CN111639865B - High-speed rail line meteorological disaster occurrence risk analysis method - Google Patents
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Abstract
The invention relates to a high-speed rail line meteorological disaster occurrence risk analysis method, which comprises the following steps: step S1: carrying out normalization processing on each meteorological element sample sequence; step S2: calculating a comprehensive risk index of the construction period, and performing reselection of the primary selection track based on the feedback of the comprehensive risk index of the construction period; step S3: and calculating the comprehensive risk index of the operation period, and carrying out lattice monitoring adjustment and risk early warning based on the index. The comprehensive disaster occurrence risk analysis method can simultaneously integrate and consider various meteorological elements to carry out comprehensive disaster occurrence risk analysis; adjusting meteorological element monitoring based on the analysis result, and giving a suggestion of track selection in the construction period; and respectively analyzing risk areas at different stages along the railway according to different sensitivities of the railway construction period and the operation period to meteorological elements.
Description
[ technical field ] A method for producing a semiconductor device
The invention belongs to the technical field of rail transit, and particularly relates to a method for analyzing risk of meteorological disaster happening along a high-speed rail.
[ background of the invention ]
Traffic has a very close relation with weather, and particularly railways are highly sensitive industries of weather disasters. The research for refining the climate background and the meteorological disasters is difficult, the railway construction and railway operation are supported less, particularly, the altitude of the Sichuan-Tibet region is reduced from more than 5000 meters to less than 1000 meters from the west to the east, in the descending process, the terrain relief is severe, the fall is large, most of the Sichuan-Tibet region is positioned on the Qinghai-Tibet plateau, the average altitude is basically more than 4000m, the Linzhi and the south of Shannan are relatively low-value regions of the Tibet region, the altitude is about 2500m, the altitude of the Sichuan-Tibet plateau region is more than 3000m, the altitude of the Sichuan-basin is 1000m, the regions experience great terrain relief from 500 meters above the altitude to 5000 meters above, the meteorological conditions face more unknown factors, and the risk analysis of the meteorological disasters along the high-speed rail has very important significance for the track setting and the monitoring point setting. Therefore, it is very important to develop the risk of disaster occurrence along the railway. The comprehensive disaster occurrence risk analysis method can simultaneously integrate and consider various meteorological elements to carry out comprehensive disaster occurrence risk analysis; adjusting meteorological element monitoring based on the analysis result, and giving a suggestion of track selection in the construction period; and respectively analyzing risk areas at different stages along the railway according to different sensitivities of the railway construction period and the operation period to meteorological elements. Selecting partial meteorological elements to carry out risk analysis in the operation period through sensitivity judgment; the data analysis can be carried out according to the grid points, the actual terrain condition is considered at the same time, the data analysis based on the track is carried out, and the depth and the precision of the analysis are greatly improved.
[ summary of the invention ]
In order to solve the above problems in the prior art, the present invention provides a method for analyzing risk of occurrence of meteorological disasters along a high-speed rail, the method comprising:
step S1: carrying out normalization processing on each meteorological element sample sequence;
step S2: calculating a comprehensive risk index of the construction period, and performing reselection of the primary selection track based on the feedback of the comprehensive risk index of the construction period;
step S3: and calculating the comprehensive risk index of the operation period, and carrying out lattice monitoring adjustment and risk early warning based on the index.
Further, in step S1, specifically, the step includes: normalization is performed by adopting a formula (1);
wherein x isiIs a sample sequence of original meteorological elements, xi ’Is a normalized meteorological element sample sequence,
is the minimum value of the sample sequence and,
is the maximum value of the sample sequence.
Further, the step S2 is specifically: calculating by adopting a formula (2);
wherein, RIi,jIs the corresponding construction period comprehensive risk index (NI) on the grid point (i, j)i,j)kIs the normalized value, W, corresponding to the kth meteorological element at the grid point (i, j)kThe weight value is corresponding to the kth meteorological element.
Further, the step S3 is specifically: selecting operation period sensitive meteorological elements, calculating an operation period comprehensive risk index based on the operation period sensitive meteorological elements, carrying out lattice monitoring adjustment based on the operation period comprehensive risk index and the risk index, and further carrying out risk early warning.
