CN114492090B - Road surface temperature short-term forecasting method - Google Patents
Road surface temperature short-term forecasting method Download PDFInfo
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- CN114492090B CN114492090B CN202210378921.6A CN202210378921A CN114492090B CN 114492090 B CN114492090 B CN 114492090B CN 202210378921 A CN202210378921 A CN 202210378921A CN 114492090 B CN114492090 B CN 114492090B
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Abstract
The present disclosure provides a road surface temperature short-term forecasting method, including: determining historical data of a traffic weather station in a sampling time period corresponding to a current forecast period, wherein the historical data of the traffic weather station comprises road surface temperature and at least one type of auxiliary weather parameters in the current forecast period; establishing a mean value generating function of the road surface temperature and a mean value generating function of each auxiliary meteorological parameter according to the historical data of the traffic meteorological station; constructing a road surface temperature short-term forecasting model by taking the mean value generating function of the road surface temperature and the mean value generating functions of various auxiliary meteorological parameters as independent variables; and outputting a pavement temperature forecast value in a forecast time period corresponding to the current forecast period according to the pavement temperature short-term forecast model corresponding to the current forecast period. The present disclosure also provides an electronic device and a computer-readable storage medium. The pavement condition can be further determined by combining the pavement temperature forecast value with various auxiliary meteorological parameters, and the driving safety is better improved.
Description
Technical Field
The present disclosure relates to the field of computers, and in particular, to a road surface temperature short-term prediction method, an electronic device, and a computer-readable medium.
Background
Extreme temperature of road surface is one of the important factors affecting road traffic safety: the vehicle tire burst is easily caused on one hand when the temperature of the road surface is too high, and the deformation or the damage of the roadbed is caused on the other hand; the road surface temperature is too low, can cause road icing, snow, has traffic safety hidden danger.
In order to eliminate the potential traffic safety hazard, the road surface temperature needs to be forecasted. However, how to accurately predict the road surface temperature is an urgent technical problem to be solved in the field.
Disclosure of Invention
An object of the present disclosure is to provide a road temperature short-term prediction method by which a road temperature can be accurately predicted, an electronic device, and a computer-readable medium.
As a first aspect of the present disclosure, there is provided a road surface temperature short-forecasting method including the steps of, periodically:
determining historical data of a traffic weather station in a sampling time period corresponding to a current forecast period, wherein the historical data of the traffic weather station comprise road surface temperature and at least one type of auxiliary weather parameters in the current forecast period, and the auxiliary weather parameters can influence the road surface temperature;
establishing a mean value generating function of the road surface temperature and mean value generating functions of various auxiliary meteorological parameters according to the historical data of the traffic meteorological station;
constructing a road surface temperature short-term forecasting model by taking the mean value generating function of the road surface temperature and the mean value generating functions of various auxiliary meteorological parameters as independent variables;
and outputting a pavement temperature forecast value in a forecast time period corresponding to the current forecast period according to the pavement temperature short-term forecast model corresponding to the current forecast period.
Optionally, the auxiliary meteorological parameters comprise at least one of air temperature, wind speed, relative humidity.
Optionally, the sampling time period corresponding to the current forecast period includes the following intervals:
[ t 0-. DELTA.t, t0], wherein,
t0 is the starting time of the current forecast period;
Δ t is the period of 2 days to 5 days.
Optionally, the establishing a mean value generating function of the road surface temperature and a mean value generating function of each auxiliary meteorological parameter according to the historical data of the traffic meteorological station includes:
performing initial processing on corresponding historical data of the traffic meteorological station to obtain modeling data meeting quality requirements, wherein the modeling data comprises road surface temperature modeling data and auxiliary meteorological parameter modeling data;
performing power spectrum analysis on various modeling data, and extracting the dominant cycle of the various modeling data;
establishing a mean value generating function of the road surface temperature according to the dominant period of the data for road surface temperature modeling;
and establishing a mean value generating function of various auxiliary meteorological parameters according to the dominant period of the data for modeling various auxiliary meteorological parameters.
Optionally, the initially processing the historical data of the corresponding traffic weather station to obtain data for modeling meeting the quality requirement includes:
filtering corresponding historical data of the traffic weather station to filter out bad data;
interpolating the filtered historical data of the traffic weather station to respectively obtain interpolated road surface temperature data and interpolated auxiliary weather parameter data;
carrying out first-order differencing processing on the interpolated road surface temperature data to obtain road surface temperature modeling data meeting quality requirements;
and performing first-order differencing processing on the various weather auxiliary parameter data after interpolation to obtain various weather auxiliary parameter modeling data meeting quality requirements.
