A method, device and storage medium for assessing geological disaster risk in power grid area based on multi-source data

Abstract

The present invention relates to the technical field of disaster assessment in power grid areas, and in particular to a method, device and storage medium for assessing geological disaster risks in power grid areas based on multi-source data. The method comprises: obtaining a multi-source data set of a power grid area to be tested, constructing a geological disaster grid network in the power grid area to be tested, and extracting evaluation factors corresponding to each geological disaster in the geological disaster grid network; using the Pearson correlation coefficient to screen key evaluation factors and establish an evaluation factor library; determining the weights of each evaluation factor by the CRITIC weight method and the independence weight coefficient method, and constructing a geological disaster susceptibility model; according to the geological disaster susceptibility model, based on the regional characteristics of the power transmission line corridor in the power grid area to be tested, assessing the geological disaster susceptibility risk in the power grid area to be tested; and drawing a geological disaster risk distribution map in the power grid area to be tested, providing a scientific basis for the planning, construction and maintenance of the power grid, and improving the scientific nature and application value of disaster risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster assessment in power grid areas, and in particular to a method, device and storage medium for geological disaster risk assessment in power grid areas based on multi-source data. Background Art

[0002] Geological disasters, including landslides, debris flows and collapses, pose a huge threat to human life and property due to their unpredictability and destructiveness. Especially in areas with complex terrain and active geology, the frequent occurrence of geological disasters has seriously restricted the stable development of local social economy. Although existing geological hazard assessment methods provide a certain degree of risk identification, they usually have problems such as data source limitations, backward technical methods, lack of real-time updates and dynamic monitoring, and low levels of automation and intelligence.

[0003] Traditional assessment methods mainly rely on ground surveys and limited remote sensing data, lack the integration and fusion of multi-source data, resulting in the inability of assessment results to fully reflect the complexity of geological hazards. In addition, many existing assessment methods rely on qualitative analysis and expert experience, lack quantitative risk assessment models, which limits the accuracy and reliability of assessment results. With the passage of time and changes in the environment, the risk of geological hazards is also constantly changing. Existing methods often cannot achieve real-time updates and dynamic monitoring, and are difficult to adapt to the needs of dynamic assessment of geological hazards. Existing mapping methods rely on manual operations and lack automation and intelligent processing, resulting in low efficiency and susceptibility to interference from human factors.

[0004] The invention patent with application number 202211305380.0 and application name "Transmission Line Geological Hazard Risk Assessment Method and System Based on Data Fusion" discloses a transmission line geological hazard risk assessment method based on data fusion, which obtains the historical multi-source data set of the transmission line; analyzes the strongly correlated operating factors of each address disaster risk category and obtains the transmission line geological hazard correlation factor library; determines the risk values ​​of each strongly correlated operating parameter of the address disaster risk category and constructs a geological hazard risk knowledge base; uses a feature matching algorithm to calculate the geological hazard risk factor and constructs a risk factor matrix; uses an evidence reasoning algorithm to calculate the geological hazard risk matrix of the transmission line, and completes the final transmission line geological hazard risk level assessment.

[0005] The above assessment method mainly uses historical multi-source data of transmission lines, including transmission line characteristics, environmental characteristics, meteorology, online monitoring and line patrol data as data sources, and establishes a "geological disaster correlation factor library" through correlation analysis. However, the overall geological disaster factors of the non-transmission line coverage area in the entire power grid area are not added to the data source, and the specific spatial distribution and specific spatial characteristics of the transmission lines are less considered. The characteristics of the transmission line corridor are ignored, so that the assessment results cannot clearly reflect the overall geological disaster risk assessment of the transmission lines and the power grid area. At the same time, although the risk matrix output method of this assessment method can reflect the risk level, it lacks spatial distribution and visualization results, and is difficult to be intuitively applied to power grid management. Summary of the invention

[0006] To solve the above technical problems, the present invention provides a method for assessing the risk of geological disasters in a power grid region based on multi-source data, comprising the following steps:

[0007] S1. Obtain a multi-source data set of the power grid area to be tested, pre-process the multi-source data set, construct a geological disaster grid network of the power grid area to be tested, and extract the evaluation factor corresponding to each geological disaster in the geological disaster grid network;

