CN117010555A - Method, device and processor for predicting disaster event risk of power transmission line - Google Patents
Method, device and processor for predicting disaster event risk of power transmission line Download PDFInfo
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Abstract
The embodiment of the invention provides a method, a device and a processor for predicting disaster event risks of a power transmission line, and belongs to the field of power grids. The method for predicting the risk of the disaster event of the power transmission line comprises the following steps: gridding a geographical area containing the power transmission line to obtain a plurality of grid points; acquiring attribute data of preset disaster recovery environmental factors related to disaster events, which correspond to the grid points respectively, and historical effective precipitation and future precipitation, which correspond to the grid points respectively; determining a risk index of disaster events occurring in the geographic area according to the attribute data; predicting grid point risk values of disaster events at grid points according to the risk indexes, the historical effective precipitation and the future precipitation based on a pre-constructed disaster event prediction model; and determining a tower risk value of a disaster event of the tower according to the distance between the tower and the grid point in the power transmission line and the grid point risk value. The embodiment of the invention can realize the risk prediction of the disaster event of the power transmission line.
Description
Technical Field
The invention relates to the field of power grids, in particular to a method, a device and a processor for predicting disaster event risks of a power transmission line.
Background
In recent years, as global climate is warmed, extreme natural disasters frequently occur, the damage degree of a power grid is continuously increased, the risk of damage is increased, and economic losses also show an increasing trend. Extreme external environments such as heavy rain, continuous heavy rainfall and the like are easy to cause secondary heavy rain disasters (such as landslide, mud-rock flow and the like) on land features such as ridges, steep slopes and the like. With the continuous and rapid construction of a power transmission network in China, a power transmission tower is inevitably used as a support body of the power transmission line of the power transmission network to cross over the zones, and a large amount of damage to the power transmission tower can be caused by secondary disasters induced by rainfall disasters in a short time, and large-area power failure accidents can be caused when the power transmission tower is serious. Therefore, the prediction of the storm secondary disasters, such as landslide risk prediction, is very significant for disaster prevention and reduction of the power grid. Therefore, how to implement risk prediction of disaster events of the power transmission line is a problem to be solved.
Disclosure of Invention
An object of an embodiment of the present invention is to provide a method, an apparatus, a processor and a storage medium for predicting a risk of a disaster event of a power transmission line, so as to solve the above-mentioned problems in the prior art.
To achieve the above object, a first aspect of an embodiment of the present invention provides a method for predicting a risk of a disaster event of an electric transmission line, the method including:
gridding a geographical area containing the power transmission line to obtain a plurality of grid points;
acquiring attribute data of preset disaster recovery environmental factors related to disaster events, which correspond to the grid points respectively, and historical effective precipitation and future precipitation, which correspond to the grid points respectively;
determining a risk index of disaster events occurring in the geographic area according to the attribute data;
predicting grid point risk values of disaster events at grid points according to the risk indexes, the historical effective precipitation and the future precipitation based on a pre-constructed disaster event prediction model;
and determining a tower risk value of a disaster event of the tower according to the distance between the tower and the grid point in the power transmission line and the grid point risk value.
In an embodiment of the present invention, determining a risk index of occurrence of a disaster event in a geographic area according to attribute data includes: determining the influence weight of a preset disaster-tolerant environmental factor on a disaster event according to the attribute data; determining a target pregnant disaster environment factor according to the influence weight and the preset weight, wherein the target pregnant disaster environment factor is a preset pregnant disaster environment factor with the influence weight greater than the preset weight in the preset pregnant disaster environment factors; determining the intensity value of the target disaster-tolerant environmental factor according to the influence weight; and determining a risk index according to the intensity value and a preset condition degree coefficient corresponding to the attribute type of the target disaster-enriched environmental factor corresponding to the grid point.
In the embodiment of the invention, the influence weight of the preset disaster-tolerant environmental factor on the disaster event is determined according to the attribute data, and the method comprises the following steps: based on the geographic detector model, determining the influence weight of the preset disaster-tolerant environmental factors on disaster events according to the attribute data.
In the embodiment of the invention, determining the risk index according to the intensity value and the preset condition degree coefficient corresponding to the attribute type of the target disaster recovery environmental factor corresponding to the grid point comprises determining the risk index according to the following formula (1):
wherein Z is a risk index, K i For intensity value, T i And n is the number of the target disaster-tolerant environmental factors for the preset condition degree coefficient.
In the embodiment of the invention, the obtaining of the historical effective precipitation amount comprises the following steps: acquiring historical daily precipitation in a preset time period; determining a historical effective precipitation according to the historical daily precipitation and the effective precipitation coefficient, wherein the historical effective precipitation is determined according to the historical daily precipitation and the effective precipitation coefficient, and comprises the following steps of determining according to the following formula (2):
wherein R is p For the effective precipitation of history, k is the effective precipitation coefficient, R i And (3) the precipitation amount is the historical daily precipitation amount of the previous i days, and n is the total number of days in a preset time period.
In the embodiment of the invention, determining the tower risk value of the disaster event of the tower according to the distance between the tower and the grid point in the power transmission line and the grid point risk value comprises the following steps: acquiring grid point positions of grid points and tower positions of a tower; determining the distance between the tower and the grid point according to the grid point position and the tower position; determining a target grid point closest to the tower in the grid points according to the distance; and determining the grid point risk value corresponding to the target grid point as a tower risk value of the disaster event of the tower.
