CN114966898B - Method and system for early warning of rainy, snowy and frozen weather of high-speed road section - Google Patents
Method and system for early warning of rainy, snowy and frozen weather of high-speed road section Download PDFInfo
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Abstract
The invention relates to the technical field of weather early warning, and provides a method and a system for early warning rainy, snowy and frozen weather of a high-speed road section, wherein the method comprises the following steps: acquiring time, place and corresponding meteorological data of the historical rainy, snowy and frozen weather of a high-speed road section area; according to historical meteorological data of each observation point in the high-speed road section, calculating correlation coefficients of different meteorological elements of each observation point and the weather of rain, snow and ice, and constructing a rain, snow and ice prediction model corresponding to each observation point; and acquiring meteorological data currently acquired at each observation point of the high-speed road section area, inputting the meteorological data into a rain, snow and ice prediction model corresponding to each observation point, and outputting a rain, snow and ice weather prediction result by the rain, snow and ice prediction model. According to the method, the meteorological data of the high-speed road section area are subjected to refinement processing, and the difference of the relevant coefficients of the rain, snow and ice weather caused by the difference of the altitudes and the terrains of different observation points is considered, so that the rain, snow and ice prediction model is constructed by dividing each observation point in the high-speed road section, and the accuracy of the rain, snow and ice prediction is ensured.
Description
Technical Field
The invention relates to the technical field of weather early warning, in particular to a method and a system for early warning of rainy, snowy and frozen weather of a high-speed road section.
Background
At present, because fewer traffic weather observation stations are arranged along the expressway, real-time weather observation data of any mileage stake marks of the expressway are difficult to obtain, and when the expressway is in early warning in rainy, snowy and frozen weather, the live data of a plurality of weather observation stations can only be used for representing the early warning condition of a road section in a large range nearby, and the road section without the weather observation stations cannot be accurately early warned.
The existing dynamic short-term early warning method for the expressway icing risk road section establishes a road surface temperature time sequence prediction model based on multi-source traffic weather historical data acquired from traffic weather monitoring stations, and rolls and predicts predicted values of the traffic weather monitoring station positions in a future short time period and the road surface temperature changes along with time; calculating according to road surface temperature difference values of other positions on the road section and monitoring station points, and calculating road surface temperature predicted values of other positions except the monitoring station points at the same moment according to road surface temperature predicted values of the monitoring station points in a short time period in the future; and finally judging whether the road surface temperature predicted value is lower than or equal to 0 ℃ or freezing point temperature or not according to the road surface temperature predicted value of the whole road section, whether the road surface temperature predicted value is lower than or equal to dew point temperature at the same moment or not, and judging possible road surface icing risks at all positions of the whole road section of the expressway in the prediction aging by combining the current observed precipitation state. However, the method only carries out temperature prediction through a temperature time sequence prediction model, and lacks of weight consideration on various meteorological elements, so that the prediction accuracy is lower.
Disclosure of Invention
The invention provides a method and a system for early warning of rain, snow and freezing weather of a high-speed road section, which overcome the defect of low prediction accuracy caused by lack of weight consideration of various weather elements when predicting the rain, snow and freezing weather.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method for early warning of rain, snow and freezing weather of a high-speed road section comprises the following steps:
S1, acquiring time, places and corresponding meteorological data of the historical rainy, snowy and frozen weather of a high-speed road section area;
s2, calculating correlation coefficients of different meteorological elements of each observation point and the rain, snow and ice weather according to historical meteorological data of each observation point in the high-speed road section, and constructing a rain, snow and ice prediction model corresponding to each observation point;
S3, acquiring meteorological data currently acquired at each observation point of the high-speed road section area, inputting the meteorological data into the rain, snow and freezing prediction model corresponding to each observation point, and outputting a rain, snow and freezing weather prediction result by the rain, snow and freezing prediction model.
According to the technical scheme, the meteorological data of the high-speed road section area are subjected to refined processing, and the difference of the relevant coefficients of the rain, snow and ice weather caused by the difference of the altitudes and the terrains of different observation points is considered, so that the rain, snow and ice prediction model is constructed by dividing each observation point in the high-speed road section, and the accuracy of the rain, snow and ice prediction is ensured.
Furthermore, the invention also provides a system for early warning the rain, snow and freezing weather of the high-speed road section, and the method for early warning the rain, snow and freezing weather of the high-speed road section is provided by applying the technical scheme.
The high-speed road section rain, snow and freezing weather early warning system provided by the invention comprises a data acquisition module and a rain, snow and freezing prediction module. The data acquisition module is used for acquiring time, places and corresponding meteorological data of the historical rainy, snowy and frozen weather of the high-speed road section area and acquiring current meteorological data of the high-speed road section area. The rain, snow and ice prediction module is constructed by calculating correlation coefficients of different meteorological elements of each observation point and rain, snow and ice weather according to the historical meteorological data of each observation point in the high-speed road section acquired by the data acquisition module; the rain, snow and ice prediction module is used for generating a rain, snow and ice weather prediction result according to the input meteorological data.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: according to the invention, the meteorological data of the high-speed road section area are subjected to refinement treatment, and the difference of the relevant coefficients of the rain, snow and ice weather caused by the difference of the altitudes and the terrains of different observation points is considered, so that the rain, snow and ice prediction model is constructed by dividing each observation point in the high-speed road section, and the accuracy of the rain, snow and ice prediction is effectively improved.