[0008] S2. Based on the evaluation factors obtained in S1, the Pearson correlation coefficient method is used to perform collinearity analysis on the evaluation factors to obtain the Pearson correlation coefficient of each evaluation factor. The evaluation factors whose Pearson correlation coefficient is greater than the Pearson correlation coefficient threshold are recorded as highly correlated factors. After traversing all evaluation factors, all highly correlated factors are eliminated, and an evaluation factor library is established;

[0009] S3. According to the evaluation factor library obtained in S2, the weight results of each evaluation factor in the evaluation factor library are calculated, and a geological disaster susceptibility model is constructed according to the weight results, wherein the geological disaster susceptibility model includes the weighted information amount of each evaluation factor in the evaluation factor library;

[0010] S4. Based on the geological disaster susceptibility model obtained in S3 and the regional characteristics of the transmission line corridors in the power grid area to be tested, the geological disaster susceptibility risk of the power grid area to be tested is evaluated;

[0011] S5. Based on the assessment results obtained in S4, a geological disaster risk distribution map of the power grid area to be tested is drawn to provide risk level assessment information of the power grid area to be tested;

[0012] The multi-source data set includes historical geological disaster data, basic geographic data, digital elevation model data, hydrogeological map data, meteorological rainfall data, earthquake catalog, geological fault data, geological disaster potential point survey data and power grid special information data;

[0013] The power grid special data includes power grid ledger, transmission line name, tower number and tower space coordinates.

[0014] Furthermore, the preprocessing in step S1 includes: constructing a geological disaster grid network of the transmission area to be tested based on the digital elevation model data of the area to be tested, and the grid construction formula can be expressed as:

[0015]

[0016] in, is the grid cell size; is the denominator of the digital elevation model data scale;

[0017] The scale of the digital elevation model data of the power grid area to be tested is brought into the grid network construction formula to calculate , and Take an integer as the grid size of the geological hazard grid network; if the digital elevation model data accuracy is inconsistent with the grid size of the geological hazard grid network, the digital elevation model data accuracy is sampled to be consistent with the grid size of the geological hazard grid network through the resampling tool of the GIS data processing platform.

[0018] Furthermore, the collinearity analysis in step S2 adopts the Pearson correlation coefficient method, and the Pearson correlation coefficient threshold is set to , the evaluation factors with Pearson correlation coefficient greater than the Pearson correlation coefficient threshold are recorded as highly correlated factors. After traversing all evaluation factors, all evaluation factors with |r|>0.5 are eliminated, and an evaluation factor library is established. The Pearson correlation coefficient method can be expressed as:

[0019]

[0020] in, is the Pearson correlation coefficient, and are the observed values ​​of the evaluation factors, and is the average value of the evaluation factor, is the current evaluation factor number, is the number of evaluation factors.

[0021] Further, step S3 includes the following steps:

[0022] S301. Calculate the first weight of each evaluation factor by using the CRITIC weight method, which can be expressed as:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028] in, is the evaluation factor matrix; For evaluation factors The standard deviation of For evaluation factors The matrix data points; For evaluation factors The sample mean of ; For evaluation factors and evaluation factors The linear correlation coefficient of For evaluation factors No. data points; For evaluation factors No. data points; For evaluation factors The sample mean of ; For evaluation factors conflict; For evaluation factors The amount of weight information; is the evaluation factor obtained by the CRITIC weight method The weight of is the number of evaluation factors;

[0029] S302. Calculate the second weight of each evaluation factor by the independence weight coefficient method, which can be expressed as:

[0030]

[0031]

[0032]

[0033] in, is the mean of the evaluation factor matrix; For evaluation factors matrix; For evaluation factors Multiple correlation coefficients with other evaluation factors; To remove The remaining evaluation factor matrix of is the inverse of the multiple correlation coefficient; is the evaluation factor obtained by the independence weight coefficient method The weight of

[0034] S303. According to the first weight of the evaluation factor and the second weight of the evaluation factor, the weight results of each evaluation factor are calculated by a comprehensive weight method, and the comprehensive weight method can be expressed as:

[0035]

[0036] in, For evaluation factors The weight result of

[0037] S304. Construct a geological disaster susceptibility model based on the weight results of each evaluation factor.

