A method and system for geological disaster early warning based on multi-source information data

Abstract

The present invention discloses a method for geological disaster early warning based on multi-source information data, comprising the following steps: S1. Constructing a digital rock gene library based on multi-source information data; S2. Determining rock standard detection items and establishing a rock basic information prediction model; S3. Establishing a geological disaster monitoring and early warning model; S4. When carrying out early warning projects, collecting rock samples from the warning area, conducting tests, and substituting the test results into the rock basic information prediction model to obtain the rock basic information of the rock samples; S5. Substituting the information into the geological disaster monitoring and early warning model, analyzing and obtaining early warning dynamic monitoring indicators and early warning thresholds; S6. Performing real-time dynamic monitoring of the early warning dynamic monitoring indicators. The early warning technology of this application uses multiple information source data, which reduces prediction deviation compared to the existing technology that uses a single monitoring source data. The early warning model fully considers the role of rock mechanical properties in geological disasters and has higher accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of rock mechanics, and in particular relates to a method and system for geological disaster early warning based on multi-source information data. Background Art

[0002] Geotechnical hazards include floods caused by rising groundwater levels and landslides, collapses, and debris flows caused by rock stability issues. These hazards not only endanger the safety of construction facilities and equipment, but can also pose serious threats to workers' lives and the environment. Geotechnical engineering geological hazard prediction is a technical method for predicting and warning of geological hazards through comprehensive investigation, analysis, and evaluation of the geological environment. It aims to quickly and accurately identify and analyze geological hazards, reduce the burden of manual investigations, provide important support for geotechnical engineering planning, design, and construction, and reduce the risks and losses of geological hazards.

[0003] Geological hazards are multifactorial phenomena. In recent years, advances in rock mechanics theory and innovations in experimental techniques have led to a deeper understanding of geological hazards and their mechanisms. However, the mechanisms of geological hazards are complex, involving factors such as lithology, topography, climate, and human activities. Traditional methods for studying them rely on field exploration and sampling, as well as on-site and laboratory experiments. However, these methods are limited to specific engineering sites and require repeated experiments, increasing both time and financial costs.

[0004] Current geological disaster on-site monitoring and early warning have the following deficiencies: (1) Due to the current limitations of geological disaster on-site monitoring information collection, most geological disaster analysis is still based on the method of exceeding the threshold of a single monitoring information; (2) Geological disaster analysis based on multi-source information fusion mainly uses data-driven methods to establish displacement prediction or risk assessment models. The quality of monitoring data seriously affects the accuracy of disaster analysis by such methods; and monitoring data often contains a large amount of noise signals, resulting in deviations in prediction and evaluation results. (3) Currently, the multi-source information fusion methods used for geological disaster analysis are mostly artificial intelligence methods. Such methods do not make sufficient use of rock mechanics mechanism knowledge, resulting in limited guidance of mechanism knowledge in the prediction and evaluation model building process. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for geological disaster early warning based on multi-source information data in order to solve the deficiencies of the existing technology.

[0006] The purpose of the present invention is achieved by the following technical solutions:

[0007] A method for geological disaster early warning based on multi-source information data includes the following steps:

[0008] S1. Construct a digital rock gene library based on multi-source information data; the multi-source information data includes rock standardization data, historical geological disaster standardization data, and current real-time dynamic monitoring data of geological disasters; the rock standardization data is obtained by the following method:

[0009] Collect different typical rock samples, conduct relevant experimental research, obtain relevant experimental data of the typical rock samples, and process the relevant experimental data to form the rock standardized data;

[0010] S2. Determine the rock standard detection items based on the digital rock gene library and establish a rock basic information prediction model based on the rock standard detection item parameters;

[0011] S3. Establish a geological disaster monitoring and early warning model based on basic rock information based on the digital rock gene library;

[0012] S4. When carrying out the early warning project, rock samples are collected from the warning area, and the rock samples in the warning area are tested according to the rock standard testing project. The test results are substituted into the rock basic information prediction model to obtain the rock basic information of the rock samples in the warning area;

[0013] S5. Substitute the basic rock information of the rock samples in the warning area into the geological disaster monitoring and warning model, and analyze the dynamic monitoring indicators and warning thresholds for warning;

[0014] S6. Perform real-time dynamic monitoring of the early warning dynamic monitoring indicators, and issue an early warning when the monitoring results reach the early warning threshold.

