Geological risk early warning method, computer-readable storage medium, and program product

Abstract

The present invention provides a geological risk warning method, a computer-readable storage medium, and a program product, wherein the geological risk warning method may include: obtaining the geological hazard risk level of the area where the high-voltage transmission tower is located, the geological hazard risk level being obtained through a comprehensive evaluation based on the geological hazard influencing factors of the area; generating a meteorological warning index based on the geological hazard risk level, the meteorological warning index including a determination threshold corresponding to different warning levels; collecting one or more meteorological valid data of the area, the meteorological valid data being calculated based on real-time meteorological data and meteorological forecast data; comparing the meteorological valid data with the determination threshold to determine the corresponding warning level; and outputting warning information according to the determined warning level. The warning level obtained by the solution of the present invention reflects the risk situation of the high-voltage transmission line tower area, making the warning level more accurate, facilitating the implementation of targeted warning measures, and thus improving safety and reliability.

Description

Technical Field

[0001] The present invention relates to geographic data processing, and in particular to a geological risk early warning method, a computer-readable storage medium, and a program product. Background Art

[0002] Power corridors are becoming increasingly crowded. High-voltage transmission lines, especially ultra-high-voltage transmission lines, are long overall, with a large number of poles and towers. They pass through areas with complex terrain. The spatial distribution of regional meteorological elements varies significantly, and there is often a risk of geological disasters at the locations of the poles and towers.

[0003] Electricity users are increasingly demanding power reliability, particularly power continuity. The safety and reliability of high-voltage transmission networks face numerous risk factors. In addition to their inherent vulnerability, numerous natural factors pose significant challenges. According to statistics, natural disasters have become the second-largest factor impacting the safety of high-voltage transmission networks, after equipment failure. Geological risks, such as landslides, floods, mudslides, and rockfalls, pose a significant threat to high-voltage transmission networks. Therefore, geological risk early warning in the areas surrounding high-voltage transmission towers is essential for improving the safety and reliability of high-voltage transmission networks.

[0004] Existing geological risk warning systems for high-voltage transmission lines primarily focus on a single type of geological disaster. These systems monitor and control the factors influencing the disaster, analyze and calculate the data, and then predict and warn of the disaster. Each type of warning indicator utilizes a grading system based on indicators from disciplines such as water conservancy, meteorology, and geology. Warnings are graded based on the corresponding observation data, allowing for the implementation of tailored measures.

[0005] However, the above method doesn't take into account the geological characteristics of the locations where high-voltage transmission towers are located. Although the construction sites have been surveyed and meet construction standards during the initial planning phase, risk tolerance varies significantly under different environmental conditions. The existing method for determining warning levels may not truly reflect the risk situation of transmission towers, easily leading to false or missed warnings. Summary of the Invention

[0006] One purpose of the present invention is to be able to accurately assess the geological risk warning level of the high-voltage transmission line tower area.

[0007] In particular, according to one aspect of the present invention, the present invention provides a geological risk early warning method for a high-voltage transmission line tower area, comprising:

[0008] Obtain the geological hazard risk level of the area where the high-voltage transmission tower is located. The geological hazard risk level is obtained through comprehensive evaluation based on the geological hazard influencing factors of the area;

[0009] Generate meteorological warning indicators based on geological disaster risk levels, including thresholds corresponding to different warning levels;

[0010] Collect one or more valid meteorological data of the area, which are calculated based on real-time meteorological data and meteorological forecast data;

[0011] Compare the effective meteorological data with the judgment threshold to determine the corresponding warning level;

[0012] Output warning information according to the determined warning level.

[0013] Optionally, after determining the corresponding warning level, the method further includes:

[0014] When the warning level is higher than the preset level threshold, determine the geological hazard influencing factors affected by the effective meteorological data;

[0015] Re-collect geological hazard influencing factors affected by effective meteorological data to obtain updated factor data;

[0016] Reassess the geological hazard risk level based on the updated factor data to update the geological hazard risk level;

[0017] A hazard visual interface is generated based on the map of the high-voltage transmission line tower area according to the updated geological disaster risk level.

[0018] Optionally, the meteorological valid data includes precipitation valid data; and

[0019] The steps to collect effective precipitation data for a region include:

[0020] Obtain historical precipitation observation data from precipitation observation stations within the area where the high-voltage transmission tower is located and the surrounding preset area;

[0021] Calculate the actual effective precipitation in the area where the high-voltage transmission tower is located based on historical precipitation observation data;

[0022] Obtain precipitation forecast information in the area where high-voltage transmission towers are located;

[0023] Calculate the expected effective precipitation based on precipitation forecast information and actual effective precipitation.

[0024] Optionally, the step of calculating the actual effective precipitation in the area where the high-voltage transmission tower is located based on historical precipitation observation data includes:

[0025] Determine the number of precipitation observing stations;

[0026] In the case where there are multiple precipitation observation stations, the distances from the multiple precipitation observation stations to the high-voltage transmission line towers are obtained respectively;

[0027] Calculate the precipitation observation evaluation data of the area where the high-voltage transmission tower is located in each historical period based on the distance and the historical precipitation observation data of each precipitation observation station;

[0028] Determine the actual impact factor of precipitation observation assessment data according to the length of time from the corresponding historical period to the current time point;

[0029] The actual effective precipitation is calculated based on the precipitation observation evaluation data of each historical period and its actual influencing factors.

[0030] Optionally, the step of calculating the expected effective precipitation based on the precipitation forecast information and the actual effective precipitation includes:

[0031] Configure the first expected impact factor according to the time from the expected time point to the current time point, and use the first expected impact factor to make an expected correction to the actual effective precipitation;

[0032] determining expected precipitation amounts within a plurality of expected time periods from an expected time point to a current time point from the precipitation forecast information;

[0033] According to the length of time from the end point of each expected time period to the expected time point, the respective second expected impact factors are configured;

[0034] Use the second expected impact factor of each expected period to make an expected correction to the corresponding expected precipitation;

[0035] The expected effective precipitation is calculated based on the actual effective precipitation after expected correction and the expected precipitation after expected correction.

