Regional high-temperature heat wave meteorological disaster thematic resource library system and construction method

Abstract

The invention discloses a regional high-temperature heat wave meteorological disaster thematic resource library system and a construction method. The system comprises a basic resource layer module, an algorithm layer module, a product layer module and a management application layer module. The method comprises the steps of data acquisition, characteristic quantity calculation, data reorganization and fusion, product production, system application service providing and the like. According to the construction method and system of the resource library, a set of high-temperature heat wave disaster multi-source data automatic acquisition and fusion method is adopted. Meanwhile, the invention provides a set of regional high-temperature heat wave dynamic identification and evaluation algorithm, the regional high-heat wave weather process can be rapidly identified, process disaster-inducing factors are automatically extracted, the process level is evaluated, disaster information is collected and fused, and data support is rapidly provided for dynamic monitoring of high-temperature heat wave disaster events. On the basis, a high-temperature heat wave disaster sequence product is provided, a high-temperature heat wave meteorological data rapid sharing service is provided through the system, and cross-department and cross-region safety service sharing of high-temperature heat wave disaster data resources is achieved. According to the construction method and system of the regional high-temperature hot-wave meteorological disaster thematic resource library, a generalizable mode is provided for fine management and utilization of disaster event data resources of different regions and different disaster types.

Description

Technical Field

[0001] This invention mainly relates to the field of meteorological disaster monitoring and prediction. Specifically, this invention relates to a regional high-temperature heat wave meteorological disaster thematic resource database system and its construction method. Background Technology

[0002] Heat waves refer to weather events characterized by high temperatures and humidity over extended periods, which can threaten public health and safety, increase energy consumption, and disrupt social production activities. They have become a major challenge for human production, life, health, urban management, and social governance, both now and in the future. Therefore, it is imperative to strengthen effective prevention measures against heat waves to mitigate their potential severe losses.

[0003] Current technologies are lacking in the construction of specialized resource databases for high-temperature heat wave meteorological disasters. Firstly, there is a lack of automated tools capable of dynamically monitoring, quantitatively assessing, and producing and verifying products for extreme weather and climate events. Secondly, the accuracy and intelligence level of disaster identification need further improvement, and the automated production capacity and precision of products remain insufficient, with a lack of unified standards for related processes and methods. Furthermore, existing products are relatively simple in content, offering limited descriptions of key characteristics in weather processes and lacking in-depth analysis of historical and disaster data. The collection of disaster and socio-economic data from outside departments is lagging, resulting in limited information and low integration with forecasting services and impact assessments. In addition, the existing systems employ traditional development models, leading to low code reuse rates, high maintenance costs, and hindering deployment and widespread application. The insufficient multi-source data fusion and analysis capabilities based on meteorological disasters, along with the low efficiency of product sharing services, directly impact the ability to support emergency response and scientific decision-making in meteorological disasters.

[0004] In the process of monitoring regional high-temperature weather, there are problems such as multiple data sources, limited and scattered information, time lag, and limited product variety that does not meet the needs of service operations. Therefore, there is an urgent need to develop a regional high-temperature heat wave meteorological disaster thematic resource database that is timely, comprehensive, and capable of meeting the needs of monitoring extreme weather processes, risk assessment, decision-making services, and retrospective analysis. Summary of the Invention

[0005] Based on existing technologies, the objective of this invention is to propose a regional high-temperature heat wave meteorological disaster thematic resource database system and its construction method. This system can automatically collect and integrate multi-source data related to high-temperature heat wave disasters, and establish a high-temperature heat wave disaster event database and a high-temperature heat wave disaster basic resource database. On this basis, it can provide high-temperature heat wave thematic products for various scenarios such as disaster prevention and mitigation and decision-making services.

[0006] According to the present invention, the above content is achieved through a regional high-temperature heat wave meteorological disaster thematic resource database system and its construction method.

[0007] In a first aspect, the present invention proposes a regional high-temperature heat wave meteorological disaster thematic resource database system, the system comprising: The basic resource layer module is configured to build a database storage and preprocessing system through data sources to provide unified data for upper-layer applications. The algorithm layer module is configured to analyze data from the basic resource layer module; The product layer module is configured to integrate, compile, and process basic resources to generate products; and The management application layer module is configured to provide application services and system management functions.

[0008] Furthermore, the algorithm layer module includes: The task scheduling module is configured to schedule tasks for the dynamic identification and feature extreme value calculation module and the data acquisition and fusion module. A dynamic identification and characteristic extreme value calculation module is configured to automatically identify regional high-temperature heat wave weather processes, assess their intensity levels, and calculate characteristic quantities based on data from the basic resource layer module and a preset algorithm; and The data acquisition and fusion module is configured to intelligently acquire multi-source high-temperature heat wave disaster data and fuse the high-temperature heat wave disaster data.

[0009] Furthermore, the product layer module includes: The High Temperature Heat Wave Disaster Event Database module is composed of integrated high temperature heat wave process characteristic quantities; The basic resource database module for high-temperature heat wave disasters is compiled from monitoring data, forecasting service data, disaster data, and / or socio-economic data related to high-temperature heat waves; and... The High Temperature and Heat Wave Disaster Thematic Product Module provides processed products related to the topic of high temperature and heat wave disasters.

[0010] Furthermore, the high-temperature heat wave disaster-themed product module includes: Real-time dynamic products are configured to display high-temperature heat wave disaster events, extreme values ​​and / or characteristic quantities during the event process, and their actual distribution maps in real time. Impact analysis products are configured to analyze the characteristics and impacts of high-temperature heat wave disasters; and Historical data products are configured to identify and / or assess the intensity of past regional high-temperature heat waves, and to filter and / or rank them.

[0011] Furthermore, the application service is a regional high-temperature heat wave meteorological disaster thematic resource database application service, including: The heatwave disaster event retrieval function is configured to search for heatwave disaster events based on filter criteria. The online display of extreme values ​​of high temperature heat wave processes is configured to display the characteristic quantities and / or extreme values ​​of high temperature heat wave weather processes online. Information on the disaster situation during the heat wave is configured to display data statistics and / or image works related to the heat wave disaster; High-temperature heat wave resource list sharing, which is configured to provide download lists and / or data interface services for products and / or resources related to high-temperature heat wave processes; High-temperature heat wave process monitoring report, which is configured to automatically generate monitoring reports on high-temperature heat wave weather processes and provide online viewing and / or download services; The historical heat wave events are arranged to showcase long-term heat wave disaster cases ranked by heat wave intensity. The analysis of the characteristics of high-temperature heat wave disaster events is configured to provide frequency and / or variation characteristics of regional high temperatures within a certain time range; A GIS-fusion map of high-temperature heat wave disasters, configured to provide a GIS-fusion spatiotemporal distribution map of the characteristic quantities and / or disaster situation of each site; and The high-temperature heat wave knowledge graph visualization is configured to visually represent the relationship between the actual situation, disaster situation and station extreme values ​​during regional high-temperature weather in the form of a knowledge graph.

