CN114966898A - Early warning method and system for rainy and snowy weather in highway section - Google Patents
Early warning method and system for rainy and snowy weather in highway section Download PDFInfo
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Abstract
The invention relates to the technical field of weather early warning, and provides a rain, snow and ice weather early warning method and system for a highway section, wherein the method comprises the following steps: acquiring time, place and corresponding meteorological data of the weather of rain, snow and ice in the history of the highway section area; calculating correlation coefficients of different meteorological elements of each observation point and the rain and snow freezing weather according to historical meteorological data of each observation point in the highway section, and constructing a rain and snow freezing prediction model corresponding to each observation point; acquiring meteorological data currently acquired by each observation point in the high-speed road section area, inputting the corresponding rain and snow freezing prediction model of each observation point, and outputting a rain and snow freezing weather prediction result by the rain and snow freezing prediction model. The method provided by the invention can be used for finely processing the meteorological data of the high-speed road section area, and dividing each observation point in the high-speed road section to construct a rain and snow freezing prediction model by considering different correlation coefficients of rain and snow freezing weather caused by different altitudes and terrains of different observation points, so that the accuracy of rain and snow freezing 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 weather of rain, snow and ice at a highway section.
Background
At present, because the number of traffic meteorological observation stations along the highway is small, real-time meteorological observation data of any mileage pile number of the highway is difficult to obtain, when the early warning of rain, snow and ice weather of the highway is carried out, the early warning condition of a large-scale road section nearby can be represented by the live data of a plurality of meteorological observation stations, and the road section without the meteorological observation stations can not be accurately early warned.
A dynamic short-term early warning method for an icing risk section of a highway comprises the steps of establishing a road surface temperature time sequence prediction model based on multi-source traffic weather historical data acquired from traffic weather monitoring stations, and rolling to predict a predicted value of road surface temperature change along with time when the traffic weather monitoring stations are positioned in a short time period in the future; calculating according to the difference value of the road temperature between other positions on the road section and the positions of the monitoring stations, and calculating the predicted value of the road temperature at other positions except the monitoring stations at the same moment according to the predicted value of the road temperature at the monitoring stations in a short time period in the future; and finally, judging whether the predicted value of the pavement temperature of the whole section is lower than or equal to 0 ℃ or the freezing point temperature or not and whether the predicted value of the pavement temperature of the whole section is lower than or equal to the dew point temperature at the same moment or not, and judging and predicting the pavement icing risk possibly existing at each position of the whole section of the expressway in time efficiency by combining the currently observed precipitation state. However, the method only carries out temperature prediction through a temperature time series prediction model, and lacks of weight consideration for various meteorological elements, so that the prediction accuracy is low.
Disclosure of Invention
The invention provides a method and a system for early warning of rain, snow and ice weather in a highway section, aiming at overcoming the defect of low prediction accuracy caused by lack of weight consideration of various meteorological elements when the rain, snow and ice weather is predicted.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a rain and snow freezing weather early warning method for a highway section comprises the following steps:
s1, acquiring time, place and corresponding meteorological data of the weather of rain, snow and ice in the history of the high-speed road section area;
s2, calculating correlation coefficients of different meteorological elements of each observation point and rain, snow and ice weather according to historical meteorological data of each observation point in the highway section, and constructing a rain, snow and ice prediction model corresponding to each observation point;
and S3, acquiring current meteorological data acquired by each observation point in the high-speed road section area, inputting the corresponding rain and snow freezing prediction model of each observation point, and outputting a rain and snow freezing weather prediction result by the rain and snow freezing prediction model.
According to the technical scheme, the meteorological data of the high-speed road section area are finely processed, and the rain and snow freezing prediction model is divided and constructed by the observation points in the high-speed road section by considering the difference of the relative coefficients of the rain and snow freezing weather caused by the difference of the altitude and the terrain of the observation points, so that the accuracy of the rain and snow freezing prediction is ensured.
Furthermore, the invention also provides a rain and snow freezing weather early warning system for the highway section and a rain and snow freezing weather early warning method for the highway section, which is provided by applying the technical scheme.
The early warning system for the rain, snow and ice weather at the highway section comprises a data acquisition module and a rain, snow and ice freezing prediction module. The data acquisition module is used for acquiring time, place and corresponding meteorological data of the weather of rain, snow and ice in the history of the highway section area and acquiring current meteorological data of the highway section area. The rain and snow freezing prediction module is constructed by calculating correlation coefficients of different meteorological elements of each observation point and rain and snow freezing weather according to historical meteorological data of each observation point in the highway section acquired by the data acquisition module; the rain and snow freezing prediction module is used for generating a rain and snow freezing 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 method, the weather data of the high-speed road section area are finely processed, and the different correlation coefficients of the rain and snow freezing weather caused by the different altitudes and terrains of the different observation points are considered, so that the rain and snow freezing prediction model is divided and constructed by the observation points in the high-speed road section, and the accuracy of the rain and snow freezing prediction is effectively improved.