[0036] Optionally, the geological hazard influencing factors affected by the effective precipitation data include hydrological conditions and vegetation coverage, and before the step of re-evaluating the geological hazard risk level based on the updated factor data, the following steps are further included:

[0037] Determine whether the weights of hydrological conditions and vegetation coverage need to be increased based on the warning level;

[0038] If so, modify the factor weights of hydrological conditions and vegetation cover based on the available precipitation data.

[0039] Optionally, the effective meteorological data also includes effective wind speed; and

[0040] The steps to collect the effective wind speed for an area include:

[0041] Obtain wind speed measurement data detected by a wind speed detection device in the area where the high-voltage transmission tower is located;

[0042] Acquire temperature and humidity data detected by temperature and humidity sensors in the area where the high-voltage transmission tower is located;

[0043] The wind speed measurement data is corrected using temperature and humidity data to obtain the effective wind speed.

[0044] Optionally, the step of generating a meteorological warning indicator according to the geological disaster risk level includes:

[0045] Obtain correction coefficients corresponding to geological hazard risk levels;

[0046] The preset initial meteorological warning indicators are corrected using the correction coefficient to obtain the judgment thresholds corresponding to different warning levels, and the judgment thresholds decrease accordingly as the geological disaster risk level increases.

[0047] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned geological risk early warning methods for high-voltage transmission line tower areas are implemented.

[0048] According to another aspect of the present invention, a computer program is provided, which, when executed by a processor, implements the steps of any of the above-mentioned geological risk early warning methods for high-voltage transmission line tower areas.

[0049] The geological risk early warning method for the high-voltage transmission line tower area of ​​the present invention generates a meteorological early warning index according to the geological disaster risk level. The geological disaster risk level is obtained through comprehensive evaluation based on the geological hazard influencing factors of the region. The generated meteorological early warning index is consistent with the geological conditions of the high-voltage transmission line tower area, ensuring that the early warning level obtained by using the meteorological early warning index reflects the risk situation of the high-voltage transmission line tower area, making the early warning level more accurate, facilitating targeted early warning measures, avoiding misreporting and omissions of early warnings, improving the risk handling efficiency of power grid operation units, and effectively ensuring the safe and stable operation of the high-voltage transmission network.

[0050] Furthermore, the present invention's geological risk early warning method for high-voltage transmission line tower areas recollects geological hazard influencing factors influenced by effective meteorological data and reassesses the geological hazard risk level when the warning level exceeds a preset threshold. This method promptly updates the geological environment of the high-voltage transmission line tower area, facilitating timely reinforcement and maintenance measures. The hazard visual interface intuitively displays the geological conditions of the high-voltage transmission line tower area, reducing the analytical workload for maintenance personnel and facilitating management.

[0051] Furthermore, the geological risk early warning method for the high-voltage transmission line tower area of ​​the present invention performs targeted analysis and processing on the collected data such as precipitation and wind speed according to their impact on geological risks, thereby ensuring the accuracy of meteorological early warning indicators.

[0052] Based on the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings, those skilled in the art will become more aware of the above and other objects, advantages and features of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0054] Figure 1 is a schematic diagram of a geological risk early warning method for a high-voltage transmission line tower area according to one embodiment of the present invention;

[0055] Figure 2 1 is a schematic diagram of determining the geological disaster risk level in a geological risk early warning method for a high-voltage transmission line tower area according to an embodiment of the present invention;

[0056] Figure 3 A hierarchical structure diagram of dangerous influencing factors and geological data thereof in a geological risk early warning method for a high-voltage transmission line tower area according to an embodiment of the present invention;

[0057] Figure 4 This is a flow chart for collecting effective precipitation data in a geological risk early warning method for a high-voltage transmission line tower area according to one embodiment of the present invention;

[0058] Figure 5 This is a flow chart of calculating effective precipitation in a geological risk early warning method for a high-voltage transmission line tower area according to one embodiment of the present invention;

[0059] Figure 6 This is a flow chart of calculating expected effective precipitation in a geological risk early warning method for a high-voltage transmission line tower area according to one embodiment of the present invention;

[0060] Figure 7 This is a flow chart of an effective wind speed in a collection area in a geological risk early warning method for a high-voltage transmission line tower area according to an embodiment of the present invention;

[0061] Figure 8 is a schematic diagram of a computer-readable storage medium according to one embodiment of the present invention; and

[0062] Figure 9is a schematic diagram of a computer program product according to one embodiment of the present invention. DETAILED DESCRIPTION

[0063] It should be understood by those skilled in the art that the embodiments described below are only some embodiments of the present invention, rather than all embodiments of the present invention, and that these embodiments are intended to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention. Based on the embodiments provided by the present invention, all other embodiments obtained by those skilled in the art without creative effort should still fall within the scope of protection of the present invention.

[0064] Figure 1 FIG. 1 is a schematic diagram of a geological risk early warning method for a high-voltage transmission line tower area according to one embodiment of the present invention. The geological risk early warning method for a high-voltage transmission line tower area generally may include:

[0065] Step S101: obtaining the geological disaster risk level of the area where the high-voltage transmission tower is located. The geological disaster risk level is obtained through comprehensive evaluation based on the geological hazard influencing factors of the area.

[0066] Step S102, a meteorological warning index is generated according to the geological disaster risk level. The meteorological warning index includes judgment thresholds corresponding to different warning levels. The meteorological warning index can be configured according to different types of meteorological data. In some embodiments, the meteorological warning index may include a precipitation warning index and a wind warning index. In addition, the meteorological warning index may also be set according to the environmental characteristics of the high-voltage transmission tower, and additional temperature and humidity indicators (for ice-prone areas and extremely cold areas), effective vegetation coverage, etc. are set. Each meteorological warning index includes multiple numerical ranges, each numerical range corresponds to a warning level, and the upper and lower limits of the numerical range are used as judgment thresholds for comparison with the meteorological valid data collected and calculated.