[0012] Furthermore, the system is deployed in the cloud and locally, and the data, algorithms, plugins, interfaces and / or frameworks of different local systems are all unified.

[0013] A second aspect of this invention proposes a method for constructing a regional high-temperature heat wave meteorological disaster thematic resource database, comprising the following steps: Step S1: Collect basic data related to high-temperature heat wave meteorological disasters; Step S2: Based on the basic data collected in Step S1, automatically identify the high temperature heat wave weather process and conduct a level assessment, extract the characteristic quantities of the high temperature heat wave weather process, and construct a high temperature heat wave disaster event database. Step S3: Automatically collect, merge, and organize the data to construct a basic resource database for high-temperature heat wave disasters; Step S4: Based on the high-temperature heat wave disaster event database described in Step S2 and the high-temperature heat wave disaster basic resource database described in Step S3, produce products; and Step S5: Provide application services based on the product described in step S4.

[0014] Furthermore, the sources of the basic data include: "Tianqing" is a meteorological big data cloud platform. GDS and M4 file servers; Meteorological record operation system; Meteorological disaster reporting system; The Great Compendium of Disasters in China; and Other official data.

[0015] Furthermore, the feature quantities mentioned in step S2 include: Regional high-temperature weather process level index (RI); The Single-Station High Temperature Weather Comprehensive Intensity Index (SI) The Heatwave Index (HI) is a high-temperature heatwave index. Regional high-temperature weather process characteristic extreme values.

[0016] Further, step S3 includes the following steps: Step S31: Construct a corpus of thematic discourse on high-temperature heat wave disasters; Step S32: Based on the two dimensions of region and disaster type, collect sample data of high temperature heat wave disaster and classify and label the sample data; Step S33: For the selected high temperature heat wave disaster information, use the general information extraction tools under the PaddlePaddle ecosystem to accurately extract high temperature heat wave knowledge and relationships. Step S34: Construct a fusion technique based on optional feature level to perform feature-level fusion on the data; Step S35: Automatically and / or manually eliminate data conflicts; and Step S36: Verify the quality of data fusion, iteratively optimize the technical model, and improve the accuracy of the fusion results.

[0017] This invention proposes a method and system for constructing a regional high-temperature heat wave meteorological disaster thematic resource database, which has at least the following beneficial effects: First, the resource database construction method and system adopt a set of automatic data collection and fusion methods for multi-source data of high temperature heat wave disasters: During each high temperature heat wave weather process, various platforms and websites inside and outside the meteorological department will generate massive event-related data. By constructing a high temperature heat wave disaster thematic corpus, and based on the corpus and knowledge graph technology, the method of discovering and extracting the characteristic information of high temperature heat wave disasters is studied. This realizes the multi-source fusion of disaster data, the supplementation of features, the identification and elimination of conflicts, and the mining of key disaster information needed for both forecasting and services. This solves the problem of inconsistent spatiotemporal scales and accuracy in the integration of multi-source heterogeneous big data of high temperature heat wave disasters.

[0018] Secondly, this invention proposes a set of algorithms for dynamic identification and assessment of regional high-temperature heat waves. This algorithm can quickly identify regional high-temperature heat wave weather processes and automatically extract disaster-causing factors, assess process levels, and collect and fuse disaster information based on these processes, providing rapid data support for the dynamic monitoring of high-temperature heat wave disaster events. Furthermore, a database of high-temperature heat wave disaster events spanning 64 years from 1961 to the present is established and supports automatic additions and updates.

[0019] Furthermore, this invention proposes a high-temperature heat wave disaster sequence product for weather and climate-related business applications, and provides a rapid sharing service for high-temperature heat wave meteorological data through the system, enabling cross-departmental and cross-regional secure sharing of high-temperature heat wave disaster data resources. Simultaneously, the system adopts a deployment architecture combining "cloud services" and "end-user applications," with unified data, algorithms, plugins, structures, and frameworks across different ports, supporting the localized construction, unified management, and widespread application of the system in different regions.

[0020] In summary, this invention proposes a method and system for constructing a regional high-temperature heat wave meteorological disaster thematic resource database. By building a complete meteorological disaster event information chain thematic resource database containing disaster monitoring data, disaster forecasting service products, and disaster impact information, it effectively solves the problems of limited and scattered relevant information and the limited variety of existing products. Through the development of a regional high-temperature weather process monitoring and identification algorithm, real-time dynamic identification, monitoring, and early warning of high temperatures are achieved, reducing time lag. Furthermore, the development of an intelligent acquisition and fusion algorithm for high-temperature heat wave disaster information, through data analysis and visualization technologies, allows for the acquisition of multi-dimensional and multi-type data, enabling dynamic disaster assessment and efficient coupling with forecasting services to meet the needs of monitoring, risk assessment, decision-making services, and post-mortem analysis of extreme weather processes. Simultaneously, component-based technology improves code reusability, reduces high code maintenance costs, and enables rapid deployment and application of the system. This invention provides a scalable model for the refined management and utilization of disaster event data resources by region and disaster type. Attached Figure Description

[0021] To further illustrate the advantages and other features of the various embodiments of the present invention, a more specific description of the embodiments of the present invention will be presented with reference to the accompanying drawings. It is understood that these drawings depict only typical embodiments of the invention and are therefore not intended to limit its scope. In the drawings, identical or corresponding parts will be indicated by the same or similar reference numerals for clarity.

[0022] Figure 1 The diagram illustrates a method for constructing a regional high-temperature heat wave meteorological disaster resource database according to an embodiment of the present invention.

[0023] Figure 2 The diagram shows a schematic of a regional high-temperature heat wave meteorological disaster thematic resource database system according to one embodiment of the present invention.

[0024] Figure 3 A flowchart illustrating the dynamic identification of regional high-temperature heat waves in one embodiment of the present invention is shown.

[0025] Figure 4 The diagram illustrates a "cloud service-end application" of a regional high-temperature heat wave meteorological disaster thematic resource database system in one embodiment of the present invention.

[0026] Figure 5 This document illustrates a visualization page of a regional high-temperature heat wave meteorological disaster resource database, representing one application embodiment of the present invention.