[0015] Preferably, the relevant experimental studies include at least one of optical microscopy observation, scanning electron microscopy observation, transmission electron microscopy observation, X-ray diffraction analysis, CT scanning, magnetic resonance imaging analysis, infrared spectroscopy analysis, relevant physical and chemical property detection experiments, sonic wave experiments and relevant mechanical experiments;

[0016] The relevant experimental data at least include the microstructure parameters, basic physical and chemical property parameters and mechanical parameter data of the rock sample;

[0017] Preferably, the geological disaster data at least includes rock-related data that causes the disaster, hydrological and climate data, geological structure data, and topographic and geomorphological data;

[0018] The real-time dynamic monitoring data includes at least satellite remote sensing data, aerial remote sensing data, and data collected by ground and underground monitoring equipment.

[0019] Preferably, the step S2 of determining the rock standard testing items further includes:

[0020] Determine the standard rock testing items corresponding to different geological disasters.

[0021] Preferably, if the detection results of the rock samples in the warning area cannot correspond to the rock basic information prediction model, relevant experimental research is carried out on the rock samples in the warning area to obtain relevant experimental data of the rock samples in the warning area, and the rock standardization data, the digital rock gene library, the rock basic information prediction model and the geological disaster monitoring and early warning model are further updated.

[0022] Preferably, step S3 utilizes the intelligent early warning technology for geological disasters under multi-dynamic coupling driven by data-rock macro-micro cross-scale fracture damage mechanism to establish the geological disaster monitoring and early warning model.

[0023] Preferably, in step S4, when the rock samples in the warning area cannot be collected, rock samples from other areas with similar rock properties to those in the warning area are collected as substitutes.

[0024] Preferably, the form of issuing the warning in step S6 includes at least one of network warning, broadcast warning, television warning and text message warning.

[0025] A geological disaster early warning system based on multi-source information data and running the above method includes a multi-source information acquisition module, a gene library construction module, a rock basic information prediction model establishment module, a geological disaster early warning model establishment module, an early warning analysis module and a real-time early warning module;

[0026] The multi-source information acquisition module is used to obtain multi-source information data and standard test item parameter value data of rock samples in the warning area; the multi-source information data includes rock standardization data of typical rock samples, historical geological disaster standardization data and current real-time dynamic monitoring data of geological disasters;

[0027] The gene library construction module is used to perform fusion analysis and unified standardization processing based on the multi-source information data obtained by the multi-source information acquisition module to construct a digital rock gene library;

[0028] The rock basic information prediction model establishment module is used to perform data analysis and processing based on the digital rock gene library, determine rock standard detection items, and establish a rock basic information prediction model based on the rock standard detection item parameters;

[0029] The geological disaster early warning model establishment module is used to perform data analysis and processing based on the digital rock gene library to establish a geological disaster monitoring and early warning model based on basic rock information;

[0030] The early warning analysis module is used to analyze and determine early warning dynamic monitoring indicators and early warning thresholds based on standard test item parameter value data of rock samples in the early warning area, the rock basic information prediction model and the geological disaster monitoring and early warning model;

[0031] The real-time early warning module is used to determine whether the early warning dynamic monitoring index monitoring data in the current real-time dynamic monitoring data of geological disasters has reached the early warning threshold, and to issue an early warning when the early warning threshold is reached.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a computer processor, enables the computer to execute the method for geological disaster early warning based on multi-source information data as described above.

[0033] This application uses three heterogeneous data sources to construct a digital rock gene library, and based on this gene library, establishes a rock basic information prediction model and a geological disaster monitoring and early warning model. When the early warning project is launched, rock samples are collected from the warning area, and then a few items are quickly tested. The basic rock information prediction model is used to analyze and predict other basic properties of the rock. Then, the geological disaster monitoring and early warning model is used to determine dynamic monitoring indicators to provide dynamic early warnings for geological disasters. This early warning technology uses multiple information source data, which reduces prediction bias compared to the existing technology that uses a single monitoring source data. In addition to using modern methods such as artificial intelligence and deep learning, the establishment of the early warning model also focuses on the use of basic rock information, fully considering the role of rock mechanical properties in geological disasters. Compared with the early warning model established solely based on historical geological disaster data and dynamic monitoring data, it is more accurate. Moreover, obtaining basic rock information only requires collecting rock samples and quickly testing a few items, without the need for repeated testing experiments, greatly reducing detection costs and time. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of the method steps for geological disaster early warning provided by this application. DETAILED DESCRIPTION