[0038] Furthermore, the construction of the geological disaster susceptibility model includes the following steps:

[0039] S304-1. Obtain the weight results of each evaluation factor;

[0040] S304-2. Divide each evaluation factor into intervals, and calculate the proportion of the number of grids in the interval to the total number of grids and the proportion of geological hazards in different intervals;

[0041] S304-3. Calculate the information amount of each evaluation factor in different intervals. The calculation formula can be expressed as:

[0042]

[0043]

[0044] in, For evaluation factors No. The amount of information of the class; is distributed in the evaluation factor No. The number of geological hazard sites within the class; is the total number of geological disaster points in the power grid area to be tested; is the disaster ratio; The power grid area to be tested contains evaluation factors No. The number of rasters in the class; is the total number of grids in the power grid area to be tested; is the grid ratio; is the total information content of the grid unit;

[0045] S304-4. Construct a geological disaster susceptibility model and calculate the weighted information of each evaluation factor in different intervals. The weighted information model can be expressed as:

[0046]

[0047] in, For evaluation factors The weighted information content of is the evaluation factor of the total information content of the grid unit The amount of original information; For evaluation factors The comprehensive weight of .

[0048] Further, the step S4 includes the following steps:

[0049] S401. Normalize the weighted information of the evaluation factors obtained in S3 to obtain a dimensionless geological disaster susceptibility evaluation result;

[0050] S402. Based on the grid-specific data, the distances and weights of the transmission lines and towers in the grid area to be tested are respectively delineated and given according to the empirical thresholds;

[0051] S403. Compare the weights of the transmission lines and transmission towers, and take the maximum weight as the weight of the geological disaster affecting the safety of the transmission lines and towers;

[0052] S404. Multiply the geological disaster susceptibility assessment result obtained in S401 by the weight of the geological disaster impact on the safety of transmission lines and towers obtained in S403 to obtain the geological disaster risk assessment result of the transmission and transformation corridor.

[0053] Further, step S5 includes the following steps:

[0054] S501. Based on the digital elevation model data, the Euclidean distance analysis result map of the transmission line and the Euclidean distance analysis result map of the transmission tower are drawn respectively, and the Euclidean distance analysis result map of the transmission line and the Euclidean distance analysis result map of the transmission tower are merged with the geological disaster grid network of the power grid area to be tested, and the threat level evaluation result map of the transmission line tower and the threat level evaluation result map of the transmission line are obtained;

[0055] S502. Based on the geological disaster grid network of the power grid area to be tested, the maximum value of the Euclidean distance analysis result of the transmission line and the Euclidean distance analysis result of the transmission tower in each grid is taken to obtain the geological disaster threat result map of the power grid area to be tested;

[0056] S503. Overlay the Euclidean distance analysis result map of the transmission line with the Euclidean distance analysis result map of the transmission tower to obtain the geological disaster risk zoning result;

[0057] Wherein, the superposition can be realized by the map algebra tool of ArcGIS, and the superposition method is selected as the product;

[0058] S504. Partition the geological disaster risk zoning result map using the natural breakpoint method to obtain a geological disaster risk zoning map for the power grid area to be tested.

[0059] Furthermore, the geological disaster risk distribution map includes a weighted information map, a geological disaster susceptibility zoning map, a transmission line threat level evaluation result map, a transmission tower threat level evaluation result map, and a typical transmission and transformation corridor geological disaster hazard result map.

[0060] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for assessing the risk of geological disasters in a power grid area based on multi-source data as described in any of the foregoing items is implemented.

[0061] The present invention also provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for assessing the risk of geological disasters in a power grid area based on multi-source data as described in any of the above items is implemented.

[0062] The beneficial effects of the present invention are:

[0063] By integrating multiple data sources such as remote sensing data, geographic information system (GIS), meteorological data and geological survey data, in-depth analysis is carried out to achieve more accurate and efficient geological disaster risk assessment. In addition, this method provides customized risk management suggestions specifically for the disaster risk assessment needs of power grid facilities to enhance the disaster resistance of the power grid. This method not only improves the accuracy and reliability of geological disaster risk assessment, but also generates high-resolution risk distribution maps, providing an effective tool for geological disaster risk management of power grid projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 , a flow chart of a method for assessing geological disaster risks in a power grid area based on multi-source data of the present invention.

[0065] Figure 2 , a flow chart of weighted information amount calculation according to an embodiment of the present invention.

[0066] Figure 3 , a flow chart of transmission line corridor hazard assessment and drawing according to an embodiment of the present invention.

