Spatiotemporal early warning method and system for urban high temperature disaster risk

Abstract

The present invention discloses a spatiotemporal early warning method and system for urban high temperature disaster risk, and relates to the technical field of urban high temperature early warning. The present invention includes a database end, a server end, and a user terminal. The database end is communicatively connected to the server end, the server end is communicatively connected to the user terminal, the server end downloads data from the database end, the database end and the server end read and write data with each other, the server end and the user terminal read and write data with each other, the database end includes a high-resolution meteorological element numerical forecast database and a storage database, and the server end includes a download storage operation module, various index calculation modules, and an urban high temperature disaster risk mapping module. The present invention is a new meteorological product integration system that can provide spatiotemporal early warning and visual display of urban high temperature disaster risks, mainly by combining multi-source data to calculate and finely characterize the spatial distribution of high temperature at the urban scale.

Description

Technical Field

[0001] The present invention relates to the technical field of urban high temperature early warning, and in particular to a method and system for spatiotemporal early warning of urban high temperature disaster risks. Background Art

[0002] Against the backdrop of global climate change and frequent natural disasters, urban heat island effects and human health issues caused by high temperature and heat waves have become social public events that trouble citizens, governments and relevant decision-making support departments;

[0003] The existing meteorological disaster forecast index products (such as high temperature) still have shortcomings, including only considering the single meteorological element of temperature, and the forecast is limited to warnings based on administrative districts, resulting in low forecast accuracy, weak reference value of forecast information, and short forecast period. Therefore, we propose a spatiotemporal warning method and system for urban high temperature disaster risks. Summary of the invention

[0004] The purpose of the present invention is to provide a spatiotemporal early warning method and system for urban high temperature disaster risks to solve the problems raised in the above background.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is a spatiotemporal early warning system for urban high temperature disaster risk, comprising a database end, a server end and a user terminal, wherein the database end is communicatively connected to the server end, the server end is communicatively connected to the user terminal, the server end downloads data from the database end, the database end and the server end read and write data with each other, the server end and the user terminal read and write data with each other, the database end comprises a high-resolution meteorological element numerical forecast database and a storage database, the server end comprises a download storage operation module, various index calculation modules and an urban high temperature disaster risk mapping module, the various index calculation modules comprise a high temperature hazard index calculation module, a high temperature exposure index calculation module and an urban high temperature risk index calculation module, the download storage operation module is communicatively connected to the various index calculation modules, the various index calculation modules are communicatively connected to the urban high temperature disaster risk mapping module, and the user terminal comprises a front-end display module for the spatial distribution of urban high temperature disaster risk.

[0007] Preferably, the download storage operation module downloads and processes high-resolution meteorological element numerical forecast data from the server, and stores the processed data into a server database table.

[0008] Preferably, the high temperature hazard index calculation module reads the air temperature and relative humidity grid data processed by the "download storage operation module" from the database table of the server, calculates the high temperature hazard, standardizes the data, and finally stores them in the corresponding database tables.

[0009] Preferably, the high temperature exposure index calculation module first reads the high temperature exposure data of the grid point from the database table of the server, then queries the cumulative number of high temperature days and the number of hours of high temperature on the day of the corresponding grid point, processes and standardizes the data, and then multiplies them by weight coefficients 0.2539 and 0.2124 respectively, and finally calculates the high temperature exposure index of the grid point and writes the data into the database table after standardization.

[0010] Preferably, the city high temperature risk index calculation module reads the high temperature hazard index calculation results and high temperature exposure index calculation results of the grid points from the database table of the server, calculates the city high temperature risk index and stores it in the database table.

[0011] Preferably, the urban high temperature disaster risk mapping module reads grid data from a database table of a server, grades urban high temperature disaster risks according to grading rules, draws a spatial distribution map of urban high temperature disaster risks, and stores it in the database table.

