A method, medium and program product for evaluating the snow hazard of highway operation based on satellite remote sensing data

Abstract

The present invention provides a method, medium and program product for evaluating the snow hazard of highway operation based on satellite remote sensing data, which mainly includes the steps of obtaining long-term series of satellite remote sensing snow depth data, counting the average number of days of unfavorable operation of highways with snow for many years, counting the maximum snow depth in extreme years, constructing a snow hazard matrix comprehensive evaluation model, drawing a snow hazard evaluation distribution map using geographic information system (GIS) technology, analyzing the snow disaster hazard of built highways, and generating a distribution map containing the hazard level of each section through spatial overlay analysis. The present invention not only takes into account the extreme value of snow depth, but also combines the influence conditions of snow on highway operation and long-term climate characteristics, realizes comprehensive analysis of multiple factors through the matrix comprehensive evaluation model, improves the accuracy and reliability of snow disaster evaluation, and provides scientific basis and decision support for the planning, construction and operation management of highways.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic safety and meteorological disaster assessment, and relates to highway operation safety assessment. Specifically, it is a method, medium and program product for assessing the snow hazard of highway operation based on satellite remote sensing data. By utilizing satellite remote sensing snow depth data and combining matrix comprehensive analysis technology, the extreme maximum snow depth and the average number of snow days over many years are used as assessment indicators to objectively and comprehensively assess the snow disaster hazard affecting highway operation. Background Art

[0002] With the rapid development of social economy, the continuous increase in the mileage of expressways and the improvement of road networks, the high sensitivity of highway transportation to meteorological factors has become increasingly apparent. Among them, snow accumulation, as a common meteorological disaster, has a particularly serious impact on highway transportation. After being rolled over by wheels for many times on the road, snow will freeze in a low temperature environment, reducing the friction coefficient of the ground, causing a serious reduction in the braking ability. Vehicles are prone to rollover, rear-end collisions, and chain collisions during emergency braking. In addition, snow accumulation can also make it difficult for cars to start, especially on uphill sections, where they are prone to sliding backwards and colliding with the following vehicles. At the same time, snow accumulation can also cover up the original potholes or obstacles on the road surface, posing a great safety hazard to driving. Existing studies have shown that the main types of meteorological disasters in highway transportation are heavy rainfall (32.8%), heavy fog (21.7%), road icing (16.4%), highway snow accumulation (11.1%), and fog (4.6%). It can be seen that highway snow accumulation is one of the main meteorological disasters affecting highway traffic safety.

[0003] Due to the needs of social and economic development, highway construction will gradually radiate to remote areas. Due to the lack of meteorological observations around the places they pass through, it is difficult to understand the actual snow conditions in these places, which affects the line selection, planning and construction of highways. It is necessary to understand the snow hazard conditions in these areas. At present, the meteorological observation sites are relatively sparse and have limited coverage, which cannot meet the needs of large-scale snow condition monitoring. In response to the above problems, satellite remote sensing technology (Satellite Remote Sensing Technology) provides a new solution for snow hazard assessment due to its wide coverage and high temporal and spatial resolution. Satellite remote sensing data can be used as alternative data for analysis and evaluation, thereby improving the spatial refinement level of snow disaster hazard identification and providing a reference for highway planning and construction and the implementation of disaster prevention and mitigation measures.

[0004] However, there are relatively few assessments of snow hazards on highways, and there is a lack of systematic assessments of large areas. Only individual lines are evaluated using traditional station observation data, and large-scale, high-resolution data provided by advanced technologies such as satellite remote sensing have not been fully and effectively utilized. In addition, existing assessment methods mostly use indicator weight methods to determine the risk of snow disasters, and the weight setting is relatively subjective and artificial, which affects the accuracy and reliability of the assessment results. In addition, existing assessment methods rarely consider extreme situations, while extreme value data plays an important reference role in the design of protective projects.

