CN110334318A - Road Operation Decision method, service platform and system based on meteorological big data - Google Patents
Road Operation Decision method, service platform and system based on meteorological big data Download PDFInfo
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Abstract
The present invention discloses a kind of road Operation Decision method based on meteorological big data, comprising the following steps: collects, assimilation data, and numerical analysis is handled, obtain final forecast result；Final forecast result is subjected to water film thickness calculating；Differentiate pavement behavior；Instruct road Operation Decision.Invention additionally discloses a kind of service platforms, comprising: for collect data data collection module, for Road Weather forecast Road Weather forecast module, for the road Operation Decision module of road Operation Decision and for the info push module of pushed information.Invention additionally discloses a kind of systems, including service platform, and the automatic weather station and terminal that connect with service platform.The present invention improves the accuracy and Road Weather accuracy of the forecast of detecting road status；The present invention establishes road surface dry and wet state inverting, for meeting the needs of highway communication Meteorological Services.
Description
Technical field
The present invention relates to meteorological detection and forecasting technique field more particularly to a kind of road road transports based on meteorological big data
Seek decision-making technique, service platform and system.
Background technique
With the development and scientific and technological progress of social economy, communications and transportation has become national economy production and people society is raw
Important lifeblood living, however, the links of communications and transportation are all directly affected by meteorological condition.According to statistics, China
Having in traffic accident on highway has the ratio for having 65% in 71%, direct economic loss hair in 50%, particularly serious traffic accident
It gives birth in the bad weather generated by bad-weather condition, and in numerous influent factors, condition of road surface (ponding or icing),
The influence of visibility is especially pronounced.The study found that rainwater is generated along road surface direction of maximum slope gravity when precipitation
Slope runoff forms moisture film, the reduction of pavement friction value is caused, to influence traffic safety.
For road water film thickness, with it is more be " British experience formula ", i.e., continue coagulation under precipitation by studying
The ponding thickness of soil and bituminous pavement, discovery ponding thickness (d) and length of drainage (L, m), precipitation intensity (I, cm/h) and road surface
The gradient (1/N) is related, proposes the relational expression between three: d=0.015 (L × I)1/2N1/5
Traffic accident and economic loss caused by reduce because of bad-weather condition, the prior art have combined meteorological detection
" British experience formula " releases traffic weather forecast and pavement behavior Warning Service, and but there are the following problems:
(1) current traffic weather reporting services mostly utilize automatic meteorological observation equipment, fixed in one or more
Place, carry out the monitoring of conventional meteorological element, then early warning is carried out with detection result, there is certain hysteresis quality；
(2) existing traffic weather reporting services, the forecast of mostly conventional meteorological element, forecast precision is unstable, and simultaneously
It is not directed to the data inversion for pavement state；
(3) " British experience formula " considers the influence of road grade and precipitation to water film thickness emphatically, does not consider
The influences of meteorological elements and vehicle to road moisture film such as evaporation, so calculated value is relatively large, and strong due to containing precipitation
Degree, so the water film thickness during only estimating precipitation.
Therefore, the prior art is defective, needs to improve.
Summary of the invention
The purpose of the present invention is overcome the deficiencies of the prior art and provide a kind of road operation based on meteorological big data to determine
Plan method, service platform and system.
Firstly, the present invention provides a kind of road Operation Decision method based on meteorological big data, comprising the following steps:
Step S1 collects the initial fields and boundary number of fields of real-time weather element observation data and Numerical Prediction Models
According to；
Step S2, initial fields and the side of real-time weather element observation data and Numerical Prediction Models that step S1 is collected
Boundary's field data imports data pool, after data assimilation, executes numerical forecast tool NONLINEAR CALCULATION；
The calculated result of step S2 and real-time weather element observation data are carried out linear analysis processing, obtained by step S3
One group of final forecast result；
Final forecast result (except visibility) is imported and customizes algorithm progress water film thickness calculating by step S4；
Step S5 sentences according to water film thickness calculated result according to the pavement behavior grade scale customized in algorithm
Other pavement behavior；
Step S6 instructs road referring to road Operation Decision standard according to the forecast result of visibility and pavement behavior
Operation Decision.