In the embodiment of the invention, the method further comprises the following steps: and carrying out uniform resolution processing on the attribute data based on an inverse distance weight interpolation method to obtain the attribute data with uniform resolution.
A second aspect of an embodiment of the invention provides a processor configured to perform a method for predicting a risk of a transmission line disaster event according to the above.
A third aspect of an embodiment of the present invention provides an apparatus for predicting a risk of a disaster event of a power transmission line, including:
the data acquisition module is used for carrying out gridding processing on a geographical area containing the transmission line so as to obtain a plurality of grid points; acquiring attribute data of preset disaster recovery environmental factors related to disaster events, which correspond to the grid points respectively, and historical effective precipitation and future precipitation, which correspond to the grid points respectively;
the grid point risk prediction module is used for determining a risk index of a disaster event in the geographic area according to the attribute data; predicting grid point risk values of disaster events at grid points according to the risk indexes, the historical effective precipitation and the future precipitation based on a pre-constructed disaster event prediction model;
and the pole risk prediction module is used for determining a pole risk value of a disaster event of the pole according to the distance between the pole and the grid point in the power transmission line and the grid point risk value.
In the embodiment of the present invention, the grid point risk prediction module is further configured to: determining the influence weight of a preset disaster-tolerant environmental factor on a disaster event according to the attribute data; determining a target pregnant disaster environment factor according to the influence weight and the preset weight, wherein the target pregnant disaster environment factor is a preset pregnant disaster environment factor with the influence weight greater than the preset weight in the preset pregnant disaster environment factors; determining the intensity value of the target disaster-tolerant environmental factor according to the influence weight; and determining a risk index according to the intensity value and a preset condition degree coefficient corresponding to the attribute type of the target disaster-enriched environmental factor corresponding to the grid point.
In the embodiment of the present invention, the grid point risk prediction module is further configured to: based on the geographic detector model, determining the influence weight of the preset disaster-tolerant environmental factors on disaster events according to the attribute data.
In the embodiment of the present invention, the grid point risk prediction module is further configured to: the risk index is determined according to the following equation (1):
wherein Z is a risk index, K i For intensity value, T i And n is the number of the target disaster-tolerant environmental factors for the preset condition degree coefficient.
In the embodiment of the present invention, the data acquisition module is further configured to: acquiring historical daily precipitation in a preset time period; determining a historical effective precipitation according to the historical daily precipitation and the effective precipitation coefficient, wherein the historical effective precipitation is determined according to the historical daily precipitation and the effective precipitation coefficient, and comprises the following steps of determining according to the following formula (2):
Wherein R is p For the effective precipitation of history, k is the effective precipitation coefficient, R i And (3) the precipitation amount is the historical daily precipitation amount of the previous i days, and n is the total number of days in a preset time period.
In the embodiment of the invention, the tower risk prediction module is further used for: acquiring grid point positions of grid points and tower positions of a tower; determining the distance between the tower and the grid point according to the grid point position and the tower position; determining a target grid point closest to the tower in the grid points according to the distance; and determining the grid point risk value corresponding to the target grid point as a tower risk value of the disaster event of the tower.
In the embodiment of the invention, the device further comprises a preprocessing module, which is used for carrying out uniform resolution processing on the attribute data based on an inverse distance weight interpolation method so as to obtain the attribute data with uniform resolution.
A fourth aspect of the embodiments of the present invention provides a machine-readable storage medium, on which a program or instructions is stored, which when executed by a processor, implement a method for predicting a risk of a transmission line disaster event according to the above.
According to the technical scheme, the grid processing is carried out on the geographic area containing the power transmission line to obtain the grid points, attribute data of preset disaster-pregnant environmental factors related to disaster events corresponding to the grid points respectively, historical effective precipitation and future precipitation corresponding to the grid points respectively are obtained, further the risk index of the disaster event occurring in the geographic area is determined according to the attribute data, the grid point risk value of the disaster event occurring in the grid points is predicted according to the risk index, the historical effective precipitation and the future precipitation based on the pre-built disaster event prediction model, and accordingly the tower risk value of the disaster event occurring in the tower is determined according to the distance between the tower and the grid points in the power transmission line and the grid point risk value. According to the scheme, the risk index of the disaster event in the geographic area is determined based on the preset disaster-tolerant environmental factors, and the risk of the disaster event in the tower in the power transmission line is predicted by combining the risk index, the historical precipitation data and the predicted precipitation data, so that the accurate and rapid prediction of the disaster event can be realized, early warning and warning work can be performed in advance, unnecessary life and property loss is reduced, the running safety of the power grid is improved, and contribution is made to disaster prevention and reduction of the power grid.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 schematically illustrates a flow chart of a method for predicting risk of a transmission line disaster event in an embodiment of the invention;
fig. 2 schematically illustrates a block diagram of an apparatus for predicting risk of a transmission line disaster event according to an embodiment of the present invention.