[0027] List of reference numerals 100 Regional High Temperature and Heat Wave Meteorological Disaster Thematic Resource Database 101 Basic Resource Layer Module 102 Algorithm Layer Module 1021 Task Scheduling Module 1022 Dynamic Recognition and Eigenvalue Calculation Module 1023 Data Acquisition and Fusion Module 103 Product Layer Module 1031 High Temperature Heatwave Disaster Event Database Module 1032 High Temperature Heat Wave Disaster Basic Resource Database Module 1033 High Temperature Heatwave Disaster Special Product Module 104 Management Application Layer Module 1041 Application Module 1042 Management module 200 data sources 300 Regional High Temperature and Heat Wave Meteorological Disaster Thematic Resource Database 301 Infrastructure Cloud Platform Layer Module 302 Cloud Service Layer Module 3021 Data Storage Management Module 3022 Component Library Module 3023 Processing Production Line Module 3024 Long-Sequence Observational Dataset 3025 Long-Series Climate Data Products 3026 Meteorological Disaster Long-Sequence Thematic Data Products 303 Application Layer Module 3031 Front-end application module 3032 Backend Management Module Detailed Implementation It should be noted that the components in the various figures may be shown exaggeratedly for illustrative purposes and are not necessarily to scale. In each figure, the same reference numerals are used for components that are identical or have the same function.

[0028] In this invention, the various embodiments are merely intended to illustrate the solutions of the invention and should not be construed as limiting.

[0029] In this invention, unless otherwise specified, the quantifiers “a” and “one” do not exclude scenarios involving multiple elements.

[0030] It should also be noted that, for clarity and simplicity, only a portion of the components may be shown in the embodiments of the present invention. However, those skilled in the art will understand that, under the teachings of the present invention, the required components can be added according to specific needs. Furthermore, unless otherwise stated, features in different embodiments of the present invention can be combined with each other. For example, a feature in the second embodiment can replace a corresponding or functionally identical or similar feature in the first embodiment, and the resulting embodiment will also fall within the scope of disclosure or description of this application.

[0031] It should also be noted that within the scope of this invention, the terms "same", "equal", and "equal to" do not mean that the two values ​​are absolutely equal, but allow for a certain reasonable error. In other words, the terms also cover "substantially the same", "substantially equal", and "substantially equal to".

[0032] In this invention, the modules of the system according to the invention can be implemented using software, hardware, firmware, or a combination thereof. When a module is implemented using software, its function can be implemented through computer program flow. For example, the module can be implemented using code segments (such as code segments in languages ​​like C and C++) stored in a storage device (such as a hard disk, memory, etc.), wherein the corresponding function of the module can be implemented when the code segment is executed by a processor. When a module is implemented using hardware, its function can be implemented by setting a corresponding hardware structure. For example, the module's function can be implemented by hardware programming a programmable device such as a field-programmable gate array (FPGA), or by designing an application-specific integrated circuit (ASIC) that includes multiple transistors, resistors, capacitors, and other electronic devices. When a module is implemented using firmware, the module's function can be written into a read-only memory such as an EPROM or EEPROM in the form of program code, and the corresponding function of the module can be implemented when the program code is executed by a processor. In addition, some functions of the module may need to be implemented by separate hardware or by working in cooperation with the hardware. For example, the detection function is implemented by a corresponding sensor (such as a proximity sensor, accelerometer, gyroscope, etc.), the signal transmission function is implemented by a corresponding communication device (such as a Bluetooth device, infrared communication device, baseband communication device, Wi-Fi communication device, etc.), the output function is implemented by a corresponding output device (such as a display, speaker, etc.), and so on.

[0033] Furthermore, the numbering of the steps in the methods of the present invention does not limit the execution order of the method steps. Unless otherwise specified, the method steps may be executed in different orders.

[0034] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0035] Figure 1 This diagram illustrates a method for constructing a regional high-temperature heat wave meteorological disaster thematic resource database according to an embodiment of the present invention. Figure 1 As shown, in one embodiment of the present invention, the method includes the following steps: Step S1: Collect basic data related to high temperature heat wave meteorological disasters.

[0036] Step S2: Based on the ground meteorological observation data collected in Step S1, automatically identify the high temperature heat wave weather process and conduct a level assessment, extract the characteristic quantities of the high temperature heat wave weather process, and construct a regional high temperature heat wave process event database.

[0037] Step S3: Automatically collect, merge, and organize the data to construct a basic resource database for regional high-temperature heat wave disasters; Step S4: Based on the regional high-temperature heat wave disaster basic resource database described in Step S3, produce special products on high-temperature heat wave disasters; and Step S5: Based on the product described in step S4, provide system application services.

[0038] In one embodiment of the present invention, the basic data may be meteorological monitoring data, forecast and service product data, disaster data and / or socio-economic activity data related to the high-temperature heat wave disaster event process. It requires comprehensive integration with relevant data systems to obtain the corresponding data, specifically including but not limited to: Connect to the meteorological archives system to obtain historical meteorological reports, disaster information, and other image, digital, and textual materials. Connect to the Tianqing system to obtain real-time historical observation data from the ground, radar, and / or satellites; connect to the disaster reporting system to obtain current disaster information; Connect with disaster risk census data to obtain disaster risk zoning, risk warning and other data from the first national natural disaster risk census; Connect with publicly available disaster databases, such as the China Disaster Encyclopedia, the Meteorological Disaster Encyclopedia, and the Ministry of Emergency Management's disaster database; Connect with social statistical data to obtain basic population, economic, agricultural and other related data; Connect with mainstream official media to obtain relevant media and public opinion information.

[0039] In one embodiment of the present invention, a set of developed regional high temperature heat wave dynamic identification and assessment algorithms is used to quickly identify and assess regional high heat wave weather processes. Based on the high temperature heat wave weather processes, the algorithm automatically extracts process-causing factors, assesses process levels, and other characteristic quantities, collects and integrates disaster information, and provides rapid data support for dynamic monitoring of high temperature heat wave disaster events. This enables the establishment of a regional high temperature heat wave process event database with a historical span of up to 50 years, and supports automatic addition and updates.

[0040] In one embodiment of the present invention, the construction of a basic resource database for high-temperature heat wave disasters is based on multi-source data intelligent acquisition technology. This technology achieves data cleaning and quality control, establishes a topic discrimination model, and constructs geographic and spatiotemporal correlations. It employs high-temperature heat wave multi-source data fusion analysis technology, which includes the construction of a topic corpus, classification of high-temperature heat wave disaster information, feature and relationship extraction, and conflict resolution, thereby achieving feature fusion of disaster data. Metadata on meteorological data related to high-temperature heat wave disasters is organized, and the monitoring data resources, forecast service product resources, disaster resources, and socio-economic data related to high-temperature heat wave disasters are organized and classified, thus constructing a basic resource database for high-temperature heat wave disasters.

[0041] In one embodiment of the present invention, based on knowledge graph technology and GIS map technology, a regional high-temperature heat wave disaster-themed product is established for real-time monitoring of meteorological disasters, historical data review, and climate impact assessment. This product includes high-temperature heat wave disaster event products, high-temperature heat wave process extreme value monitoring products, high-temperature heat wave disaster characteristic analysis products, high-temperature heat wave process monitoring and assessment reports, high-temperature heat wave disaster real-time distribution maps, and / or high-temperature heat wave disaster long-sequence products.