[0012] The spatiotemporal early warning method for urban high temperature disaster risk includes the following steps:

[0013] Step S1: downloading data from the database end according to the meteorological element database through the download storage operation module, including urban temperature data and relative humidity data;

[0014] Step S2: Calculate the high temperature hazard using the high temperature hazard index calculation module, standardize the calculated data, and store them in a corresponding database table;

[0015] Step S3: Use the high temperature exposure index calculation module to read the high temperature exposure data of the grid point, then query the cumulative number of high temperature days and the number of hours of high temperature on the day of the corresponding grid point, process and standardize the data, and write it into the database table;

[0016] Step S4: using the urban high temperature risk index calculation module to read the high temperature hazard index calculation results and high temperature exposure index calculation results of the grid points, and store them in a database table;

[0017] Step S5: Use the urban high temperature disaster risk mapping module to read grid data from the database table of the server, classify the urban high temperature disaster risk according to the classification rules, and draw a spatial distribution map of the urban high temperature disaster risk and store it in the database table.

[0018] In the plan, high temperature hazard refers to the possibility of high temperature disasters occurring in the areas where disaster-prone objects (such as cities, people, crops, etc.) are located, and is a representation of the degree of proximity of the regional environment to high temperature heat wave disasters; high temperature exposure refers to people or objects that may be adversely affected by the disaster.

[0019] The present invention has the following beneficial effects:

[0020] 1. The spatiotemporal early warning system for urban high temperature disaster risks of the present invention can be embedded in the applications of various meteorological life service mobile terminals, thereby guiding the government, community committees and residents to carry out production and life under high temperature weather, which is of great significance to reducing the economic losses and health damage caused by high temperature meteorological disasters.

[0021] 2. The spatiotemporal early warning method for urban high temperature disaster risks of the present invention combines an operating system to provide spatiotemporal early warning for urban high temperature disaster risks, has high forecast accuracy, strong reference value for forecast information, long forecast period, is easy and efficient to operate, has strong controllability, and is highly adaptable.

[0022] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0024] Figure 1 This is the architecture diagram of the spatiotemporal early warning system for urban high temperature disaster risk of the present invention;

[0025] Figure 2 This is a flow chart of the spatiotemporal early warning method for urban high temperature disaster risk of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Embodiment 1:

[0028] See also Figure 1As shown: the present invention is a spatiotemporal early warning system for urban high temperature disaster risk, including a database end, a server end and a user terminal. The database end is communicatively connected to the server end, the server end is communicatively connected to the user terminal, the server end downloads data from the database end, the database end and the server end read and write data with each other, the server end and the user terminal read and write data with each other, the database end includes a high-resolution meteorological element numerical forecast database and a storage database, the server end includes a download storage operation module, various index calculation modules and an urban high temperature disaster risk mapping module, each index calculation module includes a high temperature hazard index calculation module, a high temperature exposure index calculation module and an urban high temperature risk index calculation module, the download storage operation module is communicatively connected to each index calculation module, each index calculation module is communicatively connected to the urban high temperature disaster risk mapping module, and the user terminal includes a front-end display module for the spatial distribution of urban high temperature disaster risk.

[0029] The download storage operation module downloads and processes high-resolution numerical forecast data of meteorological elements from the server (including temperature data released at 08:00 on the same day with a resolution of 1 hour and a forecast of 24 hours; temperature data released at 20:00 on the same day with a resolution of 1 hour and a forecast of 24 hours; relative humidity data released at 08:00 on the same day with a resolution of 1 hour and a forecast of 24 hours; relative humidity data released at 20:00 on the same day with a resolution of 1 hour and a forecast of 24 hours; temperature data released at 08:00 on the same day with a resolution of 24 hours; temperature data released at 20:00 on the same day with a resolution of 24 hours; relative humidity data released at 08:00 on the same day with a resolution of 24 hours; relative humidity data released at 20:00 on the same day with a resolution of 24 hours) and stores the processed data in the server database table. It should be noted that since the forecast timeliness of the numerical forecast data of meteorological elements mentioned above is different (hourly and daily respectively), we store them in different data tables. The hourly forecast data is stored in T_TQ_XM_HTS, and the daily forecast data is stored in T_TQ_XM_THREE_DATA. Since the original data is a 2.5km*2.5km resolution grid data, it is necessary to split the data into 500m*500m resolution grid data. Secondly, re-match the new grid according to the latitude and longitude information of the original data.