[0005] In view of the above problems, how to effectively use satellite remote sensing snow data and combine it with advanced data analysis methods such as matrix comprehensive analysis to carry out snow disaster risk assessment for highway operation, so as to obtain higher spatial resolution and more targeted assessment results, while covering areas that are difficult to cover with traditional observations such as remote mountainous areas, so as to better meet the needs of highway planning, route selection and construction, is a technical problem that needs to be solved urgently. Summary of the invention

[0006] (I) Purpose of the invention

[0007] In view of the defects and problems of the existing highway snow disaster risk assessment methods, such as limited coverage, insufficient assessment accuracy, strong subjectivity, and insufficient consideration of extreme weather, in order to solve at least one of the above and other technical problems in the prior art, the present invention aims to provide a highway operation snow disaster risk assessment method, medium and program product based on satellite remote sensing snow data combined with matrix comprehensive analysis. By introducing the snow depth data inverted by satellite remote sensing and combining the matrix comprehensive analysis method, the key meteorological condition indicators affecting the operation of the highway are selected, the intensity and frequency of snow disasters are fully considered, and the risk level is divided, so as to conduct a more objective, comprehensive and refined assessment of the snow disaster risk affecting the operation of the highway, and provide a scientific basis for the planning and line selection of highways, the formulation and implementation of disaster prevention and mitigation measures, etc.

[0008] (II) Technical solution

[0009] In order to achieve the purpose of the invention and solve the technical problems, the present invention adopts the following technical solutions:

[0010] The first invention objective of the present invention is to provide a method for evaluating the snow hazard of highway operation based on satellite remote sensing data, which is used to identify and evaluate the snow disaster hazard that affects the operation of highways, and provide a decision-making basis for highway planning and line selection, disaster prevention and mitigation measures, etc. Specifically, the method includes the following steps when implemented:

[0011] SS1. Obtain satellite remote sensing snow depth data

[0012] Collect and organize long-term series data of snow depth based on satellite remote sensing inversion in the area to be evaluated. The long-term series data of snow depth is based on the passive microwave brightness temperature data obtained daily by the satellite-borne SMMR, SSM / I and SSMI / S sensors, and the passive microwave snow depth inversion algorithm is used to cross-correct and invert the passive microwave brightness temperature data. The data should be compared and verified with the observation data of the stations in the area to be evaluated;

[0013] SS2. Statistics on the average number of days per year when snow accumulation causes unfavorable highway operation

[0014] Based on the long-term series data of snow depth in the area to be evaluated collected and collated in step SS1, the snow depth is divided into several different levels according to the size of the snow depth and the degree of its impact on the operation of the highway, and the number of unfavorable operation days of the highway in the snowy years in previous years is counted according to the standard of snow depth H>0cm, and then the multi-year average is performed to obtain the unfavorable operation perennial value of the highway in the area to be evaluated;

[0015] SS3. Statistical maximum snow depth in extreme years

[0016] Based on the long-term series data of snow depth in the area to be evaluated collected and collated in step SS1, the daily snow depth values ​​in each snow year are traversed and analyzed to extract the maximum daily snow depth value of the year, and the historical highest value is selected in the entire data time series to calculate the extreme annual maximum snow depth value that occurred in the historical period of the area to be evaluated, so as to reflect the maximum extent of snow depth in the area to be evaluated;

[0017] SS4. Construct a matrix comprehensive assessment model to conduct snow hazard analysis

[0018] The extreme annual maximum snow depth determined in step SS3 and the multi-year average number of days of high-speed unfavorable operation due to snow determined in step SS2 are selected as the ordinate and abscissa of the matrix, and the ordinate and abscissa are graded and divided into several different grades respectively. The two coordinates are combined in pairs to form the row and column values ​​of the matrix, and the corresponding snow disaster hazard level is set for each row and column combination value, so as to construct a matrix comprehensive assessment model to carry out snow hazard analysis;

[0019] SS5. Draw a distribution map of snow disaster risk assessment in the area to be assessed

[0020] Based on the multi-year average number of unfavorable snow-covered highway operation days and the extreme annual maximum snow depth calculated in steps SS2 and SS3, the matrix comprehensive assessment model constructed in step SS4 is used to analyze and obtain the snow disaster hazard level of each grid, and the geographic information spatial analysis technology (GIS) is used to draw a snow disaster hazard assessment distribution map in the area to be assessed;

[0021] SS6. Analyze the snow disaster risk of existing highways in the area to be evaluated

[0022] Based on the snow disaster hazard assessment distribution map in the area to be assessed drawn in step SS5, and with respect to the route vector information of the already built expressways in the area to be assessed, the geographic information spatial overlay analysis technology is used to spatially overlay the route vector data of the already built expressways with the drawn snow disaster hazard assessment distribution map, and generate a distribution map in the area to be assessed that includes the routes of all the already built expressways and the lengths of each section, the starting and ending positions, and the corresponding snow disaster hazard levels, thereby providing a scientific basis and decision-making support for the operation management and disaster prevention and mitigation measures of the already built expressways.