Further, automatic filling of missing values in the sample sequence and consistency check are performed before normalization processing.
Further, the automatic filling is as follows: carrying out n-dimensional space fitting on the normalized meteorological element sample sequence, wherein n is the number of meteorological element types, and automatically filling missing values based on corresponding values of fitting points in an n-dimensional space fitting curve; before fitting, the sample sequence is segmented, and n-dimensional space fitting and automatic filling are respectively carried out on each segment of sample sequence.
Further, a dynamic mapping table is set to map the comprehensive risk index and the risk level.
Further, the meteorological elements are classified, and a comprehensive risk index is established based on the classified meteorological elements.
Further, one or more primary factors are selected and refined to a plurality of secondary factors for each primary factor.
Further, the primary factors correspond to meteorological element types, and the plurality of secondary factors are directly parameter-measurable.
The beneficial effects of the invention include: comprehensive disaster occurrence risk analysis can be carried out by simultaneously integrating and considering various meteorological elements; adjusting meteorological element monitoring based on the analysis result, and giving a suggestion of track selection in the construction period; and respectively analyzing risk areas at different stages along the railway according to different sensitivities of the railway construction period and the operation period to meteorological elements. Selecting partial meteorological elements to carry out risk analysis in the operation period through sensitivity judgment; the data analysis can be carried out according to the grid points, the actual terrain condition is considered at the same time, the data analysis based on the track is carried out, and the depth and the precision of the analysis are greatly improved.
[ description of the drawings ]
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, and are not to be considered limiting of the invention, in which:
fig. 1 is a schematic view of the method for analyzing risk of occurrence of meteorological disasters along a high-speed rail according to the invention.
FIG. 2 is a comprehensive meteorological risk index distribution diagram of the railway construction period of the invention.
FIG. 3 is a diagram of a comprehensive weather risk index profile during a railway operation period according to the present invention.
[ detailed description ] embodiments
The present invention will now be described in detail with reference to the drawings and specific embodiments, wherein the exemplary embodiments and descriptions are provided only for the purpose of illustrating the present invention and are not to be construed as limiting the present invention.
The invention adopts a Chinese meteorological office Chinese ground meteorological element daily data set (V3.0) with the time range of 1951-2017, which comprises temperature (highest, lowest and average daily temperature), precipitation, wind (average daily wind speed, maximum daily wind direction and maximum wind speed), relative humidity, ground temperature (shallow layer and deep layer) and station metadata. Live data is based on a Space-Time Multiscale Analysis System (STMAS). The system adopts a multiple grid sequential variation method and carries out the analysis and optimization process of data by analysis methods of different scales. The method can effectively capture short wave information and propagation information in the land, and adopts different capture modes under different weather dynamics limiting conditions. The STMAS may also give optimization and the most efficient extraction of observation information in combination with spatial information at different times. The system avoids some unreasonable assumptions about background error covariance of traditional data assimilation, saves large-scale calculation time, extracts small-scale information in detail, and accordingly reasonably shortens calculation time of an assimilation process. Functionally, the system comprises ground analysis, high-altitude analysis, cloud analysis, balance analysis and the like, wherein the analysis elements comprise ground temperature, relative humidity, wind, air pressure, precipitation, 21-layer temperature, humidity and wind at high altitude, cloud bottom, cloud height, cloud coverage, cloud ice, cloud water, cloud type and the like. The analysis takes GFS data as a background field, integrates data of 5 ten thousand multi-area automatic stations in the country, 120 pieces of sounding data, 215 pieces of weather radar data, 108 pieces of wind profile radar and satellite data. The mode time resolution is 15 minutes, the horizontal spatial resolution is 5km multiplied by 5km, the vertical direction is divided into 21 layers which are not equidistant, and the data time range is 2018 year all year round.