[0067] Step S103 collects one or more valid meteorological data for the region. The valid meteorological data is calculated based on the real-time meteorological data and the meteorological forecast data. In some embodiments, the valid meteorological data may include: effective precipitation data, effective wind speed, etc. In addition, effective temperature, effective humidity, effective vegetation coverage, etc. may also be set.

[0068] Step S104 compares the effective meteorological data with the judgment threshold to determine the corresponding warning level. In other words, the effective meteorological data is matched with the numerical range of the corresponding meteorological warning indicator. If the effective meteorological data falls within a certain numerical range, the warning level corresponding to the numerical range is used as the determined warning level.

[0069] Step S105: Output warning information according to the determined warning level. In addition to the warning level, the warning information may also include corresponding response measures. Different warning levels can send warning information to different ranges, and the output method of the warning information can be different.

[0070] The above-mentioned step of generating a meteorological warning indicator according to the geological hazard risk level includes: obtaining a correction coefficient corresponding to the geological hazard risk level; using the correction coefficient to correct the preset initial meteorological warning indicator to obtain a judgment threshold corresponding to different warning levels, and the judgment threshold decreases accordingly as the geological hazard risk level increases. For example, in the initial state, the judgment threshold intervals of the four warning levels of level one (dangerous level), level two (relatively dangerous level), level three (low dangerous level), and level four (safe level) are set to (T11, T12), (T21, T22), (T31, T32), and (T41, T42) respectively. For the geological hazard risk level of the non-risk area, the judgment is still made according to the judgment threshold of the above-mentioned preset initial meteorological warning indicator. Accordingly, the geological hazard risk level of the low-risk area is set with a correction coefficient q1, the geological hazard risk level of the medium-risk area is set with a correction coefficient q2, the geological hazard risk level of the relatively high-risk area is set with a correction coefficient q3, and the geological hazard risk level of the high-risk area is set with a correction coefficient q4. For high-risk areas, the threshold intervals for the four warning levels of geological hazard risk can be set to (q4*T11, q4*T12), (q4*T21, q4*T22), (q4*T31, q4*T32), and (q4*T41, q4*T42). By configuring the values ​​of q1 to q4, the thresholds can be lowered, thereby increasing the severity of warnings for higher geological hazard risk levels, ensuring that the warning levels accurately indicate risks.

[0071] The geological risk warning method for the high-voltage transmission line tower area in the above-mentioned embodiment generates a meteorological warning index according to the geological disaster risk level. The geological disaster risk level is obtained through comprehensive evaluation based on the geological hazard influencing factors of the region. The generated meteorological warning index is consistent with the geological conditions of the high-voltage transmission line tower area, ensuring that the warning level obtained using the meteorological warning index reflects the risk situation of the high-voltage transmission line tower area, making the warning level more accurate, facilitating targeted early warning measures, avoiding misreporting and omissions of early warnings, improving the risk handling efficiency of the power grid operation unit, and effectively ensuring the safe and stable operation of the high-voltage transmission network.

[0072] Figure 2The figure is a schematic diagram of determining the geological hazard risk level in a geological risk early warning method for a high-voltage transmission line tower region according to one embodiment of the present invention. The geological hazard risk level can be determined by comprehensively evaluating the geological hazard influencing factors in the region before and after the construction of the high-voltage transmission line tower. In some embodiments, the process of determining the geological hazard risk level may include:

[0073] Step S201: Acquire geological data of the high-voltage transmission line tower area.

[0074] The high-voltage transmission line tower area can be an area obtained by expanding outwards from a line connecting the high-voltage transmission line towers as the center line. For example, a line connecting the high-voltage transmission line towers is established, and the line is used as the center and the area is expanded outwards by a certain distance (2 kilometers) to obtain the high-voltage transmission line tower area. Geological data is classified according to the hazardous influencing factors to which it belongs. The method of this embodiment requires the use of multi-source data, including but not limited to high-precision multispectral satellite remote sensing image data, elevation data, water system data, earthquake and geological structure data, etc.

[0075] Step S202: Calculate the geological data according to the preset factor weights corresponding to each risk influencing factor to obtain an evaluation index score for each risk influencing factor. The factor weights can be configured based on the impact of the geological data type on the risk influencing factor.

[0076] An optional step S202 execution process may be: obtaining a pre-constructed element weight vector for a hazardous impact factor. The element weight vector uses the weight value of each type of geological data in the hazardous impact factor as an element; the geological data score values ​​of the hazardous impact factor are combined into a data vector in the order of the corresponding weight values ​​in the element weight vector; the element weight vector is multiplied with the data vector to obtain the evaluation index score of the hazardous impact factor. By performing the dot product calculation of the vector, the calculation efficiency can be improved and the evaluation process can be accelerated. For example, for a certain hazardous impact factor, it contains n types of geological data, the corresponding scores are a1, a2, a3...an, and the corresponding weights are p1, p2, p3...pn respectively: then the data vector can be a=[a1, a2, a3,...an], and the element weight vector is p=[p1, p2, p3,...pn] T The final evaluation index score of the risk impact factor is

[0077] The process of constructing the element weight vector may include: arbitrarily selecting two geological data from the risk-influencing factors to conduct importance evaluation; forming a judgment matrix based on the results of the importance evaluation; and performing a consistency test on the judgment matrix. The judgment matrix with a qualified consistency test result is normalized to obtain the element weight vector. One execution process of the step of performing a consistency test on the judgment matrix may include: calculating the maximum eigenvector of the judgment matrix; calculating the inconsistency index based on the maximum eigenvector; calculating the ratio of the inconsistency index to a preset random consistency index, and if the ratio is less than a preset threshold, the consistency test result is determined to be qualified. The judgment matrix is ​​generated by comparing two types of geological data. For example, for the risk-influencing factor C and its corresponding geological data factor P, the importance of the factors can be determined by comparing and analyzing the degree of risk impact using a pairwise comparison method.

