Method for monitoring road surface temperature based on satellite products

Abstract

The present disclosure provides a method for constructing a road surface temperature live analysis model based on satellite products, comprising: obtaining geographic position information of a plurality of traffic road observation stations; determining road environment characteristics within the observation range of each traffic road observation station using high-resolution satellite data; determining road environment classification attributes within the observation range of each traffic road observation station according to the road environment characteristics within the observation range of each traffic road observation station; determining climate region attributes within the observation range of each traffic road observation station according to the geographic position information of each traffic road observation station; establishing a plurality of initial road surface temperature live analysis models; and training parameters of the plurality of initial road surface temperature live analysis models to obtain a plurality of final road surface temperature live analysis models. The present disclosure also provides a highway road surface temperature monitoring method based on satellite products, an electronic device and a computer readable medium.

Description

Technical Field

[0001] This disclosure relates to the field of computers, and more specifically, to a method for constructing a road surface temperature real-time analysis model based on satellite products, a method for monitoring highway road surface temperature based on satellite products, an electronic device, and a computer-readable medium. Background Technology

[0002] Extreme road surface temperatures are one of the important factors affecting road traffic safety: excessively high road surface temperatures can easily cause tire blowouts and roadbed deformation or damage; excessively low road surface temperatures can cause road icing and snow accumulation, posing traffic safety hazards.

[0003] Currently, the main methods for determining highway pavement temperature are as follows:

[0004] The first method is to set up traffic surface observation stations to monitor highway surface temperature in real time.

[0005] The second method is to use thermal map analysis. Specifically, the road surface mobile monitoring equipment is used to collect data on the test road section. The road surface temperature at different locations on the route or road section is made into a thematic map using different colors according to the relative temperature, so as to realize the analysis of road surface temperature from limited traffic road surface observation stations along the road.

[0006] The third method involves obtaining surface temperature through inversion from meteorological satellites.

[0007] However, in the first method, the existing road surface observation stations are sparsely distributed, and many high-risk road sections with traffic meteorological disasters such as road icing and snow accumulation lack traffic meteorological observations; the second method is costly and requires multiple measurements for different weather conditions and time periods; in the third method, the spatial resolution of geostationary satellite monitoring is about 4km, while the road width is only tens of meters, and there is a large difference in spatial scale between the two, so the road surface temperature cannot be accurately determined. Summary of the Invention

[0008] The purpose of this disclosure is to provide a method for constructing a road surface temperature real-time analysis model based on satellite products and a method for monitoring highway road surface temperature based on satellite products, so as to solve at least one of the above-mentioned problems.

[0009] To achieve the above objectives, as a first aspect of this disclosure, a method for constructing a road surface temperature real-time analysis model based on satellite products is provided, comprising:

[0010] Obtain the geographical location information of multiple traffic surface observation stations;

[0011] High-resolution satellite data was used to determine the road environment characteristics within the observation range of each of the aforementioned traffic surface observation stations;

[0012] Based on the road environment characteristics within the observation range of each traffic surface observation station, the road environment classification attributes within the observation range of each traffic surface observation station are determined.

[0013] The climate zone attributes within the observation range of each traffic road surface observation station are determined based on the geographical location information of each of the traffic road surface observation stations.

[0014] Multiple initial pavement temperature real-time analysis models were established, with at least one of the climate zone attribute and road environment classification attribute being different for each initial pavement temperature real-time analysis model.

[0015] Using historical meteorological data obtained from various traffic road surface observation stations, as well as historical surface temperature and radiation flux data from the satellite products, multiple initial road surface temperature analysis models are trained to obtain multiple final road surface temperature analysis models. The historical meteorological data includes historical road surface temperature, and at least one of the following: historical air temperature, historical wind speed, and historical humidity.

[0016] Optionally, the road environment features include:

[0017] The types of land features within the observation range of the traffic road observation station, and the proportion of land features within the observation range of the traffic road observation station.

[0018] In the step of determining the road environment classification attributes within the observation range of each traffic road surface observation station based on the road environment characteristics within the observation range of each traffic road surface observation station, the road environment classification attributes within the observation range of each traffic road surface observation station are determined according to the land cover type and the proportion of land cover within the observation range of each traffic road surface observation station.

