Meteorological data-based road surface temperature annual distribution probability model construction method

Abstract

The invention discloses a meteorological data-based road surface temperature annual distribution probability model construction method. The method comprises the following steps of S1, collecting atmospheric temperature and solar radiation intensity data; s2, dividing a temperature interval, and calculating an air temperature occurrence probability corresponding to the temperature interval; s3, calculating cumulative probabilities corresponding to a plurality of temperature intervals, and forming a data set of air temperature and air temperature annual distribution cumulative probabilities; s4, constructing an air temperature-annual distribution cumulative probability model; s5, calculating feature points of the air temperature annual distribution cumulative probability model according to the air temperature-annual distribution cumulative probability model; s6, calculating annual average radiation intensity; s7, calculating feature points of the road surface temperature annual distribution cumulative probability model; and S8, constructing a road surface temperature annual distribution cumulative probability model. According to the method, the annual distribution cumulative probability model of the asphalt pavement surface temperature can be accurately estimated, an accurate temperature parameter determination basis is provided for pavement design, the calculation process is simple and convenient, the result is accurate and reliable, and road surface temperature estimation based on meteorological data can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent traffic monitoring applications, and in particular to a method for constructing an annual distribution probability model of road surface temperature based on meteorological data. Background Art

[0002] When designing asphalt pavements, it's often necessary to determine design parameters based on the annual distribution of asphalt pavement temperature. However, collecting asphalt pavement temperature data presents significant challenges. For newly constructed asphalt pavements, temperature data collection isn't readily available during design, requiring only reference to existing data from nearby roads. For existing asphalt pavements, while temperature data collection is available, embedding temperature sensors increases construction costs and can damage the pavement. my country has extensive meteorological data collection sites that don't damage the pavement. Establishing a link between meteorological data and asphalt pavement temperature could improve the reliability of asphalt pavement design and performance estimation in my country. Summary of the Invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for constructing an annual distribution probability model of road surface temperature based on meteorological data.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0005] A method for constructing a road surface temperature annual distribution probability model based on meteorological data is provided, comprising the following steps:

[0006] S1. Determine the time interval and collect meteorological data, which includes the annual atmospheric temperature and annual solar radiation intensity;

[0007] S2. Determine the temperature intervals. Divide the annual atmospheric temperature into several temperature intervals based on the temperature intervals. Determine the frequency of occurrence of each temperature interval within the year. Obtain the occurrence time of each temperature interval. Calculate the probability of occurrence of the temperature interval based on the occurrence time.

[0008] S3. Calculate the cumulative probability of annual temperature distribution in several temperature intervals to form a data set of atmospheric temperature and cumulative probability of annual temperature distribution;

[0009] S4. Constructing a cumulative probability model for annual distribution of air temperature based on the data set of atmospheric temperature and annual distribution cumulative probability of air temperature obtained in step S3;

[0010] S5. Determine several annual temperature distribution cumulative probabilities, calculate the temperatures corresponding to the annual temperature distribution cumulative probabilities according to the annual temperature distribution cumulative probability model, and obtain several characteristic points of the annual temperature distribution cumulative probability model;

[0011] S6. Calculate the annual solar radiation intensity data and annual average radiation intensity based on the annual solar radiation intensity data and solar radiation intensity frequency;

[0012] S7. Calculate characteristic points of the cumulative probability model of annual distribution of road surface temperature;

[0013] S8. Construct a cumulative probability model of the annual distribution of road surface temperature based on a number of characteristic points of the cumulative probability of the annual distribution of road surface temperature.

[0014] Furthermore, the step S2 specifically includes the following steps:

[0015] S21, select the atmospheric temperature data for the whole year, and divide the atmospheric temperature T for the whole year into fixed temperature intervals ΔT. air , divide the annual atmospheric temperature data into n temperature intervals, and obtain the time ΔT of each temperature interval;

[0016] S22. Determine the frequency N of occurrence of the i-th temperature interval throughout the year i , calculate the appearance time t of the i-th temperature interval i , appearance time t i The calculation formula is as follows:

[0017] t i =N i ΔT;

[0018] S23, according to the appearance time t i Calculate the probability of temperature occurrence corresponding to the temperature interval. The formula for calculating the probability of temperature occurrence corresponding to the temperature interval is as follows:

[0019]

[0020] Where p i is the probability of occurrence of the i-th temperature interval, is the total length of the year, and n is the number of temperature intervals.