[0078] The above importance assignment can be obtained by analyzing previous data or by scoring by experts in related fields. After the judgment matrix is ​​constructed, a consistency test is required. After the judgment matrix is ​​constructed, a consistency test is required. Let λmax(A) be the largest eigenvector in matrix A, then the inconsistency index CI can be set as: CI means CI = 0, A is completely consistent; the larger the CI, the more serious the inconsistency of A. The random consistency index RI is set to a preset value. The ratio of the inconsistency index to the preset random consistency index is calculated as CR = CI / RI. When CR < 0.1, the judgment matrix is ​​judged to meet the consistency (the consistency test result is qualified); if it does not meet the requirements, the judgment matrix can be modified and the importance of factors xi and xj can be reset until the judgment matrix meets the consistency. The above preset ratio threshold of 0.1 can be adjusted according to the evaluation requirements.

[0079] After obtaining a judgment matrix with a qualified consistency test result, the judgment matrix is ​​normalized to obtain the final factor weight vector. The normalization process can be as follows: first, the column vectors of the matrix are normalized, that is, the sum of the values ​​of the elements in each column is 1 (the proportional relationship of the elements remains unchanged), then each row is summed and normalized again, that is, the elements in each row are accumulated separately, the accumulated sum is used as the value of each row, and then normalized so that the sum of the values ​​in each row is 1 (the proportional relationship of the elements in each row remains unchanged). The final factor weight vector is obtained.

[0080] Step S203 performs a comprehensive evaluation based on the evaluation index scores of all risk influencing factors to obtain a geological hazard risk assessment value for the high-voltage transmission line tower area. This comprehensive evaluation can also be performed using weighted calculation or other calculation methods. For example, a weight can be assigned to each risk influencing factor based on its impact on the geological hazard risk, and then the geological hazard risk assessment value can be obtained through weighted calculation.

[0081] The execution process of a comprehensive evaluation may include: obtaining a pre-constructed comprehensive weight vector, which uses the weight value of each risk influencing factor as an element; combining the evaluation index scores into an index vector according to the order of the corresponding weight values ​​in the comprehensive weight vector; performing a dot product calculation between the comprehensive weight vector and the index vector to obtain a geological hazard assessment value. Thus, the weight calculation is performed again using a calculation method similar to step S102. For example, for a risk influencing factor containing m, the corresponding evaluation index scores are C1, C2, C3...Cm, and the corresponding weights are P1, P2, P3...Pm, respectively. Then the index vector can be C = [C1, C2, C3,...Cm], and the comprehensive weight vector can be P = [P1, P2, P3,...Pm]. T The final evaluation index score of the risk impact factor is

[0082] The comprehensive weight vector can also be constructed using a similar method to the element weight vector. First, by comparing the impact of various hazard influencing factors on the geological hazard in the high-voltage transmission line tower area, a judgment matrix is ​​constructed. After a consistency check, the matrix is ​​normalized to obtain the comprehensive weight vector. Because the construction process of the comprehensive weight vector is similar to that of the element weight vector, those skilled in the art can implement the construction of the comprehensive weight vector based on the construction method of the element weight vector.

[0083] The obtained geological hazard risk assessment value can be used to generate a risk visualization interface based on a map of the high-voltage transmission line tower area. The resulting risk visualization interface can intuitively show the geological hazard of the high-voltage transmission line tower area.

[0084] Figure 3 This diagram shows the hierarchical structure of risk influencing factors and their geological data in a geological risk early warning method for high-voltage transmission line tower areas according to one embodiment of the present invention. The geological hazard risk assessment value A is derived from a comprehensive evaluation of the evaluation index scores of all risk influencing factors, including: engineering lithology C1, topography C2, hydrological conditions C3, surface cover C4, and geological structure C5.

[0085] Engineering lithology geological data includes lithology type P1, which is determined based on surface lithology and interlayer lithology associated with hazards. Lithology P1 is categorized into five types: loess soft rock and severely weathered granite, soft and hard rock interlayers, jointed hard rock, hard sedimentary rock, and hard igneous rock. Table 1 shows an example of different lithology scoring options. Higher scores indicate greater hazard.

[0086] Table 1

[0087]

[0088] The lithology scores shown in Table 1 are optional example values ​​that can be summarized based on research data on different lithologies.

[0089] The geological data belonging to topography C2 include: slope height P2, slope gradient P3, slope aspect P4, slope shape P5, and slope structure P6.

[0090] Slope height (P2) refers to the height difference between the foot and top of the slope. It is extracted using the DEM (Digital Elevation Model) data of the study area through the spatial analysis function of a GIS (Geographic Information System). An example of an optional slope height score is shown in Table 2, with higher scores indicating greater risk.

[0091] Table 2

[0092] Rock slope height (m) >300 300-100 100-30 30-15 <15 score 5 4 3 2 1 Soil slope height (m) >300 300-100 100-15 15-10 <10 Rating value 5 4 3 2 1

[0093] The slope height score shown in Table 2 is an optional example value, which can be derived from the summary of hazard risk research data for different slope heights.

[0094] The slope P3 can also be extracted from DEM data. An example of an optional slope score is shown in Table 3. The higher the score, the higher the danger.

[0095] Table 3

[0096] Slope (°) 0-5 5-8 8-15 15-25 25-35 35-90 Rating value 1 3 3 5 2 2

[0097] The slope scores shown in Table 3 are optional example values ​​that can be derived from a summary of hazard risk research data for different slopes.

[0098] Slope aspect P4 can be extracted from DEM data and can be categorized into eight categories: north-northeast (0-45°), northeast (45-90°), southeast (90-135°), south-southeast (135-180°), south-southwest (180-225°), southwest (225-270°), northwest (270-315°), and northwest-northwest (315-360°). An example of an optional aspect score is shown in Table 4, where higher scores indicate greater risk.

[0099] Table 4

[0100] Slope aspect (°) 0-45 45-90 90-135 135-180 180-225 225-270 270-315 315-360 Rating value 2 3 3 2 1 4 5 3

[0101] The aspect scores shown in Table 4 are optional example values ​​that can be derived from a summary of hazard risk research data for different aspects.