Drawings
Fig. 1 is a flowchart of a method for early warning of rainy, snowy and frozen weather on a high-speed road section according to embodiment 1.
Fig. 2 is a flowchart of a method for early warning of rainy, snowy and frozen weather on a high-speed road section according to embodiment 2.
Fig. 3 is a flowchart for constructing a rain/snow ice prediction model according to example 2.
Fig. 4 is a construction diagram of a rainy, snowy and frozen weather warning system for a high-speed road section according to embodiment 3.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
For the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Example 1
The embodiment provides a method for early warning of the rainy, snowy and frozen weather of a high-speed road section, and as shown in fig. 1, the method is a flow chart of the method for early warning of the rainy, snowy and frozen weather of the high-speed road section.
The method for early warning of rain, snow and freezing weather of the high-speed road section provided by the embodiment comprises the following steps:
S1, acquiring time, places and corresponding meteorological data of the historical rainy, snowy and frozen weather of a high-speed road section area.
S2, calculating correlation coefficients of different meteorological elements of each observation point and the rain, snow and ice weather according to historical meteorological data of each observation point in the high-speed road section, and constructing a rain, snow and ice prediction model corresponding to each observation point.
S3, acquiring meteorological data currently acquired at each observation point of the high-speed road section area, inputting the meteorological data into the rain, snow and freezing prediction model corresponding to each observation point, and outputting a rain, snow and freezing weather prediction result by the rain, snow and freezing prediction model.
In this embodiment, the acquired weather data includes time, geographical location, and daily average air temperature, minimum air temperature of altitude tags with historic weather of the highway section area, and regional sounding station observation data. The regional sounding station observation data comprise air pressure, wind direction, wind power, relative humidity and atmospheric temperature.
In one implementation, first, historical data of the highway section area is collected, and the time and place (geographic position, altitude) of the highway section area in which the rainy, snowy and frozen weather process occurs are screened. And collecting daily average air temperature, minimum air temperature and precipitation data recorded by the national weather observation stations and regional weather observation stations of the high-speed road section region and the surrounding region according to the screened time, and regional sounding station observation data of the surrounding region.
Further, the process of the frozen, rain and snow weather is synthesized and analyzed to obtain weather element characteristics such as daily average air temperature, minimum air temperature, air pressure, wind direction, wind power, relative humidity, temperature of each layer of atmosphere, precipitation and the like in the frozen, rain and snow weather. And calculating correlation coefficients of different meteorological elements of each observation point and the rain, snow and ice weather according to historical meteorological data of each observation point in the high-speed road section, and constructing a rain, snow and ice prediction model.
In this embodiment, the correlation coefficients of different meteorological elements and rain, snow and ice weather are calculated, and the different meteorological element fields and the rain, snow and ice weather are analyzed by using an Empirical Orthogonal Function (EOF) analysis method, and the indicators of the upstream meteorological elements on the rain, snow and ice weather of the high-speed road section area are analyzed. And (3) screening out weather elements with strong correlation with the rain, snow and ice weather through significance test, and establishing a rain, snow and ice prediction model according to contributions of different weather elements to the rain, snow and ice weather.
In a specific application process, weather data currently collected at each observation point of a high-speed road section area are acquired and input into a corresponding rain, snow and ice prediction model, and the rain, snow and ice prediction model outputs a rain, ice and ice weather prediction result of the corresponding observation point, so that the warning of the rain, snow and ice weather of the high-speed road section is realized.
In the embodiment, the weather data of the high-speed road section area are subjected to refinement processing, so that the difference of the elevation of different observation points and the correlation coefficient of the rain, snow and freezing weather caused by different terrains is considered, the rain, snow and freezing prediction model is constructed by dividing each observation point in the high-speed road section, and the accuracy of rain, snow and freezing prediction is ensured.
Example 2
The embodiment provides a method for early warning of the rainy, snowy and frozen weather of a high-speed road section, and as shown in fig. 2, the method is a flow chart of the method for early warning of the rainy, snowy and frozen weather of the high-speed road section.
The method for early warning the rainy, snowy and frozen weather of the high-speed road section specifically comprises the following steps:
S1, acquiring time, places and corresponding meteorological data of the historical rainy, snowy and frozen weather of a high-speed road section area.
In this embodiment, the acquired meteorological data includes time, geographical location, and daily average air temperature and lowest air temperature of altitude tags with the historic occurrence of rain, snow and ice weather in the highway section area, and regional sounding station observation data. The regional sounding station observation data comprise air pressure, wind direction, wind power, relative humidity and atmospheric temperature.
S2, calculating correlation coefficients of different meteorological elements of each observation point and the rain, snow and ice weather according to historical meteorological data of each observation point in the high-speed road section, and constructing a rain, snow and ice prediction model corresponding to each observation point.