[0023] The second inventive object of the present invention is to provide a computer program product, including computer instructions, for executing the above-mentioned method for evaluating the snow hazard of highway operation based on satellite remote sensing data.

[0024] The third invention object of the present invention is to provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for evaluating the snow hazard of highway operation based on satellite remote sensing data is implemented.

[0025] (III) Technical Effect

[0026] Compared with the prior art, the method, medium and program product for evaluating the snow hazard of highway operation based on satellite remote sensing data of the present invention have the following beneficial and significant technical effects:

[0027] (1) The present invention uses satellite remote sensing technology to obtain long-term series data of snow depth with wide coverage and high temporal and spatial resolution. By using passive microwave brightness temperature data obtained daily by SMMR, SSM / I and SSMI / S sensors, and applying passive microwave snow depth inversion algorithm for data processing and cross-correction, the snow depth data is made more accurate and reliable. After comparison and verification with the observation data of the stations in the area to be evaluated, a solid data foundation is provided for snow hazard assessment.

[0028] (2) The present invention divides the average number of days of high-speed unfavorable operation due to snow accumulation and the maximum snow depth in extreme years into different levels through statistical analysis of snow depth data. The matrix comprehensive evaluation model is used to take the maximum snow depth in extreme years and the average number of days of high-speed unfavorable operation due to snow accumulation as evaluation indicators to construct a detailed hazard level classification system, making the snow hazard assessment more scientific and accurate.

[0029] (3) The present invention uses geographic information spatial analysis technology (GIS) to combine the snow hazard assessment results with geographic information to draw a snow hazard assessment distribution map. This graphical assessment result intuitively displays the snow hazard level in different areas, providing a clear visual reference for highway planning and route selection, and the formulation of disaster prevention and mitigation measures.

[0030] (4) The present invention not only provides snow hazard assessment for newly planned highways, but also applies the assessment results to existing highways through overlay analysis technology. By collecting route vector information of existing highways and performing spatial overlay analysis, the snow disaster hazard level of each section is determined, and corresponding disaster prevention and mitigation measures are formulated for different hazard levels, thereby improving the safe operation of existing highways. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The figure shows a schematic diagram of the implementation process of the method for evaluating the snow hazard of highway operation based on satellite remote sensing data of the present invention.

[0032] Figure 2 The figure shows a distribution map of snow disaster risk levels of built expressways in a certain area to be evaluated, which is drawn based on the method of the present invention. DETAILED DESCRIPTION

[0033] In order to better understand the present invention, the content of the present invention is further explained below in conjunction with embodiments. The described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limitations of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the art without making creative work are within the scope of protection of the present invention.

[0034] In view of the defects and shortcomings of the existing highway snow disaster risk assessment methods, such as limited coverage, insufficient assessment accuracy, strong subjectivity, and insufficient consideration of extreme weather, in order to solve at least one of the above and other technical problems in the prior art, the present invention aims to provide a highway operation snow disaster risk assessment method, medium and program product based on satellite remote sensing snow data combined with matrix comprehensive analysis. By introducing satellite remote sensing inversion of snow depth data and verification, combined with matrix comprehensive analysis methods, key meteorological condition indicators affecting highway operation are selected, the intensity and frequency of snow disasters are fully considered, and the risk level is divided, so as to conduct a more objective, comprehensive and refined assessment of the snow disaster risk affecting highway operation, and provide a scientific basis for highway planning and line selection, disaster prevention and mitigation measures formulation and implementation.

[0035] As a specific example, Figure 1 As shown, the method for evaluating the snow hazard of highway operation based on satellite remote sensing data of the present invention is used to identify and evaluate the snow disaster hazard affecting the operation of the highway, and provide a decision-making basis for highway planning and line selection, disaster prevention and mitigation measures, etc. The method includes the following steps when implemented:

[0036] SS1. Obtain satellite remote sensing snow depth data

[0037] The long-term series data of snow depth based on satellite remote sensing inversion in the area to be evaluated are collected and collated. The long-term series data of snow depth are based on the passive microwave brightness temperature data acquired daily by the satellite-borne SMMR, SSM / I and SSMI / S sensors. The passive microwave snow depth inversion algorithm is used to cross-correct and invert the passive microwave brightness temperature data, and the data are compared with the observation data of the stations in the area to be evaluated.

