Meteorological disaster early warning service data generation method and device and electronic equipment

Abstract

The invention provides a meteorological disaster early warning service data generation method and apparatus, and an electronic device, relates to the technical field of meteorological early warning, and solves the technical problem of low meteorological disaster early warning service data generation efficiency. The method comprises the following steps: acquiring early warning data through an early warning interface, and identifying an early warning release object, early warning time, an influence space range, an early warning type and an early warning level in the early warning data; based on an early warning release object, early warning time, an influence space range, an early warning type and an early warning level, through a multi-class data source automatic matching algorithm, target meteorological data matched with the early warning data are called from weather data, and the target meteorological data and the early warning data are fused according to the early warning level and the early warning type; obtaining fused meteorological disaster early warning data; and establishing a modularized rendering pipeline based on the fused meteorological disaster early warning data, and generating visual meteorological disaster early warning service data through a specified image visual production tool based on the modularized rendering pipeline.

Description

Technical Field

[0001] The present application relates to the field of meteorological warning technology, and in particular to a method, device and electronic equipment for generating meteorological disaster warning service data. Background Art

[0002] At present, in disaster prevention and mitigation work, meteorological disaster warning industry service data is a key bridge connecting warning issuing agencies with industry and public services. Previous technical means were relatively backward, and most of them were fragmented operations. In terms of the overall process, the data collection link simply captures the basic warning text, and the data integration capability is insufficient; on the other hand, the efficiency of the manual-led production process cannot keep up with the rhythm of emergency warning scenarios. When disasters occur, it is necessary to quickly produce response products for the industry and the public. Moreover, due to the subjectivity of manual operations, product styles and quality vary, making it difficult to form a unified and professional service image, which is not conducive to the efficient reception of warning information. Therefore, the efficiency of meteorological disaster warning service data generation in the existing technology is low. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device and electronic equipment for generating meteorological disaster warning service data, so as to solve the technical problem of low efficiency in generating meteorological disaster warning service data.

[0004] In a first aspect, the present application provides a method for generating meteorological disaster warning service data, the method comprising: Acquire warning data through the warning interface, and identify the warning issuing object, warning time, affected spatial scope, warning type and warning level in the warning data; Based on the warning issuing object, the warning time, the affected spatial range, the warning type and the warning level, the target meteorological data matching the warning data is retrieved from the weather data through an automatic matching algorithm of multiple data sources, and the target meteorological data and the warning data are fused according to the warning level and the warning type to obtain fused meteorological disaster warning data; the weather data includes at least one of meteorological observation historical data, weather actual data, weather forecast data, typhoon data and risk reminder data; the target meteorological data includes meteorological data associated with the warning type within a target geographical range, and the target geographical range is a geographical range corresponding to the affected spatial range determined by a spatiotemporal alignment engine; A componentized rendering pipeline is established based on the integrated meteorological disaster warning data, and visual meteorological disaster warning service data is generated based on the componentized rendering pipeline through a designated image visualization production tool; wherein the visual meteorological disaster warning service data contains service data in text, picture and video forms.

[0005] In a possible implementation, a componentized rendering pipeline is established based on the fused meteorological disaster warning data, and visual meteorological disaster warning service data is generated through a specified image visualization production tool based on the componentized rendering pipeline, including: Generate a warning graphic drawing template according to the warning type, the warning level, and the warning release object; the warning graphic drawing template correspondingly includes Echarts statistical chart configuration options and customization options for various aspects corresponding to the chart; Establish a componentized rendering pipeline and an integrated AI layout engine based on the fused meteorological disaster warning data, and based on the componentized rendering pipeline and the AI layout engine, use the warning graphic drawing template to generate visual meteorological disaster warning service data through a specified image visualization production tool; wherein, in the process of establishing the componentized rendering pipeline and the integrated AI layout engine, component adaptive arrangement is performed using the constraint satisfaction algorithm CSP, and the layout priority rules corresponding to the component adaptive arrangement include at least one of the following: the rule that the layout priority is that the warning identifier takes precedence over real-time data and the real-time data takes precedence over the forecast curve, the color block contrast rule in visual balance, and the minimum click area rule in mobile device adaptation; the specified image visualization production tool realizes the layer superposition operation of different images by the way of drawing multiple graphs on the same coordinate axis through Matplotlib, and / or by the way of performing pixel-level operations on the image through OpenCV.