Detailed Description
The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
It should be noted that, if directional indications (such as up, down, left, right, front, and rear … …) are included in the embodiments of the present invention, the directional indications are merely used to explain the relative positional relationship, movement conditions, etc. between the components in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indications are correspondingly changed.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Fig. 1 schematically illustrates a flow chart of a method for predicting risk of a transmission line disaster event according to an embodiment of the present invention. As shown in fig. 1, in an embodiment of the present invention, a method for predicting a risk of a disaster event of a power transmission line is provided, and the method is described by taking an application of the method to a processor as an example, and the method may include the following steps:
step S102, performing gridding processing on the geographical area including the transmission line to obtain a plurality of grid points.
Step S104, obtaining attribute data of a preset disaster recovery environmental factor related to a disaster event corresponding to each of the plurality of grid points, and historical effective precipitation and future precipitation corresponding to each of the plurality of grid points.
And step S106, determining the risk index of the disaster event in the geographic area according to the attribute data.
Step S108, based on the pre-constructed disaster event prediction model, predicting grid point risk values of disaster events of grid points according to the risk index, the historical effective precipitation and the future precipitation.
Step S110, determining a tower risk value of a disaster event of the tower according to the distance between the tower and the grid point in the power transmission line and the grid point risk value.
It is to be appreciated that the disaster event can include a geomorphic disaster event, a geological disaster event, a weather disaster event, etc., which can include landslide, debris flow, etc., for example. The grid points are the crossing points of each grid obtained after grid processing is carried out on the geographical area containing the transmission line. The preset disaster-pregnancy environmental factors are predetermined disaster-pregnancy environmental factors related to disaster events, the number of the disaster-pregnancy environmental factors can be a plurality, the disaster-pregnancy environmental factors are comprehensive earth surface environments formed by atmosphere circles, water circles, rock circles (including soil and vegetation), biospheres and human society circles, the disaster-pregnancy environmental factors are characteristic attribute factors of the disaster-pregnancy environmental factors, and the disaster-pregnancy environmental factors can specifically comprise but are not limited to topography (altitude, gradient, slope direction and topography fluctuation), geological structures (lithology and fault distribution), soil (soil type, soil texture), vegetation type and the like. The attribute data of the pre-set disaster-tolerant environmental factors are specific data or attributes of the pre-set disaster-tolerant environmental factors. The historical effective precipitation amount is an effective precipitation amount over a certain preset period of time in the past, for example, the historical effective precipitation amount may be an effective precipitation amount over the past 7 days or 3 days. The future precipitation is a predicted precipitation for a certain preset period of time in the future, for example, the future precipitation may be a predicted precipitation for 3 days or 24 hours in the future. The risk index of the disaster event in the geographic area can describe the degree of the disaster event (such as landslide) controlled by static easily-generated conditions and the tendency of the disaster event (such as landslide) to occur in the future, and the contribution of a plurality of preset disaster-enriched environmental factors (such as gradient, vegetation coverage and the like) to the disaster event (such as landslide) can be comprehensively considered. The pre-constructed disaster event prediction model is a model which is obtained through pre-training and used for predicting the occurrence risk of a disaster event, the input of the disaster event prediction model is a risk index, historical effective precipitation and future precipitation, and the output of the disaster event prediction model is a risk value of the occurrence of the disaster event. The grid point risk value is the risk value of the disaster event of the grid point. The tower risk value is a risk value of a disaster event occurring in the tower, and the tower is a part of a power transmission line.
Specifically, the processor may perform gridding processing on a geographical area including the power transmission line, that is, divide the geographical area including the power transmission line into a plurality of grids to obtain a plurality of grid points, further obtain attribute data (such as elevation data, gradient data, etc.) of a preset disaster-tolerant environmental factor corresponding to the disaster event, which correspond to the plurality of grid points, respectively, and historical effective precipitation and future precipitation corresponding to the plurality of grid points, so as to determine a risk index of the disaster event occurring in the geographical area according to the attribute data, for example, determine a risk index of the disaster event occurring in the geographical area according to a relation between the attribute data and the risk index, which are determined in advance, and use the risk index, the historical effective precipitation and the future precipitation as inputs of a disaster event prediction model, thereby obtaining grid point risk values of the disaster event occurring in the prediction model, further determine tower risk values of the disaster event occurring in the grid points according to distances between towers in the power transmission line and grid points and grid point risk values, for example, and the risk tower risk values corresponding to the grid points in the power transmission line can be processed according to the distance between the towers and the grid points, so as to obtain the risk average value of the tower risk value corresponding to the tower risk value.
According to the method for predicting the disaster event risk of the power transmission line, the grid processing is carried out on the geographical area containing the power transmission line to obtain the grid points, the attribute data of the preset disaster-tolerant environmental factors related to the disaster event corresponding to the grid points respectively, the historical effective precipitation and the future precipitation corresponding to the grid points respectively are obtained, the risk index of the disaster event in the geographical area is determined according to the attribute data, the grid point risk value of the disaster event in the grid points is predicted according to the risk index, the historical effective precipitation and the future precipitation based on the pre-constructed disaster event prediction model, and therefore the tower risk value of the disaster event in the tower is determined according to the distance between the tower and the grid points in the power transmission line and the grid point risk value. According to the method, the risk index of the disaster event in the geographic area is determined based on the preset disaster-tolerant environmental factors, and the risk of the disaster event in the tower in the power transmission line is predicted by combining the risk index, the historical precipitation data and the predicted precipitation data, so that the accurate and rapid prediction of the disaster event can be realized, early warning and warning work can be performed in advance, unnecessary life and property loss is reduced, the running safety of the power grid is improved, and contribution is made to disaster prevention and reduction of the power grid.
