CN113126182A - Accumulated snow depth prediction method and system - Google Patents

Accumulated snow depth prediction method and system Download PDF

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CN113126182A
CN113126182A CN201911405094.XA CN201911405094A CN113126182A CN 113126182 A CN113126182 A CN 113126182A CN 201911405094 A CN201911405094 A CN 201911405094A CN 113126182 A CN113126182 A CN 113126182A
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snow depth
snow
historical
precipitation phase
value
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邓卫
李慧恩
王焕晓
张亮
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Beijing Siwei Zhi Lian Technology Co ltd
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Beijing Siwei Zhi Lian Technology Co ltd
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    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions

Abstract

The invention discloses a snow depth prediction method and a snow depth prediction system, wherein the method comprises the steps of identifying precipitation phase at a future target moment of a target location based on a preset precipitation phase identification condition to obtain a precipitation phase identification result, obtaining weather data of the target location including the future target moment in the same day from high-precision gridding weather data when the precipitation phase identification result is rain and snow or snowfall, carrying out standardization processing on the weather data to obtain standard weather data, bringing the standard weather data into a snow depth prediction model to obtain a snow depth prediction value, and correcting the snow depth prediction value by adopting a snow depth optimization equation established based on geographic information system data, national map road data and dynamic traffic information to obtain the corrected optimal snow depth prediction value. When the snow depth prediction is carried out, the multi-dimensional information is integrated, the real snow and snow scenes are highly restored, the accuracy of snow depth prediction is improved, and the method has universality.

Description

Accumulated snow depth prediction method and system
Technical Field
The invention relates to the technical field of weather forecast, in particular to a snow depth prediction method and a snow depth prediction system.
Background
Snow accumulation depth: refers to the vertical depth from the surface of the snow layer to the snowy ground surface, assuming that the snow layer is evenly distributed over the snowy ground surface. The snow depth prediction means: and predicting the snow depth within 0-240 hours in the future. The snow depth prediction needs to identify precipitation phase state firstly, and then the snow depth prediction is carried out based on the precipitation phase state identification result.
Compared with foreign countries, domestic research on rainfall phase and snow depth prediction is relatively late, and currently, the main method for snow depth prediction is as follows: and analyzing based on related factors such as terrains in different regions, judging precipitation phase states by establishing corresponding forecast indexes, and predicting accumulated snow depth. Generally, the snow depth prediction scheme based on the conventional ground observation method has certain reference significance for snow forecast. However, since the prediction conditions required for the snow depth prediction are different in different regions, there is no general applicability. In addition, it is difficult to obtain precise description and comprehensive information of the aspects of the space-time distribution characteristics of the snow in a large range and the influence of topographic and geomorphic conditions on the space-time distribution of the snow in the large range only by using the traditional conventional ground observation method, so that the accuracy rate of snow depth prediction is not high.
The satellite remote sensing technology developed in the 60 s of the 20 th century provides an effective way for monitoring and researching regional to global scales of snow, is wide in monitoring range, can quickly acquire comprehensive information of ground snow in a large range, is not influenced by ground conditions, and is particularly suitable for regions with wide regions, complex landforms and frequent snow disasters, so that the satellite remote sensing technology becomes the most effective means for predicting and researching snow. However, when the satellite remote sensing technology is adopted to predict the snow depth, a relevant calculation model is still lacked in the aspect of snow depth, particularly in the aspect of combining a geographic information system and a digital elevation model.
In summary, how to provide a snow depth prediction method and system, which not only has high accuracy for snow depth prediction, but also has universality, becomes a technical problem that needs to be solved urgently by technical personnel in the field.
Disclosure of Invention
In view of this, the invention discloses a snow depth prediction method and system, so as to improve the accuracy of depth prediction and have universality.
A snow depth prediction method includes:
identifying the precipitation phase state of the target site at the future target moment based on preset precipitation phase state identification conditions to obtain a precipitation phase state identification result;
when the precipitation phase state identification result is rain and snow or snowfall, acquiring the meteorological data of the current day of the target site including the future target time from high-precision gridding weather data, wherein the meteorological data comprises: daily precipitation, average air temperature, lowest air temperature, highest air temperature, accumulated snow depth, next-day precipitation and next-day average temperature;
standardizing the meteorological data to obtain standard meteorological data;
substituting the standard meteorological data into a pre-established snow depth prediction model to obtain a snow depth prediction value;
and correcting the accumulated snow depth predicted value by adopting a pre-established accumulated snow depth optimization equation to obtain a corrected optimal accumulated snow depth predicted value, wherein the accumulated snow depth optimization equation is established on the basis of geographic information system data, national map road data and dynamic traffic information.
Optionally, the building process of the snow depth optimization equation includes:
based on geographic information system data, national map road data, and dynamic traffic information, the accumulated snow depth optimization equation shown below is established,
Figure BDA0002348415960000021
in the formula, YfFor the optimal accumulated snow depth prediction value, Y is the accumulated snow depth prediction value, pijThe matrix is an element (i, j) of a snow depth correction factor matrix m multiplied by n matrix P, wherein i is the ith row of the snow depth correction factor matrix, and j is the jth column of the snow depth correction factor matrix;
the expression of matrix P is as follows:
Figure BDA0002348415960000031
wherein the i-th line is 1 or p1jRestoring a natural snowing scene based on the landform, and correcting an accumulated snow depth predicted value from a natural accumulated snow angle, wherein an accumulated snow depth correction factor of the natural accumulated snow angle comprises: high resolution elevation index IdemSun exposure coefficient IsAnd coefficient of windward slope IwThree elements;
i 2 line i2jCorrecting the snow depth value from the wind-blowing angle based on the road scene determined by the geographic information system data and the road network angle, wherein the snow depth correction factor of the wind-blowing angle comprises: radius coefficient of curve IrAnd the included angle coefficient I between the main wind direction and the road trend in the snow accumulation periodaTwo elements;
i-3 lines i.e. p3jCorrecting the snow depth predicted value from the ice and snow ablation angle, wherein the snow depth correction factor of the ice and snow ablation angle comprises: coefficient of depth of ground colour Ic；
I-4 th row, i.e. p4jAnd correcting the accumulated snow depth predicted value from a manual intervention angle, wherein the accumulated snow depth correction factor of the manual intervention angle comprises: vehicle speed evidence coefficient Itmc。
Optionally, the high resolution elevation index IdemIs calculated as follows:
Figure BDA0002348415960000032
in the formula, HmaxIs the maximum elevation value of the Chinese highway after standard deviation processing, f is a weight adjustment factor, HsIs a standard elevation value, when H ═ HsWhen, IdemWhen H is equal to 1>Hs, depth is positively compensated; h<HsWhile the depth is compensated negatively.
Optionally, the solar radiation coefficient IsIs calculated as follows:
Figure BDA0002348415960000033
in the formula, m is a north coefficient, p is other orientation coefficients, and n is a south coefficient.
Optionally, the coefficient of windward slope IwThe expression of (a) is as follows:
Figure BDA0002348415960000041
in the formula, q is the windward slope coefficient, r is the general terrain coefficient, and s is the leeward slope coefficient.
