Geological disaster early warning method, system, equipment and medium

Abstract

The invention relates to a geological disaster early warning method, system and device and a medium, and the method comprises the steps: constructing a geological disaster evaluation module of a monitoring region according to the three-dimensional geological information and meteorological information of the monitoring region; performing single-layer analysis and multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain a first weight coefficient and a second weight coefficient of each evaluation factor; carrying out weight calculation on the evaluation factor by utilizing a mutation model to obtain a third weight coefficient of the evaluation factor; obtaining an optimal weight coefficient of the evaluation factor by combining a minimum information entropy method according to the obtained three types of weight coefficients; and according to the normalized data of the evaluation factors and the optimal weight coefficient, calculating probability data of geological disasters in the monitoring area, converting the probability data into early warning data, and sending the early warning data to a user side. According to the invention, disaster early warning of the three-dimensional geological environment is realized, the influence of meteorological conditions on the geological environment is also considered, the accuracy of geological disaster early warning is improved, and the cost investment is reduced.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster early warning technology, and in particular to a geological disaster early warning method, system, equipment and medium. Background Art

[0002] Geological disasters refer to natural disasters caused by geological factors, such as earthquakes, landslides, mud-rock flows, ground collapse, etc. Geological disaster early warning is an important part of geological disaster prevention and control work. Through early warning, signs of geological disasters can be discovered in advance, preventive measures can be taken in time, and the losses caused by disasters can be reduced.

[0003] In the prior art, geological disaster early warning is generally based on remote sensing technology to identify geological disasters and deduce their spatial distribution patterns, or based on real-time machine monitoring data, or based on manual patrol monitoring, or based on a single weather forecast. However, the above geological disaster early warning methods have the following disadvantages:

[0004] (1) Remote sensing technology analysis is based on two-dimensional images and can be used for large-scale analysis and early warning. However, due to the limitations of the accuracy of planar images and the lack of three-dimensional information, the prediction accuracy of geological disasters is limited.

[0005] (2) The real-time monitoring data of machines are generally based on various sensors deployed on site. The accuracy of the monitoring data is limited by the capacity and quantity of the deployed sensor equipment. Considering the economy and necessity, it cannot be used for large-scale geological disaster warning.

[0006] (3) Manual inspection and monitoring rely on the technical ability and sense of responsibility of the inspectors, and the manpower is limited. The efficiency of monitoring and early warning for large-area geological bodies is low, and the labor input cost is high.

[0007] (4) Relying solely on meteorological forecasts for early warning fails to take into account the specific conditions of the geological body, resulting in the expansion of the warning scope. In addition, due to the differences in geological bodies, the geological body may still pose a major safety hazard after extreme rainfall. There is currently no quantitative assessment of its risks.

[0008] Therefore, technical personnel in this field urgently need a technical solution that can efficiently realize large-scale geological disaster early warning. Summary of the invention

[0009] 1. Technical issues to be resolved

[0010] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a geological disaster early warning method, system, equipment and medium, which solve the technical problems of low early warning prediction accuracy and high investment cost when realizing large-scale geological disaster early warning.

[0011] (II) Technical solution

[0012] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0013] In a first aspect, an embodiment of the present invention provides a geological disaster early warning method, comprising:

[0014] Decomposing the acquired three-dimensional geological information and meteorological information of the monitoring area, and constructing a geological disaster assessment module of the monitoring area based on the decomposition results;

[0015] Performing single-layer analysis and multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain a first weight coefficient and a second weight coefficient of each evaluation factor;

[0016] According to the geological disaster assessment module, the assessment factors are weighted by using a preset mutation model to obtain a third weight coefficient of each assessment factor;

[0017] According to the first weight coefficient, the second weight coefficient and the third weight coefficient, the optimal weight coefficient of each evaluation factor is obtained in combination with the minimum information entropy method;

[0018] According to the normalized data of the evaluation factors and the optimal weight coefficient, the probability data of geological disasters occurring in the monitoring area is calculated, and the probability data is converted into early warning data and sent to the user end.