[0067] Figure 4 , heat map of correlation analysis of evaluation factors of an embodiment of the present invention.

[0068] Figure 5 , a geological disaster susceptibility zoning map according to an embodiment of the present invention.

[0069] Figure 6 , a weighted information map of geological hazards according to an embodiment of the present invention.

[0070] Figure 7 , a diagram showing the evaluation results of the threat level of a transmission line tower according to an embodiment of the present invention.

[0071] Figure 8 , a diagram showing the result of the threat level evaluation of a power transmission line according to an embodiment of the present invention.

[0072] Fig. 9 , a geological disaster hazard zoning map for a transmission line corridor according to an embodiment of the present invention.

[0073] Fig.10 , a schematic diagram of the internal structure of the computer device of the present invention.

[0074] Fig.11 , a schematic diagram of the storage medium of the present invention.

[0075] Symbols in the figure: 200 - terminal device, 210 - memory, 211 - RAM, 212 - cache memory, 213 - ROM, 214 - program / utility, 215 - program module, 220 - processor, 230 - bus, 240 - external device, 250 - I / O interface, 260 - network adapter, 300 - program product. DETAILED DESCRIPTION

[0076] In order to enable those skilled in the art to better understand the content of the present invention and make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. The illustrative embodiments of the present invention and their description are only used to explain the present invention and are not intended to further limit the present invention.

[0077] Attached Figure 5 , Attachment Figure 6 , Attachment Figure 7 , Attachment Figure 8 With attached Fig. 9 It is only an illustration of the data results in the embodiment of the present invention and does not represent the actual geographical location.

[0078] Embodiment 1:

[0079] like Figure 1 As shown, an embodiment of the present invention provides a method for assessing the risk of geological disasters in a power grid region based on multi-source data, comprising the following steps:

[0080] S1, data collection and integration, determine a certain power grid area as the power grid area to be tested, first collect basic data, including basic geographic data, digital elevation model (DEM), hydrogeological map, meteorological rainfall data, earthquake catalog, fault data, geological disaster risk point survey data, and detailed information of transmission lines above 110kV. These data provide comprehensive basic information for geological disaster risk assessment;

[0081] Among them, the digital elevation model (DEM) in S1 is derived from the ALOS satellite public version (12.5m) resolution data, with high accuracy and good effect; basic geographic data such as road network and water system are derived from the open data website OpenStreetMap: stratigraphic lithology data are derived from the China Geological Survey's "Spatial Database of 1:250,000 Tectonic Maps of China's Land"; vegetation cover types are derived from the National Qinghai-Tibet Plateau Science Data Center's "Sichuan-Tibet Transportation Corridor Vegetation Coverage (1985-2020) Dataset"; annual precipitation is derived from the National Earth System Science Data Center's "China 1km Resolution Annual Precipitation Dataset", with a temporal resolution of monthly precipitation and a spatial resolution of 0.0083333° (about 1km), ensuring the authority and wide use of the data.

[0082] S2, data preprocessing, after the collection is completed, the data is vectorized to establish a geohazard grid network for a certain power grid area, unified to the same coordinate system, and cleaned to remove outliers and missing values. Next, the DEM is used to extract key terrain feature data, including height, slope, aspect, humidity index and profile curvature, and resampled according to the needs of the geological disaster evaluation unit to meet the resolution requirements of subsequent analysis. The DEM is used to extract the terrain factors of the experimental area and divide the micro-topography types, and the evaluation factors corresponding to each geological disaster in the geological disaster grid network are extracted. The scale of a certain power grid area is 1:50000, and the formula is substituted and rounded to 40m, that is, the data is divided into a grid grid of 40m*40m, and the data whose resolution does not meet the unit grid is resampled.

[0083] S3, collinearity analysis, based on data preprocessing, the Pearson correlation coefficient method is used to conduct collinearity analysis on various evaluation factors in a certain power grid area. The evaluation results are as follows Figure 4 As shown in the figure, factors with high correlation (|r|>0.5), namely slope and rainfall factors, are removed, and the remaining evaluation factors are used to construct an evaluation factor library to ensure the stability and accuracy of the evaluation model; Figure 4The meanings of the evaluation factors are as follows: HL-river distance, GL-road distance, DUANC-fault distance, DZ-earthquake distance, DC-stratum type, SDZS-humidity index, PMQL-profile curvature, PD-slope, GC-elevation, DXQF-terrain undulation, ZBFG-vegetation cover type, PX-slope aspect, JYL-average annual rainfall.

