CN115983093A - Power transmission line tower footing geological disaster prediction method and device - Google Patents
Power transmission line tower footing geological disaster prediction method and device Download PDFInfo
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Abstract
The invention relates to a power transmission line tower footing geological disaster prediction method and device, belongs to the technical field of electric power, and solves the problem that the existing method is insufficient in early warning of geological disaster. The method comprises the following steps: identifying the influence factors of the power transmission line tower footing geological disaster to determine the main reason of the geological disaster; collecting and sorting historical data of geological disasters, and identifying and counting meteorological conditions and geological conditions under different geological disasters; analyzing different rainfall capacities of various regions, and constructing rainfall thresholds of different types of geological disasters by combining the rainfall capacities when the historical geological disasters occur; constructing a geological disaster risk prediction model, and generating a geological disaster risk prediction result of the complex weather elements; and counting historical disasters and early rainfall, establishing a short-term meteorological early warning criterion of the power transmission line tower footing geological disasters by combining the regional geological disaster proneness zoning results, and performing short-term meteorological early warning on the power transmission line geological disasters. The accuracy of early warning work is promoted.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a power transmission line tower footing geological disaster prediction method and device.
Background
Over the years of development, ultra-long power transportation networks spanning a wide range have been established. Along the way, people inevitably need to pass through areas with complex geological terrain, severe environmental conditions and changeable climate, such as Chongshan mountains and high mountains and canyons. The transmission tower serving as a connection node between the high-voltage transmission line and the ground is often required to be built at a mountain steep slope in a suburb region with little smoke to reduce the transmission distance and reduce the electric loss and avoid the mutual influence with human activities. Such topographical conditions increase the likelihood of a geological disaster. Meanwhile, the influence of various construction and excavation activities on the periphery of the line easily causes deformation and damage of a tower foundation, and the safe operation of the line is seriously threatened.
In the research in the field of geological disasters, the risk assessment research can comprehensively consider the dangerousness and potential consequences of the disasters, and then pertinently carry out risk control and guide the development of geological disaster prevention and control work; the early warning research can strive for precious time for geological disaster emergency risk avoidance and emergency prevention and control disposal, and avoid causing serious casualties and property loss.
Most of the current methods fully consider the intrinsic factors such as the landform, the geological condition, the mechanical property and the like of the side slope to research the corresponding relation of rainfall, rainfall intensity, rainfall process and geological disaster on the space-time distribution, and further develop the regional geological disaster early warning and forecasting research.
Disclosure of Invention
In view of the above analysis, the embodiments of the present invention provide a method and an apparatus for predicting a geological disaster of a tower footing of a power transmission line, so as to solve the problem that the existing method is insufficient in early warning of occurrence of a geological disaster.
On one hand, the embodiment of the invention provides a power transmission line tower footing geological disaster prediction method, which comprises the following steps: identifying the influence factors of the power transmission line tower footing geological disaster to determine the main reason of the geological disaster; collecting and sorting historical data of the geological disaster, and identifying and counting meteorological conditions and geological conditions under different geological disasters; analyzing different rainfall capacities of various regions based on rainfall data of meteorological satellites and cloud charts, and constructing rainfall thresholds of different types of geological disasters by combining the rainfall capacities when historical geological disasters occur; constructing a geological disaster risk prediction model, and generating a geological disaster risk prediction result of the complex weather elements through the prediction model; and counting historical disasters and early rainfall, establishing a short-term meteorological early warning criterion of the power transmission line tower footing geological disasters by combining the regional geological disaster proneness zoning results, and carrying out short-term meteorological early warning on the power transmission line geological disasters according to numerical meteorological forecasting.
The beneficial effects of the above technical scheme are as follows: by collecting data, the rainfall data is processed by applying a particle swarm and genetic hybrid optimization algorithm, effective data is extracted, and invalid and redundant data are eliminated. Based on a risk assessment theory, the geological disaster development characteristics, the regional geological environment conditions and the rainfall inducing factors along the power transmission line are integrated, and a quantitative assessment model for the geological disaster risk of the power transmission line is provided. Establishing short-term meteorological early warning criterion of the geological disaster of the power transmission line based on statistical analysis of historical geological disaster and rainfall data; and carrying out short-term meteorological early warning on the geological disaster of the power transmission line according to the numerical meteorological forecast.
Based on the further improvement of the method, the step of identifying the influence factors of the power transmission line tower footing geological disaster to determine the main reasons of the geological disaster comprises the following steps: internal influence factors of the power transmission line tower footing geological disaster comprise deep valley cutting, steep valley slope, fracture, fold development, earthquake activity intensity, rock weathering intensity, rock breakage and thickness and distribution range of loose covering layers on slopes; external influence factors of the power transmission line tower footing geological disaster comprise rainfall, earthquake, snow melting, unreasonable human engineering activities and reservoir level fluctuation, wherein the rainfall is determined to be the main reason of the geological disaster according to statistical analysis.
Based on the further improvement of the method, the current data of the geological disasters are collected and sorted, and the identification and statistics of meteorological conditions and geological conditions under different geological disasters comprise the following steps: the process of inducing geological disasters by rainfall is a dynamic evolution process which continuously develops along with time, wherein the dynamic evolution process sequentially comprises an infiltration stage, a saturation stage, a degradation stage and a destabilization stage; performing data statistics by combining the dynamic evolution process to form a data set, wherein the data set comprises specific time of occurrence of geological disasters, rainfall, geographic positions of towers, disaster types, induction factors and investigation dates; and processing the rainfall data by applying a particle swarm and genetic hybrid optimization algorithm to extract effective data and eliminate ineffective and redundant data.
Based on the further improvement of the method, based on rainfall data of meteorological satellites and cloud charts, different rainfall amounts of each region are analyzed, and in combination with the rainfall amount when historical geological disasters occur, the construction of rainfall thresholds of different types of geological disasters comprises the following steps: acquiring rainfall event data points inducing geological disasters according to the meteorological satellite and the cloud chart, and performing fitting analysis on the rainfall event data points inducing the geological disasters through the following formula:
E＝c+α×D β ；
wherein E is the accumulated rainfall, D is the duration of the rainfall event, and alpha, beta and c are statistical parameters; and drawing a scatter diagram by taking the rainfall event data points as samples, and respectively fitting rainfall threshold curves when the rainfall landslide occurrence frequency is 5% and 50% according to the scatter diagram, wherein the process of inducing the geological disaster by the rainfall event is not an average effect.