[0019] Optionally, the land feature type includes at least one of the following types:

[0020] Trees, herbaceous plants, water bodies, buildings, bare ground;

[0021] In the step of determining the road environment classification attributes within the observation range of each traffic surface observation station based on the road environment characteristics within the observation range of each traffic surface observation station...

[0022] When the proportion of trees exceeds a predetermined proportion, the road environment classification attribute is determined to be woodland;

[0023] When the proportion of herbaceous plants exceeds the predetermined proportion, the road environment classification attribute is determined to be grassland;

[0024] When the proportion of water exceeds the predetermined proportion, the road environment will be classified as a water area.

[0025] When the proportion of buildings exceeds the predetermined proportion, the road environment will be classified as building land.

[0026] When the proportion of bare land exceeds the predetermined proportion, the road environment will be classified as bare land.

[0027] Optionally, the geographical location information of the traffic road surface observation station includes the latitude and longitude information of the traffic road surface observation station.

[0028] As a second aspect of this disclosure, a method for monitoring highway pavement temperature based on satellite products is provided, comprising:

[0029] The road environment classification attributes of the highway sections to be monitored are determined based on the underlying surface type information from high-resolution satellites and highway network information.

[0030] Determine the climatic zone attributes of the highway section to be monitored;

[0031] The final road surface temperature analysis model is determined based on the road environment classification attributes and the climate zone attributes, wherein the final road surface temperature analysis model is selected from the final road surface temperature analysis model determined by the method provided in the first aspect of this disclosure.

[0032] Based on the real-time surface temperature and real-time radiation flux from satellite products, the real-time air temperature, wind speed, and humidity from intelligent grid real-time analysis products, and the geographical location information of the road segment to be monitored, the road surface temperature of the road segment to be monitored is determined using the established final road surface temperature real-time analysis model.

[0033] Optionally, determining the road environment classification attributes of the highway segment to be monitored based on high-resolution satellite underlying surface type information and highway network information includes:

[0034] The location information of the highway section to be monitored is determined based on the highway network information;

[0035] Based on the location information, high-resolution satellite data corresponding to the road segment to be monitored is determined, and the high-resolution satellite data includes information about the underlying surface of the high-resolution satellite.

[0036] Cluster analysis was used to extract and classify the underlying surface types based on the high-resolution satellite data.

[0037] The road environment classification attributes of the highway section to be monitored are determined based on the classification results.

[0038] As a third aspect of this disclosure, an electronic device is provided, comprising:

[0039] One or more first processors;

[0040] A first memory storing one or more first programs, which, when executed by the one or more first processors, cause the one or more first processors to implement the method described in the first aspect of this disclosure;

[0041] One or more first I / O interfaces are connected between the first processor and the first memory, and configured to enable information interaction between the first processor and the first memory.

[0042] As a fourth aspect of this disclosure, an electronic device is provided, comprising:

[0043] One or more second processors;

[0044] A second memory stores one or more second programs, which, when executed by the one or more second processors, enable the one or more second processors to implement the road surface temperature monitoring method provided in the second aspect of this disclosure.

[0045] One or more second I / O interfaces are connected between the second processor and the second memory and configured to enable information interaction between the second processor and the second memory.

[0046] As a fifth aspect of this disclosure, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements at least one of the following methods:

[0047] According to the method provided in the first aspect of this disclosure;

[0048] The method for monitoring road surface temperature according to the second aspect of this disclosure.

[0049] The meteorological data obtained by the traffic pavement observation station is accurate down to the road surface, including road surface temperature, air temperature at a height of 1.5 meters above the road surface, wind speed, and humidity. In addition, the pavement temperature observed by the traffic pavement observation station includes road surface temperature under various temperature conditions.

[0050] High-resolution satellite data can accurately determine the road environment characteristics within the observation range of each traffic surface observation station.

[0051] The surface temperature and radiation flux data in satellite products cover a wide area, which can make up for the limited number of traffic road observation stations.

