Multi-source data-based rainstorm disaster risk early warning method and system, and medium

Abstract

The invention discloses a rainstorm disaster risk early warning method and system based on multi-source data and a medium, and belongs to the technical field of meteorological disaster risk early warning. The method comprises the following steps: S1, a historical rainfall data reading stage; s2, a disaster-inducing factor calculation stage; s3, a rainstorm disaster disaster-causing risk index calculation stage: calculating a rainstorm disaster disaster-causing risk index based on the rainstorm process intensity index and the disaster-pregnant environment influence coefficient; s4, a disaster-bearing body data calculation stage; s5, a rainstorm disaster risk index calculation stage, wherein the rainstorm disaster risk index is calculated according to the rainstorm disaster-causing risk index, the disaster-bearing body vulnerability, the disaster-bearing body exposure degree and the weights of the disaster-bearing body vulnerability and the disaster-bearing body exposure degree; and S6, a rainstorm disaster risk early warning grade calculation stage: calculating the rainstorm disaster risk early warning grade of each preset area. The rainfall estimation precision and the spatial variability are considered, and are combined with the characteristics of long sequence observation data identification, so that the uncertainty of a risk estimation result can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of meteorological disaster risk warning technology, and in particular to a rainstorm disaster risk warning method, system and medium based on multi-source data. Background Art

[0002] The occurrence of meteorological disasters is uncontrollable. It is necessary to predict the risk level of meteorological disasters in different regions in advance and pre-assess the possible impact on the disaster-prone body, so as to achieve emergency prevention in advance and ultimately reduce the impact of disasters. In the early stage, some regions established different scales of rainstorm disaster risk warning level thresholds based on census work, but most of them determined the thresholds based on the precipitation level of national or regional stations, which were not refined enough to meet business needs. In addition, the thresholds mainly focused on the danger of disaster-causing factors, and did not take into account the sensitivity of the disaster-prone environment, the exposure and vulnerability of the disaster-prone body. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a rainstorm disaster risk warning method, system and medium based on multi-source data.

[0004] The object of the present invention is achieved through the following technical solutions: The first aspect of the present invention provides: a rainstorm disaster risk warning method based on multi-source data, comprising the following steps: S1: Historical precipitation data reading stage, reading historical precipitation data to obtain hourly precipitation data files; S2: Disaster factor calculation stage: pre-process the hourly precipitation data file, calculate the corresponding disaster factor according to the pre-processed hourly precipitation data file; calculate the weight of each disaster factor to obtain the rainstorm process intensity index; calculate the disaster-prone environmental impact coefficient based on environmental factors; S3: Calculation stage of rainstorm disaster risk index, the rainstorm disaster risk index is calculated based on the rainstorm process intensity index and the disaster-prone environmental impact coefficient; S4: Disaster-prone body data calculation stage: Based on socio-economic data and historical rainstorm disaster data, the vulnerability and exposure of the disaster-prone body are calculated through unified data range and resolution; S5: Rainstorm disaster risk index calculation stage, the rainstorm disaster risk index is calculated based on the rainstorm disaster hazard index, the vulnerability of the disaster-bearing body, the exposure of the disaster-bearing body and their respective weights; S6: In the rainstorm disaster risk warning level calculation stage, the rainstorm disaster risk warning level threshold of each grid point is calculated according to the threshold level classification standard and the rainstorm disaster risk index; then, based on the preset area, the grid point with the highest grid warning level in each preset area is calculated to obtain the rainstorm disaster risk warning level of each preset area.

[0005] Preferably, the S1: historical precipitation data reading stage further includes the following steps: The precipitation data files of the current day and the previous day are read and parsed into a first format, and the 24-hour real-time data of the previous day is spliced ​​with the current day forecast data, and saved in a second format to obtain a precipitation hourly data file; each forecast time period in the current day precipitation forecast file in the precipitation hourly data file has valid previous 24-hour data.

[0006] Preferably, the first format is the grib format, and the second format is the nc format.

[0007] Preferably, the hourly corresponding disaster factors include 1-hour cumulative precipitation disaster factors, 3-hour cumulative precipitation disaster factors, 6-hour cumulative precipitation disaster factors, and 24-hour cumulative precipitation disaster factors.

[0008] Preferably, the weights of the various disaster-causing factors are calculated by expert scoring method, information entropy weighting method or hierarchical analysis method; and the rainstorm process intensity index is obtained by comprehensive weighting method. Preferably, the environmental factors include terrain undulation, terrain slope, vegetation coverage, river network density, and distance from water bodies; and the disaster-prone environmental impact coefficient is calculated by a weighted summation method.