[0030] The high temperature hazard index calculation module reads the temperature and relative humidity grid data processed by the "download storage operation module" from the server database tables T_TQ_XM_HTS and T_TQ_XM_THREE_DATA, calculates the high temperature hazard according to formula (1), and standardizes the data according to formula (2). Finally, they are stored in the corresponding database tables.

[0031] Heat Risk Index (HRI) = T-0.55 (1-Rh) (T-14.4) Formula (1)

[0032] Where, is the high temperature hazard index; T is the forecast temperature, ℃; Rh is the forecast relative humidity, %.

[0033]

[0034] In the formula, X i is the value of grid point i, X imin The minimum value of all non-zero grid points, X imax The maximum value of all non-zero grid points;

[0035] The high temperature exposure index calculation module first reads the high temperature exposure data of the grid points from the server database tables T_TQ_XM_EXPOSE and T_TQ_XM_THREE_EXPOSE, then queries the accumulated number of high temperature days and the number of hours of high temperature on the day of the corresponding grid point, processes and standardizes the data according to formula (2), and then multiplies the weight coefficients 0.2539 and 0.2124 respectively. Finally, the high temperature exposure index of the grid point is calculated and the data is standardized according to formula (2) and written into T_TQ_XM_EXPOSE and T_TQ_XM_THREE_EXPOSE. The following two points should be noted:

[0036] (1) The cumulative number of high temperature days needs to be further processed by formula (3).

[0037]

[0038] Where n represents the cumulative number of days with temperature ≥35℃.

[0039] Note: When the intensity of high temperature exceeds the threshold (≥35℃), it will cause harm to human health. The increase in the number of days with high temperature will not only make people feel uncomfortable and increase psychological pressure, but also increase the load of urban operation. However, as the number of days with high temperature increases, the urban complex human-land system can gradually adapt to high temperature through the adjustment of various subsystems, and the city's ability to resist high temperature tends to increase and stabilize. Based on this process, we borrow the concept of logistic function to simulate the change of dangerous state when the city is subjected to high temperature stress.

[0040] (2) The hourly forecast directly uses the duration of the temperature ≥35°C on that day to characterize the exposure condition. If a daily forecast is made, the “number of hours the high temperature lasts on that day” is not included in the calculation of the high temperature exposure index.

[0041] The urban high temperature risk index calculation module reads the calculation results of the high temperature hazard index and the high temperature exposure index of the grid point from the database table of the server, calculates the urban high temperature risk index according to formula (4) and stores it in the database table. Among them, the calculation data required for forecasting the hourly urban high temperature risk index comes from the database tables T_TQ_XM_HTS and T_TQ_XM_EXPOSE, and the calculation results are written into the database table T_TQ_XM_HTS; the calculation data required for forecasting the daily urban high temperature risk index comes from the database tables T_TQ_XM_THREE_DATA and T_TQ_XM_THREE_EXPOS, and the calculation results are written into the database table T_TQ_XM_THREE_DATA.

[0042] Heat Index (HI) = HRI * Exposure Formula (4)

[0043] In the formula, HRI stands for high temperature hazard index, and Exposure stands for high temperature exposure index;

[0044] The urban high temperature disaster risk mapping module reads grid data from the server's database tables T_TQ_XM_HTS and T_TQ_XM_THREE_DATA, classes urban high temperature disaster risks according to the following classification rules, and draws a spatial distribution map of urban high temperature disaster risks, which is stored in the database table T_TQ_XM_IMG.

[0045] The standard deviation method is used as the classification rule.

[0046] Level Ⅰ, blue: index value < mean value - σ;

[0047] Level II, yellow: average value - σ ≤ index value ≤ average value;

[0048] Level III, orange: average value < index value ≤ average value + σ;

[0049] Level IV, red: index value > average value + σ.

[0050] Note: The average value is the mean of the high temperature disaster risk values ​​of non-zero cities in the region, and σ is the standard deviation of the high temperature disaster risk values ​​of non-zero cities in the region.