[0102] Slope shape P5: Slope shape refers to the cross-sectional shape of a slope, which can be categorized as linear, concave, and convex. This can be extracted using GIS spatial analysis tools. Each of these three slope shapes is assigned a corresponding evaluation score, with the peak value determined based on risk research data for each slope type.

[0103] Slope structure P6 Slope structure refers to the relationship between strata and slopes, which can be determined based on field geological surveys and is divided into steeply inclined slopes, moderately reversed slopes, gently inclined slopes, flat-overlapping slopes, oblique slopes, and transverse slopes. An example of an optional slope aspect score is shown in Table 5. A higher score indicates a higher risk.

[0104] Table 5

[0105]

[0106] The slope structure score shown in Table 5 is an optional example value, which can be summarized based on the geological hazard risk research data of different slope structures.

[0107] Geological data pertaining to hydrological condition C3 include surface moisture (P8) and distance to the river (P7). Surface moisture (P8) can be derived from remote sensing imagery. The relative moisture index (RMI 1), relative moisture index (RMI 2), normalized moisture index (NDMI), and modified normalized difference water index (MNDWI) are extracted from the imagery. Principal component analysis (PCA) is then performed using SPSS (Statistical Package for the Social Sciences) software to determine the index representing moisture. For example, if the final moisture value is λi, the assigned score can be calculated based on λi.

[0108] Distance P7 from the river is derived from river data using GIS buffer analysis. The study area is generally categorized into five levels: 0-300m, 300-600m, 600-900m, 900-1200m, and >1200m. An example of a distance P7 score from the river is shown in Table 6, where higher scores indicate greater risk.

[0109] Table 6

[0110] Distance to water system (m) 0-300 300-600 600-900 900-1200 >1200 Rating value 5 4 3 2 1

[0111] The scoring value of the distance to the water system shown in Table 6 is an optional example value, which can be obtained based on the summary of geological hazard risk research data on the distance to the water system.

[0112] The geological data belonging to surface cover C4 include vegetation cover P9. Vegetation cover P9 can be extracted using remote sensing satellite data. Four indices, namely Normalized Difference Vegetation Index (NDVI), Simple Ratio Vegetation Index (SRI), Enhanced Vegetation Index (EVI), and Atmospheric Resistance Vegetation Index (ARVI), are extracted from satellite images. Then, principal component analysis (PCA) is used with SPSS software to determine the index that best represents the surface cover. The final calculated surface cover is divided into five categories: <5%, 5%-10%, 10%-15%, 15%-20%, and >20%.

[0113] The principal component analysis (PCA) method was also used with SPSS software to determine the index that best represents vegetation coverage. The assigned score can be set according to the obtained surface coverage.

[0114] Geological data belonging to geological structure C5 may include: distance to geological faults (P10) and earthquake intensity impact (P11). Geological tectonic activity related to high-voltage transmission line towers includes active faults undergoing neotectonic movement. Tectonic factors are not considered when there is no neotectonic movement. Distance to faults is divided into five categories: 0-300m, 300-500m, 500-100m, 100-1500m, and >1500m. An example of a distance to geological fault (P10) score is shown in Table 7, with higher scores indicating greater risk.

[0115] Table 7

[0116] Distance from fracture (m) 0-300 300-500 500-1000 1000-1500 >1500 Rating value 5 4 3 2 1

[0117] The score of the distance to the geological fault P10 shown in Table 7 is an optional example value, which can be obtained based on the summary of geological hazard risk research data on the distance to the geological fault.

[0118] The impact of earthquake intensity impact P11 on the project is divided into four levels based on a 12-level intensity scale: >9, 9-7, 6-4, and ≤3. An example of a P11 earthquake intensity impact scoring is shown in Table 8, where higher scores represent greater risk.

[0119] Table 8

[0120] Earthquake intensity (magnitude) ≥9 7-9 6-4 ≤3 Rating value 5 4 3 1

[0121] The scores of earthquake intensity impact shown in Table 8 are optional example values ​​that can be summarized based on the geological hazard risk research data of earthquake intensity impact.

[0122] The pre-set scoring values ​​for each type of geological data above reflect the impact of that type of geological data on hazard. After obtaining the geological data for each sampling area, the corresponding scoring value can be obtained using the scoring method described above.

[0123] The geological data is categorized according to the risk factors it affects. The geological data for each risk factor category is then weighted to generate an evaluation index score for that category. The evaluation index scores for multiple risk factors are then comprehensively evaluated to determine a geological hazard risk assessment for the high-voltage transmission tower area. This geological hazard risk assessment can be used to generate a hazard visualization interface based on a map of the high-voltage transmission tower area, thereby intuitively reflecting the geological conditions in the area.

[0124] In some embodiments, the natural breakpoint method can be used to divide the geological hazard risk assessment value into a set number of numerical intervals, each numerical interval corresponding to a geological hazard risk level. For example, the geological hazard risk level can be divided into high-risk areas, relatively high-risk areas, medium-risk areas, low-risk areas, and no-risk areas; for example, the geological hazard risk level can be graded from 1 to 9 from low risk to high risk. The specific number of geological hazard risk levels can be set according to specific management and early warning processing specifications.

[0125] The above comprehensive evaluation process to obtain the geological hazard risk level provides a method for quickly assessing the geological hazard risks at the locations of each tower of the high-voltage transmission line. The assessment is faster and more efficient, and the results are intuitive, providing a data basis for the safety of the high-voltage transmission line, so as to provide targeted early warning forecasts and take protective measures in advance.

[0126] In the geological risk warning method for the high-voltage transmission line tower area in an embodiment of the present invention, the geological disaster risk level is further used to generate a meteorological warning index, so that the meteorological warning index corresponds to the geological conditions of the high-voltage transmission line tower area, thereby accurately warning of the risk.

