CN113516407A - Method and system for identifying snow disaster distribution in areas along high-speed rail - Google Patents
Method and system for identifying snow disaster distribution in areas along high-speed rail Download PDFInfo
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Abstract
The invention provides a method and a system for identifying snow disaster distribution in areas along a high-speed rail. The method comprises the steps of obtaining an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area; acquiring historical monitoring data, and extracting snow change data within 20 years and 5 years; acquiring snow change data within 20 years, identifying risk areas, and generating a first-class risk block and a second-class risk block; acquiring historical monitoring data, and extracting the maximum snow depth and the snow index; acquiring snow disaster distinguishing data, snow disaster evaluation weight factors and danger evaluation factors; and according to the risk evaluation factors, carrying out risk evaluation on the altitude, the gradient, the slope direction, the land coverage, the snow coverage rate and the snow depth, and further determining the snow disaster risk level. This scheme is through providing the integration of risk area and snow risk trigger mode, and then ensures to monitor the snow risk fast.
Description
Technical Field
The invention relates to the technical field of meteorological monitoring, in particular to a method and a system for identifying snow disaster distribution in areas along a high-speed rail.
Background
Snow is a common meteorological type, and a natural disaster phenomenon affecting railway traffic is caused by snow accumulation and disasters caused by long-time large-scale snowfall. Heavy snow or a large amount of snow may cause road congestion, power damage, and the like. Particularly, in northern areas of high altitude, the possibility of the weather of accumulated snow is extremely high.
Prior to the present technology, the prior art has been primarily directed to on-line monitoring via meteorological satellites or meteorological bureau acquisition devices, but has not been able to achieve on-line, reliable data monitoring for individual railways. However, this approach has not been able to effectively identify the risk of snow accumulation.
Disclosure of Invention
In view of the above problems, the invention provides a snow disaster distribution identification method and system for areas along a high-speed rail, which ensure rapid monitoring of snow risks by providing fusion of risk areas and snow risk triggering modes.
According to the first aspect of the embodiment of the invention, a snow disaster distribution identification method for areas along a high-speed rail is provided.
In one or more embodiments, preferably, the method for identifying the distribution of snow disasters in areas along the high-speed rail includes:
acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area;
acquiring historical monitoring data, and extracting snow change data within 20 years and 5 years;
acquiring snow change data within 20 years, identifying risk areas, and generating a first type of risk block larger than a first risk margin and a second type of risk block larger than a second risk margin;
acquiring historical monitoring data, and extracting the maximum snow depth and the snow index;
acquiring snow disaster distinguishing data, snow disaster evaluation weight factors and danger evaluation factors;
according to the risk evaluation factors, carrying out risk evaluation on the altitude, the gradient, the slope direction, the land coverage, the snow coverage rate and the snow depth;
and determining the risk level of the snow cover disaster according to the snow cover disaster evaluation weight factors of each risk evaluation area.
In one or more embodiments, preferably, the acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area specifically includes:
acquiring a to-be-retrieved area, wherein the to-be-retrieved area is an area within a distance of 50km along a high-speed rail;
acquiring the risk identification areas on a map by taking the areas to be retrieved as boundary lines, wherein the total width of each risk identification area is 5km, and when the length of each area is less than 5km, the sum of the adjacent 5km width areas of the current area is used for generating a risk identification area larger than 5 km;
acquiring elevation information and gradient information in all the risk identification areas;
carrying out low resolution on the elevation information, and converting the elevation information into the elevation information with the resolution of 1km multiplied by 1 km;
and carrying out low resolution on the gradient information, and converting the gradient information into the gradient information with the resolution of 1km multiplied by 1 km.
In one or more embodiments, preferably, the acquiring of the historical monitoring data, performing the extraction of the snow change data within 20 years and 5 years specifically includes:
acquiring historical monitoring data, and extracting snow change data in 20 years;
extracting a 20-year snow coverage average value of the risk identification area according to the snow variation data;
extracting an average value of the snow coverage of the risk identification area for 5 years according to the snow variation data;
wherein the 20-year snow coverage average is obtained using a first calculation formula;
wherein the 5-year snow coverage average is obtained using a second calculation formula;
the first calculation formula is:
wherein SAVG20Is the 20 year average of snow cover, sijThe snow cover rate of i years before the jth, i is an integer from 5 to 20, s1jSnow coverage of the 5 th previous year of the jth year, s2jSnow coverage of the jth previous 4 years, s3jCoverage of snow in the first 3 th year of the jth year, s4jCoverage of accumulated snow in the first 2 years of jth year, s5jThe coverage rate of the accumulated snow in the jth previous 1 year is shown, and j is the number of a measuring point;
the second calculation formula is:
SAVG5＝0.4×(0.2×∑s1j+0.2×∑s2j+0.2×∑s3j)+0.6×(0.2×∑s4j+0.2×∑s5j)
wherein SAVG5Is the average of the 5 year snow cover.