[0035] The present invention provides a method for geological disaster early warning based on multi-source information data, such as Figure 1 As shown, the following steps are included:

[0036] S1. Construct a digital rock gene library based on multi-source information data; the multi-source information data includes standardized rock data, standardized historical geological disaster data, and current real-time dynamic monitoring data of geological disasters; the standardized rock data is obtained by the following methods:

[0037] Collect different typical rock samples, carry out relevant experimental research, obtain relevant experimental data of typical rock samples, and process the relevant experimental data to form rock standardized data.

[0038] Typical rock samples should be as comprehensive as possible, covering rocks from different regions, different geological structures (such as folds, joints, faults, etc.), different topography (such as basins, mountains, plains, etc.), different diagenetic ages (such as Quaternary, Jurassic, Triassic, Ordovician, Cambrian, etc.), and different types (such as igneous rocks, sedimentary rocks, metamorphic rocks, etc.). Data and information on rock samples published or unpublished through channels such as the Internet, books, and newspapers verified by authoritative departments can also be collected as supplementary information. When the same typical rock sample is not collected, the relevant data of the collected rock sample can be directly adopted. When the same typical rock sample is collected, the experimental data can be compared and analyzed with the collected data to reduce error data and improve the accuracy of the rock data.

[0039] The relevant experimental research mainly includes: (1) some rapid non-destructive testing methods such as optical microscope observation, scanning electron microscope observation, transmission electron microscope observation, CT scanning analysis, magnetic resonance imaging analysis, infrared spectrum analysis, X-ray diffraction analysis, etc., which can quickly obtain the micro-structural parameters related to rocks; (2) relevant physical and chemical property testing experiments: such as water content experiments, density experiments, elemental analysis experiments, etc., which can experimentally obtain the basic physical and chemical property parameters related to rocks; (3) acoustic wave experiments, various related mechanical experiments such as uniaxial compressive strength experiments, direct shear experiments, conventional triaxial experiments, creep experiments, hydraulic fracturing experiments, etc., which can explore the mechanical failure mechanism of rocks and collect various mechanical parameters of rocks.

[0040] The relevant experimental data include at least the microstructure parameters, basic physical and chemical properties and mechanical parameters of the rock samples.

[0041] The microstructural parameters mainly include: microscopic crystal structure, grain boundary distribution, mineral particle morphology, mineral particle size, porosity, crack space distribution, crack number, crack length, crack area, etc., but are not limited to the above parameters;

[0042] Basic physical and chemical properties mainly include: color, gloss, transparency, moisture content, water absorption, density, specific gravity, mineral type and composition, permeability, swelling, solubility, frost resistance, etc., but are not limited to the above parameters;

[0043] Mechanical parameters include, but are not limited to, compressive strength, flexural strength, shear strength, impact strength, elastic modulus, deformation modulus, Poisson's ratio, and the like.

[0044] In general, all experiments and all parameters should be covered as much as possible. However, due to the limitations of human and material resources, the main experiments and key parameters can be selected for testing based on the actual application.

[0045] The mechanical parameter data of the three types of experimental data mentioned above directly reflect the various mechanical properties of rocks such as elasticity, plasticity, toughness, rheology, brittleness, etc. during geological processes and under various stresses. They are important indicators for studying the deformation, destruction, and changes of rocks under stress. They play an important role in geological engineering such as tunnel engineering and retaining wall engineering, as well as in mining exploration, crushing machinery research and development, and geological disaster prediction. Basic physical and chemical property parameters can, to a certain extent, reflect the stability and bearing capacity of rocks under external forces (such as hydraulics, temperature fields, and stress). Microstructural parameters can reveal the internal characteristics and mineral composition of rocks. These data can largely predict some basic physical and chemical properties and mechanical properties of rocks.