[0009] Preferably, the rainstorm disaster hazard index is calculated by the following formula: TI = (1 + PI) *RI, wherein RI, PI, and TI represent the rainstorm process intensity index, the disaster-prone environmental impact coefficient, and the rainstorm disaster hazard index, respectively.

[0010] Preferably, the weights of the rainstorm disaster risk index, the vulnerability of the disaster-bearing body and the exposure of the disaster-bearing body are calculated by an expert scoring method or an information entropy weighting method; the rainstorm disaster risk index is calculated by the following formula: MDRI = (TI we ) * (EI wh ) * (VI ws ), where MDRI is the rainstorm disaster risk index, which is used to indicate the degree of rainstorm disaster risk. The larger its value is, the greater the degree of rainstorm disaster risk is; TI, EI, and VI are the rainstorm disaster hazard, exposure degree of the disaster-bearing body, and vulnerability index of the disaster-bearing body respectively; we, wh, and ws are the weights of the rainstorm disaster hazard, exposure degree of the disaster-bearing body, and vulnerability index.

[0011] The second aspect of the present invention provides: a rainstorm disaster risk warning system based on multi-source data, which is used to implement any of the above-mentioned rainstorm disaster risk warning methods based on multi-source data, comprising: The precipitation data reading module is used to read historical precipitation data and intelligent grid forecast data to obtain hourly precipitation real-time data and forecast data files; The disaster factor calculation module is used to preprocess the hourly precipitation data file, calculate the corresponding disaster factor according to the preprocessed hourly precipitation data file; calculate the weight of each disaster factor to obtain the rainstorm process intensity index; calculate the disaster-prone environmental impact coefficient based on environmental factors; A rainstorm disaster hazard index calculation module is used to calculate the rainstorm disaster hazard index based on the rainstorm process intensity index and the disaster-prone environmental impact coefficient; The disaster-prone body data calculation module is used to calculate the vulnerability and exposure of the disaster-prone body through unified data range and resolution based on socio-economic data and historical rainstorm disaster data; A rainstorm disaster risk index calculation module is used to calculate the rainstorm disaster risk index based on the rainstorm disaster hazard index, the vulnerability of the disaster-bearing body, the exposure of the disaster-bearing body and their respective weights; The rainstorm disaster risk warning level calculation module is used to calculate the rainstorm disaster risk warning level threshold of each grid point according to the threshold level classification standard and the rainstorm disaster risk index; then based on the preset area, the grid point with the highest grid warning level in each preset area is calculated to obtain the rainstorm disaster risk warning level of each preset area.

[0012] The third aspect of the present invention provides: a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned rainstorm disaster risk warning methods based on multi-source data is implemented.

[0013] The beneficial effects of the present invention are: 1) The use of kilometer-level, hourly and daily quantitative precipitation forecast data from numerical models in business applications takes into account both rainfall estimation accuracy and spatial variability, and combines this with the features identified from long-series observation data to help reduce the uncertainty of risk prediction results.

[0014] 2) Aiming at the main technical bottlenecks of rainstorm disaster risk warning, we focus on the key scientific issues of multi-temporal and spatial scale characteristics of precipitation and the risk differences caused by them, and start from the identification of rainstorm disaster events, the determination of objective forecast indicators, the calculation of risk level thresholds, and the construction of risk warning models, so that the research problems and perspectives are more targeted. At the same time, the method comprehensively considers the ground-based encrypted meteorological stations and satellite multi-source fusion precipitation data, which has significant advantages in both temporal resolution and spatial resolution, and can provide an important basis for accurately describing the short-duration rainfall intensity characteristics and spatial variability of precipitation; and the application of various data such as grid disaster-bearing bodies and geographic information has improved the scientificity and refinement of the model.