In one or more embodiments, preferably, the acquiring snow change data within 20 years, performing risk area identification, and generating a first type of risk block larger than the first risk margin and a second type of risk block larger than the second risk margin specifically includes:
acquiring snow change data within 20 years;
extracting the risk identification areas with the snow variation data being more than 80% in 20 years, and storing the risk identification areas as a type of risk block;
and extracting the risk identification area with the snow change data being more than 40% in 20 years, and storing the risk identification area as a second-class risk block.
In one or more embodiments, preferably, the acquiring of the historical monitoring data and the extracting of the maximum snow depth and the snow index include:
acquiring the historical monitoring data, and extracting the maximum accumulated snow depth;
filling all of the maximum snow depths into the risk identification area;
judging whether the maximum accumulated snow depth data does not exist in the risk identification area or not;
when the maximum snow depth data does not exist in the risk identification area, extracting the maximum snow depth data in an area adjacent to the current risk identification area, and determining the maximum snow depth data by using a third calculation formula;
normalizing the maximum snow depth data by using a fourth calculation formula to generate a snow index in each risk identification area;
the third calculation formula:
maxi＝max(deepi1,deepi2,……,deepin)
therein, maxiFor said maximum snow depth data, deepi1、deepi2、……、deepinIdentifying snow depth data for region i for risk 1, 2, … …, n;
the fourth calculation formula:
where k is the number of risk identification areas, staiIdentifying a snow index within the area for the ith said risk.
In one or more embodiments, preferably, the obtaining snow disaster determination data, the snow disaster evaluation weighting factor, and the risk evaluation factor specifically includes:
acquiring snow disaster judging data, wherein the snow disaster judging data comprises elevation information, gradient information, slope information, land coverage, snow cover day data and maximum snow depth;
acquiring the snow cover disaster evaluation weight factors, wherein the snow cover disaster evaluation weight factors comprise an altitude factor, a gradient factor, a slope factor, a land coverage factor, a snow cover day data factor and a maximum snow cover depth factor;
and acquiring the risk evaluation factors, wherein the risk evaluation factors comprise an altitude evaluation factor, a slope evaluation factor, a land cover evaluation factor, an accumulated snow cover day data evaluation factor and a maximum accumulated snow depth evaluation factor.
In one or more embodiments, preferably, the risk assessment of altitude, gradient, slope, land cover, snow coverage rate and snow depth according to the risk evaluation factor specifically includes:
determining altitude risks by using a fifth calculation formula according to the risk evaluation factors;
determining a gradient risk by using a sixth calculation formula according to the risk evaluation factor;
determining the slope risk by using a seventh calculation formula according to the risk evaluation factor;
determining land cover risks by using an eighth calculation formula according to the risk evaluation factors;
determining the risk of high snow coverage rate by using a ninth calculation formula according to the risk evaluation factor;
determining the deep accumulated snow depth risk by utilizing a tenth calculation formula according to the risk evaluation factor;
the fifth calculation formula is:
P1＝A3(1-A)2
wherein, P1For the altitude risk, a is the altitude evaluation factor;
the sixth calculation formula is:
P2＝BC+B2C
wherein, P2The grade risk is obtained, and B is the grade evaluation factor;
the seventh calculation formula is:
P3＝BC+BC2
wherein, P3C is the slope risk and the slope evaluation factor;
the eighth calculation formula is:
P4＝D2+D
wherein, P4For said landCovering risk, D is the land covering evaluation factor;
the ninth calculation formula is:
P5＝A+E2(1+A)
wherein, P5For the risk of high snow coverage, E is the snow coverage day data evaluation factor;
the tenth calculation formula is:
P6＝A2+AF3
wherein, P6And F is the maximum accumulated snow depth evaluation factor.
In one or more embodiments, preferably, the determining a risk level of an snow disaster according to the snow disaster evaluation weighting factor of each risk evaluation area specifically includes:
determining the snow disaster risk level of each risk evaluation area by using an eleventh calculation formula according to the snow disaster evaluation weight factor of each risk evaluation area;
the eleventh calculation formula is:
Pz＝K1P1+K2P2+K3P3+K4P4+K5P5+K6P6
wherein, PzTo said snow disaster risk class, K1、K2、K3、K4、K5、K6First to sixth risk factors, respectively.
