Advanced geological forecast risk assessment method and equipment based on multi-source geophysical prospecting method

Abstract

The invention discloses the technical field of geological forecast risk assessment, and particularly discloses an advanced geological forecast risk assessment method and equipment based on a multi-source geophysical prospecting method. According to the method, on the basis of acquiring various geological exploration data for risk assessment, each type of data is complemented by crawling associated data factors, on one hand, influence factors causing geological risks can be brought into consideration of risk prediction, possible geological risks are more comprehensively identified, and the risk assessment efficiency is improved; a more reliable basis is provided for engineering design and construction; on the other hand, completeness and comprehensiveness of exploration data can be guaranteed, the situation that evaluation errors occur due to the fact that geological exploration data obtained through exploration are narrow is effectively avoided, the accuracy of advanced geological forecast is further improved, and engineering safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological prediction risk assessment, and in particular to an advanced geological prediction risk assessment method and device based on a multi-source geophysical prospecting method. Background Art

[0002] Advanced geological prediction is an important task in engineering practice. Its purpose is to understand the geological conditions of the engineering site in advance, assess possible risks, and provide a basis for engineering design and construction. CN110244344A discloses a TBM advanced geological prediction method based on deep learning. This method obtains the excavation parameters and geology through the TBM cutterhead, and trains the prediction model for geological prediction. However, due to the complexity and uncertainty of geological conditions, considering only one geological parameter can easily lead to inaccurate risk assessment, and it is impossible to comprehensively and effectively identify and warn of possible geological risks. Therefore, advanced geological prediction still faces many challenges. Summary of the invention

[0003] In order to solve the problems existing in the existing advanced geological forecast, such as the single basis parameter for assessing risk and the inability to effectively identify possible geological risks, the present invention provides an advanced geological forecast risk assessment method and device based on multi-source geophysical prospecting methods.

[0004] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0005] In a first aspect, the present invention provides a method for risk assessment of advanced geological prediction based on a multi-source geophysical prospecting method, the method comprising:

[0006] Obtain multiple geological exploration data of the target geology, and associate data factors for each geological exploration data;

[0007] Crawling corresponding network exploration data of target geology based on the data factors;

[0008] Merge the network exploration data and geological exploration data of each data factor, and convert them to generate corresponding geological distribution images;

[0009] The risk category of the geological distribution image is identified through the trained assessment model, the risk assessment result of each data factor in the target geology is obtained, and the geological forecast is completed.

[0010] According to a specific implementation, in the above-mentioned assessment method, the risk assessment result is obtained when the confidence of the risk category exceeds a preset threshold, and the confidence is used to characterize the probability of risk occurring in the geological distribution image.

[0011] According to a specific implementation, in the above evaluation method, the training process of the evaluation model includes:

[0012] An input set is constructed based on the historical geological distribution image generated by converting the historical geological exploration data, and an output set is constructed based on the risk categories corresponding to the historical address exploration data;

[0013] A training set is constructed according to the input set and the output set, and a neural network model is trained based on the training set to obtain a trained evaluation model.

[0014] According to a specific implementation, in the above evaluation method, the evaluation model adopts an RNN recurrent neural network.

[0015] According to a specific implementation, in the above evaluation method, crawling the corresponding target geological network exploration data based on the data factor specifically includes:

[0016] Constructing a web crawler, and setting a logic filling unit in the web crawler;

[0017] Setting a corresponding qualifier filling menu in the logical filling unit according to the data factor;

[0018] The web crawler fills the menu according to each qualifier and crawls the corresponding web exploration data of the target geology.

[0019] According to a specific implementation, in the above evaluation method, the data factor uses Chinese characters to characterize the characteristics of each geological exploration data; the qualifier filling menu uses a unicode type character string.

[0020] According to a specific implementation, in the above-mentioned evaluation method, the geological exploration data include: data on underground faults, folds and lithological interfaces, underground oil and gas exploration data geotechnical engineering, groundwater and landslide exploration data, soil conditions and groundwater conditions data, seismic data, geothermal data, and geoelectrical data.