In one embodiment, determining a risk index for a disaster event occurring in a geographic area based on attribute data comprises: determining the influence weight of a preset disaster-tolerant environmental factor on a disaster event according to the attribute data; determining a target pregnant disaster environment factor according to the influence weight and the preset weight, wherein the target pregnant disaster environment factor is a preset pregnant disaster environment factor with the influence weight greater than the preset weight in the preset pregnant disaster environment factors; determining the intensity value of the target disaster-tolerant environmental factor according to the influence weight; and determining a risk index according to the intensity value and a preset condition degree coefficient corresponding to the attribute type of the target disaster-enriched environmental factor corresponding to the grid point.
It can be understood that the influence weight of the preset disaster-tolerant environmental factor on the disaster event is the dominant weight of the preset disaster-tolerant environmental factor on the disaster event. The preset weight is a preset influence weight threshold. The target disaster-pregnant environment factor is a preset disaster-pregnant environment factor with the influence weight larger than the preset weight in the preset disaster-pregnant environment factors. The intensity value of the target disaster-pregnant environment factor is the intensity score of the target disaster-pregnant environment factor determined according to the influence weight of the target disaster-pregnant environment factor. The preset condition degree coefficient is a condition degree coefficient corresponding to each attribute type of each predetermined target disaster-pregnant environment factor, for example, when the target disaster-pregnant environment factor is a gradient, the gradient may include four attribute types of 40 degrees to 60 degrees, 20 degrees to 40 degrees, 0 degrees to 20 degrees and more than 60 degrees, and the corresponding condition degree coefficient, that is, the preset condition degree coefficient, may be set for different attribute types. Further, in some embodiments, the sum of the intensity values of all the target disaster-pregnant environmental factors may be 1, and the sum of the preset condition degree coefficients corresponding to all the attribute types of each target disaster-pregnant environmental factor may be 1.
Specifically, the processor may determine an impact weight of a preset disaster environment factor on a disaster event according to attribute data, for example, the processor may determine an impact weight of the preset disaster environment factor on the disaster event according to attribute data based on a geographic detector model, further may determine a target disaster environment factor according to the impact weight and the preset weight, determine a preset disaster environment factor with an impact weight greater than the preset weight in the preset disaster environment factor as the target disaster environment factor, and determine an intensity value of the target disaster environment factor according to the impact weight, for example, may search a corresponding relation table according to the impact weight and the intensity value, and thus determine an intensity value of the target disaster environment factor according to the intensity value and a preset condition degree coefficient corresponding to an attribute type of the target disaster environment factor corresponding to the grid point.
In one embodiment, determining the risk index according to the intensity value and the preset condition degree coefficient corresponding to the attribute type of the target disaster recovery environmental factor corresponding to the grid point includes determining the risk index according to the following formula (1):
Wherein Z is a risk index, K i For intensity value, T i And n is the number of the target disaster-tolerant environmental factors for the preset condition degree coefficient.
In one embodiment, the obtaining of the historical effective precipitation amount includes: acquiring historical daily precipitation in a preset time period; determining a historical effective precipitation according to the historical daily precipitation and the effective precipitation coefficient, wherein the historical effective precipitation is determined according to the historical daily precipitation and the effective precipitation coefficient, and comprises the following steps of determining according to the following formula (2):
wherein R is p For the effective precipitation of history, k is the effective precipitation coefficient, R i And (3) the precipitation amount is the historical daily precipitation amount of the previous i days, and n is the total number of days in a preset time period.
It is understood that the preset time period is a preset time period, for example, 3 days or 7 days. The effective precipitation coefficient is a preset coefficient related to historical precipitation data, and the value of the effective precipitation coefficient is usually less than 1, for example, the effective precipitation coefficient is 0.84.
Specifically, the processor may obtain the historical daily precipitation in a preset time period (for example, the past 7 days), based on the historical daily precipitation, combine the date and the effective precipitation coefficient, and calculate the effective precipitation of each day in the past 7 days by adopting the method of the power exponent, that is, calculate the product value of the historical daily precipitation and the power exponent based on the effective precipitation coefficient and using the current day of the historical daily precipitation as the exponent, so as to obtain the effective precipitation of the day corresponding to the historical daily precipitation, where the sum of the effective precipitation of the past 7 days is the historical effective precipitation of the past 7 days.
In the embodiment of the invention, the historical effective precipitation is determined by combining the historical daily precipitation and the effective precipitation coefficient so as to predict the risk value of the disaster event according to the historical effective precipitation, and the accuracy of disaster event prediction can be further improved.
In one embodiment, determining a tower risk value for a disaster event for a tower from a distance of the tower from a grid point in a transmission line and the grid point risk value includes: acquiring grid point positions of grid points and tower positions of a tower; determining the distance between the tower and the grid point according to the grid point position and the tower position; determining a target grid point closest to the tower in the grid points according to the distance; and determining the grid point risk value corresponding to the target grid point as a tower risk value of the disaster event of the tower.
