Geological disaster risk hidden danger prediction method and system based on model algorithm

Abstract

The invention relates to the technical field of geological disaster risk hidden danger prediction, in particular to a geological disaster risk hidden danger prediction method and system based on a model algorithm, and the method comprises the steps: obtaining target data related to geological disasters; extracting features from the target data to construct a risk prediction model; and geological disasters are monitored and early warned through the risk prediction model. The method has the beneficial effects that potential geological disaster risks can be identified and early warned in time through comprehensive data acquisition, feature extraction, model construction and optimization and real-time monitoring. Particularly, an online learning mechanism and an incremental learning technology are introduced, so that the prediction model can dynamically adapt to the change of a geological environment and continuously absorb new data, and the prediction reliability is improved. In addition, through fine feature selection and statistical analysis, the model keeps efficient operation, complexity is reduced, prediction accuracy is enhanced, and powerful technical support and decision assistance are provided for prevention and reduction of geological disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster risk hidden danger prediction, and in particular to a geological disaster risk hidden danger prediction method and system based on a model algorithm. Background Art

[0002] Geological disasters, including landslides, mudslides and earthquakes, are natural disasters that frequently occur and pose a major threat to human life and economic development. Due to the destructiveness of these disasters, accurate prediction of the occurrence of geological disasters is crucial for taking preventive measures and reducing losses. However, traditional geological disaster risk assessment methods mainly rely on manual collection and analysis of geological data, which is not only inefficient, but also limited in accuracy and reliability due to the intervention of human factors. Therefore, developing a new and efficient geological disaster risk prediction method to improve the accuracy and efficiency of prediction has become a technical problem that urgently needs to be solved. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for predicting geological disaster risk hazards based on a model algorithm, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for predicting geological disaster risk hazards based on a model algorithm, including obtaining target data related to geological disasters;

[0008] Build a risk prediction model by extracting features from target data;

[0009] Monitor and warn of geological disasters through risk prediction models.

[0010] As a preferred solution of the geological disaster risk prediction method based on the model algorithm of the present invention, wherein: obtaining target data related to geological disasters includes

[0011] Acquire target data related to geological hazards by using remote sensing satellite technology, GIS technology and geological hazard monitoring equipment;

[0012] Geological disaster monitoring equipment includes GPS, 3D laser scanners, surface deformation monitoring instruments, earth pressure gauges, pore water pressure gauges, rain gauges, water level gauges and flow meters.

[0013] As a preferred solution of the geological disaster risk prediction method based on the model algorithm of the present invention, the target data includes geological, topographic, meteorological, humanistic and seismic data.

[0014] As a preferred solution of the geological disaster risk prediction method based on the model algorithm of the present invention, the feature extraction includes extracting features that are meaningful to geological disaster prediction and data features that have a significant impact on the prediction results.

[0015] As a preferred solution of the method for predicting geological disaster risk hazards based on the model algorithm of the present invention, the features meaningful for geological disaster prediction include but are not limited to landslides, mud-rock flows, and earthquakes;

[0016] Data features that have a significant impact on the prediction results include, but are not limited to, geology, topography, rainfall, vegetation, hydrology, and the time, location, magnitude, and waveform of earthquakes.

[0017] As a preferred solution of the geological disaster risk prediction method based on the model algorithm of the present invention, wherein: the risk prediction model is constructed by using a support vector machine algorithm to construct a target risk prediction model.

[0018] As a preferred solution of the method for predicting geological disaster risk hazards based on the model algorithm of the present invention, wherein: constructing the risk prediction model also includes evaluating and optimizing the risk prediction model;

[0019] Optimization of the risk prediction model, including optimization of the risk prediction model through parameter tuning and feature selection based on the evaluation results.

[0020] In a second aspect, the present invention provides a method for predicting geological disaster risk hazards based on a model algorithm, comprising: an acquisition module for acquiring target data related to geological disasters;

[0021] A building module for building a risk prediction model by extracting features from target data;

[0022] The early warning module is used to monitor and warn of geological disasters through risk prediction models.

[0023] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0025] Compared with the prior art, the present invention has the following beneficial effects: through comprehensive data collection, feature extraction, model construction and optimization, and real-time monitoring, it is possible to timely identify and warn of potential geological disaster risks. In particular, the introduction of online learning mechanisms and incremental learning technologies enables the prediction model to dynamically adapt to changes in the geological environment and continuously absorb new data, thereby improving the reliability of the prediction. In addition, through sophisticated feature selection and statistical analysis, the model reduces complexity and enhances the accuracy of prediction while maintaining efficient operation, providing strong technical support and decision-making assistance for the prevention and mitigation of geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0027] Figure 1 This is a flowchart of the geological disaster risk prediction method based on model algorithm.