[0021] In a second aspect, the present invention provides an advanced geological prediction risk assessment device based on a multi-source geophysical prospecting method, characterized in that the device comprises:

[0022] A data acquisition unit, used to obtain various geological exploration data of the target geology;

[0023] A data processing unit for associating a data factor for each geological exploration data;

[0024] A data crawling unit, used to set a Python web crawler using the data factors, so that the Python web crawler crawls the data factors to obtain the corresponding network exploration data of the target geology;

[0025] A data conversion unit, used to merge the network exploration data and geological exploration data of each data factor, and convert and generate a corresponding geological distribution image;

[0026] The risk identification unit is used to identify the risk category of the geological distribution image through a trained assessment model to obtain a risk assessment result of each data factor in the target geology.

[0027] According to a specific implementation, in the above evaluation device, the data acquisition unit includes:

[0028] The geological structure acquisition module is used to collect data on underground faults, folds and lithology interfaces;

[0029] Resource exploration and acquisition module, used to collect underground oil and gas exploration data geotechnical engineering;

[0030] Geological survey collection module, used to collect groundwater and landslide survey data;

[0031] Environmental geophysical acquisition module, used to collect soil condition and groundwater condition data;

[0032] A seismic data acquisition module, used for acquiring seismic data;

[0033] Ground temperature data acquisition module, used to collect ground temperature data;

[0034] The geoelectric data acquisition module is used to collect geoelectric data.

[0035] In a third aspect, the present invention provides an electronic device comprising at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the above-mentioned methods for advanced geological prediction risk assessment based on multi-source geophysical exploration methods.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] Based on the technical solution provided by the present invention, on the basis of acquiring a variety of geological exploration data for risk assessment, each type of data is supplemented by crawling related data factors. On the one hand, the influencing factors that cause geological risks can be included in the consideration of risk forecasting, and possible geological risks can be identified more comprehensively, providing a more reliable basis for engineering design and construction; on the other hand, the integrity and comprehensiveness of the survey data can be guaranteed, and the situation in which the narrow geological exploration data obtained through exploration leads to errors in the assessment is effectively avoided, and the accuracy of the advanced geological forecast is further improved, and the safety of the project is improved; at the same time, the present invention proposes a method of associating various exploration data with data factors in the forecasting process, classifies each type of geological exploration data, and then conducts risk assessment through a trained assessment model, thereby realizing a quantitative assessment of geological risks, and has good scalability, and can be adjusted and optimized according to different engineering requirements and geological conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of the structure of an advanced geological prediction risk assessment device based on a multi-source geophysical prospecting method provided by an embodiment of the present invention;

[0039] Figure 2 A schematic flow chart of a method for risk assessment of advanced geological prediction based on multi-source geophysical prospecting provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present invention is further described in detail below in conjunction with test examples and specific implementation methods. However, this should not be understood as the scope of the above subject matter of the present invention being limited to the following embodiments, and all technologies realized based on the content of the present invention belong to the scope of the present invention.

[0041] In order to better understand the technical solutions of the embodiments of the present invention, some terms are explained below.

[0042] Multi-source geophysical prospecting refers to the use of a variety of geophysical exploration methods for geological surveys, including seismic exploration, electrical exploration, magnetic exploration, gravity exploration, etc. Seismic exploration is a method of detecting geological structures by studying the propagation law of seismic waves underground; electrical exploration is the use of electrical principles to study the distribution law of underground electrical properties and infer the structure and properties of underground rock and soil media; magnetic exploration is the use of the change law of the geomagnetic field to detect underground ore bodies, faults and other geological structures; gravity exploration is the use of the relationship between gravity and gravity to study the density changes of underground geological bodies.

[0043] In order to more accurately assess geological risks and improve the accuracy and reliability of forecasts, the embodiments of the present invention propose an advanced geological forecast risk assessment method and device based on multi-source geophysical prospecting methods. It can fully integrate the data of multiple geophysical prospecting methods, realize a comprehensive assessment of geological risks, and provide strong support for engineering practice.