[0019] Optionally, decomposing the acquired three-dimensional geological information and meteorological information of the monitoring area, and constructing a geological disaster assessment module of the monitoring area based on the decomposition results includes:

[0020] Acquire three-dimensional geological information and meteorological information of the monitoring area, wherein the three-dimensional geological information includes three-dimensional vector information of geological bodies, geophysical parameters, engineering geological information and water temperature geological information;

[0021] The three-dimensional geological information and the meteorological information are split into a number of evaluation factors, and the evaluation factors are used as factor layers to construct a geological disaster evaluation module of target layer-criterion layer-factor layer;

[0022] in,

[0023] The evaluation factors of the three-dimensional vector information of the geological body include: the slope height of the geological body, the slope of the geological body and the slope direction of the geological body;

[0024] The evaluation factors of the geotechnical physical parameters include: elastic parameters and strength parameters;

[0025] The evaluation factors of engineering geological information include: topography, geological structure, adverse geology and special rock and soil;

[0026] The evaluation factors of the hydrogeological information include: surface water level, groundwater level and groundwater recharge path;

[0027] The evaluation factors of the meteorological information include: temperature, humidity, precipitation and evaporation.

[0028] Optionally, performing single-layer analysis and multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain the first weight coefficient and the second weight coefficient of each evaluation factor includes:

[0029] According to the geological disaster assessment module, a judgment matrix of the target layer-criterion layer, a judgment matrix of the criterion layer-factor layer and a judgment matrix of the target layer-factor layer are established by using a 1-9 scaling method;

[0030] According to the judgment matrix of the target layer-factor layer, a single-layer analysis is performed on the evaluation factors in the geological disaster evaluation module to obtain the first weight coefficient of each evaluation factor;

[0031] According to the judgment matrix of the target layer-criterion layer and the judgment matrix of the criterion layer-factor layer, a multi-layer analysis is performed on the evaluation factors in the geological disaster evaluation module to obtain the second weight coefficient of each evaluation factor.

[0032] Optionally, according to the geological disaster assessment module, the evaluation factors are weighted using a preset mutation model to obtain a third weight coefficient for each evaluation factor, including:

[0033] According to the number of evaluation factors belonging to each type of information in the criterion layer of the geological disaster evaluation module, the type of mutation model is determined, and the mutation model includes a folding mutation model, a cusp mutation model, a swallowtail mutation model and a butterfly mutation model;

[0034] After each evaluation factor is standardized, the standardized evaluation factor is normalized using the mutation model to obtain a fuzzy membership function value of each evaluation factor;

[0035] According to the fuzzy membership function value, the weight coefficients of various types of information in the criterion layer are obtained in sequence;

[0036] According to the weight coefficients of various types of information in the criterion layer, the weight coefficient of each evaluation factor is obtained in combination with the fuzzy membership function value, and the third weight coefficient of each evaluation factor is calculated.

[0037] Optionally, obtaining the optimal weight coefficient of each evaluation factor according to the first weight coefficient, the second weight coefficient and the third weight coefficient in combination with a minimum information entropy method includes:

[0038]

[0039] (1) In the formula, minH represents the minimum information entropy, n represents the number of evaluation factors, and W i Represents the optimal weight coefficient of the i-th evaluation factor, W 1i represents the first weight coefficient of the i-th evaluation factor, W 2i represents the second weight coefficient of the i-th evaluation factor, W 3i Represents the third weight coefficient of the ith evaluation factor.

[0040] Optionally, before calculating the probability data of geological disasters occurring in the monitoring area based on the normalized data of the evaluation factors and the optimal weight coefficient, and converting the probability data into early warning data and sending it to the user end, it also includes:

[0041] Get the evaluation rating scale;

[0042] Using the evaluation grade classification table, normalizing the evaluation factors of the engineering geological information and the hydrogeological information;

[0043] Formula (2) is used to normalize the three-dimensional vector information of the geological body, geotechnical physical parameters, and evaluation factors in meteorological information;

[0044] Wherein, formula (2) is:

[0045]

[0046] Among them, X norm represents the normalized data of the evaluation factor, X represents the original collected data of the evaluation factor, and X min Represents the minimum value of the data in this type of evaluation factor, X max Represents the maximum value of the data in this type of evaluation factor.

[0047] Optionally, calculating the probability data of geological disasters occurring in the monitoring area according to the normalized data of the evaluation factor and the optimal weight coefficient, and converting the probability data into early warning data and sending it to the user end includes:

[0048] According to the normalized data of the evaluation factors and the optimal weight coefficient, the risk index of geological disasters occurring in the monitoring area is obtained through the risk index calculation formula;

[0049] Normalizing the risk index to obtain probability data of geological disasters occurring in the monitoring area;

[0050] When the probability data exceeds a preset threshold range, the probability data is combined with the map information of the monitoring area to generate warning information, and the warning information is sent to the user terminal;

[0051] The calculation formula of the risk index is:

[0052]

[0053] (3) In the formula, S j Represents the risk index of geological disasters in the j-th monitoring area. The normalized data of the risk index is [0,1]. When the normalized data is [0.45, 1], the probability level of geological disasters is extremely high risk. When the normalized data is [0.30, 0.45), the probability level of geological disasters is high risk. When the normalized data is [0.19-0.29), the probability level of geological disasters is medium-high risk. When the normalized data is [0-0.19), the probability level of geological disasters is low risk.