Optionally, the curve radius coefficient IrThe expression of (a) is as follows:
Figure BDA0002348415960000042
in the formula, R is the radius of an actual road curve;
rs is a basic standard value;
Rmaxmaximum value of minimum radius of circular curve specified in the design specification for road route;
Rmina minimum value of a minimum radius of a circular curve specified in a highway route design specification;
Nbfor the balance coefficient, the expression is
Figure BDA0002348415960000043
NmEnabling the roadbed to be close to the mountain coefficient and only enabling the roadbed to be effective in the mountain road section, wherein the expression is as follows:
Figure BDA0002348415960000044
optionally, the included angle coefficient I between the main wind direction and the road trend in the snow accumulation periodaThe expression of (a) is as follows:
Figure BDA0002348415960000045
wherein beta is the included angle between the main wind direction and the road trend in the snow accumulation period,
Figure BDA0002348415960000046
k is a balance coefficient calculated by a normalization formula and aims at reducing IaAnd (4) weighting the influence on the original snow depth.
Optionally, the ground color depth coefficient IcThe expression of (a) is as follows:
Figure BDA0002348415960000051
in the formula, t is a black road coefficient, u is a yellow road coefficient, and v is an off-white road coefficient.
Optionally, the vehicle speed evidence factor ItmcThe expression of (a) is as follows:
Figure BDA0002348415960000052
in the formula ItmcTake an integer of ItmcIs effective only when said accumulated snow depth predicted value Y has an assigned value, I when the vehicle is traveling at a higher speed than the driving speed in the expected accumulated snow state of the roadtmcNegative, when the vehicle is running at a speed lower than the driving speed expected under snow conditions on the road, ItmcPositive, σ is the standard deviation of the vehicle speed data for k minutes, and p is within a range of one standard deviation [ v [ ]0-σ，v0+σ]The number of data of the new vehicle speed data set obtained from all vehicle speed values, v0Is the minute-scale road speed data vnFor the nth speed in the data set,
Figure BDA0002348415960000053
as congestion coefficients
Figure BDA0002348415960000054
vsThe driving speed is standard for normal driving environment.
Optionally, the method further includes:
and carrying out grade division on the optimal accumulated snow depth predicted value according to a preset grade division standard to obtain accumulated snow grades with different influence degrees.
Optionally, before the identifying the precipitation phase at the future target time of the target location based on the preset precipitation phase identification condition to obtain the precipitation phase identification result, the method further includes: acquiring a value of a precipitation phase state;
the process of obtaining the value of the precipitation phase state specifically comprises the following steps:
obtaining the predicted ground temperature and the predicted ground dew point of a target site at the future target moment from the high-precision gridding weather data;
standardizing the predicted ground temperature to obtain a predicted standard ground temperature, and standardizing the predicted ground dew point to obtain a predicted standard ground dew point;
and substituting the predicted standard ground temperature and the predicted standard ground dew point into a pre-established precipitation phase state prediction model to obtain the precipitation phase state of the future target moment.
Optionally, the process of constructing the precipitation phase prediction model includes:
acquiring a historical ground temperature data set and a historical ground dew point data set in a first preset time period, calculating to obtain a historical ground temperature average value based on the historical ground temperature data set, and obtaining a historical ground dew point average value based on the historical ground dew point data set;
obtaining a historical ground temperature standard difference value based on the historical ground temperature data set and the historical ground temperature average value, and obtaining a historical ground dew point standard difference value based on the historical ground dew point data set and the historical ground dew point average value;
standardizing the ground temperature in the historical ground temperature data set based on the historical ground temperature average value and the historical ground temperature standard difference value to obtain historical standard ground temperature, and standardizing the ground dew point in the historical ground dew point data set based on the historical ground dew point average value and the historical ground dew point standard difference value to obtain historical standard ground dew point;
and obtaining the precipitation phase state prediction model by adopting a gradient descent method based on the historical precipitation phase state, the historical ground temperature data set and the historical ground dew point data set in the first preset time period.
Optionally, the obtaining the precipitation phase prediction model by using a gradient descent method based on the historical precipitation phase in the first preset time period, the historical ground temperature data set, and the historical ground dew point data set specifically includes:
calculating a first weight parameter k in the precipitation phase state prediction model according to the following two formulas1A second weight parameter k2And an intercept parameter b;
Figure BDA0002348415960000061
Figure BDA0002348415960000062
where n is the number of elements in a data set, the data set being: i is an element serial number and xiIs the ith element, and r is the historical precipitation phase;
according to the first weight parameter k1The second weight parameter k2And the distance parameter b, obtaining an expression of the precipitation phase state prediction model shown as follows:
Y＝b+k1x1+k2x2；
in the formula, Y is a precipitation phase, x1For the historical ground temperature data setOf (1) historical ground temperature, x2Historical ground dew points in the historical ground dew point data set.
Optionally, based on the preset precipitation phase recognition condition, the precipitation phase at the future target time of the target location is recognized, and a precipitation phase recognition result is obtained, which specifically includes:
judging whether the precipitation phase state is smaller than a first critical value or not;
if so, determining that the rainfall phase is rainfall;
if not, continuously judging whether the precipitation phase state is less than or equal to a second critical value, wherein the second critical value is greater than the first critical value;
if so, determining that the precipitation phase state is snowfall;
and if not, determining that the precipitation phase state is rain and snow.
Optionally, the building process of the snow depth prediction model includes:
according to the formula
Figure BDA0002348415960000071
The following weight formula is obtained:
w＝(XTX)-1XTy；
wherein X is [1, X ]3,…,x9]W is the weight corresponding to X, y is the historical observed snow depth data, 1 is a constant term, and X3Is the historical daily precipitation, x4Is a historical average air temperature, x5Is the historical lowest temperature, x6Is the historical maximum temperature, x7For historical accumulated snow depth, x8Precipitation of water and x for the historical next day9The average temperature of the historical next day;
obtaining an expression of the accumulated snow depth prediction model according to a weight formula, wherein the expression comprises the following steps:
Y＝XT(XTX)-1XTy；
wherein Y is the depth of accumulated snow and X isTIs the transpose of matrix X.
A snow depth prediction system comprising:
the identification unit is used for identifying the precipitation phase state of the target site at the future target moment based on the preset precipitation phase state identification condition to obtain a precipitation phase state identification result;
a first obtaining unit, configured to obtain, from high-precision gridded weather data, weather data of a current day in which the target location includes the future target time when the precipitation phase recognition result is rain and snow or snowfall, where the weather data includes: daily precipitation, average air temperature, lowest air temperature, highest air temperature, accumulated snow depth, next-day precipitation and next-day average temperature;
the standard processing unit is used for carrying out standardized processing on the meteorological data to obtain standard meteorological data;
the prediction unit is used for substituting the standard meteorological data into a pre-established snow depth prediction model to obtain a snow depth prediction value;
and the correction unit is used for correcting the accumulated snow depth predicted value by adopting a pre-established accumulated snow depth optimization equation to obtain a corrected optimal accumulated snow depth predicted value, wherein the accumulated snow depth optimization equation is established on the basis of geographic information system data, national map road data and dynamic traffic information.
Optionally, the method further includes:
and the grade division unit is used for carrying out grade division on the optimal accumulated snow depth predicted value according to a preset grade division standard to obtain accumulated snow grades with different influence degrees.
Optionally, the method further includes:
the second acquisition unit is used for acquiring the value of the precipitation phase state before the identification unit identifies the precipitation phase state of the target site at the future target moment based on the preset precipitation phase state identification condition to obtain the precipitation phase state identification result;
the second obtaining unit is specifically configured to:
obtaining the predicted ground temperature and the predicted ground dew point of a target site at the future target moment from the high-precision gridding weather data;
standardizing the predicted ground temperature to obtain a predicted standard ground temperature, and standardizing the predicted ground dew point to obtain a predicted standard ground dew point;
and substituting the predicted standard ground temperature and the predicted standard ground dew point into a pre-established precipitation phase state prediction model to obtain the value of the precipitation phase state at the future target moment.
