CN-122024458-A - Disaster early warning method and system for intelligent weather desktop

CN122024458ACN 122024458 ACN122024458 ACN 122024458ACN-122024458-A

Abstract

The invention provides a disaster early warning method and system for an intelligent weather table top, and relates to the technical field of data processing, wherein the method comprises the following steps: dividing rainfall into a plurality of time units according to fixed intervals, taking the rainfall difference value of adjacent time units as positive variation as effective increment, calculating the ratio of the effective increment of each adjacent time unit, calculating the continuous enhancement degree and mutation strength of rainfall in the time dimension to obtain a time evolution index, dividing a target area into a plurality of grid units, recording the land height value and drainage capacity value of each grid unit, determining the water flow direction, accumulating the transmission value of each grid unit to adjacent low-grade grid units, calculating the risk concentration degree formed by superposition of the low drainage capacity value and the low grade in the target area, and obtaining the disaster warning grade. The method and the device can dynamically analyze whether the target area has potential disasters or not, and avoid early warning lag.

Inventors

YU LIZHENG

Assignees

福建智天气信息技术有限公司

Dates

Publication Date: 20260512
Application Date: 20260416

Claims (10)

1. A disaster early warning method for an intelligent weather desk, the method comprising: Acquiring meteorological element data and regional attribute data; Dividing rainfall into a plurality of time units according to fixed intervals according to meteorological element data, and taking the change quantity with the rainfall difference value of adjacent time units positive as an effective increase quantity to obtain a progressive change sequence; Calculating the ratio of the effective increment of each adjacent time unit according to the progressive change sequence, recording the ratio as an acceleration section when the ratio is greater than 1, and recording the acceleration multiple of the acceleration section to obtain a mutation identification sequence; according to the mutation identification sequence, calculating the continuous enhancement degree and mutation strength of rainfall in the time dimension to obtain a time evolution index; dividing a target area into a plurality of grid cells according to the area attribute data, recording the relief height value and the drainage capacity value of each grid cell, and determining the water flow direction to obtain a water quantity collecting path; the time evolution index is transmitted to the low topography grid cells step by step along the water volume collecting path, and the transmission values of the grid cells are accumulated to the adjacent low topography grid cells to obtain an accumulation response value set; According to the accumulation response value set, calculating a risk concentration degree formed by superposition of a low drainage capacity value and a low topography in the target area to obtain an area bearing index; And the time evolution index is used as a time dimension parameter, the region bearing index is used as a space dimension parameter, and the region bearing index is matched with a preset disaster grade interval to obtain a disaster early warning grade.
2. The disaster warning method for intelligent weather desktops according to claim 1, wherein dividing rainfall into a plurality of time units at fixed intervals according to weather element data, and obtaining a progressive variation sequence by taking a variation in which rainfall difference of adjacent time units is positive as an effective increment, comprises: According to meteorological element data, extracting rainfall and marking in a segmented mode according to a time sequence, distinguishing time intervals of continuous rising trend and non-rising trend, and obtaining a trend segmented set; According to the trend segmentation set, calculating the rainfall difference value in each time interval, and inhibiting the rainfall difference value in the time interval which is not the rising trend to obtain a trend screening difference value set; overlapping the rainfall difference values in each ascending trend time interval according to a time sequence according to a trend screening difference value set, terminating overlapping at a trend interruption position, and recording a current overlapping result to obtain a segmented growing set; and according to the segment growing set, re-splicing each segment growing result into a continuous enhancement sequence according to the time sequence, and mapping the enhancement result of each time position with the original time unit to obtain a progressive change sequence.
3. The disaster warning method for intelligent weather desktops according to claim 2, wherein calculating a ratio of effective increment of each adjacent time unit according to the progressive change sequence, recording the ratio as an acceleration segment when the ratio is greater than 1, and recording acceleration multiples of the acceleration segment to obtain the mutation identification sequence, comprises: defining the ratio of the effective increment of the next time unit relative to the previous time unit as an acceleration multiple according to the progressive change sequence to obtain a multiple sequence; Marking time units with acceleration times larger than 1 as candidate units according to the multiple sequences, and determining continuous time intervals of the candidate units as candidate sections to obtain a candidate section set; Defining a candidate segment as an acceleration segment when the effective growth in the candidate segment remains increasing or does not decrease in an opposite direction according to the candidate segment set, and obtaining a segment multiple; And according to the acceleration sections and the section multiples, correlating the starting time position, the ending time position and the section multiples of each acceleration section according to the time sequence to obtain a mutation identification sequence.
4. The disaster warning method for intelligent weather desktops according to claim 3, wherein calculating the continuous enhancement degree and the mutation strength of rainfall in the time dimension according to the mutation recognition sequence to obtain the time evolution index comprises the following steps: extracting the effective increment of each time unit and the corresponding time position according to the progressive change sequence, and identifying the backward aggregation degree of the effective increment on the whole time axis to obtain a time progressive item; extracting the segment multiple, the starting time position and the ending time position of each accelerating segment according to the mutation identification sequence, and identifying the expansion degree of each accelerating segment in the time dimension to obtain a segment potential expansion item; Extracting effective increment and segment multiple in each acceleration segment according to the mutation identification sequence, and identifying linkage degree between the increment and segment multiple in the acceleration segment to obtain a sudden increase coupling item; Calculating the central position of each acceleration section according to the mutation identification sequence, and identifying the concentration degree of the mutation process in the rear section of the time axis to obtain a rear section bias item; And adding the time-position accumulation item and the segment potential expansion item to obtain a basic evolution value, adding the sudden increase coupling item and the rear-segment bias item to obtain a mutation correction value, and fusing the basic evolution value and the mutation correction value to obtain a time evolution index.