[0044] Specifically, please refer to Figure 1 , which shows a schematic diagram of the structure of an advanced geological prediction risk assessment device based on a multi-source geophysical prospecting method provided by an embodiment of the present invention. The device includes: a data acquisition unit, a data processing unit, a data crawling unit, a data conversion unit and a risk identification unit. In the data acquisition unit, a geological structure acquisition module is included, which is used to collect data on underground faults, folds and lithological interfaces; a resource exploration acquisition module is used to collect geotechnical engineering data for underground oil and gas exploration; a geological exploration acquisition module is used to collect groundwater and landslide exploration data; an environmental geophysical exploration acquisition module is used to collect soil condition and groundwater condition data; a seismic data acquisition module is used to collect seismic data; a geothermal data acquisition module is used to collect geothermal data; and a geoelectric data acquisition module is used to collect geoelectric data. Correspondingly, each data acquisition module can select corresponding geophysical prospecting equipment to collect geological exploration data. For example, the seismic data acquisition module can use equipment such as seismic detectors to obtain seismic data, the geoelectric data can use equipment such as ground penetrating radars to obtain geoelectric data, and the geothermal data can use equipment such as geothermometers to obtain geothermal data. The remaining corresponding modules can be set accordingly according to the equipment for obtaining the exploration data through geophysical prospecting, and will not be described in detail here.

[0045] Among them, for each type of geological exploration data collected by the exploration data collection module, the data processing unit, the data crawling unit, the data conversion unit and the risk identification unit all process the geological exploration data accordingly. The above processing is further introduced and illustrated by the advanced geological prediction risk assessment method based on the multi-source geophysical prospecting method provided by the embodiment of the present invention.

[0046] Please refer to Figure 2 , which shows a flow chart of an advanced geological prediction risk assessment method based on a multi-source geophysical prospecting method provided by an embodiment of the present invention, the method comprising:

[0047] Step 1: Obtain multiple geological exploration data of the target geology and associate data factors for each geological exploration data;

[0048] Step 2: crawling corresponding network exploration data of target geology based on the data factors;

[0049] Step 3: Merge the network exploration data and geological exploration data of each data factor and convert them to generate the corresponding geological distribution image;

[0050] Step 4: Identify the risk category of the geological distribution image through the trained assessment model, obtain the risk assessment result of each data factor in the target geology, and complete the geological forecast.

[0051] Specifically, in step 1, the data factors are information labels used to extract and analyze geological information in the multi-source geophysical prospecting method, such as seismic data, gravity data, magnetic field data, geothermal data, and geoelectric data, etc. Each label contains the corresponding complete geological exploration data collected. In the embodiment of the present invention, the data factors use Chinese characters to characterize the characteristics of each geological exploration data. Several of these data factors are selected and used for geological prediction and risk assessment to describe the technical solution of the embodiment of the present invention.

[0052] In a possible implementation, the seismic data, geothermal data and geoelectric data are taken as examples. Since the names and characteristics of the above three types of data are relatively simple, for these three types of data, the corresponding "seismic data", "geothermal data" and "geoelectric data" are used as data factors for subsequent processing steps.

[0053] Specifically, in step 2, crawling the corresponding target geological network exploration data based on the data factor specifically includes:

[0054] Constructing a web crawler, and setting a logic filling unit in the web crawler;

[0055] Setting a corresponding qualifier filling menu in the logical filling unit according to the data factor;

[0056] The web crawler fills the menu according to each qualifier and crawls the corresponding web exploration data of the target geology.

[0057] In a possible implementation, the qualifier populates the menu using a unicode type string.

[0058] Specifically, the embodiment of the present application uses Python to construct the web crawler. It can be understood that Python crawling is a web crawler program written in Python programming language for automatically obtaining data on web pages. The principle is to write a program in Python to simulate the behavior of humans accessing web pages, send HTTP requests to the target website (which can be the public Internet or an internal LAN), obtain the HTML source code of the web page, and then use a parsing library (such as BeautifulSoup, lxml, etc.) to parse the HTML source code and extract the required data.