[0054] In a second aspect, an embodiment of the present invention provides a geological disaster early warning system, including:

[0055] An information acquisition unit, used to acquire three-dimensional geological information and meteorological information of the monitoring area;

[0056] An evaluation module construction unit is used to decompose the acquired three-dimensional geological information and meteorological information of the monitoring area, and construct a geological disaster evaluation module of the monitoring area based on the decomposition results;

[0057] A weight calculation unit based on hierarchical analysis is used to perform single-layer analysis and multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain a first weight coefficient and a second weight coefficient of each evaluation factor;

[0058] A weight calculation unit based on a mutation model, used to perform weight calculation on the evaluation factors according to the geological disaster evaluation module using a preset mutation model to obtain a third weight coefficient of each evaluation factor;

[0059] an optimal weight calculation unit, configured to obtain an optimal weight coefficient of each evaluation factor according to the first weight coefficient, the second weight coefficient and the third weight coefficient in combination with a minimum information entropy method;

[0060] The geological disaster early warning unit is used to calculate the probability data of geological disasters occurring in the monitoring area based on the normalized data of the evaluation factors and the optimal weight coefficient, and convert the probability data into early warning data and send it to the user end.

[0061] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0062] processor;

[0063] A memory stores the steps of a geological disaster early warning method described above for the processor to control.

[0064] In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having computer-executable instructions stored thereon, which, when executed by a processor, implement the above-mentioned steps of a geological disaster early warning method.

[0065] (III) Beneficial effects

[0066] The beneficial effect of the present invention is that the geological disaster early warning method proposed by the present invention adopts three-dimensional geological information and meteorological information to conduct a comprehensive geological disaster early warning evaluation on the monitoring area. Compared with the existing technology, it realizes the disaster early warning of the three-dimensional geological environment, and it also takes into account the impact of meteorological conditions on the geological environment, further improving the accuracy of geological disaster early warning and reducing cost investment.

[0067] At the same time, the present invention obtains three weight coefficients of each evaluation factor from the constructed geological disaster evaluation module by adopting hierarchical analysis and mutation model, and after obtaining the optimal weight coefficient through the three weight coefficients, uses the optimal weight coefficient to conduct risk evaluation on the geological disasters in the monitoring area, so that the evaluation result is more reasonable and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A schematic diagram of a flow chart of a geological disaster early warning method provided by an embodiment of the present invention;

[0069] Figure 2 A schematic diagram of the composition of a geological disaster assessment module provided by an embodiment of the present invention;

[0070] Figure 3 A schematic diagram of the composition of a geological disaster early warning system provided by one embodiment of the present invention;

[0071] Figure 4 A schematic diagram of the structure of a computer system of an electronic device provided by the present invention.

[0072] [Description of Reference Numerals]

[0073] 300: geological disaster early warning system; 301: information acquisition unit; 302: evaluation module construction unit; 303: weight calculation unit based on hierarchical analysis; 304: weight calculation unit based on mutation model; 305: optimal weight calculation unit; 306: geological disaster early warning unit;

[0074] 400: computer system; 401: CPU; 402: ROM; 403: RAM; 404: bus; 405: I / O interface; 406: input part; 407: output part; 408: storage part; 409: communication part; 410: drive; 411: removable medium. DETAILED DESCRIPTION

[0075] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation modes in conjunction with the accompanying drawings.

[0076] refer to Figure 1 As shown, a geological disaster early warning method proposed in an embodiment of the present invention includes: first, decomposing the acquired three-dimensional geological information and meteorological information of the monitoring area, and constructing a geological disaster evaluation module of the monitoring area based on the decomposition results; secondly, performing single-layer analysis and multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain a first weight coefficient and a second weight coefficient of each evaluation factor; then, according to the geological disaster evaluation module, using a pre-set mutation model to calculate the weight of the evaluation factor to obtain a third weight coefficient of each evaluation factor; then, according to the first weight coefficient, the second weight coefficient and the third weight coefficient, combined with the minimum information entropy method, the optimal weight coefficient of each evaluation factor is obtained; finally, based on the normalized data and the optimal weight coefficient of the evaluation factor, the probability data of the occurrence of geological disasters in the monitoring area is calculated, and the probability data is converted into early warning data and sent to the user end.