Optionally, the performing power spectrum analysis on the various types of modeling data to extract dominant cycles of the various types of modeling data includes:
determining power spectral densities corresponding to different sampling periods of the road surface temperature differential sequence in the current forecasting period and power spectral densities corresponding to different sampling periods of each type of auxiliary meteorological parameter differential sequence in the current forecasting period;
the current forecast weekDividing power spectral density corresponding to different sampling periods of pavement temperature differential sequence in period by
The red noise standard spectrum of the road surface temperature sequence obtains the spectrum ratio of the road surface temperature sequence, and for each type of auxiliary meteorological parameters, the power spectral density corresponding to different sampling periods of the auxiliary meteorological parameter differential sequence in the current forecast period is divided by the power spectral density
Obtaining the spectrum ratio of the auxiliary meteorological parameter sequence according to the red noise standard spectrum;
taking the sampling period of the road surface temperature difference sequence with the spectrum ratio value larger than the preset value as the dominant period of the data for road surface temperature modeling, and taking the sampling period of the auxiliary meteorological parameter difference sequence with the spectrum ratio value larger than the preset value as the dominant period of the auxiliary meteorological parameter for modeling, wherein the preset value is not less than 1.
Optionally, the time interval of each modeling data time series is the same as the time interval of the road temperature forecast in the forecast time period.
As a second aspect of the present disclosure, there is provided an electronic apparatus including:
one or more processors;
a memory having one or more applications stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the road surface temperature imminent prediction method according to the first aspect of the disclosure;
one or more I/O interfaces connected between the processor and the memory and configured to enable information interaction between the processor and the first memory.
As a third aspect of the present disclosure, there is provided a computer-readable medium on which a computer program is stored, the program, when executed by a processor, implementing the road surface temperature short-prediction method provided by the first aspect of the present disclosure.
In the disclosure, when a road temperature short-term forecasting model is constructed, not only the road temperature but also auxiliary meteorological parameters which can influence the road temperature are considered. Therefore, when the pavement temperature short-term forecasting method provided by the disclosure is used for forecasting the pavement temperature, a more accurate pavement temperature forecasting value can be output.
Through the combination of the pavement temperature forecast value and various auxiliary meteorological parameters, the pavement condition (such as icing or water accumulation) can be further determined, and the driving safety can be improved. Particularly, in winter, whether the road surface is frozen or not can be accurately judged according to the predicted value of the road surface temperature and the weather condition, and the driving safety in winter is improved.
Drawings
FIG. 1 is a flow chart of one embodiment of a method for the near prediction of road temperature provided by the present disclosure;
FIG. 2 is a flow chart of one embodiment of step S120;
FIG. 3 is a flowchart of one embodiment of step S121;
FIG. 4 is a flowchart of one embodiment of step S122.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present disclosure, the method for road surface temperature prediction, the electronic device and the computer readable medium provided by the present disclosure are described in detail below with reference to the accompanying drawings.
Example embodiments will be described more fully hereinafter with reference to the accompanying drawings, but which may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As a first aspect of the present disclosure, there is provided a road surface temperature short-forecasting method, as shown in fig. 1, including the following steps performed periodically:
in step S110, determining historical data of a traffic weather station in a sampling time period corresponding to a current forecast period, where the historical data of the traffic weather station includes road surface temperature and at least one type of auxiliary weather parameter in the current forecast period, and the auxiliary weather parameter can affect the road surface temperature;
in step S120, a mean value generating function of the road surface temperature and mean value generating functions of various auxiliary meteorological parameters are established according to the historical data of the traffic meteorological station;
in step S130, a road temperature short-term prediction model is constructed by using the mean generation function of the road temperature and the mean generation functions of various auxiliary meteorological parameters as arguments;
in step S140, a predicted value of the road temperature in the prediction time period corresponding to the current prediction cycle is output according to the short-term prediction model of the road temperature corresponding to the current prediction cycle.
