Geological disaster early warning method and system based on multi-source information data

Abstract

The invention discloses a geological disaster early warning method based on multi-source information data. The method comprises the following steps: S1, constructing a digital rock gene pool based on the multi-source information data; s2, determining a rock standard detection item, and establishing a rock basic information prediction model; s3, establishing a geological disaster monitoring and early warning model; s4, when an early warning project is carried out, collecting a rock sample of an early warning area, carrying out detection, and substituting a detection result into the rock basic information prediction model to obtain rock basic information of the rock sample; s5, substituting the information into a geological disaster monitoring and early warning model, and analyzing to obtain an early warning dynamic monitoring index and an early warning threshold value; and S6, carrying out real-time dynamic monitoring on the early warning dynamic monitoring index. According to the early warning technology, multiple pieces of information source data are adopted, and compared with single monitoring source data adopted in the prior art, prediction deviation is reduced. The early warning model fully considers the effect of rock mechanical properties in geological disasters, and the accuracy is higher.

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 early warning of geological disasters based on multi-source information data. Background Art

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

[0003] Geological disasters are a multi-factor phenomenon. In recent years, with the development of the theoretical basis of rock mechanics and the innovation of experimental technology, we have a deeper understanding of geological disasters and their mechanisms. However, the mechanism of geological disasters is very complex, involving factors such as lithology, landforms, climate conditions, and human activities. The traditional method for studying it is to conduct field and indoor experiments through on-site exploration and sampling. The disadvantage is that it only targets a certain engineering site and is repeated repeatedly, which increases time and economic costs.

[0004] The current geological disaster on-site monitoring and early warning have the following shortcomings: (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) At present, 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 prior art.

[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 comprises 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, carry out 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 according to the digital rock gene library, and establish a rock basic information prediction model based on the parameters of the rock standard detection items;

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

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

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

[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 research includes at least one of optical microscope observation, scanning electron microscope observation, transmission electron microscope observation, X-ray diffraction analysis, CT scanning, magnetic resonance imaging analysis, infrared spectrum analysis, relevant physical and chemical property detection experiments, sonic wave experiments and relevant mechanical experiments;

[0016] The relevant experimental data at least include 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, hydrological and climatic data, geological structure data and topographic data that cause the disaster;

[0018] The real-time dynamic monitoring data at least includes 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 test 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 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 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 the early warning dynamic monitoring index and early warning threshold according to the standard detection item parameter value data of the 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 processor of a computer, 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 establishes a rock basic information prediction model and a geological disaster monitoring and early warning model based on the gene library. When the early warning project is launched, rock samples in the early warning area are collected, and then a few items are quickly tested. The other basic properties of the rock are predicted by the rock basic information prediction model, and then the dynamic monitoring indicators are determined by the geological disaster monitoring and early warning model to dynamically warn 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 means 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, and is more accurate than the early warning model established only based on historical geological disaster data and dynamic monitoring data. In addition, the acquisition of basic rock information only requires the collection of rock samples, which can be obtained by quickly testing a few items, without repeated testing experiments, greatly reducing the detection cost 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 early warning of geological disasters 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 rock standardization data, historical geological disaster standardization data and current real-time dynamic monitoring data of geological disasters; rock standardization data is obtained by the following method:

[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.). You can also collect relevant data and information of rock samples that are publicly available and unpublished through the Internet, books, newspapers and other channels verified by authoritative departments as a supplement. When the same typical rock samples are not collected, the relevant data of the collected rock samples can be directly adopted. When the same typical rock samples are collected, the experimental data can be compared and analyzed with the collected data to reduce error data and improve the accuracy of 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-structure parameters related to rocks; (2) relevant physical and chemical property detection 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 property parameters and mechanical parameter data of the rock samples.

[0041] The microstructure 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 property parameters mainly include: color, gloss, transparency, moisture content, water absorption, density, specific gravity, mineral type and composition, permeability, swelling, solubility, frost resistance, etc., but not limited to the above parameters;

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

[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, major experiments and key parameters can be selected for testing based on actual application conditions.

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

[0046] Some basic physical and chemical properties and microstructure 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 (such as rock type, mechanical strength, cracks, etc.) that cause disasters, hydroclimatic data (such as groundwater, rainstorms, climate environment, human activities, etc.), geological structures (such as folds, joints, faults) and topographic data (slopes, side slopes, steep slopes), etc.

[0049] The formation mechanism of geological disasters is complex. 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) Lithological 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 reflect the current surface information from the far end and near end, and identify the sensitive areas of geological disasters. The data collected by ground and underground monitoring equipment, such as the internal observation data of the slope, can reflect the current stratum lithology and internal geological information. The above three types of dynamic monitoring data reflect the current topography, stratum lithology, geological structure and other information in an all-round, multi-level and three-dimensional manner, which plays an important role in the early identification, early warning and prevention of geological disasters.