According to the technical scheme, the invention discloses a snow depth prediction method and a snow depth prediction system, based on preset precipitation phase recognition conditions, the precipitation phase of a target site at a future target moment is recognized to obtain a precipitation phase recognition result, when the precipitation phase recognition result is rain and snow, the meteorological data of the target site at the same day including the future target moment are obtained from high-precision gridding weather data, the meteorological data are standardized to obtain standard meteorological data, the standard meteorological data are substituted into a snow depth prediction model to obtain a snow depth prediction value, and the snow depth prediction value is corrected by adopting a snow depth optimization equation established based on geographic information system data, national map road data and dynamic traffic information to obtain the corrected optimal snow depth prediction value. Therefore, the snow depth prediction method and the snow depth prediction device have universality and improve the accuracy of snow depth prediction by integrating the multi-dimensional information and highly restoring real snow and snow scenes when the snow depth is predicted.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the disclosed drawings without creative efforts.
FIG. 1 is a flowchart of a snow depth prediction method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for establishing a precipitation phase prediction model according to an embodiment of the present invention;
FIG. 3 is a graph illustrating solar radiation coefficients according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a snow depth prediction system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a snow depth prediction method and a snow depth prediction system, which are based on a preset precipitation phase state identification condition, identifying the precipitation phase state of the target site at the future target moment to obtain a precipitation phase state identification result, when the precipitation phase state identification result is rain and snow or snowfall, acquiring the weather data of the day, including the future target time, of the target location from the high-precision gridding weather data, standardizing the meteorological data to obtain standard meteorological data, substituting the standard meteorological data into an accumulated snow depth prediction model to obtain an accumulated snow depth prediction value, adopting an accumulated snow depth optimization equation established based on geographic information system data, national map road data and dynamic traffic information, and correcting the snow depth predicted value to obtain a corrected optimal snow depth predicted value. Therefore, the snow depth prediction method and the snow depth prediction device have universality and improve the accuracy of snow depth prediction by integrating the multi-dimensional information and highly restoring real snow and snow scenes when the snow depth is predicted.
Referring to fig. 1, a flowchart of a snow depth prediction method according to an embodiment of the present invention includes:
s101, identifying precipitation phase states of future target moments of a target site based on preset precipitation phase state identification conditions to obtain precipitation phase state identification results;
wherein, step S101 specifically includes:
judging whether the precipitation phase state is smaller than a first critical value or not;
if so, determining that the rainfall phase is rainfall;
if not, continuously judging whether the precipitation phase state is less than or equal to a second critical value, wherein the second critical value is greater than the first critical value;
if so, determining that the precipitation phase state is snowfall;
and if not, determining that the precipitation phase state is rain and snow.
It should be particularly noted that the values of the first critical value and the second critical value are determined according to the precipitation phase values of snowfall and rainfall, for example, the first critical value is 1.8, the second critical value is 2.1, and at this time, when Y is greater than or equal to 1.8 and less than or equal to 2.1, the precipitation phase is rain and snow; when Y is more than 2.1, the precipitation phase is snowfall; when Y is less than 1.8, the precipitation phase is rainfall, and Y is the precipitation phase.
It is understood that before executing step S101, the method may further include the steps of:
values of precipitation phase at a future target time of the target site are obtained.
The process of acquiring the precipitation phase state of the target site at the future target moment specifically comprises the following steps:
(1) acquiring the predicted ground temperature and the predicted ground dew point of a target site at the future target moment from the high-precision gridding weather data;
the ground temperature means: general term for soil temperature at the surface and at different depths below. Refers to the depth at which water in an evaporator of a given diameter is reduced by evaporation. Units are degrees Celsius (. degree. C.).
The dew point/dew point temperature Td means: under the condition that the water vapor content in the air is unchanged and the air pressure is kept constant, the temperature when the air is cooled to reach saturation is called dew point temperature, which is called dew point for short, and the unit is expressed by DEG C or DEG F.
In practical application, the predicted ground temperature of the future target time T can be obtained from the high-precision gridding weather data
Figure BDA0002348415960000117
And predicting the ground dew point
Figure BDA0002348415960000118
The high-precision gridding weather data comes from the national weather bureau, the grid precision can reach 1km x 1km at most, and weather information such as precipitation and wind vectors is contained.
(2) Standardizing the predicted ground temperature to obtain a predicted standard ground temperature, and standardizing the predicted ground dew point to obtain a predicted standard ground dew point;
(3) substituting the prediction standard ground temperature and the prediction standard ground dew point into a pre-established precipitation phase state prediction model to obtain a precipitation phase state value at the future target moment;
specifically, referring to fig. 2, an embodiment of the present invention discloses a flowchart of a method for building a precipitation phase prediction model, where the method includes the steps of:
step S201, obtaining a historical ground temperature data set and a historical ground dew point data set in a first preset time period, calculating to obtain a historical ground temperature average value based on the historical ground temperature data set, and obtaining a historical ground dew point average value based on the historical ground dew point data set;
specifically, assume that the surface temperature in the historical surface temperature data set is: x is the number of1Ground dew point temperature with historical ground dew point data setComprises the following steps: x is the number of2. The historical ground temperature average value can be respectively calculated according to the formula (1)
Figure BDA0002348415960000111
And historical ground dew point average
Figure BDA0002348415960000112
Equation (1) is as follows:
Figure BDA0002348415960000113
where n is the number of elements in the data set, i is the number of elements, xiIs the ith element.
Step S202, obtaining a historical ground temperature standard deviation value based on the historical ground temperature data set and the historical ground temperature average value, and obtaining a historical ground dew point standard deviation value based on the historical ground dew point data set and the historical ground dew point average value;
specifically, the historical ground temperature standard deviation values are respectively calculated according to the formula (2)
Figure BDA0002348415960000114
And historical ground dew point standard deviation value
Figure BDA0002348415960000115
Equation (2) is as follows:
Figure BDA0002348415960000116
step S203, based on the historical ground temperature average value and the historical ground temperature standard difference value, conducting standardization processing on the ground temperature in the historical ground temperature data set to obtain historical standard ground temperature, and based on the historical ground dew point average value and the historical ground dew point standard difference value, conducting standardization processing on the ground dew point in the historical ground dew point data set to obtain historical standard ground dew point;
specifically, in practical applications, the ground temperature x in the historical ground temperature data set can be respectively adjusted according to the formula (3)1Carrying out standardization to obtain historical standard ground temperature y1And ground dew point x in a historical set of ground dew point data2Standardizing to obtain historical standard ground dew point y2Equation (3) is as follows:
Figure BDA0002348415960000121
and S204, obtaining a precipitation phase state prediction model by adopting a gradient descent method based on the historical precipitation phase state, the historical ground temperature data set and the historical ground dew point data set in the first preset time period.
It should be noted that the precipitation phase is divided into three categories: rain, rain and snow, and the precipitation phase in the invention only relates to the two conditions of rain and snow.
Specifically, A, calculating according to a formula (4) and a formula (5) to obtain a first weight parameter k in the precipitation phase prediction model1A second weight parameter k2And an intercept parameter b, equation (4) and equation (5) are as follows:
Figure BDA0002348415960000122
Figure BDA0002348415960000123
where n is the number of elements in a data set, the data set being: i is an element serial number and xiIs the ith element, and r is the historical precipitation phase.
B. According to the first weight parameter k1The second weight parameter k2And the distance parameter b to obtain a table of a precipitation phase state prediction model shown in formula (6)Expression, equation (6) is as follows:
Y＝b+k1x1+k2x2 (6)；
in the formula, Y is a precipitation phase, x1For historical ground temperature, x, in the historical ground temperature dataset2Historical ground dew points in the historical ground dew point data set.