First, the sampling time period of the current forecast period refers to a time period before (including) the start time of the current forecast period. For example, the starting time of the current forecast cycle is 12 am # 1/20 m 2022, and the corresponding sampling period of the current forecast cycle should be a period of time (e.g., 3 days) before 12 am # 20 m 2022 (including 12 am). The forecast time period corresponding to the current forecast period refers to a period of time after the starting time of the current forecast period. For example, the starting time of the current forecast period is 12 am # 1/20 at 2022, and the forecast time period corresponding to the current forecast period is also a period of time (e.g., 2 hours) after the starting time of the current forecast period.
During a sampling period, a number of different weather types may be experienced. For example, during a sampling period, weather patterns of sunny, cloudy, rainy, snowy, etc. may be experienced. The specific values of the auxiliary meteorological parameters are different in different weather types, which also have an effect on the road surface temperature. In the present disclosure, the type of the auxiliary meteorological parameters is not particularly limited. Optionally, the auxiliary meteorological parameters may include at least one of meteorological elements that affect the road surface temperature, such as air temperature, wind speed, relative humidity, and the like. Taking the relative humidity as an example, the relative humidity in four weather types, namely sunny days, cloudy days, rainy days and snowy days, is obviously different.
In the disclosure, when a road temperature short-term forecasting model is constructed, not only the road temperature but also auxiliary meteorological parameters which can influence the road temperature are considered. Therefore, when the pavement temperature short-term forecasting method provided by the disclosure is used for forecasting the pavement temperature, a more accurate pavement temperature forecasting value can be output.
Through the combination of the pavement temperature forecast value and various auxiliary meteorological parameters, the pavement condition (such as icing or water accumulation) can be further determined, and the driving safety can be improved. Particularly in winter, whether the road surface is frozen or not can be accurately judged according to the predicted value of the road surface temperature and the weather condition, and the driving safety in winter is improved.
In the disclosure, no special limitation is made on how to construct the road surface temperature short prediction model according to the road surface temperature mean value generating function and the mean value generating functions of various auxiliary meteorological parameters. For example, the road surface temperature short-term prediction model may be generated by means of linear regression. Alternatively, the road surface temperature short-term prediction model may be generated by a nonlinear regression method.
It should be noted that, when a road surface temperature short-term prediction model based on a homogenesis function is constructed, a road surface temperature time sequence of historical data of a traffic meteorological station and time sequences of various auxiliary meteorological parameters need to be extracted. Therefore, in the road surface temperature short-circuit forecasting method, a road surface temperature time sequence is extracted in a rolling mode, time sequences of various auxiliary meteorological parameters are extracted in a rolling mode, a road surface temperature short-circuit forecasting model is generated in a rolling mode, and the road surface temperature is subjected to rolling forecasting.
In the disclosure, the time interval of each time sequence is not particularly limited, and optionally, the time interval of each time sequence may be between 10 minutes and 20 minutes, so that not only can the calculation resources be saved, but also the road surface temperature can be accurately forecasted.
In the present disclosure, the time interval of the road temperature prediction in the prediction period of the current prediction cycle is the same as the time interval of each of the modeling-use data time series. For example, when the time interval of each modeling data time series is 10 minutes, the road surface temperature 10 minutes after the current time can be predicted by the road surface temperature short prediction model.
It should be noted that the road surface temperature statistically obtained by each traffic weather station is targeted, and therefore, the road surface temperature predicted by the road surface temperature short-term prediction method provided by the present disclosure is also the road surface temperature within the coverage area of the traffic weather station. Similarly, the "auxiliary weather parameters" described above are also auxiliary weather parameters within the coverage area of the traffic weather station.
As described above, the sampling time period corresponding to the current forecast period refers to a time period before the start time of the current forecast period (including the start time). That is, the sampling time period corresponding to the current forecast period includes the following intervals:
[ t0- Δ t, t0], wherein:
t0 is the starting time of the current forecast period;
Δ t is the period of 2 days to 5 days.
For traffic weather stations, the sampling period is fixed. The longer the sampling period, the larger the amount of samples contained. Theoretically, the more the number of samples is, the more detailed the model describes the road surface temperature change rule, and the more accurate the prediction result is. However, the larger the sample size, the larger the subsequent calculation amount. Moreover, some traffic weather stations have relatively common data quality due to lack of maintenance. The historical data of the traffic weather station provided by the traffic weather station with general data quality needs to be preprocessed, abnormal data is removed, interpolation is carried out again, and data meeting quality requirements are obtained. If the sample size is larger, the amount of data preprocessing work increases. In order to balance the accuracy of the prediction model with the calculation amount, optionally, the sampling time period corresponding to the current prediction cycle includes: a period of 2 to 5 days before (including the start time of) the start time of the current forecast period. Further optionally, the sampling time period corresponding to the current forecast period may include a time period of 3 days before (including) the start time of the current forecast period.