[0052] In constructing the initial road surface temperature analysis model, historical meteorological data obtained from traffic road surface observation stations, historical temperature data from satellite products, and historical radiation flux data were used. High-resolution satellite data was also combined to analyze the road environment characteristics within the observation range of each traffic road surface observation station. This enabled the final road surface temperature analysis model obtained by training the initial road surface temperature analysis model to perform road surface temperature analysis on roads without traffic road surface observation stations and obtain a relatively accurate road surface temperature. Attached Figure Description

[0053] Figure 1 This is a flowchart of one implementation of the method for constructing a road surface temperature real-time analysis model based on satellite products provided in this disclosure;

[0054] Figure 2 This is a flowchart of one implementation of the satellite-based highway pavement temperature monitoring method provided in this disclosure. Detailed Implementation

[0055] To enable those skilled in the art to better understand the technical solutions of this disclosure, the following describes in detail, with reference to the accompanying drawings, the method for constructing a road surface temperature analysis model based on satellite products, the method for monitoring highway road surface temperature based on satellite products, the electronic equipment, and the computer-readable medium provided in this disclosure.

[0056] Exemplary embodiments will be described more fully below with reference to the accompanying drawings; however, these exemplary embodiments 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 enable those skilled in the art to fully understand the scope of this disclosure.

[0057] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0058] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0059] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded.

[0060] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined herein.

[0061] As the first aspect of this disclosure, a method for constructing a real-time road surface temperature analysis model based on satellite products is provided, such as... Figure 1 As shown, the method includes:

[0062] In step S110, the geographical location information of multiple traffic surface observation stations is obtained;

[0063] In step S120, high-resolution satellite data is used to determine the road environment characteristics within the observation range of each of the traffic surface observation stations;

[0064] In step S130, the road environment classification attribute within the observation range of each traffic surface observation station is determined based on the road environment characteristics within the observation range of each traffic surface observation station.

[0065] In step S140, the climate zone attributes within the observation range of each traffic road surface observation station are determined based on the geographical location information of each traffic road surface observation station.

[0066] In step S150, multiple initial pavement temperature real-time analysis models are established, and at least one of the climate zone attribute and road environment classification attribute is different for each initial pavement temperature real-time analysis model.

[0067] In step S160, the parameters of the initial road surface temperature analysis model are trained by using historical meteorological data obtained from various traffic road surface observation stations and historical surface temperature and radiation flux data from the satellite products, according to seasonal time periods, to obtain the final road surface temperature analysis model. The historical meteorological data includes historical road surface temperature and at least one of the following: historical air temperature, historical wind speed, and historical humidity.

[0068] The inventors of this disclosure have discovered that differences in the local environment surrounding different road segments have a significant impact on the relationship between road meteorological conditions and the average meteorological conditions of the grid cells containing that road segment. Specifically, different "local environments surrounding road segments" result in different road environment classification attributes within the observation range of traffic surface observation stations. The road environment within the observation range of traffic surface observation stations can be classified into several different environmental classification attributes based on the local environment surrounding road segments. For example, when the proportion of trees in the local area surrounding a road segment is high, its corresponding environmental classification attribute is woodland; when the proportion of grassland in the local area surrounding a road segment is high, its corresponding environmental classification attribute is grassland; when the proportion of water bodies in the local area surrounding a road segment is high, its corresponding environmental classification attribute is water body; when the proportion of bare land in the local area surrounding a road segment is high, its corresponding environmental classification attribute is bare land; and when the proportion of buildings in the local area surrounding a road segment is high, its corresponding environmental classification attribute is built-up land.

[0069] In this disclosure, climate region attributes may include temperate climate attributes, tropical climate attributes, etc.

[0070] In this disclosure, the types of initial pavement temperature observation models are related to the types of climate region attributes and road environment classification attributes. For example, if multiple traffic pavement observation stations have type A climate region attribute and type B road environment classification attribute, then the maximum number of initial pavement temperature observation models is A*B. Of course, this disclosure is not limited to this.

[0071] The following explanation and illustration will be based on a specific implementation of the "Initial Road Surface Temperature Real-Time Analysis Model".

[0072] In this embodiment, an initial road surface temperature analysis model is determined based on three traffic road surface observation stations.

[0073] The three traffic surface observation stations are located in two different climate zones. Therefore, the three traffic surface observation stations can be divided into two groups based on their climate zone attributes. For ease of description, the two climate zone attributes are denoted as attribute A1 and attribute A2, respectively.