It should be noted that, in practical applications, the predicted ground temperature may be used
Figure BDA0002348415960000124
Substituting into formula (3) for standardization to obtain predicted standard ground temperature
Figure BDA0002348415960000131
Also, the predicted ground dew point
Figure BDA0002348415960000132
Substituting into formula (3) for standardization to obtain the predicted standard ground dew point
Figure BDA0002348415960000133
Step S102, when the precipitation phase state identification result is rain and snow or snowfall, acquiring the current-day meteorological data of the target location including the future target time from high-precision gridding weather data;
wherein the meteorological data comprises: daily precipitation, average air temperature, lowest air temperature, highest air temperature, snow accumulation depth, next-day precipitation and next-day average temperature.
In this example, the daily precipitation is
Figure BDA0002348415960000134
Average air temperature of
Figure BDA0002348415960000135
The lowest temperature is
Figure BDA0002348415960000136
The maximum air temperature is
Figure BDA0002348415960000137
The depth of accumulated snow is
Figure BDA0002348415960000138
The precipitation of the next day is
Figure BDA0002348415960000139
And the next-day average temperature of
Figure BDA00023484159600001310
It should be noted that when the precipitation phase recognition result is rain and snow or snowfall, snow depth prediction is further performed; and when the precipitation phase state recognition result is rainfall, not predicting the depth of accumulated snow.
Step S103, carrying out standardization processing on the meteorological data to obtain standard meteorological data;
wherein, standard meteorological data includes: standard daily precipitation, standard average air temperature, standard lowest air temperature, standard highest air temperature, standard snow depth, standard next-day precipitation and standard next-day average temperature.
Assuming that the standard daily precipitation is
Figure BDA00023484159600001311
The standard average air temperature is
Figure BDA00023484159600001312
The standard minimum air temperature is
Figure BDA00023484159600001313
The standard maximum air temperature is
Figure BDA00023484159600001314
Standard depth of snow cover
Figure BDA00023484159600001315
Standard amount of precipitation next day
Figure BDA00023484159600001316
And a standard next-day average temperature of
Figure BDA00023484159600001317
That is, the standard data obtained in this embodiment is
Figure BDA00023484159600001318
Specifically, the historical daily precipitation x in the second preset time period is respectively calculated according to the formula (1)3Historical average air temperature x4Historical minimum air temperature x5Historical maximum air temperature x6Historical accumulated snow depth x7Historical next day precipitation x8And the historical next-day average temperature x9Respective corresponding mean values
Figure BDA00023484159600001328
Respectively calculating daily precipitation x according to formula (2)3Average air temperature x4Minimum air temperature x5Maximum air temperature x6And snow depth x7The next day precipitation x8And the next-day average temperature x9Corresponding standard deviation value of
Figure BDA00023484159600001327
According to the formula (7), respectively comparing the historical daily precipitation x3Historical average air temperature x4Historical minimum air temperature x5Historical maximum air temperature x6Historical accumulated snow depth x7Historical next day precipitation x8And the historical next-day average temperature x9Standardized to obtain the standard daily precipitation of
Figure BDA00023484159600001319
The standard average air temperature is
Figure BDA00023484159600001320
The standard minimum air temperature is
Figure BDA00023484159600001321
The standard maximum air temperature is
Figure BDA00023484159600001322
Standard depth of snow cover
Figure BDA00023484159600001323
Standard amount of precipitation next day
Figure BDA00023484159600001324
And a standard next-day average temperature of
Figure BDA00023484159600001325
That is, the standard data obtained in this embodiment is
Figure BDA00023484159600001326
Equation (7) is as follows:
Figure BDA0002348415960000141
step S104, substituting the standard meteorological data into a pre-established snow depth prediction model to obtain a snow depth prediction value;
specifically, the building process of the snow depth prediction model comprises the following steps:
equation (9) is obtained from equation (8), and equations (8) and (9) are as follows:
Figure BDA0002348415960000142
w＝(XTX)-1XTy (9)；
wherein X is [1, X ]3,…,x9]W is the weight corresponding to X, y is the historical observed snow depth data, 1 is a constant term, and X3Is the historical daily precipitation, x4Is a historical average air temperature, x5Is the lowest historyAir temperature, x6Is the historical maximum temperature, x7For historical accumulated snow depth, x8Precipitation of water and x for the historical next day9The historical next-day average temperature.
Obtaining an expression of the snow depth prediction model shown in formula (10) according to the weight formula shown in formula (9), wherein formula (10) is as follows:
Y＝XT(XTX)-1XTy (10)；
wherein Y is the depth of accumulated snow and X isTIs the transpose of matrix X.
And S105, correcting the snow depth predicted value by adopting a pre-established snow depth optimization equation to obtain a corrected optimal snow depth predicted value.
It should be noted that the predicted snow depth value in step S104 predicts the snow depth based on meteorological factors (parameters of meteorological observation), and the obtained predicted snow depth is the snow depth under natural snowing, and the meteorological factors include: ground temperature, ground dew point temperature, daily precipitation, average air temperature, lowest air temperature, highest air temperature, and snow depth, next-day precipitation, next-day average temperature, etc. In order to further improve the accuracy of snow depth prediction, the method also utilizes the characteristics of terrain and road data in the GIS, such as curve radius, road trend, road surface material and the like, to restore the real snow scene in step S105, and introduces dynamic traffic information manual intervention verification to further refine and optimize the snow depth predicted based on meteorological factors.
The snow depth optimization equation is established based on geographic information system data, national map road data and dynamic traffic information.
Specifically, based on geographic information system data, national map road data and dynamic traffic information, an accumulated snow depth optimization equation shown in formula (11) is established, wherein the formula (11) is as follows:
Figure BDA0002348415960000151
in the formula, YfThe snow depth value is calculated for the optimal snow depth predicted value, namely the snow depth value is combined with the snow depth correction factor matrix, Y is the snow depth predicted value obtained in the step S107, and p isijIs the (i, j) element of the snow depth correction factor matrix m × n matrix P, i is the ith row of the snow depth correction factor matrix, and j is the jth column of the snow depth correction factor matrix.
The expression of the matrix P is shown in equation (12), where equation (12) is as follows:
Figure BDA0002348415960000152
in the m × n matrix P shown in formula (12), the original accumulated snow depth prediction result is refined from different layers for each row from top to bottom.
a) I-1 line, i.e. p1jRestoring a natural snowing scene based on the landform, and correcting an accumulated snow depth predicted value from a natural accumulated snow angle, wherein an accumulated snow depth correction factor of the natural accumulated snow angle comprises: high resolution elevation index IdemSun exposure coefficient IsAnd coefficient of windward slope IwThree elements, thereby making up the deficiency of the prior art that the granularity is predicted roughly.
b) I 2 line i2jAnd correcting the snow depth value from the wind snow blowing angle based on the road scene determined by the GIS and the road network angle, wherein the snow depth correction factor of the wind snow blowing angle comprises: radius coefficient of curve IrAnd the included angle coefficient I between the main wind direction and the road trend in the snow accumulation periodaTwo elements.