In the present disclosure, how to generate the average value generating function of the road surface temperature and how to generate the average value generating function of the various auxiliary meteorological parameters are not particularly limited.
As an alternative implementation, in the present disclosure, as shown in fig. 2, step S120 may include:
in step S121, performing initial processing on historical data of a corresponding traffic meteorological station to obtain modeling data meeting quality requirements, where the modeling data includes data for modeling road surface temperature and data for modeling auxiliary meteorological parameters;
in step S122, performing power spectrum analysis on each type of modeling data, and extracting dominant cycles of each type of modeling data;
in step S123, a mean value generating function of the road surface temperature is established according to the dominant cycle of the road surface temperature modeling data;
in step S124, a mean value generating function of each type of auxiliary meteorological parameters is established according to the dominant period of each type of auxiliary meteorological parameter modeling data.
It should be noted that, in the present disclosure, the sequence between step S123 and step S124 is not particularly limited. Step S123 may be executed first and then step S124 may be executed, step S124 may be executed first and then step S123 may be executed, and step S123 and step S124 may be executed simultaneously.
In step S122, "each type of modeling data" refers to road surface temperature modeling data and each type of auxiliary meteorological parameter modeling data.
When the auxiliary meteorological parameters comprise air temperature, air speed and relative humidity, the various auxiliary meteorological parameter modeling data respectively comprise air temperature modeling data, air speed modeling data and relative humidity modeling data. Correspondingly, the average value generating functions of the auxiliary meteorological parameters respectively comprise an air temperature average value generating function, a wind speed average value generating function and a relative humidity average value generating function.
It should be noted that when acquiring the weather data within the coverage area of the traffic weather station, the data may be influenced by objective factors such as acquisition equipment and acquisition environment, or human factors such as operation mode, which may result in acquiring significantly inaccurate data, and the purpose of performing initial processing in step S121 is to remove significantly inaccurate data and obtain data for modeling that meets the quality requirement, thereby improving the accuracy of the imminent prediction.
As an alternative embodiment, as shown in fig. 3, the initial processing of the historical data of the corresponding traffic weather station to obtain the data for modeling meeting the quality requirement includes:
in step S121a, filtering the historical data of the corresponding traffic weather station to filter out bad data;
in step S121b, interpolating the filtered historical data of the traffic weather station, and obtaining interpolated road surface temperature data and interpolated weather auxiliary parameters, respectively;
in step S121c, performing first order differencing on the interpolated road surface temperature data to obtain road surface temperature modeling data that satisfies quality requirements;
in step S121d, the interpolated weather assistance parameters are subjected to first order differencing processing, and data for modeling the weather assistance parameters satisfying the quality requirement are obtained.
The "bad data" described in step S121a is obviously inaccurate data. In this condition, there is no particular limitation on how bad data is determined. For example, if the road surface temperature at time a is 20 ℃ higher than the road surface temperature at time b, the road surface temperature difference between time a and time c is only 1 ℃, time a is 10 minutes earlier than time b, and time a is 10 minutes later than time c, then the road surface temperature at time b can be considered to be bad data. After the defective data is removed, data interpolation is required for the time corresponding to the defective data. In the present disclosure, how to interpolate data is not particularly limited. For example, the interpolation data may be an average value of data obtained at two sampling times adjacent to each other before and after the sampling time corresponding to the defective data.
In addition to data loss at a certain sampling time due to "bad data removal", there is data loss due to a device cause such as a storage failure. For such data loss, it is also necessary to complement the data by interpolation.
In the present disclosure, the interpolation method is not particularly limited, and when data loss is small, data interpolation may be performed by using a linear interpolation method. For data loss in a longer period of time, the average value of the road surface temperature of 2 days before and after can be adopted to replace the data loss.
The historical data of the traffic weather station is analyzed, and the road surface temperature has obvious non-stationarity characteristics, and the sequence stationarity is an important premise of a time sequence analysis model. In view of this, in the present disclosure, the interpolated data (also, the road surface temperature sequence) is smoothed by the first order differencing process, and the obtained data is a smoothed sequence (that is, data for modeling that satisfies the quality requirement).