[0084] S4, weight determination and model construction, wherein weight determination includes the following steps:

[0085] A1. Calculate the first weight of each evaluation factor by the CRITIC weight method, which can be expressed as:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] in, is the evaluation factor matrix; For evaluation factors The standard deviation of For evaluation factors The matrix data points; For evaluation factors The sample mean of ; For evaluation factors and evaluation factors The linear correlation coefficient of For evaluation factors No. data points; For evaluation factors No. data points; For evaluation factors The sample mean of ; For evaluation factors conflict; For evaluation factors The amount of weight information; is the evaluation factor obtained by the CRITIC weight method The weight of is the number of evaluation factors;

[0092] A2. Calculate the second weight of each evaluation factor by the independence weight coefficient method, which can be expressed as:

[0093]

[0094]

[0095]

[0096] in, is the mean of the evaluation factor matrix; For evaluation factors matrix; For evaluation factors Multiple correlation coefficients with other evaluation factors; To remove The remaining evaluation factor matrix of is the inverse of the multiple correlation coefficient; is the evaluation factor obtained by the independence weight coefficient method The weight of

[0097] A3. According to the first weight of the evaluation factor and the second weight of the evaluation factor, the weight results of each evaluation factor are calculated by the comprehensive weight method, and the comprehensive weight method can be expressed as:

[0098]

[0099] in, For evaluation factors The weight result of

[0100] A4. A geological disaster susceptibility model is constructed based on the weight results of each evaluation factor. The model comprehensively considers factors such as topography, stratum lithology, geological structure, and hydrogeology to evaluate the susceptibility of geological disasters.

[0101] Furthermore, the construction of the geological disaster susceptibility model includes the following steps:

[0102] B1. Obtain the weight results of each evaluation factor;

[0103] B2. Divide each evaluation factor into intervals, calculate the proportion of the number of grids in the interval to the total number of grids, and the proportion of geological hazards in different intervals;

[0104] B3. Calculate the information content of each evaluation factor in different intervals. The calculation formula can be expressed as:

[0105]

[0106]

[0107] in, For evaluation factors No. The amount of information of the class; is distributed in the evaluation factor No. The number of geological hazard sites within the class; is the total number of geological disaster points in the power grid area to be tested; is the disaster ratio; The power grid area to be tested contains evaluation factors No. The number of rasters in the class; is the total number of grids in the power grid area to be tested; is the grid ratio; is the total information content of the grid unit;

[0108] B4. Construct a geological disaster susceptibility model and calculate the weighted information of each evaluation factor in different intervals. The geological disaster susceptibility model can be expressed as:

[0109]

[0110] in, For evaluation factors The weighted information content of is the evaluation factor of the total information content of the grid unit The amount of original information; For evaluation factors The comprehensive weight of .

[0111] According to the above steps, the weight results of the evaluation factors of the CRITIC method of a certain power grid area as shown in Table 1, the weight results of the evaluation factors of the independence weight coefficient method of a certain power grid area as shown in Table 2, the weight results of the comprehensive evaluation factors of a certain power grid area as shown in Table 3, the information table of a certain power grid area as shown in Table 4 and the weighted information table of a certain power grid area as shown in Table 5 are calculated respectively:

[0112] Table 1 CRITIC evaluation factor weight results for a certain power grid area

[0113]

[0114] Table 2 Evaluation factor weights of a power grid regional independence weight coefficient method

[0115]

[0116] Table 3 Weight results of comprehensive evaluation factors of a certain power grid area

[0117]

[0118] Table 4 Information table of a certain power grid area

[0119]

[0120] Table 5 Weighted information table of a certain power grid area

[0121]

[0122] S5, geological hazard assessment of transmission line corridors, combined with the regional characteristics of the transmission line corridors, such as line direction, line tower location distribution, etc., further assess the potential threat of geological hazards to power grid facilities, and analyze the spatial relationship between geological hazards and power grid facilities, including the following steps:

[0123] S501. Normalize the weighted information of the evaluation factors to obtain a dimensionless geological disaster susceptibility evaluation result;