Drawings
Fig. 1 is a flowchart of a rain, snow and ice weather early warning method in an expressway in embodiment 1.
Fig. 2 is a flowchart of the early warning method for the rainy, snowy and frozen weather in the highway section in embodiment 2.
Fig. 3 is a flowchart of the construction of the rain and snow freezing prediction model of example 2.
Fig. 4 is an architecture diagram of a rain, snow, ice and weather early warning system in embodiment 3.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The embodiment provides a method for early warning of rain, snow and ice weather at a highway section, which is a flowchart of the method for early warning of rain, snow and ice weather at a highway section in the embodiment as shown in fig. 1.
The early warning method for the rainy, snowy and frozen weather at the highway section provided by the embodiment comprises the following steps of:
and S1, acquiring time, place and corresponding meteorological data of the historical rainy, snowy and frozen weather of the highway section area.
And 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 highway section, and constructing a rain, snow and ice prediction model corresponding to each observation point.
S3, acquiring meteorological data currently acquired by each observation point of the high-speed road section area, inputting the corresponding rain and snow freezing prediction model of each observation point, and outputting a rain and snow freezing weather prediction result by the rain and snow freezing prediction model.
In this embodiment, the acquired meteorological data includes time of a weather that rain, snow and ice appear in the history of the highway section area, daily average temperature, minimum temperature of a geographical position and an altitude label, and observation data of the area air station. The observation data of the regional sounding station comprise air pressure, wind direction, wind power, relative humidity and temperature of each layer of atmosphere.
In one implementation, historical data of the highway section area is collected, and the time and the place (geographical position and altitude) of the rain, snow and ice weather process in the highway section area are screened. And collecting daily average air temperature, minimum air temperature and precipitation data recorded by the national weather observation station and the regional weather observation station 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 weather with the frozen rain and snow is subjected to synthesis analysis, and meteorological element characteristics such as daily average temperature, lowest temperature, air pressure, wind direction, wind power, relative humidity, temperature of each layer of atmosphere, precipitation and the like of the weather with the frozen rain and snow are obtained. And calculating correlation coefficients of different meteorological elements of each observation point and the rain and snow freezing weather according to historical meteorological data of each observation point in the highway section, and constructing a rain and snow freezing prediction model.
In the embodiment, correlation coefficients of different meteorological elements and rain, snow and ice weather are calculated, and an Empirical Orthogonal Function (EOF) analysis method is used for analyzing different meteorological element fields and the rain, snow and ice weather and analyzing the indicative property of upstream meteorological elements on the rain, snow and ice weather in the high-speed road section. And (3) screening meteorological elements with strong correlation with the rain and snow freezing weather through significance test, and establishing a rain and snow freezing prediction model according to the contribution of different meteorological elements to the rain and snow freezing weather.
In a specific application process, acquiring meteorological data currently acquired by each observation point in a high-speed road section area and inputting the meteorological data into the corresponding rain and snow freezing prediction model, wherein the rain and snow freezing prediction model outputs a rain and snow freezing weather prediction result of the corresponding observation point, and early warning of rain and snow freezing weather in the high-speed road section is realized.
In the embodiment, the weather data of the high-speed road section area is finely processed, and the rain and snow freezing prediction model is divided and constructed by each observation point in the high-speed road section by considering the difference of the altitude and the terrain of different observation points to cause the difference of the correlation coefficients of the rain and snow freezing weather, so that the accuracy of the rain and snow freezing prediction is ensured.
Example 2
The embodiment provides a method for early warning of rain, snow and ice weather at a highway section, and is a flowchart of the method for early warning of rain, snow and ice weather at a highway section in the embodiment, as shown in fig. 2.
The early warning method for the rainy, snowy and frozen weather at the highway section provided by the embodiment specifically comprises the following steps:
and S1, acquiring time, place and corresponding meteorological data of the historical rainy, snowy and frozen weather of the highway section area.
In this embodiment, the acquired meteorological data includes time of a historical rainy, snowy and iced weather in a highway section area, daily average temperature and minimum temperature of a geographical position and an altitude label, and observation data of an area sounding station. The observation data of the regional sounding station comprise air pressure, wind direction, wind power, relative humidity and temperature of each layer of atmosphere.