The analysis method comprises the following steps:
step S1: carrying out normalization processing on each meteorological element sample sequence; specifically, normalization is performed by adopting a formula (1);
wherein x isiIs a sample sequence of original meteorological elements, xi ’Is a normalized meteorological element sample sequence,
is the minimum value of the sample sequence and,
is the maximum value of the sample sequence; preferably: the meteorological elements comprise thunder, temperature, precipitation, wind and the like;
preferably: before normalization processing, automatic filling and consistency check of missing values in a sample sequence are carried out;
the automatic filling is as follows: carrying out n-dimensional space fitting on the normalized meteorological element sample sequence, wherein n is the number of meteorological element types, and automatically filling missing values based on corresponding values of fitting points in an n-dimensional space fitting curve; before fitting, sample sequences need to be segmented, and n-dimensional space fitting and automatic filling are respectively carried out on each segment of sample sequences; in fact, the loss of meteorological element sample sequences is very common, but in the prior art, the sample sequences are not processed, mainly because the number of the sample sequences is too complicated, manual filling cannot be performed, and the overall fitting basically cannot obtain an effective fitting curve; actually, the deviation of the following data is necessarily brought by not processing the sample sequence; the present invention therefore solves this problem by a segmented approach; in fact, the sample sequence values of meteorological elements are always in strong continuity, so that the filling accuracy of data after automatic filling is high; the segmentation is based on natural time periods and meteorological element change rules for division; one way of doing this is: dividing the variation phases of the sample sequences of the meteorological elements according to the sequence values, finding out the minimum value of the variation phases for various sample sequences in the same period, and performing piecewise fitting based on the minimum value of the variation phases; the range of variation of the sequence values within the same variation phase is limited or regular;
the consistency check is: for the sample sequence subjected to n-dimensional space fitting, determining the number of samples of which the distance from a fitting curve exceeds a preset range, and determining that the meteorological element sample sequence does not pass consistency check when the number of samples exceeds an upper discrete limit; the analysis result of the sample sequence which fails the consistency check may have a large deviation; the complexity of the sample sequence inspection is simplified through the analysis of the number of discrete points;
step S2: calculating a comprehensive risk index of the construction period, and performing reselection of the primary selection track based on the feedback of the comprehensive risk index of the construction period; specifically, the formula (2) is adopted for calculation;
wherein, RIi,jIs the corresponding construction period comprehensive risk index (NI) on the grid point (i, j)i,j)kIs the normalized value, W, corresponding to the kth meteorological element at the grid point (i, j)kThe weight value is corresponding to the kth meteorological element. Through the comprehensive risk index in the construction period, a high-risk area formed by overlapping a plurality of elements can be shown. A closer to 1 of the composite risk indicates a higher degree of influence by the composite meteorological factors on the grid point (i, j), and a closer to 0 indicates a lower degree of influence by the meteorological factors. The high risk area is the meteorological monitoring key area. The feedback can be feedback to a user to perform manual re-screening or machine selection alternative, and a feedback suggestion is given based on the construction position of the track to be selected;
preferably: grading meteorological elements, and establishing a comprehensive risk index based on the graded meteorological elements; specifically, the method comprises the following steps: selecting one or more primary factors according to the sensitivity of the construction period to meteorological disasters, and refining each primary factor into a plurality of secondary factors, wherein the primary factors correspond to meteorological element types, and the secondary factors are directly measured by parameters; calculating a first-level summary and index based on the second-level factor, and calculating a summary and risk index based on the first-level summary and index; for example: the first-level factors comprise thunder, temperature, precipitation and wind, and the second-level factors corresponding to the thunder are thunderstorm days and thunder density; the secondary factors corresponding to the temperature are the temperature, the daily worse and the maximum frozen soil depth; the secondary factor corresponding to the wind is the maximum wind pressure and the large wind day number; the second-level factors corresponding to precipitation are the maximum daily precipitation and the accumulated snow depth;
as can be seen from fig. 2, the comprehensive risk index of the areas in the state of kanzizhou, rassa and sichuan, tibet, in the process of railway line construction in the tibetan region is relatively high, and is an area to be monitored. The comprehensive risk index of the Linzhi and Changdu junction areas and most areas of the Sichuan basin in the line construction stage is relatively low. From the distribution of the primary route, the high-value risk index area is basically avoided.