[0042] In one embodiment of the present invention, the regional high-temperature heat wave disaster thematic resource database provides users with regional high-temperature heat wave thematic resource database services for decision-making services, public services and climate impact assessments, including but not limited to monitoring and assessment of high-temperature heat wave disaster events, statistics and multi-dimensional retrieval of historical high-temperature heat wave disaster events, reproduction, analysis and application of historical typical processes, and related high-temperature heat wave disaster data resource sharing services.

[0043] Figure 2 A schematic diagram of a regional high-temperature heat wave meteorological disaster thematic resource database system is shown in one embodiment of the present invention. For example... Figure 2 As shown, the regional high temperature heat wave meteorological disaster special resource database 100 includes a basic resource layer module 101, an algorithm layer module 102, a product layer module 103, and a management application layer module 104.

[0044] The basic resource layer module 101 collects and aggregates data and / or information from the data source 200 after it has been processed by the algorithm layer module 102, providing unified data for upper-layer applications.

[0045] Specifically, the data source 200 includes: The meteorological big data cloud platform "Tianqing" provides basic ground station observation data for the automatic identification of regional high temperature and heat wave weather processes, and provides upper-air, satellite, radar, smart grid products, forecast service products, and disaster risk survey data for the compilation of basic resources for high temperature and heat wave weather processes. GDS and M4 file servers provide ground data, upper-air data, satellite data, and numerical forecasts in MICAPS format for the compilation of basic resources for high-temperature heat wave weather processes, which can be used for retrospective analysis of high-temperature heat wave meteorological disaster weather processes. The meteorological archives system provides historical meteorological reports, as well as images, text, and / or digital materials related to high-temperature heat wave meteorological disasters. The meteorological disaster reporting system provides real-time meteorological data, historical data queries, and / or real-time disaster information sharing for high-temperature heat wave meteorological disasters. The "China Disaster Encyclopedia" provides information on high-temperature heat wave disasters from 2000 onwards. It is highly official, systematic, and authoritative, filling the gaps in early observation data. Other official data, including but not limited to government statistical departments, emergency management departments, global disaster data platforms, and official websites, provide social statistical information such as population, economy, and agriculture, as well as information on the impact of high-temperature heat wave meteorological disasters.

[0046] The algorithm layer module 102 includes a task scheduling module 1021, a dynamic recognition and feature extreme value calculation module 1022, and a data acquisition and fusion module 1023.

[0047] The task scheduling module 1021 schedules tasks for the dynamic identification and feature extreme value calculation module 1022 to achieve dynamic identification of high temperature heat wave weather processes; and the task scheduling module 1021 schedules tasks for the data acquisition and fusion module 1023 to achieve the acquisition and fusion of multi-source data of high temperature heat wave disasters.

[0048] The dynamic identification and feature extreme value calculation module 1022 automatically identifies regional high temperature heat wave weather processes, assesses the intensity level, and calculates feature quantities based on the data of the basic resource layer module 101 and through a preset algorithm.

[0049] The data acquisition and fusion module 1023 intelligently acquires multi-source data on high-temperature heat wave disasters from data source 200 and performs feature-level fusion on multi-source heterogeneous disaster data of high-temperature heat waves.

[0050] Product layer module 103 includes a high-temperature heat wave disaster event database module 1031, a high-temperature heat wave disaster basic resource database module 1032, and a high-temperature heat wave disaster special product module 1033.

[0051] The high-temperature heat wave disaster event database module 1031 is integrated from the high-temperature heat wave process characteristic quantities calculated and / or recorded by the dynamic identification and characteristic extreme value calculation module 1022.

[0052] The high-temperature heat wave disaster basic resource database module 1032 is compiled from high-temperature heat wave monitoring data, forecast service data, disaster data and / or socio-economic data collected and integrated by the data acquisition and fusion module 1023.

[0053] The high-temperature heat wave disaster event database module 1031 and the high-temperature heat wave disaster basic resource database module 1032 are linked in the geographic-temporal dimension and applied to the high-temperature heat wave disaster special product module 1033, providing a foundation for the production and research and development of high-temperature heat wave disaster special products.

[0054] The management application layer module 104 includes an application-side module 1041 and a management-side module 1042.

[0055] Application module 1041 provides services such as multi-dimensional retrieval of high temperature heat wave events, GIS fusion map of high temperature heat wave disasters, high temperature heat wave monitoring reports, high temperature heat wave characteristic analysis, and high temperature heat wave data sharing.

[0056] The management module 1042 manages the tasks, algorithms, databases, products, permissions, and logs of the regional high-temperature heat wave meteorological disaster resource database 100.

[0057] The following describes the process of dynamic identification and feature extremum calculation of regional high-temperature heat waves by the dynamic identification and feature extremum calculation module 1022 in the algorithm layer module 102.

[0058] In one embodiment of the present invention, an algorithm for dynamic identification of regional high-temperature processes and assessment of the intensity level of high-temperature processes is developed based on the meteorological industry standard "QX / T 228—2014 Classification of Regional High-Temperature Weather Processes". The high-temperature heat wave level of each national station during the regional high-temperature process is dynamically assessed based on "GB / T29475—2012 High-Temperature Heat Wave Levels".

[0059] Figure 3 A flowchart illustrating the dynamic identification of regional high-temperature heat waves in one embodiment of the present invention is shown. Figure 3 As shown, the specific process is as follows: After the task scheduling module 1021 releases the dynamic identification plan task, the data acquisition and fusion module obtains the daily maximum temperature of national stations in the region from the meteorological big data cloud platform "Tianqing". The system identifies whether a region is experiencing a high-temperature day based on the daily maximum temperature; if not, the process is terminated. If a region is identified as having a high-temperature day, the process will further identify whether a regional high-temperature weather event has occurred in that region. If not, the process will terminate. If a regional heatwave is identified in the area, perform the following steps: Calculate the regional high-temperature weather process index (RI), classify regional high-temperature weather processes into levels, and calculate process characteristic quantities; and Calculate the comprehensive intensity index (SI) of high temperature weather at a single station, classify the high temperature heat wave level at a single station, and calculate the characteristic quantities of a single station.

[0060] Specifically, the regional high-temperature weather process level is classified according to the Regional High-Temperature Weather Process Index (RI), and the calculation formula for the Regional High-Temperature Weather Process Index (RI) is as follows: In the formula: RI is a regional high-temperature weather process level indicator; G kThis represents the comprehensive intensity level of high-temperature weather at a single station within the region. W k Within the region G k The proportion of the corresponding number of sites to the total number of sites.

[0061] Comprehensive intensity level of high temperature weather at a single station in the region G k The value of is related to the single-station comprehensive intensity index of high temperature weather (SI), and the calculation formula of the single-station comprehensive intensity index of high temperature weather (SI) is as follows: In the formula: SI is the comprehensive intensity index of high-temperature weather at a single station. I j The high temperature intensity of a single station is classified into three levels, namely the highest daily temperature, with values ​​of 1, 2, and 3, corresponding to three temperature ranges: [35℃, 37℃), [37℃, 40℃), and [40℃, +∞). T j To and I j The corresponding number of high-temperature days.