[0046] Some basic physical and chemical properties and microstructural parameters can reflect the mechanical parameters of rocks and can be obtained more quickly and easily than mechanical parameters, which has important application value.

[0047] The standardized data of historical geological disasters are formed by collecting and processing data of typical geological disasters that have occurred in history.

[0048] The geological disaster data include at least rock-related data that cause disasters (such as rock type, mechanical strength, cracks, etc.), hydroclimatic data (such as groundwater, rainstorms, climate environment, human activities, etc.), geological structure (such as folds, joints, faults) and topographic data (slopes, side slopes, steep slopes).

[0049] The formation mechanism of geological disasters is complex and there are many factors that cause geological disasters. For example, the main conditions for the formation of landslides are:

[0050] (1) Topographic conditions: slope (10-45 degrees);

[0051] (2) Lithological conditions: rock and soil with loose structure and low shear strength;

[0052] (3) Structural conditions: Landslides are more likely to occur when there are structural surfaces such as joints and faults;

[0053] (4) Groundwater: can reduce shear strength;

[0054] (5) Triggering conditions: earthquakes, heavy rains, and unreasonable human activities (such as excavation at the foot of the slope).

[0055] The main conditions for collapse are:

[0056] (1) Topographic conditions: steep terrain (greater than 45 degrees);

[0057] (2) Lithologic conditions: The rock is hard;

[0058] (3) Structural conditions: with joints, faults and other structural surfaces;

[0059] (4) Climate conditions: large daily and annual temperature ranges;

[0060] (5) Trigger conditions: earthquakes, heavy rains, melting ice and snow, etc.

[0061] Historical geological disaster data can be used to analyze the causes, development trends, impacts, and the role of various factors such as rock properties in geological disasters. This plays an important role in predicting and preventing future geological disasters and guiding human activities.

[0062] Real-time dynamic monitoring data includes at least satellite remote sensing data, aerial remote sensing data, and data collected by ground and underground monitoring equipment, namely, sky-air-ground-depth real-time dynamic monitoring data.

[0063] Satellite remote sensing data and aerial remote sensing data can respectively provide current surface information from the far and near ends, identifying areas susceptible to geological hazards. Data collected by ground and underground monitoring equipment, such as observations of slope interiors, can reflect current stratum lithology and geological internal information. These three types of dynamic monitoring data provide a comprehensive, multi-layered, and three-dimensional picture of current topography, stratum lithology, and geological structure, playing a crucial role in the early identification, early warning, and prevention of geological hazards.

[0064] Modern methods such as artificial intelligence, machine learning, and deep learning can be used to extract useful information data from the above-mentioned standard multi-source data, form a digital rock gene library, realize data sharing and services, and provide the best data platform for geological disaster prediction and human activity guidance.

[0065] Through the digital rock gene library, we can not only query data information related to different rocks, historical geological disaster data and dynamic monitoring data, but most importantly, as described later, we can also analyze rock gene information, extract useful data, establish rock basic information prediction models and geological disaster monitoring and early warning models, and provide a technical basis for geological disaster prediction and early warning, and guide human activities.

[0066] S2. Determine standard rock testing items based on the digital rock gene library and establish a rock basic information prediction model based on the parameters of the standard rock testing items;

[0067] S3. Based on the digital rock gene library, establish a geological disaster monitoring and early warning model based on basic rock information;

[0068] S4. When implementing the early warning project, collect rock samples from the warning area, test the rock samples in the warning area according to the rock standard testing project, substitute the test results into the rock basic information prediction model to obtain the rock basic information of the rock samples in the warning area;

[0069] S5. Substitute basic rock information of rock samples in the warning area into the geological disaster monitoring and early warning model, and analyze and obtain early warning dynamic monitoring indicators and warning thresholds;

[0070] S6. Conduct real-time dynamic monitoring of early warning dynamic monitoring indicators, and issue an early warning when the monitoring results reach the early warning threshold.

[0071] As mentioned above, the digital rock gene library includes a large amount of relevant experimental data information on typical rock samples. Moreover, certain parameters have a certain correspondence with other parameters. In particular, the mineral composition and microstructure parameters of rocks have an important influence on the mechanical properties of rocks. Through modern data processing methods, useful data can be extracted from the digital rock gene library information, rock standard testing items can be determined, and a basic rock information prediction model based on the parameters of rock standard testing items can be established.