[0006] In a possible implementation, the generating a warning graphic drawing template according to the warning type, the warning level, and the warning release object includes: Construct a corresponding visual warning graphic display template according to the warning type, the warning level, and the warning release object, and based on the association rule mining model of the FP-Growth algorithm, splice and combine the warning graphic display template through the combination of historical selection recommendation components to generate various forms of warning graphic drawing templates.

[0007] In a possible implementation, after identifying the warning release object, warning time, affected spatial range, warning type, and warning level in the warning data, it further includes: Perform data cleaning on the warning data to screen out warning data of a specified level from the warning data as specified high-level warning data, calculate and exclude duplicate data in the specified high-level warning data to obtain first warning data; Group the first warning data according to the level of the warning release object, determine the storage array data for storing the first warning data, and sort the storage array data according to the warning level and the warning type; Synchronize historical warning data through automatic task scheduling, and use the Redis cache mechanism to synchronize the warning release object, the warning type, and the warning time into Redis array data. Compare the effective warning data corresponding to a specified moment with the Redis array data, and determine the duration days of the effective warning data according to the comparison result.

[0008] In a possible implementation, retrieving the target meteorological data matching the warning data from the weather data includes: Send multiple scraping requests to multiple servers simultaneously through the method of asynchronous network request processing, and use the built-in middleware mechanism to process the scraping requests and responses; wherein, the data in the multiple servers includes real-time monitoring data in the meteorological department system and topographic and geomorphic data in the geographic information system. Use the proxy IP mechanism to automatically identify and process verification codes, analyze the website rules of the target websites corresponding to the multiple servers, and based on the scraping requests and the website rules, scrape and integrate multi-source heterogeneous data from the multiple target websites through a distributed crawler framework to obtain the meteorological data in the target websites, and retrieve the target meteorological data matching the warning data from the meteorological data; wherein, the anti-crawling strategy is implemented in the distributed crawler framework by including the method of setting the request header User-Agent and controlling the request frequency random mechanism. Generate an initial meteorological data cube storage system through the method of distributed storage optimization, use the improved GeoHash coding method to dynamically adapt the spatial resolution of the initial meteorological data cube storage system from 1km to 100m to obtain a meteorological big data cube storage system, and store the scraped meteorological data into the meteorological big data cube storage system through the method of persistent storage; the meteorological big data cube storage system contains databases in multiple formats.

[0009] In a possible implementation, before retrieving the target meteorological data matching the warning data from the weather data through the multi-class data source automatic matching algorithm based on the warning release object, the warning time, the affected spatial range, the warning type, and the warning level, it further includes: Construct a multi-source perception layer with spatio-temporal alignment function through a multi-modal fusion architecture, and use the non-structured data to vectorize the engine corresponding to the multi-source perception layer to obtain the spatio-temporal alignment engine, so as to realize the standardized parsing of radar-based data, sensor time-series data, and geographic grid heterogeneous data.

[0010] In a possible implementation, the fusion of the target meteorological data and the warning data according to the warning level and the warning type to obtain fused meteorological disaster warning data includes: Construct a multi-dimensional credibility evaluation model based on the attention mechanism, and use the multi-dimensional credibility evaluation model to remove abnormal data from the target meteorological data and the warning data in real time by means of feature-level cross-validation, so as to realize a dynamic quality evaluation mechanism through a dynamic weight allocation strategy, and obtain the target meteorological data and warning data after the abnormal data are removed; According to the warning level and the warning type, the target meteorological data after the abnormal data is eliminated and the warning data are fused to obtain fused meteorological disaster warning data.