[0062] The SI value of each station during all regional high-temperature weather events in the designated area (e.g., 1981-2010 or a longer period) was calculated using the formula for calculating the comprehensive intensity index (SI) of high-temperature weather at a single station. The frequency of each SI value was then statistically analyzed, and the SI values ​​were sorted in ascending order. The percentile method was used to divide the SI values, determining the range of values ​​for each level. According to QX / T 228—2014, 95%, 85%, and 60% were taken as the critical values ​​for the intensity levels, thus obtaining the comprehensive intensity level of high-temperature weather at a single station within the region. G k The discrimination method is shown in Table 1.

[0063] Table 1. Method for determining the comprehensive intensity level of high temperature weather at a single station within the region. Therefore, the regional high-temperature weather process level index (RI) can be calculated. Based on the RI value, the intensity level of regional high-temperature processes can be classified, as shown in Table 2.

[0064] Table 2. Classification Method of Intensity Levels of Regional High Temperature Weather Processes According to QX / T 228—2014, a regional high-temperature process is defined based on data from national meteorological stations. It begins when a regional high-temperature day meets the criteria and ends the day before the criteria are no longer met, lasting for two or more days. During this period, at least one day the high-temperature area covers 50% or more of the designated area. A regional high-temperature day is defined as a day in which 20% or more of the area within the region experiences high temperatures (daily maximum temperature ≥ 35℃).

[0065] In one application embodiment of the present invention, all regional high-temperature events in Anhui Province from 1961 to 2020 are used as samples. The SI value of each station is calculated, and the corresponding percentage values ​​are obtained after sorting in ascending order. The SI values ​​corresponding to the 95th, 85th, and 60th percentiles are 19, 10, and 4, respectively. That is, the SI value ranges corresponding to G1, G2, G3, G4, and G5 are [19, +∞), [10, 19), [4, 10), (0, 4), and 0. Based on this, the comprehensive intensity level of high-temperature weather at a single station is determined, and the RI value of the high-temperature weather process level index is further obtained, thereby obtaining the regional high-temperature weather process level.

[0066] The classification of high-temperature heat waves is based on the High Temperature Heat Wave Index (HI), which is calculated using the following formula: In the formula: TI is the heat index for the day; TI' is the heat threshold value. It is calculated based on daily ground-based meteorological data from May to September of each year from 1961 to 2010. The heat index of the samples with a daily maximum temperature greater than 33°C is calculated and the heat index series is arranged in ascending order. The 50th quantile is selected as the local heat threshold value. TI i This represents the heat index of the i-th day prior to the current day. nd i This represents the number of days from the current day to the i-th day prior to the current day. n represents the duration of the hot weather event, measured in days.

[0067] The formula for calculating the Heat Index (TI) is as follows: When the daily average relative humidity (RH) is ≤60%, the formula for calculating TI is: When the daily average relative humidity (RH) is greater than 60%, the formula for calculating TI is: In the formula: T max This represents the highest temperature of the day, expressed in degrees Celsius (°C). RH is the daily average relative humidity (%) The high-temperature heat wave index HI can be calculated using the above method, and the high-temperature heat wave level can be determined accordingly.

[0068] According to GB / T 29475—2012, high-temperature heat waves are classified into three levels: mild heat wave (Level III), moderate heat wave (Level II), and severe heat wave (Level I). The specific indicators and definitions are shown in Table 3.

[0069] Table 3 Classification and Definition of High Temperature Heatwave Levels In one embodiment of the present invention, the characteristic extreme values ​​of regional high-temperature weather processes are calculated. Specifically, the following extreme values ​​of regional high-temperature heatwave processes are calculated and / or recorded by recording the duration, affected area (total number of stations), regional high-temperature weather process index value (RI), and regional high-temperature weather process level: ①The extreme maximum temperature of the process, the time of occurrence of the extreme maximum temperature of the process, the station number and / or the station name of the extreme maximum temperature of the process; ②The extreme minimum temperature of the process, the time of occurrence of the extreme minimum temperature of the process, the station number and / or the station name for observing the extreme minimum temperature of the process; and ③ The maximum average relative humidity of the process, the date of occurrence of the maximum average relative humidity of the process, the station number of the maximum average relative humidity of the process and / or the name of the station where the maximum average relative humidity of the process was observed.

[0070] As described above, in one embodiment of the present invention, the dynamic identification and feature extreme value calculation module 1022 realizes the dynamic identification and feature extreme value calculation and extraction of regional high temperature heat waves.

[0071] In one embodiment of the present invention, the data acquisition and fusion module 1023 intelligently acquires multi-source data on high-temperature heat wave disasters from the data source 200, and the specific process is as follows: First, establish rules for data collection and data reliability assessment of high-temperature heat wave disasters.

[0072] In one application embodiment of the present invention, the data collection sources include: official websites of emergency management departments, statistical departments, and government departments, whose data has the highest authenticity and can be configured with a credibility level of 1; internal systems of meteorological departments such as disaster reporting systems and meteorological archive business systems, whose data has relatively high authenticity and can be configured with a credibility level of 2; and data from platforms such as Weibo, Toutiao, and Douyin, whose data requires further cleaning and verification and is for reference only, therefore their credibility level is configured with a credibility level of 3. The credibility level decreases progressively from 1 to 3.

[0073] Subsequently, multi-source data on high-temperature heat wave disasters were intelligently collected.

[0074] In one embodiment of the present invention, a multi-source data acquisition template for meteorological departments based on timed tasks is configured.

[0075] Specifically, in one application embodiment of the present invention, a multi-source data acquisition template is configured for provincial "Tianqing" and MICAPS4 file servers. The template includes source system, data category, data name, data encoding, service interface, interface parameters, file address, etc., and sets acquisition tasks for basic data such as ground, upper air, satellite, numerical forecast, and smart grid based on the Quartz task framework and geospatial correlation algorithm to achieve timed intelligent data acquisition.

[0076] In one embodiment of the present invention, the data collection strategy can be adjusted according to actual business needs, and the system can be connected to channels such as meteorological archive business system and meteorological disaster direct reporting system. Through geographic spatiotemporal correlation algorithm, the system can automatically collect data such as ground meteorological monthly report metadata and disaster information.

[0077] In one embodiment of the invention, data is collected from the Internet using web crawlers. Specifically, based on historical text content related to high-temperature heatwave natural disasters, a topic modeling algorithm is used to analyze and calculate the scores of key words. The word with the highest score in each topic is selected as a key phrase, and the top K (preferably K=10) most relevant topics are selected as the core topic clues for high-temperature heatwaves in the current time period, thereby constructing a high-temperature heatwave topic clue database. Subsequently, data is crawled from the official websites of emergency management departments, statistical departments, and government departments, as well as their official WeChat accounts, Weibo, Douyin, and other platforms, using web crawlers, Selenium simulated access, and other methods. Based on the aforementioned high-temperature heatwave topic clue database, a pre-trained discriminator model is used to calculate the topic similarity value corresponding to the crawled data. If the similarity value exceeds a set topic similarity threshold, it indicates that the data is related to the target topic; otherwise, the data is discarded. In addition, a multi-technology composite approach is used to deduplicate and filter the collected data.