It is understood that the target grid point is the nearest grid point to the tower.
Specifically, the processor may first obtain a grid point position (for example, longitude and latitude coordinates of a grid point) and a tower position (for example, longitude and latitude coordinates of a tower) of the tower, and then determine a distance between the tower and the grid point according to the grid point position and the tower position, so as to determine a target grid point closest to the tower in the grid point according to the distance, and determine a grid point risk value corresponding to the target grid point as a tower risk value of a disaster event of the tower.
In one embodiment, the method for predicting the risk of a transmission line disaster event further comprises: and carrying out uniform resolution processing on the attribute data based on an inverse distance weight interpolation method to obtain the attribute data with uniform resolution.
It may be understood that before acquiring attribute data of preset disaster recovery environmental factors corresponding to each grid point, the number of times of gridding processing may be multiple, attribute data of different preset disaster recovery environmental factors may be obtained by each gridding processing, resolutions of grid points obtained after multiple gridding processing may be the same or may be different, and when resolutions of grid points are different, uniform resolution processing may be performed, specifically, based on an inverse distance weight interpolation method, uniform resolution processing may be performed on the attribute data to obtain attribute data after uniform resolution.
In a specific embodiment, taking a disaster event as a landslide as an example, a method for predicting the risk of the disaster event of a power transmission line is provided, and the specific implementation steps are as follows:
step 1, determining a preset disaster recovery environment factor.
The disaster-pregnant environment reflects the spatial probability of landslide occurrence. The preset disaster recovery environmental factors generally include: topography (elevation, slope direction, topography relief), geologic structure (lithology, fault distribution), soil (soil type, soil texture), vegetation type, etc. For this purpose, the above factors are initially selected as the pre-set pregnancy disaster environment factors.
And 2, collecting a risk evaluation data set (namely attribute data of a preset disaster recovery environmental factor).
Based on the preset pregnancy disaster environment factors, landslide hazard evaluation can be further carried out. The data used for carrying out landslide hazard evaluation mainly comprise DEM (elevation) data, fault structures, stratum lithology, historical disaster points and the like. Parameters such as gradient, slope direction, topography fluctuation degree and the like can be further calculated according to the DEM data, and the historical disaster point data comprise disaster occurrence time, place (longitude and latitude coordinates), hazard area and the like.
And 3, carrying out landslide hazard evaluation based on attribute data of the preset pregnancy disaster environment factors.
1) Basic data preprocessing for risk assessment
Because of the differences of various data types and resolutions, various data needs to be unified to the same resolution before the evaluation process is carried out. Therefore, it is necessary to set the resolution of the analysis grid (e.g., 5 km, 1 km, 100 meters, etc.) in advance, and then unify various types of data to the resolution. The method commonly adopted is an inverse distance weight interpolation method. The calculation formula is as follows:
in which Z is 0 Representing the estimated value of the new grid point, Z i For the property value of the ith sample (terrain height, soil type, etc.), p is a power of distance, which significantly affects the result of interpolation, its selection criteria is the minimum mean absolute error, p often taking 2.D (D) i Is the distance X o And Y o To study longitude and latitude coordinates of points, X i And Y i For the longitude and latitude coordinates of the surrounding grid points, 4 are usually taken i, namely, the surrounding 4 grid points containing the research point.
2) Landslide hazard susceptibility analysis based on geographic detector model
Geographic probes are a set of statistical methods that detect spatial diversity and reveal their driving force behind. The core idea is as follows: if an independent variable has a significant effect on an independent variable, then the spatial distribution of the independent and dependent variables should have similarity. The geographic probe comprises a plurality of probes, where the diversity and factor probes are selected to explore the spatial diversity of dependent variables (landslide, denoted Y) and to detect how much a factor (soil type, elevation, etc., denoted X) accounts for the spatial diversity of attribute Y. The expression is as follows:
SST＝Nσ 2
wherein: h=1, …, L is a stratification, i.e. a classification or partition, of the variable Y or factor X; n (N) h And N is the number of units of layer h and the full area respectively;sum sigma 2 The variance of the Y values for layer h and full region, respectively. SSW and SST are the sum of intra-layer variances and total full-zone variances, respectively. The value range of the influence weight q is [0,1 ]]The larger the value, the more pronounced the spatial anisotropy of Y; if the hierarchy is generated by an argument X, the larger the impact weight q is, the stronger the interpretation of the attribute Y by the argument X is, and vice versa. In the extreme case, a q value of 1 indicates that the factor X completely controls the spatial distribution of Y, a q value of 0 indicates that the factor X has no relation to Y, and an impact weight q value indicates that X interprets 100 xq% of Y.
After the detection by the geographic detector, the influence weight q of each preset disaster-tolerant environmental factor can be obtained, namely the dominant weight of each preset disaster-tolerant environmental factor on disaster occurrence.
TABLE 1 presetting the relationship between the environmental factors and the influence weights of the pregnant disaster
3) Disaster risk index calculation
According to the influence weight q in the step 2), several target disaster-pregnant environmental factors with the greatest influence on landslide can be obtained, and the intensity value of the target disaster-pregnant environmental factors is given (generally, the larger the influence weight q is, the larger the intensity value can be given appropriately). Further, establishing a risk index of the landslide disaster through the intensity value of each target disaster-pregnant environmental factor and the preset condition degree coefficient, wherein the risk index comprehensively considers the influence of a plurality of target disaster-pregnant environmental factors.
