High-temperature heat wave risk analysis and evaluation method, medium, equipment and system

Abstract

The invention belongs to the technical field of high-temperature heat wave risk analysis and evaluation, and relates to a high-temperature heat wave risk analysis and evaluation method, medium, equipment and system, and the method comprises the steps: collecting historical day-by-day highest temperature meteorological observation data, historical social economic statistical data, historical geographic space data and historical remote sensing product data of a research area; constructing a high-temperature heat wave disaster risk index system; constructing a high-temperature heat wave risk index model; and generating a high-temperature heat wave risk grade distribution diagram and a risk evolution trend diagram of each year in the research area. According to the method, the multi-dimensional data can be adopted to construct the high-temperature heat wave disaster risk index system, the high-temperature heat wave risk index model is constructed, the data type and the risk assessment dimension are increased, the constructed model is more standardized, the replicability of the assessment system is improved, and meanwhile, the assessment efficiency is improved. And generating a high-temperature heat wave risk grade distribution diagram and a risk evolution trend diagram of each year in the research area through a geographic information system platform, and carrying out visual display on the high-temperature heat wave risk grade distribution diagram and the risk evolution trend diagram.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-temperature heat wave risk analysis and assessment, and relates to a high-temperature heat wave risk analysis and assessment method, medium, equipment and system. Background Art

[0002] A heat wave, also known as a "scorching summer," is a meteorological term that refers to weather events with temperatures reaching 35°C or higher for three or more consecutive days. Heat waves can harm human health, increase the risk of illness and death, strain water and electricity supplies, impact agricultural production, and increase the likelihood of public safety incidents. Therefore, scientific analysis and risk assessment of heat waves are crucial for proactively addressing their hazards.

[0003] However, existing methods for analyzing and assessing heatwave risks suffer from numerous shortcomings. First, they lack comprehensive data support at high spatial resolution due to their limited data types. Traditional methods rely heavily on meteorological station data for heatwave identification and trend analysis, neglecting the integration of multi-source data such as remote sensing, socioeconomic data, and geographic data, making it difficult to identify detailed regional differences. Second, risk expression is crude, lacking the ability to visualize spatial outputs. Most traditional research methods offer only quantitative analysis or simple statistical results, hindering their readability. Summary of the Invention

[0004] The purpose of the present invention is to provide a high-temperature heat wave risk analysis and assessment method that can increase the data type and risk assessment dimension, and perform visual display, thereby improving the readability of the risk assessment results.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: A high temperature heat wave risk analysis and assessment method includes the following steps: Collect historical daily maximum temperature meteorological observation data, historical socioeconomic statistics, historical geographic spatial data and historical remote sensing product data of the study area.

[0006] A high temperature heat wave disaster risk indicator system is constructed based on historical daily maximum temperature meteorological observation data, historical socioeconomic statistical data, historical geographic spatial data and historical remote sensing product data.

[0007] All data in the high temperature heat wave disaster risk index system are preprocessed, and the preprocessed data are used to construct a high temperature heat wave risk index model.

[0008] The high temperature heat wave risk index model is loaded into the geographic information system platform, and the geographic information system platform is used to generate the high temperature heat wave risk level distribution map and risk evolution trend map for each year in the study area.

[0009] The present invention is also characterized in that: Among them, the high temperature heat wave disaster risk indicator system includes hazard indicators, vulnerability indicators, exposure indicators and adaptability indicators. Hazard indicators include surface temperature, frequency of high temperature heat waves, intensity of high temperature heat waves, and number of days of high temperature heat waves. Vulnerability indicators include human settlement index, GDP, and the number of people under 14 years old and over 60 years old. Exposure indicators include population density, normalized vegetation index, improved water body index, and normalized building index. Adaptability indicators include the number of beds in urban health institutions, the number of urban medical and health technicians, and general public budget expenditure.

[0010] The calculation formula for the high temperature heat wave intensity in the danger index is: .

[0011] Where H L is the intensity of high temperature heat wave, T L is the maximum daily temperature on the Lth day during a high temperature heat wave, 35 is the high temperature threshold, L is the number of days the high temperature heat wave lasts, L=1, 2, 3, …, n, and n is the maximum number of days the high temperature heat wave lasts.

[0012] When preprocessing all the data in the high temperature heat wave disaster risk index system, all the data are normalized to a value between 0 and 1.