[0051] The urban high temperature disaster risk spatiotemporal early warning system of the present invention has the functions of downloading data, saving data, calculating data, reading data and viewing data; downloading meteorological element files from a server, and writing them into a database table as required for the next step of calculation and reading; calculating the sub-indicators of the urban high temperature risk index according to the algorithm, including the high temperature hazard index, the high temperature exposure index and the final urban high temperature risk index, and writing them into the database table for the next step after the program automatically calculates and completes; reading the data in the database to draw a real-time spatial distribution map of the urban high temperature risk at the front end, and being able to display the high temperature risk of the selected point by clicking.

[0052] Embodiment 2:

[0053] like Figure 2 As shown: The spatiotemporal early warning method for urban high temperature disaster risk includes the following steps:

[0054] Step S1: downloading data from the database end according to the meteorological element database through the download storage operation module, including urban temperature data and relative humidity data;

[0055] Step S2: Calculate the high temperature hazard using the high temperature hazard index calculation module, standardize the calculated data, and store them in a corresponding database table;

[0056] Step S3: Use the high temperature exposure index calculation module to read the high temperature exposure data of the grid point, then query the cumulative number of high temperature days and the number of hours of high temperature on the day of the corresponding grid point, process and standardize the data, and write it into the database table;

[0057] Step S4: using the urban high temperature risk index calculation module to read the high temperature hazard index calculation results and high temperature exposure index calculation results of the grid points, and store them in a database table;

[0058] Step S5: Use the urban high temperature disaster risk mapping module to read grid data from the database table of the server, classify the urban high temperature disaster risk according to the classification rules, and draw a spatial distribution map of the urban high temperature disaster risk and store it in the database table.

[0059] Database Description

[0060] Taking into account the design requirements of the software itself, the collaboration of team work and the scalability of the later programs, the database is deployed on the FTP server, which has the characteristics of real-time access, high performance and high scalability.

[0061] Database software: Navicat

[0062] The names of the database tables created are: T_TQ_XM_HTS, T_TQ_XM_EXPOSE, T_TQ_XM_THREE_DATA, T_TQ_XM_THREE_EXPOSE, T_TQ_XM_IMG

[0063] Tables for reading and writing numerical forecast data of meteorological elements: T_TQ_XM_HTS, T_TQ_XM_THREE_DATA

[0064] Tables for reading and writing high temperature hazard index data: T_TQ_XM_HTS, T_TQ_XM_THREE_DATA

[0065] Tables for reading and writing high temperature exposure index data: T_TQ_XM_EXPOSE, T_TQ_XM_THREE_EXPOSE

[0066] Tables for reading and writing urban high temperature risk index data: T_TQ_XM_HTS, T_TQ_XM_THREE_DATA

[0067] Table for reading and writing cartographic data: T_TQ_XM_IMG

[0068] Encapsulate the operations on the database into modules. The benefits of separating the data storage into a separate module are obvious, which achieves weak coupling between system modules and facilitates data writing, reading and other operations.

[0069] CalculationDataDao module function: operate database tables T_TQ_XM_HTS, T_TQ_XM_EXPOSE, T_TQ_XM_IMG

[0070] getGridDao module function: operate database tables T_TQ_XM_THREE_DATA, T_TQ_XM_THREE_EXPOSE, T_TQ_XM_IMG

[0071] Database structure

[0072]

[0073]

[0074] Table Structure

[0075] The fields of each table in the database are designed as follows.

[0076] Table name: T_TQ_XM_HTS

[0077] Field Name Chinese description Data Types W_DATETIME date Varchar UPDATE_TIME Data update (calculation) time Date GRID_ID Grid ID Varchar WD_ACTUAL_VALUE Forecast temperature Varchar SD_ACTUAL_VALUE Forecast relative humidity Varchar FORECAST_VALUE Heat Hazard Index Varchar NORMALIZE High temperature hazard index standard value Varchar HEATINDEX Urban Heat Risk Index Varchar AVERAGEHI Average value of urban heat risk index Varchar L Standard deviation of urban high temperature risk index Varchar

[0078] Table name: T_TQ_XM_EXPOSE

[0079]

[0080]

[0081] Table name: T_TQ_XM_THREE_DATA

[0082] Field Name Chinese description Data Types W_DATETIME date Varchar UPDATE_TIME Data update (calculation) time Date GRID_ID Grid ID Varchar WD_ACTUAL_VALUE Forecast temperature Varchar SD_ACTUAL_VALUE Forecast relative humidity Varchar FORECAST_VALUE Heat Hazard Index Varchar NORMALIZE High temperature hazard index standard value Varchar HEATINDEX Urban Heat Risk Index Varchar AVERAGEHI Average value of urban heat risk index Varchar L Standard deviation of urban high temperature risk index Varchar