Based on a further improvement of the above method, the process of inducing the geological disaster by the rainfall event is not an average effect, comprising: the rainfall of the day of the geological disaster plays a full role in the occurrence of the geological disaster; rainfall before the geological disaster is effective to the geological disaster part, wherein the rainfall event threshold index inducing the geological disaster is represented by effective rainfall, and the effective rainfall is calculated by the following formula:
R c ＝R 0 +αR 1 +α 2 R 2 +…+α n R n ；
wherein R is c For the effective rainfall, R 0 For geological disasters, rainfall on the day, R n The rainfall n days before geological disaster, alpha is attenuation coefficient, and n is days.
Based on the further improvement of the method, the construction of the geological disaster risk prediction model comprises the following steps: according to the danger probability of the geological disaster, the vulnerability of a disaster bearing body and the direct and indirect economic losses caused by the damage of the tower footing of the power transmission pole under the action of the geological disaster, a geological disaster risk prediction model is constructed, wherein the geological disaster risk prediction model comprises the following steps:
R＝H×V×E；
wherein R is the risk of the power transmission line tower footing geological disaster, H is the danger probability, V is the vulnerability, and E is the comprehensive loss; calculating the hazard probability of the geological disaster by combining the E-D rainfall threshold model and the rainfall threshold transcendental probability; the vulnerability V is the degree of damage to a disaster bearing body caused by geological disasters, wherein the loss degree of the disaster bearing body when the geological disasters with certain intensity occur is represented by 0-1, 0 represents no loss, and 1 represents complete loss; and the comprehensive loss comprises direct economic loss and indirect economic loss, wherein the direct economic loss is reduction of the scale of the transmission power caused by power failure time, and the indirect economic loss causes extremely adverse social influence on large-scale power failure, and the indirect economic loss is determined by artificial evaluation.
Based on further improvement of the method, the step of calculating the danger probability of the geological disaster by combining the E-D rainfall threshold model and the rainfall threshold transcendental probability comprises the following steps: in N groups of random numbers there are M times F s Results of ≦ 1 occur when N is sufficiently large, according to F s Acquiring the hazard probability of the geological disaster at a frequency less than or equal to 1:
when N is sufficiently large, its mean value μ F Sum standard deviation σ F Respectively as follows:
the danger probability of the geological disaster is as follows:
P f ＝1-Φ(β)；
where Φ (β) is the result F from the Monte Carlo simulation i Calculated stability factor, F s Representing the stability factor of the geological disaster when F s The time disaster is in an unstable state less than or equal to 1; calculating the vulnerability V of the transmission line tower through the following formula:
wherein x is the horizontal displacement of the earth's surface obtained by numerical simulation, and λ, k are constants determined by fitting a curve.
Based on further improvement of the method, the steps of counting historical disasters and early rainfall, establishing a power transmission line tower footing geological disaster weather early warning criterion index by combining the area geological disaster proneness zoning result, and carrying out power transmission line geological disaster short-term weather early warning according to numerical weather forecast comprise: the method comprises the steps that a geological disaster meteorological early warning criterion index of each subarea is constructed on the basis of early rainfall of historical geological disaster points, wherein the early warning criterion index comprises geological disaster easiness, early 3-day monitoring rainfall and 24-hour future forecasting rainfall, the geological disaster easiness is the degree of describing geological disaster forming condition combination favorable for geological disaster occurrence, and the 24-hour future forecasting rainfall and the early 3-day monitoring rainfall are updated in real time by connecting a rainfall monitoring and rainfall forecasting system of a meteorological department; establishing three-dimensional risk early warning matrixes of different partitions by combining the effective rainfall in the early 3 days and the predicted rainfall in the future 24 hours; and taking the danger degree of the disaster as a boundary to obtain a final early warning result.
On the other hand, the embodiment of the invention provides a power transmission line tower footing geological disaster prediction device, which comprises the following components: the influence factor determination module is used for identifying influence factors of the power transmission line tower footing geological disaster so as to determine a main reason for generating the geological disaster; the data acquisition and identification module is used for collecting and sorting historical data of geological disasters, and identifying and counting meteorological conditions and geological conditions under different geological disasters; the rainfall threshold determination module is used for analyzing different rainfall capacities of various regions based on rainfall data of meteorological satellites and cloud charts, and constructing rainfall thresholds of different types of geological disasters by combining the rainfall capacities when historical geological disasters occur; the risk prediction model is used for constructing a geological disaster risk prediction model and generating a geological disaster risk prediction result of the complex weather elements through the prediction model; and the early warning module is used for counting historical disasters and early rainfall, establishing short-term meteorological early warning criteria of the power transmission line tower footing geological disasters by combining the easy-to-send zoning results of the regional geological disasters, and carrying out short-term meteorological early warning on the power transmission line geological disasters according to numerical meteorological forecasting.
Based on further improvement of the device, the influence factor determination module is used for determining internal influence factors of the power transmission line tower footing geological disaster and external influence factors of the power transmission line tower footing geological disaster, wherein the internal influence factors of the power transmission line tower footing geological disaster comprise deep valley cutting, steep valley slope, fracture, fold development, strong seismic activity degree, strong rock weathering degree, rock mass breakage, thickness and distribution range of loose covering layers on the slope; and external influence factors of the power transmission line tower footing geological disaster comprise rainfall, earthquake, snow melting, unreasonable human engineering activities and reservoir level fluctuation, wherein the rainfall is determined to be the main cause of the geological disaster according to statistical analysis.
Compared with the prior art, the invention can realize at least one of the following beneficial effects:
1. data is first collected and collated. When the complex mass data is processed, advanced computer particle swarm algorithm and genetic algorithm are introduced to process the data, the extracted effective data are removed from invalid and redundant data, the data processing efficiency can be improved, and meanwhile the accuracy of early warning work can be improved.
2. Aiming at the aspect of rainfall threshold calculation, the effective rainfall model is combined with the rainfall threshold model, so that rainfall events inducing geological disasters can be better analyzed, and the analysis result is more reasonable.