[0127] In some embodiments, after determining the corresponding warning level, the following steps may be performed: if the warning level is higher than a preset level threshold, determining the geological hazard influencing factors affected by the effective meteorological data; re-collecting the geological hazard influencing factors affected by the effective meteorological data to obtain updated factor data; re-evaluating the geological hazard risk level based on the updated factor data to update the geological hazard risk level; and generating a hazard visual interface based on a map of the high-voltage transmission line tower area according to the updated geological hazard risk level. For example, the warning levels determined based on the effective precipitation data include level one (dangerous level), level two (relatively dangerous level), level three (low dangerous level), and level four (safe level), and the level colors can be represented by red, orange, yellow, and green in sequence. If it is determined that the warning level reaches level two or above, the geological hazard risk level needs to be re-evaluated.

[0128] The above-mentioned recollection of geological hazard influencing factors and reassessment of geological hazard risk level can still be performed using the process of steps S201 to S203. The above-mentioned geological hazard influencing factors affected by the effective meteorological data are selected from all geological hazard influencing factors based on the geological impact generated by the corresponding meteorological data. For example, precipitation (rainfall, snowfall, etc.) has a greater impact on hydrological conditions, surface cover, and slope structure; for another example, wind force has an impact on factors such as surface cover, slope structure, and lithology.

[0129] Figure 4 This is a flow chart of collecting effective precipitation data in a geological risk early warning method for a high-voltage transmission line tower area according to one embodiment of the present invention. The steps of collecting effective precipitation data in the area include:

[0130] Step S401, obtaining historical precipitation observation data of precipitation observation stations in the area where the high-voltage transmission tower is located and the surrounding preset area;

[0131] Step S402, calculating the actual effective precipitation in the area where the high-voltage transmission tower is located based on historical precipitation observation data;

[0132] Step S403, obtaining precipitation forecast information for the area where the high-voltage transmission tower is located;

[0133] Step S404: Calculate the expected effective precipitation based on the precipitation forecast information and the actual effective precipitation.

[0134] Precipitation, especially heavy rain, is a major factor in inducing geological disasters, especially landslides. The impact of precipitation on geological disasters is mainly related to parameters such as rainfall amount, rainfall time, and rainfall intensity. Under different geological conditions, the impact of precipitation is also different. The method of this embodiment sets corresponding precipitation early warning indicators for different geological disaster risk levels, for example, setting four levels: level one (danger level), level two (relatively dangerous level), level three (low dangerous level), and level four (safe level), and each level is set with a judgment threshold, that is, a numerical range.

[0135] The above process of collecting effective precipitation data takes into account both historical precipitation observation data and expected effective precipitation, thus achieving the purpose of providing effective warnings before danger occurs.

[0136] Figure 5 This is a flowchart of calculating effective precipitation in a geological risk early warning method for a high-voltage transmission line tower area according to an embodiment of the present invention. The process of calculating actual effective precipitation in the area where the high-voltage transmission tower is located based on historical precipitation observation data may include:

[0137] Step S501: determine the number of precipitation observation stations.

[0138] During the calculation process, the high-voltage transmission tower to be calculated is referred to as the target tower. The actual effective precipitation in the area where the target tower is located is calculated using measurements from surrounding precipitation observation stations. Precipitation observation stations are typically deployed by meteorological authorities and typically do not coincide with the location of the tower. Therefore, in this embodiment, the actual effective precipitation is calculated using observation data from precipitation observation stations adjacent to the target tower.

[0139] When selecting a precipitation observation station, if the precipitation observation station meets the set geographical conditions, the precipitation observation data of the single precipitation observation station can be used alone. The above geographical conditions can be that the distance between the precipitation observation station and the target tower is close (for example, within 1km) and there are no obvious obstacles between the two (for example, there are no mountains, tall buildings, or rivers in between). If there is no precipitation observation station that meets the above geographical conditions, multiple nearby precipitation observation stations will be selected.

[0140] In some embodiments, the nearest precipitation observation station can be first selected as the first precipitation observation station Y1, and the line from Y1 to the target tower can be used as the baseline. Then, one or more precipitation observation stations can be selected on both sides of the baseline in a set direction (for example, clockwise or counterclockwise), so as to minimize the influence of terrain on precipitation calculation.

[0141] Step S502: When there are multiple precipitation observation stations, respectively obtain the distances from the multiple precipitation observation stations to the high-voltage transmission line towers.

[0142] Step S503 calculates the precipitation observation assessment data for the area where the high-voltage transmission tower is located during each historical period based on the distance and the historical precipitation observation data of each precipitation observation station. For example, three precipitation observation stations, Y1, Y2, and Y3, are selected. The distances from the target tower are L1, L2, and L3, respectively, and the precipitation observation values ​​during a certain period are P1, P2, and P3, respectively. The formula for calculating the precipitation observation assessment data P for the area where the high-voltage transmission tower is located during this period can be:

[0143] P=((L2+L3)*P1+(L1+L3)*P2+(L1+L2)*P3)) / (2*(L1+L2+L3)).

[0144] Step S504 determines the actual impact factor of the precipitation observation and evaluation data based on the duration from the corresponding historical period to the current time point. For example, in the case of hourly precipitation determination, the precipitation observation and evaluation data calculated within one hour before the current moment is PA0, the precipitation observation and evaluation data calculated for the previous two hours is PA1, the precipitation observation and evaluation data calculated for the previous three hours is PA2, and so on. The precipitation observation and evaluation data calculated for the previous hour 24 hours ago is PA23. Since the longer the time from the current moment, the more gradually the impact on water storage capacity, groundwater permeability, and surface water flow will be attenuated, the coefficients (i.e., actual impact factors) can be configured for precipitation observation and evaluation data of different durations based on the duration and the specific attenuation effect. For example, a coefficient k1 can be set for PA1, a coefficient k2 can be set for PA2, and a coefficient k23 can be set for PA23. k1, k2, ..., k23 can gradually decrease.