[0021] Furthermore, in step S3, the formula for calculating the annual distribution cumulative probability of several temperature intervals is as follows:

[0022]

[0023] Where, P i is the annual distribution cumulative probability of the i-th temperature interval, and the cumulative probability P of the i-th temperature interval i =P i-1 +p i The cumulative probability of the lowest temperature is P1=p1=0, the cumulative probability of the second temperature interval is P2=P1+p2, and the data set of temperature and annual temperature distribution cumulative probability is formed after calculating the annual distribution cumulative probability of each temperature interval.

[0024] Furthermore, in step S4, the cumulative probability model of annual temperature distribution is constructed as follows:

[0025]

[0026] Where, T air-min is the annual minimum temperature, a, b, c are the regression coefficients of the model, T air is the atmospheric temperature, F(T air ) is the cumulative probability corresponding to the temperature;

[0027] Furthermore, in step S5, the cumulative probabilities of the annual distribution of temperature are 0%, 5%, 25%, 50%, 75%, 95% and 100% respectively.

[0028] Furthermore, in step S6, the formula for calculating the annual average radiation intensity is as follows:

[0029]

[0030] Where N Qs>0 is the frequency of solar radiation intensity, N Qs>0 >0w / m2,Q total is the total solar radiation intensity throughout the year. is the annual average solar radiation intensity.

[0031] Furthermore, in step S7, the formula for calculating the characteristic points of the annual cumulative probability model of the road surface temperature is as follows:

[0032]

[0033] Where Q max is the annual maximum radiation intensity, is the annual average solar radiation intensity, T s is the annual cumulative probability of road surface temperature distribution, T s-min is the minimum value of the road surface temperature, that is, the minimum temperature that may occur on the road surface in a year;

[0034] Furthermore, in step S8, the constructed cumulative probability model of annual distribution of road surface temperature is as follows:

[0035]

[0036] Where, T s-min is the minimum characteristic point of the annual cumulative probability model of road surface temperature distribution, A, B, C are the regression coefficients of the model, F(T s ) is the cumulative probability corresponding to the road surface temperature.

[0037] Furthermore, the temperature interval is less than or equal to 3°C.

[0038] Furthermore, the time interval is less than or equal to 1 hour.

[0039] The beneficial effects of the present invention are:

[0040] The method presented in this paper accurately estimates the annual distribution cumulative probability model of asphalt pavement surface temperature based on meteorological data (including air temperature and solar radiation intensity), providing a precise basis for determining temperature parameters for pavement design. The method offers a simple calculation process and reliable results, enabling accurate and reliable estimation of road surface temperature based on meteorological data. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a graph showing the relationship between temperature and occurrence probability in Example 1;

[0042] Figure 2 This is a relationship diagram between temperature and annual distribution cumulative probability in Example 1;

[0043] Figure 3 Schematic diagram of characteristic points of cumulative probability of annual temperature distribution in Example 1;

[0044] Figure 4 Schematic diagram of characteristic points of the cumulative probability model of annual distribution of road surface temperature in Example 1;

[0045] Figure 5 This is a test diagram of the cumulative probability model of annual distribution of road surface temperature in Example 1;

[0046] Figure 6 This is a graph showing the relationship between temperature and occurrence probability in Example 2;

[0047] Figure 7 This is a relationship diagram between temperature and annual distribution cumulative probability in Example 2;

[0048] Figure 8 Schematic diagram of characteristic points of cumulative probability of annual temperature distribution in Example 2;

[0049] Figure 9 Schematic diagram of characteristic points of the cumulative probability model of annual distribution of road surface temperature in Example 2;

[0050] Figure 10 This is a test diagram of the cumulative probability model of annual distribution of road surface temperature in Example 2; DETAILED DESCRIPTION

[0051] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0052] The method for constructing a road surface temperature annual distribution probability model based on meteorological data includes the following steps:

[0053] S1. Determine the time interval and collect meteorological data, which includes the annual atmospheric temperature and annual solar radiation intensity;

[0054] Specifically, the atmospheric temperature T is collected at a fixed time interval Δt (Δt≤1h) air and solar radiation intensity Q s , collect the time t and atmospheric temperature T throughout the year air and solar radiation intensity Q s The data forms a data set;

[0055] S2. Determine the temperature interval. Divide the annual atmospheric temperature into several temperature intervals according to the temperature intervals. Determine the frequency of occurrence of the temperature intervals within the year. Obtain the occurrence time of the temperature intervals. Calculate the probability of occurrence of the temperature intervals according to the occurrence time. Specifically,