3. In the invention, the regional landslide easiness, the early-stage 3-day effective rainfall and the future 24-hour rainfall are simultaneously considered, a three-dimensional scale evaluation matrix is established and used as a criterion, and the rainfall prediction is used as input data, so that a rainfall classification chart can be obtained. And inputting the rainfall classification and easiness result into a risk early warning criterion matrix to obtain a short-term weather risk early warning result. And (4) taking the rainfall classification and the proneness subarea as input data, and judging through a meteorological risk criterion matrix to obtain a final geological disaster early warning result.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a flowchart of a power transmission line tower footing geological disaster prediction method according to an embodiment of the invention;
FIG. 2 is a rainfall threshold graph according to an embodiment of the invention;
FIG. 3 is a graph illustrating vulnerability of a geological disaster degree and a damage degree of a disaster-bearing body under the action of the geological disaster according to an embodiment of the present invention;
FIG. 4 is a flow chart of the hazard probability of a geological disaster according to an embodiment of the present invention;
FIG. 5 is a flow chart of pre-warning of geological disaster risk prediction results based on complex weather elements according to an embodiment of the present invention; and
fig. 6 is a block diagram of a power transmission line tower footing geological disaster prediction device according to an embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Referring to fig. 1, an embodiment of the present invention discloses a power transmission line tower footing geological disaster prediction method, including: in step S102, identifying influence factors of the power transmission line tower footing geological disaster to determine a main cause of the geological disaster; in step S104, collecting and sorting historical data of geological disasters, and identifying and counting meteorological conditions and geological conditions under different geological disasters; in step S106, analyzing different rainfall capacities of each region based on rainfall data of meteorological satellites and cloud charts, and constructing rainfall thresholds of different types of geological disasters by combining the rainfall capacities when historical geological disasters occur; in step S108, a geological disaster risk prediction model is constructed, and a geological disaster risk prediction result of the complex weather elements is generated through the prediction model; and in step S110, counting historical disasters and early rainfall, establishing short-term meteorological early warning criteria for the power transmission line tower footing geological disasters by combining the regional geological disaster proneness zoning results, and performing short-term meteorological early warning on the power transmission line geological disasters according to numerical meteorological forecasts.
Compared with the prior art, the method for predicting the power transmission line tower footing geological disasters, provided by the embodiment, processes rainfall data by adopting a particle swarm and genetic hybrid optimization algorithm based on data collection, extracts effective data, and eliminates ineffective and redundant data. Based on a risk assessment theory, the geological disaster development characteristics, the regional geological environment conditions and the rainfall induction factors along the power transmission line are integrated, and a quantitative assessment model for the geological disaster risk of the power transmission line is provided. Establishing short-term meteorological early warning criterion of the geological disaster of the power transmission line based on statistical analysis of historical geological disaster and rainfall data; and carrying out short-term meteorological early warning on the geological disaster of the power transmission line according to numerical meteorological forecasting.
Hereinafter, a power transmission line tower footing geological disaster prediction method according to an embodiment of the present invention will be described with reference to fig. 1.
In step S102, the influence factors of the power transmission line tower footing geological disaster are identified to determine the main cause of the geological disaster. Specifically, identifying the influencing factors of the power transmission line tower footing geological disaster to determine the main causes of the geological disaster comprises the following steps: internal influence factors of power transmission line tower footing geological disasters comprise deep valley cutting, steep valley slope, fracture, fold development, seismic activity intensity, rock weathering intensity, rock mass breakage and thickness and distribution range of loose covering layers on the slope; external influence factors of the power transmission line tower footing geological disaster comprise rainfall, earthquake, snow melting, unreasonable human engineering activities and reservoir level fluctuation, wherein the rainfall is determined to be the main cause of the geological disaster according to statistical analysis.
In step S104, the historical data of the geological disaster is collected and collated, and the meteorological conditions and geological conditions under different geological disasters are identified and counted. Specifically, collecting and sorting current data of geological disasters, and identifying and counting meteorological conditions and geological conditions under different geological disasters comprise: the process of inducing geological disasters by rainfall is a dynamic evolution process which continuously develops along with time, wherein the dynamic evolution process sequentially comprises an infiltration stage, a saturation stage, a degradation stage and a destabilization stage; performing data statistics by combining a dynamic evolution process to form a data set, wherein the data set comprises specific time of occurrence of geological disasters, rainfall, geographic positions of towers, disaster types, induction factors and investigation dates; and processing the rainfall data by applying a particle swarm and genetic hybrid optimization algorithm to extract effective data and eliminate ineffective and redundant data.
In step S106, different rainfall amounts of each region are analyzed based on rainfall data of the meteorological satellite and the cloud map, and rainfall thresholds of different types of geological disasters are constructed in combination with the rainfall amount when the historical geological disasters occur. Specifically, based on rainfall data of meteorological satellites and cloud charts, different rainfall amounts of all regions are analyzed, and in combination with the rainfall amount when historical geological disasters occur, the construction of rainfall thresholds of different types of geological disasters comprises the following steps: acquiring rainfall event data points inducing the geological disaster according to the meteorological satellite and the cloud chart, and performing fitting analysis on the rainfall event data points inducing the geological disaster through the following formula:
E＝c+α×D β ；
wherein E is the accumulated rainfall, D is the duration of the rainfall event, and alpha, beta and c are statistical parameters; and drawing a scatter diagram by taking the rainfall event data points as samples, and respectively fitting rainfall threshold curves when the rainfall landslide occurrence frequency is 5% and 50% according to the scatter diagram, wherein the process of inducing geological disasters by rainfall events is not an average effect. The process of inducing geological disasters from rainfall events is not evenly functional and includes: the rainfall of the day of the geological disaster plays a full role in the occurrence of the geological disaster; rainfall before the geological disaster is effective to the geological disaster part, wherein the rainfall event threshold index inducing the geological disaster is represented by effective rainfall, and the effective rainfall is calculated by the following formula:
R c ＝R 0 +αR 1 +α 2 R 2 +…+α n R n ；
wherein R is c For effective rainfall, R 0 For geological disasters, rainfall on the day, R n The rainfall n days before geological disaster, alpha is attenuation coefficient, and n is days.
In step S108, a geological disaster risk prediction model is constructed, and a geological disaster risk prediction result of the complex weather element is generated by the prediction model. Specifically, the construction of the geological disaster risk prediction model comprises the following steps: according to the hazard probability of the geological disaster, the vulnerability of a disaster bearing body and the direct and indirect economic losses caused by the damage of the power transmission pole tower footing under the action of the geological disaster, a geological disaster risk prediction model is constructed, wherein the geological disaster risk prediction model comprises the following steps:
R＝H×V×E；
wherein R is the risk of the power transmission line tower footing geological disaster, H is the danger probability, V is the vulnerability, and E is the comprehensive loss.