[0013] When using the preprocessed data to construct the high temperature heat wave risk index model, all data normalized to between 0 and 1 are weighted and superimposed to obtain the high temperature heat wave risk index model.

[0014] When all the data normalized to between 0 and 1 were weighted and superimposed, the weights were calculated using the AHP analytic hierarchy process (AHP) using MATLAB software and a consistency test was performed.

[0015] The specific calculation formula of the high temperature heat wave risk index model is: .

[0016] Where B i is the index of the i-th criterion layer. There are four criterion layers, namely, hazard index, vulnerability index, exposure index and adaptability index. Each criterion layer contains multiple indicators, i=1, 2, 3..., N, that is, N is 4, W ij is the first criterion layer under the i-th criterion layer The weight of each indicator, j=1, 2, 3..., m, R j For the The normalized value of each indicator, HWRI is the high temperature heat wave risk index, W i is the weight of the i-th criterion layer.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned high-temperature heat wave analysis and risk assessment method.

[0018] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned high-temperature heat wave analysis and risk assessment method is implemented.

[0019] A high temperature heat wave risk analysis and assessment system, comprising: The data collection module is used to collect historical daily maximum temperature meteorological observation data, historical socioeconomic statistics, historical geographic spatial data and historical remote sensing product data of the study area.

[0020] The indicator system construction module is used to construct a high temperature heat wave disaster risk indicator system based on historical daily maximum temperature meteorological observation data, historical socio-economic statistical data, historical geographic spatial data and historical remote sensing product data.

[0021] The data processing and model building module is used to preprocess all data in the high temperature heat wave disaster risk index system and use the preprocessed data to build a high temperature heat wave risk index model.

[0022] The display module is used to load the high temperature heat wave risk index model into the geographic information system platform, and use the geographic information system platform to generate the high temperature heat wave risk level distribution map and risk evolution trend map for each year in the study area.

[0023] The high-temperature heat wave risk analysis and assessment method, medium, equipment, and system of the present invention have the following advantages: The present invention collects historical daily maximum temperature meteorological observation data, historical socio-economic statistical data, historical geographic spatial data and historical remote sensing product data of the study area, and then constructs a high-temperature heat wave disaster risk index system based on the historical daily maximum temperature meteorological observation data, historical socio-economic statistical data, historical geographic spatial data and historical remote sensing product data. All data in the high-temperature heat wave disaster risk index system are then preprocessed, and a high-temperature heat wave risk index model is constructed using the preprocessed data. Finally, the high-temperature heat wave risk index model is loaded into a geographic information system platform, and the geographic information system platform is used to generate a high-temperature heat wave risk level distribution map and a risk evolution trend map for each year in the study area. The present invention can use multi-dimensional data to construct a high-temperature heat wave disaster risk indicator system and a high-temperature heat wave risk index model, increase the data type and risk assessment dimension, make the constructed model more standardized, and thus improve the replicability of the assessment system. At the same time, the high-temperature heat wave risk level distribution map and the risk evolution trend map for each year in the study area are generated through the geographic information system platform for visual display, thereby improving the readability of the results. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the overall process of the present invention. DETAILED DESCRIPTION

[0025] The technical solutions in the present invention will be described clearly and in detail below with reference to the accompanying drawings. In the description of the embodiments of the present invention, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, such as A and / or B, which can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" refers to two or more than two. The following terms "first" and "second" are used for descriptive purposes only and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0026] like Figure 1 As shown, the present invention provides a high temperature heat wave risk analysis and assessment method, comprising: Collect historical daily maximum temperature meteorological observation data, historical socioeconomic statistics, historical geographic spatial data and historical remote sensing product data of the study area.

[0027] A high temperature heat wave disaster risk indicator system is constructed based on historical daily maximum temperature meteorological observation data, historical socioeconomic statistical data, historical geographic spatial data and historical remote sensing product data.

[0028] All data in the high temperature heat wave disaster risk index system are preprocessed, and the preprocessed data are used to construct a high temperature heat wave risk index model.

[0029] The high temperature heat wave risk index model is loaded into the geographic information system platform, and the geographic information system platform is used to generate the high temperature heat wave risk level distribution map and risk evolution trend map for each year in the study area.