TABLE 2 relation between intensity value of target disaster-enriched environmental factor and preset condition degree coefficient
The calculation formula of the risk index Z of landslide hazard is as follows:
wherein: n is the environmental factor number of the target pregnant disaster, K i For the intensity value of each target pregnancy and disaster environment factor, T i And the preset condition degree coefficient corresponding to the attribute type of each target disaster-tolerant environmental factor.
And 4, calculating landslide risk based on the risk index and rainfall data.
And establishing a landslide disaster prediction model (namely a disaster event prediction model) based on the risk index, the historical live precipitation and the future 24-hour precipitation of the landslide disaster, and obtaining a risk prediction value of the landslide disaster. The specific formula is as follows:
F＝f(R d ,R p ,Z)
f is the risk value of landslide hazard occurrence, R d For future 24-hour daily rainfall (mm), R p The early effective rainfall (mm) refers to the effective rainfall which affects the geological disaster in the rainfall process before the occurrence of the disaster. Effective rainfall R in early stage p The calculation of (a) may take the form of a power exponentAnd (3) calculating:
wherein k is an effective rainfall coefficient, and generally 0.84 is taken; r is R i Live daily rainfall (mm) for the previous i days; n is the number of days of effective rainfall, and according to practical experience, n is generally taken to be 7, namely, the effect of rainfall in one week is mainly affected.
The selection of the landslide hazard prediction model f is usually performed by using a classification model in a machine learning method, for example, a classification model XGBClassifier of XGBOOST, and the input data set (predictor) is as follows: the future 24-hour daily rainfall, the early effective rainfall, landslide and debris flow disaster risk index, and the output is a submerged state (predicted amount). Model training has been performed using 80% of the samples as training sets and 20% of the samples as test sets to verify model performance.
During the training process, three key parameters of the model are continuously adjusted: the maximum depth of the tree (max_depth), learning rate (learning rate), number of trees (n_identifiers) to adjust the optimization model to maximize accuracy on the training set. The maximum depth (max_depth) of the tree is 6 by default, and can be gradually reduced to 1; the learning rate (learning rate) is set to a default value of 0.1, which can be gradually reduced to 0.01, and the number of trees (n_identifiers) is set to a default value of 100, which can be gradually reduced to 80. And obtaining the accuracy rate of the test set under different parameters through the change process, wherein the parameter with the maximum accuracy rate is the model parameter. The calculation formula of the accuracy rate is as follows:
in the above expression, TP is a true example (landslide hazard is predicted and landslide hazard is actually occurred), TN is a true counterexample (landslide hazard is predicted and landslide hazard is not actually occurred), FN is a false counterexample (landslide hazard is predicted and landslide hazard is actually occurred, i.e., missing report), and FP is a false positive example (landslide hazard is predicted and landslide hazard is not actually occurred, i.e., false report).
And 5, obtaining landslide at the tower of the specific transmission line through space matching of the grid points and the positions of the towers.
The commonly adopted method is a proximity interpolation method, the proximity interpolation needs to calculate the distance between grid points and the position of a transmission line tower, and the calculation formula is as follows:
Wherein: d (D) i Is the distance X o And Y o To predict longitude and latitude coordinates of grid point, X i And Y i Is the longitude and latitude coordinates of the transmission line tower. For each grid point, the coordinates i and j should be found to satisfy D i And at the moment, the value of the landslide risk at the transmission line tower is the landslide risk value corresponding to the grid point closest to the transmission line tower.
According to the technical scheme provided by the embodiment of the invention, the heavy rain landslide risk index of the specific area is obtained based on the relief environmental factors such as the topography, the geological structure conditions, the soil and the vegetation, and the like, the rainfall prediction data can be combined to develop a large-scale geological landslide prediction later, so that precious time is won for disaster prevention and reduction of the power grid, the principle is clear, the operation is convenient, the practical value is high, in addition, the calculation process is rapid, the subsequent calculation can be used for developing a large-area landslide calculation, early warning and warning work can be conveniently performed in advance, and the loss of life and property is reduced.
An embodiment of the invention provides a processor configured to perform a method for predicting a risk of a transmission line disaster event according to the above-described embodiments.
As shown in fig. 2, an embodiment of the present invention provides an apparatus 200 for predicting a risk of a disaster event of a power transmission line, including:
A data acquisition module 210, configured to perform gridding processing on a geographical area including the power transmission line, so as to obtain a plurality of grid points; and acquiring attribute data of preset disaster recovery environmental factors related to disaster events, which correspond to the grid points respectively, and historical effective precipitation and future precipitation, which correspond to the grid points respectively.
A grid point risk prediction module 220, configured to determine a risk index of a disaster event occurring in the geographic area according to the attribute data; based on a pre-constructed disaster event prediction model, predicting grid point risk values of disaster events occurring at grid points according to the risk index, the historical effective precipitation and the future precipitation.
The tower risk prediction module 230 is configured to determine a tower risk value of a disaster event of the tower according to a distance between the tower and the grid point in the power transmission line and the grid point risk value.