[0038] In some preferred examples, the geographical scope of the long-term series data of snow depth should at least fully cover the area to be evaluated, for example, the entire area to be evaluated or a larger area, to ensure the integrity and continuity of the data required for the evaluation work. Its temporal resolution is preferably 1 day, so that it can more accurately reflect the daily changes in the snow accumulation process. Its spatial resolution is preferably 0.25°×0.25° or higher, so as to obtain a sufficiently high spatial resolution to capture the detailed differences in the distribution of snow in the region, and provide high-quality and high-precision basic data support for subsequent evaluation work.

[0039] SS2. Statistics on the average number of days per year when snow accumulation causes unfavorable highway operation

[0040] Based on the long-term series data of snow depth in the area to be evaluated collected and sorted in step SS1, the snow depth is divided into several different levels according to the size of the snow depth and the degree of its impact on the operation of the highway. The number of unfavorable operation days of highways in snowy years in previous years is counted according to the standard of snow depth H>0cm, and then the multi-year average is performed to obtain the annual value of the unfavorable operation days of highways in the area to be evaluated.

[0041] In some preferred examples, when this step is implemented, the number of unfavorable operation days of highways in previous snowy years is counted according to the snow depth H>0 cm, and then the 30-year average is performed to obtain the annual value of the unfavorable operation days of highways, and July 1 of the previous year to June 30 of the current year is counted as a snowy year. The time series length of the long-term series data of snow depth collected and sorted out in step SS1 is at least 30 years to ensure that the regional extreme snow events can be captured more comprehensively.

[0042] SS3. Count the maximum snow depth in the extreme year in the area to be evaluated

[0043] Based on the long-term time series data of snow depth in the area to be evaluated collected and sorted in step SS1, by traversing and analyzing the daily snow depth values within each snow year, the maximum daily snow depth value of the current year is extracted, and the historical highest value is selected from the entire data time series to statistically obtain the extreme annual maximum snow depth value that occurred in the historical period of the area to be evaluated, so as to reflect the maximum degree reached by the snow depth in the area to be evaluated.

[0044] SS4. Construct a matrix comprehensive evaluation model to carry out snow hazard analysis

[0045] Select the extreme annual maximum snow depth determined in step SS3 and the number of days of adverse operation of the multi-year average snow depth on the expressway determined in step SS2 as the vertical and horizontal coordinates of the matrix. The vertical and horizontal coordinates are respectively graded and divided into several different levels. The two coordinates are combined pairwise to form the row and column values of the matrix, and a corresponding snow disaster hazard level is set for each row and column combination value, so as to construct a matrix comprehensive evaluation model to carry out snow hazard analysis.

[0046] In some preferred examples, the extreme annual maximum snow depth of the vertical coordinate is graded and divided into several levels according to a preset critical value to reflect the severity of the impact of snow intensity on the operation of the expressway; the number of days of adverse operation of the multi-year average snow depth on the expressway of the horizontal coordinate is graded and divided into several levels according to a preset critical value to reflect the degree of influence of snow frequency on the operation of the expressway.

[0047] Furthermore, according to the severity of the impact of snow intensity on the operation of the expressway, the extreme annual maximum snow depth is divided into 4 levels: "slight impact", "certain impact", "relatively large impact" and "severe impact". Among them, the snow depth H corresponding to the "slight impact" level is 0 cm < H < 1 cm, the snow depth H corresponding to the "certain impact" level is 1 cm ≤ H < 3 cm, the snow depth H corresponding to the "relatively large impact" level is 3 cm ≤ H < 5 cm, and the snow depth H corresponding to the "severe impact" level is H ≥ 5 cm.

[0048] At the same time, according to the degree of influence of snow frequency on the operation of the expressway, the number of days of adverse operation of the multi-year average snow depth on the expressway of the horizontal coordinate is divided into 4 levels: "few", "general", "relatively many", "many". Among them, the number of days of adverse operation D of the expressway corresponding to the "few" level is D < 0.1 day, the number of days of adverse operation D of the expressway corresponding to the "general" level is 0.1 day ≤ D < 5 days, the number of days of adverse operation D of the expressway corresponding to the "relatively many" level is 5 days ≤ D < 50 days, and the number of days of adverse operation D of the expressway corresponding to the "many" level is D ≥ 50 days.