[0074] The surrounding local areas of the road sections at the three traffic surface observation stations are classified into three types of road environments. For ease of description, the road environment classification attributes of the three types of road environments are denoted as B1, B2, and B3.

[0075] The climate zone attribute of the first traffic surface observation station is A1, the road environment classification attribute is B1, and the corresponding initial road surface temperature analysis model is (A1, B1); the climate attribute of the second traffic surface observation station is A2, the road environment classification attribute is B2, and the corresponding initial road surface temperature analysis model is (A2, B2); the climate attribute of the third traffic surface observation station is A1, the road environment classification attribute is B3, and the corresponding initial road surface temperature analysis model is (A1, B3).

[0076] By training the initial parameters of the initial road surface temperature analysis model (A1, B1) using observation data from the first traffic road surface observation station and surface temperature and radiation flux data from satellite products of the grid cells where the first traffic road surface observation station is located, the final road surface temperature analysis model (A1', B1') can be obtained. By inputting the geographic location information, intelligent grid real-time analysis data on air temperature, wind speed, and humidity, as well as real-time surface temperature and radiation flux data from satellite products of that geographic location into the final road surface analysis model (A1', B1'), the road surface temperature of the road segment represented by that geographic location information can be obtained.

[0077] By training the initial parameters of the initial pavement temperature analysis model (A2, B2) using observation data from the second traffic pavement rule observation station and surface temperature and radiation flux data from satellite products of the grid cells where the second traffic pavement rule observation station is located, the final pavement temperature analysis model (A2', B2') can be obtained. By inputting the geographic location information and the real-time surface temperature and radiation flux data of the satellite products of that geographic location into the final pavement temperature analysis model (A2', B2'), the pavement temperature of the road segment represented by that geographic location information can be obtained.

[0078] As another optional implementation, the data input to the final road surface temperature analysis model can also include real-time data such as air temperature, wind speed, and humidity from intelligent grid analysis, which can make the road surface temperature output by the final road surface temperature analysis model more accurate.

[0079] By training the initial parameters of the initial road surface temperature analysis model (A1, B3) using observation data from the third traffic road surface rule observation station and surface temperature and radiation flux data from satellite products of the grid cells where the third traffic road surface rule observation station is located, the final road surface temperature analysis model (A1', B3') can be obtained. By inputting geographic location information, or intelligent grid real-time analysis data of air temperature, wind speed, and humidity, as well as real-time surface temperature and radiation flux data from satellite products of that geographic location into the final road surface analysis model (A1', B3'), the road surface temperature of the road segment represented by that geographic location information can be obtained.

[0080] The historical meteorological data obtained by traffic surface observation stations is accurate down to the road surface level. In addition, the road surface temperature observed by these stations includes road surface temperatures under various temperature conditions. As an optional implementation, temperature conditions may include clear skies in winter, cloudy skies in winter, rain and snow in winter, clear skies in summer, cloudy skies in summer, rain in summer, clear skies in spring, cloudy skies in spring, rain in spring, clear skies in autumn, cloudy skies in autumn, and rain in autumn.

[0081] High-resolution satellite data can be used to accurately determine the road environment characteristics within the observation range of each traffic surface observation station.

[0082] Historical surface temperature data and historical radiation flux data in satellite products cover a wide geographical area, which can make up for the limited number of traffic road observation stations.

[0083] In constructing the initial road surface temperature analysis model, historical meteorological data obtained from traffic road surface observation stations, historical surface temperature data from satellite products, and historical radiation flux data were used. High-resolution satellite data was also combined to analyze the road environment characteristics within the observation range of each traffic road surface observation station. This enabled the final road surface temperature analysis model obtained by training the initial road surface temperature analysis model to perform road surface temperature analysis on roads without traffic road surface observation stations and obtain a relatively accurate road surface temperature.

[0084] Since the initial road surface temperature analysis model corresponding to the final road surface temperature analysis model is related to the road environment classification attributes and the memory climate region attributes, and the training data uses data observed by traffic road surface observation stations and satellite product data, the road surface temperature calculated using the final road surface temperature analysis model has the advantages of high resolution, low cost, and applicability to various temperature conditions.