Preferably: setting a dynamic mapping table to map the comprehensive risk index and the risk grade; considering that index ranges corresponding to different regional risk levels can be dynamically changed in different construction periods, the method sets a dynamic mapping table, automatically selects the dynamic mapping table based on mapping influence factors such as seasons, time, construction periods, regions and the like, and maps the comprehensive risk index into a corresponding risk level based on the selected dynamic mapping table; the specific index is invisible to the user through a form dynamic selection mode, and the user can only see the converted grade, so that the user experience is greatly improved.
preferably: continuously monitoring according to the summary and risk indexes, and adjusting and reselecting the track construction position according to the continuous monitoring result, specifically: determining whether the comprehensive risk level of the corresponding construction period on the grid point (i, j) exceeds a first risk threshold value, if so, selecting an adjacent grid point of the grid point (i, j) as a construction position of the track to be selected; otherwise, the original sample sequence xiDividing into m sub-sample sequences xi1,xi2…ximNormalizing each sub-sample sequence again and calculating the corresponding sub-composite risk index RIi，j，pIf the number of the continuous sub-comprehensive risk indexes with continuous sub-comprehensive risk indexes larger than the second risk threshold is larger than the second number threshold, feeding back to reselect the primary selection track, and selecting the adjacent grid points of the grid points (i, j) as the construction positions of the tracks to be selected; wherein the continuous length is limited, for example, the continuous length of the continuous sub-integrated risk index is greater than mz, wherein mz is a preset value; preferably: the m word sample sequences are mutually overlapped, wherein m is a preset value; the first risk threshold is greater than the second risk threshold; in the case that the independent risk index exceeds a large value, the risk analysis is easy to achieve, but actually, the occurrence of risks can be avoided in the process of track construction, the risks need to be reckoned based on time continuity to predict the risks more accurately, and the accuracy of prediction can be improved by splitting a sample sequence under the condition that continuous risks easily cause risk accumulation on time although an extreme risk does not appear;
preferably: the value of m is chosen in a trade-off way according to the length of the sample sequence and the running capacity of the computer;
step S3: calculating an operation period comprehensive risk index, and carrying out lattice monitoring adjustment and risk early warning based on the index, wherein the method specifically comprises the following steps: selecting operation period sensitive meteorological elements, calculating an operation period comprehensive risk index based on the operation period sensitive meteorological elements, carrying out lattice monitoring adjustment based on the operation period comprehensive risk index and the risk index, and further carrying out risk early warning;
the railway operation stage is mainly influenced by short-time extreme meteorological elements and cannot uniformly process the construction period and the operation period; the selection of the sensitive meteorological elements in the operation period specifically comprises the following steps: judging whether each of the plurality of primary comprehensive indexes obtained in the construction period is higher than a basic risk index, and if so, determining meteorological elements corresponding to the primary comprehensive indexes as selected operation period sensitive meteorological elements; wherein: different primary summary indexes correspond to different basic risk thresholds; the basic risk threshold value is a preset value, and is a low-value threshold value which is not easy to meet, and the meteorological elements basically without risk are screened out in such a way and are not considered; for example: through the calculation of the first-level comprehensive index, the influence of the temperature on the operation period is small, and the first-level factor temperature is screened out in the evaluation process of the Sichuan-Tibet railway operation period, so that the influence of the temperature factor is not considered for sensitive meteorological elements in the operation period; by the mode, the number of parameters for evaluation is reduced, and the influence of invalid parameters on the whole index is reduced, so that the index sensitivity, the prediction accuracy and the prediction sensitivity are improved.