Further, the real-time weather element observation data in step S1 are by being installed on vehicle or specifying after integrating
The automatic weather station in location obtains, and acquired real-time weather element is selected from: dry-bulb temperature (temperature), wet-bulb temperature, wind speed,
It is several in rainfall, snowfall intensity, evaporation capacity, radiation flux, pavement temperature, roadbed temperature and visibility, and by having
Line or wireless transmission method are real-time transmitted to cloud server.
Further, the data assimilation in step S2 passes through three-dimensional variational algorithm (3D-Var) and realizes, three-dimensional variational algorithm
(3D-Var) takes following formula to calculate:
Wherein, O is the observation of meteorological variables, and F is the background value (forecast of average value or numerical model of meteorological variables
Value), σOFor the variance of meteorological variables observation, σFFor the variance of meteorological variables background value.
Further, the numerical forecast tool in step S2 is Mesoscale Numerical Forecast tool (WRF), mesoscale numerical value
The physico parametric scheme of the WRF used described in forecasting tool includes: SLAB heat diffusion scheme, Noah scheme and the side RUC
Case, each scheme calculate separately out one group of forecast result.
Further, the linear analysis in step S3 is by way of establishing multiple linear regression model, by time series
It is divided into training period and forecast period, training period is sliding training period；Multiple linear regression model is executed in training period and is tied to forecast
The multiple linear regression analysis of fruit and real-time weather element observation data, calculates separately the weight coefficient of forecast result, and according to
The final forecast result of weight coefficient acquisition forecast period.
Further, the linear analysis in step S3 passes through following equation to a certain meteorological element of a certain Time effect forecast
Carry out regression analysis:
O is the average observed value of a certain meteorological element training period, αiFor participate in set ith member weight coefficient,
FiWithIt is the predicted value and its forecast average value in training period of i-th of mode respectively, N is to participate in superset to forecast
Mode sum；
Wherein, weight coefficient αiIt is calculated and is obtained by the minimum of the error term G in training period equation:
N is the sum of training period time samples, S 'tWith O 'tThe respectively deviation of the superset of training period and observation field.
Further, the customization algorithm in step S4 is Sass surface gathered water icing model, and water film thickness passes through
Sass surface gathered water icing model calculates, and calculates especially by following formula:
Wherein, WlFor the liquid water accumulating volume (kg/m on road surface2), WsFor the solid-state water accumulating volume (kg/m on road surface2), PrFor rainfall
Intensity (mm/min), PsFor snowfall intensity (mm/min),Icing or melted mass for the surface water, R are net radiation flux,
LfFor the ablation heat of water, GlFor downward heat flux, r is run-off, and E is evaporation capacity.
Secondly, the present invention provides a kind of road Operation Decision service platform based on meteorological big data, comprising:
Data collection module, including real-time weather acquisition unit, forecast numerical search unit and data storage cell,
Real-time weather acquisition unit is installed on real-time weather acquired in vehicle or the automatic weather station in specified location and wants for collecting
Element observation data, forecast numerical search unit are used to search for and collect the initial fields and boundary field data of Numerical Prediction Models, number
It is used to store real-time weather acquisition unit according to storage unit and forecasts the data that numerical search unit is collected；
Road Weather forecast module, including data assimilation unit, weather simulation unit and forecast unit, data assimilation
Unit is embedded with three-dimensional variational algorithm (3D-Var), for assimilating the data of data storage cell；Weather simulation unit is embedded with
Mesoscale Numerical Forecast tool (WRF), the data parameters that Mesoscale Numerical Forecast tool (WRF) assimilates data assimilation unit
Change to simulate synoptic process, forecast unit is embedded with multiple linear regression model algorithm, passes through the data to weather simulation unit
Linear regression analysis is carried out, one group of final Road Weather forecast result is generated；
Road Operation Decision module, including road condition analyzing unit and Operation Decision unit, road condition analyzing unit are embedded with
Road Weather forecast result (except visibility) is imported Sass surface gathered water knot by Sass surface gathered water icing model algorithm
Ice model calculates rain water depth on road surface, provides pavement behavior forecast result, and Operation Decision unit is used for according to visibility and road surface
Condition prognosis is as a result, provide road Operation Decision opinion.