[0015] 3) The warning threshold can be refined to the township scale, and can meet the application of rainstorm disaster risk warning business at the provincial, municipal, county and township levels in space; at the same time, through the hourly precipitation forecast data as the disaster factor, the rainstorm disaster risk warning service at different time scales such as 0-3 days, 3-7 days, 7-15 days and extended period (15-30 days) can be realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the rainstorm disaster risk warning method based on multi-source data; Figure 2 This is the business process diagram for rainstorm disaster risk warning. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1-Figure 2 The present invention provides a technical solution: The first aspect of the present invention provides: a rainstorm disaster risk warning method based on multi-source data, comprising the following steps: S1: Historical precipitation data reading stage, reading historical precipitation data to obtain hourly precipitation data files; S2: Disaster factor calculation stage: pre-process the hourly precipitation data file, calculate the corresponding disaster factor according to the pre-processed hourly precipitation data file; calculate the weight of each disaster factor to obtain the rainstorm process intensity index; calculate the disaster-prone environmental impact coefficient based on environmental factors; S3: Calculation stage of rainstorm disaster risk index, the rainstorm disaster risk index is calculated based on the rainstorm process intensity index and the disaster-prone environmental impact coefficient; S4: Disaster-prone body data calculation stage: Based on socio-economic data and historical rainstorm disaster data, the vulnerability and exposure of the disaster-prone body are calculated through unified data range and resolution; S5: Rainstorm disaster risk index calculation stage, the rainstorm disaster risk index is calculated based on the rainstorm disaster hazard index, the vulnerability of the disaster-bearing body, the exposure of the disaster-bearing body and their respective weights; S6: In the rainstorm disaster risk warning level calculation stage, the rainstorm disaster risk warning level threshold of each grid point is calculated according to the threshold level classification standard and the rainstorm disaster risk index; then, based on the preset area, the grid point with the highest grid warning level in each preset area is calculated to obtain the rainstorm disaster risk warning level of each preset area.

[0019] In this embodiment, the preset area is at least one of a township administrative unit, a county-level administrative unit, a municipal-level administrative unit, and a provincial-level administrative unit; the environmental factors have an amplifying or mitigating effect on the rainstorm disaster; and the socio-economic data are socio-economic data such as population, economy, and agriculture and animal husbandry obtained through official channels. The present invention selects the rainstorm disaster-causing factors involved in the calculation based on historical station and grid precipitation data and meteorological disaster losses, uses the information entropy weighting method to calculate the weight of each disaster-causing factor index, and uses the weighted summation method to calculate the rainstorm process intensity index within the evaluation range, and combines the terrain slope, terrain undulation, river network density, and distance from the water body to obtain the disaster-prone environmental impact coefficient, and then calculates the disaster risk index in the evaluation area. The disaster-prone body data (including population, economy, crop area, animal husbandry, etc.) and historical meteorological disaster loss data are obtained through official channels to calculate the vulnerability index of the disaster-prone body. Using the indicators such as the rainstorm disaster hazard index, the vulnerability index of the disaster-bearing body and the exposure index of the disaster-bearing body in the study area, the weight coefficients of the corresponding factors are confirmed according to the expert scoring method or the information entropy weight method, and the weighted summation method is used to calculate the rainstorm disaster risk index. The rainstorm disaster risk level is divided according to the natural breakpoint method, percentile method and other grading standards, and finally the rainstorm disaster risk assessment model and different risk thresholds are obtained. Further using the intelligent grid forecast data of precipitation, by connecting to the rainstorm disaster risk assessment model and the risk level threshold, the precipitation forecast data is divided into the precipitation warning signal release level threshold according to the 24-hour precipitation standard, and the rainstorm disaster risk warning level and precipitation warning signal release level of different administrative units are calculated, and the two are classified and counted to verify the applicability of the model and threshold. At the same time, the precipitation grid data monitored daily in real time is connected to the rainstorm disaster risk assessment model to obtain the actual risk level of the rainstorm disaster, and the precipitation warning signal release level is obtained according to the actual 24-hour cumulative precipitation. The actual rainstorm risk level and precipitation warning signal issuance level of each administrative unit are calculated, and the two are classified and statistically analyzed to verify the accuracy of the model.

[0020] In some embodiments, the S1: historical precipitation data reading stage further includes the following steps: The precipitation data files of the current day and the previous day are read and parsed into a first format, and the 24-hour real-time data of the previous day is spliced ​​with the current day forecast data, and saved in a second format to obtain a precipitation hourly data file; each forecast time period in the current day precipitation forecast file in the precipitation hourly data file has valid previous 24-hour data.

[0021] In some embodiments, the first format is a grib format, and the second format is a nc format.

[0022] In some embodiments, the hourly corresponding disaster factors include 1-hour cumulative precipitation disaster factors, 3-hour cumulative precipitation disaster factors, 6-hour cumulative precipitation disaster factors, and 24-hour cumulative precipitation disaster factors.

[0023] In some embodiments, the weights of the various disaster-causing factors are calculated by expert scoring method, information entropy weighting method or hierarchical analysis method; the rainstorm process intensity index is obtained by comprehensive weighting method. In some embodiments, the environmental factors include terrain undulation, terrain slope, vegetation coverage, river network density, and distance from water bodies; the disaster-prone environmental impact coefficient is calculated by a weighted summation method.