[0011] In a possible implementation, generating visualized meteorological disaster warning service data based on the componentized rendering pipeline through a specified image visualization production tool includes: Determine the target scene requirements of the media to be disseminated corresponding to the visualized meteorological disaster warning service data, and generate the visualized meteorological disaster warning service data suitable for the media to be disseminated by means of video stream synthesis and / or picture synthesis using a designated image visualization production tool based on the componentized rendering pipeline and the target scene requirements; Based on the text information in the visualized meteorological disaster warning service data, the natural language large model NLP is used to perform audio synthesis of multilingual speech, and based on the audio synthesis results, speech scene simulation is performed to obtain the meteorological disaster warning service scene simulation results.

[0012] In a second aspect, the present application provides a device for generating meteorological disaster warning service data, comprising: An acquisition module is used to acquire warning data through the warning interface and identify the warning issuing object, warning time, affected spatial range, warning type and warning level in the warning data; A retrieval module is used to retrieve target meteorological data matching the warning data from weather data based on the warning issuing object, the warning time, the affected spatial range, the warning type and the warning level through an automatic matching algorithm of multiple data sources, and fuse the target meteorological data with the warning data according to the warning level and the warning type to obtain fused meteorological disaster warning data; the weather data includes at least one of meteorological observation history data, weather actual data, weather forecast data, typhoon data and risk reminder data; the target meteorological data includes meteorological data associated with the warning type within a target geographical range, and the target geographical range is a geographical range corresponding to the affected spatial range determined by a spatiotemporal alignment engine; A generation module, configured to establish a componentized rendering pipeline based on the fused meteorological disaster warning data, and generate visual meteorological disaster warning service data through a specified image visualization production tool based on the componentized rendering pipeline; wherein, the visual meteorological disaster warning service data includes service data in text form, picture form, and video form.

[0013] In a third aspect, the present application further provides an electronic device, including a memory and a processor. A computer program that can run on the processor is stored in the memory. When the processor executes the computer program, the method described in the first aspect above is implemented.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to run the method described in the first aspect above.

[0015] The present application brings the following beneficial effects: The present application provides a method, device and electronic device for generating meteorological disaster warning service data, which can obtain warning data through a warning interface, and identify the warning issuing object, warning time, affected spatial range, warning type and warning level in the warning data; based on the warning issuing object, the warning time, the affected spatial range, the warning type and the warning level, an automatic matching algorithm of multiple data sources is used to retrieve target meteorological data matching the warning data from weather data, and the target meteorological data and the warning data are fused according to the warning level and the warning type to obtain fused meteorological disaster warning data; the weather data It contains at least one of meteorological observation historical data, actual weather data, weather forecast data, typhoon data and risk warning data; the target meteorological data contains meteorological data associated with the warning type within the target geographical range, and the target geographical range is the geographical range corresponding to the affected spatial range determined by the spatiotemporal alignment engine; a componentized rendering pipeline is established based on the fused meteorological disaster warning data, and visual meteorological disaster warning service data is generated based on the componentized rendering pipeline through a specified image visualization production tool; wherein the visual meteorological disaster warning service data contains service data in text form, picture form and video form. In this scheme, a method is used to automatically identify warning text information by machine and match the corresponding meteorological observation history, actual conditions, forecasts, typhoon data, risk warnings and other information to generate pictures and synthesize them into long pictures suitable for media dissemination. This realizes the deep integration of data from different industries such as meteorology and improves the data integration capability. Moreover, by associating warning information with relevant weather conditions, historical daily values, weather forecasts and typhoon data through modular processing and combining video streams and picture synthesis, warning graphic data or warning video data is automatically generated. This not only achieves the unification of warning data style and quality, but also improves the efficiency of generating meteorological disaster warning service data, solving the technical problem of low efficiency of generating meteorological disaster warning service data.

[0016] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1Schematic flowchart of the method for generating meteorological disaster warning service data provided by an embodiment of the present application; Figure 2 Another schematic flowchart of the method for generating meteorological disaster warning service data provided by an embodiment of the present application; Figure 3 Schematic structural diagram of a device for generating meteorological disaster warning service data provided by an embodiment of the present application; Figure 4 Shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0020] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0021] Currently, the efficiency of generating meteorological disaster warning service data is relatively low. Based on this, the embodiments of the present application provide a method, a device, and an electronic device for generating meteorological disaster warning service data, and the technical problem of low efficiency in generating meteorological disaster warning service data can be solved by this method.