And 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 highway 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 the n kinds of meteorological data of each observation point and the rain, snow and ice weather result based on an empirical orthogonal function analysis method for k observation points in the high-speed road section area, and screening meteorological elements corresponding to the meteorological data with obvious correlation with the rain, snow and ice weather result through a stepwise regression algorithm and significance test.
S2.2, calculating a correlation coefficient of a weather element and a rain and snow freezing weather result with obvious correlation in the corresponding observation point, and constructing a rain and snow freezing prediction model of the corresponding observation point; the expression of the rain and snow freezing prediction model of the jth observation point is as follows:
y′ j ＝b 0 +b 1 x′ 1 +b 2 x′ 2 +...+b l x′ l
wherein, b 0 Constant term representing regression equation, b l Denotes a meteorological element x 'whose correlation is significant' l A coefficient of correlation with rain and snow freezing weather results, and l ≦ n, j ≦ 1, 2.
Fig. 3 is a flowchart of the method for constructing a rain/snow freezing prediction model according to this embodiment.
In this embodiment, the step of screening meteorological elements corresponding to meteorological data whose correlation with the result of the sleet frozen weather is significant through the significance test and calculating the correlation coefficient thereof includes:
step A, for the j observation point, there are n meteorological elements x corresponding to meteorological data j1 ,x j2 ,...,x jn And corresponding rain, snow and ice weather results y j (ii) a Taking the meteorological elements corresponding to the n kinds of meteorological data as regression independent variables, and taking the corresponding n kinds of rain, snow and ice weather results y j Constructing a normalized coefficient correlation matrix R of (n +1) × (n +1) order as a dependent variable j The expression is as follows:
in the formula, r ab Expressing a standardized variable between the meteorological element factor a in the jth observation point and the b-th result in the rain, snow and ice weather result y, wherein a is 1,2, the. r is a(n+1) The regression coefficient of the b-th result in the weather element factor a and the rain, snow and ice weather result y is represented; r is (n+1)b Expressing regression coefficients of the meteorological element factor n and the type b result in the rain, snow and frozen weather result y; x is the number of ta Representing the tth data of the meteorological element factor a;
representing the mean value of the variables for the meteorological element factor a in all observation points;
and represents the mean value of the dependent variable of the rain and snow freezing result b in all observation points.
B, calculating partial regression square sum V according to each standardized variable i The expression is as follows:
step C, calculating partial regression square sum V according to each standardized variable i And (4) judging:
if V i If the value is less than 0, the corresponding ith meteorological element factor x is calculated i Adding a selected regression equation factor set;
otherwise, corresponding ith meteorological element factor x i Adding the factor set to be selectedAnd (6) mixing.
D, selecting V with the minimum absolute value from the selected regression equation factor set i Corresponding meteorological element factor x i Carrying out significance test; when the following conditions are satisfied:
then eliminating the corresponding meteorological element factor x from the selected regression equation factor set i And performing element elimination transformation on the factor in the coefficient correlation matrix R;
selecting V with the maximum absolute value from the factor set to be selected i Corresponding meteorological element factor x i Carrying out significance test; when the following conditions are satisfied:
then factor x will be the meteorological element i Adding the selected regression equation factor set, and performing elimination transformation on the factor in the coefficient correlation matrix R;
wherein phi is the degree of freedom of the residual sum of squares; f 1 、F 2 Is F-Y distribution value, the value of which depends on the number of observation points, the number of selected factors and the selected significant level of the alternative, and F 1 >F 2 When there are many observation points, it can be taken as a constant; v i,max V representing the maximum absolute value i ，V i,min V representing the smallest absolute value i 。
And E, repeating the step D until all factors are selected or removed, and obtaining each regression coefficient b of the normalized regression equation according to the selected regression equation factor set 0 ,b 1 ,...,b l 。
In the above steps, when a certain factor x is to be removed or selected jn All need to correlate the coefficient with the array pitch R j And (3) performing elimination transformation, wherein the algorithm is as follows:
wherein a is 1,2,., n, y, b is 1,2,., n, y, and a, b is not equal to l; when the screening is finished, obtaining each regression coefficient b of the normalized regression equation 0 ,b 1 ,...,b n . The coefficient with a value of 0 indicates that the corresponding argument is a culled factor.
S3, acquiring meteorological data currently acquired by each observation point of the high-speed road section area, inputting the corresponding rain and snow freezing prediction model of each observation point, and outputting a rain and snow freezing weather prediction result by the rain and snow freezing prediction model.
S4, performing gridding processing on meteorological data currently acquired by each observation point of the high-speed road section area and a rain, snow and ice weather prediction result; according to the geographical information of the mileage stake marks of the highway sections, carrying out interpolation processing on the meteorological data and the rain, snow and ice weather prediction results of different areas of the highway sections subjected to gridding processing to obtain the meteorological data and the rain, snow and ice weather prediction results of the mileage stake marks; and (4) judging conditions according to a preset forecast early warning condition threshold value, and generating the rain and snow freezing forecast early warning grade of each mileage stake number.