It should be noted that, in the present disclosure, the order of the steps S121c and S121d is not particularly limited. Step S121c may be executed first and step S121d may be executed later, step S121d may be executed first and step S121c may be executed later, and step S121c and step S121d may be executed in synchronization.
The temperature change of the road surface is similar to the temperature change, and the periodic characteristics such as obvious daily change and the like are provided. In the present disclosure, how to perform step S122 is not particularly limited. As an alternative embodiment, as shown in fig. 4, the performing a power spectrum analysis on the data for modeling to extract a dominant cycle includes:
in step S122a, determining power spectral densities corresponding to different sampling periods of the road surface temperature differential sequence in the current forecast period and power spectral densities corresponding to different sampling periods of each type of auxiliary meteorological parameter differential sequence in the current forecast period;
in step S122b, dividing the power spectral density corresponding to the different sampling periods of the road surface temperature difference sequence in the current forecast period by the red noise standard spectrum with α =0.05 to obtain the spectral ratio of the road surface temperature sequence, and for each type of auxiliary meteorological parameters, dividing the power spectral density corresponding to the different sampling periods of the auxiliary meteorological parameter difference sequence in the current forecast period by the red noise standard spectrum with α =0.05 to obtain the spectral ratio of the auxiliary meteorological parameter sequence;
in step S122c, the road surface temperature difference sequence sampling period with the spectral ratio greater than the predetermined value is used as the dominant period of the road surface temperature modeling data, and the auxiliary meteorological parameter difference sequence sampling period with the spectral ratio greater than the predetermined value is used as the dominant period of the auxiliary meteorological parameter modeling data, where the predetermined value is not less than 1.
When the spectrum ratio is larger than 1.0, the significance level of the sequence period exceeding 0.05 is represented, and the period is more significant when the spectrum ratio is larger.
In the present disclosure, the road surface temperature sequence may be extracted every 10 minutes for the data for modeling within the current forecast period.
As a second aspect of the present disclosure, there is provided an electronic apparatus including:
one or more processors;
a memory having one or more applications stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method for short-term prediction of road surface temperature according to the first aspect of the present disclosure;
one or more I/O interfaces connected between the processor and the memory and configured to enable information interaction between the processor and the memory.
Wherein, the processor is a device with data processing capability, which includes but is not limited to a Central Processing Unit (CPU) and the like; the first memory is a device with data storage capability including, but not limited to, random access memory (RAM, more specifically SDRAM, DDR, etc.), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), FLASH memory (FLASH); the I/O interface (read/write interface) is connected between the processor and the memory, and can realize information interaction between the processor and the memory, including but not limited to a data Bus (Bus) and the like.
In some embodiments, the processor, memory, and I/O interface are interconnected by a bus, which in turn connects with other components of the computing device.
As a third aspect of the present disclosure, there is provided a computer-readable medium on which a computer program is stored, the program, when executed by a processor, implementing the road surface temperature short-forecasting method provided by the first aspect of the present disclosure.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as is well known to those skilled in the art.
Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted in a generic and descriptive sense only and not for purposes of limitation. In some instances, features, characteristics and/or elements described in connection with a particular embodiment may be used alone or in combination with features, characteristics and/or elements described in connection with other embodiments, unless expressly stated otherwise, as would be apparent to one skilled in the art. Accordingly, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the disclosure as set forth in the appended claims.