[0072] As mentioned above, rock properties, topography, geological structure, hydrological and climatic conditions, and human activities are important influencing factors in the formation of geological disasters. Parameters such as rock properties, topography, and geological structure generally do not change in a short period of time, while hydrological and climatic conditions and human activities such as heavy rain, melting ice and snow, and slope excavation are important dynamic influencing factors that trigger the occurrence of geological disasters and often directly cause the occurrence of geological disasters.

[0073] Relying on the rich data in the digital rock gene bank, we can use the intelligent early warning technology of geological disasters under the multi-dynamic coupling driven by data-rock macro-micro cross-scale fracture damage mechanism to construct multi-indicator geological disaster monitoring and early warning algorithms and models, and extract early warning dynamic monitoring indicators and warning thresholds.

[0074] When implementing an early warning project, rock samples are first collected from the warning area. These samples are then tested according to standard rock testing procedures. The test results are then fed into a rock basic information prediction model to obtain the basic rock information for the rock samples in the warning area. This eliminates the need for comprehensive testing of the rock samples, significantly saving experimental time and costs.

[0075] Standard rock testing items should preferably be representative items that can be tested by rapid non-destructive testing methods. For example, standard rock testing items may include: rock density testing, infrared spectroscopy analysis, transmission electron microscopy observation, mineral composition analysis, etc. Of course, destructive testing methods may also be used when necessary, such as rock fracturing experiments.

[0076] The digital rock gene library of this application includes three types of heterogeneous data, among which the relevant experimental data of typical rock samples directly reveal the relationship between the standard test items of rock and other parameters. However, there are many types of rock property parameters, and the other parameters reflected by a standard test item are also limited. By testing a few items, it is impossible to reflect all the properties of the rock. Through historical geological disaster data, it is possible to analyze which specific rock properties have caused the corresponding geological disasters. Therefore, for different geological disasters, different rock standard test items can be determined to reflect the rock properties that have the greatest impact on the geological disaster. For example, for landslides, when determining the rock standard test items, you can choose a test item that can reflect the shear resistance of the rock. For collapses, when determining the rock standard test items, you can choose a test item that can reflect the hardness of the rock. This can not only improve the detection efficiency, but also accurately obtain the key properties of the rock. However, historical disaster data can only reflect historical geological disaster information, but cannot reflect current geological information. Moreover, geological disasters are sporadic and intermittent. Historical disaster data are incomplete in both time and space. The rock properties and other conditions that cause current geological disasters may have changed significantly. By collecting real-time dynamic monitoring data, we can understand the relationship between current rock properties and other conditions and geological disasters, make up for the lack of timeliness of historical disaster data, and improve the reliability and applicability of newly collected rock sample testing.

[0077] After obtaining the basic information data of the rock, the basic information of the rock is substituted into the geological disaster monitoring and early warning model, and the early warning dynamic monitoring indicators and early warning thresholds are obtained through analysis.

[0078] For example, for landslide geological disaster early warning, a digital rock gene library can be used to analyze and develop a landslide geological disaster monitoring and early warning model based on rock properties, rainfall, and surface rock displacement. After collecting rock samples from the warning area and conducting standard testing to obtain basic rock information, rainfall and surface rock displacement are determined as dynamic early warning monitoring indicators. These indicators can be monitored in real time using methods such as rain gauges, satellite monitoring (locating displacement points or elevation points at the top and toe of the slope), and ground electronic levels. When the monitoring results reach the early warning threshold, an early warning is issued to remind people to take precautions and initiate emergency response. Warnings can be issued online, through radio broadcasts, television, and text messages. If the monitoring data indicates that the landslide is stable, real-time monitoring is not required; only relevant parameters need to be calculated regularly to monitor the landslide's development trends.

[0079] After the initial construction of the digital rock gene library, as time goes by, real-time dynamic monitoring data and historical geological disaster data are constantly updated, and the digital rock gene library as well as prediction and early warning models also need to be continuously iterated and updated.