[0059] Taking the above three data factors of "seismic data", "geothermal data" and "geoelectric data" as examples, the network geological exploration data of the target geology is crawled.

[0060] For example, when crawling earthquake data, select several recent earthquake data for crawling.

[0061] At 07:55:14 on July 19, 2023, a magnitude 3.1 earthquake occurred in Qilian County, Haibei Prefecture; at 07:25:29 on July 19, 2023, a magnitude 3.0 earthquake occurred in Yutian County, Hotan Prefecture; at 03:42:46 on July 18, 2023, a magnitude 3.2 earthquake occurred in Xiangyun County, Dali Prefecture; at 16:22:55 on July 17, 2023, a magnitude 5.4 earthquake occurred in Gegye County, Ali Prefecture; at 12: At 42:16, a magnitude 6.7 earthquake occurred in the Maluku Sea, at 11:05:09 on July 17, 2023, a magnitude 7.2 earthquake occurred in Argentina, at 14:48:21 on July 16, 2023, a magnitude 3.4 earthquake occurred in the sea south of Alaska, and at 18:39:36 on July 15, 2023, a magnitude 6.2 earthquake occurred in Shaya County, Aksu Prefecture. By crawling recent earthquake data, it helps to improve the geological exploration data of data factors.

[0062] For example, when crawling geothermal data, choose custom data to crawl.

[0063] Ground temperature is the temperature condition at the junction of the atmosphere and the ground surface, and refers to the depth to which the water in an evaporator of a certain diameter decreases due to evaporation. Ground temperature should be measured with a special ground thermometer. In order to facilitate reading and accurately measure the soil temperature at a certain depth, the ground temperature is usually measured with a special curved tube ground temperature meter. The sensing ball of the curved tube ground temperature meter is connected to the meter body at an angle of 135 degrees. When installing, just bury the meter body in the soil at an angle of 45 degrees to the ground. Meteorological stations generally observe the ground temperature at the ground and at depths of 5 cm, 10 cm, 15 cm, 20 cm, 40 cm, 80 cm, 160 cm and 320 cm below the ground, as well as the highest and lowest temperatures of the ground every day.

[0064] For geoelectric data, select custom data to crawl.

[0065] The measurement components involved in geoelectric data include: resistivity NS, resistivity EW, resistivity NE, potential NS, potential EW, potential NE, electric field NS, electric field EW, electric field NE, etc.

[0066] Further, as described in step 3 above, after crawling is completed, the network exploration data and geological exploration data of each data factor are merged. In an embodiment of the present invention, a database is used to store and process the data corresponding to each data factor. In a possible implementation, an embodiment of the present invention uses geological system simulation to retrieve the data corresponding to each data factor in the database, and establish a geological model accordingly, and then convert it to generate a geological distribution image.

[0067] Specifically, in step 4, the assessment model adopts an RNN recurrent neural network. The RNN recurrent neural network includes an input, an output, and a circulation, and the input, output, and circulation all use the above-mentioned data factors as reference quantities. The input is the geological distribution image generated by the data conversion corresponding to each data factor in step 3; the circulation is the data factor associated with each geological distribution image; and the output is the risk assessment result of each data factor.

[0068] It is understandable that since the RNN recurrent neural network is a recurrent neural network used to process sequence data. Therefore, in the advanced geological prediction risk assessment method based on the multi-source geophysical exploration method, the RNN recurrent neural network can perform cyclic analysis and processing on the input geological distribution image, by combining the input at the current moment with the output at the previous moment, and then calculating through the recurrent neural network to obtain the output at the current moment, which is the risk assessment result. Specifically, the RNN recurrent neural network is a convolutional neural network for image classification, which includes multiple convolutional layers, pooling layers and fully connected layers, and can realize automatic extraction and classification of image features. The RNN recurrent neural network has been trained on a large number of image data sets and has achieved good classification results.

