time; The foundation instability fusion evaluation module regularly determines the parameter characteristics of various monitoring parameters, and then determines the foundation instability evaluation index of the slope; The evolutionary instability fusion evaluation module, after determining the basic instability evaluation index of the slope, simultaneously determines the time series prediction characteristics of various monitoring parameters, constructs a slope analysis model, obtains the parameter linkage influence index of various monitoring parameters, and then determines the evolutionary instability evaluation index of the slope; The comprehensive instability warning module determines the comprehensive instability warning level of the slope based on the basic instability evaluation index and the evolutionary instability evaluation index of the slope.

2. The slope monitoring system based on multi-parameter fusion according to claim 1 is characterized in that: The parameter characteristics of the monitoring parameters are determined as follows: a type of monitoring parameters is selected, all data of this type of monitoring parameters in a period are obtained, the data of the monitoring parameters are preprocessed and feature extracted, and the parameter characteristics of the monitoring parameters are extracted.

3. The slope monitoring system based on multi-parameter fusion according to claim 1 is characterized in that: The foundation instability evaluation index of the slope is determined as follows: the parameter characteristics of various monitoring parameters are combined into a parameter feature set in the form of a feature set, a foundation instability evaluation model is selected, the parameter feature set is input into the foundation instability evaluation model, and the foundation instability evaluation model outputs the foundation instability evaluation index of the slope.

4. The slope monitoring system based on multi-parameter fusion according to claim 1 is characterized in that: The time series prediction characteristics of the monitoring parameters are determined as follows: select a type of monitoring parameters, obtain the parameter characteristics of the monitoring parameters in the previous j periods, combine the parameter characteristics of the j periods into a parameter time series feature set in the form of a feature set, select the parameter time series prediction model corresponding to this type of monitoring parameters, input the parameter time series feature set into the parameter time series prediction model, and the parameter time series prediction model outputs the time series prediction characteristics of the monitoring parameters.

5. The slope monitoring system based on multi-parameter fusion according to claim 1 is characterized in that: The parameter linkage influence index of the monitoring parameters is obtained by the following process: select a type of monitoring parameters, obtain the time series prediction characteristics of this type of monitoring parameters, mark the remaining types of monitoring parameters as evolution influencing parameters, obtain the parameter characteristics of each evolution influencing parameter, synchronously input the time series prediction characteristics of this type of monitoring parameters and the parameter characteristics of each evolution influencing parameter into the slope analysis model, and simulate the slope analysis model for one cycle. After the simulation, obtain the simulation parameter characteristics of each evolution influencing parameter in the slope analysis model, combine the simulation parameter characteristics and parameter characteristics of the same evolution influencing parameters into a feature comparison group, obtain the feature evolution comparison model corresponding to each evolution influencing parameter, input the feature comparison group of each evolution influencing parameter into the corresponding feature evolution comparison model, obtain the evolution anomaly index of each evolution influencing parameter, obtain the number of times the evolution anomaly intensifies, and obtain the parameter linkage influence index of the monitoring parameters based on the evolution anomaly index of each evolution influencing parameter and the number of times the evolution anomaly intensifies.

6. The slope monitoring system based on multi-parameter fusion according to claim 5 is characterized in that: The acquisition process of the number of evolution anomaly aggravation is as follows: an evolution anomaly threshold index is set, and when the evolution anomaly index of the evolution influencing parameter is greater than the evolution anomaly threshold index, the number of evolution anomaly aggravation is increased by one.

7. The slope monitoring system based on multi-parameter fusion according to claim 1 is characterized in that: The evolutionary instability evaluation index of the slope is determined as follows: the parameter linkage influence index of each monitoring parameter is summed and averaged to obtain the average parameter linkage influence index, the number of linkage promotions is obtained, and the evolutionary instability evaluation index of the slope is determined based on the average parameter linkage influence index and the number of linkage promotions.

8. The slope monitoring system based on multi-parameter fusion according to claim 7 is characterized in that: The acquisition process of the number of linkage promotions is as follows: compare each type of monitoring parameters in pairs, calculate the sum of the parameter linkage influence indexes of the two types of compared monitoring parameters, calculate the linkage promotion sum index, set the linkage promotion sum threshold index, and when the linkage promotion sum index is greater than the linkage promotion sum threshold index, increase the number of linkage promotions by one.

9. A slope monitoring method based on multi-parameter fusion, applied to a slope monitoring system based on multi-parameter fusion as claimed in any one of claims 1 to 8, characterized in that: The steps include: Step 1: Regularly determine the parameter characteristics of various monitoring parameters, and then determine the foundation instability evaluation index of the slope; Step 2: Determine the time series prediction characteristics of various monitoring parameters, build a slope analysis model, obtain the parameter linkage influence index of various monitoring parameters, and then determine the evolution instability evaluation index of the slope; Step 3: Determine the comprehensive instability warning level of the slope based on the basic instability evaluation index and the evolutionary instability evaluation index of the slope.

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