[0078] In one embodiment of the present invention, the data acquisition and fusion module 1023 fuses multi-source data on high-temperature heat wave disaster events. Addressing the challenges of inconsistent spatiotemporal scales and lack of accuracy in integrating multi-source heterogeneous big data on high-temperature heat wave disasters, a thematic corpus on high-temperature heat wave disasters is constructed. Based on the corpus and knowledge graph technology, methods for discovering and extracting characteristic information of high-temperature heat wave disasters are studied, initially forming a real-time thematic corpus on high-temperature heat wave disasters for specific regions. This achieves multi-source fusion of disaster data, feature completion and supplementation, conflict identification and elimination, and continuous improvement of the accuracy of the fusion results. Specifically, it consists of the following six steps: Step S31: Construct a corpus of thematic discourse on high-temperature heat wave disasters; First, the original corpus data is cleaned. The construction of the corpus on the topic of high-temperature heatwave disasters includes collecting corpus data on high-temperature disasters, segmenting the corpus into words, and removing stop words and / or special symbols. This removes incomplete and duplicate data from the corpus, ensuring that all retained data pertains to the field of high-temperature heatwave disasters.

[0079] In one application embodiment of the present invention, a corpus on the theme of high temperature heat wave disasters in Anhui Province was constructed, which specifically includes a corpus of two dimensions: "region" and "disaster type". The regional corpus contains keywords covering the administrative regions of Anhui Province, cities, and counties, while the disaster type corpus includes keywords related to high temperature heat wave disasters.

[0080] Subsequently, the format of the corpus was standardized and validated. Basic format information such as the source of news data and the title, content, and keywords of information on high-temperature heat wave disasters were extracted. Data that did not meet the requirements was deleted to ensure that the key basic attributes (source, title, and content) of the retained corpus met the format requirements.

[0081] Finally, the required attributes of the corpus are validated. The data content is extracted, and the required attribute information is extracted, including the region, time, and theme of the heat wave disaster event, to ensure that the retained corpus is all related to heat wave disasters in that region. Step S32: Classify data information; Based on the two dimensions of region and disaster type, sample data of high-temperature heat wave disasters were collected, and the sample data were classified and labeled to create labels including "forecast information," "early warning information," "disaster information," "emergency response," and "post-disaster relief," forming high-temperature heat wave disaster labeled training data. The disaster data classification was trained using the BERT classification model.

[0082] The BERT classification model, as a pre-trained language model, can capture the deep semantic relationships between words in the text, thereby efficiently identifying information on high-temperature heat wave disasters in the collected corpus.

[0083] Step S33: Knowledge Features and Relationship Extraction; For the high-temperature heat wave disaster information filtered by the BERT model, the UIE (Universal Information Extraction) tool under the PaddlePaddle ecosystem is used to accurately extract the knowledge features and relationships of the high-temperature heat wave.

[0084] In one application embodiment of the present invention, the knowledge features to be extracted are first determined, such as the disaster situation information including the region, time, affected population, death toll, direct economic loss, affected area, disaster-stricken area, and area of ​​complete crop failure, and the relationships (e.g., "occurrence area - disaster event", "affected objects - damage situation"). Then, the text filtered by the BERT model (e.g., "In July 2013, due to continuous high temperatures, 588,691 people in Ma'anshan City, Anhui Province were affected, with direct economic losses of 349.3486 million yuan") is input into U. The IE model, through learning from a small number of disaster-related samples, can automatically identify and extract knowledge features from text such as "Ma'anshan City, Anhui Province" (region), "July 2013" (time), "direct economic loss" (affected entities), and "349.3486 million yuan" (damage situation). At the same time, it establishes relationships between entities such as "Ma'anshan City, Anhui Province - direct economic loss" and "direct economic loss - 349.3486 million yuan", ultimately transforming unstructured disaster text into structured data tables, providing data support for the analysis and decision-making of high-temperature heat wave disasters.

[0085] Step S34: Data feature-level fusion; A fusion technology method based on optional features is constructed. Key features, optional features, and feature relationships are extracted and fused to complete the knowledge of high temperature heat wave disasters. The target is a set of high temperature heat wave disaster feature attributes (high temperature disaster event area, time, affected population, death population, direct economic loss, affected area of ​​crops, disaster-stricken area, area of ​​crop failure, etc.). When the key features are mandatory feature matching, optional features are extracted and fused to complete the knowledge.

[0086] Step S35: Eliminate data feature conflicts; When the amount of raw data to be processed increases significantly, it becomes difficult to obtain the cross-correlation between different features. In this case, conflict resolution methods are used for fusion and completion.

[0087] Based on the credibility of the information source and the similarity of the information extraction results, an automatic conflict resolution scheme for feature extraction in response to high-temperature heat wave disasters is implemented using ensemble learning technology. When ensemble learning cannot automatically resolve conflicts, feature fusion can be performed through manual interaction.

[0088] Step S36: Iterate and improve the accuracy of data fusion.

[0089] Develop technical indicators for data acquisition and fusion to evaluate the accuracy of data acquisition in the aforementioned steps. These indicators include: data classification accuracy, information extraction accuracy, and feature fusion accuracy.

[0090] In one embodiment of the present invention, as evaluation criteria, the data classification accuracy C1 ≥ 90%, information extraction accuracy C2 ≥ 90%, and feature fusion accuracy C3 ≥ 85% are pre-set. A sample dataset is constructed, including a training set, a validation set, and a test set. The data collection and fusion accuracy is automatically calculated. If the data fusion quality does not meet the standards, the process is traced back from "fusion result - information extraction - data classification - data collection" to identify problems in the training dataset, supplement the collection of corresponding data, and correct the annotation rules.

[0091] For example, in one application embodiment of the present invention, in response to the situation of "large granularity of high temperature heat wave disaster feature annotation", the granularity of the impact feature attributes of high temperature heat wave disaster is further refined from aspects such as "population", "disease", "crops", "animal husbandry", "energy and electricity" and "economic losses". Using the updated training sample database, incremental retraining is performed on the data classification model, information extraction model and feature fusion model, and iteratively. The data classification accuracy C1, information extraction accuracy C2 and feature fusion accuracy C3 are recalculated on the test set until the accuracy reaches the preset standard.