[0077] The geological disaster warning method proposed in this embodiment uses three-dimensional geological information and meteorological information to conduct a comprehensive geological disaster warning evaluation in the monitoring area. Compared with the existing technology, it realizes disaster warning in the three-dimensional geological environment, and it also takes into account the impact of meteorological conditions on the geological environment, further improving the accuracy of geological disaster warning and reducing cost investment.

[0078] At the same time, this embodiment obtains three weight coefficients of each evaluation factor from the constructed geological hazard evaluation module by using hierarchical analysis and mutation model, and after obtaining the optimal weight coefficient through the three weight coefficients, uses the optimal weight coefficient to conduct risk assessment of geological hazards in the monitoring area, so that the evaluation results are more reasonable and accurate.

[0079] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0080] Specifically, refer to Figure 1 As shown, this embodiment proposes a geological disaster early warning method, which includes:

[0081] S1. Decompose the acquired three-dimensional geological information and meteorological information of the monitoring area, and build a geological disaster assessment module for the monitoring area based on the decomposition results.

[0082] In this embodiment, step S1 may include the following sub-steps S11-S12:

[0083] S11, obtaining three-dimensional geological information and meteorological information of the monitoring area, wherein the three-dimensional geological information includes three-dimensional vector information of geological bodies, geophysical parameters, engineering geological information and water temperature geological information.

[0084] S12, split the three-dimensional geological information and meteorological information into several evaluation factors, and use the evaluation factors as factor layers to construct a geological disaster evaluation module with target layer, criterion layer and factor layer. Figure 2 As shown, the evaluation factors of the three-dimensional vector information of the geological body include: the slope height of the geological body, the slope of the geological body and the slope direction of the geological body; the evaluation factors of the geotechnical physical parameters include: elastic parameters and strength parameters; the evaluation factors of engineering geological information include: topography, geological structure, unfavorable geology and special rock and soil; the evaluation factors of hydrogeological information include: surface water level, groundwater level and groundwater recharge path; the evaluation factors of meteorological information include: temperature, humidity, precipitation and evaporation.

[0085] S2. Perform single-layer analysis and multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain the first weight coefficient and the second weight coefficient of each evaluation factor.

[0086] In this embodiment, step S2 may include the following sub-steps S21-S23:

[0087] S21. According to the geological disaster assessment module, the 1-9 scaling method is used to establish the judgment matrix of the target layer-criterion layer, the judgment matrix of the criterion layer-factor layer, and the judgment matrix of the target layer-factor layer.

[0088] The 1-9 scale uses 1 to 9 and their reciprocals as evaluation elements to qualitatively describe the relative importance of each function or factor. The specific meaning of each number in the 1-9 scale is shown in Table 1 below.

[0089] Table 1. Judgment matrix scale and its meaning

[0090] Scale value meaning 1 Indicates that two factors are equally important. 3 Indicates that compared with the two factors, the former is slightly more important than the latter 5 Indicates that compared with the two factors, the former is significantly more important than the latter 7 Indicates that compared with two factors, the former is more important than the latter 9 Indicates that compared with the two factors, the former is extremely more important than the latter 2、4、6、8 Indicates the middle value of the above adjacent descriptions Countdown from 1 to 9 Contrary to the above situation

[0091] S22. According to the judgment matrix of the target layer-factor layer, a single-layer analysis is performed on the evaluation factors in the geological disaster evaluation module to obtain the first weight coefficient of each evaluation factor.

[0092] S23. According to the judgment matrix of the target layer-criterion layer and the judgment matrix of the criterion layer-factor layer, a multi-layer analysis is performed on the evaluation factors in the geological disaster evaluation module to obtain the second weight coefficient of each evaluation factor.

[0093] In a specific embodiment, after a certain mountainous area is selected as a geological disaster early warning monitoring area, first, a geological disaster evaluation module of the mountainous area is constructed according to the three-dimensional geological information and meteorological information of the mountainous area. Secondly, based on the geological disaster evaluation module, the user inputs the scale value between two elements in the quasi-measurement layer and the scale value between two elements in the factor layer. Then, based on the scale value between the elements, the following judgment matrix of the target layer-criterion layer, the judgment matrix of the criterion layer-factor layer and the judgment matrix of the target layer-factor layer are established. Then, according to the maximum eigenvalue of each judgment matrix, the judgment matrix is ​​subjected to consistency test. Finally, after the judgment matrix satisfies the consistency test, the hierarchical single sorting method in the analytic hierarchy process (AHP) is used to combine the judgment matrix of the target layer-factor layer to perform a single-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain the first weight coefficient of each evaluation factor; the hierarchical single sorting-hierarchical total sorting method in the analytic hierarchy process (AHP) is used to combine the judgment matrix of the target layer-criterion layer and the judgment matrix of the criterion layer-factor layer to perform a multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain the second weight coefficient of each evaluation factor.