Calculating the hazard probability of the geological disaster by combining the E-D rainfall threshold model and the rainfall threshold transcendental probability, comprising:
in N groups of random numbers there are M times F s Results of ≦ 1 occur when N is sufficiently large, according to F s Acquiring the hazard probability of the geological disaster at a frequency less than or equal to 1:
when N is sufficiently large, its mean value μ F Sum standard deviation σ F Respectively as follows:
the probability of danger of a geological disaster is:
P f ＝1-Φ(β)；
where Φ (β) is the result F from the Monte Carlo simulation i Calculated stability factor, F s Representing the stability factor of the geological disaster when F s The time quality disaster is in an unstable state less than or equal to 1.
The vulnerability V is the degree of damage to the disaster-bearing body caused by geological disasters, wherein the damage degree of the disaster-bearing body when a geological disaster with certain intensity occurs is represented by 0-1, 0 represents no loss, and 1 represents complete loss. Calculating the vulnerability V of the power transmission line tower through the following formula:
wherein x is the horizontal displacement of the earth's surface obtained by numerical simulation, and λ, k are constants determined by fitting a curve.
The comprehensive loss includes direct economic loss and indirect economic loss, wherein the direct economic loss is the reduction of the scale of the transmission power caused by the power failure time, and the indirect economic loss causes extremely adverse social influence on large-scale power failure, and the indirect economic loss is determined by artificial evaluation.
In step S110, historical disasters and early rainfall are counted, short-term meteorological early warning criteria for power transmission line tower footing geological disasters are established in combination with regional geological disaster susceptibility zoning results, and short-term meteorological early warning for power transmission line geological disasters is performed according to numerical meteorological forecasts. The method comprises the following steps of counting historical disasters and early rainfall, establishing a power transmission line tower footing geological disaster meteorological early warning criterion index by combining regional geological disaster happened zoning results, and carrying out early warning on geological disaster risk prediction results based on complex weather elements, wherein the steps comprise: constructing geological disaster and weather early warning criterion indexes of each subarea based on early rainfall of historical geological disaster points, wherein the early warning criterion indexes comprise geological disaster easiness, early 3-day monitoring rainfall and future 24-hour (h) forecasting rainfall, the geological disaster easiness is a degree for describing a geological disaster forming condition combination to facilitate occurrence of geological disasters, and the future 24-hour forecasting rainfall and the early 3-day monitoring rainfall are updated in real time by connecting a rainfall monitoring and rainfall forecasting system of a meteorological department; establishing three-dimensional risk early warning matrixes of different partitions by combining the effective rainfall in the early 3 days and the predicted rainfall in the future 24 hours; and taking the danger degree of the disaster as a boundary to obtain a final early warning result.
Referring to fig. 6, an embodiment of the present invention discloses a power transmission line tower footing geological disaster prediction apparatus, including: the influence factor determination module 602 is configured to identify influence factors of the power transmission line tower footing geological disaster to determine a main cause of the geological disaster; the data acquisition and identification module 604 is used for collecting and sorting historical data of geological disasters, and identifying and counting meteorological conditions and geological conditions under different geological disasters; a rainfall threshold determining module 606, configured to analyze different rainfall amounts in various regions based on rainfall data of meteorological satellites and cloud charts, and construct rainfall thresholds of different types of geological disasters in combination with the rainfall amounts when historical geological disasters occur; the risk prediction model 608 is used for constructing a geological disaster risk prediction model and generating a geological disaster risk prediction result of the complex weather elements through the prediction model; and the early warning module 610 is used for counting historical disasters and early rainfall, establishing short-term meteorological early warning criteria of the power transmission line tower footing geological disasters by combining the regional geological disaster proneness zoning results, and carrying out short-term meteorological early warning on the power transmission line geological disasters according to numerical meteorological forecasting.
The influence factor determination module 602 is configured to determine internal influence factors of the power transmission line tower footing geological disaster and external influence factors of the power transmission line tower footing geological disaster, where the internal influence factors of the power transmission line tower footing geological disaster include deep valley cutting, steep valley slope, fracture, fold development, earthquake activity intensity, rock weathering intensity, rock mass breakage, and thickness and distribution range of a loose covering layer on the slope; and external influence factors of the power transmission line tower footing geological disaster, including rainfall, earthquake, snow melting, unreasonable human engineering activities and reservoir water level fluctuation, wherein the rainfall is determined to be the main cause of the geological disaster according to statistical analysis.
Hereinafter, a method for predicting a geological disaster of a tower footing of a power transmission line according to an embodiment of the present invention is described in detail by way of specific examples.
On one hand, the embodiment of the invention provides a power transmission line tower footing geological disaster early warning technology considering complex meteorological elements, which comprises the following steps: and (4) identifying the influence factors of the geological disaster of the tower footing of the power transmission line by combining the actual current situation, and determining the main reason of the geological disaster. And then collecting and sorting the current data of the geological disasters, and identifying and counting meteorological conditions, geological conditions and the like under different geological disasters. Analyzing different rainfall capacities of various regions based on rainfall data of meteorological satellites and cloud charts, and constructing rainfall thresholds of different types of geological disasters by combining the rainfall capacities when historical geological disasters occur; and (3) constructing a geological disaster risk prediction method, and calculating direct and indirect economic risks caused by the damage of the power transmission tower base under the action of the geological disaster by combining the damage probability and the vulnerability curve. And finally, counting historical disasters and early rainfall, establishing short-term meteorological early warning criteria of the power transmission line tower footing geological disasters by combining the regional geological disaster proneness zoning results, and providing a geological disaster risk early warning method based on complex weather elements.
The beneficial effects of the above technical scheme are as follows: by collecting data, the rainfall data is processed by applying a particle swarm and genetic hybrid optimization algorithm, effective data is extracted, and invalid and redundant data are eliminated. Based on a risk assessment theory, the geological disaster development characteristics, the regional geological environment conditions and the rainfall induction factors along the power transmission line are integrated, and a quantitative assessment model for the geological disaster risk of the power transmission line is provided. Establishing short-term meteorological early warning criterion of the geological disaster of the power transmission line based on statistical analysis of historical geological disaster and rainfall data; and carrying out short-term meteorological early warning on the geological disaster of the power transmission line according to numerical meteorological forecasting.
Firstly, the actual current situation is combined, the influence factors of the geological disaster of the tower footing of the power transmission line are identified, and the main reason of the geological disaster is determined.