[0028] Figure 2 A schematic diagram of the internal structure of a computer device. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0031] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0032] Example 1

[0033] Reference Figure 1, which is the first embodiment of the present invention, and provides a method for predicting geological disaster risk hazards based on a model algorithm, which includes:

[0034] S1. Obtain target data related to geological disasters.

[0035] Furthermore, target data related to geological hazards are obtained, including

[0036] Acquire target data related to geological hazards by using remote sensing satellite technology, GIS technology and geological hazard monitoring equipment;

[0037] Geological disaster monitoring equipment includes GPS, 3D laser scanners, surface deformation monitoring instruments, earth pressure gauges, pore water pressure gauges, rain gauges, water level gauges and flow meters.

[0038] Furthermore, the target data include geological, topographical, meteorological, humanistic and seismic data.

[0039] S2. Construct a risk prediction model by extracting features from the target data.

[0040] Furthermore, feature extraction includes extracting features that are meaningful to geological disaster prediction and data features that have a significant impact on prediction results.

[0041] It should be noted that image processing technology is used to extract features that are meaningful for geological disaster prediction. On the one hand, geological disasters are a dynamic process, and data features also change over time. Therefore, it is necessary to monitor and analyze geological disaster data in real time to grasp its dynamic features; on the other hand, geological disaster data often contains multiple types of data (such as remote sensing images, ground monitoring data, GIS data, etc.), and these data have complex relationships. Therefore, when extracting geological disaster data features, it is necessary to comprehensively consider multiple factors and data sources.

[0042] It should be further explained that statistical analysis and machine learning techniques are used to screen out data features that have a significant impact on the prediction results.

[0043] Furthermore, features that are meaningful for geological disaster prediction include, but are not limited to, landslides, debris flows, and earthquakes;

[0044] Data features that have a significant impact on the prediction results include, but are not limited to, geology, topography, rainfall, vegetation, hydrology, and the time, location, magnitude, and waveform of earthquakes.

[0045] Furthermore, a risk prediction model is constructed by using a support vector machine algorithm to construct a target risk prediction model, wherein the target risk prediction model is a debris flow risk prediction model.

[0046] Specifically, the formula of the support vector machine algorithm is:

[0047]

[0048] In the formula, f(x) represents the predicted result of the sample;

[0049] a i is the Lagrange multiplier, corresponding to the weight of each training sample;

[0050] y i is the label of the i-th training sample, indicating whether a debris flow occurs,

[0051] K(x i , x) is the kernel function, measuring the similarity between sample x and xi,

[0052] b is the intercept term of the decision boundary, indicating the output of the decision function when all features are zero,

[0053] x i is the input feature of the i-th training sample in the training dataset, including features such as geological conditions, rainfall, slope, soil, etc., for predicting whether a debris flow will occur,

[0054] x represents the input feature of the sample to be predicted, containing the same type of features as the training samples, but from new, unclassified data points.

[0055] It should be noted that x in the formula represents the input feature, that is, the feature extracted from the target data, and f(x) is a numerical result, not text, representing the magnitude of the geological disaster risk predicted by the model at a specific point x. It can be used to determine the risk level and trigger an early warning. Further, by setting thresholds T1 and T2, the risk is quantified and divided into different levels: specifically, f(x) < T1 is low risk, T1 ≤ f(x) < T2 is medium risk, and f(x) ≥ T2 is high risk. This quantification and level division are the basis for early warning, which allows corresponding early warnings to be issued according to the risk level.

[0056] It should be further noted that on the one hand, the risk prediction model is trained by using historical geological disaster data and the extracted features; on the other hand, data cleaning and preprocessing are required before inputting the input features into the risk prediction model, specifically including handling missing values, outliers, data normalization or standardization, etc.

[0057] It should be further explained that the risk prediction model introduces an online learning mechanism. The model can learn immediately when it receives new data, that is, new target data actually obtained, and update the model's prediction rules based on the new data, so that the model can continuously learn and update. At the same time, through incremental learning technology, the model can gradually absorb new target data and knowledge while retaining existing knowledge, thereby improving the model's predictive ability and adaptability.

[0058] Furthermore, constructing a risk prediction model also includes evaluating and optimizing the risk prediction model;

[0059] It should be noted that the evaluation of the risk prediction model includes calculating the accuracy, recall rate, F1 score and other indicators of the risk prediction model, and further using cross-validation such as K-fold cross-validation to evaluate the model, and optimizing the prediction model based on the evaluation results.

[0060] Optimization of the risk prediction model, including optimization of the risk prediction model through parameter tuning, feature selection and regularization techniques based on the evaluation results.