Claims (8)
1. A road surface temperature short-term forecasting method comprises the following steps which are carried out periodically:
determining historical data of a traffic weather station in a sampling time period corresponding to a current forecast period, wherein the historical data of the traffic weather station comprises road surface temperature and at least one type of auxiliary weather parameters in the current forecast period, and the auxiliary weather parameters can influence the road surface temperature;
establishing a mean value generating function of the road surface temperature and a mean value generating function of each auxiliary meteorological parameter according to the historical data of the traffic meteorological station;
constructing a road surface temperature short-term forecasting model by taking the mean generating function of the road surface temperature and the mean generating functions of various auxiliary meteorological parameters as independent variables;
outputting a pavement temperature forecast value in a forecast time period corresponding to the current forecast period according to a pavement temperature short-term forecast model corresponding to the current forecast period;
the method for establishing the mean value generating function of the road surface temperature and the mean value generating function of each auxiliary meteorological parameter according to the historical data of the traffic meteorological station comprises the following steps:
performing initial processing on corresponding historical data of the traffic meteorological station to obtain modeling data meeting quality requirements, wherein the modeling data comprises road surface temperature modeling data and auxiliary meteorological parameter modeling data;
performing power spectrum analysis on various modeling data, and extracting the dominant cycle of the various modeling data;
establishing a mean value generating function of the road surface temperature according to the dominant period of the data for road surface temperature modeling;
establishing a mean value generating function of various auxiliary meteorological parameters according to the dominant period of the data for modeling various auxiliary meteorological parameters; wherein, the first and the second end of the pipe are connected with each other,
in the road surface temperature short-circuit forecasting method, a road surface temperature time sequence is extracted in a rolling mode, time sequences of various auxiliary meteorological parameters are extracted in a rolling mode, a road surface temperature short-circuit forecasting model is generated in a rolling mode, and the road surface temperature is subjected to rolling forecasting.
2. The method of claim 1, wherein the auxiliary meteorological parameters include at least one of air temperature, wind speed, and relative humidity.
3. The method of claim 1, wherein the sampling time period corresponding to the current prediction cycle comprises the following interval:
[ t 0-. DELTA.t, t0], wherein,
t0 is the starting time of the current forecast period;
Δ t is the period of 2 days to 5 days.
4. The method for short-term forecasting of road surface temperature according to any one of claims 1 to 3, wherein the initial processing of the historical data of the corresponding traffic weather station to obtain the data for modeling satisfying the quality requirement comprises:
filtering corresponding historical data of the traffic meteorological station to filter bad data;
interpolating the filtered historical data of the traffic meteorological station to respectively obtain interpolated road surface temperature data and interpolated various meteorological auxiliary parameters;
performing first-order differencing processing on the interpolated road surface temperature data to obtain road surface temperature modeling data meeting quality requirements;
and performing first-order differencing processing on the various weather auxiliary parameters after interpolation to obtain various weather auxiliary parameter modeling data meeting quality requirements.
5. The method for short-term prediction of road surface temperature according to claim 4, wherein the performing power spectrum analysis on each type of modeling data to extract dominant cycles of each type of modeling data includes:
determining power spectral densities corresponding to different sampling periods of the pavement temperature differential sequence in the current forecasting period and power spectral densities corresponding to different sampling periods of each type of auxiliary meteorological parameter differential sequence in the current forecasting period;
dividing the power spectral density corresponding to different sampling periods of the road surface temperature differential sequence in the current forecasting period by the red noise standard spectrum with the value of alpha =0.05 to obtain the spectral ratio corresponding to different sampling periods of the road surface temperature sequence, and dividing the power spectral density corresponding to different sampling periods of the auxiliary meteorological parameter differential sequence in the current forecasting period by the red noise standard spectrum with the value of alpha =0.05 for each type of auxiliary meteorological parameters to obtain the spectral ratio corresponding to different sampling periods of the auxiliary meteorological parameter differential sequence;
and taking the road surface temperature difference sequence sampling period with the spectral ratio value larger than a preset value as an advantage period of the data for road surface temperature modeling, and taking the auxiliary meteorological parameter difference sequence sampling period with the spectral ratio value larger than the preset value as an advantage period of the auxiliary meteorological parameter for modeling, wherein the preset value is not less than 1.
6. The road surface temperature short-forecasting method according to claim 5, wherein a time interval of each modeling data time series is the same as a time interval of the road surface temperature forecast in the forecast time period.
7. An electronic device, comprising:
one or more processors;
a memory having one or more application programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the road surface temperature short-forecasting method according to any one of claims 1 to 6;
one or more I/O interfaces connected between the processor and the memory and configured to enable information interaction between the processor and the first memory.
8. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out a road surface temperature short-prediction method according to any one of claims 1 to 6.
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| JP2006017501A (en) * | 2004-06-30 | 2006-01-19 | Japan Weather Association | Road surface temperature prediction system, method, and program |
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| CN107203827A (en) * | 2017-06-21 | 2017-09-26 | 南京信息工程大学 | A kind of wind turbine forecasting wind speed optimization method based on multiscale analysis |
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| CN109738091A (en) * | 2019-01-28 | 2019-05-10 | 象谱信息产业有限公司 | System and method based on highway geographical environment prediction pavement temperature |
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