[0024] In some embodiments, the rainstorm disaster hazard index is calculated by the following formula: TI = (1 +PI) * RI, wherein RI, PI, and TI represent the rainstorm process intensity index, the disaster-prone environment impact coefficient, and the rainstorm disaster hazard index, respectively.

[0025] In some embodiments, the weights of the rainstorm disaster hazard index, the vulnerability of the disaster-bearing body, and the exposure of the disaster-bearing body are calculated by an expert scoring method or an information entropy weighting method; the rainstorm disaster risk index is calculated by the following formula: MDRI = (TI we ) * (EI wh ) * (VI ws ), where MDRI is the rainstorm disaster risk index, which is used to indicate the degree of rainstorm disaster risk. The larger its value is, the greater the degree of rainstorm disaster risk is; TI, EI, and VI are the rainstorm disaster hazard, exposure degree of the disaster-bearing body, and vulnerability index of the disaster-bearing body respectively; we, wh, and ws are the weights of the rainstorm disaster hazard, exposure degree of the disaster-bearing body, and vulnerability index.

[0026] The present invention introduces refined precipitation data such as regional station precipitation data, grid precipitation data, and reanalysis precipitation data, while comprehensively considering grid data such as population, GDP, agriculture and animal husbandry, topography, and water systems. While improving data accuracy, it also improves the scientificity and pertinence of risk warning thresholds, providing technical support for improving the refined and targeted risk warning business for multiple disaster-prone bodies caused by rainstorm disasters, and also providing guarantees for local meteorological disaster prevention and mitigation services.

[0027] The second aspect of the present invention provides: a rainstorm disaster risk warning system based on multi-source data, which is used to implement any of the above-mentioned rainstorm disaster risk warning methods based on multi-source data, comprising: The precipitation data reading module is used to read historical precipitation data and intelligent grid forecast data to obtain hourly precipitation real-time data and forecast data files; The disaster factor calculation module is used to preprocess the hourly precipitation data file, calculate the corresponding disaster factor according to the preprocessed hourly precipitation data file; calculate the weight of each disaster factor to obtain the rainstorm process intensity index; calculate the disaster-prone environmental impact coefficient based on environmental factors; A rainstorm disaster hazard index calculation module is used to calculate the rainstorm disaster hazard index based on the rainstorm process intensity index and the disaster-prone environmental impact coefficient; The disaster-prone body data calculation module is used to calculate the vulnerability and exposure of the disaster-prone body through unified data range and resolution based on socio-economic data and historical rainstorm disaster data; A rainstorm disaster risk index calculation module is used to calculate the rainstorm disaster risk index based on the rainstorm disaster hazard index, the vulnerability of the disaster-bearing body, the exposure of the disaster-bearing body and their respective weights; The rainstorm disaster risk warning level calculation module is used to calculate the rainstorm disaster risk warning level threshold of each grid point according to the threshold level classification standard and the rainstorm disaster risk index; then based on the preset area, the grid point with the highest grid warning level in each preset area is calculated to obtain the rainstorm disaster risk warning level of each preset area.

[0028] The third aspect of the present invention provides: a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by a processor, any of the above-mentioned rainstorm disaster risk warning methods based on multi-source data is implemented.

[0029] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A rainstorm disaster risk early warning method based on multi-source data, characterized by: The following steps are involved: S1: Historical precipitation data reading stage, reading historical precipitation data to obtain hourly precipitation data files; S2: Disaster factor calculation stage: preprocess the hourly precipitation data file, calculate the corresponding disaster factor according to the preprocessed hourly precipitation data file; calculate the weight of each disaster factor and obtain the rainstorm process intensity index; Calculate the disaster-prone environmental impact coefficient based on environmental factors; S3: Calculation stage of rainstorm disaster risk index, the rainstorm disaster risk index is calculated based on the rainstorm process intensity index and the disaster-prone environmental impact coefficient; S4: Disaster-prone body data calculation stage: Based on socio-economic data and historical rainstorm disaster data, the vulnerability and exposure of the disaster-prone body are calculated through unified data range and resolution; S5: Rainstorm disaster risk index calculation stage, the rainstorm disaster risk index is calculated based on the rainstorm disaster hazard index, the vulnerability of the disaster-bearing body, the exposure of the disaster-bearing body and their respective weights; S6: In the rainstorm disaster risk warning level calculation stage, the rainstorm disaster risk warning level threshold of each grid point is calculated according to the threshold level classification standard and the rainstorm disaster risk index; then, based on the preset area, the grid point with the highest grid warning level in each preset area is calculated to obtain the rainstorm disaster risk warning level of each preset area.