[0092] In one embodiment of the present invention, based on the acquisition and fusion results of multi-source high-temperature heat wave disaster meteorological data and the technical specifications related to the compilation of the high-temperature heat wave disaster basic resource database, the high-temperature heat wave disaster meteorological data is organized and imported into the high-temperature heat wave disaster basic resource database module 1032. Specifically, the high-temperature heat wave disaster basic resource database module 1032 includes: The high-temperature heat wave disaster monitoring data resource database includes the structure rules and data formats of various types of data such as basic meteorological data, surface data, upper-air data, radar data, and satellite data related to high-temperature heat wave disasters; The resource library for high-temperature heat wave disaster forecasting services includes the structural rules and data formats of various types of data such as intelligent grid data for high-temperature heat wave disasters, numerical forecast data, weather forecast and early warning data, decision service products, and prediction and assessment products; The heatwave disaster resource database includes data on the source, time, location, impact, characteristics, and auxiliary information of heatwave disasters, as well as the data formats; and... The socio-economic resource database includes the structure and format of data on natural resources, population, economy, environment, and monitoring and early warning capabilities in areas affected by high-temperature heatwave disasters.

[0093] In one embodiment of the present invention, based on the high-temperature heat wave disaster event database module 1031 and / or the high-temperature heat wave disaster basic resource database module 1032, and processed according to specific business scenarios, a high-temperature heat wave disaster-themed product module 1033 is generated. Specifically, the high-temperature heat wave disaster-themed product module 1033 includes: Real-time dynamic products are configured to display high-temperature heat wave disaster events, extreme values ​​and / or characteristic quantities during the event process, and their actual distribution maps in real time. Impact analysis products are configured to analyze the characteristics and impacts of high-temperature heat wave disasters; and Historical data products are configured to identify and / or assess the intensity of past regional high-temperature heat waves, and to filter and / or rank them.

[0094] The specific list of products related to high temperature and heat wave disasters is shown in Table 4.

[0095] Table 4. List of Products Related to High Temperature and Heatwave Disasters Figure 4 This diagram illustrates a "cloud service-client application" of a regional high-temperature heat wave meteorological disaster thematic resource database system according to one embodiment of the present invention. Figure 4 As shown, in one embodiment of the present invention, the regional high temperature heat wave meteorological disaster special resource database system 300 includes an infrastructure cloud platform layer module 301, a cloud service layer module 302, and an end application layer module 303.

[0096] The infrastructure cloud platform layer module 301 is configured as the hardware and basic resource support layer of the high temperature heat wave meteorological disaster special resource database system 300, providing basic resources such as cloud computing, storage, and network, and serving as the operating carrier for all upper-layer functional modules.

[0097] The cloud service layer module 302 includes a data storage management module 3021, a component library module 3022, and a processing pipeline module 3023. In one embodiment of the present invention, the data storage management module 3021 includes a distributed relational database for storing management-type structured data, a distributed relational database for storing business-type structured data, and a distributed NAS for storing unstructured data. The processing pipeline module 3023 acquires data from the long-sequence observation base dataset 3024 and / or long-sequence climate data product 3025 of the unified service data interface, and realizes the write-back of the long-sequence meteorological disaster thematic product 3026 with the help of the component orchestration of the component library module 3022.

[0098] The cloud service layer module 302 outputs its processing capabilities and data to the outside world through standardized interfaces (such as RESTful API for web services and JDBC for database connections) for the end application layer module 303 to call.

[0099] The application layer module 303 includes a front-end application module 3031 and a back-end management module 3032.

[0100] Among them, the front-end application module 3031 provides the following business functions to users: Multi-dimensional search for high-temperature heat wave events, supporting queries for historical high-temperature events based on multiple conditions (time, region, intensity, etc.); The high-temperature heat wave disaster GIS fusion map combines high-temperature data with GIS (Geographic Information System) to visualize the spatial distribution of disasters; High-temperature heatwave monitoring and reporting services, generating and providing standardized and / or customized monitoring reports (such as process overview, disaster images, and historical comparisons); High-temperature heatwave characteristics analysis includes statistical analysis of the intensity, duration, and affected area of ​​the high temperatures; and... High Temperature Heatwave Data Sharing: Enables secure sharing of disaster data across departments and regions (provides download lists and data interfaces).

[0101] The backend management module 3032 manages the tasks, algorithms, disaster event database, basic resource database and / or products of the regional high temperature heat wave meteorological disaster special resource database system 300, and performs permission management and / or log management.

[0102] In summary, in one embodiment of the present invention, the various levels of the regional high-temperature heat wave meteorological disaster resource database system 300 are interconnected through data flow and service calls. The application functions of each microservice are constructed in a component-based manner, and a deployment architecture combining "cloud services" and "end-side applications" is adopted. By unifying data, algorithms, plugins, interfaces, and frameworks, the component-based technology improves code reusability and reduces code maintenance costs, thereby enhancing the reusability, rapid adaptability, and scalability of cross-regional deployment, and ultimately achieving efficient management and convenient services for high-temperature heat wave disaster resources.

[0103] Figure 5 This illustrates a visualization page of a regional high-temperature heat wave meteorological disaster resource database, representing one application embodiment of the present invention. For example... Figure 5 As shown in one application embodiment of the present invention, a special resource database system for high-temperature heat wave disasters in Anhui Province was constructed using the construction method proposed in this invention. The system provides online application services including high-temperature heat wave disaster event retrieval, high-temperature heat wave disaster GIS fusion map, high-temperature heat wave disaster monitoring reports, high-temperature heat wave disaster characteristic analysis, and high-temperature heat wave data resource sharing. Specific online application services and their functional descriptions are shown in Table 5.

[0104] Table 5 Application Services of Anhui Province High Temperature and Heat Wave Disaster Thematic Resource Database System The advantages and beneficial effects of the regional high-temperature heat wave meteorological disaster thematic resource database system proposed in this invention are illustrated below with specific application examples.

[0105] First, the regional high-temperature heat wave meteorological disaster resource database system proposed in this invention can provide timely and accurate dynamic monitoring data on high-temperature heat wave disasters to relevant departments and / or units. For example, in one application embodiment of this invention, from August 13 to August 30, 2025, Anhui Province experienced continuous high temperatures, reaching the level of extremely high temperatures, the highest in the same period in history. Based on the regional high-temperature heat wave meteorological disaster resource database system proposed in this invention, a total of 5,100,807 MB of various types of data was collected, along with 17 disaster images, resulting in 18 monitoring reports, which were promptly provided to units such as the Anhui Provincial Meteorological Observatory and the Anhui Provincial Climate Center.