The method comprises the steps of gridding and interpolating meteorological data of each observation point of the current high-speed road section area and a corresponding rain, snow and ice weather prediction result to be a prediction numerical value of each mileage stake number of the high-speed road section area, wherein the prediction numerical value comprises data of daily rainfall, daily average temperature, daily minimum temperature and potential occurrence index of sleet, or data of accumulated rainfall, daily minimum temperature, daily average temperature, real-time rainfall and the like, and rain, snow and ice prediction early warning grade. And further, the forecasting results after gridding and interpolation processing are visually displayed, so that a driver driving in a corresponding high-speed road section area can intuitively judge the rain, snow and ice conditions of a target driving road section approach area, and the route avoidance is carried out under necessary conditions, so that driving accidents are avoided.
In another embodiment, further comprising the steps of:
according to observation data of regional sounding stations of the high-speed road section, performing synthetic analysis on different phase precipitation temperature stratification junctions, and establishing different phase precipitation sounding curve models;
and reading humidity and temperature profile changes of the target highway section area according to the different phase rainfall sounding curve models to obtain the temperature and humidity conditions of each atmospheric layer, analyzing and obtaining the rainfall phase state of the target highway section area by judging the adverse humidity level, the adverse temperature condition and the level of water vapor, and correcting the rain and snow freezing weather prediction result output by the rain and snow freezing prediction model.
In this step, considering that there is a certain difference in the distribution of temperature stratification of different phase rainfall such as rain, freezing rain, rain with snow, and the like, the present embodiment performs synthetic analysis on the temperature stratification of different phase rainfall to obtain the exploration curve model of different phase rainfall. And (4) adopting a thermodynamic equation to diagnose and analyze the advection cooling of the rain and snow freezing and freezing event. The thermodynamic equation is shown in the following expression:
temperature level advection term of the first term on the right side of the formula
And calculating the temperature advection difference of the selected ground index stations one by one so as to determine the intensity of the cold air and the weakening and influence degree in the process of the south-to-south.
Then further adopting a water vapor flux equation, and selecting the specific humidity q sum of each upper-level index station and each lower-level index station
And calculating the water vapor flux of the atmosphere of each layer in the region so as to judge the possible precipitation condition. The water vapor flux equation is shown as the following expression:
the method comprises the steps of reading temperature and humidity profile changes by selecting a sounding situation of an upstream area of a target high-speed road section area, obtaining the temperature and humidity of each layer of the atmosphere, judging the adverse humidity level, the adverse temperature condition and the level where water vapor mainly exists, and comprehensively analyzing to obtain the precipitation phase state.
Example 3
The embodiment provides a rain, snow and ice weather early warning system for a highway section, which is an architecture diagram of the rain, snow and ice weather early warning system for a highway section in the embodiment, as shown in fig. 4.
In the early warning system for the rainy, snowy and frozen weather in the highway section, the early warning system comprises a data acquisition module 1 and a rainy, snowy and frozen prediction module 2. Wherein:
the data acquisition module 1 is used for acquiring time, place and corresponding meteorological data of the weather of rain, snow and ice in the history of the highway section area, and acquiring current meteorological data of the highway section area.
The rain and snow freezing prediction module 2 is constructed by calculating correlation coefficients of different meteorological elements of each observation point and rain and snow freezing weather according to historical meteorological data of each observation point in the highway section acquired by the data acquisition module 1. The rain and snow freezing prediction module 2 is used for generating a rain and snow freezing weather prediction result according to the input meteorological data.
Further, the meteorological data acquired by the data acquisition module 1 in this embodiment includes time of a rainy, snowy and frozen weather in the regional history of the highway section, daily average temperature, lowest temperature of a geographical position and an altitude label, and observation data of a regional sounding station; the observation data of the regional sounding station comprise air pressure, wind direction, wind power, relative humidity and temperature of each layer of atmosphere.
In the construction process of the rain and snow freezing prediction module 2 in the embodiment, for k observation points in a high-speed road section area, n kinds of meteorological data of each observation point and a rain and snow freezing weather result are analyzed based on an empirical orthogonal function analysis method, and meteorological elements corresponding to the meteorological data with obvious correlation with the rain and snow freezing weather result are screened out through a stepwise regression algorithm and significance test; and calculating the correlation coefficient of the weather elements with obvious correlation in the corresponding observation points and the rain and snow freezing weather results, and constructing the rain and snow freezing prediction model of the corresponding observation points.