[0094] S3. According to the geological disaster assessment module, the assessment factors are weighted using a pre-set mutation model to obtain a third weight coefficient for each assessment factor.

[0095] In this embodiment, step S3 may include the following sub-steps S31-S34:

[0096] S31, according to the number of evaluation factors belonging to each type of information in the criterion layer of the geological disaster evaluation module, determine the type of mutation model. The mutation model includes a folding mutation model, a cusp mutation model, a swallowtail mutation model and a butterfly mutation model.

[0097] Based on the number of evaluation factors belonging to each type of information in the criterion layer of the geological hazard assessment module, the type of mutation model is determined. When the number of evaluation factors belonging to each type of information in the criterion layer is 1, the mutation model uses the folding mutation model, when the number of evaluation factors belonging to each type of information in the criterion layer is 2, the mutation model uses the cusp mutation model, when the number of evaluation factors belonging to each type of information in the criterion layer is 3, the mutation model uses the swallowtail mutation model, and when the number of evaluation factors belonging to each type of information in the criterion layer is 4, the mutation model uses the butterfly mutation model.

[0098] S32, after standardizing each evaluation factor, normalize the standardized evaluation factor using a mutation model to obtain a fuzzy membership function value of each evaluation factor.

[0099] S33. According to the fuzzy membership function value, the weight coefficients of various types of information in the criterion layer are obtained in sequence.

[0100] S34. According to the weight coefficients of various types of information in the criterion layer, the weight coefficient of each evaluation factor is obtained in combination with the fuzzy membership function value, and the third weight coefficient of each evaluation factor is calculated.

[0101] S4. According to the first weight coefficient, the second weight coefficient and the third weight coefficient, the optimal weight coefficient of each evaluation factor is obtained in combination with the minimum information entropy method.

[0102]

[0103] (1) In the formula, minH represents the minimum information entropy, n represents the number of evaluation factors, and W i Represents the optimal weight coefficient of the i-th evaluation factor, W 1i represents the first weight coefficient of the i-th evaluation factor, W 2i represents the second weight coefficient of the i-th evaluation factor, W 3i Represents the third weight coefficient of the i-th evaluation factor.

[0104] This embodiment uses three weight coefficients to obtain the optimal weight coefficient, and uses the optimal weight coefficient to conduct risk assessment of geological disasters in the monitoring area, so that the assessment results are more reasonable and accurate. In addition, this assessment method is suitable for erosional structural mountainous areas with large terrain fluctuations, developed structures, severe rock weathering, and developed river networks within a large scale range, and can effectively provide new methods and reliable basis for geological disaster risk assessment and disaster prevention and mitigation work.

[0105] S5. Based on the normalized data of the evaluation factors and the optimal weight coefficient, the probability data of geological disasters occurring in the monitoring area is calculated, and the probability data is converted into early warning data and sent to the user end.

[0106] In this embodiment, before step S5, the following steps F1-F3 are also included:

[0107] F1. Obtain the evaluation grade table.

[0108] F2. Use the evaluation grade classification table to normalize the evaluation factors of engineering geological information and hydrogeological information.

[0109] For example, when the landform type is plateau and mountainous area, the normalized index is 1, when the landform type is hilly and basin, the normalized index is 0.5, and when the landform type is plain, the normalized index is 0. When the geological structure is a fault, the normalized index is 1, and when the geological structure is erosion and erosion, the normalized index is 0.7. When unfavorable geology (such as karst, landslide, collapse, mudslide, ground subsidence, etc.) occurs, the normalized index of unfavorable geology is set to 1. When special rock and soil (easy to soften rock and soil: siltstone, layered soft mudstone, mud siltstone, etc., rock and soil with poor weathering resistance: clay rock, shale, ultrabasic rock, basic rock, etc.) appears, the normalized index of special rock and soil is set to 1. The surface water level and groundwater level take normalized indexes between 0 and 1 according to different water levels.

[0110] F3. Use formula (2) to normalize the three-dimensional vector information of the geological body, geotechnical physical parameters and evaluation factors in meteorological information.

