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CN-121998342-A - Manual rainfall enhancement operation demand index calculation method based on multi-source data fusion

CN121998342ACN 121998342 ACN121998342 ACN 121998342ACN-121998342-A

Abstract

The invention discloses a rainmaking operation demand index calculation method based on multi-source data fusion, which comprises the steps of obtaining multi-source heterogeneous data of a target area, respectively carrying out normalization processing on the multi-source heterogeneous data for the rainmaking operation demand to obtain four types of normalized characteristic values, converging the four types of normalized characteristic values to a county-level administrative district through spatial interpolation or attribute to form four-dimensional characteristic vectors corresponding to each county contained in the target area, carrying out weighted fusion on the four-dimensional characteristic vectors of each county, introducing drought grades of the county, and carrying out constraint adjustment to obtain the rainmaking operation demand index of each county. According to the invention, multisource heterogeneous data such as weather, environment, water conservancy and forestry are comprehensively utilized, and the demand index of the artificial precipitation operation refined to the county-level administrative district is calculated and output through intelligent weighted fusion, so that a more scientific, accurate and timely quantitative decision basis is provided for the artificial precipitation operation.

Inventors

  • LI DAN
  • LIN WEN
  • XU LUJING
  • ZHANG ZUYI
  • XIE ZUXIN

Assignees

  • 福建省气象科学研究所

Dates

Publication Date
20260508
Application Date
20260127

Claims (7)

  1. 1. The method for calculating the artificial precipitation work demand index based on multi-source data fusion is characterized by comprising the following steps of: s1, acquiring multi-source heterogeneous data of a target area, wherein the multi-source heterogeneous data comprise a weather drought comprehensive index, an air quality index, reservoir water potential state information and forest fire weather grade, and the target area comprises a plurality of county administrative areas; S2, respectively carrying out normalization processing on the multi-source heterogeneous data for the requirements of artificial precipitation enhancement operation to obtain four types of normalized characteristic values; s3, dividing the four types of normalized characteristic values into county-level administrative regions through spatial interpolation or attribute aggregation, and forming four-dimensional characteristic vectors corresponding to each county contained in the target region; And S4, carrying out weighted fusion on the four-dimensional feature vectors of each county, introducing drought grades of the county, and carrying out constraint adjustment to obtain the artificial precipitation work demand index of each county.
  2. 2. The method according to claim 1, wherein step S2 specifically comprises: (a) Reversely normalizing the weather drought comprehensive index based on the extreme value of the history weather drought comprehensive index to obtain a drought demand characteristic value a 1 ; (b) Normalizing the air quality index AQI by a piecewise function to calculate an air demand characteristic value a 2 , wherein a 2 =AQI/100 when the AQI is more than or equal to 0 and less than or equal to 100, and a 2 =1 when the AQI is more than or equal to 100; (c) Mapping the water potential state information of the reservoirs into numerical values, and taking the maximum value of all the reservoir mapping values in each county-level administrative district as a water conservancy demand characteristic value a 3 of the current county; (d) Mapping forest fire weather grade into binary variable, when fire grade is first grade, fire demand characteristic value a 4 =0, when fire grade is second grade and above, fire demand characteristic value a 4 =1.
  3. 3. The method of claim 2, wherein the weather drought composite index is inversely normalized based on an extremum of the historical weather drought composite index to obtain a drought demand characteristic value a 1 , and wherein the method is expressed as: Wherein, MCI represents a weather drought comprehensive index, max hist represents a historical maximum drought value, and Min hist represents a historical minimum drought value.
  4. 4. The method of claim 2, wherein the numerical mapping rule of the reservoir water condition information is: When the water potential state information of the reservoir is 'falling', mapping to 1, and indicating high requirements for artificial precipitation work; when the water potential state information of the reservoir is 'flat', mapping to 0, and representing the general requirement for artificial precipitation; when the water potential state information of the reservoir is 'rising', the mapping is-1, and the low requirement on the artificial precipitation is indicated.
  5. 5. The method according to claim 2, characterized in that step S3 comprises in particular: The weather drought comprehensive index MCI and the air quality index AQI are used as site type data, corresponding characteristic values are interpolated into continuous grid surfaces by adopting a Kriging interpolation method, and then the average value of the corresponding characteristic values of each county is obtained through regional statistics; the water potential state information of the reservoir is used as punctiform data, a government district to which the water potential state information belongs is positioned, and then characteristic values of water conservancy demands are aggregated according to counties and counties, and the maximum value is taken; forest fire insurance weather grade is used as planar data, and is corresponding to the characteristic value of the related fire insurance demand directly divided according to administrative areas.
  6. 6. The method of claim 1, wherein in step S4, the four-dimensional feature vectors of each county are weighted and fused, and expressed as: Wherein I j,init represents an initial value of a demand index of a manual rainfall augmentation operation, a 1j represents a drought demand characteristic value of a jth county, a 2j represents an air demand characteristic value of the jth county, a 3j represents a water conservancy demand characteristic value of the jth county, a 4j represents a fire insurance demand characteristic value of the jth county, and w 1 、w 2 、w 3 and w 4 represent weights corresponding to the drought demand, the air demand, the water conservancy demand and the fire insurance demand, respectively.
  7. 7. The method of claim 6, wherein in step S4, drought-class constraint adjustments are introduced, comprising: introducing drought grade as a forced constraint condition, and performing lower limit adjustment on the initial value of the artificial precipitation work demand index: When the drought grade of the corresponding county is light drought, the final artificial precipitation operation demand index is I j =max(I j,init and T1); When the drought grade of the corresponding county is the middle drought, the final artificial precipitation operation demand index is I j =max(I j,init and T2); when the drought grade of the corresponding county is heavy or extremely drought, the final artificial precipitation operation demand index is I j =max(I j,init and T3; Wherein, T1, T2 and T3 respectively represent the predefined artificial precipitation work demand index threshold values of different drought grades.