[0124] S502. Based on the data of a power grid topic, the distances and weights of the transmission lines and towers in a power grid area are delineated and given according to the empirical thresholds. The delineation rules and empirical thresholds are shown in Table 6:

[0125] Table 6 Geological hazard threat levels and weights in the transmission line corridor area

[0126]

[0127] S503. Compare the weights of the transmission lines and transmission towers, and take the maximum weight as the weight of the geological disaster affecting the safety of the transmission lines and towers;

[0128] S504. Multiply the geological disaster susceptibility assessment result obtained in S501 by the weight of the geological disaster impact on the safety of transmission lines and towers obtained in S503 to obtain the geological disaster risk assessment result of the transmission and transformation corridor.

[0129] S6. Drawing of geological hazard distribution map Based on the assessment results, a geological hazard distribution map of a certain power grid area is drawn, that is, Euclidean distance analysis is performed on the transmission lines and transmission towers respectively, and the following are obtained respectively: Figure 7 The results of the evaluation of the threat level of transmission line towers shown in the figure are similar to those in the figure below. Figure 8 The transmission line threat level evaluation result map is shown in the figure. The values ​​of the two maps are then extracted to a grid grid in a certain power grid area. The maximum value of the Euclidean distance analysis result of the transmission line and the Euclidean distance analysis result of the transmission tower in each grid is taken respectively, and finally a geological disaster threat result map is formed.

[0130] Then, the map algebra tool of ArcGIS is used to overlay the Euclidean distance analysis result map of the transmission line with the Euclidean distance analysis result map of the transmission tower, and the product is selected as the overlay method to obtain the geological disaster risk zoning result map.

[0131] Then the natural breakpoint method is used to divide the data into five categories, and the geological disaster hazard zoning map of a certain power grid is obtained, such as Fig. 9 It intuitively displays key information such as the geological disaster susceptibility level, geological disaster type, and impact range, providing scientific decision-making support for power grid planning, construction, and maintenance.

[0132] Example 2

[0133] Further, as a preferred embodiment of the present invention, a terminal device for a method for assessing the risk of geological disasters in a power grid region based on multi-source data is proposed, such as Fig.10 As shown, the terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0134] The memory 210 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 211 and / or a cache memory 212 , and may further include a read-only memory (ROM) 213 .

[0135] Among them, the memory 210 also stores a computer program, and the computer program can be executed by the processor 220, so that the processor 220 executes any one of the above-mentioned methods for assessing the risk of geological disasters in the power grid area based on multi-source data in the embodiments of the present application, and its specific implementation method is consistent with the implementation method and the technical effect achieved in the above-mentioned embodiments, and some contents are not repeated. The memory 210 can also include a program / utility 214 having a group (at least one) of program modules 215, such program modules include but are not limited to: an operating system, one or more application programs, other program modules and program data, each of these examples or some combination may include the implementation of a network environment.

[0136] Accordingly, the processor 220 may execute the above-mentioned computer programs, and may execute the program / utility 214 .

[0137] The bus 230 may be a bus representing one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0138] The terminal device 200 may also communicate with one or more external devices 240 such as keyboards, pointing devices, Bluetooth devices, etc., and may also communicate with one or more devices that can interact with the terminal device 200, and / or communicate with any device (such as a router, a modem, etc.) that enables the terminal device 200 to communicate with one or more other computing devices. Such communication may be performed through an I / O interface 250. In addition, the terminal device 200 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and / or a public network, such as the Internet) through a network adapter 260. The network adapter 260 may communicate with other modules of the terminal device 200 through a bus 230. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0139] Further, as a preferred implementation scheme of this embodiment, a computer-readable storage medium for a method for assessing the risk of geological disasters in a power grid region based on multi-source data is proposed, and instructions are stored on the computer-readable storage medium, and when the instructions are executed by the processor, any of the above-mentioned methods for assessing the risk of geological disasters in a power grid region based on multi-source data is implemented. Its specific implementation method is consistent with the implementation method and the technical effect achieved in the above-mentioned embodiment, and some contents are not repeated here.

[0140] like Fig.11As shown, the program product 300 provided in this embodiment for implementing the above-mentioned method for assessing the risk of geological disasters in power grid areas based on multi-source data can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not limited to this. In this document, the readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The program product 300 can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable 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.