In this embodiment, the step of constructing the rain and snow freezing prediction model includes:
S2.1, analyzing n kinds of weather data and rain, snow and ice weather results of each observation point based on an empirical orthogonal function analysis method for k observation points of a high-speed road section area, and screening weather elements corresponding to weather data with obvious correlation with the rain, snow and ice weather results through a stepwise regression algorithm and saliency test.
S2.2, calculating correlation coefficients of weather elements with obvious correlation in corresponding observation points and weather results of rain, snow and freezing, and constructing a rain, snow and freezing prediction model of the corresponding observation points; the expression of the rain and snow freezing prediction model of the j observation point is as follows:
y′j＝b0+b1x′1+b2x′2+...+blx′l
Wherein b 0 represents a constant term of the regression equation, b l represents a correlation coefficient of the first weather element x' l with the weather result of rain, snow and ice, and l.ltoreq.n, j=1, 2.
As shown in fig. 3, a flowchart of the construction of the rain and snow freezing prediction model according to the present embodiment is shown.
In this embodiment, the step of screening weather elements corresponding to weather data with significant correlation with the weather results of rain, snow and ice through the significance test and calculating the correlation coefficient thereof includes:
Step A, for the j observation point, n meteorological elements x j1,xj2,...,xjn corresponding to meteorological data and a corresponding rainy, snowy and frozen weather result y j are provided; taking the corresponding meteorological elements of n meteorological data as regression independent variables and the corresponding n weather results y j of rain, snow and freezing as dependent variables, constructing a (n+1) x (n+1) order normalized coefficient correlation matrix R j, wherein the expression is as follows:
wherein r ab represents a normalized variable between the meteorological element factor a in the j-th observation point and the b-th result in the rainy, snowy, or frozen weather result y, where a=1, 2. r a(n+1) represents the regression coefficient of the meteorological element factor a and the b-th result in the rainy, snowy and frozen weather result y; r (n+1)b represents a regression coefficient of the meteorological element factor n and the b-th result in the rainy, snowy and frozen weather result y; x ta represents the nth data of the meteorological element factor a; representing the variable average value of the meteorological element factor a in all observation points; /(I) And the mean value of the dependent variable of the rain and snow freezing result b in all observation points is represented.
And step B, calculating partial regression square sum V i according to each standardized variable, wherein the expression is as follows:
step C, calculating partial regression square sum V i according to each standardized variable to judge:
If V i is smaller than 0, adding the corresponding ith meteorological element factor x i into the selected regression equation factor set;
Otherwise, adding the corresponding ith meteorological element factor x i into the factor set to be selected.
Step D, selecting a meteorological element factor x i corresponding to V i with the minimum absolute value from the selected regression equation factor set for significance test; when the following condition is satisfied:
removing the corresponding meteorological element factor x i from the selected regression equation factor set, and performing elimination transformation on the factor in the coefficient correlation matrix R;
From the factor set to be selected, weather element factor x i corresponding to V i with the largest absolute value is selected for significance test; when the following condition is satisfied:
then the meteorological element factor x i is added into the regression equation factor set, and the factor is subjected to the elimination transformation in the coefficient correlation matrix R;
Wherein phi is the degree of freedom of the sum of squares of the residuals; f 1、F2 is an F-Y distribution value, the value of which depends on the number of observation points, the number of factors selected and the selected optional significant level, and F 1>F2 can be taken as a constant when the number of observation points is large; v i,max represents V i,Vi,min having the largest absolute value and V i having the smallest absolute value.
And E, repeating the step D until all factors are selected or eliminated, and obtaining each regression coefficient b 0,b1,...,bl of the normalized regression equation according to the selected regression equation factor set.
In the above steps, when a certain factor x jn is to be removed or selected, the coefficient correlation matrix R j needs to be subjected to the binary conversion, and the algorithm is as follows:
wherein a=1, 2,..n, y, b=1, 2,..n, y and a, b+.l; when the screening is completed, each regression coefficient b 0,b1,...,bn of the normalized regression equation is obtained. Where a coefficient with a value of 0 indicates that the corresponding argument is the factor of culling.
S3, acquiring meteorological data currently acquired at each observation point of the high-speed road section area, inputting the meteorological data into the rain, snow and freezing prediction model corresponding to each observation point, and outputting a rain, snow and freezing weather prediction result by the rain, snow and freezing prediction model.
S4, meshing the meteorological data currently collected at each observation point of the high-speed road section area and the prediction results of the rain, snow and freezing weather; interpolation processing is carried out on the meteorological data and the rain, snow and ice weather prediction results of different high-speed road section areas subjected to meshing processing according to the geographical information of the mileage stake marks of the high-speed road section, so that the meteorological data and the rain, snow and ice weather prediction results of the mileage stake marks are obtained; and carrying out condition judgment according to a preset forecasting and early-warning condition threshold value, and generating the rain and snow freezing forecasting and early-warning level of each mileage stake mark.