[0069] Specifically, the processing steps of the assessment model are further introduced and explained. First, the input geological distribution image needs to be preprocessed, including: normalizing the RGB image data of 224x224 pixels to the range of [0,1]. Then, the image is restricted, including: batch size: 32 images are input per batch; data enhancement: random cropping, rotation and other operations are performed on the input image to increase the generalization ability of the model. The output result is the image classification result, that is, the risk category corresponding to each geological distribution image. In the assessment model, the confidence level and the preset threshold are set. Only when the confidence level of the risk category exceeds the preset threshold, the corresponding risk assessment result can be obtained. The confidence level is used to characterize the probability of risk in the geological distribution image. .

[0070] Furthermore, the training process of the evaluation model includes:

[0071] An input set is constructed based on the historical geological distribution image generated by converting the historical geological exploration data, and an output set is constructed based on the risk categories corresponding to the historical address exploration data;

[0072] A training set is constructed according to the input set and the output set, and a neural network model is trained based on the training set to obtain a trained evaluation model.

[0073] In the embodiment of the present invention, the evaluation model needs to be evaluated, including: for each input image, determining its classification result according to the confidence threshold in the output parameter; and calculating the accuracy, recall rate, F1 value and other evaluation indicators of the classification result to comprehensively evaluate the performance of the evaluation model. According to the calculation results of the evaluation indicators, the performance of the evaluation model on different data factors is analyzed to find out possible problems and improvement directions. Finally, a trained evaluation model is obtained.

[0074] Taking the construction of a highway as an example, two geophysical exploration methods, seismic exploration and electrical exploration, were used for advanced geological prediction. By establishing a geological model, the geological risks along the route were evaluated. During the evaluation process, some sections of the road were found to have high risks, and corresponding engineering measures needed to be taken to reduce the risks. At the same time, the evaluation results also provided an important reference for the design and construction of highways. In practical applications, the system has achieved remarkable results, improved the accuracy and reliability of geological risk assessment, and provided strong support for engineering construction and mineral resource development.

[0075] Furthermore, based on the above technical solution provided by the embodiment of the present invention, on the basis of obtaining a variety of geological exploration data for risk assessment, each type of data is supplemented by crawling related data factors. On the one hand, the influencing factors that cause geological risks can be included in the consideration of risk forecasting, and possible geological risks can be identified more comprehensively, providing a more reliable basis for engineering design and construction; on the other hand, the integrity and comprehensiveness of the survey data can be guaranteed, effectively avoiding the situation where the narrow geological exploration data obtained through exploration leads to errors in the assessment, and further improves the accuracy of advanced geological forecasts and improves the safety of the project; at the same time, the present invention proposes a method of associating various exploration data with data factors during the forecasting process, classifies each type of geological exploration data, and then performs risk assessment through a trained assessment model, thereby realizing a quantitative assessment of geological risks, and has good scalability, and can be adjusted and optimized according to different engineering requirements and geological conditions.

[0076] In the description of the embodiments of the present invention, it should be noted that, for ordinary technicians in the field, the specific meanings of the above-mentioned various exploration terms in the technical solutions of the embodiments of the present invention can be understood according to specific circumstances.

[0077] In addition, an embodiment of the present invention also provides an electronic device, comprising at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above-mentioned methods for advanced geological prediction risk assessment based on multi-source geophysical exploration methods.

[0078] In the embodiment of the present invention, the processor may be an integrated circuit chip having the ability to process signals. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0079] The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The processor reads the information in the storage medium and completes the steps of the above method in combination with its hardware.

[0080] The storage medium may be a memory, which may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0081] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0082] The volatile memory may be a random access memory (RAM) which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DRRAM).

[0083] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0084] It should be understood that the various units and corresponding modules in the device disclosed in the embodiment of the present invention can be implemented in other ways. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the communication connection between the units can be through some interfaces, servers or indirect coupling or communication connection, which can be electrical or other forms.