[0080] If the test results of rock samples in the warning area cannot correspond to the rock basic information prediction model, then the relevant experimental research will be carried out on the rock sample, just like the typical rock sample, to obtain the relevant experimental data of the rock sample, and further update the rock standardization data, digital rock gene library, and prediction and warning models.

[0081] When rock samples in the warning area cannot be collected, rock samples from other areas with similar rock properties to those in the warning area can be collected as substitutes.

[0082] Therefore, this application uses three heterogeneous data sources to construct a digital rock gene library, and based on this gene library, establishes a rock basic information prediction model and a geological disaster monitoring and early warning model. When the early warning project is launched, by collecting rock samples in the warning area, and then through rapid testing of a few items, the basic properties of the rock are predicted and analyzed by the rock basic information prediction model. Then, the geological disaster monitoring and early warning model is used to determine the dynamic monitoring indicators to provide dynamic early warning of geological disasters. This early warning technology uses multiple information source data, which reduces the prediction deviation compared to the existing technology that uses a single monitoring source data. In addition to using modern methods such as artificial intelligence and deep learning, the establishment of the early warning model also focuses on the use of basic rock information, fully considering the role of rock mechanical properties in geological disasters. Compared with the early warning model established solely based on historical geological disaster data and dynamic monitoring data, it is more accurate. Moreover, the acquisition of basic rock information only requires the collection of rock samples, which can be obtained by rapid testing of a few items, without the need for repeated testing experiments, greatly reducing the detection cost and time.

[0083] This application for geological disaster warning is applicable to types of geological disasters related to rock properties, such as landslides, collapses, mudslides, karst collapses, ground collapses, ground fissures, ground subsidence, etc.

[0084] This application also provides a geological disaster early warning system based on multi-source information data that implements the above-described method. This system provides scientific decision-making for geological assessment, geological evaluation, geological disaster monitoring and early warning, and geological disaster emergency response and rescue for major projects. Specifically, the system includes a multi-source information acquisition module, a gene library construction module, a rock basic information prediction model establishment module, a geological disaster early warning model establishment module, an early warning analysis module, and a real-time early warning module.

[0085] Multi-source information acquisition module, used to obtain multi-source information data and standard test item parameter value data of rock samples in the warning area; multi-source information data includes rock standardization data of typical rock samples, historical geological disaster standardization data and current real-time dynamic monitoring data of geological disasters;

[0086] The gene library construction module is used to conduct fusion analysis and unified standardization processing based on the multi-source information data obtained through the multi-source information acquisition module to construct a digital rock gene library;

[0087] The rock basic information prediction model establishment module is used to perform data analysis and processing based on the digital rock gene library, determine rock standard detection items, and establish a rock basic information prediction model based on the rock standard detection item parameters;

[0088] The geological disaster early warning model establishment module is used to analyze and process data based on the digital rock gene library and establish a geological disaster monitoring and early warning model based on basic rock information;

[0089] The early warning analysis module is used to analyze and determine the early warning dynamic monitoring indicators and early warning thresholds based on the standard test item parameter value data of rock samples in the early warning area, the rock basic information prediction model and the geological disaster monitoring and early warning model;

[0090] The real-time early warning module is used to determine whether the early warning dynamic monitoring indicator monitoring data in the current real-time dynamic monitoring data of geological disasters has reached the early warning threshold. When the early warning threshold is reached, an early warning is issued and emergency response work is carried out.

[0091] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A method for geological disaster early warning based on multi-source information data, characterized in that: The following steps are involved: S1. Construct a digital rock gene library based on multi-source information data; the multi-source information data includes rock standardization data, historical geological disaster standardization data, and current real-time dynamic monitoring data of geological disasters; the rock standardization data is obtained by the following method: Collect different typical rock samples, conduct relevant experimental research, obtain relevant experimental data of the typical rock samples, and process the relevant experimental data to form the rock standardized data; S2. Determine the rock standard detection items based on the digital rock gene library and establish a rock basic information prediction model based on the rock standard detection item parameters; S3. Establish a geological disaster monitoring and early warning model based on basic rock information based on the digital rock gene library; S4. When carrying out the early warning project, rock samples are collected from the warning area, and the rock samples in the warning area are tested according to the rock standard testing project. The test results are substituted into the rock basic information prediction model to obtain the rock basic information of the rock samples in the warning area; S5. Substitute the basic rock information of the rock samples in the warning area into the geological disaster monitoring and warning model, and analyze the dynamic monitoring indicators and warning thresholds for warning; S6. Perform real-time dynamic monitoring of the early warning dynamic monitoring indicators, and issue an early warning when the monitoring results reach the early warning threshold.