[0085] As an optional implementation, as described above, the road environment characteristics may include: the types of land features within the observation range of the traffic road observation station, and the proportion of land features within the observation range of the traffic road observation station.

[0086] Therefore, in the step of determining the road environment classification attribute within the observation range of each traffic road surface observation station based on the road environment characteristics within the observation range of each traffic road surface observation station (i.e., step S130), the road environment classification attribute within the sensing range of the traffic road surface observation station can be determined based on the land cover type within the observation range of the traffic road surface observation station and the proportion of land cover within the observation range of the traffic road surface observation station.

[0087] As an alternative implementation, the land feature type includes at least one of the following types: trees, herbaceous plants, water bodies, buildings, and bare land.

[0088] Accordingly, in the step of determining the road environment classification attributes within the observation range of each traffic surface observation station based on the road environment characteristics within the observation range of each traffic surface observation station (i.e., step S130),

[0089] When the proportion of trees exceeds a predetermined proportion, the road environment classification attribute is determined to be woodland;

[0090] When the proportion of herbaceous plants exceeds the predetermined proportion, the road environment classification attribute is determined to be grassland;

[0091] When the proportion of water exceeds the predetermined proportion, the road environment will be classified as a water area.

[0092] When the proportion of buildings exceeds the predetermined proportion, the road environment will be classified as building land.

[0093] When the proportion of bare land exceeds the predetermined proportion, the road environment will be classified as bare land.

[0094] In this disclosure, no special limitation is made to the predetermined ratio shown. For example, the predetermined ratio may be selected from 40% to 60%.

[0095] In this disclosure, no special limitation is made on how to represent the geographical location information of the road traffic observation station. As an optional implementation, the geographical location information of the road traffic observation station includes the latitude and longitude information of the road traffic observation station.

[0096] This disclosure does not specifically limit the type of satellite product. For example, the satellite product may be the FY-4A satellite product.

[0097] In this disclosure, surface temperature can be obtained through inversion using the FY-4A satellite as the aforementioned "historical surface temperature data in satellite products".

[0098] This disclosure does not impose specific limitations on how to use high-resolution satellite data to determine the road environment characteristics within the observation range of each traffic surface observation station. For example, cluster analysis can be used to classify the road environment characteristics within the observation range of each traffic surface observation station.

[0099] As an alternative implementation method, high-resolution satellite data can be used to extract road environment characteristics of highway sections within a 1km radius of traffic road surface observation stations and determine their road environment classification attributes.

[0100] The following describes a specific embodiment.

[0101] In step S110, latitude and longitude information of multiple traffic road surface observation stations across the country is obtained;

[0102] In step S120, based on the latitude and longitude location information of multiple traffic road surface observation stations across the country, high-resolution satellite data is used to extract the types of land cover and the percentage of each land cover within a 1 km road segment and a 1 km buffer zone around the traffic station, including forest land, grassland, farmland, water bodies, building land, bare land, etc. The extracted results are then classified to classify the road environment categories and determine the road environment characteristics of each traffic road surface observation station.

[0103] In step S130, the road environment classification attribute within the observation range of each traffic surface observation station is determined based on the road environment characteristics within the observation range of each traffic surface observation station.

[0104] In step S140, the climate zone attributes within the observation range of each traffic road surface observation station are determined based on China's climate zoning and the geographical location information of each traffic road surface observation station.

[0105] In step S150, hourly observation data of road surface temperature, air temperature, wind speed, and relative humidity from the traffic road surface meteorological observation station for the past three years (2018-2020) are compiled, along with hourly surface temperature (LST), total solar radiation (SSI), longwave radiation (ULR), and longwave radiation (DLR) data of the satellite grid pixels where the traffic road surface observation station is located for the past three years (2018-2020). This is used to construct a historical database for road surface temperature analysis and an initial road surface temperature analysis model.

[0106] In step S160, multiple linear stepwise regression or artificial intelligence deep learning methods are used to train the initial parameters of the initial road surface temperature analysis model by climate region, road environment category, season and time period, and obtain the relevant final parameters to obtain multiple final road surface temperature analysis models.