[0030] In summary, the present invention collects historical daily maximum temperature meteorological observation data, historical socio-economic statistical data, historical geographic spatial data and historical remote sensing product data of the study area, and then constructs a high-temperature heat wave disaster risk index system based on the historical daily maximum temperature meteorological observation data, historical socio-economic statistical data, historical geographic spatial data and historical remote sensing product data. All data in the high-temperature heat wave disaster risk index system are then preprocessed, and a high-temperature heat wave risk index model is constructed using the preprocessed data. Finally, the high-temperature heat wave risk index model is loaded into a geographic information system platform, and the geographic information system platform is used to generate a high-temperature heat wave risk level distribution map and a risk evolution trend map for each year in the study area. Multidimensional data can be used to construct a high-temperature heat wave disaster risk indicator system and a high-temperature heat wave risk index model, which increases the data type and risk assessment dimension, makes the constructed model more standardized, and thus improves the replicability of the assessment system. At the same time, the high-temperature heat wave risk level distribution map and the risk evolution trend map for each year in the study area are generated through the geographic information system platform for visual display, thereby improving the readability of the results.

[0031] Historical daily maximum temperature observations are obtained from meteorological stations. Based on the Meteorological Bureau's criteria for heat waves (maximum temperatures ≥ 35°C for three or more consecutive days), the frequency, intensity, and duration of heat waves are extracted from these daily maximum temperature observations. Historical remote sensing product data include surface temperature, normalized difference vegetation index, improved water index, and normalized building index. Historical geospatial data include population density, GDP, human settlement index, and the number of people under 14 and over 60. Historical socioeconomic statistics include the number of beds in urban health institutions, the number of medical and health technicians in urban areas, and general public budget expenditures.

[0032] like Figure 1 As shown in the figure, the high temperature heat wave disaster risk indicator system includes hazard indicators, vulnerability indicators, exposure indicators and adaptability indicators. Hazard indicators include surface temperature, frequency of high temperature heat waves, intensity of high temperature heat waves, and number of days of high temperature heat waves. Vulnerability indicators include human settlement index, GDP, and the number of people under 14 years old and over 60 years old. Exposure indicators include population density, normalized vegetation index, improved water body index, and normalized building index. Adaptability indicators include the number of beds in urban health institutions, the number of urban medical and health technicians, and general public budget expenditure.

[0033] Among them, when constructing the high temperature heat wave disaster risk indicator system, a four-dimensional risk assessment framework is based on hazard indicators, vulnerability indicators, exposure indicators, and adaptability indicators.

[0034] Among them, the calculation formula for the high temperature heat wave intensity in the danger index is: .

[0035] Where H L is the intensity of high temperature heat wave, T L is the maximum daily temperature on the Lth day during a high temperature heat wave, 35 is the high temperature threshold, L is the number of days the high temperature heat wave lasts, L=1, 2, 3, …, n, and n is the maximum number of days the high temperature heat wave lasts.

[0036] like Figure 1 As shown in the figure, when preprocessing all the data in the high temperature heat wave disaster risk index system, all the data are normalized to a value between 0 and 1 to ensure the additivity and comparability of data in different dimensions.

[0037] like Figure 1 As shown in Figure 2, when using the preprocessed data to construct a high temperature heat wave risk index model, all data normalized to between 0 and 1 are weighted and superimposed to obtain the high temperature heat wave risk index model.

[0038] Among them, when all data normalized to between 0 and 1 are weighted and superimposed, the hierarchical analysis method is used to construct a judgment matrix and scientifically assign weights to each indicator.

[0039] Among them, when all the data normalized to between 0 and 1 were weighted and superimposed, the AHP analytical hierarchy process was used through MATLAB software to calculate the weights and perform consistency tests.

[0040] like Figure 1 As shown in Figure 2, the specific calculation formula of the high temperature heat wave risk index model is: .

[0041] Where B i is the index of the i-th criterion layer. There are four criterion layers, namely, hazard index, vulnerability index, exposure index, and adaptability index. Each criterion layer contains multiple indicators. For example, the first criterion layer (hazard index) includes four indicators including surface temperature, frequency of high temperature heat waves, intensity of high temperature heat waves, and duration of high temperature heat waves. i = 1, 2, 3, ..., N, that is, N is 4, W ij is the weight of the jth indicator under the i-th criterion layer, j = 1, 2, 3..., m, R j is the normalized value of the jth indicator, HWRI is the high temperature heat wave risk index, W i is the weight of the i-th criterion layer.