[0022] The embodiments of the present invention will be further introduced below with reference to the accompanying drawings.

[0023] Figure 1 Schematic flowchart of a method for generating meteorological disaster warning service data provided by an embodiment of the present application. As Figure 1 shown, the method includes: Step S110, obtaining warning data through a warning interface, and identifying the warning release object, warning time, affected spatial range, warning type, and warning level in the warning data.

[0024] For high-level warning information data in various forms such as text and images obtained through the warning interface, identify information such as the release unit, release time, affected spatial range, warning type, and warning level in the warning data.

[0025] As an alternative implementation, such as Figure 2 shown, the overall technical route of the solution provided by the embodiments of the present application can be carried out according to the idea of designing, sorting, collecting, aggregating and analyzing multi-industry and multi-source heterogeneous data, classifying data, triggering the product production algorithm by the rule engine, and promoting the layer rendering and synthesis of the map by the decision service.

[0026] Step S120, based on the warning release object, warning time, affected spatial range, warning type and warning level, through the automatic matching algorithm of multiple data sources, retrieve the target meteorological data matching the warning data from the weather data, and fuse the target meteorological data and the warning data according to the warning level and warning type to obtain the fused meteorological disaster warning data.

[0027] Among them, the weather data includes at least one of meteorological observation historical data, weather actual situation data, weather forecast data, typhoon data and risk reminder data; the target meteorological data includes meteorological data associated with the warning type within the target geographical range, and the target geographical range is the geographical range corresponding to the affected spatial range determined by the spatio-temporal alignment engine.

[0028] In a possible implementation manner, for the fusion of high-level warning data, combining data such as weather actual situation, weather forecast, typhoon warning, etc., retrieve the relevant meteorological data in the release area based on information such as the release unit, warning type, warning level, release time, etc. of the warning information, construct a spatio-temporal alignment engine, implement the automatic matching algorithm of multiple data sources, and perform the fusion of meteorological data and warning data according to the warning type and level. For example, if the warning information file name shows a red rainstorm warning in Beijing, then retrieve the rainfall actual situation in Beijing within 24 hours and the rainfall forecast information in Beijing within 24 hours.

[0029] As an alternative implementation, before retrieving the target meteorological data matching the warning data from the weather data through the automatic matching algorithm of multiple data sources based on the warning release object, warning time, affected spatial range, warning type and warning level, it further includes: constructing a multi-source perception layer with spatio-temporal alignment function through a multi-modal fusion architecture, and using the non-structured data vectorization engine corresponding to the multi-source perception layer to obtain a spatio-temporal alignment engine to realize the standardized parsing of radar-based data, sensor time-series data, and geographical grid heterogeneous data.

[0030] In the prior art, the traditional meteorological data system has defects such as single dimension, inconsistent spatio-temporal benchmark, and poor compatibility of heterogeneous data, resulting in difficulty in effectively aggregating multi-modal data such as satellite remote sensing, ground observation, and industry IoT devices, and insufficient data utilization rate.

[0031] In the embodiments of the present application, for the multi-modal fusion architecture, a multi-source perception layer with spatio-temporal alignment ability is constructed, and an unstructured data vectorization engine is adopted to achieve the standardized parsing of radar-based data, sensor time-series data, and heterogeneous geographical grid data (supporting automatic conversion of multiple formats such as HDF5 / NetCDF / GRIB), and to implement a multi-source heterogeneous data integration strategy. Moreover, the method for generating meteorological disaster warning industry service data in the embodiments of the present application takes a new route, which can comprehensively collect and integrate multi-source heterogeneous data. Whether it is the real-time monitoring data of authoritative meteorological departments or the topographic and geomorphic data in the geographical information system, all are gathered together to prepare sufficient raw material data for product production.