[0061] It should be noted that parameter tuning refers to tuning model parameters using algorithms such as grid search, random search or Bayesian optimization to find the optimal parameter combination, focusing on key parameters that have a greater impact on model performance, such as learning rate, batch size, number of iterations, etc.; feature selection refers to screening out key features that have a greater impact on prediction results through feature importance evaluation methods (such as feature importance evaluation based on tree models), removing redundant or irrelevant features to reduce model complexity and improve prediction efficiency; regularization technology refers to the introduction of regularization terms (such as L1 regularization, L2 regularization) in the model training process to prevent the occurrence of overfitting, and balancing the relationship between model complexity and prediction accuracy by adjusting the weight of the regularization term.

[0062] S3. Monitor and warn of geological disasters through risk prediction models.

[0063] It should be noted that by quantifying the prediction results, when the prediction results exceed the set warning threshold or an abnormal situation occurs, the warning mechanism will be automatically triggered to issue warning information to relevant departments and the public. The warning information includes the type of geological disaster, possible impact range, recommended response measures, etc., to achieve accurate prediction and warning of geological disaster risk hazards.

[0064] In summary, the beneficial effect of the geological disaster risk prediction method based on the model algorithm of the present invention is to significantly improve the accuracy and response speed of geological disaster risk prediction. Through comprehensive data collection, feature extraction, model construction and optimization, and real-time monitoring, potential geological disaster risks can be identified and warned in a timely manner. In particular, the introduction of online learning mechanism and incremental learning technology enables the prediction model to dynamically adapt to changes in the geological environment and continuously absorb new data, thereby improving the reliability of the prediction. In addition, through sophisticated feature selection and statistical analysis, the model reduces complexity and enhances the accuracy of prediction while maintaining efficient operation, providing strong technical support and decision-making assistance for the prevention and mitigation of geological disasters.

[0065] Example 2

[0066] This embodiment provides a geological disaster risk prediction system based on a model algorithm, which includes an acquisition module for acquiring target data related to geological disasters;

[0067] A building module for building a risk prediction model by extracting features from target data;

[0068] The early warning module is used to monitor and warn of geological disasters through risk prediction models.

[0069] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0070] Example 3

[0071] This embodiment provides a computer device, which may be a terminal, and its internal structure diagram may be as follows: Figure 2As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for predicting geological disaster risk hazards based on a model algorithm is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0072] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements: obtaining target data related to geological disasters; constructing a risk prediction model by extracting features from the target data; and monitoring and warning geological disasters through the risk prediction model.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for predicting geological disaster risk hazards based on a model algorithm, characterized by: include, Obtain target data related to geological hazards; Build a risk prediction model by extracting features from target data; Monitor and warn of geological disasters through risk prediction models.

2. The method for predicting geological disaster risk hazards based on a model algorithm as claimed in claim 1, characterized in that: The acquisition of target data related to geological hazards includes: Acquire target data related to geological hazards by using remote sensing satellite technology, GIS technology and geological hazard monitoring equipment; The geological disaster monitoring equipment includes GPS, three-dimensional laser scanner, surface deformation monitoring instrument, soil pressure gauge, pore water pressure gauge, rain gauge, water level meter and flow meter.

3. The method for predicting geological disaster risk hazards based on a model algorithm as claimed in claim 2, characterized in that: The target data include geological, topographical, meteorological, humanistic and seismic data.

4. The method for predicting geological disaster risk hazards based on a model algorithm as claimed in claim 3, characterized in that: The feature extraction includes extracting features that are meaningful to geological disaster prediction and data features that have a significant impact on prediction results.

5. The method for predicting geological disaster risk hazards based on a model algorithm as claimed in claim 4, characterized in that: The characteristics meaningful for geological disaster prediction include but are not limited to landslides, mud-rock flows, and earthquakes; The data features that significantly affect the prediction results include, but are not limited to, geology, topography, rainfall, vegetation, hydrology, and the time, location, magnitude, and waveform of earthquakes.

6. The method for predicting geological disaster risk hazards based on a model algorithm according to any one of claims 1 to 5, characterized in that: The risk prediction model is constructed by using a support vector machine algorithm to construct a target risk prediction model.

7. The method for predicting geological disaster risk hazards based on a model algorithm as claimed in claim 6, characterized in that: The constructing of the risk prediction model also includes evaluating and optimizing the risk prediction model; Optimization of the risk prediction model, including optimization of the risk prediction model through parameter tuning, feature selection and regularization techniques based on the evaluation results.

8. A geological disaster risk prediction system based on a model algorithm, characterized in that: include: An acquisition module is used to acquire target data related to geological disasters; A building module for building a risk prediction model by extracting features from target data; The early warning module is used to monitor and warn of geological disasters through risk prediction models.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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