The operation period comprehensive risk index is calculated based on the operation period sensitive meteorological elements, and specifically comprises the following steps: determining whether an operation period risk grid point exists, if so, enhancing the monitoring of the operation period risk grid point, and carrying out risk early warning on a region corresponding to the operation period risk grid point; further, whether an operation period risk track exists is determined, if yes, joint monitoring of a grid point set corresponding to the operation period risk track is enhanced, and risk early warning is carried out on an area corresponding to the operation period risk track;
the enhancing the monitoring of the operation period risk lattice point specifically comprises the following steps: increasing the monitoring frequency of the operation period risk grid points;
the determining whether the operation period risk lattice exists specifically includes: selecting first-level factors corresponding to the sensitive meteorological elements in the operation period and second-level factors corresponding to the sensitive meteorological elements in the operation period respectively, and substituting the first-level factors and the second-level factors into the formulas (3) and (4) to calculate the comprehensive risk index in the operation period; if the operation period comprehensive risk index is larger than an operation period risk threshold, taking a lattice point corresponding to the operation period comprehensive risk index as an operation period risk lattice point; the risk threshold value in the operation period is a preset value;
for example: the first-level factor and the second-level factor are respectively a lightning factor, a wind factor and a precipitation factor, and a thunderstorm day number and a lightning density of the second-level factor respectively corresponding to the lightning factor, the wind pressure, the wind day number, the precipitation maximum value and the snow accumulation depth;
the determining whether the operation period risk track exists specifically includes: for each grid point (i, j), calculating a first-level comprehensive sum index corresponding to each sensitive meteorological element; for each meteorological element, determining a primary comprehensive index track, determining whether the primary comprehensive index track corresponding to the meteorological element is a risk track, and if so, enhancing the joint monitoring of a grid point set corresponding to the operation period risk track; considering that for the operation period, although the conditions may be severe for specific meteorological elements, the influence of the train and the track thereof in the operation period is discontinuous, comprehensive meteorological conditions related to the terrain are not formed among the meteorological elements, and the risk avoidance of the operation period can be effectively carried out by considering the continuous performance of the meteorological elements on the terrain in combination with the combination of the terrain;
the determining of the first-level comprehensive index track specifically comprises the following steps: acquiring all grid points with the first-stage comprehensive index larger than a track grid point threshold value as grid points to be communicated, and if two grid points to be communicated are adjacent, connecting the two grid points to be communicated to form communicated grid points; all the connected grid points form one or more first-level comprehensive and exponential tracks; wherein: the trace grid points are preset values, and when the trace grid points are larger than the preset values, continuous meteorological elements possibly cause risks of matching with terrain;
the determining whether the first-level comprehensive index track corresponding to the meteorological element is a risk track specifically includes: comparing each primary comprehensive index track with the risk track template, and if the primary comprehensive index track is matched with the risk track template, determining that the primary comprehensive index track is a risk track; otherwise, determining that the primary comprehensive index track is not a risk track; the risk track template is obtained through intelligent learning according to topographic data and meteorological element influence conditions; or obtaining the data from the cloud;
the enhancing the joint monitoring of the grid point set corresponding to the operation period risk track specifically comprises: splitting grid points in the grid point set to increase the number of grid points covered by the operation period risk track;
as can be seen from the attached figure 3, the areas with higher comprehensive meteorological risks in the Sichuan-Tibet railway operation period are in the Sichuan basin, the northern part of Ganzui and the Western part of Linzhi; the current route selection of the railway focuses on the Yaan section, the Cumin section, the Changdu section and the southern Shanxi section in the operation period.
As can be seen by comparing the attached drawings 2 and 3, the meteorological risk of the Sichuan basin is low in the construction process, the risk index is increased in the later operation process, the meteorological risk index is high in the middle area of the Ganzui in the construction process, the risk index is reduced in the operation process, the risk index is low in the Changdu to the Linzhi area in both the construction process and the later operation process, the meteorological risk index is also high in the railway from the West Linzhi area to the Lasa area in the construction process, and the risk index of the Lasa area is reduced in the operation process. Therefore, attention is paid to strengthen the monitoring of the corresponding meteorological risk factors in the high-value risk index areas at different stages.
The above description is only a preferred embodiment of the present invention, and all equivalent changes or modifications of the structure, characteristics and principles described in the present invention are included in the scope of the present invention.