[0032] In an alternative embodiment, the step of retrieving the target meteorological data matching the warning data from the weather data in step S120 may specifically include the following steps: Send multiple scraping requests to multiple servers simultaneously through the asynchronous network request processing method, and use the built-in middleware mechanism to process the scraping requests and responses; wherein, the data in the multiple servers includes real-time monitoring data in the meteorological department system and topographic and geomorphic data in the geographical information system; Automatically identify and process verification codes using the proxy IP mechanism, analyze the website rules of the target websites corresponding to the multiple servers, and scrape and integrate multi-source heterogeneous data from the multiple target websites based on the scraping requests and website rules to obtain the meteorological data in the target websites, and retrieve the target meteorological data matching the warning data from the meteorological data; wherein, the anti-crawling strategy is implemented in the distributed crawler framework by including the method of setting the request header User-Agent and controlling the request frequency random mechanism; Generate an initial meteorological data cube storage system through the method of distributed storage optimization, dynamically adapt the spatial resolution of the initial meteorological data cube storage system from 1km to 100m using the improved GeoHash coding method to obtain a meteorological big data cube storage system, and store the scraped meteorological data into the meteorological big data cube storage system through the method of persistent storage; the meteorological big data cube storage system contains databases in multiple formats.

[0033] In the embodiments of the present application, a meteorological big data cube storage system is developed. An improved GeoHash coding is adopted to achieve dynamic adaptation of the spatial resolution from 1 km to 100 m, with the storage efficiency increased by 3.8 times, further optimizing the distributed storage. Moreover, for multi-modal data fusion, a spatio-temporal consistency verification model is constructed in the embodiments of the present application to solve the problem of spatio-temporal resolution differences in data from different industries. The 1 km grid of meteorological data can be seamlessly matched with the traffic data of the flow rate at the 100-meter level, improving the fusion accuracy. In addition, in the industry knowledge graph mapping system, tens of thousands of semantic association rules between meteorological elements and business scenarios are established, realizing the deep integration of early warning information and industry data.

[0034] For the collection of professional meteorological website data, for example, a self-developed crawler framework is used, which improves the highly customizable and scalable functions and is suitable for large-scale web scraping projects. The efficient asynchronous network request processing can send multiple requests simultaneously, improving the scraping efficiency. The built-in middleware mechanism can facilitate the processing of requests and responses, such as setting proxies and handling verification codes. It supports the persistent storage of data and can save the scraped data in various formats (such as JSON, CSV, databases, etc.). Powerful anti-crawling strategies include reasonably setting the request header User-Agent, reasonably controlling the request frequency with the random mechanism, reasonably using the proxy IP mechanism, automatically identifying and processing verification codes, analyzing website rules such as JavaScript encryption and dynamic page loading technology, and the distributed crawler strategy for large-scale data scraping.

[0035] By using the self-developed distributed crawler framework in the data collection link, which supports large-scale web scraping and anti-crawling strategies and can send multiple requests concurrently, the scraping efficiency can be significantly improved. Moreover, through the built-in middleware mechanism, it is more convenient to process requests and responses, such as setting dynamic proxies, automatically handling verification codes, and collecting with a delayed step size.

[0036] As an optional implementation manner, the step of fusing the target meteorological data and the warning data according to the warning level and warning type in the above step S120 to obtain the fused meteorological disaster warning data may specifically include the following steps: Construct a multi-dimensional credibility evaluation model based on the attention mechanism, and use the multi-dimensional credibility evaluation model to perform real-time abnormal data elimination on the target meteorological data and the warning data through feature-level cross-validation, so as to implement a dynamic quality evaluation mechanism through a dynamic weight allocation strategy, obtaining the target meteorological data and the warning data after abnormal data elimination; fuse the target meteorological data and the warning data after abnormal data elimination according to the warning level and warning type to obtain the fused meteorological disaster warning data.

[0037] For the above dynamic quality evaluation mechanism, a multi-dimensional credibility evaluation model based on the attention mechanism is constructed, and real-time abnormal data elimination is achieved through feature-level cross-validation (recall rate > 93%), and a dynamic weight allocation strategy is established to improve the data fusion accuracy.