Further, the early warning system for the rainy, snowy and frozen weather at 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 judgment unit 303.
The gridding processing unit 301 in this embodiment is configured to perform gridding processing on the current meteorological data of the highway section area acquired by the data acquisition module 1 and the rain and snow freezing weather prediction result generated by the rain and snow freezing prediction module 2.
The interpolation processing unit 302 is configured to perform interpolation processing on the gridded meteorological data and the rain, snow and ice weather prediction result according to the geographic information of each mileage stake number of the highway segment to obtain the meteorological data and the rain, snow and ice weather prediction result of each mileage stake number.
The early warning level judgment unit 303 is configured to perform condition judgment according to a preset forecast early warning condition threshold value, and generate a rain and snow freezing forecast early warning level for each mileage stake number.
Further, the early warning system for the rainy, snowy and frozen weather at the highway section in the embodiment further comprises a prediction correction module 4; the prediction correction module 4 is preset with different phase precipitation sounding curve models obtained by performing synthetic analysis on different phase precipitation temperature level junctions according to regional sounding station observation data of the highway section.
In this embodiment, the prediction and correction module 4 reads humidity and temperature profile changes of a target highway section area according to different phase rainfall sounding curve models to obtain temperature and humidity conditions of each layer of the atmosphere, analyzes and obtains rainfall phase states of the target highway section area by judging a reversed humidity level, a reversed temperature condition and a water vapor level, and corrects a rain and snow freezing weather prediction result output by the rain and snow freezing prediction model.
In this embodiment, a specific implementation process is exemplified by the weather process of the cold tide in 12 th of 2021 in the high-speed northern Guangdong section of Kyoto hong Kong.
(1) And acquiring a forecast data source.
And (3) starting the early warning system for the rainy, snowy and frozen weather on the highway section at 12 months, 24 days and 16 days in 2021. According to a rain and snow freezing forecast early warning condition threshold value configuration table customized by a user, a forecast data source which is reported at 12 months and 24 days (UTC) is obtained through a data acquisition module 1, and the forecast data source comprises GIFTDAILY (refined grid point interactive forecast system) daily rainfall in the future 7 days, 2 m daily average temperature, 2 m daily minimum temperature and ECMWFTHIN (European mid-term weather forecast center fine grid numerical forecast) gridded daily average temperature, daily minimum temperature and gridded FRGPI (freezing rain potential occurrence index) at the height of 925 hectopascals.
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 gridding data, and writes the forecast gridding data into the cache.
Meanwhile, the data acquisition module 1 sends the acquired meteorological data to the constructed rain and snow freezing prediction module 2, and the rain and snow freezing prediction module 2 generates a rain and snow freezing weather prediction result according to the input meteorological data.
(2) And converting the forecast grid data into single-point data.
The interpolation processing unit 302 interpolates the grid data output by the calculation in the step (1) to the geographical position of the highway milepost number by a near point interpolation method (horizontal grid data is interpolated to a discrete point of a plane, and the value of each point is set to be the value of the nearest one of four grid points around the point) according to the geographic information (the stake number ID, the longitude, the latitude and the altitude) of the highway milepost number, reads temperature forecast data of different levels according to different altitudes of different milepost numbers in a temperature forecast condition, generates the future 7-day rainfall, the daily average air temperature, the daily minimum air temperature, the forecast result of rain, snow, ice and weather and the like of each milepost number of the highway, and writes the data into a cache.
(3) And (6) judging the early warning grade condition of each mileage stake number.
The early warning level judgment unit 303 generates a rain and snow freezing forecast early warning level condition judgment (i) for each milepost number by performing condition judgment on data such as future 7-day rainfall, daily average temperature, daily minimum temperature, rain and snow freezing weather prediction results and the like of each milepost number output in the step (2) in a traversing manner according to a rain and snow freezing situation early warning condition threshold configuration table defined by a user, and writes the data into a cache.
At the moment, the rain and snow freezing forecast early warning grade condition of each mileage pile number calculated through the forecast condition is as follows: the risk grade of each mileage stake number in three sections of K1842-K1853 and K1863-K1918 is 4, namely the early warning grade of 27 days in 12 months is judged to be 4.
(4) A live data source is acquired.
The data acquisition module 1 acquires a live data source at 24 days and 16 days in 12 months through a data interface according to a rain and snow freezing forecast early warning condition threshold configuration table customized by a user, comprises refined multi-source fusion live meteorological element field data in a Shaoguan area, and comprises CLDAS (China weather service bureau land data assimilation system) air temperature data and CMPA (China multi-source precipitation fusion system) rainfall data, and stores the data in a local file in time.