[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 different rock-related data information, 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 the rock standard detection items based on the digital rock gene library, and establish a rock basic information prediction model based on the parameters of the rock standard detection 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 carrying out the early warning project, collect rock samples in the early warning area, test the rock samples in the early warning area according to the rock standard test project, substitute the test results into the rock basic information prediction model, and obtain the rock basic information of the rock samples in the early warning area;

[0069] S5. Substitute the basic rock information of the rock samples in the warning area into the geological disaster monitoring and early warning model, and analyze and obtain the early warning dynamic monitoring indicators and early 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 of typical rock samples. Moreover, some 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 detection items can be determined, and a rock basic information prediction model based on the parameters of rock standard detection 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 rains, ice and snow melting, 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, build multi-indicator geological disaster monitoring and early warning algorithms and models, and extract early warning dynamic monitoring indicators and early warning thresholds.

[0074] When carrying out the early warning project, we first collect rock samples from the early warning area, test them according to the rock standard test project, and substitute the test results into the rock basic information prediction model to obtain the basic rock information of the rock samples in the early warning area. There is no need to conduct comprehensive experiments on the rock samples, which significantly saves experimental time and cost.

[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 spectrum 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 bank 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 rocks and other parameters. However, there are many types of rock property parameters, and 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 standard rock test items, you can choose a test item that can reflect the shear resistance of the rock. For collapse, when determining the standard rock 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 somewhat 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 warning, through the digital rock gene library, the landslide geological disaster monitoring and early warning model based on rock properties, rainfall, and surface rock displacement is analyzed. After collecting rock samples in the warning area and conducting standard test items to obtain basic rock information, rainfall and surface rock displacement are determined as early warning dynamic monitoring indicators. Rain gauges, satellite monitoring (locating displacement points or elevation points at the top and foot of the slope), ground electronic levels and other methods can be used to conduct real-time dynamic monitoring of these two items. When the monitoring results reach the early warning threshold, an early warning is issued to remind people to take precautions and launch emergency response work. The forms of issuing early warnings may include network warnings, radio warnings, television warnings, and text message warnings. If the landslide is determined to be in a stable state through monitoring data, it does not need to be monitored in real time. It only needs to calculate relevant parameters regularly and pay attention to the development trend of the landslide.

[0079] After the initial construction of the digital rock gene bank, as time goes by, real-time dynamic monitoring data and historical geological disaster data are constantly updated, and the digital rock gene bank 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 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 early 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 establishes a rock basic information prediction model and a geological disaster monitoring and early warning model based on the gene library. When the early warning project is launched, rock samples in the early warning area are collected, and then a few items are quickly tested. The other basic properties of the rock are predicted by the rock basic information prediction model, and then the dynamic monitoring indicators are determined by the geological disaster monitoring and early warning model to dynamically warn 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 means 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, and is more accurate than the early warning model established only based on historical geological disaster data and dynamic monitoring data. In addition, the acquisition of basic rock information only requires the collection of rock samples, which can be obtained by quickly testing a few items, without 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] The present application also provides a geological disaster early warning system based on multi-source information data that runs the above method, providing 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 collection 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 construct a digital rock gene library by conducting fusion analysis and unified standardization processing based on the multi-source information data obtained through the multi-source information acquisition module;

[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 the rock standard detection items, and establish the 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 index 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, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. 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 present 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 present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for early warning of geological disasters 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, carry out 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 according to the digital rock gene library, and establish a rock basic information prediction model based on the parameters of the rock standard detection items; S3. Based on the digital rock gene library, a geological disaster monitoring and early warning model based on basic rock information is established; S4. When carrying out the early warning project, rock samples are collected from the early warning area, and the rock samples in the early warning area are tested according to the rock standard test project, and the test results are substituted into the rock basic information prediction model to obtain the rock basic information of the rock samples in the early warning area; S5. Substitute the basic rock information of the rock samples in the warning area into the geological disaster monitoring and early warning model, and analyze and obtain the early warning dynamic monitoring indicators and early warning thresholds; 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 microscope observation, scanning electron microscope observation, transmission electron microscope observation, X-ray diffraction analysis, CT scanning, magnetic resonance imaging analysis, infrared spectrum analysis, relevant physical and chemical property detection experiments, sound 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 geological disaster data at least include rock-related data, hydrological and climatic data, geological structure data and topographic data that cause the disaster; The real-time dynamic monitoring data at least includes 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 test 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 uses 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.

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 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 the early warning dynamic monitoring index and early warning threshold according to the standard detection item parameter value data of the 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.

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