[0141] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, wherein readable program codes are carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium may also be any readable medium other than the readable storage medium, which may send, propagate, or transmit a program used by or in combination with an instruction execution system, an apparatus, or a device. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above. The program code for performing the operation of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device, partially on a remote computing device, or entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0142] The present invention is explained from the viewpoints of purpose of use, efficiency, progress and novelty, and the practical progress it has is in line with the functional enhancement and use requirements emphasized by the Patent Law. The above description and drawings of this application are only preferred embodiments of this application, and are not intended to limit this application. Therefore, all structures, devices, features, etc. that are similar or identical to those of this application, that is, all equivalent replacements or modifications made in accordance with the scope of the patent application of this application, should fall within the scope of protection of the patent application of this application.

[0143] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for assessing the risk of geological disasters in power grid areas based on multi-source data, characterized in that: The following steps are involved: S1. Obtain a multi-source data set of the power grid area to be tested, pre-process the multi-source data set, construct a geological disaster grid network of the power grid area to be tested, and extract the evaluation factor corresponding to each geological disaster in the geological disaster grid network; S2. Based on the evaluation factors obtained in S1, the Pearson correlation coefficient method is used to perform collinearity analysis on the evaluation factors to obtain the Pearson correlation coefficient of each evaluation factor. The evaluation factors whose Pearson correlation coefficient is greater than the Pearson correlation coefficient threshold are recorded as highly correlated factors. After traversing all evaluation factors, all highly correlated factors are eliminated, and an evaluation factor library is established; S3. According to the evaluation factor library obtained in S2, the weight results of each evaluation factor in the evaluation factor library are calculated, and a geological disaster susceptibility model is constructed according to the weight results, wherein the geological disaster susceptibility model includes the weighted information amount of each evaluation factor in the evaluation factor library; S4. Based on the geological disaster susceptibility model obtained in S3 and the regional characteristics of the transmission line corridors in the power grid area to be tested, the geological disaster susceptibility risk of the power grid area to be tested is evaluated; S5. Based on the assessment results obtained in S4, a geological disaster risk distribution map of the power grid area to be tested is drawn to provide risk level assessment information of the power grid area to be tested; The multi-source data set includes historical geological disaster data, basic geographic data, digital elevation model data, hydrogeological map data, meteorological rainfall data, earthquake catalog, geological fault data, geological disaster potential point survey data and power grid special information data; The power grid special data includes power grid ledger, transmission line name, tower number and tower space coordinates; The preprocessing in step S1 includes: constructing a geological disaster grid network of the power grid area to be tested based on the digital elevation model data of the power grid area to be tested, and the network construction formula can be expressed as: in, is the grid cell size; is the denominator of the digital elevation model data scale; The scale of the digital elevation model data of the power grid area to be tested is brought into the grid network construction formula to calculate , and Take an integer as the grid size of the geological hazard grid network; if the accuracy of the digital elevation model data is inconsistent with the grid size of the geological hazard grid network, the digital elevation model data accuracy is sampled to be consistent with the grid size of the geological hazard grid network through the resampling tool of the GIS data processing platform; Step S3 includes the following steps: S301. Calculate the first weight of each evaluation factor by using the CRITIC weight method, which can be expressed as: in, is the evaluation factor matrix; For evaluation factors The standard deviation of For evaluation factors The matrix data points; For evaluation factors The sample mean of ; For evaluation factors and evaluation factors The linear correlation coefficient of For evaluation factors No. data points; For evaluation factors No. data points; For evaluation factors The sample mean of ; For evaluation factors conflict; For evaluation factors The weight information of is the evaluation factor obtained by the CRITIC weight method The weight of is the number of evaluation factors; S302. Calculate the second weight of each evaluation factor by the independence weight coefficient method, which can be expressed as: in, is the mean of the evaluation factor matrix; For evaluation factors matrix; For evaluation factors Multiple correlation coefficients with other evaluation factors; To remove The remaining evaluation factor matrix of is the inverse of the multiple correlation coefficient; is the evaluation factor obtained by the independence weight coefficient method The weight of S303. According to the first weight of the evaluation factor and the second weight of the evaluation factor, the weight result of each evaluation factor is calculated by a comprehensive weight method, and the comprehensive weight method can be expressed as: in, For evaluation factors The weight result of S304. Constructing a geological disaster susceptibility model according to the weight results of each evaluation factor; The construction of the geological disaster susceptibility model comprises the following steps: S304-1. Obtain the weight results of each evaluation factor; S304-2. Divide each evaluation factor into intervals, and calculate the proportion of the number of grids in the interval to the total number of grids and the proportion of geological hazards in different intervals; S304-3. Calculate the information amount of each evaluation factor in different intervals. The calculation formula can be expressed as: in, For evaluation factors No. The amount of information of the class; is distributed in the evaluation factor No. The number of geological hazard sites within the class; is the total number of geological disaster points in the power grid area to be tested; is the disaster ratio; The power grid area to be tested contains evaluation factors No. The number of rasters in the class; is the total number of grids in the power grid area to be tested; is the grid ratio; is the total information content of the grid unit; S304-4. Construct a geological disaster susceptibility model and calculate the weighted information of each evaluation factor in different intervals. The geological disaster susceptibility model can be expressed as: in, For evaluation factors The weighted information content of is the evaluation factor of the total information content of the grid unit The amount of original information; For evaluation factors The comprehensive weight of .