The method comprises the steps of meshing weather data of each observation point of a current high-speed road section area and corresponding weather prediction results of rain, snow and ice, interpolating and processing the weather data and the corresponding weather prediction results into forecast values of each mileage stake mark of the high-speed road section area, wherein the forecast values comprise forecast daily rainfall, daily average air temperature, daily minimum air temperature, and data of a potential occurrence index of freezing rain, or data of accumulated rainfall, daily minimum air temperature, daily average air temperature, real-time rainfall and the like, and a rain, snow and ice forecast early warning grade. And further, visually displaying the forecast results after the gridding and interpolation processing, and enabling a driver driving in a corresponding high-speed road section area to intuitively judge the rain and snow freezing condition of the target driving road section path area, and carrying out route avoidance under the necessary condition so as to avoid driving accidents.
In another embodiment, further comprising the steps of:
according to the observation data of the regional sounding stations of the high-speed road section, carrying out synthetic analysis on the combination of different phase precipitation temperature layers, and establishing different phase precipitation sounding curve models;
and reading the humidity and temperature profile changes of the target high-speed road section area according to the different phase precipitation exploration curve models, obtaining the temperature and humidity conditions of each layer of the atmosphere, analyzing to obtain the precipitation phase state of the target high-speed road section area by judging the reverse humidity level, the reverse temperature condition and the level of water vapor, and correcting the rain, snow and freezing weather prediction result output by the rain, snow and freezing prediction model.
In this step, in consideration of certain differences in temperature layer junction distribution of different phase precipitation such as rain, freezing rain, snow and the like, the embodiment performs synthesis analysis on the temperature layer junction of different phase precipitation to obtain a sounding curve model of different phase precipitation. And adopting a thermodynamic equation to carry out diagnosis and analysis on the advection cooling of the rain, snow and ice events. The thermodynamic equation is shown as follows:
the temperature level advection term of the first term on the right of the formula And calculating the temperature advection difference for the selected ground index stations one by one, thereby determining the cold air strength and the weakening and influencing degree in the south-down process.
Then further adopting a water vapor flux equation, and selecting the specific humidity q and the specific humidity q of each high-altitude index station at the upstream and the downstreamAnd calculating the water vapor flux of each layer of atmosphere in the area, so as to judge the possible precipitation condition. Wherein the water vapor flux equation is shown as follows:
And (3) reading temperature and humidity profile changes by selecting a sounding situation of an upstream area of a target highway section area to obtain the temperature and humidity of each layer of the atmosphere, judging a reverse humidity layer, a reverse temperature condition and a layer where water vapor is mainly located, and comprehensively analyzing to obtain a precipitation phase state.
Example 3
The embodiment provides a rainy, snowy and frozen weather early warning system for a high-speed road section, and as shown in fig. 4, the system is a framework diagram of the rainy, snowy and frozen weather early warning system for the high-speed road section.
The high-speed road section rain, snow and freezing weather early warning system provided by the embodiment comprises a data acquisition module 1 and a rain, snow and freezing prediction module 2. Wherein:
The data acquisition module 1 is used for acquiring time, place and corresponding meteorological data of the historical rainy, snowy and frozen weather of the high-speed road section area and acquiring current meteorological data of the high-speed road section area.
The rain, snow and ice prediction module 2 is constructed by calculating correlation coefficients of different meteorological elements of each observation point and rain, snow and ice weather according to the historical meteorological data of each observation point in the high-speed road section acquired by the data acquisition module 1. The rain, snow and ice prediction module 2 is used for generating a rain, snow and ice weather prediction result according to the input meteorological data.
Further, the meteorological data collected by the data collection module 1 in the embodiment include time, geographical position and daily average air temperature and lowest air temperature of altitude labels of the historical rainy, snowy and frozen weather of the high-speed road section area, and regional sounding station observation data; the regional sounding station observation data comprise air pressure, wind direction, wind power, relative humidity and atmospheric temperature.
In the construction process of the rain, snow and freezing prediction module 2 in the embodiment, for k observation points of a high-speed road section area, n kinds of weather data and rain, snow and freezing weather results of each observation point are analyzed based on an empirical orthogonal function analysis method, and weather elements corresponding to weather data with obvious correlation with the rain, snow and freezing weather results are screened out through a stepwise regression algorithm and a saliency check; and calculating the correlation coefficient of the weather element with obvious correlation in the corresponding observation point and the rain, snow and freezing weather result, and constructing a rain, snow and freezing prediction model of the corresponding observation point.
Further, the system for early warning of rainy, snowy and frozen weather of the highway section in the embodiment further comprises a data conversion module 3. The data conversion module 3 includes a gridding processing unit 301, an interpolation processing unit 302, and an early warning level judging unit 303.
The meshing processing unit 301 in this embodiment is configured to perform meshing processing on current weather data of the high-speed road section area acquired by the data acquisition module 1 and the weather prediction result generated by the weather prediction module 2.
The interpolation processing unit 302 is configured to interpolate the weather data and the weather prediction result of the rain, snow and ice according to the geographic information of each mileage stake mark of the high-speed road section, so as to obtain the weather data and the weather prediction result of the rain, snow and ice of each mileage stake mark.
The early warning level judging unit 303 is configured to perform condition judgment according to a preset early warning condition threshold value, and generate a rain, snow and ice early warning level of each mileage stake mark.