[0111] Wherein, formula (2) is:

[0112]

[0113] Among them, X norm represents the normalized data of the evaluation factor, X represents the original collected data of the evaluation factor, and X min Represents the minimum value of the data in this type of evaluation factor, X max Represents the maximum value of the data in this type of evaluation factor.

[0114] In this embodiment, step S5 may include the following sub-steps S51-S53:

[0115] S51. Based on the normalized data of the evaluation factors and the optimal weight coefficient, the hazard index of geological disasters in the monitoring area is obtained through the hazard index calculation formula.

[0116] S52. Normalize the hazard index to obtain probability data of geological disasters occurring in the monitoring area.

[0117] S53: When the probability data exceeds a preset threshold range, the probability data is combined with map information of the monitoring area to generate warning information, and the warning information is sent to the user end.

[0118] The calculation formula of the risk index is:

[0119]

[0120] (3) In the formula, S jRepresents the risk index of geological disasters in the j-th monitoring area. The normalized data of the risk index is [0,1]. When the normalized data is [0.45, 1], the probability level of geological disasters is extremely high risk. When the normalized data is [0.30, 0.45), the probability level of geological disasters is high risk. When the normalized data is [0.19-0.29), the probability level of geological disasters is medium-high risk. When the normalized data is [0-0.19), the probability level of geological disasters is low risk.

[0121] It is worth mentioning that the user terminal includes one or more of the user's mobile terminal, email, and online chat application.

[0122] also, Figure 3 A schematic diagram of the composition of a geological disaster early warning system provided by the present invention is shown in FIG. Figure 3 As shown, the present invention also provides a geological disaster early warning system 300, which includes:

[0123] The information acquisition unit 301 is used to acquire three-dimensional geological information and meteorological information of the monitoring area.

[0124] The evaluation module construction unit 302 is used to decompose the acquired three-dimensional geological information and meteorological information of the monitoring area, and construct a geological disaster evaluation module of the monitoring area based on the decomposition results.

[0125] The weight calculation unit 303 based on hierarchical analysis is used to perform single-layer analysis and multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain the first weight coefficient and the second weight coefficient of each evaluation factor.

[0126] The weight calculation unit 304 based on the mutation model is used to perform weight calculation on the evaluation factors according to the geological disaster evaluation module using the pre-set mutation model to obtain the third weight coefficient of each evaluation factor.

[0127] The optimal weight calculation unit 305 is used to obtain the optimal weight coefficient of each evaluation factor according to the first weight coefficient, the second weight coefficient and the third weight coefficient in combination with the minimum information entropy method.

[0128] The geological disaster early warning unit 306 is used to calculate the probability data of geological disasters occurring in the monitoring area based on the normalized data of the evaluation factors and the optimal weight coefficient, and convert the probability data into early warning data and send it to the user end.

[0129] The functions of each unit in the system refer to the relevant description in the above method embodiment, which will not be repeated here.

[0130] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores steps of a geological disaster early warning method described above for the processor to control.

[0131] Reference below Figure 4 , which shows a schematic diagram of the structure of a computer system 400 suitable for implementing the electronic device of this embodiment. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0132] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage part 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the computer system 400 are also stored. The CPU 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0133] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.

[0134] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 409, and / or installed from a removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, the above-mentioned functions defined in the system of the present application are executed.

[0135] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable medium that can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0136] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0137] The units involved in the embodiments described in this application may be implemented by software or hardware. The units described may also be arranged in a processor, wherein the names of these units do not constitute limitations on the units themselves in certain circumstances.

[0138] Finally, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes the following method steps:

[0139] S1. Decompose the acquired three-dimensional geological information and meteorological information of the monitoring area, and build a geological disaster assessment module for the monitoring area based on the decomposition results.

[0140] S2. Perform single-layer analysis and multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain the first weight coefficient and the second weight coefficient of each evaluation factor.

[0141] S3. According to the geological disaster assessment module, the assessment factors are weighted using a pre-set mutation model to obtain a third weight coefficient for each assessment factor.

[0142] S4. According to the first weight coefficient, the second weight coefficient and the third weight coefficient, the optimal weight coefficient of each evaluation factor is obtained in combination with the minimum information entropy method.

[0143] S5. Based on the normalized data of the evaluation factors and the optimal weight coefficient, the probability data of geological disasters occurring in the monitoring area is calculated, and the probability data is converted into early warning data and sent to the user end.