[0145] Step S505: Calculate the actual effective precipitation based on the precipitation observation evaluation data and its actual influencing factors for each historical period. Still taking the above hourly precipitation determination as an example, the formula for the final calculated actual effective precipitation PA can be:

[0146] PA=PA0+k1*PA1+k2*PA2+k3*PA3+……+k23*PA23.

[0147] Figure 6 This is a flowchart of calculating expected effective precipitation in a geological risk early warning method for a high-voltage transmission line tower area according to one embodiment of the present invention. The step of calculating expected effective precipitation based on precipitation forecast information and actual effective precipitation includes:

[0148] Step S601 configures a first expected impact factor based on the duration from the expected time point to the current time point, and uses the first expected impact factor to perform an expected correction on the actual effective precipitation. As the duration from the expected time point to the current time point increases, the impact of the current actual effective precipitation on the expected time point may decrease. Therefore, by configuring the first expected impact factor and performing an expected correction on the actual effective precipitation, the actual effective precipitation after the expected correction can reflect the geological impact of the current precipitation on the expected time point.

[0149] The formula for the corrected actual effective precipitation PAf can be:

[0150] PAf=m1*PA, where m1 is the first expected impact factor, which is set according to the time from the expected time point to the current time point and the degree to which the geological conditions are affected by precipitation.

[0151] Step S602: Determine the expected precipitation within multiple expected time periods from the expected time point to the current time point from the precipitation forecast information. This embodiment can calculate the expected precipitation at multiple expected time points, such as the expected precipitation after 24 hours, the expected precipitation after 36 hours, the expected precipitation after 48 hours, and the expected precipitation after 72 hours. The period from the expected time point to the current time point can be divided into multiple expected time periods based on the forecast period of the precipitation forecast. Taking a precipitation forecast with a 12-hour period as an example, the expected precipitation after 24 hours includes two expected time periods, the expected precipitation after 36 hours includes three expected time periods, and so on.

[0152] Step S603 configures a second expected impact factor based on the duration from the end point of each expected period to the expected time point. Taking the 12-hour precipitation forecast as an example, the expected precipitation after 36 hours encompasses three expected periods. Second expected impact factors mf1, mf2, and mf3 can be configured for the expected precipitation within these three expected periods, Pf1, Pf2, and Pf3.

[0153] Step S604: Use the second expected impact factor for each expected time period to perform an expected correction on the corresponding expected precipitation. The expected precipitation amounts Pf1, Pf2, and Pf3 for the three expected time periods can be corrected to Pf1*mf1, Pf2*mf2, and Pf3*mf3, respectively. The final corrected expected precipitation amount Pf can be calculated using the formula: Pf = Pf1*mf1 + Pf2*mf2 + Pf3*mf3.

[0154] Step S605: Calculate the expected effective precipitation based on the actual effective precipitation after expected correction and the expected precipitation after expected correction. The final calculated expected effective precipitation Pftotal=PAf+Pf.

[0155] Through the above calculations, we can accurately estimate the impact of precipitation on geological disasters in the future, and further accurately determine the warning level.

[0156] The geological hazard influencing factors affected by the effective precipitation data may include hydrological conditions and vegetation coverage, and before the step of re-evaluating the geological disaster risk level based on the updated factor data, it also includes: judging whether the factor weights of hydrological conditions and vegetation coverage need to be increased based on the warning level; if so, modifying the factor weights of hydrological conditions and vegetation coverage based on the effective precipitation data. That is, redetermining the factor weight vector. The basis for judging whether the factor weights of hydrological conditions and vegetation coverage need to be increased based on the warning level can be that if the warning level reaches the set level, for example, reaches a more dangerous level, the factor weights of hydrological conditions and vegetation coverage can be automatically increased. In other embodiments, the above-mentioned operation of increasing the weight can be triggered by the management personnel after the warning information is issued.

[0157] Extreme temperature fluctuations, such as freeze-thaw cycles, can cause changes in the physical properties of soil and rock, increasing the risk of geological instability. In cold regions, permafrost thawing or glacial retreat can lead to ground subsidence or landslides. Increased humidity can affect soil moisture content, particularly under conditions of sustained high humidity, increasing soil saturation and the likelihood of landslides. High winds, which weaken the ground surface, also have their effects on geological factors affected by extreme temperatures and humidity.

[0158] In some embodiments, the meteorological validity data may also include effective wind speed. Figure 7 This is a flow chart of collecting effective wind speed in a region in a method for early warning of geological risks in a high-voltage transmission line tower region according to one embodiment of the present invention. The step of collecting effective wind speed in a region may include:

[0159] Step S701: obtaining wind speed measurement data detected by a wind speed detection device in an area where a high-voltage transmission tower is located.

[0160] Step S70: Acquire temperature and humidity data detected by temperature and humidity sensors in the area where the high-voltage transmission tower is located.

[0161] Step S703 uses the temperature and humidity data to correct the wind speed measurement data to obtain an effective wind speed. This correction can be performed by multiplying the wind speed measurement data by a set coefficient if the temperature and humidity data reach preset extremes, such as extreme heat or extreme cold. This results in an effective wind speed greater than the wind speed measurement data, thereby fully accounting for the effects of high temperatures or extreme cold on ice formation, rock weathering, and the like.

[0162] The process of wind warning is similar to the precipitation warning process mentioned above and will not be elaborated here.

[0163] This embodiment also provides a computer-readable storage medium 10 and a computer program product 30 according to an embodiment of the present invention. Figure 8 FIG2 is a schematic diagram of a computer-readable storage medium 10 according to an embodiment of the present invention. The computer-readable storage medium 10 stores a computer program 11, which, when executed by a processor 20, implements the steps of the geological risk early warning method for a high-voltage transmission line tower area according to any of the above embodiments.

[0164] Figure 9 FIG3 is a schematic diagram of a computer program product 30 according to an embodiment of the present invention. The computer program product 30 includes a computer program 11, which, when executed by a processor 20, implements the steps of the geological risk early warning method for a high-voltage transmission line tower area according to any of the above embodiments.