[0049] Furthermore, for the 16 types of situations formed by the combination of the horizontal and vertical coordinates, according to the comprehensive degree of influence of snow intensity and frequency on the operation of the highway, they are divided into five dangerous levels: "low", "lower", "medium", "higher" and "high", as shown in Table 1 below, where:

[0050] The "low" level corresponds to the situation where the maximum snow depth in an extreme year is at the "slightly affected" level and the number of days in a year with unfavorable snow cover is at the "few" level, which basically has no impact on the operation of the expressway;

[0051] The “lower” level corresponds to the following three situations: 1) The maximum snow depth in an extreme year is “slightly affected” and the number of days in a year with adverse snow operation is “average”, “more” or “more”; 2) The number of days in a year with adverse snow operation is “few” and the maximum snow depth in an extreme year is “certainly affected”, “relatively affected” or “severely affected”; 3) The maximum snow depth in an extreme year is “certainly affected” and the number of days in a year with adverse snow operation is “average”. This level will have a certain slight impact on the operation of the expressway;

[0052] The "medium" level corresponds to the following three situations: 1) The maximum snow depth in an extreme year is "certain impact" and the number of days in a year with adverse snow operation is "relatively" or "many"; 2) The number of days in a year with adverse snow operation is "average" and the maximum snow depth in an extreme year is "relatively impacted" or "severely impacted"; 3) The maximum snow depth in an extreme year is "relatively impacted" and the number of days in a year with adverse snow operation is "relatively", which will have a moderate impact on highway operation;

[0053] The "higher" level corresponds to the following two situations: 1) the maximum snow depth in an extreme year is "significant impact" and the number of days in a year with unfavorable snow operation is "many"; 2) the maximum snow depth in an extreme year is "severe impact" and the number of days in a year with unfavorable snow operation is "many", which will have a serious impact on the operation of the expressway;

[0054] The "high" level corresponds to the situation where the maximum annual snow depth is "severe impact" and the number of days in a year when snow accumulation is unfavorable for operation is "many", which will have extremely serious and catastrophic impacts on highway operations.

[0055] Table 1 Snow hazard comprehensive assessment matrix

[0056]

[0057] Notes: For the convenience of representation, the five hazard levels “low”, “lower”, “medium”, “higher” and “high” correspond to Arabic numerals 1 to 5 respectively in the table.

[0058] SS5. Draw a distribution map of snow disaster risk assessment in the area to be assessed

[0059] Based on the multi-year average number of unfavorable snow-covered highway operation days and the extreme annual maximum snow depth counted in steps SS2 and SS3, the matrix comprehensive assessment model constructed in step SS4 is used to analyze and obtain the snow disaster hazard level of each grid, and the geographic information spatial analysis technology (GIS) is used to draw the snow disaster hazard assessment distribution map in the area to be assessed.

[0060] In some preferred examples, different colors are used to represent different risk levels in the snow disaster risk assessment distribution map drawn in the area to be assessed, so as to intuitively display the snow disaster risk level of each location in the area to be assessed. When used for highway construction planning and line selection, appropriate routes can be selected for planning according to the degree of risk, and at the same time, a decision-making basis can be provided for the operation management of existing highways and the formulation of disaster prevention and mitigation measures.

[0061] SS6. Analyze the snow disaster risk of existing highways in the area to be evaluated

[0062] Based on the snow disaster risk assessment distribution map drawn in the above step SS5, the route vector information of the built expressways in the area to be assessed is collected, and the geographic information space (GIS) overlay analysis technology is used to spatially overlay the built expressway route vector data with the drawn snow disaster risk assessment distribution map to generate a distribution map in the area to be assessed that includes the routes of all the built expressways and the lengths of each section, the starting and ending positions, and the corresponding snow disaster risk levels, such as Figure 2 As shown in the figure (the distribution map uses different colors to represent different hazard levels, so as to more intuitively display the snow disaster hazard level of various locations in the area to be assessed, where the "low" level corresponds to green, the "lower" level corresponds to blue, the "medium" level corresponds to yellow, the "higher" level corresponds to orange, and the "high" level corresponds to red), thereby providing a scientific basis and decision-making support for the operation management and disaster prevention and mitigation measures of existing highways.