(5) And processing the local file data.
The data acquisition module 1 sends the data acquired in step (4) to the gridding processing unit 301 for data conversion, so as to obtain the following data: CMPA (China multi-source precipitation fusion system) gridding accumulated rainfall data for the last 3 days and 5 days, CLDAS (China weather service bureau land data assimilation system) gridding daily minimum temperature data for the last 3 days and 5 days, and gridding daily average 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 and snow freezing prediction module 2, the rain and snow freezing prediction module 2 generates a rain and snow freezing weather prediction result according to the input meteorological data, and writes the rain and snow freezing weather actual prediction result into a cache.
(6) The live mesh data is converted into single point data.
The interpolation processing unit 302 interpolates the grid data output by the calculation in the step (5) to the geographical position of the highway mileage stake number by a near point interpolation method (horizontal grid data is interpolated to discrete points of a plane, and the value of each point is set to be the value of the nearest one of four grid points around) according to the geographic information (stake number ID, longitude and latitude) of the highway mileage stake number, generates the past N days accumulated rainfall, the daily minimum air temperature and the daily average air temperature of each mileage stake number of the highway, and writes the data into a cache.
(7) Judging the early warning grade condition of each mileage stake mark.
The early warning level judgment unit 303 performs condition judgment by traversing the accumulated rainfall, daily minimum temperature and daily average temperature data of the past 3 days and 5 days of each mileage stake number output in the step (6) according to the rain and snow freezing condition threshold configuration table defined by the user, generates rain and snow freezing forecast early warning level condition judgment of each mileage stake number, and writes the data into the cache.
At the moment, the rain and snow freezing forecast early warning level condition of each mileage stake number calculated through the live condition is as follows: and the risk grade of the stake number of each mileage in three sections of K1842-K1853 and K1863-K1918 is 0 grade, namely that each section does not reach the condition of rain, snow and freezing live condition.
(8) And comprehensively judging the early warning grade condition of each mileage stake mark.
The early warning level judgment unit 303 outputs early warning levels of the mileage stake marks through traversing the early warning levels of the mileage stake marks output in the step (3) and the step (7) according to a rain and snow freezing condition threshold configuration table customized by the user, comprehensively forecasts the early warning levels of the mileage stake marks and judges the actual warning levels of the mileage stake marks of the mileage.
At the moment, the forecasting conditions and the actual conditions are comprehensively judged, and only the forecasting conditions are met because the actual conditions are not met, and 4-level yellow early warning of rain and snow freezing is expected to occur in mileage stake numbers in three sections of K1842-K1853 and K1863-K1918 of 27 months according to a rain and snow freezing forecasting early warning level condition threshold table.
(9) And generating branch section forecasting and early warning.
And (4) processing each mileage pile number forecasting and early warning grade output in the step (8) into component road rain and snow freezing forecasting and early warning data according to a user-defined Shaoguan expressway segment table. The system output at this time is shown in table 1 below:
TABLE 1 prediction and early warning grade of each mileage stake number
| Mileage column number | Road section name | Type of early warning | Early warning level |
| K1842-K1853 | Pond-terrace stone | Rain and snow freezing forecast early warning | 4 |
| K1863-K1879 | Plum blossom-cloud rock | Rain and snow freezing forecast early warning | 4 |
| K1879-K1899 | Cloud rock-bridge | Rain and snow freezing forecast early warning | 4 |
The early warning is carried out by adopting the early warning system for the rainy and snowy frozen weather in the high-speed Yuebei section area in Beijing hong Kong and Australia in 12 th of 2021, after the related departments receive early warning information of rainy and snowy frozen 4-grade forecast issued by a city meteorological department in 24 days of 12 months, the emergency plan is started in time, and traffic control measures are started in 19 days of 26 days of 12 months, and the measures are as follows: beijing pearl Beijing of Ping Shi Beijing (K5) is closed towards Guangzhou at high speed, and the bridge station is divided into streams towards the exit of Hunan; the entrance of the terrace stone station towards the Guangzhou direction is closed, and the entrance of the plum blossom station towards the Guangzhou direction is closed; the entrance of the large bridge station to the Hunan direction is closed; the milk source station is closed at the inlet towards the Hunan direction; the entrance of the Dongtan station towards the Hunan direction is closed. 12, 27, 8, 30 minutes, and the investigation conditions of the field part road sections are as follows: k1882 prevents throwing the net and has icing phenomenon, the road surface is moist. Thin ice is arranged on the bridge surface from K1874 to K1885 and the guardrail. The K1863-K1879 plum blossom-nephrite, K1879-K1899 nephrite-bridge section are consistent with the rain and snow freezing forecast early warning section result actually triggered by the system.