And then collecting and sorting the current data of the geological disasters, and identifying and counting meteorological conditions, geological conditions and the like under different geological disasters.
Analyzing different rainfall capacities of various regions based on rainfall data of meteorological satellites and cloud charts, and constructing rainfall thresholds of different types of geological disasters by combining the rainfall capacities when historical geological disasters occur;
and (3) constructing a geological disaster risk prediction method, and calculating direct and indirect economic risks caused by the damage of the power transmission tower base under the action of the geological disaster by combining the damage probability and the vulnerability curve.
And finally, counting historical disasters and early rainfall, establishing short-term meteorological early warning criteria of the power transmission line tower footing geological disasters by combining the regional geological disaster proneness zoning results, and providing a geological disaster risk early warning method based on complex weather factors.
And (4) identifying the influence factors of the geological disaster of the tower footing of the power transmission line by combining the actual current situation, and determining the main reason of the geological disaster.
Internal causes of geological disasters include: deep cutting of valley, steep valley slope, fracture, fold development, strong earthquake activity, strong rock weathering, rock fragmentation, large thickness of loose covering layer on slope and wide distribution.
The external cause of geological disasters refers to the inducing effect of external environmental condition changes on geological disasters, including rainfall, earthquake, snow melting, unreasonable human engineering activities, reservoir level fluctuation and the like. In the statistical analysis, rainfall is the main cause of geological disasters, so the rainfall is mainly taken as the key element of consideration in the application.
Collecting and sorting current data of the geological disasters, and identifying and counting meteorological conditions, geological conditions and the like under different geological disasters.
Rainfall-induced geological disasters are a dynamic evolution process that develops constantly over time. There is a clear time sequence of infiltration-saturation-deterioration-destabilization. Hysteresis effects are a relatively common feature in observing rainfall-induced geological disaster events. Along with the continuous infiltration of rainwater in the soil, the water content of the rock-soil body rises gradually, and the physical and mechanical properties mainly including shear strength weaken, and the rock-soil body begins to deform gradually. Subsequently, as the cracks formed penetrate the rock mass, destabilization begins. Rainfall thus plays an important role in the formation of geological disasters. However, the rainfall process is not always consistent, but changes continuously with time, so the rainfall process needs to be analyzed to determine the rainfall amount inducing geological disasters.
And performing data statistics by combining the analysis, wherein the data statistics comprises information such as specific time of occurrence of geological disasters, rainfall, geographic positions of towers, disaster types, induction factors, investigation dates and the like, and a data multi-domain set is formed.
And processing the rainfall data by applying a particle swarm and genetic hybrid optimization algorithm, extracting effective data and removing ineffective and redundant data. The specific treatment process is as follows:
the main principle is that a particle swarm algorithm is introduced into a genetic algorithm, a mutation operator in the genetic algorithm is constructed by the particle swarm algorithm, randomness of chromosomes during mutation is damaged, directional mutation is carried out along with previous information, the advantages and the benefits of filial generations are guaranteed, and the population is rapidly and virtuously developed.
(1) Generation of initial population and parameter coding. The method comprises the steps of classifying a plurality of rainfall data, then equally dividing a limit value interval of a parameter to generate an initial group, and finally carrying out binary coding on the parameter.
Setting a parameter A to be corrected ri Is [ A ] in a variable range MIN ,A MAX ]Parameter A si The binary number of (c) is b.
All parameters A to be corrected are added s1 、A s2 、…、A sm If m-k parameters to be identified exist and the binary code of each parameter is q bits, the binary string p has (m-k) q bits.
(2) Establishment of a fitness function, namely individual fitness f:
f＝C/[W+f(e)]；
and C and W are a proportionality coefficient and a nonzero adjusting parameter and are used for adjusting the fitness value.
3) And determining a genetic operator. The selection mode combining a random remainder selection method without putting back and 'elite reservation' is adopted: in order to change the convergence rate of the algorithm, replacing the individual with the worst fitness in each generation by the optimal individual of each iteration and the existing optimal individual; in order to avoid the loss of the individuals with the optimal fitness in the parent in the selection, crossing and mutation processes, the optimal individuals in the parent are stored and directly copied in the algorithm execution process without crossing and mutation operations.
In order to improve the time efficiency of the algorithm, the self-adaptive cross rate is provided, and the formula is as follows
In the formula P c1 、P c2 The value can be set manually; f. of max Maximum fitness value in each generation population; f. of avg Is the average fitness value; f' is the larger value of fitness among the individuals to be crossed.
(4) Iteration termination
Analyzing different rainfall capacities of various regions, and constructing rainfall thresholds of different types of geological disasters by combining the rainfall capacities when the historical geological disasters occur;
recording the duration time of a rainfall event inducing the geological disaster event as D, recording the accumulated rainfall in the time period as E, and recording the ratio of E to D as the average rainfall intensity.
E＝c+α×D β
E is cumulative rainfall (mm); d is the duration of rainfall, and alpha, beta and c are statistical parameters.
Fig. 2 is a rainfall threshold curve graph of a certain area, which counts the cumulative rainfall days (D) and rainfall (E) of 1000 rainfall events in 1990-2020, and a scatter diagram is drawn by taking the rainfall events as a sample, and threshold curves of the rainfall landslide occurrence frequency of 5% and 50% are fitted to the scatter diagram respectively. The blank point is a rainfall event which does not induce landslide, and the gray point and the black point are rainfall events which induce landslide and correspond to different threshold curves.
However, the process of inducing a geological disaster by a rainfall event is not an average effect, and it is generally considered that only the daily rainfall of the geological disaster plays a full role in the occurrence of the geological disaster, and the rest is considered to be only partially effective for the geological disaster. Therefore, the threshold indicator of rainfall-induced geological disasters is typically characterized by an effective rainfall. The method mainly adopts a power exponent formula to calculate the corresponding effective rainfall.
R c ＝R 0 +αR 1 +α 2 R 2 +...+α n R n
R c For effective rainfall, R 0 For geological disasters, rainfall on the day, R n The rainfall n days before the disaster. α is the attenuation coefficient and n is the number of days.
And (3) constructing a geological disaster risk prediction method, and calculating direct and indirect economic risks caused by the damage of the power transmission tower base under the action of the geological disaster by combining the damage probability and the vulnerability curve.
The risk calculation mode of the power transmission line tower footing geological disaster is as follows.