Further, service platform further includes info push module, and info push module includes information push unit, is used
In forecast result and road Operation Decision opinion to terminal push Road Weather, pavement behavior, and road is broadcasted by terminal
The meteorological, forecast result of pavement behavior and road Operation Decision opinion.
Finally, the present invention also provides a kind of road Operation Decision system based on meteorological big data, including above-mentioned road
Operation Decision service platform, and the automatic weather station and terminal that are connect with road Operation Decision service platform.
Compared with prior art, the invention has the following advantages:
1, vehicle-mounted automatic weather station is added in the present invention, and realization is observed a variety of meteorological elements in moving process, compared with
In fixed point monitoring, expand observation section, improve observation accuracy；
2, invention introduces Sass surface gathered water icing model, which introduces according to ground water household principle
The meteorological factors such as precipitation, temperature, relative humidity, wind speed, evaporation, sun net radiation, road pavement moisture film predicted, compared to
Commonly " British experience formula ", improves the accuracy of detecting road status；
3, the present invention carries out express highway pavement using the physico parametric scheme of WRF and Sass surface gathered water icing model
Dry and wet state inverting establishes the inverse model of the situations such as road surface drying, humidity, ponding, accumulated snow, icing, traffic weather is observed
Logging data application also further expands or the perfect research method of traffic weather in the forecast of traffic weather pavement behavior, meets
The demand of highway communication Meteorological Services；
4, when the present invention gathers forecast result, using sliding training period, weight coefficient changes at any time,
Improve accuracy.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will to embodiment or
Attached drawing needed to be used in the description of the prior art is briefly described, it should be apparent that, the accompanying drawings in the following description is only
Some embodiments of the present invention, for those of ordinary skill in the art, without creative efforts, also
The structure that can be shown according to these attached drawings obtains other attached drawings.
Fig. 1 is road Operation Decision flow chart of the invention；
Fig. 2 is the structural schematic diagram of service platform of the present invention；
Fig. 3 is system structure diagram of the invention.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.
Shown in referring to Fig.1, the present invention provides a kind of road Operation Decision method based on meteorological big data, including following
Step:
Step S1 collects the initial fields and boundary number of fields of real-time weather element observation data and Numerical Prediction Models
According to；
Step S2, initial fields and the side of real-time weather element observation data and Numerical Prediction Models that step S1 is collected
Boundary's field data imports data pool, after data assimilation, executes numerical forecast tool NONLINEAR CALCULATION；
The calculated result of step S2 and real-time weather element observation data are carried out linear analysis processing, obtained by step S3
One group of final forecast result；
Final forecast result (except visibility) is imported and customizes algorithm progress water film thickness calculating by step S4；
Step S5 sentences according to water film thickness calculated result according to the pavement behavior grade scale customized in algorithm
Other pavement behavior；
Step S6 instructs road referring to road Operation Decision standard according to the forecast result of visibility and pavement behavior
Operation Decision.
As one embodiment, the real-time weather element observation data in step step S1 are by being installed on vehicle after integrating
Or the automatic weather station in specified location obtain, acquired real-time weather element is selected from: dry-bulb temperature (temperature), wet bulb
If in temperature, 2m wind speed, rainfall, snowfall intensity, evaporation capacity, radiation flux, pavement temperature, roadbed temperature and visibility
Dry kind, and cloud server is real-time transmitted to by wired or wireless transmission mode.
And Numerical Prediction Models are GFS Numerical Prediction Models, the initial fields and boundary field data of GFS Numerical Prediction Models
From Environmental forecasting centre (NECP), data are more accurate and stablize, so that the forecast knot that data processing generates
The accuracy of fruit is ensured.
In the present embodiment, the data assimilation in step S2 passes through three-dimensional variational algorithm (3D-Var) and realizes, three-dimensional variation
Algorithm (3D-Var) takes following formula to calculate:
Wherein, O is the observation of meteorological variables, and F is the background value (forecast of average value or numerical model of meteorological variables
Value), σOFor the variance of meteorological variables observation, σFFor the variance of meteorological variables background value.