Further, the high-speed road section rainy and snowy and frozen weather early warning system in the embodiment further comprises a prediction correction module 4; and the prediction correction module 4 is preset with different phase precipitation sounding curve models obtained by performing synthetic analysis on different phase precipitation temperature layer combinations according to the observation data of the regional sounding stations of the high-speed road section.
According to the different phase precipitation exploration curve models, the prediction correction module 4 reads the humidity and temperature profile changes of the target high-speed road section area to obtain the temperature and humidity conditions of each layer of the atmosphere, analyzes the precipitation phase of the target high-speed road section area by judging the reverse humidity layer, the reverse temperature condition and the layer where water vapor is located, and corrects the rain, snow and freezing weather prediction result output by the rain, snow and freezing prediction model.
This example illustrates an implementation of the weather process of the cold wave in the region of North Guangdong, gang, and high speed in 2021, 12, and the next ten days.
(1) A source of forecast data is obtained.
And starting a high-speed road section rain, snow and ice weather early warning system at 2021, 12 months and 24 days and 16 days. According to a user-defined rain and snow freezing prediction early warning condition threshold value configuration table, a prediction data source reported at the time of 12 months and 24 days and 00 hours (UTC) is obtained through a data acquisition module 1, wherein the prediction data source comprises a daily rainfall of GIFTDAILY (refined lattice point interaction prediction system) in the future and 7 days, a 2-meter daily average air temperature, a 2-meter daily minimum air temperature and a ECMWFTHIN (European medium weather prediction center fine lattice numerical prediction) grid daily average air temperature and daily minimum air temperature with the height of 925 hundred Pa, and a grid FRGPI (freezing rain potential occurrence index).
The data acquisition module 1 sends the acquired meteorological data to the gridding processing unit 301, and the gridding processing unit 301 performs gridding processing on various meteorological data to obtain forecast grid data and writes the forecast grid data into a cache.
Meanwhile, the data acquisition module 1 sends the acquired meteorological data to the constructed rain, snow and ice prediction module 2, and the rain, snow and ice prediction module 2 generates a rain, snow and ice weather prediction result according to the input meteorological data.
(2) The forecast grid data is converted into single point data.
The interpolation processing unit 302 interpolates the grid data calculated and output in step (1) to the geographical position of the highway mileage stake marks by a method of interpolating adjacent points (interpolating horizontal grid data to discrete points on a plane, setting the value of each point to the value of the nearest one of four surrounding grid points) according to the geographical information (stake mark ID, longitude, latitude and altitude) of the highway mileage stake marks, reads temperature forecast data of different levels according to different altitudes of different mileage stake marks in a temperature forecast condition, generates data such as future 7-day daily rainfall, daily average air temperature, daily minimum air temperature, rainy, snowy and frozen weather forecast result of each mileage stake mark of the highway, and writes the data into a cache.
(3) And judging ① the early warning grade conditions of the mileage stake marks.
The early warning level judging unit 303 performs condition judgment on the future 7-day daily rainfall, the daily average air temperature, the daily minimum air temperature, the predicted result of the rainy, snowy and frozen weather and other data of each mileage stake mark output in the step (2) by traversing according to a user-defined rainy, snowy and frozen live early warning condition threshold configuration table, generates the rainy, snowy and frozen early warning level condition judgment ① of each mileage stake mark, and writes the data into a cache.
The rain and snow freezing forecast and early warning grade conditions of the mileage stake marks obtained through calculation of the forecast conditions are as follows: the risk level of each mileage stake mark in the three sections K1842-K1853 and K1863-K1918 is 4, namely, the early warning level of the 12 months and 27 days is 4.
(4) A live data source is acquired.
The data acquisition module 1 acquires a live data source at the time of 16 days of 12 months and 24 days through a data interface according to a user-defined rain and snow freezing forecast early warning condition threshold configuration table, wherein the live data source comprises refined multisource fusion live meteorological element field data of a Shaog region, air temperature data of CLDAS (China meteorological office land data assimilation system) and rainfall data of CMPA (China multisource precipitation fusion system), and the live meteorological element field data is stored in a local file on time.
(5) And processing the local file data.
The data acquisition module 1 sends the data acquired in the step (4) to the gridding processing unit 301 for data conversion, so as to obtain the following data: the method comprises the steps of meshing accumulated rainfall data of CMPA (China multisource precipitation fusion system) of the past 3 days and 5 days, meshing lowest air temperature data of the past 3 days and 5 days CLDAS (China weather office land surface data assimilation system) and meshing average air temperature data, and writing the data into a cache.
Meanwhile, the data acquisition module 1 sends the data acquired in the step (4) to the rain, snow and freezing prediction module 2, and the rain, snow and freezing prediction module 2 generates a rain, snow and freezing weather prediction result according to the input meteorological data and writes the rain, snow and freezing weather live prediction result into a cache.
(6) The live grid data is converted to single point data.