[0063] Through the above steps, the present invention provides a method for evaluating the snow hazard of highway operation based on satellite remote sensing data combined with matrix comprehensive analysis. The method obtains satellite remote sensing snow depth data, counts the average number of days of unfavorable snow-covered highway operation and the maximum snow depth in extreme years, constructs a matrix comprehensive evaluation model, and uses geographic information spatial analysis technology to draw a snow hazard assessment distribution map, and finally conducts snow disaster hazard analysis on the built highway. This method not only takes into account the extreme values ​​of snow depth, but also combines the impact of snow on highway operation and long-term climate characteristics. Through the matrix comprehensive evaluation model, a comprehensive analysis of multiple factors is achieved, which improves the accuracy and reliability of snow disaster assessment, provides a scientific basis and decision-making support for the planning, construction and operation management of highways, and effectively improves the highway network's ability to respond to snow disasters and its safe operation level.

[0064] Through the above embodiments, the purpose of the present invention is fully and effectively achieved. Those skilled in the art can understand that the present invention includes but is not limited to the contents described in the drawings and the above specific embodiments. Although the present invention has been described with respect to the most practical and preferred embodiments currently considered, it should be understood that the present invention is not limited to the disclosed embodiments, and any modification that does not deviate from the functional and structural principles of the present invention will be included in the scope of the claims.

Claims

1. A method for evaluating the snow hazard of highway operation based on satellite remote sensing data, characterized in that: The method includes the following steps when implemented: SS1. Obtain satellite remote sensing snow depth data Collect and collate long-term time series data of snow depth retrieved from satellite remote sensing for the area to be evaluated. The long-term time series data of snow depth is generated after cross-calibration and inversion processing using a passive microwave snow depth inversion algorithm based on passive microwave brightness temperature data obtained daily by spaceborne sensors, and is compared and verified with the observed data at stations in the area to be evaluated; SS2. Statistically calculate the average number of days with adverse operation of expressways due to snow over the years Based on the long-term time series data of snow depth in the area to be evaluated, count the number of days with adverse operation of expressways due to snow in each year according to the standard of snow depth H > 0 cm, and then calculate the average over the years to obtain the annual value of adverse operation of expressways in the area to be evaluated; SS3. Statistically calculate the extreme maximum snow depth in a year Based on the long-term time series data of snow depth in the area to be evaluated, by traversing and analyzing the daily snow depth values within each snow year, extract the maximum daily snow depth value of the current year, and select the historical highest value in the entire data time series to statistically calculate the extreme maximum snow depth value that occurred during the historical period; SS4. Construct a matrix comprehensive evaluation model to conduct snow hazard analysis Select the extreme maximum snow depth in a year and the average number of days with adverse operation of expressways due to snow over the years as the vertical and horizontal coordinates of the matrix. Grade the vertical and horizontal coordinates respectively and divide them into several different grades. The pairwise combination of the two coordinates forms the row and column values of the matrix, and set corresponding snow disaster hazard levels for each row and column combination value to construct a matrix comprehensive evaluation model to conduct snow hazard analysis; Among them, The extreme maximum snow depth in the vertical coordinate is divided into 4 grades: "slight impact", "certain impact", "relatively large impact", and "severe impact". Among them, the snow depth H corresponding to the "slight impact" grade is 0 cm < H < 1 cm, the snow depth H corresponding to the "certain impact" grade is 1 cm ≤ H < 3 cm, the snow depth H corresponding to the "relatively large impact" grade is 3 cm ≤ H < 5 cm, and the snow depth H corresponding to the "severe impact" grade is H ≥ 5 cm; The average number of days with adverse operation of expressways due to snow in the horizontal coordinate is divided into 4 grades: "few", "general", "more", and "many". The number of days D with adverse operation of expressways due to snow corresponding to the "few" grade is D < 0.1 days, the number of days D with adverse operation of expressways due to snow corresponding to the "general" grade is 0.1 days ≤ D < 5 days, the number of days D with adverse operation of expressways due to snow corresponding to the "more" grade is 5 days ≤ D < 50 days, and the number of days D with adverse operation of expressways due to snow corresponding to the "many" grade is D ≥ 50 days; For the 16 cases formed by the pairwise combination of the horizontal and vertical coordinates, according to the comprehensive degree of the impact of snow intensity and frequency on the operation of expressways, divide them into 5 hazard levels: "low", "relatively low", "medium", "relatively high", and "high". Among them: The "low" level corresponds to the situation where the extreme maximum snow depth in a year is at the "slight impact" level and the number of days with adverse operation of expressways due to snow is at the "few" level, and has basically no impact on the operation of expressways; The "lower" level corresponds to the following three situations: 1) The maximum snow depth in an extreme year is "slightly affected" and the number of days in a year with adverse snow operation is "average", "more" or "more"; 2) The number of days in a year with adverse snow operation is "few" and the maximum snow depth in an extreme year is "certainly affected", "relatively affected" or "severely affected"; 3) The maximum snow depth in an extreme year is "certainly affected" and the number of days in a year with adverse snow operation is "average", which will cause certain adverse effects on the operation of the expressway; The "medium" level corresponds to the following three situations: 1) The maximum snow depth in an extreme year is "certain impact" and the number of days in a year with adverse snow operation is "relatively" or "relatively"; 2) The number of days in a year with adverse snow operation is "average" and the maximum snow depth in an extreme year is "relatively impacted" or "severely impacted"; 3) The maximum snow depth in an extreme year is "relatively impacted" and the number of days in a year with adverse snow operation is "relatively"; this level will have a moderate impact on highway operation; The "higher" level corresponds to the following two situations: 1) The maximum snow depth in an extreme year is "significant impact" and the number of days in a year with unfavorable snow cover is "many"; 2) The maximum snow depth in an extreme year is "severe impact" and the number of days in a year with unfavorable snow cover is "many", which will have a serious impact on the operation of the expressway; The "high" level corresponds to the situation where the maximum snow depth in an extreme year is "severe impact" and the number of days in a year with unfavorable snow conditions is "many", which will cause extremely serious and disastrous impacts on highway operations; SS5. Draw a distribution map of snow disaster risk assessment in the area to be assessed Based on the matrix comprehensive assessment model, the average number of days of unfavorable operation of highways due to snow accumulation over many years and the maximum snow depth in extreme years are comprehensively counted to obtain the snow disaster hazard level of each grid, and the distribution map of snow disaster hazard assessment in the area to be assessed is drawn using geographic information spatial analysis technology; SS6. Analyze the snow disaster risk of existing highways in the assessment area Based on the drawn snow disaster hazard assessment distribution map in the area to be assessed, and the route vector information of the already built expressways in the area to be assessed is collected, the geographic information spatial overlay analysis technology is used to spatially overlay the route vector data of the already built expressways with the drawn snow disaster hazard assessment distribution map, and generate a distribution map in the area to be assessed that contains the routes of all the already built expressways and the length, starting and ending positions of each section, and the corresponding snow disaster hazard level.