R＝H×V×E
Wherein R is the geological disaster risk of the tower footing of the power transmission line. H is risk probability analysis, V is vulnerability analysis, and E is comprehensive loss including direct and indirect economic loss.
Referring to fig. 3, the vulnerability curve describes the relationship between the degree of geological disaster and the damage degree of the disaster-bearing body under the action of geological disaster, and the process of gradually increasing damage of the disaster-bearing body on the slowly deformed landslide can be better understood through the vulnerability curve.
Based on Bayesian theory, H is calculated by combining an E-D rainfall threshold model and a rainfall threshold exceeding probability, and the time probability of the rainfall-induced geological disaster is divided into two parts, namely the probability of a certain rainfall and the probability of the occurrence of the geological disaster under the rainfall. Firstly, determining rainfall corresponding to different extreme value recurrence period conditions in a certain area by a statistical method; then, obtaining rainfall thresholds inducing geological disasters in each local area through statistical analysis, and statistically analyzing the probability of occurrence of the geological disasters under rainfall exceeding the thresholds through historical geological disaster events; and integrating the two to obtain the time probability of the geological disaster.
Assuming that the rainfall event is A, the geological disaster event is B, and P [ A ] and P [ B | A ] are the probability of occurrence of the geological disaster under the rainfall respectively. Time probability P (AB) of rainfall induced geological disaster in Bayes conditional probability calculation
Expressed as:
P(AB)＝P[A]·P[B|A]
and in the calculation of the transcendental probability determined based on the E-D effective rainfall threshold model, after the geological disaster threshold of each research unit is obtained, the times of occurrence of rainfall events which historically exceed the threshold rainfall and the times of inducing geological disasters by the events are analyzed in combination with historical rainfall records of the research units, and the probability P [ B | A ] of occurrence of the geological disasters can be obtained by combining the times of occurrence of the rainfall events which historically exceed the threshold rainfall and the times of inducing the geological disasters by the events. The probability distribution of geological disasters in the same long time can be evaluated by binomial distribution and poisson distribution. When the time length is a discrete time interval of one year, the average annual geological disaster probability is related to the time period T of occurrence of the geological disaster time under the condition, and the annual geological disaster probability can be expressed as:
t is the occurrence period of geological disaster events, and lambda is geological disasterThe frequency of occurrence of the pest. Such a method is also suitable for calculating the probability of occurrence of a geological disaster in a given time period. Let P 0 There is no probability of a geological disaster occurring for a given period of time (within 1 year). Then P 0t The probability of no geological disaster occurring within t years is shown, so the probability of geological disaster occurring within years can be expressed as:
wherein e is a natural constant, and lambda is the frequency of occurrence of geological disasters under a limited rainfall. Thus, in the future t years, the probability of one or more geological disasters occurring is:
P＝1-e -λt ；
the risk probability V of the qualification disasters is calculated mainly through a Monte Carlo simulation method, and according to a statistical sampling theory, the Monte Carlo method researches a numerical calculation method which generates random variables through a computer, and is also called as a random simulation method or a statistical test method.
Referring to fig. 4, a function representing the steady state of a geological disaster is first established:
each parameter is a random variable for controlling the stability of the geological disaster and is determined by factors such as the property of the rock and soil mass, the deformation mechanism and the like of the geological disaster. In this study, the above parameters are defined as rainfall, single maximum rainfall, duration of rainfall, type of rock and soil. These parameters are generally considered to have a certain value range, and have randomness within the value range. The reasons for these randomness include inhomogeneity of the geologic body, variation of external environmental factors, measurement errors, etc.
F s The stability coefficient of geological disaster is represented, and is considered as F s The geological disaster is in an unstable state less than or equal to 1. In N groups of random numbers there are M times F s Results of ≦ 1 occurred, when N was large enough,consider F s The frequency ≦ 1 is approximately probabilistic. The probability of danger of a geological disaster is expressed as follows:
when N is sufficiently large, its mean value μ F And standard deviation σ F Respectively as follows:
the probability of danger of a geological disaster is:
P f ＝1-Φ(β)；
vulnerability refers to the degree of damage to a disaster-bearing body caused by a disaster within a certain space-time range. The degree of damage to a threatened object when a geological disaster of a certain intensity occurs is generally expressed by 0 to 1, where 0 represents no loss and 1 represents complete loss. The research disaster-bearing body is a transmission tower, the disaster-resisting capability of the disaster-bearing body is represented by the characteristic of tower foundation deformation, and the transmission tower with complete foundation and no deformation is considered to have strong disaster-resisting capability and vulnerability of 1; the foundation deformation is strong, the disaster resistance of the transmission tower in the maximum allowable deformation state is weak, and the vulnerability is 0.
Firstly, analyzing the deformation and movement characteristics of the geological disaster by establishing a geological model and numerical simulation to obtain the displacement of the geological disaster under different rainfall working conditions as an intensity index; and then converting the geological disaster deformation into the transmission tower foundation deformation through a calculation formula. And establishing a corresponding relation between the vulnerability and the geological disaster deformation through the foundation root opening deformation under the surface displacement of different geological disasters, and representing by using a vulnerability curve method.
The weibull distribution is a continuous probability distribution that is widely used in reliability analysis. The method can deduce corresponding distribution parameters by using the probability value more conveniently, and is applied to data processing of various life tests. With a probability density of
Where x is a random variable, λ >0 is a scale parameter, and k >0 is a shape parameter. The cumulative distribution function is an extended exponential distribution function.
A value of k <1 indicates that the failure rate decreases over time. The value of k =1 indicates that the failure rate is constant over time. Values with k >1 indicate that the failure rate increases over time.
And fitting the relationship between the vulnerability of the transmission line tower and the intensity of the geological disaster by using Weibull distribution by using the value of the horizontal displacement of the geological disaster as the intensity of the geological disaster. Because the displacement value is an accumulated value, the corrected Weibull distribution for calculating the physical vulnerability of the power transmission line tower by fitting the accumulated distribution function is defined as follows:
wherein: v is the vulnerability of the transmission line tower calculated by using an equation, x is the horizontal displacement of the earth surface, is obtained by numerical simulation, and lambda and k are constants and are determined by fitting a curve.
The economic loss Q includes a direct loss, which is a reduction in the scale of the amount of power transmitted due to the time of power failure, and an indirect loss, which is a large-scale power failure and causes extremely adverse social effects, especially as the society develops, stable power supply, and a fundamental demand for people's life. Unplanned sudden power outages can have a serious impact on many important large meetings and events, which require human assessment.