In the embodiment, the early warning system for the rainy and snowy frozen weather of the highway section with high refinement degree is established, the basic geographic information data of the highway mileage pile number is introduced, the meteorological data is comprehensively analyzed according to the actual mileage pile number altitude, and the forecast for the rainy and snowy frozen weather is generated, so that the early warning for the rainy and snowy frozen weather of the highway section is further performed, and the high early warning accuracy is ensured.
Further, according to the time gradient influenced by the severe weather of rain, snow and freezing, the early warning system can provide three progressive early warning products of forecast early warning, approach early warning and live early warning.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A rain and snow freezing weather early warning method for a highway section is characterized by comprising the following steps:
s1, acquiring time, place and corresponding meteorological data of the weather of rain, snow and ice in the history of the high-speed road section area;
s2, calculating correlation coefficients of different meteorological elements of each observation point and rain, snow and ice weather according to historical meteorological data of each observation point in the highway section, and constructing a rain, snow and ice prediction model corresponding to each observation point;
s3, acquiring meteorological data currently acquired by each observation point of the high-speed road section area, inputting the corresponding rain and snow freezing prediction model of each observation point, and outputting a rain and snow freezing weather prediction result by the rain and snow freezing prediction model.
2. The method for warning rainy and snowy frozen weather on highway sections according to claim 1, wherein the acquired meteorological data in the step S1 comprises time of occurrence of rainy and snowy frozen weather in highway sections, daily average air temperature, minimum air temperature with geographical position and altitude labels, and observation data of regional sounding stations; the observation data of the regional sounding station comprise air pressure, wind direction, wind power, relative humidity and temperature of each layer of atmosphere.
3. The method for early warning of rainy and snowy freezing weather on the highway section according to claim 1, wherein the step of constructing a rainy and snowy freezing prediction model in the step of S2 comprises:
analyzing n kinds of meteorological data and the rain, snow and ice weather results of each observation point on the basis of an empirical orthogonal function analysis method for k observation points in the high-speed road section area, and screening out meteorological elements corresponding to the meteorological data with obvious correlation with the rain, snow and ice weather results through a stepwise regression algorithm and significance test;
calculating the correlation coefficient of the weather elements with obvious correlation in the corresponding observation points and the rain and snow freezing weather results, and constructing a rain and snow freezing prediction model of the corresponding observation points; the expression of the rain and snow freezing prediction model of the jth observation point is as follows:
y′ j ＝b 0 +b 1 x′ 1 +b 2 x′ 2 +...+b l x′ l
wherein, b 0 Constant term representing regression equation, b l Denotes a meteorological element x 'in which the l-th correlation is significant' l A coefficient of correlation with rain and snow freezing weather results, and l ≦ n, j ≦ 1, 2.
4. The method for early warning of rainy and snowy frozen weather on highway sections according to claim 3, wherein the step of screening meteorological elements corresponding to meteorological data with significant correlation to the result of rainy and snowy frozen weather through significance test and calculating correlation coefficients of the meteorological elements comprises the following steps:
step A, for the j observation point, there are n meteorological elements x corresponding to meteorological data j1 ,x j2 ,...,x jn And n corresponding weather results y j (ii) a Taking the meteorological elements corresponding to the n kinds of meteorological data as regression independent variables, and taking the corresponding n kinds of rain, snow and ice weather results y j Constructing a normalized coefficient correlation matrix R of (n +1) × (n +1) order as a dependent variable j The expression is as follows:
in the formula, r ab Expressing a standardized variable between the weather element factor a in the jth observation point and the b-th result in the rain, snow and ice weather result y, wherein a is 1,2, the. r is a(n+1) The regression coefficient of the b-th result in the weather element factor a and the rain, snow and ice weather result y is represented; r is (n+1)b Expressing regression coefficients of the meteorological element factor n and the b-th result in the rain, snow and frozen weather result y; x is the number of ta Representing the tth data of the meteorological element factor a;
representing the mean value of the variables for the meteorological element factor a in all observation points;
representing the mean value of the dependent variables of the rain and snow freezing result b in all the observation points;
b, calculating partial regression square sum V according to each standardized variable i The expression is as follows:
step C, calculating partial regression square sum V according to each standardized variable i And (4) judging:
if V i If the value is less than 0, the corresponding ith meteorological element factor x is calculated i Adding a selected regression equation factor set;
otherwise, corresponding ith meteorological element factor x i Adding a factor set to be selected;
step D, selecting the minimum absolute value from the selected regression equation factor setV of i Corresponding meteorological element factor x i Carrying out significance test; when the following conditions are satisfied:
then eliminating the corresponding meteorological element factor x from the selected regression equation factor set i And performing element elimination transformation on the factor in the coefficient correlation matrix R;