In using Numerical Prediction Models simulation synoptic process, in order to preferably describe the physical process of sub-grid scale,
Numerical forecast tool is Mesoscale Numerical Forecast tool (WRF) in the present embodiment, and uses Mesoscale Numerical Forecast tool
(WRF) the land-surfac e process scheme in improves the effect of simulation to the physical process parameter of sub-grid scale.As
A kind of embodiment, land-surfac e process scheme include:
SLAB heat diffusion scheme, from 5 layers of soil moisture mode of MM5,5 layer depths be respectively 1cm, 2cm,
4cm, 8cm and 16cm are deeper partially fixed on daily mean, the NONLINEAR CALCULATION to energy include: radiation, sensible heat and
Latent heat flux, and allow snow-clad presence；
Noah scheme, the Forecast Mode of 4 layers of soil moisture and humidity, and can calculate tree crown transpiration and etc. waters snow depth,
Export the runoff volume on ground and underground；In processing evaporation and transpiration, it is contemplated that vegetation, soil types and vegetation refer to
Number；Noah scheme can forecast that soil ice and snow covering influence, and improve urban canopy layer, simultaneously, it is also contemplated that surface radiation
Coefficient；
RUC scheme contains 6 layers of soil and 2 layers of snow covering treatment, it is contemplated that the temperature and close of frozen soil, broken snow and snow
Degree variation, Vegetation Effect and tree crown are rising；The program solves the energy of 6 layers of soil heat and humidification delivery equation and earth's surface
And water household equation, and surface flux is calculated with implicit aspect, the latent heat that the parametrization of frozen soil considers phase transformation in soil is released
It puts, while considering influence of the ice in soil to vapor transfer；
By the NONLINEAR CALCULATION of three groups of schemes, each scheme calculates separately out one group of forecast result.
In the present embodiment, the linear analysis in step S3 is by way of establishing multiple linear regression model, by the time
Sequence is divided into training period and forecast period；Multiple linear regression model wants forecast result and real-time weather in training period execution
The multiple linear regression analysis of element observation data calculates separately the weight coefficient of forecast result, and is obtained in advance according to weight coefficient
The final forecast result of report phase.As one embodiment, training period is sliding training period, and system constantly accesses subsequent real-time
Meteorological measuring and forecast result compare verifying, obtain newest weight coefficient.
In the present embodiment, the linear analysis in step S3 passes through a certain meteorological element of a certain Time effect forecast following
Formula carries out regression analysis:
O is the average observed value of a certain meteorological element training period, αiFor participate in set ith member weight coefficient,
FiWithIt is the predicted value and its forecast average value in training period of i-th of mode respectively, N is to participate in superset to forecast
Mode sum；
Wherein, weight coefficient αiIt is calculated and is obtained by the minimum of the error term G in training period equation:
N is the sum of training period time samples, S 'tWith O 'tThe respectively deviation of the superset of training period and observation field.
In the present embodiment, the customization algorithm in step S4 is Sass surface gathered water icing model, and water film thickness is logical
The calculating of Sass surface gathered water icing model is crossed, is calculated especially by following formula:
Wherein, WlFor the liquid water accumulating volume (kg/m on road surface2), WsFor the solid-state water accumulating volume (kg/m on road surface2), PrFor rainfall
Intensity (mm/min), PsFor snowfall intensity (mm/min),For ground water freezing or thawing only active near 0 DEG C
Amount, R is net radiation flux, LfFor the ablation heat of water, GlFor downward heat flux, r is run-off, and E is evaporation capacity；
Wherein, λ is the heat conductivity of pitch, TrFor pavement temperature (0cm), TrbFor roadbed temperature (10cm), Δ Z is
The difference in height of roadbed and road surface；
Wherein, C=0.003s-1, Wr=1.0kg/m2；
Wherein, it in temperature is T that Δ, which is saturation vapour pressure curve,αThe slope at place, r are wet and dry bulb constant, EαTo be air-dried
Rate；
Eα=0.138 × (1+0.725 × V2)(es-e)
Wherein, (es- e) it is saturated water draught head, V2For 2m wind speed；
H is Surface radiation budget value
H=S (1- α)+ε I- ε σ Tr 4
Wherein, S is incident radiation amount, and α is Planetary albedo (no snow road surface takes 0.1), and ε I is the infrared spoke of ground absorption
It penetrates, ε is emissivity (taking 0.92), ε σ T4 rFor the averaged long wave radiation flux of ground launch.