[0056] S21. Select the atmospheric temperature data for the whole year and divide the atmospheric temperature T into the fixed temperature interval ΔT (ΔT≤3℃) air , divide the annual atmospheric temperature data into n temperature intervals, and obtain the time ΔT of each temperature interval;

[0057] S22. Determine the frequency N of occurrence of the i-th temperature interval throughout the year i , calculate the appearance time t of the i-th temperature interval i , appearance time t i The calculation formula is as follows:

[0058] t i =N i ΔT;

[0059] S23, according to the appearance time t i Calculate the probability of temperature occurrence corresponding to the temperature interval. The formula for calculating the probability of temperature occurrence corresponding to the temperature interval is as follows:

[0060]

[0061] Where p i is the probability of occurrence of the i-th temperature interval, is the total length of the year, n is the number of temperature intervals;

[0062] S3. Calculate the cumulative probability of annual temperature distribution in several temperature intervals to form a data set of atmospheric temperature and cumulative probability of annual temperature distribution;

[0063] The formula for calculating the annual distribution cumulative probability of each temperature range is as follows:

[0064]

[0065] Where, P i is the annual distribution cumulative probability of the i-th temperature interval, and the cumulative probability P of the i-th temperature interval i =P i-1 +p i The cumulative probability of the lowest temperature is P1=p1=0, the cumulative probability of the second temperature interval is P2=P1+p2, and the annual distribution cumulative probability of each temperature interval is calculated to form a data set of temperature and annual distribution cumulative probability of temperature.

[0066] S4. Constructing a cumulative probability model for annual distribution of air temperature based on the data set of atmospheric temperature and annual distribution cumulative probability of air temperature obtained in step S3;

[0067] The constructed cumulative probability model of annual temperature distribution is as follows:

[0068]

[0069] Where, T air-min is the annual minimum temperature, a, b, c are the regression coefficients of the model, T air is the atmospheric temperature, F(T air ) is the cumulative probability corresponding to the temperature;

[0070] The model shown in the cumulative probability model of annual temperature distribution is used to fit the data set of temperature and cumulative probability of annual temperature distribution to determine the values of regression parameters a, b, and c in the cumulative probability model of annual temperature distribution. Specifically, based on the cumulative probability data set of annual temperature distribution, the least squares method is used to determine the values of regression parameters a, b, and c when the gap between the observed value and the predicted value (mean square error MSE) is minimized. The mean square error MSE is as follows:

[0071]

[0072] Where n is the number of samples; i is the sum index, from 1 to n; F(T air ) is the cumulative probability corresponding to the temperature (true value function); is the estimated value function.

[0073] S5. Determine several annual temperature distribution cumulative probabilities, calculate the temperatures corresponding to the annual temperature distribution cumulative probabilities according to the annual temperature distribution cumulative probability model, and obtain several characteristic points of the annual temperature distribution cumulative probability model;

[0074] Specifically, based on the cumulative probability model of annual temperature distribution, the temperatures corresponding to the cumulative probability of 0%, 5%, 25%, 50%, 75%, 95%, and 100% of the annual temperature distribution are calculated, and 7 characteristic points of the cumulative probability model of annual temperature distribution are determined;

[0075] S6. Calculate the annual solar radiation intensity data and annual average radiation intensity based on the annual solar radiation intensity data and solar radiation intensity frequency;

[0076] Specifically, select the solar radiation intensity data for the whole year and count the frequency N of solar radiation intensity > 0w / m2 Qs>0 , calculate the total annual solar radiation intensity Q total , and calculate the annual average solar radiation intensity And calculate the annual maximum radiation intensity Q max , calculate the annual average solar radiation intensity The formula is as follows:

[0077]

[0078] S7. Calculate characteristic points of the cumulative probability model of annual distribution of road surface temperature;

[0079] Specifically, the formula for calculating the characteristic points of the annual cumulative probability model of road surface temperature distribution is as follows:

[0080]

[0081] Where Q max is the annual maximum radiation intensity, is the annual average solar radiation intensity, T s is the annual cumulative probability of road surface temperature distribution, T s-min is the minimum value of the road surface temperature, that is, the minimum temperature that may occur on the road surface in a year;

[0082] S8. Constructing a cumulative probability model of the annual distribution of road surface temperature based on a number of characteristic points of the cumulative probability of the annual distribution of road surface temperature;

[0083] The cumulative probability model of the annual distribution of road surface temperature is constructed based on the seven characteristic points of the cumulative probability of the annual distribution of road surface temperature. The cumulative probability model of the annual distribution of road surface temperature is constructed as follows:

[0084]

[0085] Where, T s-min is the minimum characteristic point of the annual cumulative probability model of road surface temperature distribution, A, B, C are the regression coefficients of the model, F(T s ) is the cumulative probability corresponding to the road surface temperature. Specifically, based on the annual cumulative probability data set of the road surface temperature distribution, the least squares method is used to determine the values of regression parameters A, B, and C when the gap between the observed value and the predicted value is minimized. The specific mean square error formula is similar to that shown in step S4.