Claims (4)
1. A method for analyzing risk of meteorological disaster occurrence along a high-speed rail is characterized by comprising the following steps:
step S1: carrying out normalization processing on each meteorological element sample sequence;
before normalization processing, automatic filling and consistency check of missing values in a sample sequence are carried out;
the automatic filling is as follows: carrying out n-dimensional space fitting on the normalized meteorological element sample sequence, wherein n is the number of meteorological element types, and automatically filling missing values based on corresponding values of fitting points in an n-dimensional space fitting curve; before fitting, sample sequences need to be segmented, and n-dimensional space fitting and automatic filling are respectively carried out on each segment of sample sequences; dividing the variation phases of the sample sequences of the meteorological elements according to the sequence values, finding out the minimum value of the variation phases for various sample sequences in the same period, and performing piecewise fitting based on the minimum value of the variation phases;
the consistency check is: for the sample sequence subjected to n-dimensional space fitting, determining the number of samples of which the distance from a fitting curve exceeds a preset range, and determining that the meteorological element sample sequence does not pass consistency check when the number of samples exceeds an upper discrete limit;
step S2: calculating a comprehensive risk index of the construction period, and performing reselection of the primary selection track based on the feedback of the comprehensive risk index of the construction period;
the step S2 specifically includes: calculating by adopting a formula (2);
grading meteorological elements, and establishing a comprehensive risk index based on the graded meteorological elements; specifically, the method comprises the following steps: selecting one or more primary factors according to the sensitivity of the construction period to meteorological disasters, and refining each primary factor into a plurality of secondary factors, wherein the primary factors correspond to meteorological element types, and the secondary factors are directly measured by parameters; calculating a first-level comprehensive index based on the second-level factor, and calculating a comprehensive risk index based on the first-level comprehensive index; for example: the first-level factors comprise thunder, temperature, precipitation and wind, and the second-level factors corresponding to the thunder are thunderstorm days and thunder density; the secondary factors corresponding to the temperature are the temperature, the daily worse and the maximum frozen soil depth; the secondary factor corresponding to the wind is the maximum wind pressure and the large wind day number; the second-level factors corresponding to precipitation are the maximum daily precipitation and the accumulated snow depth;
carrying out continuous monitoring according to the comprehensive risk index, and carrying out reselection of the track construction position according to the continuous monitoring result adjustment, specifically: determining whether the comprehensive risk level of the corresponding construction period on the grid point (i, j) exceeds a first risk threshold value, if so, selecting an adjacent grid point of the grid point (i, j) as a construction position of the track to be selected; otherwise, the original sample sequence xiDividing into m sub-sample sequences xi1，xi2…ximNormalizing each subsample sequence again and calculating the corresponding sub-composite risk index Rl thereof respectivelyi，j，pIf the number of the continuous sub-comprehensive risk indexes with continuous sub-comprehensive risk indexes larger than the second risk threshold is larger than the second number threshold, feeding back to reselect the primary selection track, and selecting the adjacent grid points of the grid points (i, j) as the construction positions of the tracks to be selected; wherein, the continuous length is limited, and the continuous length of the continuous sub-integrated risk index is greater than mz, wherein mz is a preset value;
the m sub-sample sequences are mutually overlapped, wherein m is a preset value; the first risk threshold is greater than the second risk threshold;
step S3: and calculating the comprehensive risk index of the operation period, and carrying out lattice monitoring adjustment and risk early warning based on the index.
2. The method for analyzing the risk of occurrence of meteorological disasters along high-speed rails according to claim 1, wherein the step S1 specifically comprises: normalization is performed by adopting a formula (1);
wherein x isiIs original meteorological element sample sequence, x'iTo be normalizedThe transformed meteorological element sample sequence is processed by the following steps,
is the minimum value of the sample sequence and,
is the maximum value of the sample sequence.
3. The method for analyzing the risk of occurrence of meteorological disasters along high-speed rails according to claim 2, wherein the step S3 specifically comprises: selecting operation period sensitive meteorological elements, calculating an operation period comprehensive risk index based on the operation period sensitive meteorological elements, carrying out lattice monitoring adjustment based on the operation period comprehensive risk index, and further carrying out risk early warning.
4. The method for analyzing risk of occurrence of meteorological disasters along high-speed rails according to claim 3, wherein a dynamic mapping table is arranged to map the comprehensive risk index and the risk level.
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| CN111639865B (en) * | 2020-06-02 | 2021-03-19 | 中国气象局气象探测中心 | High-speed rail line meteorological disaster occurrence risk analysis method |
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