[0085] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each module may exist physically separately, or two or more modules may be integrated into one processing unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional unit.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0087] Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way for easy understanding.

[0088] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A risk assessment method for advanced geological prediction based on multi-source geophysical prospecting method, characterized in that: The method comprises: Obtain multiple geological exploration data of the target geology, and associate data factors for each geological exploration data; Crawling corresponding network exploration data of target geology based on the data factors; Merge the network exploration data and geological exploration data of each data factor, and convert them to generate corresponding geological distribution images; The risk category of the geological distribution image is identified through the trained assessment model, the risk assessment result of each data factor in the target geology is obtained, and the geological forecast is completed.

2. The method for risk assessment of advanced geological prediction based on multi-source geophysical prospecting method according to claim 1, characterized in that: The risk assessment result is obtained when the confidence of the risk category exceeds a preset threshold, and the confidence is used to characterize the probability of risk occurring in the geological distribution image.

3. The method for risk assessment of advanced geological prediction based on multi-source geophysical prospecting method according to claim 1, characterized in that: The training process of the evaluation model includes: An input set is constructed based on the historical geological distribution image generated by converting the historical geological exploration data, and an output set is constructed based on the risk categories corresponding to the historical address exploration data; A training set is constructed according to the input set and the output set, and a neural network model is trained based on the training set to obtain a trained evaluation model.

4. The method for risk assessment of advanced geological prediction based on multi-source geophysical prospecting method according to claim 3 is characterized in that: The evaluation model adopts RNN recurrent neural network.

5. The method for risk assessment of advanced geological prediction based on multi-source geophysical prospecting method according to claim 1, characterized in that: Based on the data factors, crawling corresponding network exploration data of target geology specifically includes: Constructing a web crawler, and setting a logic filling unit in the web crawler; Setting a corresponding qualifier filling menu in the logical filling unit according to the data factor; The web crawler fills the menu according to each qualifier and crawls the corresponding web exploration data of the target geology.

6. The method for risk assessment of advanced geological prediction based on multi-source geophysical prospecting method according to claim 5, characterized in that: The data factors use Chinese characters to characterize the characteristics of each geological exploration data; the qualifier filling menu uses a unicode type character string.

7. The method for risk assessment of advanced geological prediction based on multi-source geophysical prospecting method according to claim 1, characterized in that: The geological exploration data include: underground fault, fold and lithology interface data, underground oil and gas exploration data geotechnical engineering, groundwater and landslide exploration data, soil condition and groundwater condition data, earthquake data, geothermal data, and geoelectric data.

8. An advanced geological prediction risk assessment device based on multi-source geophysical prospecting method, characterized in that: The device comprises: a data acquisition unit for acquiring a variety of geological exploration data of the target geology; A data processing unit for associating a data factor for each geological exploration data; A data crawling unit, used to set a network crawler using the data factor, so that the network crawler crawls the data factor to obtain network exploration data of the corresponding target geology; A data conversion unit, used to merge the network exploration data and geological exploration data of each data factor, and convert and generate a corresponding geological distribution image; The risk identification unit is used to identify the risk category of the geological distribution image through a trained assessment model to obtain a risk assessment result of each data factor in the target geology.

9. The advanced geological prediction risk assessment device based on multi-source geophysical prospecting method according to claim 8 is characterized in that: The data acquisition unit comprises: The geological structure acquisition module is used to collect data on underground faults, folds and lithology interfaces; Resource exploration and acquisition module, used to collect underground oil and gas exploration data geotechnical engineering; Geological survey collection module, used to collect groundwater and landslide survey data; Environmental geophysical acquisition module, used to collect soil condition and groundwater condition data; A seismic data acquisition module, used for acquiring seismic data; Ground temperature data acquisition module, used to collect ground temperature data; The geoelectric data acquisition module is used to collect geoelectric data.

10. An electronic device, characterized in that: It includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an advanced geological prediction risk assessment method based on a multi-source geophysical prospecting method as described in any one of claims 1 to 7.

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