According to the second aspect of the embodiment of the invention, a snow disaster distribution identification system for areas along a high-speed rail is provided.
In one or more embodiments, preferably, the snow disaster distribution identification system for the areas along the high-speed rail preferably includes:
the geographic information extraction submodule is used for acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area;
the historical data extraction submodule is used for acquiring historical monitoring data and extracting the snow change data within 20 years and 5 years;
the risk field identification submodule is used for acquiring snow change data within 20 years, identifying risk areas and generating a first type of risk block larger than a first risk margin and a second type of risk block larger than a second risk margin;
the depth index extraction submodule is used for acquiring historical monitoring data and extracting the maximum snow depth and the snow index;
the risk evaluation factor extraction submodule is used for obtaining snow disaster distinguishing data, snow disaster evaluation weight factors and risk evaluation factors;
the comprehensive data extraction submodule is used for carrying out risk assessment on the altitude, the gradient, the slope direction, the land coverage, the snow coverage rate and the snow depth according to the risk evaluation factor;
and the snow cover disaster grade generation submodule is used for determining the snow cover disaster risk grade according to the snow cover disaster evaluation weight factors of each risk evaluation area.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method according to any one of the first aspect of embodiments of the present invention.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
1) the embodiment of the invention provides a method for arranging an accumulated snow monitoring mode according to the grade of an accumulated snow disaster, and the reliability of accumulated snow monitoring is improved.
2) The embodiment of the invention provides a calculation method of a risk evaluation factor, which combines historical snow monitoring data to perform on-line snow index analysis;
3) in the embodiment of the invention, supplementary data are provided for historical data which cannot acquire the maximum snow depth due to the absence of monitoring points, and a snow depth map can be drawn according to the data.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a snow disaster distribution identification method in a region along a high-speed rail according to an embodiment of the present invention.
Fig. 2 is a flowchart of acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area in the snow disaster distribution identification method for the area along the high-speed rail according to the embodiment of the present invention.
Fig. 3 is a flowchart of acquiring historical monitoring data and extracting snow change data within 20 years and 5 years in the snow disaster distribution identification method for the areas along the high-speed rail according to the embodiment of the invention.
Fig. 4 is a flowchart of acquiring snow change data within 20 years, performing risk area identification, and generating a first type of risk block larger than a first risk margin and a second type of risk block larger than a second risk margin in the snow disaster distribution identification method for the areas along the high-speed rail according to an embodiment of the present invention.
Fig. 5 is a flowchart of extracting the maximum snow depth and the snow index from the acquired historical monitoring data in the snow disaster distribution identification method for the areas along the high-speed rail according to an embodiment of the present invention.
Fig. 6 is a flowchart of acquiring snow disaster determination data, snow disaster evaluation weight factors, and risk evaluation factors in a snow disaster distribution identification method for a high-speed rail along a line area according to an embodiment of the present invention.
Fig. 7 is a flowchart of risk assessment of altitude, gradient, slope direction, land coverage, snow coverage rate, and snow depth according to the risk evaluation factor in the snow disaster distribution identification method for the areas along the high-speed rail according to an embodiment of the present invention.
Fig. 8 is a schematic diagram of a snow disaster distribution identification system in a region along a high-speed rail according to an embodiment of the invention.
Detailed Description
In some of the flows described in the present specification and claims and in the above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being indicated as 101, 102, etc. merely to distinguish between the various operations, and the order of the operations by themselves does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Snow is a common meteorological type, and a natural disaster phenomenon affecting railway traffic is caused by snow accumulation and disasters caused by long-time large-scale snowfall. Heavy snow or a large amount of snow may cause road congestion, power damage, and the like. Particularly, in northern areas of high altitude, the possibility of the weather of accumulated snow is extremely high.
Prior to the present technology, the prior art has been primarily directed to on-line monitoring via meteorological satellites or meteorological bureau acquisition devices, but has not been able to achieve on-line, reliable data monitoring for individual railways. However, this approach has not been able to effectively identify the risk of snow accumulation.
The embodiment of the invention provides a method and a system for identifying snow disaster distribution in areas along a high-speed rail. This scheme is through providing the integration of risk area and snow risk trigger mode, and then ensures to monitor the snow risk fast.
Fig. 1 is a flowchart of a snow disaster distribution identification method in a region along a high-speed rail according to an embodiment of the present invention.