The method comprises the steps of counting historical disasters and early rainfall, establishing short-term meteorological early warning criteria of the power transmission line tower footing geological disasters by combining regional geological disaster happily zoning results, and providing a geological disaster risk early warning method based on complex weather elements.
Firstly, referring to fig. 5, based on early rainfall of historical geological disaster points, a geological disaster meteorological risk early warning criterion index of each subarea is constructed, the early warning criterion index is composed of geological disaster susceptibility achievement, early-day 3-day monitoring rainfall and future 24-hour rainfall prediction, the three indexes are selected as evaluation basis, and consciousness risk susceptibility and the other two are rainfall. The susceptibility is calculated by the above formula, and the other two are obtained by meteorological data collection. The geological disaster proneness is the possible degree of the geological disaster forming condition combination which is beneficial to the occurrence of the geological disaster, and the proneness is a natural attribute and generally has long-term stability in time; the rainfall data is predicted in the future 24 hours and the rainfall data is monitored in the early 3 days and is updated in real time by connecting a rainfall monitoring and rainfall forecasting system of a meteorological department. Secondly, on the basis of the evaluation of the easiness of geological disasters in the research area, the three-dimensional risk early warning matrixes of different subareas of the research area are established by combining the effective rainfall in the early 3 days and the forecast rainfall in the future 24 hours. And finally, taking the danger degree of the disaster as a boundary to obtain a final early warning result.
Meanwhile, the three-dimensional scale evaluation matrix is established by considering the easiness of regional geological disasters, the effective rainfall in the early 3 days and the rainfall intensity in the future 24 hours. The rainfall threshold level is combined with the susceptibility ranking results.
TABLE 1
Referring to the above table 1, according to the proportion of inducing 5%, 50% and 80% of the number of the historical landslide disasters, one to four different rainfall threshold levels are divided, areas below 5% are divided into low-risk areas, areas below 50% and 80% threshold lines are medium-risk areas and high-risk areas respectively, and areas above 90% are extremely high-risk areas; the susceptibility grades are divided according to risk probability, wherein less than 5 percent is a low susceptibility region, less than 50 percent and less than 80 percent are medium susceptibility regions and high susceptibility regions Yi Faou respectively, and more than 90 percent are extremely high susceptibility regions.
And finally obtaining a risk calculation index of the tower geological disaster according to the tower geological disaster risk evaluation index and the rainfall index, and judging and issuing a corresponding early warning level.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A power transmission line tower footing geological disaster prediction method is characterized by comprising the following steps:
identifying the influence factors of the power transmission line tower footing geological disaster to determine the main reason of the geological disaster;
collecting and sorting historical data of the geological disasters, and identifying and counting meteorological conditions and geological conditions under different geological disasters;
analyzing different rainfall capacities of various regions based on rainfall data of meteorological satellites and cloud charts, and constructing rainfall thresholds of different types of geological disasters by combining the rainfall capacities when historical geological disasters occur;
constructing a geological disaster risk prediction model, and generating a geological disaster risk prediction result of the complex weather elements through the prediction model; and
and (3) counting historical disasters and early rainfall, establishing a short-term meteorological early warning criterion of the power transmission line tower footing geological disasters by combining the regional geological disaster proneness zoning results, and performing short-term meteorological early warning of the power transmission line geological disasters according to numerical meteorological forecasting.
2. The method for predicting the power transmission line tower footing geological disaster according to claim 1, wherein the step of identifying the influence factors of the power transmission line tower footing geological disaster to determine the main cause of the geological disaster comprises the following steps:
internal influence factors of the power transmission line tower footing geological disaster comprise deep valley cutting, steep valley slope, fracture, fold development, strong degree of earthquake activity, strong degree of rock weathering, rock mass crushing and thickness and distribution range of loose covering layers on the slope;
external influence factors of the power transmission line tower footing geological disaster comprise rainfall, earthquake, snow melting, unreasonable human engineering activities and reservoir water level fluctuation, wherein the rainfall is determined to be the main reason of the geological disaster according to statistical analysis.
3. The method for predicting the power transmission line tower footing geological disaster according to claim 2, wherein the step of collecting and sorting the current data of the geological disaster, and the step of identifying and counting the meteorological conditions and the geological conditions under different geological disasters comprises the following steps:
the process of inducing geological disasters by rainfall is a dynamic evolution process which continuously develops along with time, wherein the dynamic evolution process sequentially comprises an infiltration stage, a saturation stage, a degradation stage and a destabilization stage;
performing data statistics in combination with the dynamic evolution process to form a data set, wherein the data set comprises specific time when a geological disaster occurs, rainfall, the geographic position of a tower, a disaster type, an inducing factor and a troubleshooting date; and
and processing the rainfall data by applying a particle swarm and genetic hybrid optimization algorithm to extract effective data and eliminate ineffective and redundant data.
4. The method for predicting the power transmission line tower footing geological disaster according to claim 1, wherein the step of analyzing different rainfall capacities of each region based on rainfall data of meteorological satellites and cloud charts, and the step of constructing rainfall thresholds of different types of geological disasters by combining the rainfall capacities when historical geological disasters occur comprises the steps of:
acquiring rainfall event data points inducing geological disasters according to the meteorological satellite and the cloud chart, and performing fitting analysis on the rainfall event data points inducing the geological disasters through the following formula:
E＝c+α×D β ；
wherein E is the accumulated rainfall, D is the duration of the rainfall event, and alpha, beta and c are statistical parameters;
and drawing a scatter diagram by taking the rainfall event data points as samples, and respectively fitting rainfall threshold curves when the rainfall landslide occurrence frequency is 5% and 50% according to the scatter diagram, wherein the process of inducing the geological disaster by the rainfall event is not an average effect.
5. The method for predicting the geological disaster of the power transmission line tower footing according to claim 4, wherein the process of inducing the geological disaster by the rainfall events is not an average effect and comprises the following steps:
the rainfall of the day of the geological disaster plays a full role in the occurrence of the geological disaster;
rainfall before the geological disaster is effective to the geological disaster part, wherein the rainfall event threshold index inducing the geological disaster is represented by effective rainfall, and the effective rainfall is calculated by the following formula:
R c ＝R 0 +αR 1 +α 2 R 2 +…+α n R n ；
wherein R is c For the effective rainfall, R 0 For geological disasters, rainfall on the day, R n The rainfall n days before geological disaster, alpha is attenuation coefficient, and n is days.