5. The disaster warning method for intelligent weather desktops according to claim 4, wherein dividing a target area into a plurality of grid cells according to area attribute data, recording a relief height value and a drainage capacity value of each grid cell, and determining a water flow direction to obtain a water flow collecting path, comprises: Dividing a target area into a plurality of grid cells according to a preset space scale according to the area attribute data, and establishing a space position mark for each grid cell to obtain a grid cell set; Extracting the topography height value and the drainage capacity value of each grid cell according to the grid cell set, and calculating the water retention degree of each grid cell to obtain a cell bearing index, wherein the topography height value and the drainage capacity value are inversely proportional; calculating index difference values of cell bearing indexes of each grid cell and adjacent grid cells according to the grid cell set, determining the adjacent grid cells with positive index difference values and maximum values as water quantity transmission targets, and obtaining a cell pointing relationship set; And connecting the transmission directions of the grid cells according to the cell pointing relation set, and combining paths of the grid cells continuously pointing to the same direction to obtain a water quantity collecting path.
6. The disaster warning method for intelligent weather desktops according to claim 5, wherein the step-by-step transmission of the time evolution index to the low topography grid cells along the water volume collecting path and the accumulation of the transmission values of each grid cell to the adjacent low topography grid cells to obtain the accumulation response value set comprises: The method comprises the steps that initial distribution is carried out on a time evolution index according to grid cells, the same initial response value is ensured to be obtained by each grid cell, and an initial response set is formed according to the spatial position identification of each grid cell; Normalizing the cell bearing index of each grid cell according to the grid cell set, calculating the product result of the cell bearing index and the initial response value and taking the product result as a transmission base value of the grid cell to obtain a cell transmission base value set; Distributing the unit transfer base values of each grid unit according to the water quantity transfer targets of the unit transfer base values according to the unit transfer base value sets, and when a plurality of adjacent low topography grid units exist, distributing the unit transfer base values in proportion according to the unit load index occupation ratio of each target grid unit to obtain a unit transfer value set; and according to the unit transfer value set, the transfer value of each grid unit is transferred to the target grid unit step by step along the water quantity collecting path, and a plurality of transfer values received by the same target grid unit are accumulated with the transfer base value of the same target grid unit to obtain the accumulation response value of each grid unit.
7. The disaster early warning method for intelligent weather desktops according to claim 6, wherein calculating a risk concentration degree formed by overlapping a low drainage capacity value and a low topography in a target area according to an accumulation response value set to obtain an area bearing index comprises: according to the accumulation response value set, identifying the basic bearing degree of each grid unit after the accumulation response of each grid unit and the own topography and drainage capacity act together, and obtaining a bearing response item; according to the unit transfer value set, calculating the transfer ratio of the total transfer value received by each grid unit in all grid units, and identifying the concentration degree of local collection risk in the target area to obtain a collection bunching item; according to the cell bearing indexes, the degree of amplifying the risk aggregation by the bearing difference formed by the differences of the topography and the drainage capacity among the grid cells is identified, and a difference strengthening item is obtained; and respectively taking the bearing response item, the converging and bunching item and the difference strengthening item as a basic bearing component, a space concentration component and a difference amplification component to be fused, and identifying the overall risk concentration degree of the target area under the water quantity transmission and local converging effects to obtain an area bearing index.
8. A disaster warning system for smart weather desktops for performing the method of any of claims 1 to 7, said system comprising: the data module is used for acquiring meteorological element data and regional attribute data; The accumulation module is used for dividing the rainfall into a plurality of time units according to the meteorological element data and obtaining a progressive change sequence by taking the change quantity with the rainfall difference value of the adjacent time units positive as an effective increase quantity; The mutation module is used for calculating the ratio of the effective increment of each adjacent time unit according to the progressive change sequence, recording the ratio as an acceleration section when the ratio is more than 1, and recording the acceleration multiple of the acceleration section to obtain a mutation identification sequence; the time module is used for calculating the continuous enhancement degree and the mutation strength of rainfall in the time dimension according to the mutation identification sequence to obtain a time evolution index; the path module is used for dividing the target area into a plurality of grid cells according to the area attribute data, recording the relief height value and the drainage capacity value of each grid cell, determining the water flow direction and obtaining a water quantity collecting path; The accumulation module is used for transmitting the time evolution index to the low-topography grid cells step by step along the water volume collecting path, and accumulating the transmission values of each grid cell to the adjacent low-topography grid cells to obtain an accumulation response value set; The space module is used for calculating a risk concentration degree formed by superposition of a low drainage capacity value and a low topography in the target area according to the accumulation response value set to obtain an area bearing index; The early warning module is used for obtaining disaster early warning grades by taking the time evolution index as a time dimension parameter, taking the region bearing index as a space dimension parameter and matching the region bearing index with a preset disaster grade interval.
9. A computing device, comprising: one or more processors; Storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program which, when executed by a processor, implements the method according to any of claims 1 to 7.