[0038] In the data cleaning process, based on the rule engine and machine learning model, an intelligent cleaning algorithm is developed to automatically identify and eliminate abnormal data, improving the recall rate. Moreover, through the dynamic quality evaluation mechanism, feature-level cross-validation is achieved to ensure data accuracy and consistency.

[0039] Step S130: Based on the fused meteorological disaster warning data, a componentized rendering pipeline is established, and visual meteorological disaster warning service data is generated through a specified image visualization production tool based on the componentized rendering pipeline.

[0040] Among them, the visual meteorological disaster warning service data includes service data in text form, picture form, and video form.

[0041] In an optional implementation manner, this step S130 may specifically include the following steps: generating a warning graphic drawing template according to the warning type, warning level, and warning release object; the warning graphic drawing template correspondingly includes Echarts statistical chart configuration options and customization options for each aspect of the chart; establishing a componentized rendering pipeline and integrating an AI layout engine based on the fused meteorological disaster warning data, and based on the componentized rendering pipeline and the AI layout engine, using the warning graphic drawing template to generate visual meteorological disaster warning service data through a specified image visualization production tool; among them, in the process of establishing the componentized rendering pipeline and integrating the AI layout engine, component adaptive arrangement is performed using the constraint satisfaction algorithm CSP, and the layout priority rules corresponding to the component adaptive arrangement include at least one of the following: the rule that the layout priority is that the warning identifier takes precedence over real-time data and real-time data takes precedence over the forecast curve, the color block contrast rule in visual balance, and the minimum click area rule in mobile device adaptation; the specified image visualization production tool realizes the layer overlay operation of different images by the way of drawing multiple graphs on the same coordinate axis through Matplotlib, and / or by the way of performing pixel-level operations on the image through OpenCV.

[0042] Exemplarily, during the generation of the long-form warning graphic text, based on the fused data, a componentized rendering pipeline is established, integrating an AI typesetting engine. Professional graphic visualization production tools such as geographical maps and Echarts are used to generate visual graphic data according to the warning graphic text template. Among them, the constraint satisfaction algorithm (CSP) is used to achieve adaptive arrangement of components. The typesetting priority rules can be: {"Priority": ["Warning identifier > Real-time data > Forecast curve"], "Visual balance": "Color block contrast ratio ≥ 4.5:1", "Mobile device adaptation": "Minimum click area 48×48px"} Through template-based management of graphic drawing, the graphic drawing process can be comprehensively improved, bringing a more efficient, accurate, and standardized drawing experience to users. Compared with the traditional graphic drawing method, in the embodiment of the present application, a rich and highly standardized graphic drawing template library is pre-constructed. These templates are highly professional Echarts statistical charts, supporting rich chart configuration options, including bar charts, line charts, pie charts, maps, etc. They have high scalability and customizability, and various aspects of the charts can be customized in detail, including colors, fonts, coordinate axes, titles, legends, etc., to meet the personalized needs of different scenarios; Matplotlib can easily create high-quality 2D graphics. In terms of layer superposition, the layer effect can be achieved by drawing multiple graphics on the same coordinate axis; OpenCV can also be used for layer superposition operations of images. Different images can be superimposed by performing pixel-level operations on the images. Its advantages lie in its efficient image processing algorithms and wide support for various image formats. It can easily process large-scale image data, doubling the graphic drawing efficiency and bringing the drawing results to a new height in terms of standardization and aesthetics.

[0043] As an optional implementation manner, generating the warning graphic text drawing template according to the warning type, warning level, and warning release object in step S130 above may specifically include the following steps: constructing a corresponding visual warning graphic text display template according to the warning type, warning level, and warning release object, and based on the association rule mining model of the FP-Growth algorithm, splicing and combining the warning graphic text display template through the combination method of historically selected recommended components to generate various forms of warning graphic text drawing templates.