[0086] Example 1

[0087] S1. Meteorological data collection. The atmospheric temperature T is collected at 15-minute intervals. air and solar radiation intensity Q s , collect the time t and atmospheric temperature T throughout the year air and solar radiation intensity Q s The data forms a data set.

[0088] S2. Temperature probability. Select the atmospheric temperature data for the whole year and divide the atmospheric temperature T into 1°C intervals. air , divide the annual temperature into 43 temperature intervals. Calculate the probability of occurrence p of each temperature interval according to the formula in step S2 i ,See Figure 1 .

[0089] S3, annual distribution cumulative probability of temperature. The formula in step S3 calculates the annual distribution cumulative probability P of each temperature interval i ,See Figure 2 .

[0090] S4. Constructing a cumulative probability model for annual temperature distribution. Use the model shown in the formula in step S4 to fit the data set of temperature and annual temperature cumulative probability distribution, and determine the values of regression parameters a, b, and c in the formula in step S3. air-min is the annual minimum temperature, and the regression parameter values are shown in Table 1.

[0091] Table 1 Regression parameters of the cumulative probability model of temperature-annual distribution

[0092] parameter a b c <![CDATA[T air-min ]]> <![CDATA[R 2 ]]> value 176.2 0.038 2.18 -4.5 0.995

[0093] S5. Determination of characteristic points of the annual temperature distribution cumulative probability model. Based on formula (3), the corresponding temperatures of 0%, 5%, 25%, 50%, 75%, 95%, and 100% annual temperature distribution cumulative probability are calculated, and 7 characteristic points of the annual temperature distribution cumulative probability model are determined. Figure 3 The corresponding temperature and annual distribution cumulative probability of the characteristic points are shown in Table 2.

[0094] Table 2 Characteristic points of the cumulative probability model of temperature-annual distribution

[0095] Feature Points 1 2 3 4 5 6 7 Temperature (%) -4.5 0.9 9.0 17.8 25.0 30.4 37.0 Annual distribution cumulative probability (%) 0 5 25 50 75 95 100

[0096] S6. Calculation of annual average radiation intensity. Select the solar radiation intensity data for the whole year and count the frequency N of solar radiation intensity > 0w / m2 Qs>0 , calculate the total annual solar radiation intensity Q total , and calculate the annual average solar radiation intensity according to the formula in step S6 The annual maximum radiation intensity Q max =1486w / m2.

[0097] S7, determine the characteristic points of the cumulative probability model of the annual distribution of road surface temperature. Calculate the characteristic points of the cumulative probability model of the annual distribution of road surface temperature according to the formula in step S7, see Figure 4 The road surface temperature corresponding to the characteristic points and the annual distribution cumulative probability are shown in Table 3.

[0098] Table 3 Regression parameters of the cumulative probability model for road surface temperature-annual distribution

[0099] Feature Points 1 2 3 4 5 6 7 Temperature (%) -1 3.1 11.2 23.0 34.9 52.7 66.9 Annual distribution cumulative probability (%) 0 5 25 50 75 95 100

[0100] S8. Construction of the cumulative probability model of the annual distribution of road surface temperature. The cumulative probability model of the annual distribution of road surface temperature is constructed with 7 characteristic points of the annual distribution of road surface temperature. The model parameters are shown in Table 4. The predicted annual distribution of road surface temperature cumulative probability and the measured annual distribution of road surface temperature cumulative probability are shown in Table 4. Figure 5 , the maximum error is <5%.

[0101] Table 4 Characteristic points of the cumulative probability model of road surface temperature-annual distribution

[0102] parameter a b c <![CDATA[T air-min ]]> <![CDATA[R 2 ]]> value 113.6 0.044 2.05 -1.0 0.997

[0103] Example 2

[0104] According to the technical solution of the present invention, this implementation uses a certain asphalt pavement as an example to calculate the correlation between meteorological data and the annual cumulative probability of the road surface temperature distribution, and constructs a cumulative probability model of the annual distribution of the road surface temperature based on the meteorological data. The specific steps are as follows:

[0105] S1. Meteorological data collection. The atmospheric temperature T is collected at 15-minute intervals. air and solar radiation intensity Q s , collect the time t and atmospheric temperature T throughout the year air and solar radiation intensity Q s The data forms a data set.