selecting the V with the maximum absolute value from the factor set to be selected i Corresponding meteorological element factor x i Carrying out significance test; when the following conditions are satisfied:
then factor x the meteorological element i Adding the selected regression equation factor set, and performing elimination transformation on the factor in the coefficient correlation matrix R;
where φ is the degree of freedom of the sum of the squares of the residuals, F 1 、F 2 Is a distribution value of F-Y, and F 1 >F 2 ；V i,max V representing the maximum absolute value i ，V i,min V representing the minimum absolute value i ；
E, repeating the step D until all factors are selected or removed, and obtaining each regression coefficient b of the normalized regression equation according to the selected regression equation factor set 0 ,b 1 ,...,b l 。
5. A rain, snow and ice weather early warning method for a highway section according to any one of claims 1 to 4, further comprising the following steps of:
s4, performing gridding processing on meteorological data currently acquired by each observation point of the high-speed road section area and a rain, snow and ice weather prediction result;
according to the geographical information of the mileage stake marks of the highway sections, carrying out interpolation processing on the meteorological data and the rain, snow and ice weather prediction results of different areas of the highway sections subjected to gridding processing to obtain the meteorological data and the rain, snow and ice weather prediction results of each mileage stake mark;
and (4) judging conditions according to a preset forecast early warning condition threshold value, and generating the rain and snow freezing forecast early warning grade of each mileage stake number.
6. The early warning method for rainy and snowy frozen weather on highway section according to claim 5, further comprising the following steps:
according to observation data of regional sounding stations of the high-speed road section, performing synthetic analysis on different phase precipitation temperature level junctions, and establishing different phase precipitation sounding curve models;
and reading humidity and temperature profile changes of the target highway section area according to the different phase rainfall sounding curve models to obtain the temperature and humidity conditions of each atmospheric layer, analyzing and obtaining the rainfall phase state of the target highway section area by judging the adverse humidity level, the adverse temperature condition and the level of water vapor, and correcting the rain and snow freezing weather prediction result output by the rain and snow freezing prediction model.
7. A rain and snow freezing weather early warning system for a highway section is applied to the rain and snow freezing weather early warning method for the highway section according to any one of claims 1 to 6, and is characterized by comprising the following steps:
the data acquisition module is used for acquiring time, place and corresponding meteorological data of the weather of rain, snow and ice in the history of the highway section area and acquiring current meteorological data of the highway section area;
the rain and snow freezing prediction module is used for constructing and obtaining the rain and snow freezing weather by calculating correlation coefficients of different meteorological elements of each observation point and the rain and snow freezing weather according to historical meteorological data of each observation point in the highway section acquired by the data acquisition module;
the rain and snow freezing prediction module is used for generating a rain and snow freezing weather prediction result according to the input meteorological data.
8. The rain, snow and ice weather early warning system for the highway section according to claim 7, wherein the meteorological data collected by the data collection module comprises time of the historical rain, snow and ice weather of the highway section, daily average air temperature, minimum air temperature of geographical position and altitude labels, and observation data of a regional sounding station; the observation data of the regional sounding station comprise air pressure, wind direction, wind power, relative humidity and temperature of each layer of atmosphere.
9. The system of claim 7, further comprising a data conversion module; the data conversion module comprises:
the gridding processing unit is used for gridding the current meteorological data of the high-speed road section area acquired by the data acquisition module and the rain and snow freezing weather prediction result generated by the rain and snow freezing prediction module;
the interpolation processing unit is used for carrying out interpolation processing on the meteorological data subjected to gridding processing and the rain, snow and ice weather prediction result according to the geographic information of each mileage stake number of the high-speed road section to obtain the meteorological data and the rain, snow and ice weather prediction result of each mileage stake number;
and the early warning grade judging unit is used for carrying out condition judgment according to a preset forecast early warning condition threshold value and generating the rain and snow freezing forecast early warning grade of each mileage stake number.
10. The system of claim 7, further comprising a predictive correction module; the prediction correction module is internally preset with different phase precipitation sounding curve models obtained by performing synthetic analysis on different phase precipitation temperature level junctions according to regional sounding station observation data of a high-speed road section;
the forecasting and correcting module reads humidity and temperature profile changes of the target high-speed road section area according to different phase rainfall sounding curve models to obtain the temperature and humidity conditions of each atmospheric layer, analyzes and obtains the rainfall phase state of the target high-speed road section area by judging the adverse humidity level, the adverse temperature condition and the level of water vapor, and corrects the rain-snow freezing weather forecasting result output by the rain-snow freezing forecasting model.
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