As shown in fig. 1, according to a first aspect of the embodiment of the present invention, a method for identifying a snow disaster distribution in areas along a high-speed rail is provided.
In one or more embodiments, preferably, the method for identifying the distribution of snow disasters in areas along the high-speed rail includes:
s101, acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area;
s102, acquiring historical monitoring data, and extracting snow change data within 20 years and 5 years;
s103, acquiring snow change data within 20 years, carrying out risk area identification, and generating a first-class risk block larger than a first risk margin and a second-class risk block larger than a second risk margin;
s104, acquiring historical monitoring data, and extracting the maximum snow depth and the snow index;
s105, acquiring snow disaster distinguishing data, snow disaster evaluation weight factors and danger evaluation factors;
s106, carrying out risk assessment on the altitude, the gradient, the slope direction, the land coverage, the snow coverage rate and the snow depth according to the risk assessment factors;
and S107, determining the risk level of the snow cover disaster according to the snow cover disaster evaluation weight factors of each risk evaluation area.
Fig. 2 is a flowchart of acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area in the snow disaster distribution identification method for the area along the high-speed rail according to the embodiment of the present invention.
In the embodiment of the invention, information such as elevation, gradient, slope direction, land coverage, snow coverage rate, snow depth and the like is acquired, so that a method for arranging snow monitoring modes according to the grade of snow disasters is provided, and the reliability of snow monitoring is improved; further provided is a risk evaluation factor calculation method, which combines historical snow monitoring data to perform on-line snow index analysis.
As shown in fig. 2, in one or more embodiments, preferably, the acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area specifically includes:
s201, obtaining a region to be retrieved, wherein the region to be retrieved is a region within a distance of 50km along a high-speed rail;
s202, acquiring the risk identification areas on a map by taking the areas to be retrieved as boundary lines, wherein the total width of each risk identification area is 5km, and when the length of each area is less than 5km, summing up the adjacent 5km width areas of the current area to generate a risk identification area larger than 5 km;
s203, acquiring elevation information and gradient information in all the risk identification areas;
s204, carrying out low resolution on the elevation information, and converting the elevation information into elevation information with resolution of 1km multiplied by 1 km;
and S205, carrying out low resolution on the gradient information, and converting the gradient information into gradient information with the resolution of 1km multiplied by 1 km.
In the embodiment of the invention, the depth analysis is carried out according to all topographic data, the position of a high-speed rail along the line is firstly obtained according to an actual map, the area to be searched is further determined according to the high-speed rail along the line, a risk identification area with the minimum width of 5km is determined according to the area to be searched, and finally a slope and elevation monitoring range with relatively high resolution is determined according to the risk identification area, wherein the slope monitoring precision and the elevation monitoring precision are both 1km, and the reason is that the weather change is relatively small in a kilometer area. If the accuracy of monitoring is too high, the detection change may be too high, and an identification blind area may be generated.
Fig. 3 is a flowchart of acquiring historical monitoring data and extracting snow change data within 20 years and 5 years in the snow disaster distribution identification method for the areas along the high-speed rail according to the embodiment of the invention.
As shown in fig. 3, in one or more embodiments, preferably, the acquiring historical monitoring data, and performing the extraction of the snow change data within 20 years and 5 years specifically includes:
s301, acquiring historical monitoring data, and extracting snow change data in 20 years;
s302, extracting an average snow cover value of the risk identification area for 20 years according to the snow change data;
s303, extracting an average value of the snow coverage of the risk identification area for 5 years according to the snow change data;
wherein the 20-year snow coverage average is obtained using a first calculation formula;
wherein the 5-year snow coverage average is obtained using a second calculation formula;
the first calculation formula is:
wherein SAVG20Is the 20 year average of snow cover, sijThe snow cover rate of i years before the jth, i is an integer from 5 to 20, s1jSnow coverage of the 5 th previous year of the jth year, s2jSnow coverage of the jth previous 4 years, s3jCoverage of snow in the first 3 th year of the jth year, s4jCoverage of accumulated snow in the first 2 years of jth year, s5jThe coverage rate of the accumulated snow in the jth previous 1 year is shown, and j is the number of a measuring point;
the second calculation formula is:
SAVG5＝0.4×(0.2×∑s1j+0.2×∑s2j+0.2×∑s3j)+0.6×(0.2×∑s4j+0.2×∑s5j)
wherein SAVG5Is the average of the 5 year snow cover.