[0042] The present invention also provides a high temperature heat wave risk analysis and assessment system, comprising: The data collection module is used to collect historical daily maximum temperature meteorological observation data, historical socioeconomic statistics, historical geographic spatial data, and historical remote sensing product data of the study area; An indicator system construction module is used to construct a high-temperature heat wave disaster risk indicator system based on historical daily maximum temperature meteorological observation data, historical socioeconomic statistics, historical geospatial data, and historical remote sensing product data; The data processing and model building module is used to preprocess all data in the high temperature heat wave disaster risk indicator system and use the preprocessed data to build a high temperature heat wave risk index model; The display module is used to load the high temperature heat wave risk index model into the geographic information system platform, and use the geographic information system platform to generate the high temperature heat wave risk level distribution map and risk evolution trend map for each year in the study area.

[0043] The present invention also provides a computer-readable storage medium storing a computer program, which implements the above-mentioned high-temperature heat wave analysis and risk assessment method when executed by a processor.

[0044] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned high-temperature heat wave analysis and risk assessment method is implemented.

[0045] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0046] Example 1 1) This paper selects Hebei Province from 2002 to 2022 as the research period, and collects and organizes the following multi-source data: Meteorological data: Daily maximum temperature data from 142 meteorological stations in Hebei Province were obtained from the China Meteorological Data Sharing Service Network, covering the daily time series required for identifying high temperature days. Remote sensing product data: land surface temperature, normalized difference vegetation index, improved water index, normalized building index, pre-processed using Google Earth Engine; Geospatial data: Obtain population density, GDP, number of people under 14 and over 60, and human settlement index at 1km resolution.

[0047] Social and economic statistical data: Statistical yearbooks of Hebei Province and its cities over the years are collected, including the number of beds in medical institutions, the number of medical and health technicians in urban areas, general public budget expenditures, etc.

[0048] 2) Based on the Meteorological Bureau's definition of a heat wave, defined as a period of three or more consecutive days with daily maximum temperatures reaching or exceeding 35°C, we used Excel and R scripts to identify heat wave events from daily temperature data. We extracted core heat wave indicators for each station each year: the frequency of heat waves (times / year), which refers to the number of heat wave events per year, and the duration of heat waves (days), which refers to the cumulative number of heat wave events. We also calculated the intensity of heat waves.

[0049] The above indicators were spatially interpolated and integrated at each site to provide foundational heat wave risk factors for subsequent GIS spatial modeling. All daily temperature data were stored in CSV format, while spatial data such as surface temperature, normalized difference vegetation index, and remote sensing data products were stored in GeoTIFF format. The frequency, duration, and intensity of heat waves were screened and calculated, then output in CSV format. This data was then processed using the Kriging spatial interpolation method within the GIS platform and output as raster data.

[0050] 3) The extracted heatwave frequency, duration, and intensity indicators were loaded into a geographic information system (GIS) platform. Kriging spatial interpolation was used to perform continuous spatial surface modeling, generating a spatial distribution map of the three heatwave indicators for Hebei Province. To enhance explanatory power, a layer of urban boundaries, land use in major functional areas, and population density distribution maps were overlaid to analyze the relationship between high-incidence heatwave areas and urbanization and population exposure. The results showed that heatwave intensity in the southern urban agglomeration regions of Handan, Hengshui, and Shijiazhuang was significantly higher than that in the northern mountainous ecological barrier zone, indicating that heatwaves are spatially concentrated in densely populated urban areas.

[0051] 4) Based on the four-dimensional risk assessment framework, an indicator system covering four dimensions, namely, hazard index, vulnerability index, exposure index, and adaptability index, was constructed, and 14 specific evaluation indicators were compiled: Danger indicators (4 items): surface temperature, frequency of high-temperature heat waves, intensity of high-temperature heat waves, and duration of high-temperature heat waves.

[0052] Vulnerability indicators (3 items): GDP, human settlement index, and the number of people under 14 and over 60.

[0053] Exposure indicators (4 items): population density, normalized difference vegetation index, improved water body index, and normalized difference building index.

[0054] Adaptability (3 items): number of beds in urban health institutions, number of urban medical and health technicians, and general public budget expenditure.