Description

Disaster early warning method and system for intelligent weather desktop Technical Field The invention relates to the technical field of data processing, in particular to a disaster early warning method and system for an intelligent weather table top. Background At present, a disaster early warning technology for an intelligent weather table is generally realized by constructing a weather information platform integrating data acquisition, processing and display, a system is accessed with multi-source data such as a weather station, a radar and a satellite, weather elements such as rainfall, wind speed and temperature are acquired and fused in real time, a short-time or near-range prediction result is generated by a numerical weather prediction model or a radar extrapolation method, threshold comparison is carried out on various weather data by combining with a preset disaster judgment rule to identify potential disaster risks, and early warning results are presented on the intelligent weather table in the forms of layer superposition, charts or warning prompts and the like, so that visual monitoring and early warning release of weather disasters are realized. However, in the prior art, a fixed threshold value set uniformly is generally adopted to perform early warning triggering in the disaster judgment process, and adaptability to actual disaster bearing capability differences of different areas may be lacking. For example, in urban inland inundation early warning scenes, obvious differences exist between drainage system capacities and topography conditions of different areas, water accumulation can occur in partial low-lying areas under the condition of small rainfall, but the system can trigger early warning only by reaching a uniformly set rainfall threshold, so that early warning information can not be obtained in time when the areas are at risk of water accumulation in practice. Disclosure of Invention The invention aims to provide a disaster early warning method and system for an intelligent weather table top, and aims to solve the problems in the background technology. In order to solve the technical problems, the technical scheme of the invention is as follows: in a first aspect, a disaster warning method for a smart weather desk, the method comprising: Acquiring meteorological element data and regional attribute data; Dividing rainfall into a plurality of time units according to fixed intervals according to meteorological element data, and taking the change quantity with the rainfall difference value of adjacent time units positive as an effective increase quantity to obtain a progressive change sequence; Calculating the ratio of the effective increment of each adjacent time unit according to the progressive change sequence, recording the ratio as an acceleration section when the ratio is greater than 1, and recording the acceleration multiple of the acceleration section to obtain a mutation identification sequence; according to the mutation identification sequence, calculating the continuous enhancement degree and mutation strength of rainfall in the time dimension to obtain a time evolution index; dividing a target area into a plurality of grid cells according to the area attribute data, recording the relief height value and the drainage capacity value of each grid cell, and determining the water flow direction to obtain a water quantity collecting path; the time evolution index is transmitted to the low topography grid cells step by step along the water volume collecting path, and the transmission values of the grid cells are accumulated to the adjacent low topography grid cells to obtain an accumulation response value set; According to the accumulation response value set, calculating a risk concentration degree formed by superposition of a low drainage capacity value and a low topography in the target area to obtain an area bearing index; And the time evolution index is used as a time dimension parameter, the region bearing index is used as a space dimension parameter, and the region bearing index is matched with a preset disaster grade interval to obtain a disaster early warning grade. Further, dividing the rainfall into a plurality of time units according to the weather element data at fixed intervals, and obtaining a progressive variation sequence by taking the variation of the rainfall difference of adjacent time units as positive as an effective increment, wherein the method comprises the following steps: According to meteorological element data, extracting rainfall and marking in a segmented mode according to a time sequence, distinguishing time intervals of continuous rising trend and non-rising trend, and obtaining a trend segmented set; According to the trend segmentation set, calculating the rainfall difference value in each time interval, and inhibiting the rainfall difference value in the time interval which is not the rising trend to obtain a trend screening difference value set;