[0044] For the generation process of the above warning graphic text drawing template, exemplarily, a corresponding visual display template is generated according to the type, level, and level of the releasing unit of the warning, and the template is intelligently constructed. Based on the association rule mining model of the FP-Growth algorithm, according to the historically selected recommended component combinations, free splicing and combination are realized to form diverse product forms. Through this processing method, various forms of warning graphic text drawing templates are made more comprehensive and flexible to use.

[0045] As an alternative implementation, generating the visualized meteorological disaster warning service data through the component-based rendering pipeline in step S130 above may specifically include the following steps: Determine the target scenario requirements of the visualized meteorological disaster warning service data corresponding to the media to be disseminated, and use the specified image visualization production tool based on the component-based rendering pipeline and the target scenario requirements to generate the visualized meteorological disaster warning service data suitable for the media to be disseminated through video stream synthesis and / or picture synthesis; Perform audio synthesis of multi-language voices on the text information in the visualized meteorological disaster warning service data using the natural language large model NLP, and perform voice scene simulation based on the audio synthesis result to obtain the meteorological disaster warning service scene simulation result.

[0046] In the embodiments of the present application, through audio voice synthesis based on the voice large model, this model supports the advantages of multi-language support and performs excellently in multi-language voice synthesis. It can cover many mainstream languages and provides strong support for various scenarios. Secondly, through high anthropomorphism, it can synthesize highly anthropomorphic voices, which can be close to human natural voices in terms of timbre, intonation, emotional expression, etc., presenting vividly and bringing a more natural and comfortable auditory experience to users. Thirdly, through efficient voice feature learning, the voice features can be learned from a three-second audio sample. In practical applications, it can quickly generate voices with specific voice features based on a small amount of sample data, providing rapid adaptation for scenarios such as personalized voice synthesis and voice imitation. In addition, this model has a powerful context learning ability, can well understand the context information of the text, and the voice is coherent, natural and fluent according to the given text information. Finally, it is the scene simulation, which enhances the realism and intelligibility of the voice. In some specific application scenarios, it can improve the adaptability and effect of the voice.

[0047] It can be seen that in terms of voice synthesis, based on the voice large model, it supports voice synthesis in more than 50 languages, and the anthropomorphism of the synthesized voice is as high as 99%. The model has a powerful context learning ability and can generate coherent and natural voices according to the context information of the text, significantly improving the user experience. In the audio-video long picture synthesis link, the system also integrates graphic libraries such as Echarts, Matplotlib, and OpenCV, supports the dynamic generation of various chart types such as bar charts, line charts, pie charts, and maps, and through the intelligent layout engine, it can automatically adjust the product style according to information such as warning types, issuing units, and target audiences to ensure the professionalism and aesthetics of the output content.

[0048] In the embodiments of the present application, through multi-industry intelligent algorithms, the automated generation of long pictures and short videos can be driven with one key. The text can be typeset intelligently and precisely, and images and sound effects that match the scene can be selected. Moreover, the product style can be quickly adjusted according to special reports, the target audience, and the characteristics of the publishing platform, greatly improving the production efficiency and the quality of the finished product, and meeting the urgent need for rapid and accurate reception of early warning information in the scenario of disaster prevention and mitigation. In addition, through one-key publishing on multimedia terminals and docking with the interfaces of multi-channel publishing terminals of multiple new media, the one-key rapid publishing of early warning text and picture products can be realized.

[0049] In terms of the adaptability of the dissemination platform, the long picture format has more advantages. Various online platforms such as social media and news and information apps are friendly to the display of text and picture content. Long pictures can be presented completely and shared and forwarded quickly, facilitating the viral spread of early warning information. However, traditional text early warning information is easily submerged by a large amount of information in the fragmented dissemination scenario, with poor integrity and visibility. The long pictures synthesized by the solution provided in the embodiments of the present application can ensure that the early warning information reaches the audience completely in different dissemination scenarios, making the dissemination adaptability wider, expanding the early warning coverage, and enhancing the early warning effect of the whole society in disaster prevention and mitigation.