In the embodiment of the invention, the historical data of the snow cover rate can reflect whether the snow cover in the area is possibly in a state of snow cover all year round, in this case, the requirements of the temperature, equipment and the like along the high-speed rail will be increased, and meanwhile, the data of 5 years and 20 years can be used for judging whether the snow cover rate changes in recent years, so that measures such as arrangement of monitoring equipment are reduced.
Fig. 4 is a flowchart of acquiring snow change data within 20 years, performing risk area identification, and generating a first type of risk block larger than a first risk margin and a second type of risk block larger than a second risk margin in the snow disaster distribution identification method for the areas along the high-speed rail according to an embodiment of the present invention.
As shown in fig. 4, in one or more embodiments, preferably, the acquiring the snow change data within 20 years, performing risk area identification, and generating a first type of risk block larger than the first risk margin and a second type of risk block larger than the second risk margin specifically includes:
s401, acquiring snow change data within 20 years;
s402, extracting the risk identification area with the snow change data being more than 80% in 20 years, and storing the risk identification area as a type of risk block;
and S403, extracting the risk identification area with the snow change data being more than 40% in 20 years, and storing the risk identification area as a second-class risk block.
In the embodiment of the invention, in order to evaluate the snow accumulation state in detail, the areas of 80% of snow accumulation and 40% of snow accumulation are divided respectively, and the areas can be directly dyed into different colors in actual operation to be displayed visually, so that the prediction of the snow accumulation state can be realized.
Fig. 5 is a flowchart of extracting the maximum snow depth and the snow index from the acquired historical monitoring data in the snow disaster distribution identification method for the areas along the high-speed rail according to an embodiment of the present invention.
As shown in fig. 5, in one or more embodiments, preferably, the acquiring historical monitoring data and extracting the maximum snow depth and the snow index includes:
s501, acquiring the historical monitoring data, and extracting the maximum accumulated snow depth;
s502, filling all the maximum snow depth into the risk identification area;
s503, judging whether the maximum accumulated snow depth data does not exist in the risk identification area;
s504, when the maximum snow depth data does not exist in the risk identification area, extracting the maximum snow depth data in an area adjacent to the current risk identification area, and determining the maximum snow depth data by using a third calculation formula;
s505, normalizing the maximum snow depth data by using a fourth calculation formula to generate a snow index in each risk identification area;
the third calculation formula:
maxi＝max(deepi1,deepi2,……,deepin)
therein, maxiFor said maximum snow depth data, deepi1、deepi2、……、deepinIdentifying snow depth data for region i for risk 1, 2, … …, n;
the fourth calculation formula:
where k is the number of risk identification areas, staiIdentifying a snow index within the area for the ith said risk.
In the embodiment of the invention, as the maximum snow depth has a certain deviation and monitoring points possibly do not exist in some monitoring areas, the historical data of the maximum snow depth cannot be obtained, the method supplements the data and performs normalization processing to ensure that the maximum snow depth data exist in each risk identification area, and the snow depth map can be drawn according to the data.
Fig. 6 is a flowchart of acquiring snow disaster determination data, snow disaster evaluation weight factors, and risk evaluation factors in a snow disaster distribution identification method for a high-speed rail along a line area according to an embodiment of the present invention.
As shown in fig. 6, in one or more embodiments, preferably, the obtaining snow disaster determination data, snow disaster evaluation weighting factor, and risk evaluation factor specifically includes:
s601, acquiring snow disaster distinguishing data, wherein the snow disaster distinguishing data comprises elevation information, gradient information, slope information, land coverage rate, snow coverage day data and maximum snow depth;
s602, acquiring the snow cover disaster evaluation weight factors, wherein the snow cover disaster evaluation weight factors comprise an altitude factor, a gradient factor, a slope factor, a land coverage factor, a snow cover day data factor and a maximum snow cover depth factor;
s603, obtaining the risk evaluation factors, wherein the risk evaluation factors comprise an altitude evaluation factor, a slope evaluation factor, a land cover evaluation factor, an accumulated snow cover day data evaluation factor and a maximum accumulated snow depth evaluation factor.
In the embodiment of the invention, in order to comprehensively perform risk assessment, the snow disaster judgment data, the snow disaster evaluation weight factor and the risk evaluation factor are obtained, and further, the snow disaster judgment data, the snow disaster evaluation weight factor and the risk evaluation factor are used for performing comprehensive operation to generate different types of risk levels.