The interpolation processing unit 302 interpolates the grid data calculated and output in step (5) to the geographical position of the highway mileage stake marks by a method of interpolating adjacent points (interpolating horizontal grid data to discrete points on a plane, setting the value of each point to the value of the nearest one of four surrounding grid points) according to the geographical information (stake mark ID, longitude and latitude) of the highway mileage stake marks, generates the accumulated rainfall, the lowest daily air temperature and the average daily air temperature of the past N days of each mileage stake mark of the highway, and writes the data into a cache.
(7) And judging ② the early warning grade conditions of the mileage stake marks.
The early warning level judging unit 303 performs condition judgment on the accumulated rainfall, the daily minimum air temperature and the daily average air temperature data of the last 3 days and 5 days of each mileage stake mark output in the step (6) through traversal according to a user-defined threshold configuration table of the rain, snow and ice live early warning conditions, generates the condition judgment ② of the rain, snow and ice forecast early warning level of each mileage stake mark, and writes the data into a cache.
The rain and snow freezing forecast and early warning grade conditions of the mileage stake marks obtained through calculation under the live condition are as follows: and the risk level of each mileage stake mark in the three sections K1842-K1853 and K1863-K1918 is 0 level, namely judging that each section does not reach the condition of the live condition of rain, snow and ice.
(8) And comprehensively judging the early warning grade conditions of the mileage stake marks.
The early warning level judging unit 303 judges and outputs the early warning level of the rain, snow and ice of each mileage stake mark by traversing the early warning level of each mileage stake mark output in the step (3) and the step (7) according to the user-defined threshold configuration table of the rain, snow and ice live early warning condition, comprehensively predicts and judges the early warning level of the rain, snow and ice of each mileage stake mark, and writes data into a cache.
At the moment, through the judgment of the comprehensive forecasting condition and the live condition, as the live condition is not reached, only the forecasting condition is reached, and according to a rain and snow freezing forecasting and early warning level condition threshold value table, 4-level yellow early warning of rain and snow freezing is expected to occur for each mileage stake number in three sections of K1842-K1853 and K1863-K1918 in 12 months and 27 days.
(9) And generating a shunt section forecasting and early warning.
And (3) processing the forecasting and early-warning grades of the mileage stake marks output in the step (8) into the rain and snow freezing forecasting and early-warning data of the component road sections according to the user-defined Shaog expressway segmentation table. The system output at this time is shown in table 1 below:
table 1 forecast and early-warning grades of pile numbers of mileage
| Mileage column number | Road section name | Early warning type | Early warning level |
| K1842-K1853 | Small pond-lawn stone | Rain and snow freezing prediction and early warning device | 4 |
| K1863-K1879 | Plum blossom-Yun Yan | Rain and snow freezing prediction and early warning device | 4 |
| K1879-K1899 | Yun Yan bridge | Rain and snow freezing prediction and early warning device | 4 |
Through adopting the high-speed road section rain, snow and freezing weather early warning system of the embodiment to the cold and damp weather process of the high-speed North Guangdong section area in 2021 in the next 12 th month of the Beijing harbor, relevant departments start an emergency plan in time after receiving 4-level forecast early warning information released by the city weather department in 24 th month, and start to implement traffic control measures in 19 th 26 th month, the measures are as follows: the Pingshi and North intercommunication (K5) Beijing Zhubei is sealed in the Guangzhou direction at high speed, and the bridge stands for diversion to the outlet in the Hunan direction; the lawn stone station is closed towards the Guangzhou direction entrance, and the plum blossom station is closed towards the Guangzhou direction entrance; the entrance of the bridge station towards the Hunan direction is closed; the inlet of the milk source station towards the Hunan direction is closed; the entrance of the Dongtian station towards the Hunan direction is closed. The investigation conditions of the on-site part road section are as follows: k1882 prevents throwing the net and has icing phenomenon, and the road surface is moist. The deck and the guardrails of K1874 to K1885 have thin ice. The K1863-K1879 plum blossom-Yun Yan and the K1879-K1899 Yun Yan-bridge road sections are consistent with the result of the rain and snow ice forecast and early warning road sections actually triggered by the system.
In the embodiment, by establishing the high-definition rainy, snowy and frozen weather early warning system of the high-speed road section, meanwhile, the basic geographic information data of the speedway mileage stake mark is introduced, the meteorological data are comprehensively analyzed according to the altitude of the actual mileage stake mark, the rainy, snowy and frozen weather prediction is generated, the method is further used for early warning of the rainy, snowy and frozen weather of the high-speed road section, and high early warning accuracy is ensured.
Furthermore, the embodiment can provide three progressive early warning products of forecasting early warning, approaching early warning and live early warning according to the time gradient of the influence of the rain, snow and severe freezing weather.