2. A method for assessing the risk of geological disasters in power grid areas based on multi-source data according to claim 1, characterized in that: The collinearity analysis in step S2 adopts the Pearson correlation coefficient method. According to the historical geological disaster data of the power grid area to be tested, the Pearson correlation coefficient threshold is set to , the evaluation factors whose Pearson correlation coefficient is greater than the Pearson correlation coefficient threshold are recorded as highly correlated factors. After traversing all evaluation factors, all The evaluation factors are used to establish an evaluation factor library. The Pearson correlation coefficient method can be expressed as: in, is the Pearson correlation coefficient, and are the observed values ​​of the evaluation factors, and is the average value of the evaluation factor, is the current evaluation factor number, is the number of evaluation factors.

3. A method for assessing the risk of geological disasters in power grid areas based on multi-source data according to claim 1, characterized in that: The step S4 comprises the following steps: S401. Normalize the weighted information of the evaluation factors obtained in S3 to obtain a dimensionless geological disaster susceptibility evaluation result; S402. Based on the grid-specific data, the distances and weights of the transmission lines and towers in the grid area to be tested are respectively delineated and given according to the empirical thresholds; S403. Compare the weights of the transmission lines and transmission towers, and take the maximum weight as the weight of the geological disaster affecting the safety of the transmission lines and towers; S404. Multiply the geological disaster susceptibility assessment result obtained in S401 by the weight of the geological disaster impact on the safety of transmission lines and towers obtained in S403 to obtain the geological disaster risk assessment result of the transmission and transformation corridor.

4. A method for assessing the risk of geological disasters in power grid areas based on multi-source data according to claim 1, characterized in that: Step S5 includes the following steps: S501. Based on the digital elevation model data, the Euclidean distance analysis result map of the transmission line and the Euclidean distance analysis result map of the transmission tower are drawn respectively, and the Euclidean distance analysis result map of the transmission line and the Euclidean distance analysis result map of the transmission tower are merged with the geological disaster grid network of the power grid area to be tested, and the threat level evaluation result map of the transmission line tower and the threat level evaluation result map of the transmission line are obtained; S502. Based on the geological disaster grid network of the power grid area to be tested, the maximum value of the Euclidean distance analysis result of the transmission line and the Euclidean distance analysis result of the transmission tower in each grid is taken to obtain the geological disaster threat result map of the power grid area to be tested; S503. Overlay the Euclidean distance analysis result map of the transmission line with the Euclidean distance analysis result map of the transmission tower to obtain the geological disaster risk zoning result; S504. Partition the geological disaster risk zoning results by natural breakpoint method to obtain a geological disaster risk zoning map of the power grid area to be tested.

5. The method for assessing the risk of geological disasters in power grid areas based on multi-source data according to claim 1, characterized in that: The geological disaster risk distribution map includes a weighted information map, a geological disaster susceptibility zoning map, a transmission line threat level evaluation result map, a transmission tower threat level evaluation result map, and a typical transmission and transformation corridor geological disaster hazard result map.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements a method for assessing the risk of geological disasters in a power grid area based on multi-source data as described in any one of claims 1 to 5.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements a method for assessing the risk of geological disasters in a power grid area based on multi-source data as described in any one of claims 1 to 5.

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