Fig. 7 is a flowchart of risk assessment of altitude, gradient, slope direction, land coverage, snow coverage rate, and snow depth according to the risk evaluation factor in the snow disaster distribution identification method for the areas along the high-speed rail according to an embodiment of the present invention.
As shown in fig. 7, in one or more embodiments, preferably, the risk assessment of altitude, gradient, slope direction, land coverage, snow coverage rate, and snow depth according to the risk evaluation factor specifically includes:
s701, determining an altitude risk by using a fifth calculation formula according to the risk evaluation factor;
s702, determining a gradient risk by using a sixth calculation formula according to the risk evaluation factor;
s703, determining the slope risk by using a seventh calculation formula according to the risk evaluation factor;
s704, determining the land cover risk by using an eighth calculation formula according to the risk evaluation factor;
s705, determining the risk of high snow coverage rate by using a ninth calculation formula according to the risk evaluation factor;
s706, determining the deep accumulated snow depth risk by using a tenth calculation formula according to the risk evaluation factor;
the fifth calculation formula is:
P1＝A3(1-A)2
wherein, P1For the altitude risk, a is the altitude evaluation factor;
the sixth calculation formula is:
P2＝BC+B2C
wherein, P2As said gradient risk, B as said gradient evaluationA factor;
the seventh calculation formula is:
P3＝BC+BC2
wherein, P3C is the slope risk and the slope evaluation factor;
the eighth calculation formula is:
P4＝D2+D
wherein, P4D is the land cover risk and the land cover evaluation factor;
the ninth calculation formula is:
P5＝A+E2(1+A)
wherein, P5For the risk of high snow coverage, E is the snow coverage day data evaluation factor;
the tenth calculation formula is:
P6＝A2+AF3
wherein, P6And F is the maximum accumulated snow depth evaluation factor.
In the embodiment of the invention, in order to form comprehensive risk evaluation, the risk evaluation of the altitude, the gradient, the slope direction, the land coverage, the snow coverage rate and the snow depth is carried out according to the risk evaluation factors, and then source data of multiple dimensions are generated, and the comprehensive evaluation of snow disasters can be carried out by using all the source data.
In one or more embodiments, preferably, the determining a risk level of an snow disaster according to the snow disaster evaluation weighting factor of each risk evaluation area specifically includes:
determining the snow disaster risk level of each risk evaluation area by using an eleventh calculation formula according to the snow disaster evaluation weight factor of each risk evaluation area;
the eleventh calculation formula is:
Pz＝K1P1+K2P2+K3P3+K4P4+K5P5+K6P6
wherein, PzTo said snow disaster risk class, K1、K2、K3、K4、K5、K6First to sixth risk factors, respectively.
In the embodiment of the invention, after a specific calculation result is obtained, the grade value of the snow disaster can be displayed on line in a visualized manner according to the grade values of different risk evaluation areas.
According to the second aspect of the embodiment of the invention, a snow disaster distribution identification system for areas along a high-speed rail is provided.
Fig. 8 is a schematic diagram of a snow disaster distribution identification system in a region along a high-speed rail according to an embodiment of the invention.
In one or more embodiments, as shown in fig. 8, the snow disaster distribution identification system for the area along the high-speed rail preferably includes:
the geographic information extraction submodule 801 is used for acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area;
a historical data extraction submodule 802, configured to acquire historical monitoring data and extract snow change data within 20 years and 5 years;
a risk area identification submodule 803, configured to acquire snow change data within 20 years, perform risk area identification, and generate a first type of risk block larger than a first risk margin and a second type of risk block larger than a second risk margin;
the depth index extraction submodule 804 is used for acquiring historical monitoring data and extracting the maximum snow depth and the snow index;
a risk evaluation factor extraction submodule 805 for obtaining snow disaster discrimination data, snow disaster evaluation weight factors, and risk evaluation factors;
the comprehensive data extraction submodule 806 is used for carrying out risk assessment on the altitude, the gradient, the slope direction, the land coverage, the snow coverage rate and the snow depth according to the risk evaluation factor;
an accumulated snow disaster grade generation submodule 807 is configured to determine an accumulated snow disaster risk grade according to the accumulated snow disaster evaluation weighting factor of each risk evaluation area.
According to a third aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method according to any one of the first aspect of embodiments of the present invention.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
1) the embodiment of the invention provides a method for arranging an accumulated snow monitoring mode according to the grade of an accumulated snow disaster, and the reliability of accumulated snow monitoring is improved.