It is the pavement behavior classification practical application in step S5, based on Sass surface gathered water icing model below.
Table 1 is detecting road status grade scale table, including liquid water accumulating volume, solid-state water accumulating volume and pavement behavior.
Table 1
| Liquid water accumulating volume | Solid-state water accumulating volume | Pavement behavior |
| 0 | 0 | It is dry |
| 0-1 | 0 | It is moist |
| >1 | 0 | Ponding |
| - | >0 | Ice/snow |
Pavement behavior classification and judgment method:
Road surface solid-state water accumulating volume Ws> 0 is ice or snow road surface；Road surface solid-state water accumulating volume Ws=0, road surface liquid water accumulating volume Wl>1
For ponding road surface；Road surface solid-state water accumulating volume Ws=0, road surface 0 < W of liquid water accumulating volumel< 1 is wet road surface；Road surface solid-state water accumulating volume
WsWith road surface liquid water accumulating volume WlZero is equal to as dry pavement.
As one embodiment, Visibility Forecast result based in conventional weather forecast cloud water density, cloud ice concentration,
The COMPREHENSIVE CALCULATING of rainwater density and snow density, and Visibility Forecast knot is further corrected as reference standard using aerosol density
Fruit.In the present embodiment, the forecast result based on visibility and pavement behavior instructs road Operation Decision in step S6
Practice with reference to road Operation Decision standard.
Table 2 be road Operation Decision standard scale, including under visibility, DIFFERENT METEOROLOGICAL CONDITIONS safe speed and emergency arrange
It applies.
Table 2
The classification of road operation emergency measure and judgment method under DIFFERENT METEOROLOGICAL CONDITIONS:
In low visibility in the case where 50m, icy road road surface, wet road surface and dry pavement are all selected at road closure
Reason；
It is within the scope of 50-500m in visibility, icy road road surface, wet road surface and dry pavement all select speed limit
Processing；
It is within the scope of 500-1000m in visibility, icy road road surface, wet road surface and dry pavement all select to warn
Show processing.
Referring to shown in Fig. 2, the present invention provides a kind of road Operation Decision service platform based on meteorological big data.As
A kind of embodiment, service platform build cloud server by the server cluster technology based on Infiniband, realize parallel
Operation, and the bandwidth and capacity problem of data processing interaction storage are solved, it is a large amount of required for process meteorological data to meet
Intensive operations.Service platform of the invention includes:
Data collection module 11, including real-time weather acquisition unit, forecast numerical search unit and data storage are single
Member, real-time weather acquisition unit are installed on real-time gas acquired in vehicle or the automatic weather station in specified location for collecting
As element observation data, forecast numerical search unit is used to search for and collect the initial fields and boundary number of fields of Numerical Prediction Models
According to data storage cell is used to store real-time weather acquisition unit and forecasts the data that numerical search unit is collected；
Road Weather forecast module 12, including data assimilation unit, weather simulation unit and forecast unit, as one
Kind embodiment, data assimilation unit are embedded with the selected three-dimensional variation of data assimilation in above-mentioned road Operation Decision method and calculate
Method (3D-Var), for assimilating the data of data storage cell；Weather simulation unit is embedded with above-mentioned road Operation Decision method
Used in Mesoscale Numerical Forecast tool (WRF), Mesoscale Numerical Forecast tool (WRF) assimilates data assimilation unit
Data parameterization simulate synoptic process, forecast unit is embedded in above-mentioned road Operation Decision method selected by linear analysis
The multiple linear regression model algorithm selected carries out linear regression analysis by the data to weather simulation unit, generates one group most
Whole Road Weather forecast result；
Road Operation Decision module 13, including road condition analyzing unit and Operation Decision unit, road condition analyzing unit are embedded with
Water film thickness calculates used Sass surface gathered water icing model algorithm in above-mentioned road Operation Decision method, by road
Weather forecast result (except visibility) imports Sass surface gathered water icing model and calculates rain water depth on road surface, provides road surface shape
Condition forecast result, Operation Decision unit are used to provide road Operation Decision meaning according to visibility and pavement behavior forecast result
See.