[0165] For the purposes of the present description, the computer-readable storage medium 10 can be any device that can contain, store, communicate, propagate, or transport a program for use with or in conjunction with an instruction execution system, device, or apparatus. More specific examples (not an exhaustive list) of computer-readable storage medium 10 include the following: an electrical connection having one or more wires (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM).

[0166] The computer program 11 described herein can be downloaded from the computer-readable storage medium 10 to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions to the computer-readable storage medium 10 in each computing / processing device for storage.

[0167] The computer program 11 for performing operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages.

[0168] The processor 20 that executes the above-mentioned computer program 11 can be any suitable processing device (e.g., processor core, microprocessor, ASIC, FPGA, controller, microcontroller, etc.) and can be one processor 20 or multiple processors 20 that are operably connected.

[0169] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0170] The flowchart provided in this embodiment is not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in all every case. In addition, the method may include additional operations. Within the scope of the technical ideas provided by the method of this embodiment, additional changes can be made to the above method.

[0171] At this point, those skilled in the art will recognize that, although a number of exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications consistent with the principles of the present invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and deemed to cover all such other variations or modifications.

Claims

1. A geological risk early warning method for a high-voltage transmission line tower area, characterized in that include: Obtaining a geological disaster risk level for an area where a high-voltage transmission tower is located, the geological disaster risk level being obtained through a comprehensive evaluation based on geological hazard influencing factors of the area; Generate a meteorological warning indicator according to the geological disaster risk level, wherein the meteorological warning indicator includes a determination threshold corresponding to different warning levels; Collecting one or more valid meteorological data of the area, wherein the valid meteorological data is calculated based on real-time meteorological data and meteorological forecast data; Comparing the effective meteorological data with the determination threshold to determine the corresponding warning level; Outputting warning information according to the determined warning level, and After the step of determining the corresponding warning level, the method further includes: When the warning level is higher than a preset level threshold, determining geological hazard influencing factors affected by the effective meteorological data; Recollecting geological hazard influencing factors affected by the effective meteorological data to obtain updated factor data; Reassessing the geological hazard risk level according to the updated factor data to update the geological hazard risk level; A hazard visual interface is generated based on the map of the high-voltage transmission line tower area according to the updated geological disaster risk level.

2. The geological risk early warning method for the high-voltage transmission line tower area according to claim 1, wherein: The said effective meteorological data includes effective precipitation data; and The step of collecting the effective precipitation data in the area includes: Obtaining historical precipitation observation data of precipitation observation stations within the area where the high-voltage transmission tower is located and within a surrounding preset area; Calculating the actual effective precipitation in the area where the high-voltage transmission tower is located based on the historical precipitation observation data; Obtaining precipitation forecast information for the area where the high-voltage transmission tower is located; The expected effective precipitation is calculated based on the precipitation forecast information and the actual effective precipitation.

3. The geological risk early warning method for the high-voltage transmission line tower area according to claim 2, wherein: The step of calculating the actual effective precipitation in the area where the high-voltage transmission tower is located based on the historical precipitation observation data includes: determining the number of said precipitation observing stations; In the case where there are multiple precipitation observation stations, respectively obtaining the distances from the multiple precipitation observation stations to the high-voltage transmission line tower; Calculate the precipitation observation evaluation data of the area where the high-voltage transmission tower is located in each historical period according to the distance and the precipitation observation historical data of each precipitation observation station; Determine the actual impact factor of the precipitation observation evaluation data according to the length of time from the corresponding historical period to the current time point; The actual effective precipitation is calculated based on the precipitation observation evaluation data and its actual influencing factors for each historical period.

4. The geological risk early warning method for high-voltage transmission line tower areas according to claim 2, wherein: The step of calculating the expected effective precipitation based on the precipitation forecast information and the actual effective precipitation includes: configuring a first expected impact factor according to the time from the expected time point to the current time point, and using the first expected impact factor to make an expected correction to the actual effective precipitation; Determining the expected precipitation in a plurality of expected time periods from the expected time point to the current time point from the precipitation forecast information; According to the time length from the end point of each expected time period to the expected time point, respectively configure the respective second expected impact factors; Using the second expected impact factor of each expected period to perform expected correction on the corresponding expected precipitation; The expected effective precipitation is calculated based on the actual effective precipitation after the expected correction and the expected precipitation after the expected correction.

5. The geological risk early warning method for high-voltage transmission line tower areas according to claim 2, wherein: The geological hazard influencing factors affected by the effective precipitation data include hydrological conditions and vegetation coverage, and before the step of re-evaluating the geological hazard risk level according to the updated factor data, the method further includes: Determining whether the weights of the hydrological conditions and the vegetation coverage need to be increased according to the warning level; If so, modify the element weights of the hydrological conditions and the vegetation coverage according to the effective precipitation data.

6. The geological risk early warning method for a high-voltage transmission line tower area according to any one of claims 1 to 5, wherein: The effective meteorological data also includes effective wind speed; and The step of collecting the effective wind speed in the area includes: Obtaining wind speed measurement data detected by a wind speed detection device in the area where the high-voltage transmission tower is located; Acquire temperature and humidity data detected by a temperature and humidity sensor in the area where the high-voltage transmission tower is located; The wind speed measurement data is corrected using the temperature and humidity data to obtain the effective wind speed.

7. The geological risk early warning method for a high-voltage transmission line tower area according to any one of claims 1 to 5, wherein: The step of generating a meteorological warning indicator according to the geological disaster risk level includes: Obtaining a correction coefficient corresponding to the geological hazard risk level; The correction coefficient is used to correct the preset initial meteorological warning index to obtain the judgment threshold corresponding to the different warning levels, and the judgment threshold decreases accordingly as the geological disaster risk level increases.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that When the computer program is executed by a processor, the steps of the geological risk early warning method for a high-voltage transmission line tower area according to any one of claims 1 to 7 are implemented.

9. A computer program product, characterized in that The method comprises a computer program which, when executed by a processor, implements the steps of the geological risk early warning method for a high-voltage transmission line tower area according to any one of claims 1 to 7.

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