[0106] S2. Temperature probability. Select the atmospheric temperature data for the whole year and divide the atmospheric temperature T into 1°C intervals. air , divide the annual temperature into 49 temperature intervals. Calculate the probability of occurrence p of each temperature interval according to the formula in step S2 i ,See Figure 6 .

[0107] S3, annual distribution cumulative probability of temperature. The formula in step S3 calculates the annual distribution cumulative probability P of each temperature interval i ,See Figure 7 .

[0108] S4. Constructing a temperature-annual distribution cumulative probability model. Use the model shown in the formula in step S4 to fit the data set of temperature and annual distribution cumulative probability of temperature, and determine the values of regression parameters a, b, and c in the formula in step S3. air-min is the annual minimum temperature. The regression parameter values are shown in Table 5.

[0109] Table 5 Regression parameters of the cumulative probability model of temperature-annual distribution

[0110] parameter a b c <![CDATA[T air-min ]]> <![CDATA[R 2 ]]> value 141.2 0.052 3.30 -9.5 0.997

[0111] S5. Determination of characteristic points of the cumulative probability model of annual temperature distribution. Based on formula (3), the temperatures corresponding to 100% of the cumulative probability of annual temperature distribution of 0%, 5%, 25%, 50%, 75%, and 95% are calculated, and 7 characteristic points of the cumulative probability model of annual temperature distribution are determined. Figure 8 The corresponding temperatures and annual distribution cumulative probabilities of the characteristic points are shown in Table 6.

[0112] Table 6 Characteristic points of the cumulative probability model of temperature-annual distribution

[0113] Feature Points 1 2 3 4 5 6 7 Temperature (%) -9.5 -1.1 7.5 17.0 24.1 31.5 38.5 Annual distribution cumulative probability (%) 0 5 25 50 75 95 100

[0114] S6. Calculation of annual average radiation intensity. Select the solar radiation intensity data for the whole year and count the frequency N of solar radiation intensity > 0w / m2 Qs>0 , calculate the total annual solar radiation intensity Q total , and calculate the annual average solar radiation intensity according to the formula in step S6 The annual maximum radiation intensity Q max =1513w / m2.

[0115] S7, determine the characteristic points of the cumulative probability model of the annual distribution of road surface temperature. Calculate the characteristic points of the cumulative probability model of the annual distribution of road surface temperature according to the formula in step S7, see Figure 9 The road surface temperature and annual distribution cumulative probability corresponding to the characteristic points are shown in Table 7.

[0116] Table 7 Regression parameters of the cumulative probability model for road surface temperature-annual distribution

[0117] Feature Points 1 2 3 4 5 6 7 Temperature (%) -3.4 1.5 12.0 24.3 35.8 54.9 70.0 Annual distribution cumulative probability (%) 0 5 25 50 75 95 100

[0118] S8. Construction of the cumulative probability model of the annual distribution of road surface temperature. The cumulative probability model of the annual distribution of road surface temperature is constructed with 7 characteristic points of the annual distribution of road surface temperature. The model parameters are shown in Table 8. The predicted annual distribution of road surface temperature cumulative probability and the measured annual distribution of road surface temperature cumulative probability are shown in Table 8. Figure 10 , the maximum error is <3%.

[0119] Table 8 Characteristic points of the cumulative probability model of road surface temperature-annual distribution

[0120] parameter a b c <![CDATA[T air-min ]]> <![CDATA[R 2 ]]> value 109.9 0.045 2.19 -3.4 0.997