[0083] Table name: T_TQ_XM_THREE_EXPOSE

[0084] Field Name Chinese description Data Types GRID Grid ID Varchar DAYS Cumulative number of high temperature days Varchar W_DATETIME Data update (calculation) time Date EXPOSURE High temperature exposure part value Varchar D Standardized value of the number of hours the high temperature lasts on that day Varchar POINTS - Varchar EI Heat Exposure Index Varchar

[0085] Table name: T_TQ_XM_IMG

[0086] Field Name Chinese description Data Types W_DATETIME Data update (calculation) time Date URL City heat risk index layer address Varchar CREATIME Layer creation time Varchar TYPE Layer Type Varchar NUM Total number of grid points in the layer Varchar TAILLE - Varchar

[0087] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0088] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Urban high temperature disaster risk spatiotemporal early warning system, characterized by: It includes a database end, a server end and a user terminal. The database end is connected to the server end in communication, and the server end is connected to the user terminal in communication. The server end downloads data from the database end. The database end and the server end read and write data with each other, and the server end and the user terminal read and write data with each other. The database end includes a high-resolution meteorological element numerical forecast database and a storage database. The server end includes a download storage operation module, various index calculation modules and an urban high temperature disaster risk mapping module. The various index calculation modules include a high temperature hazard index calculation module, a high temperature exposure index calculation module and an urban high temperature risk index calculation module. The download storage operation module is connected to the various index calculation modules in communication, and the various index calculation modules are connected to the urban high temperature disaster risk mapping module in communication. The user terminal includes a front-end display module for the spatial distribution of urban high temperature disaster risks. The high temperature hazard index calculation module reads the temperature and relative humidity grid data processed by the "download storage calculation module" from the database table of the server, calculates the high temperature hazard, standardizes the data, and finally stores them in the corresponding database tables; The high temperature exposure index calculation module first reads the high temperature exposure data of the grid point from the database table of the server, then queries the accumulated number of high temperature days and the number of hours of high temperature on the day of the corresponding grid point, processes and standardizes the data, and multiplies them by weight coefficients 0.2539 and 0.2124 respectively, and finally calculates the high temperature exposure index of the grid point and writes the data into the database table after standardization; The city high temperature risk index calculation module reads the high temperature hazard index calculation results and the high temperature exposure index calculation results of the grid points from the database table of the server, calculates the city high temperature risk index and stores it in the database table.

2. The urban high temperature disaster risk spatiotemporal early warning system according to claim 1 is characterized in that: The download storage operation module downloads and processes high-resolution meteorological element numerical forecast data from the server, and stores the processed data in the server database table.

3. The urban high temperature disaster risk spatiotemporal early warning system according to claim 1 is characterized in that: The urban high temperature disaster risk mapping module reads grid data from a database table on a server, grades urban high temperature disaster risks according to classification rules, draws a spatial distribution map of urban high temperature disaster risks, and stores the map in a database table.

4. A spatiotemporal early warning method for urban high temperature disaster risk, characterized in that: The steps include: Step S1: downloading data from the database end according to the meteorological element database through the download storage operation module, including urban temperature data and relative humidity data; Step S2: Calculate the high temperature hazard using the high temperature hazard index calculation module, standardize the calculated data, and store them in a corresponding database table; Step S3: Use the high temperature exposure index calculation module to read the high temperature exposure data of the grid point, then query the cumulative number of high temperature days and the number of hours of high temperature on the day of the corresponding grid point, process and standardize the data, and write it into the database table; Step S4: using the urban high temperature risk index calculation module to read the high temperature hazard index calculation results and high temperature exposure index calculation results of the grid points, and store them in a database table; Step S5: Use the urban high temperature disaster risk mapping module to read grid data from the database table of the server, classify the urban high temperature disaster risk according to the classification rules, and draw a spatial distribution map of the urban high temperature disaster risk and store it in the database table.

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