2) The embodiment of the invention provides a calculation method of a risk evaluation factor, which combines historical snow monitoring data to perform on-line snow index analysis;
3) in the embodiment of the invention, supplementary data are provided for historical data which cannot acquire the maximum snow depth due to the absence of monitoring points, and a snow depth map can be drawn according to the data.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for identifying snow disaster distribution in areas along a high-speed rail is characterized by comprising the following steps:
acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area;
acquiring historical monitoring data, and extracting snow change data within 20 years and 5 years;
acquiring snow change data within 20 years, identifying risk areas, and generating a first type of risk block larger than a first risk margin and a second type of risk block larger than a second risk margin;
acquiring historical monitoring data, and extracting the maximum snow depth and the snow index;
acquiring snow disaster distinguishing data, snow disaster evaluation weight factors and danger evaluation factors;
according to the risk evaluation factors, carrying out risk evaluation on the altitude, the gradient, the slope direction, the land coverage, the snow coverage rate and the snow depth;
and determining the risk level of the snow cover disaster according to the snow cover disaster evaluation weight factors of each risk evaluation area.
2. The method for identifying distribution of snow disasters in areas along a high-speed rail according to claim 1, wherein the acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area specifically comprises:
acquiring a to-be-retrieved area, wherein the to-be-retrieved area is an area within a distance of 50km along a high-speed rail;
acquiring the risk identification areas on a map by taking the areas to be retrieved as boundary lines, wherein the total width of each risk identification area is 5km, and when the length of each area is less than 5km, the sum of the adjacent 5km width areas of the current area is used for generating a risk identification area larger than 5 km;
acquiring elevation information and gradient information in all the risk identification areas;
carrying out low resolution on the elevation information, and converting the elevation information into the elevation information with the resolution of 1km multiplied by 1 km;
and carrying out low resolution on the gradient information, and converting the gradient information into the gradient information with the resolution of 1km multiplied by 1 km.
3. The method for identifying distribution of snow disasters in areas along a high-speed rail according to claim 1, wherein the acquiring of historical monitoring data and the extracting of snow change data within 20 years and 5 years specifically comprise:
acquiring historical monitoring data, and extracting snow change data in 20 years;
extracting a 20-year snow coverage average value of the risk identification area according to the snow variation data;
extracting an average value of the snow coverage of the risk identification area for 5 years according to the snow variation data;
wherein the 20-year snow coverage average is obtained using a first calculation formula;
wherein the 5-year snow coverage average is obtained using a second calculation formula;
the first calculation formula is:
wherein SAVG20Is the 20 year average of snow cover, sijThe snow cover rate of i years before the jth, i is an integer from 5 to 20, s1jSnow coverage of the 5 th previous year of the jth year, s2jSnow coverage of the jth previous 4 years, s3jCoverage of snow in the first 3 th year of the jth year, s4jCoverage of accumulated snow in the first 2 years of jth year, s5jThe coverage rate of the accumulated snow in the jth previous 1 year is shown, and j is the number of a measuring point;
the second calculation formula is:
SAVG5＝0.4×(0.2×∈s1j+0.2×∑s2j+0.2×∑s3j)
+0.6×(0.2×∑s4j+0.2×∑s5j)
wherein SAVG5Is the average of the 5 year snow cover.
4. The method for identifying distribution of snow cover disasters in areas along a high-speed rail according to claim 1, wherein the acquiring snow cover change data within 20 years, performing risk area identification, and generating a first type of risk block larger than a first risk margin and a second type of risk block larger than a second risk margin specifically comprises:
acquiring snow change data within 20 years;
extracting the risk identification areas with the snow variation data being more than 80% in 20 years, and storing the risk identification areas as a type of risk block;
and extracting the risk identification area with the snow change data being more than 40% in 20 years, and storing the risk identification area as a second-class risk block.
5. The method for identifying distribution of snow cover disasters in areas along a high-speed rail according to claim 1, wherein the acquiring of historical monitoring data and the extracting of the maximum snow cover depth and the snow cover index specifically comprise:
acquiring the historical monitoring data, and extracting the maximum accumulated snow depth;
filling all of the maximum snow depths into the risk identification area;
judging whether the maximum accumulated snow depth data does not exist in the risk identification area or not;
when the maximum snow depth data does not exist in the risk identification area, extracting the maximum snow depth data in an area adjacent to the current risk identification area, and determining the maximum snow depth data by using a third calculation formula;
normalizing the maximum snow depth data by using a fourth calculation formula to generate a snow index in each risk identification area;
the third calculation formula:
maxi＝max(deepi1,deepi2,……,deepin)
therein, maxiFor said maximum snow depth data, deepi1、deepi2、……、deepinIdentifying snow depth data for region i for risk 1, 2, … …, n;
the fourth calculation formula:
where k is the number of risk identification areas, staiIdentifying a snow index within the area for the ith said risk.