It is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (5)
1. The method for early warning the rainy, snowy and frozen weather of the high-speed road section is characterized by comprising the following steps of:
S1, acquiring time, place and corresponding meteorological data of the historical rainy and snowy frozen weather of a high-speed road section area, wherein the meteorological data comprise daily average air temperature, lowest air temperature and regional sounding station observation data with the time, geographic position and altitude labels of the historical rainy and snowy frozen weather of the high-speed road section area; the regional sounding station observation data comprise air pressure, wind direction, wind power, relative humidity and atmospheric temperature;
s2, calculating correlation coefficients of different meteorological elements of each observation point and the rain, snow and ice weather according to historical meteorological data of each observation point in the high-speed road section, and constructing a rain, snow and ice prediction model corresponding to each observation point;
the step of constructing the rain and snow freezing prediction model comprises the following steps:
for k observation points of a high-speed road section area, analyzing n kinds of weather data and rain, snow and ice weather results of each observation point based on an empirical orthogonal function analysis method, and screening weather elements corresponding to weather data with obvious correlation with the rain, snow and ice weather results through a stepwise regression algorithm and a saliency check;
calculating the correlation coefficient of weather elements with obvious correlation in corresponding observation points and weather results of rain, snow and ice, and constructing a rain, snow and ice prediction model of the corresponding observation points; the expression of the rain and snow freezing prediction model of the j observation point is as follows:
y′j＝b0+b1x′1+b2x′2+...+blx′l
Wherein b 0 represents a constant term of a regression equation, b l represents a correlation coefficient of a weather element x' i with significant correlation and a weather result of rain, snow and ice, and l is less than or equal to n, j=1, 2, & gt, k;
S3, acquiring meteorological data currently acquired at each observation point of a high-speed road section area, inputting the meteorological data into the rain, snow and ice prediction model corresponding to each observation point, and outputting a rain, snow and ice weather prediction result by the rain, snow and ice prediction model;
s4, meshing the meteorological data currently collected at each observation point of the high-speed road section area and the prediction results of the rain, snow and freezing weather;
Interpolation processing is carried out on the meteorological data and the rain, snow and ice weather prediction results of different high-speed road section areas subjected to meshing processing according to the geographical information of the mileage stake marks of the high-speed road section, so that the meteorological data and the rain, snow and ice weather prediction results of the mileage stake marks are obtained;
And carrying out condition judgment according to a preset forecasting and early-warning condition threshold value, and generating the rain and snow freezing forecasting and early-warning level of each mileage stake mark.
2. The method for warning of rainy, snowy and frozen weather on a highway section according to claim 1, wherein the step of screening out weather elements corresponding to weather data whose correlation with the result of rainy, snowy and frozen weather is significant through the significance test and calculating the correlation coefficient thereof comprises:
Step A, regarding the j observation point, n meteorological element factors x j1,xj2,...,xjn corresponding to meteorological data and a corresponding rainy, snowy and frozen weather result y are provided; the method comprises the steps of constructing a (n+1) x (n+1) order normalized coefficient correlation matrix R j by taking the meteorological elements corresponding to n meteorological data as regression independent variables and the corresponding rainy and snowy frozen weather results y as dependent variables, wherein the expression is as follows:
Wherein r ab represents a normalized variable between the a-th and b-th meteorological element factors in the j-th observation point, where a=1, 2,..n, b=1, 2,., n; r ay represents the regression coefficient of the a-th meteorological element factor and the weather result y; r yb represents regression coefficients of the weather result y and the b weather factor; x ta represents the data of the a-th meteorological element factor in the t observation point, and x tb represents the data of the b-th meteorological element factor in the t observation point; Representing the variable average value of the a-th meteorological element factor in all observation points; /(I) Representing the variable average value of the b-th meteorological element factor in all observation points;
And step B, calculating partial regression square sum V a according to each standardized variable, wherein the expression is as follows:
step C, calculating partial regression square sum V a according to each standardized variable to judge:
If V a is smaller than 0, adding the corresponding a-th meteorological element factor into the selected regression equation factor set;
Otherwise, adding the corresponding a-th meteorological element factors into the factor set to be selected;
Step D, selecting a meteorological element factor corresponding to V a with the minimum absolute value from the selected regression equation factor set for significance test; when the following condition is satisfied:
removing the corresponding meteorological element factors from the selected regression equation factor set, and performing elimination transformation on the factors in a coefficient correlation matrix;
selecting a meteorological element factor corresponding to V a with the largest absolute value from the factor set to be selected for significance test; when the following condition is satisfied:
Then the meteorological element factors are added into a regression equation factor set, and the factors are subjected to meta-transformation in a coefficient correlation matrix;
Wherein phi is the degree of freedom of the sum of squares of the residuals, F 1、F2 is the F-Y distribution value, F 1>F2;Va,max represents V a,Va,min with the largest absolute value and V a with the smallest absolute value;
and E, repeating the step D until all factors are selected or eliminated, and obtaining each regression coefficient b 0,b1,...,bl of the normalized regression equation according to the selected regression equation factor set.
3. The method for early warning of rainy, snowy and frozen weather on a highway section according to claim 1, further comprising the steps of:
according to the observation data of the regional sounding stations of the high-speed road section, carrying out synthetic analysis on the combination of different phase precipitation temperature layers, and establishing different phase precipitation sounding curve models;
and reading the humidity and temperature profile changes of the target high-speed road section area according to the different phase precipitation exploration curve models, obtaining the temperature and humidity conditions of each layer of the atmosphere, analyzing to obtain the precipitation phase state of the target high-speed road section area by judging the reverse humidity level, the reverse temperature condition and the level of water vapor, and correcting the rain, snow and freezing weather prediction result output by the rain, snow and freezing prediction model.