In the present embodiment, service platform further includes info push module 14, and info push module includes that information push is single
Member for the forecast result and road Operation Decision opinion to terminal push Road Weather, pavement behavior, and is broadcast by terminal
Report the forecast result and road Operation Decision opinion of Road Weather, pavement behavior.
Referring to shown in Fig. 3, the present invention provides a kind of road Operation Decision system based on meteorological big data, including above-mentioned
Road Operation Decision service platform 1 based on cloud server, and the automatic gas being connect with road Operation Decision service platform
As station 2 and terminal 3.As one embodiment, automatic weather station 2 and terminal 3 can be flat by wired or wireless mode and service
The cloud server of platform 1 establishes communication relations.
Compared with prior art, the invention has the following advantages:
1, vehicle-mounted automatic weather station is added in the present invention, and realization is observed a variety of meteorological elements in moving process, compared with
In fixed point monitoring, expand observation section, improve observation accuracy；
2, invention introduces Sass surface gathered water icing model, which introduces according to ground water household principle
The meteorological factors such as precipitation, temperature, relative humidity, wind speed, evaporation, sun net radiation, road pavement moisture film predicted, compared to
Commonly " British experience formula ", improves the accuracy of detecting road status；
3, the present invention carries out express highway pavement using the physico parametric scheme of WRF and Sass surface gathered water icing model
Dry and wet state inverting establishes the inverse model of the situations such as road surface drying, humidity, ponding, accumulated snow, icing, traffic weather is observed
Logging data application also further expands or the perfect research method of traffic weather in the forecast of traffic weather pavement behavior, meets
The demand of highway communication Meteorological Services；
4, when the present invention gathers forecast result, using sliding training period, weight coefficient changes at any time,
Improve accuracy.
The above is merely preferred embodiments of the present invention, be not intended to restrict the invention, it is all in spirit of the invention and
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within principle.
Claims (10)
1. a kind of road Operation Decision method based on meteorological big data, which is characterized in that the described method comprises the following steps:
Step S1 collects the initial fields and boundary field data of real-time weather element observation data and Numerical Prediction Models；
Step S2, the initial fields and boundary number of fields of real-time weather element observation data and Numerical Prediction Models that step S1 is collected
According to data pool is imported, after data assimilation, numerical forecast tool NONLINEAR CALCULATION is executed；
The calculated result of step S2 and real-time weather element observation data are carried out linear analysis processing, obtain one group by step S3
Final forecast result；
Final forecast result (except visibility) is imported and customizes algorithm progress water film thickness calculating by step S4；
Step S5 differentiates road surface according to the pavement behavior grade scale customized in algorithm according to water film thickness calculated result
Situation；
Step S6 instructs road operation to determine according to the forecast result of visibility and pavement behavior referring to road Operation Decision standard
Plan.
2. the road Operation Decision method according to claim 1 based on meteorological big data, which is characterized in that the step
Real-time weather element observation data in S1 are obtained by being installed on the automatic weather station of vehicle or specified location after integrating, institute
The real-time weather element of acquisition is selected from: dry-bulb temperature (temperature), wet-bulb temperature, wind speed, rainfall, snowfall intensity, evaporation capacity, radiation
It is several in flux, pavement temperature, roadbed temperature and visibility, and be real-time transmitted to by wired or wireless transmission mode
Cloud server.
3. the road Operation Decision method according to claim 1 based on meteorological big data, which is characterized in that the step
Data assimilation in S2 passes through three-dimensional variational algorithm (3D-Var) and realizes, the three-dimensional variational algorithm (3D-Var) takes following public affairs
Formula calculates:
Wherein, the O is the observation of meteorological variables, the F be meteorological variables background value (average value or numerical model it is pre-
Report value), the σOFor the variance of meteorological variables observation, the σFFor the variance of meteorological variables background value.