According to the device 200 for predicting the disaster event risk of the power transmission line, the grid processing is performed on the geographical area containing the power transmission line to obtain a plurality of grid points, attribute data of preset disaster-tolerant environmental factors related to the disaster event corresponding to the grid points respectively, historical effective precipitation and future precipitation corresponding to the grid points respectively are obtained, further, the risk index of the disaster event in the geographical area is determined according to the attribute data, the grid point risk value of the disaster event in the grid points is predicted according to the risk index, the historical effective precipitation and the future precipitation based on the pre-constructed disaster event prediction model, and accordingly the tower risk value of the disaster event in the tower is determined according to the distance between the tower and the grid points in the power transmission line and the grid point risk value. The device determines the risk index of the disaster event in the geographical area based on the preset disaster-tolerant environmental factors, predicts the risk of the disaster event of the tower in the power transmission line by combining the risk index, the historical precipitation data and the predicted precipitation data, can realize accurate and rapid prediction of the disaster event, so as to perform early warning and warning work in advance, reduce unnecessary life and property loss, improve the running safety of the power grid and make a contribution to disaster prevention and reduction of the power grid.
In one embodiment, the grid point risk prediction module is further configured to: determining the influence weight of a preset disaster-tolerant environmental factor on a disaster event according to the attribute data; determining a target pregnant disaster environment factor according to the influence weight and the preset weight, wherein the target pregnant disaster environment factor is a preset pregnant disaster environment factor with the influence weight greater than the preset weight in the preset pregnant disaster environment factors; determining the intensity value of the target disaster-tolerant environmental factor according to the influence weight; and determining a risk index according to the intensity value and a preset condition degree coefficient corresponding to the attribute type of the target disaster-enriched environmental factor corresponding to the grid point.
In one embodiment, grid point risk prediction module 220 is further to: based on the geographic detector model, determining the influence weight of the preset disaster-tolerant environmental factors on disaster events according to the attribute data.
In one embodiment, grid point risk prediction module 220 is further to: the risk index is determined according to the following equation (1):
wherein Z is a risk index, K i For intensity value, T i And n is the number of the target disaster-tolerant environmental factors for the preset condition degree coefficient.
In one embodiment, the data acquisition module 210 is further configured to: acquiring historical daily precipitation in a preset time period; determining a historical effective precipitation according to the historical daily precipitation and the effective precipitation coefficient, wherein the historical effective precipitation is determined according to the historical daily precipitation and the effective precipitation coefficient, and comprises the following steps of determining according to the following formula (2):
Wherein R is p For the effective precipitation of history, k is the effective precipitation coefficient, R i And (3) the precipitation amount is the historical daily precipitation amount of the previous i days, and n is the total number of days in a preset time period.
In one embodiment, the tower risk prediction module 230 is further configured to: acquiring grid point positions of grid points and tower positions of a tower; determining the distance between the tower and the grid point according to the grid point position and the tower position; determining a target grid point closest to the tower in the grid points according to the distance; and determining the grid point risk value corresponding to the target grid point as a tower risk value of the disaster event of the tower.
In an embodiment, the apparatus 200 for predicting a risk of a disaster event of a power transmission line further includes a preprocessing module, configured to perform a uniform resolution processing on the attribute data based on an inverse distance weight interpolation method, so as to obtain attribute data after the uniform resolution.
The embodiment of the invention provides a machine-readable storage medium, on which a program or instructions are stored, which when executed by a processor, implement a method for predicting a risk of a transmission line disaster event according to the above-described embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. Various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are to be included in the scope of the claims of the present invention.
Claims (16)
1. A method for predicting the risk of a transmission line disaster event, the method comprising:
gridding a geographical area containing the power transmission line to obtain a plurality of grid points;
Acquiring attribute data of preset disaster recovery environmental factors related to disaster events, corresponding to the grid points, and historical effective precipitation and future precipitation corresponding to the grid points;
determining a risk index of the disaster event occurring in the geographic area according to the attribute data;
predicting a grid point risk value of the grid point for generating the disaster event according to the risk index, the historical effective precipitation and the future precipitation based on a pre-constructed disaster event prediction model;
and determining a tower risk value of the disaster event of the tower according to the distance between the tower in the power transmission line and the grid point risk value.
2. The method of claim 1, wherein determining the risk index for the occurrence of the disaster event for the geographic area based on the attribute data comprises:
determining the influence weight of the preset disaster-tolerant environmental factor on the disaster event according to the attribute data;
determining a target disaster-pregnant environment factor according to the influence weight and the preset weight, wherein the target disaster-pregnant environment factor is a preset disaster-pregnant environment factor with the influence weight greater than the preset weight in the preset disaster-pregnant environment factors;
Determining the intensity value of the target disaster-tolerant environmental factor according to the influence weight;
and determining the risk index according to the intensity value and a preset condition degree coefficient corresponding to the attribute type of the target disaster recovery environmental factor corresponding to the grid point.
3. The method of claim 2, wherein determining the impact weight of the pre-set disaster recovery environmental factor on the disaster event based on the attribute data comprises:
and determining the influence weight of the preset disaster-tolerant environmental factor on the disaster event according to the attribute data based on a geographic detector model.