Claims

1. A method for constructing a road surface temperature annual distribution probability model based on meteorological data, characterized in that: The steps include: S1. Determine the time interval and collect meteorological data, which includes the annual atmospheric temperature and annual solar radiation intensity; S2. Determine the temperature intervals. Divide the annual atmospheric temperature into several temperature intervals based on the temperature intervals. Determine the frequency of occurrence of each temperature interval within the year. Obtain the occurrence time of each temperature interval. Calculate the probability of occurrence of the temperature interval based on the occurrence time. S3. Calculate the cumulative probability of annual temperature distribution in several temperature intervals to form a data set of atmospheric temperature and cumulative probability of annual temperature distribution; S4. Constructing a cumulative probability model for annual distribution of air temperature based on the data set of atmospheric temperature and annual distribution cumulative probability of air temperature obtained in step S3; S5. Determine several annual temperature distribution cumulative probabilities, calculate the temperatures corresponding to the annual temperature distribution cumulative probabilities according to the annual temperature distribution cumulative probability model, and obtain several characteristic points of the annual temperature distribution cumulative probability model; S6. Calculate the annual solar radiation intensity data and annual average radiation intensity based on the annual solar radiation intensity data and solar radiation intensity frequency; S7. Calculate characteristic points of the cumulative probability model of annual distribution of road surface temperature; S8. Construct a cumulative probability model of the annual distribution of road surface temperature based on a number of characteristic points of the cumulative probability of the annual distribution of road surface temperature.

2. The method for constructing a road surface temperature annual distribution probability model based on meteorological data according to claim 1, characterized in that: The step S2 specifically includes the following steps: S21, select the atmospheric temperature data for the whole year, and divide the atmospheric temperature T for the whole year into fixed temperature intervals ΔT. air , divide the annual atmospheric temperature data into n temperature intervals, and obtain the time ΔT of each temperature interval; S22. Determine the frequency N of occurrence of the i-th temperature interval throughout the year i , calculate the appearance time t of the i-th temperature interval i , appearance time t i The calculation formula is as follows: t i =N i ΔT; S23, according to the appearance time t i Calculate the probability of temperature occurrence corresponding to the temperature interval. The formula for calculating the probability of temperature occurrence corresponding to the temperature interval is as follows: Where p i is the probability of occurrence of the i-th temperature interval, is the total length of the year, and n is the number of temperature intervals.

3. The method for constructing a road surface temperature annual distribution probability model based on meteorological data according to claim 1, characterized in that: In step S3, the formula for calculating the annual distribution cumulative probability of several temperature intervals is as follows: Where, P i is the annual distribution cumulative probability of the i-th temperature interval, and the cumulative probability P of the i-th temperature interval i =P i-1 +p i The cumulative probability of the lowest temperature is P1 = p1 = 0, and the cumulative probability of the second temperature interval is P2 = P1 + p2; After calculating the annual distribution cumulative probability of each temperature range, a data set of air temperature and annual distribution cumulative probability of air temperature is formed.

4. The method for constructing a road surface temperature annual distribution probability model based on meteorological data according to claim 1, characterized in that: In step S4, the cumulative probability model of annual temperature distribution is constructed as follows: Where, T air-min is the annual minimum temperature, a, b, c are the regression coefficients of the model, T air is the atmospheric temperature, F(T air ) is the cumulative probability corresponding to the temperature.

5. The method for constructing a road surface temperature annual distribution probability model based on meteorological data according to claim 1, characterized in that: In step S5, the cumulative probabilities of the annual temperature distribution are 0%, 5%, 25%, 50%, 75%, 95% and 100% respectively.

6. The method for constructing a road surface temperature annual distribution probability model based on meteorological data according to claim 1, characterized in that: In step S6, the formula for calculating the annual average radiation intensity is as follows: Where N Qs>0 is the frequency of solar radiation intensity, N Qs>0 >0w / m 2 , Q total is the total solar radiation intensity throughout the year. is the annual average solar radiation intensity.

7. The method for constructing a road surface temperature annual distribution probability model based on meteorological data according to claim 1, characterized in that: In step S7, the formula for calculating the characteristic points of the annual cumulative probability model of road surface temperature is as follows: Where Q max is the annual maximum radiation intensity, is the annual average solar radiation intensity, T s is the annual cumulative probability of road surface temperature distribution, T s-min is the minimum value of the road surface temperature.

8. The method for constructing a road surface temperature annual distribution probability model based on meteorological data according to claim 1, characterized in that: In step S8, the constructed cumulative probability model of annual distribution of road surface temperature is as follows: Where, T s-min is the minimum characteristic point of the annual cumulative probability model of road surface temperature distribution, A, B, C are the regression coefficients of the model, F(T s ) is the cumulative probability corresponding to the road surface temperature.

9. The method for constructing a road surface temperature annual distribution probability model based on meteorological data according to claim 1, characterized in that: The temperature interval is less than or equal to 3°C.

10. The method for constructing a road surface temperature annual distribution probability model based on meteorological data according to claim 1, characterized in that: The time interval is less than or equal to 1 hour.

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