[0107] As a second aspect of this disclosure, a method for monitoring highway pavement temperature based on satellite products is provided, such as... Figure 2 As shown, the method for monitoring highway pavement temperature includes:

[0108] In step S210, the road environment classification attributes of the highway section to be monitored are determined based on the underlying surface type information of the high-resolution satellite and the highway network information.

[0109] In step S220, the climate zone attributes of the highway section to be monitored are determined;

[0110] In step S230, a final road surface temperature analysis model is determined based on the road environment classification attribute and the climate zone attribute, wherein the final road surface temperature analysis model is selected from the final road surface temperature analysis model determined by the method provided in the first aspect of this disclosure.

[0111] In step S240, the road surface temperature of the road section to be monitored is determined using the determined final road surface temperature real-time analysis model based on the real-time air temperature, wind speed, and humidity data in the smart grid real-time product, the real-time surface temperature and radiation flux data in the satellite product, and the geographical location information of the road section to be monitored.

[0112] In the aforementioned highway pavement temperature monitoring method, based on the road environment classification attributes and climate region attributes of the highway segment to be monitored, a final pavement temperature real-time analysis model corresponding to these attributes can be selected. The air temperature, wind speed, and humidity data from the obtained smart grid real-time products, the real-time surface temperature and radiation flux data from satellite products, and the geographical location information of the highway segment to be monitored are input into the final pavement temperature real-time analysis model. The output of this model is the pavement temperature of the highway segment to be monitored. The pavement temperature output by the final pavement temperature real-time analysis model can accurately reflect the pavement temperature of the highway segment to be monitored.

[0113] As an optional implementation, the step of determining the road environment classification attribute of the highway section to be monitored based on the underlying surface type information from high-resolution satellites and highway network information (i.e., step S210) may include:

[0114] In step S211, the location information of the highway segment to be monitored is determined based on the highway network information;

[0115] In step S212, the data of the high-resolution satellite corresponding to the road section to be monitored is determined based on the location information. This data includes information about the underlying surface of the high-resolution satellite.

[0116] In step S213, cluster analysis is used to extract and classify the underlying surface types based on the high-resolution satellite data.

[0117] In step S214, the road environment classification attribute of the highway section to be monitored is determined based on the classification results.

[0118] As a third aspect of this disclosure, an electronic device is provided, comprising:

[0119] One or more first processors;

[0120] A memory having stored one or more first programs, which, when executed by the one or more first processors, cause the one or more first processors to implement the method described in the first aspect of this disclosure;

[0121] One or more first I / O interfaces are connected between the first processor and the first memory, and configured to enable information interaction between the first processor and the first memory.

[0122] The first processor is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the first memory is a device with data storage capabilities, 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), and flash memory (FLASH); the I / O interface (read-write interface) is connected between the first processor and the first memory, enabling information exchange between the first processor and the first memory, including but not limited to a data bus (Bus).

[0123] In some embodiments, the first processor, the first memory, and the first I / O interface are interconnected via a bus, and thus connected to other components of the computing device.

[0124] As a fourth aspect of this disclosure, an electronic device is provided, comprising:

[0125] One or more second processors;

[0126] A second memory stores one or more second programs, which, when executed by the one or more second processors, enable the one or more second processors to implement the road surface temperature monitoring method provided in the second aspect of this disclosure.

[0127] One or more second I / O interfaces are connected between the second processor and the second memory and configured to enable information interaction between the second processor and the second memory.

[0128] The second processor is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the second memory is a device with data storage capabilities, 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), and flash memory (FLASH); the second I / O interface (read-write interface) is connected between the second processor and the second memory, enabling information exchange between the second processor and the second memory, including but not limited to a data bus (Bus).

[0129] In some embodiments, the second processor, the second memory, and the second I / O interface are interconnected via a bus, and thus connected to other components of the computing device.

[0130] As a fifth aspect of this disclosure, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements at least one of the following methods:

[0131] According to the method provided in the first aspect of this disclosure;

[0132] The method for monitoring road surface temperature according to the second aspect of this disclosure.