[0144] In summary, the present invention proposes a geological disaster early warning method, system, equipment and medium, and the method includes: first, constructing a geological disaster evaluation module according to three-dimensional geological information and meteorological information; then, using the analytic hierarchy process and mutation theory to obtain the weights of each evaluation factor in the geological disaster evaluation module, and obtaining the optimal weight of each evaluation factor; then, according to the normalized data and optimal weight of the evaluation factor, calculating the probability data of geological disasters occurring in the monitoring area; finally, generating early warning information based on the probability data of geological disasters, and sending it to the user end. The present invention is based on three-dimensional geological information and interactive meteorological information, and displays the risk of geological disasters occurring in the monitoring area in real time, and obtains the probability of geological disasters occurring in a scientific and quantitative manner, which solves the problem that traditional two-dimensional remote sensing interpretation cannot effectively combine the three-dimensional size information of the monitoring area for analysis, and improves the accuracy of early warning. At the same time, after obtaining the optimal weight coefficient through three weight coefficients, the present invention uses the optimal weight coefficient to conduct risk assessment of geological disasters in the monitoring area, so that the evaluation result is more reasonable and accurate.

[0145] Since the system / device described in the above embodiments of the present invention is a system / device used to implement the method of the above embodiments of the present invention, a person skilled in the art can understand the specific structure and deformation of the system / device based on the method described in the above embodiments of the present invention, and thus will not be described in detail here. All systems / devices used in the method of the above embodiments of the present invention belong to the scope of protection of the present invention.

[0146] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0147] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0148] It should be noted that in the description of the present invention, the word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. The use of the words first, second, third, etc., is only for convenience of expression and does not indicate any order. These words may be understood as part of the name of the component.

[0149] In addition, it should be noted that, in the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0150] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments after obtaining the basic inventive concepts.

[0151] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the invention.

Claims

1. A geological disaster early warning method, characterized in that: include: Decomposing the acquired three-dimensional geological information and meteorological information of the monitoring area, and constructing a geological disaster assessment module of the monitoring area based on the decomposition results; Performing single-layer analysis and multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain a first weight coefficient and a second weight coefficient of each evaluation factor; According to the geological disaster assessment module, the assessment factors are weighted by using a preset mutation model to obtain a third weight coefficient of each assessment factor; According to the first weight coefficient, the second weight coefficient and the third weight coefficient, the optimal weight coefficient of each evaluation factor is obtained in combination with the minimum information entropy method; According to the normalized data of the evaluation factors and the optimal weight coefficient, the probability data of geological disasters occurring in the monitoring area is calculated, and the probability data is converted into early warning data and sent to the user end.

2. The method according to claim 1, characterized in that Decomposing the acquired three-dimensional geological information and meteorological information of the monitoring area, and constructing a geological disaster assessment module of the monitoring area based on the decomposition results include: Acquire three-dimensional geological information and meteorological information of the monitoring area, wherein the three-dimensional geological information includes three-dimensional vector information of geological bodies, geophysical parameters, engineering geological information and water temperature geological information; The three-dimensional geological information and the meteorological information are split into a number of evaluation factors, and the evaluation factors are used as factor layers to construct a geological disaster evaluation module of target layer-criterion layer-factor layer; in, The evaluation factors of the three-dimensional vector information of the geological body include: the slope height of the geological body, the slope of the geological body and the slope direction of the geological body; The evaluation factors of the geotechnical physical parameters include: elastic parameters and strength parameters; The evaluation factors of engineering geological information include: topography, geological structure, adverse geology and special rock and soil; The evaluation factors of the hydrogeological information include: surface water level, groundwater level and groundwater recharge path; The evaluation factors of the meteorological information include: temperature, humidity, precipitation and evaporation.

3. The method according to claim 2, characterized in that Single-layer analysis and multi-layer analysis are performed on the evaluation factors in the geological disaster evaluation module to obtain the first weight coefficient and the second weight coefficient of each evaluation factor, including: According to the geological disaster assessment module, a judgment matrix of the target layer-criterion layer, a judgment matrix of the criterion layer-factor layer and a judgment matrix of the target layer-factor layer are established by using a 1-9 scaling method; According to the judgment matrix of the target layer-factor layer, a single-layer analysis is performed on the evaluation factors in the geological disaster evaluation module to obtain the first weight coefficient of each evaluation factor; According to the judgment matrix of the target layer-criterion layer and the judgment matrix of the criterion layer-factor layer, a multi-layer analysis is performed on the evaluation factors in the geological disaster evaluation module to obtain the second weight coefficient of each evaluation factor.