c) I-3 lines i.e. p3jCorrecting the snow depth predicted value from the ice and snow ablation angle, wherein the snow depth correction factor of the ice and snow ablation angle comprises: coefficient of depth of ground colour Ic。
d) I-4 th row, i.e. p4jAnd correcting the accumulated snow depth predicted value from a manual intervention angle, wherein the accumulated snow depth correction factor of the manual intervention angle comprises: vehicle speed evidence coefficient Itmc。
For ease of understanding, the various snow depth correction factors referred to in equation (12) are explained below as follows:
1) high resolution elevation index Idem
The temperature of the atmosphere mainly comes from long-wave radiation on the ground, and the higher the altitude is, the less the ground long-wave radiation is absorbed; meanwhile, the thinner the air is, the poorer the heat preservation capability at night, the lower the temperature is. In the convection layer, the vertical decreasing rate of the air temperature is that the air temperature decreases by 0.6 degrees when the altitude rises by 100 m. Under the condition that influence factors such as precipitation, air volume and the like are not changed, the temperature and the snowing depth are in positive correlation, so that the elevation and the snowing depth are in positive correlation. From this, a high-resolution elevation index I can be deriveddemIs calculated as shown in equation (13):
Figure BDA0002348415960000161
in the formula, HmaxThe maximum elevation value of the Chinese highway road processed by standard deviation (+ -3 sigma) is explained as the theoretical highest point that the vehicle can run, f is a weight adjustment factor and aims to reduce IdemWeight of influence on the original depth of snow, HsThe standard elevation value has no influence on the snow depth under the ideal condition, namely when H is HsWhen, Idem1. When H is present>Hs, depth is positively compensated; h<HsAnd then, depth negative compensation is specifically as follows:
Figure BDA0002348415960000162
2) sun exposure coefficient Is
On two sides of a hillside with the same altitude, the solar radiation quantity received by the hillside is more, the air temperature is high, and the snow line position is higher along with the solar radiation quantity received by the hillside; the opposite is true for the back-yang slope. The direct sunlight irradiation range is between the south-north return lines, and the 23-degree 26' N north return lines pass through provincial administrative units in China and are sequentially from west to east: yunnan, Guangxi and Guangdong, so the accumulated snow area in China is located in the area north to the return line of the north China, and the sunshine amount in the area is characterized by comprising the following components: south, west, east and north. From this, equation (14) and fig. 3 can be derived, equation (14) being as follows:
Figure BDA0002348415960000163
in the formula, m is a north coefficient, p is other orientation coefficients, and n is a south coefficient.
3) Coefficient of windward slope Iw
The warm and humid air flow is blocked by the terrain on the windward slope, so that the terrain is forced to be lifted and cooled, the water circulation is more active, the terrain is easy to be cloudy and rain is caused, and more precipitation is caused. The leeward slope is subjected to the submerged airflow to be heated and dried, so that the snow is difficult to cloud and rain, and the accumulated snow is easier to melt and evaporate while the rainfall is less. Therefore, coefficient of windward slope IwIs shown in equation (15), equation (15) is as follows:
Figure BDA0002348415960000171
in the formula, q is the windward slope coefficient, r is the general terrain coefficient, and s is the leeward slope coefficient.
4) Radius coefficient of curve Ir
The wind blows the snow and has the redistribution effect to natural snow, and the snow depth that it formed is 3 ~ 10 times than natural snow. When the wind and snow flow passes through terrain change positions such as roads, partial snow particles are deposited due to the fact that the moving speed is reduced due to local terrain change, and accumulated snow is formed. The accumulation forms are various, such as snow eaves, snow dams, snow dunes, snow tongues, wave-type snow heaps and the like, and snow damage is formed when snow in the road area reaches a certain degree. At the turning of the road, if the radius of the wind field is too small, a vortex potential field is formed at the turning, the speed of wind and snow flow is reduced, and accumulated snow is deepened. I isrThe effect of the size of the turning radius on the snow depth is described effectively.
According to the 'national design Standard of road routes' annex 2, set the speed VstTaking the radius Rs of the road as a basic standard value, and assigning a curve radius coefficient I by taking the standard value as a reference valuerRadius coefficient of curve IrIs shown in equation (16), equation (16) is as follows:
Figure BDA0002348415960000172
in the formula, R is the radius of an actual road curve;
rs is a basic standard value;
Rmaxmaximum value of minimum radius (general value) of circular curve specified in the design specifications for road route;
Rminminimum value of the minimum radius (general value) of the circular curve specified in the road route design specifications;
Nbto balance the coefficients, it is prevented that the modification of the original snow depth Y by the actual curve radius R at the extreme value is too great, thus causing a large deviation of the result. N is a radical ofbThe effective starting is only carried out when R is more than or equal to Rs, and the expression is
Figure BDA0002348415960000181
NmThe coefficient that the roadbed is close to the mountain body is adopted, and the roadbed on the inner side of the curve is closer to the mountain body, the more serious the snow damage tends to be; enabling the vehicle to be effective only in the mountain road section, wherein the expression is as follows:
Figure BDA0002348415960000182
wherein, when R is not less than Rmax(1000m), the default road approaches a straight road, and R is taken as Rmax. At the moment, the wind flow field can pass through the ground without obstacles, no vortex is formed, the wind force has no weakening trend, and the wind, snow and snow flow accumulated snow can not be caused; meanwhile, the snow particles are easily carried by the wind flow field passing through at high speed, and the depth of the snow on the ground is reduced and compensated to a certain degree.
The minimum radius of circular curvature can be seen in table 1, table 1 below:
TABLE 1
Minimum radius of circular curve
Figure BDA0002348415960000183
Note: "general value" is a value used in a normal case; "limit value" is a value that can be used when the condition is limited: "Icnax"is the maximum superhigh value employed; "one" is the case without regard to the use of the corresponding maximum superhigh value.
5) Main wind direction and road trend included angle coefficient I in snow accumulation perioda
According to the definition of the critical value of the skew angle in annex 2 of the national design for road routes, the I can be deduced by summarizing the two critical values of the skew angle when the angle alpha is x DEGa1, the meaning is that when the oblique angle is alpha, the snow blowing by wind has no influence on the snow accumulation depth. Taking the reference standard as a coefficient I of included angle between main wind direction and road trend in snow accumulation periodaAssignment, and coefficient of included angle between main wind direction and road trend in snow accumulation periodaIs shown in equation (17), equation (17) is as follows:
Figure BDA0002348415960000184
wherein beta is the included angle between the main wind direction and the road trend in the snow accumulation period,
Figure BDA0002348415960000191
k is a balance coefficient calculated by a normalization formula and aims at reducing IaAnd (4) weighting the influence on the original snow depth.
Calculated, when the wind direction is the same as the road direction, Ia<1, the snow cover has a function of relieving the depth of accumulated snow; when the wind direction is orthogonal to the road direction, Ia>1, has the function of strengthening the depth of accumulated snow.
6) Ground color depth coefficient Ic
Natural light is composite light, a white object reflects all visible light, a black object absorbs all visible light, and the deeper the color is, the stronger the heat absorption capacity is. Therefore, the ice and snow on the object with dark color are easy to melt.
For paving roads, black pavements (asphalt) and off-white pavements (cement concrete) can be divided according to the material of the surface layer; for unpaved roads, the color of the original material, i.e., the yellow brown color of a sandy soil mud road, is generally presented.
From the ordering of the thermal effects of the colored light waves in the spectrum, equation (18) can be derived:
Figure BDA0002348415960000192
in the formula, t is a black road coefficient, u is a yellow road coefficient, and v is an off-white road coefficient.
7) Vehicle speed evidence coefficient Itmc
a) According to the minute-level road speed data v0(vehicle speed data updated once per minute), and according to the formula (19) and the formula (20), v/min per k-min road section is calculated1、v2，…vkAverage vehicle speed of
Figure BDA0002348415960000193
And calculating the standard deviation sigma of the vehicle speed data of k minutes, wherein k is a positive integer.