[0133] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all 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 (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0134] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in connection with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in connection with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A method for constructing a road surface temperature real-time analysis model based on satellite products, comprising: Obtain the geographical location information of multiple traffic surface observation stations; High-resolution satellite data is used to determine the road environment characteristics within the observation range of each traffic road surface observation station. The road environment characteristics include: the types of land features within the observation range of the traffic road surface observation station and the proportion of land features within the observation range of the traffic road surface observation station. The road environment classification attributes within the observation range of each traffic surface observation station are determined based on the road environment characteristics within the observation range of each traffic surface observation station. The road environment classification attributes are determined based on the land feature types and the proportion of land features within the observation range of each traffic surface observation station. The climate zone attributes within the observation range of each traffic road surface observation station are determined based on the geographical location information of each of the traffic road surface observation stations. Multiple initial pavement temperature condition analysis models are established. At least one of the climate region attribute and road environment classification attribute is different for each initial pavement temperature condition analysis model. The number of initial pavement temperature condition analysis models is related to the types of climate region attributes and the types of road environment classification attributes. Using historical meteorological data obtained from various traffic road surface observation stations, as well as historical surface temperature data and historical radiation flux data from the satellite products, the parameters of multiple initial road surface temperature analysis models are trained in different seasonal periods to obtain multiple final road surface temperature analysis models. The historical meteorological data includes historical road surface temperature and at least one of the following: historical air temperature, historical wind speed, and historical humidity.

2. The method according to claim 1, wherein, The land cover type includes at least one of the following types: Trees, herbaceous plants, water bodies, buildings, bare ground; In the step of determining the road environment classification attributes within the observation range of each traffic surface observation station based on the road environment characteristics within the observation range of each traffic surface observation station... When the proportion of trees exceeds a predetermined proportion, the road environment classification attribute is determined to be woodland; When the proportion of herbaceous plants exceeds the predetermined proportion, the road environment classification attribute is determined to be grassland; When the proportion of water exceeds the predetermined proportion, the road environment will be classified as a water area. When the proportion of buildings exceeds the predetermined proportion, the road environment will be classified as building land. When the proportion of bare land exceeds the predetermined proportion, the road environment will be classified as bare land.

3. The method according to any one of claims 1 to 2, wherein, The geographical location information of the traffic road surface observation station includes its latitude and longitude.

4. A method for monitoring highway pavement temperature based on satellite products, comprising: The road environment classification attributes of the highway sections to be monitored are determined based on the underlying surface type information from high-resolution satellites and highway network information. Determine the climatic zone attributes of the highway section to be monitored; The final road surface temperature analysis model is determined based on the road environment classification attributes and the climate zone attributes, wherein the final road surface temperature analysis model is selected from the final road surface temperature analysis model determined by the method described in any one of claims 1 to 3. Based on real-time air temperature, wind speed, and humidity data from the smart grid real-time data, real-time surface temperature and radiation flux data from satellite products, and the geographical location information of the road segment to be monitored, the road surface temperature of the road segment to be monitored is determined using the established final road surface temperature real-time analysis model.

5. The method for monitoring highway pavement temperature according to claim 4, wherein, The process of determining the road environment classification attributes of the highway sections to be monitored based on high-resolution satellite surface type information and highway network information includes: The location information of the highway section to be monitored is determined based on the highway network information; Based on the location information, high-resolution satellite data corresponding to the road segment to be monitored is determined, and the high-resolution satellite data includes information about the underlying surface of the high-resolution satellite. Cluster analysis was used to extract and classify the underlying surface types based on the high-resolution satellite data. The road environment classification attributes of the highway section to be monitored are determined based on the classification results.

6. An electronic device, comprising: One or more first processors; A first memory storing one or more first programs, which, when executed by the one or more first processors, cause the one or more first processors to implement the method according to any one of claims 1 to 3; One or more first I / O interfaces are connected between the first processor and the first memory, and configured to enable information interaction between the first processor and the first memory.

7. An electronic device, comprising: One or more second processors; A second memory stores one or more second programs, which, when executed by the one or more second processors, enable the one or more second processors to implement the road surface temperature monitoring method according to claim 4 or 5. One or more second I / O interfaces are connected between the second processor and the second memory and configured to enable information interaction between the second processor and the second memory.

8. A computer-readable medium having a computer program stored thereon, said program, when executed by a processor, implementing at least one of the following methods: The method according to any one of claims 1 to 3; The method for monitoring road surface temperature according to claim 4 or 5.

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