2. The method for geological disaster early warning based on multi-source information data according to claim 1, characterized in that: The relevant experimental research includes at least one of optical microscopy observation, scanning electron microscopy observation, transmission electron microscopy observation, X-ray diffraction analysis, CT scanning, magnetic resonance imaging analysis, infrared spectroscopy analysis, relevant physical and chemical property detection experiments, acoustic wave experiments and relevant mechanical experiments; The relevant experimental data at least include the microstructure parameters, basic physical and chemical property parameters and mechanical parameter data of the rock sample.

3. The method for geological disaster early warning based on multi-source information data according to claim 1, characterized in that: The standardized data of historical geological disasters is formed by collecting and processing data of typical geological disasters that occurred in history, and the geological disaster data at least includes rock-related data that caused the disaster, hydrological and climate data, geological structure data, and topographic and geomorphological data; The real-time dynamic monitoring data includes at least satellite remote sensing data, aerial remote sensing data, and data collected by ground and underground monitoring equipment.

4. The method for geological disaster early warning based on multi-source information data according to claim 1, characterized in that: The step S2 of determining the rock standard testing items further includes: Determine the standard rock testing items corresponding to different geological disasters.

5. The method for geological disaster early warning based on multi-source information data according to claim 1, characterized in that: If the test results of the rock samples in the warning area cannot correspond to the rock basic information prediction model, relevant experimental research will be carried out on the rock samples in the warning area to obtain relevant experimental data of the rock samples in the warning area, and further update the rock standardization data, the digital rock gene library, the rock basic information prediction model and the geological disaster monitoring and early warning model.

6. The method for geological disaster early warning based on multi-source information data according to claim 1, characterized in that: Step S3 utilizes the geological disaster intelligent early warning technology under the multi-dynamic coupling driven by data-rock macro-micro cross-scale fracture damage mechanism to establish the geological disaster monitoring and early warning model.

7. The method for geological disaster early warning based on multi-source information data according to claim 1, characterized in that: Step S4: When rock samples in the warning area cannot be collected, rock samples from other areas with similar rock properties to those in the warning area are collected as substitutes.

8. The method for geological disaster early warning based on multi-source information data according to claim 1, characterized in that: The form of issuing the warning in step S6 includes at least one of network warning, broadcast warning, television warning and text message warning.

9. A geological disaster early warning system based on multi-source information data running the method according to any one of claims 1 to 8, characterized in that: It includes multi-source information collection module, gene library construction module, rock basic information prediction model establishment module, geological disaster early warning model establishment module, early warning analysis module and real-time early warning module; The multi-source information acquisition module is used to obtain multi-source information data and standard test item parameter value data of rock samples in the warning area; the multi-source information data includes rock standardization data of typical rock samples, historical geological disaster standardization data and current real-time dynamic monitoring data of geological disasters; The gene library construction module is used to perform fusion analysis and unified standardization processing based on the multi-source information data obtained by the multi-source information acquisition module to construct a digital rock gene library; The rock basic information prediction model establishment module is used to perform data analysis and processing based on the digital rock gene library, determine rock standard detection items, and establish a rock basic information prediction model based on the rock standard detection item parameters; The geological disaster early warning model establishment module is used to perform data analysis and processing based on the digital rock gene library to establish a geological disaster monitoring and early warning model based on basic rock information; The early warning analysis module is used to analyze and determine early warning dynamic monitoring indicators and early warning thresholds based on standard test item parameter value data of rock samples in the early warning area, the rock basic information prediction model and the geological disaster monitoring and early warning model; The real-time early warning module is used to determine whether the early warning dynamic monitoring index monitoring data in the current real-time dynamic monitoring data of geological disasters has reached the early warning threshold, and to issue an early warning when the early warning threshold is reached.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the method for geological disaster early warning based on multi-source information data as described in any one of claims 1 to 8.

Images