Figure BDA0002348415960000194
Figure BDA0002348415960000195
b) Obtaining within one time standard deviation range [ v0-σ，v0+σ]And obtaining a new vehicle speed data set according to all the vehicle speed values, calculating the average value of the new vehicle speed data set, and obtaining the credible average vehicle speed of the new vehicle speed data set in the time period based on the data number p of the new vehicle speed data set.
c) Defining standard driving speed of normal driving environment of the road grade according to the road grade, and for part of speed-limiting road sections (such as road sections with severe environment, curves, ramps and the like), taking lower standard driving speed and speed-limiting speedValue, defined as the normal driving environment standard driving speed v of the roads。
d) According to the congestion index data of the road at different moments, defining congestion coefficients
Figure BDA0002348415960000201
When the congestion coefficient of a given link at a given time is higher,
Figure BDA0002348415960000202
the lower the value of (c). On the contrary, when the congestion coefficient of the specified road section at the specified time is lower, namely the road real-time road condition is less congested,
Figure BDA0002348415960000203
the higher the value of (c).
e) Vehicle speed evidence coefficient ItmcIs shown in equation (21), equation (21) is as follows:
Figure BDA0002348415960000204
in the formula ItmcTaking an integer. I istmcAnd the snow depth prediction value Y is effective only when assigned, namely the natural snow depth value, or is not assigned. When the vehicle running speed is higher than the driving speed in the expected snow state of the road, ItmcNegative, when the vehicle is running at a speed lower than the driving speed expected under snow conditions on the road, ItmcPositive values.
In summary, the snow depth prediction method disclosed by the invention is characterized in that the rainfall phase state of the target location at the future target time is recognized based on the preset rainfall phase state recognition condition to obtain the rainfall phase state recognition result, when the rainfall phase state recognition result is rain and snow or snowfall, the meteorological data of the target location at the same day including the future target time are obtained from the high-precision gridding weather data, the meteorological data are standardized to obtain the standard meteorological data, the standard meteorological data are substituted into the snow depth prediction model to obtain the snow depth prediction value, and the snow depth prediction value is corrected by adopting a snow depth optimization equation established based on the geographic information system data, the national map road data and the dynamic traffic information to obtain the corrected optimal snow depth prediction value. Therefore, the snow depth prediction method and the snow depth prediction device have universality and improve the accuracy of snow depth prediction by integrating the multi-dimensional information and highly restoring real snow and snow scenes when the snow depth is predicted.
To further optimize the above embodiment, after step S105, the method may further include the steps of:
and outputting the precipitation phase state identification result and the optimal accumulated snow depth prediction value.
To further optimize the above embodiment, after step S105, the method may further include the steps of:
and carrying out grade division on the optimal accumulated snow depth predicted value according to a preset grade division standard to obtain accumulated snow grades with different influence degrees.
Specifically, according to the optimal snow depth value obtained by calculation and analysis and referring to the regulations of the Chinese meteorological office on the warning and early warning signals, the optimal snow depth Y is determinedfAnd (4) grading. The deeper the accumulated snow is, the higher the possibility of issuing early warning of heavy snow or even snowstorm is, and the greater the influence strength on traffic, crops and animal husbandry is. The grading criteria for small/medium/large/snowstorm forecasting may be specified according to the optimal snow depth value.
The following is a reference to a ranking of small/medium/large/snowstorm forecast release based on national snow depth:
a) level 1: snow which has no influence on traffic, crops and animal husbandry exists in the future 6 hours;
b) level 2: snow which has slight influence on traffic, crops and animal husbandry exists in the future 6 hours;
c) level 3: accumulated snow on roads which have influences on traffic, crops and animal husbandry exists for 6 hours in the future;
d) level 4: snow which has great influence on traffic, crops and animal husbandry exists in the future for 6 hours;
e) level 5: in the future, 6 hours, the snow cover has great influence on traffic, crops and animal husbandry.
In summary, the snow depth prediction method disclosed by the invention is based on the snow depth prediction model established according to the high-precision data, starts from four angles of natural snow accumulation, wind snow blowing, snow and ice ablation and manual intervention evidence, performs multi-dimensional data fusion and information evidence based on high-precision weather forecast information and in combination with a map, a road network, road attributes and dynamic traffic information factors, and can realize high-precision national snow vertical depth prediction with the granularity not lower than the weather data. The objective and real snow and snow accumulation scenes are highly restored, and the snow accumulation depth tends to a true value. Meanwhile, different damage influence levels are divided according to different accumulated snow depth result values, and the blank of risk prediction in a service scene is filled. And the development of related industries for disaster prevention is promoted while good social benefits are generated, and the whole society is enabled.
Corresponding to the embodiment of the method, the invention also discloses a snow depth prediction system.
Referring to fig. 4, a schematic structural diagram of a snow depth prediction system according to an embodiment of the present invention is disclosed,
the identification unit 301 is configured to identify a precipitation phase at a future target time of the target location based on a preset precipitation phase identification condition, so as to obtain a precipitation phase identification result;
wherein, the identifying unit 301 is specifically configured to:
judging whether the precipitation phase state is smaller than a first critical value or not;
if so, determining that the rainfall phase is rainfall;
if not, continuously judging whether the precipitation phase state is less than or equal to a second critical value, wherein the second critical value is greater than the first critical value;
if so, determining that the precipitation phase state is snowfall;
and if not, determining that the precipitation phase state is rain and snow.
It should be particularly noted that the values of the first critical value and the second critical value are determined according to the precipitation phase values of snowfall and rainfall, for example, the first critical value is 1.8, the second critical value is 2.1, and at this time, when Y is greater than or equal to 1.8 and less than or equal to 2.1, the precipitation phase is rain and snow; when Y is more than 2.1, the precipitation phase is snowfall; when Y is less than 1.8, the precipitation phase is rainfall, and Y is the precipitation phase.
A first obtaining unit 302, configured to obtain, from high-precision gridded weather data, weather data of a current day in which the target location includes the future target time when the precipitation phase recognition result is rain and snow or snowfall, where the weather data includes: daily precipitation, average air temperature, lowest air temperature, highest air temperature, accumulated snow depth, next-day precipitation and next-day average temperature;
the standard processing unit 303 is configured to perform standardized processing on the meteorological data to obtain standard meteorological data;
wherein, standard meteorological data includes: standard daily precipitation, standard average air temperature, standard lowest air temperature, standard highest air temperature, standard snow depth, standard next-day precipitation and standard next-day average temperature.
The prediction unit 304 is used for substituting the standard meteorological data into a pre-established snow depth prediction model to obtain a snow depth prediction value;
the correcting unit 305 is configured to correct the predicted snow depth value by using a pre-established snow depth optimization equation to obtain a corrected optimal predicted snow depth value, where the snow depth optimization equation is established based on geographic information system data, national map road data, and dynamic traffic information.
It should be noted that, in this embodiment, please refer to the corresponding portion of the method embodiment for the specific working principle of each component of the snow depth prediction system, which is not described herein again.
In summary, the snow depth prediction system disclosed by the invention identifies the precipitation phase state of the target location at the future target time based on the preset precipitation phase state identification condition to obtain the precipitation phase state identification result, when the precipitation phase state identification result is rain and snow or snowfall, the weather data of the target location at the same day including the future target time is obtained from the high-precision gridding weather data, the weather data is standardized to obtain the standard weather data, the standard weather data is substituted into the snow depth prediction model to obtain the snow depth prediction value, and the snow depth prediction value is corrected by adopting a snow depth optimization equation established based on the geographic information system data, the national map road data and the dynamic traffic information to obtain the corrected optimal snow depth prediction value. Therefore, the snow depth prediction method and the snow depth prediction device have universality and improve the accuracy of snow depth prediction by integrating the multi-dimensional information and highly restoring real snow and snow scenes when the snow depth is predicted.