4. The method according to claim 2, wherein the determining the risk index according to the intensity value and a preset condition degree coefficient corresponding to an attribute type of a target disaster recovery environmental factor corresponding to the grid point includes determining the risk index according to the following formula (1):
wherein Z is the risk index, K i For the intensity value, T i And n is the number of the target disaster recovery environmental factors for the preset condition degree coefficient.
5. The method of claim 1, wherein the obtaining of the historical effective precipitation amount comprises:
Acquiring historical daily precipitation in a preset time period;
determining the historical effective precipitation according to the historical daily precipitation and the effective precipitation coefficient, wherein the determining the historical effective precipitation according to the historical daily precipitation and the effective precipitation coefficient comprises determining according to the following formula (2):
wherein R is p For the effective precipitation of the history, k is the effective precipitation coefficient, R i And (3) the precipitation amount is the historical daily precipitation amount of the previous i days, and n is the total number of days in the preset time period.
6. The method of claim 1, wherein the determining a tower risk value for the tower to experience the disaster event based on a distance of the tower from the grid point in the transmission line and the grid point risk value comprises:
acquiring grid point positions of the grid points and tower positions of the towers;
determining the distance between the tower and the grid point according to the grid point position and the tower position;
determining a target grid point closest to the tower in the grid points according to the distance;
and determining the grid point risk value corresponding to the target grid point as the tower risk value of the disaster event of the tower.
7. The method according to claim 1, wherein the method further comprises:
and carrying out uniform resolution processing on the attribute data based on an inverse distance weight interpolation method to obtain attribute data with uniform resolution.
8. A processor configured to perform the method for predicting the risk of a transmission line disaster event according to any one of claims 1 to 7.
9. An apparatus for predicting a risk of a transmission line disaster event, comprising:
the data acquisition module is used for carrying out gridding processing on a geographical area containing the transmission line so as to obtain a plurality of grid points; acquiring attribute data of preset disaster recovery environmental factors related to disaster events, corresponding to the grid points, and historical effective precipitation and future precipitation corresponding to the grid points;
a grid point risk prediction module, configured to determine a risk index of the occurrence of the disaster event in the geographic area according to the attribute data; predicting a grid point risk value of the grid point for generating the disaster event according to the risk index, the historical effective precipitation and the future precipitation based on a pre-constructed disaster event prediction model;
And the pole risk prediction module is used for determining a pole risk value of the disaster event of the pole according to the distance between the pole in the power transmission line and the grid point risk value.
10. The apparatus of claim 9, wherein the grid point risk prediction module is further configured to:
determining the influence weight of the preset disaster-tolerant environmental factor on the disaster event according to the attribute data;
determining a target disaster-pregnant environment factor according to the influence weight and the preset weight, wherein the target disaster-pregnant environment factor is a preset disaster-pregnant environment factor with the influence weight greater than the preset weight in the preset disaster-pregnant environment factors;
determining the intensity value of the target disaster-tolerant environmental factor according to the influence weight;
and determining the risk index according to the intensity value and a preset condition degree coefficient corresponding to the attribute type of the target disaster recovery environmental factor corresponding to the grid point.
11. The apparatus of claim 10, wherein the grid point risk prediction module is further configured to: and determining the influence weight of the preset disaster-tolerant environmental factor on the disaster event according to the attribute data based on a geographic detector model.
12. The apparatus of claim 10, wherein the grid point risk prediction module is further configured to: determining the risk index according to the following formula (1):
wherein Z is the risk index, K i For the intensity value, T i And n is the number of the target disaster recovery environmental factors for the preset condition degree coefficient.
13. The apparatus of claim 9, wherein the data acquisition module is further configured to:
acquiring historical daily precipitation in a preset time period;
determining the historical effective precipitation according to the historical daily precipitation and the effective precipitation coefficient, wherein the determining the historical effective precipitation according to the historical daily precipitation and the effective precipitation coefficient comprises determining according to the following formula (2):
wherein R is p For the effective precipitation of the history, k is the effective precipitation coefficient, R i And (3) the precipitation amount is the historical daily precipitation amount of the previous i days, and n is the total number of days in the preset time period.
14. The apparatus of claim 9, wherein the tower risk prediction module is further configured to:
acquiring grid point positions of the grid points and tower positions of the towers;
determining the distance between the tower and the grid point according to the grid point position and the tower position;
Determining a target grid point closest to the tower in the grid points according to the distance;
and determining the grid point risk value corresponding to the target grid point as the tower risk value of the disaster event of the tower.
15. The apparatus of claim 9, further comprising a preprocessing module configured to perform uniform resolution processing on the attribute data based on inverse distance weight interpolation to obtain uniform resolution attribute data.
16. A machine readable storage medium having stored thereon a program or instructions, which when executed by a processor, implements a method for predicting a risk of a transmission line disaster event according to any one of claims 1 to 7.
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| CN117612339B (en) * | 2023-11-09 | 2024-06-04 | 应急管理部大数据中心 | Geological disaster monitoring method and system based on iron tower big data |
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| CN117612339A (en) * | 2023-11-09 | 2024-02-27 | 应急管理部大数据中心 | Geological disaster monitoring method and system based on iron tower big data |
| CN117612339B (en) * | 2023-11-09 | 2024-06-04 | 应急管理部大数据中心 | Geological disaster monitoring method and system based on iron tower big data |
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