4. A system for early warning of rain, snow and freezing weather of a high-speed road section, which is applied to the method for early warning of rain, snow and freezing weather of a high-speed road section as claimed in any one of claims 1 to 3, and is characterized by comprising:
The data acquisition module is used for acquiring time, place and corresponding meteorological data of the historical rainy and snowy frozen weather of the high-speed road section area and acquiring current meteorological data of the high-speed road section area; the meteorological data collected by the data collection module comprises time, geographic position and daily average air temperature and lowest air temperature of the altitude label of the historical rainy, snowy and frozen weather of the high-speed road section area, and regional sounding station observation data; the regional sounding station observation data comprise air pressure, wind direction, wind power, relative humidity and atmospheric temperature;
the rain, snow and ice prediction module is constructed by calculating the correlation coefficients of different meteorological elements and the rain, snow and ice weather of each observation point according to the historical meteorological data of each observation point in the high-speed road section acquired by the data acquisition module;
the rain, snow and ice prediction module is used for generating a rain, snow and ice weather prediction result according to the input meteorological data;
a data conversion module, the data conversion module comprising:
the meshing processing unit is used for meshing the current meteorological data of the high-speed road section area acquired by the data acquisition module and the rain, snow and freezing weather prediction result generated by the rain, snow and freezing prediction module;
The interpolation processing unit is used for carrying out interpolation processing on the weather data and the rain, snow and freezing weather prediction results which are subjected to gridding processing according to the geographic information of each mileage stake mark of the high-speed road section to obtain the weather data and the rain, snow and freezing weather prediction results of each mileage stake mark;
And the early warning level judging unit is used for carrying out condition judgment according to a preset early warning condition threshold value and generating the rain and snow freezing early warning level of each mileage stake mark.
5. The high-speed road section rainy, snowy and frozen weather warning system of claim 4, further comprising a predictive correction module; the prediction correction module is preset with different phase precipitation sounding curve models obtained by performing synthetic analysis on different phase precipitation temperature layer combinations according to regional sounding station observation data of a high-speed road section;
and the prediction correction module reads the humidity and temperature profile changes of the target high-speed road section area according to different phase precipitation and exploration curve models to obtain the temperature and humidity conditions of each layer of the atmosphere, analyzes the precipitation phase of the target high-speed road section area by judging the reverse humidity layer, the reverse temperature condition and the layer where water vapor is located, and corrects the rain and snow freezing weather prediction result output by the rain and snow freezing prediction model.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06300860A (en) * | 1993-04-15 | 1994-10-28 | Koito Ind Ltd | Road surface freezing prediction method |
| JP2006030139A (en) * | 2004-07-21 | 2006-02-02 | Japan Weather Association | Road surface state predicting system, road surface state prediction method, and road surface state prediction program |
| CN102221714A (en) * | 2011-03-11 | 2011-10-19 | 钱维宏 | Medium-range forecast system and method for low temperature, rain and snow and freezing weather based on atmospheric variable physical decomposition |
| CN111145543A (en) * | 2019-12-24 | 2020-05-12 | 江苏省新通智能交通科技发展有限公司 | Road snow disaster grading early warning system based on snow thickness detection |
| CN111986464A (en) * | 2020-07-15 | 2020-11-24 | 遵义同望智能科技有限公司 | System for forecasting road icing based on dynamic method |
| CN113358040A (en) * | 2021-05-14 | 2021-09-07 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Method and device for acquiring icing thickness of power transmission line and computer equipment |
| KR20220026400A (en) * | 2020-08-25 | 2022-03-04 | 이예슬 | System for real-time weather changes prediction and method thereof |
| CN114280696A (en) * | 2021-12-23 | 2022-04-05 | 无锡九方科技有限公司 | Intelligent sea fog level forecasting method and system |
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06300860A (en) * | 1993-04-15 | 1994-10-28 | Koito Ind Ltd | Road surface freezing prediction method |
| JP2006030139A (en) * | 2004-07-21 | 2006-02-02 | Japan Weather Association | Road surface state predicting system, road surface state prediction method, and road surface state prediction program |
| CN102221714A (en) * | 2011-03-11 | 2011-10-19 | 钱维宏 | Medium-range forecast system and method for low temperature, rain and snow and freezing weather based on atmospheric variable physical decomposition |
| CN111145543A (en) * | 2019-12-24 | 2020-05-12 | 江苏省新通智能交通科技发展有限公司 | Road snow disaster grading early warning system based on snow thickness detection |
| CN111986464A (en) * | 2020-07-15 | 2020-11-24 | 遵义同望智能科技有限公司 | System for forecasting road icing based on dynamic method |
| KR20220026400A (en) * | 2020-08-25 | 2022-03-04 | 이예슬 | System for real-time weather changes prediction and method thereof |
| CN113358040A (en) * | 2021-05-14 | 2021-09-07 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Method and device for acquiring icing thickness of power transmission line and computer equipment |
| CN114280696A (en) * | 2021-12-23 | 2022-04-05 | 无锡九方科技有限公司 | Intelligent sea fog level forecasting method and system |
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