To further optimize the above embodiment, the snow depth prediction system may further include:
and the grade division unit is used for carrying out grade division on the optimal accumulated snow depth predicted value according to a preset grade division standard to obtain accumulated snow grades with different influence degrees.
Specifically, according to the optimal snow depth value obtained by calculation and analysis and referring to the regulations of the Chinese meteorological office on the warning and early warning signals, the optimal snow depth Y is determinedfAnd (4) grading. The deeper the accumulated snow is, the higher the possibility of issuing early warning of heavy snow or even snowstorm is, and the greater the influence strength on traffic, crops and animal husbandry is. The grading criteria for small/medium/large/snowstorm forecasting may be specified according to the optimal snow depth value.
The following is a reference to a ranking of small/medium/large/snowstorm forecast release based on national snow depth:
f) level 1: snow which has no influence on traffic, crops and animal husbandry exists in the future 6 hours;
g) level 2: snow which has slight influence on traffic, crops and animal husbandry exists in the future 6 hours;
h) level 3: accumulated snow on roads which have influences on traffic, crops and animal husbandry exists for 6 hours in the future;
i) level 4: snow which has great influence on traffic, crops and animal husbandry exists in the future for 6 hours;
j) level 5: in the future, 6 hours, the snow cover has great influence on traffic, crops and animal husbandry.
To further optimize the above embodiment, the snow depth prediction system may further include:
the second obtaining unit is configured to obtain a value of the precipitation phase before the identifying unit 301 identifies the precipitation phase at the future target time of the target location based on the preset precipitation phase identifying condition to obtain a precipitation phase identifying result;
the second obtaining unit is specifically configured to:
obtaining the predicted ground temperature and the predicted ground dew point of a target site at the future target moment from the high-precision gridding weather data;
standardizing the predicted ground temperature to obtain a predicted standard ground temperature, and standardizing the predicted ground dew point to obtain a predicted standard ground dew point;
and substituting the predicted standard ground temperature and the predicted standard ground dew point into a pre-established precipitation phase state prediction model to obtain the value of the precipitation phase state at the future target moment.
In summary, the snow depth prediction system disclosed by the invention is based on a snow depth prediction model established according to high-precision data, starts from four angles of natural snow, wind snow blowing, snow ablation and manual intervention evidence, performs multi-dimensional data fusion and information evidence based on high-precision weather forecast information and in combination with a map, a road network, road attributes and dynamic traffic information factors, and can realize high-precision national snow vertical depth prediction with a granularity not lower than that of weather data. The objective and real snow and snow accumulation scenes are highly restored, and the snow accumulation depth tends to a true value. Meanwhile, different damage influence levels are divided according to different accumulated snow depth result values, and the blank of risk prediction in a service scene is filled. And the development of related industries for disaster prevention is promoted while good social benefits are generated, and the whole society is enabled.
It should be noted that, in the system embodiment, please refer to the corresponding portion of the method embodiment for the specific working principle of each unit, which is not described herein again.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (18)

1. A snow depth prediction method is characterized by comprising:
identifying the precipitation phase state of the target site at the future target moment based on preset precipitation phase state identification conditions to obtain a precipitation phase state identification result;
when the precipitation phase state identification result is rain and snow or snowfall, acquiring the meteorological data of the current day of the target site including the future target time from high-precision gridding weather data, wherein the meteorological data comprises: daily precipitation, average air temperature, lowest air temperature, highest air temperature, accumulated snow depth, next-day precipitation and next-day average temperature;
standardizing the meteorological data to obtain standard meteorological data;
substituting the standard meteorological data into a pre-established snow depth prediction model to obtain a snow depth prediction value;
and correcting the accumulated snow depth predicted value by adopting a pre-established accumulated snow depth optimization equation to obtain a corrected optimal accumulated snow depth predicted value, wherein the accumulated snow depth optimization equation is established on the basis of geographic information system data, national map road data and dynamic traffic information.
2. The snow depth prediction method according to claim 1, wherein the building process of the snow depth optimization equation includes:
based on geographic information system data, national map road data, and dynamic traffic information, the accumulated snow depth optimization equation shown below is established,
Figure FDA0002348415950000011
in the formula, YfFor the optimal accumulated snow depth predicted value, Y is the accumulated snow depth predicted value，pijThe matrix is an element (i, j) of a snow depth correction factor matrix m multiplied by n matrix P, wherein i is the ith row of the snow depth correction factor matrix, and j is the jth column of the snow depth correction factor matrix;
the expression of matrix P is as follows:
Figure FDA0002348415950000012
wherein the i-th line is 1 or p1jRestoring a natural snowing scene based on the landform, and correcting an accumulated snow depth predicted value from a natural accumulated snow angle, wherein an accumulated snow depth correction factor of the natural accumulated snow angle comprises: high resolution elevation index IdemSun exposure coefficient IsAnd coefficient of windward slope IwThree elements;
i 2 line i2jThe snow depth value is corrected from the wind-blowing angle based on the road scene determined by the geographic information system GIS data and the road network angle, and the snow depth correction factor of the wind-blowing angle comprises: radius coefficient of curve IrAnd the included angle coefficient I between the main wind direction and the road trend in the snow accumulation periodaTwo elements;
i-3 lines i.e. p3jCorrecting the snow depth predicted value from the ice and snow ablation angle, wherein the snow depth correction factor of the ice and snow ablation angle comprises: coefficient of depth of ground colour Ic；
I-4 th row, i.e. p4jAnd correcting the accumulated snow depth predicted value from a manual intervention angle, wherein the accumulated snow depth correction factor of the manual intervention angle comprises: vehicle speed evidence coefficient Itmc。
3. A snow depth prediction method according to claim 2, characterized in that said high resolution elevation index IdemIs calculated as follows:
Figure FDA0002348415950000021
in the formula, HmaxIs the maximum elevation value of the Chinese highway after standard deviation processing, f is a weight adjustment factor, HsIs a standard elevation value, when H ═ HsWhen, Idem1, when H > Hs, depth is positively compensated; h is less than HsWhile the depth is compensated negatively.
4. The snow depth prediction method according to claim 2, wherein the sunshine coefficient IsIs calculated as follows:
Figure FDA0002348415950000022
in the formula, m is a north coefficient, p is other orientation coefficients, and n is a south coefficient.
5. The snow depth prediction method according to claim 2, wherein the leeward slope coefficient IwThe expression of (a) is as follows:
Figure FDA0002348415950000031
in the formula, q is the windward slope coefficient, r is the general terrain coefficient, and s is the leeward slope coefficient.
6. A snow depth prediction method according to claim 2, wherein the curve radius coefficient IrThe expression of (a) is as follows:
Figure FDA0002348415950000032
in the formula, R is the radius of an actual road curve;
rs is a basic standard value;
Rmaxmaximum value of minimum radius of circular curve specified in the design specification for road route;
Rmina minimum value of a minimum radius of a circular curve specified in a highway route design specification;
Nbfor the balance coefficient, the expression is
Figure FDA0002348415950000033
NmEnabling the roadbed to be close to the mountain coefficient and only enabling the roadbed to be effective in the mountain road section, wherein the expression is as follows:
Figure FDA0002348415950000034
7. a snow depth prediction method according to claim 2, wherein the snow-age prevailing wind direction and road heading angle coefficient IaThe expression of (a) is as follows:
Ia＝1+k*sin(β-α)，
Figure FDA0002348415950000035
wherein beta is the included angle between the main wind direction and the road trend in the snow accumulation period,
Figure FDA0002348415950000036
k is a balance coefficient calculated by a normalization formula and aims at reducing IaAnd (4) weighting the influence on the original snow depth.