2. The rainstorm disaster risk early warning method based on multi-source data according to claim 1 is characterized by: The S1: historical precipitation data reading stage also includes the following steps: The precipitation data files of the current day and the previous day are read and parsed into a first format, and the 24-hour real-time data of the previous day is spliced ​​with the current day forecast data, and saved in a second format to obtain a precipitation hourly data file; each forecast time period in the current day precipitation forecast file in the precipitation hourly data file has valid previous 24-hour data.

3. The rainstorm disaster risk early warning method based on multi-source data according to claim 2 is characterized by: The first format is the grib format, and the second format is the nc format.

4. The rainstorm disaster risk early warning method based on multi-source data according to claim 1 is characterized by: The hourly corresponding disaster factors include 1-hour cumulative precipitation disaster factors, 3-hour cumulative precipitation disaster factors, 6-hour cumulative precipitation disaster factors, and 24-hour cumulative precipitation disaster factors.

5. The rainstorm disaster risk early warning method based on multi-source data according to claim 1 is characterized by: The weights of the various disaster-causing factors are calculated by expert scoring method, information entropy weighting method or hierarchical analysis method; the rainstorm process intensity index is obtained by comprehensive weighting method.

6. The rainstorm disaster risk early warning method based on multi-source data according to claim 1 is characterized by: The environmental factors include terrain undulation, terrain slope, vegetation coverage, river network density, and distance to water bodies; the disaster-prone environmental impact coefficient is calculated by weighted summation method.

7. The rainstorm disaster risk early warning method based on multi-source data according to claim 1 is characterized by: The rainstorm disaster hazard index is calculated by the following formula: TI = (1 + PI) * RI, wherein RI, PI, and TI represent the rainstorm process intensity index, the disaster-prone environmental impact coefficient, and the rainstorm disaster hazard index, respectively.

8. The rainstorm disaster risk early warning method based on multi-source data according to claim 1 is characterized by: The weights of the rainstorm disaster hazard index, the vulnerability of the disaster-bearing body and the exposure of the disaster-bearing body are calculated by expert scoring method or information entropy weighting method; the rainstorm disaster risk index is calculated by the following formula: MDRI = (TI we ) * (EI wh ) *(VI ws ), where MDRI is the rainstorm disaster risk index, which is used to indicate the degree of rainstorm disaster risk. The larger its value is, the greater the degree of rainstorm disaster risk is; TI, EI, and VI are the rainstorm disaster hazard, exposure degree of the disaster-bearing body, and vulnerability index of the disaster-bearing body respectively; we, wh, and ws are the weights of the rainstorm disaster hazard, exposure degree of the disaster-bearing body, and vulnerability index.

9. A rainstorm disaster risk warning system based on multi-source data, characterized by: A method for implementing a rainstorm disaster risk warning method based on multi-source data as claimed in any one of claims 1 to 8, comprising: The precipitation data reading module is used to read historical precipitation data and intelligent grid forecast data to obtain hourly precipitation real-time data and forecast data files; The disaster factor calculation module is used to preprocess the hourly precipitation data file, calculate the corresponding disaster factor according to the preprocessed hourly precipitation data file; calculate the weight of each disaster factor to obtain the rainstorm process intensity index; calculate the disaster-prone environmental impact coefficient based on environmental factors; A rainstorm disaster hazard index calculation module is used to calculate the rainstorm disaster hazard index based on the rainstorm process intensity index and the disaster-prone environmental impact coefficient; The disaster-prone body data calculation module is used to calculate the vulnerability and exposure of the disaster-prone body through unified data range and resolution based on socio-economic data and historical rainstorm disaster data; A rainstorm disaster risk index calculation module is used to calculate the rainstorm disaster risk index based on the rainstorm disaster hazard index, the vulnerability of the disaster-bearing body, the exposure of the disaster-bearing body and their respective weights; The rainstorm disaster risk warning level calculation module is used to calculate the rainstorm disaster risk warning level threshold of each grid point according to the threshold level classification standard and the rainstorm disaster risk index; then based on the preset area, the grid point with the highest grid warning level in each preset area is calculated to obtain the rainstorm disaster risk warning level of each preset area.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are loaded and executed by the processor, the rainstorm disaster risk warning method based on multi-source data as described in any one of claims 1-8 is implemented.

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