[0106] Secondly, the regional high-temperature heat wave meteorological disaster resource database system proposed in this invention can enhance the ability to respond to and protect the ecosystem in the event of such disasters. High-temperature heat wave meteorological disasters can affect human health and work efficiency, damage public facilities, and disrupt ecosystems. The regional high-temperature heat wave meteorological disaster resource database system proposed in this invention can provide meteorological support and / or service decision-making in response to high-temperature heat wave meteorological disasters. For example, in one application embodiment of this invention, in August 2025, there was a sustained period of high temperatures, and water resources in the Anhui section of the Huai River Basin were 20% lower than the average for the same period. The high temperatures caused high evaporation in the basin, leading to a continuous increase in water consumption and a continuous drop in water levels. The regional high-temperature heat wave meteorological disaster resource database system proposed in this invention can continuously publish high-temperature disaster monitoring reports and decision-making service materials, while also submitting climate impact assessment materials, assisting relevant departments in water resource allocation, thereby supervising and managing the water ecology, and ensuring water security and water ecological protection in the basin.

[0107] Furthermore, the regional high-temperature heat wave meteorological disaster thematic resource database system and construction method proposed in this invention have high system deployment efficiency and strong portability. For example, in one application embodiment of this invention, based on the above-mentioned deployment architecture combining "cloud services" and "end-user applications," the high-temperature heat wave meteorological disaster thematic resource database was deployed in the Hubei Provincial Meteorological Bureau and the Shandong Provincial Meteorological Bureau, and applied locally, identifying 284 and 248 historical high-temperature regional weather processes respectively, achieving good application results.

[0108] Although various embodiments of the invention have been described above, it should be understood that they are presented by way of example only and not as limitations. It will be apparent to those skilled in the art that various combinations, modifications, and alterations can be made without departing from the spirit and scope of the invention. Therefore, the breadth and scope of the invention disclosed herein should not be limited by the exemplary embodiments disclosed above, but should be defined solely by the appended claims and their equivalents.

Claims

1. A regional high-temperature heat wave meteorological disaster thematic resource database system, characterized in that, The system includes: The basic resource layer module is configured to build a database storage and preprocessing system through data sources to provide unified data for upper-layer applications. The algorithm layer module is configured to analyze data from the basic resource layer module; The product layer module is configured to integrate, compile, and process basic resources to generate products; and The management application layer module is configured to provide application services and system management functions.

2. The system according to claim 1, characterized in that, The algorithm layer module includes: The task scheduling module is configured to schedule tasks for the dynamic identification and feature extreme value calculation module and the data acquisition and fusion module. A dynamic identification and characteristic extreme value calculation module is configured to automatically identify regional high-temperature heat wave weather processes, assess their intensity levels, and calculate characteristic quantities based on data from the basic resource layer module and a preset algorithm; and The data acquisition and fusion module is configured to intelligently acquire multi-source high-temperature heat wave disaster data and fuse the high-temperature heat wave disaster data.

3. The system according to claim 1, characterized in that, The product layer module includes: The High Temperature Heat Wave Disaster Event Database module is composed of integrated high temperature heat wave process characteristic quantities; The basic resource database module for high-temperature heat wave disasters is compiled from monitoring data, forecasting service data, disaster data, and / or socio-economic data related to high-temperature heat waves; and... The High Temperature and Heat Wave Disaster Thematic Product Module provides processed products related to the topic of high temperature and heat wave disasters.

4. The system according to claim 3, characterized in that, The high-temperature heat wave disaster-themed product module includes: Real-time dynamic products are configured to display high-temperature heat wave disaster events, extreme values ​​and / or characteristic quantities during the event process, and their actual distribution maps in real time. Impact analysis products are configured to analyze the characteristics and impacts of high-temperature heat wave disasters; and Historical data products are configured to identify and / or assess the intensity of past regional high-temperature heat waves, and to filter and / or rank them.

5. The system according to claim 1, characterized in that, The application service is a regional high-temperature heat wave meteorological disaster thematic resource database application service, including: The heatwave disaster event retrieval function is configured to search for heatwave disaster events based on filter criteria. The online display of extreme values ​​of high temperature heat wave processes is configured to display the characteristic quantities and / or extreme values ​​of high temperature heat wave weather processes online. Information on the disaster situation during the heat wave is configured to display data statistics and / or image works related to the heat wave disaster; High-temperature heat wave resource list sharing, which is configured to provide download lists and / or data interface services for products and / or resources related to high-temperature heat wave processes; High-temperature heat wave process monitoring report, which is configured to automatically generate monitoring reports on high-temperature heat wave weather processes and provide online viewing and / or download services; The historical heat wave events are arranged to showcase long-term heat wave disaster cases ranked by heat wave intensity. The analysis of the characteristics of high-temperature heat wave disaster events is configured to provide frequency and / or variation characteristics of regional high temperatures within a certain time range; A GIS-fusion map of high-temperature heat wave disasters, configured to provide a GIS-fusion spatiotemporal distribution map of the characteristic quantities and / or disaster situation of each site; and The high-temperature heat wave knowledge graph visualization is configured to visually represent the relationship between the actual situation, disaster situation and station extreme values ​​during regional high-temperature weather in the form of a knowledge graph.

6. The system according to any one of claims 1-5, characterized in that, The system is deployed in the cloud and on the local machine, and the data, algorithms, plugins, interfaces and / or frameworks of the different local machine systems are all unified.

7. A method for constructing a regional high-temperature heat wave meteorological disaster thematic resource database, characterized in that, Includes the following steps: Step S1: Collect basic data related to high-temperature heat wave meteorological disasters; Step S2: Based on the basic data collected in Step S1, automatically identify the high temperature heat wave weather process and conduct a level assessment, extract the characteristic quantities of the high temperature heat wave weather process, and construct a high temperature heat wave disaster event database. Step S3: Automatically collect, merge, and organize the data to construct a basic resource database for high-temperature heat wave disasters; Step S4: Based on the high-temperature heat wave disaster event database described in step S2 and the high-temperature heat wave disaster basic resource database described in step S3, produce products; as well as Step S5: Provide application services based on the product described in step S4.

8. The method according to claim 7, characterized in that, The sources of the basic data include: "Tianqing" is a meteorological big data cloud platform. GDS and M4 file servers; Meteorological record operation system; Disaster reporting system; The Great Compendium of Disasters in China; and Other official data.

9. The method according to claim 7, characterized in that, The feature quantities mentioned in step S2 include: Regional high-temperature weather process level index (RI); The Single-Station High Temperature Weather Comprehensive Intensity Index (SI) The Heatwave Index (HI) is a high-temperature heatwave index. Regional high-temperature weather process characteristic extreme values.

10. The method according to claim 7, characterized in that, Step S3 includes the following steps: Step S31: Construct a corpus of thematic discourse on high-temperature heat wave disasters; Step S32: Based on the two dimensions of region and disaster type, collect sample data of high temperature heat wave disaster and classify and label the sample data; Step S33: For the selected high temperature heat wave disaster information, use a general information extraction tool to accurately extract high temperature heat wave knowledge and relationships; Step S34: Construct a fusion technique based on optional feature level to perform feature-level fusion on the data; Step S35: Automatically and / or manually eliminate data conflicts; and Step S36: Verify the quality of data fusion, iteratively optimize the technical model, and improve the accuracy of the fusion results.

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