4. The method according to claim 2, characterized in that According to the geological disaster assessment module, the evaluation factors are weighted by using a preset mutation model to obtain a third weight coefficient of each evaluation factor, including: According to the number of evaluation factors belonging to each type of information in the criterion layer of the geological disaster evaluation module, the type of mutation model is determined, and the mutation model includes a folding mutation model, a cusp mutation model, a swallowtail mutation model and a butterfly mutation model; After each evaluation factor is standardized, the standardized evaluation factor is normalized using the mutation model to obtain a fuzzy membership function value of each evaluation factor; According to the fuzzy membership function value, the weight coefficients of various types of information in the criterion layer are obtained in sequence; According to the weight coefficients of various types of information in the criterion layer, the weight coefficient of each evaluation factor is obtained in combination with the fuzzy membership function value, and the third weight coefficient of each evaluation factor is calculated.

5. The method according to claim 1, characterized in that According to the first weight coefficient, the second weight coefficient and the third weight coefficient, the optimal weight coefficient of each evaluation factor is obtained by combining the minimum information entropy method, including: (1) In the formula, minH represents the minimum information entropy, n represents the number of evaluation factors, and W i Represents the optimal weight coefficient of the i-th evaluation factor, W 1i represents the first weight coefficient of the i-th evaluation factor, W 2i represents the second weight coefficient of the i-th evaluation factor, W 3i Represents the third weight coefficient of the ith evaluation factor.

6. The method according to claim 2, characterized in that Before calculating the probability data of geological disasters occurring in the monitoring area based on the normalized data of the evaluation factors and the optimal weight coefficient, and converting the probability data into early warning data and sending it to the user end, the method further includes: Get the evaluation rating scale; Using the evaluation grade classification table, normalizing the evaluation factors of the engineering geological information and the hydrogeological information; Formula (2) is used to normalize the three-dimensional vector information of the geological body, geotechnical physical parameters, and evaluation factors in meteorological information; Wherein, formula (2) is: Among them, X norm represents the normalized data of the evaluation factor, X represents the original collected data of the evaluation factor, and X min Represents the minimum value of the data in this type of evaluation factor, X max Represents the maximum value of the data in this type of evaluation factor.

7. The method according to claim 1, characterized in that Calculating the probability data of geological disasters occurring in the monitoring area according to the normalized data of the evaluation factors and the optimal weight coefficient, and converting the probability data into early warning data and sending it to the user end includes: According to the normalized data of the evaluation factors and the optimal weight coefficient, the risk index of geological disasters occurring in the monitoring area is obtained through the risk index calculation formula; Normalizing the risk index to obtain probability data of geological disasters occurring in the monitoring area; When the probability data exceeds a preset threshold range, the probability data is combined with the map information of the monitoring area to generate warning information, and the warning information is sent to the user terminal; The calculation formula of the risk index is: (3) In the formula, S j Represents the risk index of geological disasters in the j-th monitoring area. The normalized data of the risk index is [0,1]. When the normalized data is [0.45, 1], the probability level of geological disasters is extremely high risk. When the normalized data is [0.30, 0.45), the probability level of geological disasters is high risk. When the normalized data is [0.19-0.29), the probability level of geological disasters is medium-high risk. When the normalized data is [0-0.19), the probability level of geological disasters is low risk.

8. A geological disaster early warning system, characterized in that: include: An information acquisition unit, used to acquire three-dimensional geological information and meteorological information of the monitoring area; An evaluation module construction unit is used to decompose the acquired three-dimensional geological information and meteorological information of the monitoring area, and construct a geological disaster evaluation module of the monitoring area based on the decomposition results; A weight calculation unit based on hierarchical analysis is used to perform single-layer analysis and multi-layer analysis on the evaluation factors in the geological disaster evaluation module to obtain a first weight coefficient and a second weight coefficient of each evaluation factor; A weight calculation unit based on a mutation model, used to perform weight calculation on the evaluation factors according to the geological disaster evaluation module using a preset mutation model to obtain a third weight coefficient of each evaluation factor; an optimal weight calculation unit, configured to obtain an optimal weight coefficient of each evaluation factor according to the first weight coefficient, the second weight coefficient and the third weight coefficient in combination with a minimum information entropy method; The geological disaster early warning unit is used to calculate the probability data of geological disasters occurring in the monitoring area based on the normalized data of the evaluation factors and the optimal weight coefficient, and convert the probability data into early warning data and send it to the user end.

9. An electronic device, characterized in that: include: processor; A memory storing steps of a geological disaster early warning method as described in any one of claims 1 to 7 for the processor to control.

10. A computer-readable medium having computer-executable instructions stored thereon, characterized in that: When the executable instructions are executed by the processor, the steps of a geological disaster early warning method as described in any one of claims 1-7 are implemented.

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