[0055] The AHP (Analytical Hierarchy Process) is used in MATLAB software to calculate weights and perform consistency checks. The weight results are used to build a weighted evaluation model. The weight calculation uses the AHP (Analytical Hierarchy Process) to build a pairwise comparison matrix, and the consistency ratio (CR) is controlled within 0.1. MATLAB software is a high-performance numerical calculation and data analysis software that integrates calculation, visualization, and programming functions in an easy-to-use environment. It is widely used in engineering, scientific research, mathematical modeling, algorithm development, and other fields. AHP is a commonly used decision analysis method that gradually decomposes complex problems by building a hierarchical structure and weighting different factors to ultimately reach the optimal decision. The analysis process steps of AHP are as follows: ① Establish a hierarchical structure: divide the decision-making problem into the goal layer, the criterion layer and the plan layer. The goal layer represents the overall goal of the research, the criterion layer is the key factors affecting the goal, and the plan layer is the specific selection plan.

[0056] ② Construct a judgment (paired comparison) matrix: Compare the factors at the criterion level and the solution level in pairs, establish a pairwise comparison matrix, and use the 1-9 scale to assess importance. The elements of the judgment matrix meet the following requirements: .

[0057] Where a ij are the elements of the matrix, specifically the indicators under the criterion layer, for example, the surface temperature, frequency of high temperature heat waves, intensity of high temperature heat waves and duration of high temperature heat waves under the first criterion layer (hazard index).

[0058] ③Consistency test: Calculate the consistency index (CI) to judge the rationality of the matrix: .

[0059] Where CI is the consistency index, λ max is the maximum eigenvalue of the judgment matrix, and k is the matrix order.

[0060] RI is used to determine the size of CI. RI is positively correlated with the order of the judgment matrix. The corresponding relationship is shown in Table 1: Table 1 Standard value of average random consistency index RI

[0061] Further calculate the consistency ratio (CR): .

[0062] Where CR is the consistency ratio and RI is the average random consistency index. If CR < 0.1, the judgment matrix passes the consistency test, otherwise the matrix needs to be adjusted.

[0063] ④ Hierarchical total ranking and consistency test: Comprehensively analyze the calculated weights to obtain the final weight values ​​of each decision-making plan, and determine the optimal decision-making plan based on the final weight values.

[0064] 5) Preprocess all indicator data by normalizing their ranges to between 0 and 1 to ensure comparability across different metrics. The adaptability factor is negatively correlated with risk. The standardized indicator data are weighted and superimposed using the weights derived from the AHP to construct a heat wave risk index model.

[0065] 6) The heat wave risk index model was loaded into a geographic information system (GIS) platform. All preprocessed indicator data was then calculated using the GIS platform to generate heat wave risk level distribution maps and risk evolution trend maps for each year within the study area. The results showed that the southern plain urban cluster of Hebei Province reached the highest heat wave risk level in 2017. Typical high-risk cities included Handan, Hengshui, and Shijiazhuang, while northern ecological barrier cities such as Zhangjiakou and Chengde had relatively low risks. Comparison with the results obtained in 3) verified the accuracy of the heat wave risk index model.

[0066] 7) The heat wave analysis and risk assessment method provided by this invention supports deployment in a variety of GIS (Geographic Information System) platforms and analysis environments. It can be run on desktop platforms, such as ArcGIS Pro and QGIS, to build risk assessment models; it can be embedded in WebGIS systems (such as SuperMap and GeoServer) to enable online updates of heat wave warning layers; it can build automated script processes using Python, GeoPandas, and Rasterio to achieve regular assessments and batch layer output; and it supports the output of assessment results in GeoTIFF, Shapefile, or Web Map formats, facilitating multi-department sharing and platform integration.

[0067] Other advantages of the high-temperature heat wave risk analysis and assessment method, medium, equipment, and system of the present invention are as follows: First, the present invention has strong data fusion capabilities, breaking through the four data barriers of remote sensing observation, meteorological monitoring, geographic space and social statistics, realizing the integrated application of multi-source information, and improving the comprehensiveness of data support and model adaptability.

[0068] Second, the indicator system of the present invention is scientific and standardized. Based on the internationally accepted disaster risk assessment framework, it systematically constructs evaluation indicators covering the three levels of disaster causing, disaster bearing and disaster relief, which significantly improves the rationality and academic advancement of the evaluation model.

[0069] Third, the present invention has high assessment accuracy and fine spatial resolution: by constructing a raster-level risk index model on the GIS platform, it can finely model the high temperature heat wave risk at a spatial scale of 500 meters to 1 kilometer, which is significantly better than traditional city-level or administrative district-level assessments.

[0070] Fourth, the risk expression of the present invention is intuitive and visual, and can output various spatial visualization products such as annual heat wave risk level maps, trend evolution maps, high-risk aggregation maps, etc. The results are easy to understand and apply.