Description

Manual rainfall enhancement operation demand index calculation method based on multi-source data fusion Technical Field The invention relates to the technical field of weather forecast and decision support, in particular to a method for calculating a demand index of artificial precipitation work based on multi-source data fusion. Background The artificial precipitation is used as an important means for weather disaster prevention and reduction and water resource regulation and control, and is widely applied to the fields of agricultural drought resistance, reservoir water storage, forest fire prevention, atmospheric pollution control and the like. The scientific and accurate identification and quantification of the requirements of the artificial precipitation work are key preconditions of improving the work benefit, optimizing the resource allocation and avoiding invalid or excessive work. However, the conventional artificial precipitation demand judgment is mostly dependent on a single meteorological element (such as precipitation level percentage, soil humidity or meteorological drought index), lacks systematic fusion and quantitative evaluation of actual demands in multiple fields, and is difficult to comprehensively reflect comprehensive precipitation urgency of areas. In recent years, along with the continuous improvement of informatization level of departments such as weather, environmental protection, water conservancy, forestry and the like, multi-source heterogeneous data are increasingly abundant. For example, the drought degree of an area can be objectively represented by a weather drought comprehensive index (MCI) issued by a weather department, the atmospheric pollution condition is reflected by an Air Quality Index (AQI) issued by an ecological environment department in real time, emergency requirements for improving the air quality through increasing rain are frequently met in heavy pollution weather, reservoir water level, water storage capacity and water potential change information monitored by a water conservancy department are directly related to water resource scheduling and water supply safety, and a forest fire weather grade issued by the forestry department forms a remarkable rain increasing and fire prevention requirement in a high fire period. The data respectively reveal potential application scenes of artificial precipitation from different dimensions, but a unified and standardized fusion calculation frame is not formed at present, so that the demand information of each department is isolated, and collaborative decisions are difficult to support. In the prior art, partial researches try to construct a rain-increasing potential index or an operation condition index, but focus on cloud physical conditions (such as cloud top temperature, liquid water content, wind field structure and the like), focus on feasibility judgment of operation, and focus on insufficient social-ecological-economic comprehensive demand assessment of operation. There are few other methods to introduce multi-factor weighting, but the weighting setting is subjective, lacks business basis, and does not consider the threshold constraint mechanism of different demand factors under extreme events (e.g. even if other factors are higher during mild drought, the overall demand should still be limited). In addition, the existing method has the defect in data scale processing, and the problems of space matching and scale unification among site observation data, planar administrative division data and punctiform facility (such as reservoir) data cannot be effectively solved, so that accuracy of county-level refined demand assessment is affected. Therefore, the technical scheme capable of integrating multisource business data such as weather, environmental protection, water conservancy and forestry and realizing quantitative evaluation of the demand of the county-level scale artificial precipitation operation based on a scientific normalization and dynamic weight mechanism is provided, and is a problem to be solved by those skilled in the art. Disclosure of Invention In view of the above problems, the invention provides a method for calculating the artificial precipitation work demand index based on multi-source data fusion, which overcomes the above problems or at least partially solves the above problems, not only solves the limitation that the traditional method depends on a single weather index on one side, but also constructs a standardized, generalized and business-friendly artificial precipitation demand assessment system. In order to achieve the above purpose, the present invention adopts the following technical scheme: The embodiment of the invention provides a method for calculating a demand index of artificial precipitation operation based on multi-source data fusion, which comprises the following steps: s1, acquiring multi-source heterogeneous data of a target area, wherein the multi-source heterogeneous data