4. the road Operation Decision method according to claim 1 based on meteorological big data, which is characterized in that the step
Numerical forecast tool in S2 is Mesoscale Numerical Forecast tool (WRF), and the Mesoscale Numerical Forecast tool (WRF) includes:
SLAB heat diffusion scheme, Noah scheme and RUC scheme, each scheme calculate separately out one group of forecast result.
5. the road Operation Decision method according to claim 1 based on meteorological big data, which is characterized in that the step
Linear analysis in S3 is divided into training period and forecast period by way of establishing multiple linear regression model, by time series,
The training period is sliding training period；The multiple linear regression model is executed in the training period to the forecast result and reality
When meteorological element observation data multiple linear regression analysis, calculate separately the weight coefficient of the forecast result, and according to institute
State the final forecast result that weight coefficient obtains the forecast period.
6. the road Operation Decision method according to claim 5 based on meteorological big data, which is characterized in that the step
Linear analysis in S3 carries out regression analysis by following equation to a certain meteorological element of a certain Time effect forecast:
The O is the average observed value of a certain meteorological element training period, the αiFor the weight system for participating in the ith member gathered
Number, the FiWithIt is the predicted value and its forecast average value in training period of i-th of mode respectively, the N is that participation is super
The mode sum of DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM；
Wherein, the weight coefficient αiIt is calculated and is obtained by the minimum of the error term G in training period equation:
The N is the sum of training period time samples, the S 'tWith O 'tRespectively the superset of training period and observation field is inclined
Difference.
7. the road Operation Decision method according to claim 1 based on meteorological big data, which is characterized in that the step
Customization algorithm in S4 is Sass surface gathered water icing model, and the water film thickness passes through Sass surface gathered water icing model
It calculates, is calculated especially by following formula:
Wherein, the WlFor the liquid water accumulating volume (kg/m on road surface2), the WsFor the solid-state water accumulating volume (kg/m on road surface2), the Pr
For rainfall intensity (mm/min), the PsIt is described for snowfall intensity (mm/min)Icing or melted mass for the surface water,
The R is net radiation flux, the LfFor the ablation heat of water, the GlFor downward heat flux, the r is run-off, and the E is
Evaporation capacity.
8. a kind of road Operation Decision service platform based on meteorological big data characterized by comprising
Data collection module, including real-time weather acquisition unit, forecast numerical search unit and data storage cell, the reality
When meteorology acquisition unit be installed on real-time weather element acquired in vehicle or the automatic weather station in specified location for collecting
Data are observed, the forecast numerical search unit is used to search for and collect the initial fields and boundary field data of Numerical Prediction Models,
The data storage cell is used to store the real-time weather acquisition unit and forecasts the data that numerical search unit is collected；
Road Weather forecast module, including data assimilation unit, weather simulation unit and forecast unit, the data assimilation list
Member is for assimilating the data of the data storage cell；The weather simulation unit is for simulating synoptic process, the pre- declaration form
Member carries out linear regression analysis by the data to the weather simulation unit, generates one group of final Road Weather and forecasts knot
Fruit；
Road Operation Decision module, including road condition analyzing unit and Operation Decision unit, the road condition analyzing unit provide road surface
Condition prognosis is as a result, the Operation Decision unit is used to provide road operation according to visibility and pavement behavior forecast result and determine
Plan opinion.
9. the road Operation Decision service platform according to claim 8 based on meteorological big data, which is characterized in that described
Service platform further includes info push module, and the info push module includes information push unit, for pushing road to terminal
Road meteorology, the forecast result of pavement behavior and road Operation Decision opinion, and Road Weather, road surface shape are broadcasted by the terminal
The forecast result and road Operation Decision opinion of condition.
10. a kind of road Operation Decision system based on meteorological big data, which is characterized in that any including such as claim 8-9
Road Operation Decision service platform described in, and the automatic weather station that is connect with the road Operation Decision service platform and
Terminal.
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