6. The method for identifying distribution of snow cover disasters along high-speed rails according to claim 1, wherein the obtaining of snow cover disaster discrimination data, snow cover disaster evaluation weighting factors and risk evaluation factors specifically comprises:
acquiring snow disaster judging data, wherein the snow disaster judging data comprises elevation information, gradient information, slope information, land coverage, snow cover day data and maximum snow depth;
acquiring the snow cover disaster evaluation weight factors, wherein the snow cover disaster evaluation weight factors comprise an altitude factor, a gradient factor, a slope factor, a land coverage factor, a snow cover day data factor and a maximum snow cover depth factor;
and acquiring the risk evaluation factors, wherein the risk evaluation factors comprise an altitude evaluation factor, a slope evaluation factor, a land cover evaluation factor, an accumulated snow cover day data evaluation factor and a maximum accumulated snow depth evaluation factor.
7. The method for identifying distribution of snow disasters in areas along high-speed rails according to claim 6, wherein the risk assessment of altitude, gradient, slope direction, land coverage, snow coverage rate and snow depth according to the risk evaluation factors specifically comprises:
determining altitude risks by using a fifth calculation formula according to the risk evaluation factors;
determining a gradient risk by using a sixth calculation formula according to the risk evaluation factor;
determining the slope risk by using a seventh calculation formula according to the risk evaluation factor;
determining land cover risks by using an eighth calculation formula according to the risk evaluation factors;
determining the risk of high snow coverage rate by using a ninth calculation formula according to the risk evaluation factor;
determining the deep accumulated snow depth risk by utilizing a tenth calculation formula according to the risk evaluation factor;
the fifth calculation formula is:
P1＝A3(1-A)2
wherein, P1For the altitude risk, a is the altitude evaluation factor;
the sixth calculation formula is:
P2＝BC+B2C
wherein, P2The grade risk is obtained, and B is the grade evaluation factor;
the seventh calculation formula is:
P3＝BC+BC2
wherein, P3C is the slope risk and the slope evaluation factor;
the eighth calculation formula is:
P4＝D2+D
wherein, P4D is the land cover risk and the land cover evaluation factor;
the ninth calculation formula is:
P5＝A+E2(1+A)
wherein, P5For the risk of high snow coverage, E is the snow coverage day data evaluation factor;
the tenth calculation formula is:
P6＝A2+AF3
wherein, P6And F is the maximum accumulated snow depth evaluation factor.
8. The method according to claim 7, wherein the determining of the snow disaster risk level according to the snow disaster evaluation weighting factor of each risk evaluation area specifically includes:
determining the snow disaster risk level of each risk evaluation area by using an eleventh calculation formula according to the snow disaster evaluation weight factor of each risk evaluation area;
the eleventh calculation formula is:
Pz＝K1P1+K2P2+K3P3+K4P4+K5P5+K6P6
wherein, PzTo said snow disaster risk class, K1、K2、K3、K4、K5、K6First to sixth risk factors, respectively.
9. The utility model provides a high-speed railway area snow calamity distribution identification system along line which characterized in that, this system includes:
the geographic information extraction submodule is used for acquiring an area to be retrieved, dividing a monitoring area, generating a risk identification area, and extracting elevation information and gradient information in the risk identification area;
the historical data extraction submodule is used for acquiring historical monitoring data and extracting the snow change data within 20 years and 5 years;
the risk field identification submodule is used for acquiring snow change data within 20 years, identifying risk areas and generating a first type of risk block larger than a first risk margin and a second type of risk block larger than a second risk margin;
the depth index extraction submodule is used for acquiring historical monitoring data and extracting the maximum snow depth and the snow index;
the risk evaluation factor extraction submodule is used for obtaining snow disaster distinguishing data, snow disaster evaluation weight factors and risk evaluation factors;
the comprehensive data extraction submodule is used for carrying out risk assessment on the altitude, the gradient, the slope direction, the land coverage, the snow coverage rate and the snow depth according to the risk evaluation factor;
and the snow cover disaster grade generation submodule is used for determining the snow cover disaster risk grade according to the snow cover disaster evaluation weight factors of each risk evaluation area.
10. A computer-readable storage medium on which computer program instructions are stored, which, when executed by a processor, implement the method of any one of claims 1-8.
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