6. The power transmission line tower footing geological disaster prediction method as claimed in claim 1, wherein the construction of the geological disaster risk prediction model comprises: according to the danger probability of the geological disaster, the vulnerability of a disaster bearing body and the direct and indirect economic losses caused by the damage of the tower footing of the power transmission pole under the action of the geological disaster, a geological disaster risk prediction model is constructed, wherein the geological disaster risk prediction model comprises the following steps:
R＝H×V×E；
wherein R is the risk of the power transmission line tower footing geological disaster, H is the hazard probability, V is the vulnerability, and E is the comprehensive loss;
calculating the hazard probability of the geological disaster by combining the E-D rainfall threshold model and the rainfall threshold transcendental probability;
the vulnerability V is the degree of damage to a disaster bearing body caused by geological disasters, wherein the loss degree of the disaster bearing body when the geological disasters with certain intensity occur is represented by 0-1, 0 represents no loss, and 1 represents complete loss; and
the comprehensive loss comprises direct economic loss and indirect economic loss, wherein the direct economic loss is reduction of the scale of the transmission electric quantity caused by power failure time, and the indirect economic loss causes extremely adverse social influence on large-scale power failure, and the indirect economic loss is determined by artificial evaluation.
7. The method of predicting the geological disaster of the tower footing of the power transmission line according to claim 6, wherein calculating the hazard probability of the geological disaster by combining the E-D rainfall threshold model and the rainfall threshold transcendental probability comprises:
in N groups of random numbers there are M times F s Results of ≦ 1 occur when N is sufficiently large, according to F s Acquiring the danger probability of the geological disaster at a frequency less than or equal to 1:
when N is sufficiently large, its mean value μ F Sum standard deviation σ F Respectively as follows:
the danger probability of the geological disaster is as follows:
P f ＝1-Φ(β)；
where Φ (β) is the result F from the Monte Carlo simulation i Calculated stability factor, F s Representing the stability factor of the geological disaster when F s The ground disaster is in an unstable state less than or equal to 1;
calculating the vulnerability V of the power transmission line tower through the following formula:
wherein x is the horizontal displacement of the earth's surface obtained by numerical simulation, and λ, k are constants determined by fitting a curve.
8. The method for predicting the geological disaster of the tower footing of the power transmission line according to claim 7, wherein the step of carrying out statistics on historical disasters and early rainfall, establishing weather early warning criterion indexes of the geological disaster of the tower footing of the power transmission line by combining the easy-to-send zoning results of the regional geological disaster, and carrying out short-term weather early warning on the geological disaster of the power transmission line according to numerical weather forecast comprises the steps of:
the method comprises the steps that a geological disaster meteorological early warning criterion index of each subarea is constructed on the basis of early rainfall of historical geological disaster points, wherein the early warning criterion index comprises geological disaster easiness, early 3-day monitoring rainfall and 24-hour future forecasting rainfall, the geological disaster easiness is the degree of describing geological disaster forming condition combination favorable for geological disaster occurrence, and the 24-hour future forecasting rainfall and the early 3-day monitoring rainfall are updated in real time by connecting a rainfall monitoring and rainfall forecasting system of a meteorological department;
establishing three-dimensional risk early warning matrixes of different partitions by combining the effective rainfall in the early 3 days and the predicted rainfall in the future 24 hours; and
and taking the danger degree of the disaster as a boundary to obtain a final early warning result.
9. The utility model provides a transmission line tower footing geological disasters prediction device which characterized in that includes:
the influence factor determination module is used for identifying influence factors of the power transmission line tower footing geological disaster so as to determine a main reason for generating the geological disaster;
the data acquisition and identification module is used for collecting and sorting historical data of geological disasters, and identifying and counting meteorological conditions and geological conditions under different geological disasters;
the rainfall threshold determination module is used for analyzing different rainfall capacities of various regions based on rainfall data of meteorological satellites and cloud charts, and constructing rainfall thresholds of different types of geological disasters by combining the rainfall capacities when historical geological disasters occur;
the risk prediction model is used for constructing a geological disaster risk prediction model and generating a geological disaster risk prediction result of the complex weather elements through the prediction model; and
and the early warning module is used for counting historical disasters and early rainfall, establishing short-term meteorological early warning criteria of the power transmission line tower footing geological disasters by combining the easy-to-send zoning results of the regional geological disasters, and carrying out short-term meteorological early warning on the power transmission line geological disasters according to numerical meteorological forecasting.
10. The apparatus for predicting power transmission line tower footing geological disaster according to claim 9, wherein the influence factor determining module is configured to determine an internal influence factor of the power transmission line tower footing geological disaster and an external influence factor of the power transmission line tower footing geological disaster, wherein,
internal influence factors of the power transmission line tower footing geological disaster comprise deep valley cutting, steep valley slope, fracture, fold development, strong degree of earthquake activity, strong degree of rock weathering, rock mass crushing and thickness and distribution range of loose covering layers on the slope; and
external influence factors of the power transmission line tower footing geological disaster comprise rainfall, earthquake, snow melting, unreasonable human engineering activities and reservoir level fluctuation, wherein the rainfall is determined to be the main reason of the geological disaster according to statistical analysis.
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| CN117114428A (en) * | 2023-10-25 | 2023-11-24 | 国网山西省电力公司电力科学研究院 | Meteorological disaster analysis and early warning method for power equipment |
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| CN117668040A (en) * | 2024-01-30 | 2024-03-08 | 北京江河惠远科技有限公司 | Geological data processing method for high-voltage transmission line pole tower area and related products |
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| CN117114428A (en) * | 2023-10-25 | 2023-11-24 | 国网山西省电力公司电力科学研究院 | Meteorological disaster analysis and early warning method for power equipment |
| CN117114428B (en) * | 2023-10-25 | 2024-01-30 | 国网山西省电力公司电力科学研究院 | Meteorological disaster analysis and early warning method for power equipment |
| CN117493805A (en) * | 2023-11-04 | 2024-02-02 | 广东省核工业地质调查院 | Grading and value-taking method for slope unit in geological disaster evaluation process |
| CN117609900A (en) * | 2023-11-10 | 2024-02-27 | 山东省地质矿产勘查开发局第三地质大队(山东省第三地质矿产勘查院、山东省海洋地质勘查院) | Method for monitoring hydrogeological risk |
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