[0071] Fifth, the present invention has strong scalability and portability, and the model is highly universal and can be expanded to different regions, with broad promotion prospects.

[0072] It will be understood that the present invention is described by way of some embodiments, and it will be appreciated by those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of the present invention are intended to be protected by the present invention.

Claims

1. A high temperature heat wave risk analysis and assessment method, characterized in that: The following steps are involved: Collect historical daily maximum temperature meteorological observation data, historical socioeconomic statistics, historical geographic spatial data, and historical remote sensing product data for the study area; A high temperature heat wave disaster risk indicator system is constructed based on historical daily maximum temperature meteorological observation data, historical socioeconomic statistics, historical geographic spatial data, and historical remote sensing product data; Preprocess all data in the high temperature heat wave disaster risk index system and use the preprocessed data to construct a high temperature heat wave risk index model; The high temperature heat wave risk index model is loaded into the geographic information system platform, and the geographic information system platform is used to generate the high temperature heat wave risk level distribution map and risk evolution trend map for each year in the study area.

2. The high temperature heat wave risk analysis and assessment method according to claim 1, characterized in that: The high-temperature heat wave disaster risk indicator system includes hazard indicators, vulnerability indicators, exposure indicators and adaptability indicators. The hazard indicators include surface temperature, frequency of high-temperature heat waves, intensity of high-temperature heat waves, and number of days of high-temperature heat waves. The vulnerability indicators include human settlement index, GDP, and the number of people under 14 years old and over 60 years old. The exposure indicators include population density, normalized vegetation index, improved water body index, and normalized building index. The adaptability indicators include the number of beds in urban health institutions, the number of urban medical and health technicians, and general public budget expenditure.

3. The high temperature heat wave risk analysis and assessment method according to claim 2, characterized in that: The calculation formula for the high temperature heat wave intensity in the risk index is: ， Where H L is the intensity of high temperature heat wave, T L is the maximum daily temperature on the Lth day during a high temperature heat wave, 35 is the high temperature threshold, L is the number of days the high temperature heat wave lasts, L=1, 2, 3, …, n, and n is the maximum number of days the high temperature heat wave lasts.

4. The method for risk analysis and assessment of a high temperature heat wave according to claim 2, characterized in that: When preprocessing all data in the high temperature heat wave disaster risk index system, all data are normalized to a value between 0 and 1.

5. The method for risk analysis and assessment of a high temperature heat wave according to claim 4, characterized in that: When using the preprocessed data to construct a high temperature heat wave risk index model, all data normalized to between 0 and 1 are weighted and superimposed to obtain the high temperature heat wave risk index model.

6. The method for risk analysis and assessment of a high temperature heat wave according to claim 5, characterized in that: When all data normalized to between 0 and 1 were weighted and superimposed, the weights were calculated using the AHP analytic hierarchy process (AHP) using MATLAB software and a consistency test was performed.

7. The method for risk analysis and assessment of a high temperature heat wave according to claim 2, characterized in that: The specific calculation formula of the high temperature heat wave risk index model is: ， Where B i is the index of the i-th criterion layer. There are four criterion layers, namely, hazard index, vulnerability index, exposure index and adaptability index. Each criterion layer contains multiple indicators, i=1, 2, 3..., N, that is, N is 4, W ij is the first criterion layer under the i-th criterion layer The weight of each indicator, j=1, 2, 3..., m, R j For the The normalized value of each indicator, HWRI is the high temperature heat wave risk index, W i is the weight of the i-th criterion layer.

8. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A high temperature heat wave risk analysis and assessment system, characterized in that: The method according to claim 1, comprising: The data collection module is used to collect historical daily maximum temperature meteorological observation data, historical socioeconomic statistics, historical geographic spatial data, and historical remote sensing product data of the study area; An indicator system construction module is used to construct a high-temperature heat wave disaster risk indicator system based on historical daily maximum temperature meteorological observation data, historical socioeconomic statistics, historical geospatial data, and historical remote sensing product data; The data processing and model building module is used to preprocess all data in the high temperature heat wave disaster risk indicator system and use the preprocessed data to build a high temperature heat wave risk index model; The display module is used to load the high temperature heat wave risk index model into the geographic information system platform, and use the geographic information system platform to generate the high temperature heat wave risk level distribution map and risk evolution trend map for each year in the study area.

Images