8. A snow depth prediction method according to claim 2, wherein the ground color depth coefficient IcThe expression of (a) is as follows:
Figure FDA0002348415950000041
in the formula, t is a black road coefficient, u is a yellow road coefficient, and v is an off-white road coefficient.
9. A snow depth prediction method according to claim 2, wherein the vehicle speed evidence factor ItmcThe expression of (a) is as follows:
Figure FDA0002348415950000042
in the formula ItmcTake an integer of ItmcIs effective only when said accumulated snow depth predicted value Y has an assigned value, I when the vehicle is traveling at a higher speed than the driving speed in the expected accumulated snow state of the roadtmcNegative, when the vehicle is running at a speed lower than the driving speed expected under snow conditions on the road, ItmcPositive, σ is the standard deviation of the vehicle speed data for k minutes, and p is within a range of one standard deviation [ v [ ]0-σ，v0+σ]The number of data of the new vehicle speed data set obtained from all vehicle speed values, v0Is the minute-scale road speed data vnFor the nth speed in the data set,
Figure FDA0002348415950000043
as congestion coefficients
Figure FDA0002348415950000044
vsThe driving speed is standard for normal driving environment.
10. The snow depth prediction method according to claim 1, further comprising:
and carrying out grade division on the optimal accumulated snow depth predicted value according to a preset grade division standard to obtain accumulated snow grades with different influence degrees.
11. A snow depth prediction method according to claim 1, wherein before the identifying the precipitation phase at the target time in the future of the target location based on the preset precipitation phase identification condition to obtain the precipitation phase identification result, the method further comprises: acquiring a value of a precipitation phase state;
the process of obtaining the value of the precipitation phase state specifically comprises the following steps:
obtaining the predicted ground temperature and the predicted ground dew point of a target site at the future target moment from the high-precision gridding weather data;
standardizing the predicted ground temperature to obtain a predicted standard ground temperature, and standardizing the predicted ground dew point to obtain a predicted standard ground dew point;
and substituting the predicted standard ground temperature and the predicted standard ground dew point into a pre-established precipitation phase state prediction model to obtain the value of the precipitation phase state at the future target moment.
12. The snow depth prediction method according to claim 11, wherein the precipitation phase prediction model is constructed by:
acquiring a historical ground temperature data set and a historical ground dew point data set in a first preset time period, calculating to obtain a historical ground temperature average value based on the historical ground temperature data set, and obtaining a historical ground dew point average value based on the historical ground dew point data set;
obtaining a historical ground temperature standard difference value based on the historical ground temperature data set and the historical ground temperature average value, and obtaining a historical ground dew point standard difference value based on the historical ground dew point data set and the historical ground dew point average value;
standardizing the ground temperature in the historical ground temperature data set based on the historical ground temperature average value and the historical ground temperature standard difference value to obtain historical standard ground temperature, and standardizing the ground dew point in the historical ground dew point data set based on the historical ground dew point average value and the historical ground dew point standard difference value to obtain historical standard ground dew point;
and obtaining the precipitation phase state prediction model by adopting a gradient descent method based on the historical precipitation phase state, the historical ground temperature data set and the historical ground dew point data set in the first preset time period.
13. A snow depth prediction method according to claim 12, wherein the obtaining the precipitation phase prediction model by using a gradient descent method based on the historical precipitation phase, the historical ground temperature data set and the historical ground dew point data set in the first preset time period specifically comprises:
calculating a first weight parameter k in the precipitation phase state prediction model according to the following two formulas1A second weight parameter k2And an intercept parameter b;
Figure FDA0002348415950000051
Figure FDA0002348415950000052
where n is the number of elements in a data set, the data set being: i is an element serial number and xiIs the ith element, and r is the historical precipitation phase;
according to the first weight parameter k1The second weight parameter k2And the distance parameter b, obtaining an expression of the precipitation phase state prediction model shown as follows:
Y＝b+k1x1+k2x2；
in the formula, Y is a precipitation phase, x1For historical ground temperature, x, in the historical ground temperature dataset2Historical ground dew points in the historical ground dew point data set.
14. A snow depth prediction method according to claim 1, wherein the identifying the precipitation phase at a future target time of the target location based on a preset precipitation phase identification condition to obtain a precipitation phase identification result specifically comprises:
judging whether the precipitation phase state is smaller than a first critical value or not;
if so, determining that the rainfall phase is rainfall;
if not, continuously judging whether the precipitation phase state is less than or equal to a second critical value, wherein the second critical value is greater than the first critical value;
if so, determining that the precipitation phase state is snowfall;
and if not, determining that the precipitation phase state is rain and snow.
15. The snow depth prediction method according to claim 1, wherein the building process of the snow depth prediction model includes:
according to the formula
Figure FDA0002348415950000061
The following weight formula is obtained:
w＝(XTX)-1XTy；
wherein X is [1, X ]3，...，x9]W is the weight corresponding to X, y is the historical observed snow depth data, 1 is a constant term, and X3Is the historical daily precipitation, x4Is a historical average air temperature, x5Is the historical lowest temperature, x6Is the historical maximum temperature, x7For historical accumulated snow depth, x8Precipitation of water and x for the historical next day9The average temperature of the historical next day;
obtaining an expression of the accumulated snow depth prediction model according to a weight formula, wherein the expression comprises the following steps:
Y＝XT(XTX)-1XTy；
wherein Y is the depth of accumulated snow and X isTIs the transpose of matrix X.
16. A snow depth prediction system, comprising:
the identification unit is used for identifying the precipitation phase state of the target site at the future target moment based on the preset precipitation phase state identification condition to obtain a precipitation phase state identification result;
a first obtaining unit, configured to obtain, from high-precision gridded weather data, weather data of a current day in which the target location includes the future target time when the precipitation phase recognition result is rain and snow or snowfall, where the weather data includes: daily precipitation, average air temperature, lowest air temperature, highest air temperature, accumulated snow depth, next-day precipitation and next-day average temperature;
the standard processing unit is used for carrying out standardized processing on the meteorological data to obtain standard meteorological data;
the prediction unit is used for substituting the standard meteorological data into a pre-established snow depth prediction model to obtain a snow depth prediction value;
and the correction unit is used for correcting the accumulated snow depth predicted value by adopting a pre-established accumulated snow depth optimization equation to obtain a corrected optimal accumulated snow depth predicted value, wherein the accumulated snow depth optimization equation is established on the basis of geographic information system data, national map road data and dynamic traffic information.
17. The snow depth prediction system of claim 16, further comprising:
and the grade division unit is used for carrying out grade division on the optimal accumulated snow depth predicted value according to a preset grade division standard to obtain accumulated snow grades with different influence degrees.
18. The snow depth prediction system of claim 16, further comprising:
the second acquisition unit is used for acquiring the value of the precipitation phase state before the identification unit identifies the precipitation phase state of the target site at the future target moment based on the preset precipitation phase state identification condition to obtain the precipitation phase state identification result;
the second obtaining unit is specifically configured to:
obtaining the predicted ground temperature and the predicted ground dew point of a target site at the future target moment from the high-precision gridding weather data;
standardizing the predicted ground temperature to obtain a predicted standard ground temperature, and standardizing the predicted ground dew point to obtain a predicted standard ground dew point;
and substituting the predicted standard ground temperature and the predicted standard ground dew point into a pre-established precipitation phase state prediction model to obtain the value of the precipitation phase state at the future target moment.
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