2. The method for evaluating the snow hazard of highway operation based on satellite remote sensing data according to claim 1 is characterized in that: In the above step SS1, the geographical scope of the long-term series data of snow depth should at least fully cover the area to be evaluated to ensure the integrity and continuity of the data required for the evaluation work, with a temporal resolution of at least 1 day and a spatial resolution of 0.25°×0.25° or higher. The data should be compared and verified with the observation data of the stations in the area to be evaluated.

3. The method for evaluating the snow hazard of highway operation based on satellite remote sensing data according to claim 1 is characterized in that: In the above step SS2, the number of unfavorable operation days of highways in previous snowy years is counted according to the snow depth H>0cm, and then the 30-year average is performed to obtain the annual value of the unfavorable operation days of highways. July 1 of the previous year to June 30 of the current year is counted as a snowy year. The time series length of the long-term series data of snow depth collected and sorted out in step SS1 is at least 30 years.

4. The method for evaluating the snow hazard of highway operation based on satellite remote sensing data according to claim 1 is characterized in that: In the above step SS4, the maximum annual snow depth on the vertical axis is graded and divided into several levels according to a preset critical value to reflect the severity of the impact of snow intensity on highway operation; the average annual number of days of unfavorable highway operation due to snow accumulation on the horizontal axis is graded and divided into several levels according to a preset critical value to reflect the impact of snow frequency.

5. The method for evaluating the snow hazard of highway operation based on satellite remote sensing data according to claim 1 is characterized in that: In the above step SS5, different colors are used to represent different risk levels in the snow disaster risk assessment distribution map in the area to be assessed, so as to intuitively display the snow disaster risk level of each location in the area to be assessed. When it is used for highway construction planning and line selection, appropriate routes are selected for planning according to the risk level, and at the same time, a decision-making basis is provided for the operation management of existing highways and the formulation of disaster prevention and mitigation measures.

6. A computer program product, characterized in that It includes computer instructions for executing the method for evaluating the snow hazard of highway operation based on satellite remote sensing data as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for evaluating the snow hazard of highway operation based on satellite remote sensing data as described in any one of claims 1 to 5 is implemented.

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