[0050] In the prior art, the previous technical means were relatively backward and mostly fragmented operations. In terms of the overall process, in the data collection link, only basic early warning text was simply captured, lacking in-depth integration of data from different industries such as meteorology, transportation, and agriculture. Moreover, the data integration ability was insufficient, and it was difficult for data in different formats and from different sources to cooperate, resulting in thin production content and lack of depth. On the other hand, the production process dominated by humans could not keep up with the rhythm of the emergency early warning scenario. When a disaster occurred suddenly, it was necessary to quickly produce response products for the industry and the public. Moreover, due to the subjectivity of manual operations, the product styles and qualities were uneven, making it difficult to form a unified and professional brand image, which was not conducive to the efficient reception and trust building of early warning information.

[0051] In the embodiments of the present application, through the method of automatically identifying early warning text information by machine and matching corresponding meteorological observation history, real-time situation, forecast, typhoon data, and risk warnings and other information to generate pictures and synthesize long pictures suitable for media dissemination, the in-depth integration of data from different industries such as meteorology has been realized, and the data integration ability has been improved. Moreover, by associating early warning information with relevant weather real-time situations, historical daily values, weather forecasts, and typhoon data, through modular processing and combining video streams and picture synthesis methods, early warning graphic data or early warning video data are automatically generated, which not only realizes the unity of the style and quality of early warning data but also improves the generation efficiency of meteorological disaster early warning service data.

[0052] The system corresponding to this method for generating meteorological disaster early warning service data has improved the production line work process engine, covering the entire process from the import, processing, review to the final release of the original materials, such asFigure 2 As shown, it realizes the full - process automated management from data collection to product release, and also reduces manual intervention, thus ensuring the consistency and quality of the output. At the same time, through templates, it supports custom combination of multiple processing steps to form a customized production line. Each processing step supports detailed parameter configuration, including the interface collection configuration of the data collection source API, the collection configuration of the data source file, the data conversion and field processor configuration, the interface configuration of the data storage API, the data storage repository table configuration, and the callback business node service processor configuration. In addition, it has built - in dependency management, error - handling mechanism and message notification mechanism to ensure that the dependency relationships between processing steps are properly managed, and provide detailed error reports in case of errors for easy troubleshooting.

[0053] In the prior art, simple warning texts are usually published in a scattered manner. If the public wants to comprehensively understand various warning - related information, they have to switch back and forth between different platforms and sections to search. In the embodiments of this application, it can integrate rich content such as the daily warning information overview, the national actual situation map and ranking, the typhoon warning map and warning prevention, the future weather trend, and the warning risk reminder into a long picture, making the information integration degree higher. For example, when industry decision - makers view warning information, they don't need to spend a lot of time searching. They can quickly grasp the current weather situation ranking across the country from a long picture and know whether their local area and travel destinations are in high - risk areas, greatly improving the information acquisition efficiency.

[0054] Compared with the way of simply relying on text to spread warning information in the prior art, this visual presentation of the long picture better suits the public's habit of quickly browsing and intuitively understanding information, making the visual effect better. Among them, for the national actual situation map and the typhoon warning map, different weather conditions, disaster types and levels are marked with graphics and colors, and the public can clearly see at a glance which areas are experiencing bad weather, and the typhoon's moving route and influence range are also clear at a glance. This can enable the audience to accurately receive key warning information in a short time compared with reading large paragraphs of text descriptions.

[0055] The future weather trend and warning risk reminder sections in the solution provided by the embodiments of this application are generated based on professional meteorological data analysis. Different from the broad - style text warnings in the prior art, it can accurately calculate the subsequent weather trends in the corresponding regions according to the geographical environment of different regions and recent climate data, and provide customized risk prompts for the public, making the warning more targeted. For example, mountain residents can learn in advance the risk probability of mountain landslides caused by heavy rain, and coastal residents can know the estimated intensity of storm surges caused by typhoons, so as to make precise preventive measures in advance.

[0056] Regarding the pipeline workflow engine, it should be noted that the pipeline template is a predefined workflow used to automate and standardize the processing process of product content. Such as Figure 2As shown, the pipeline template covers the entire process from the import, processing, review to the final release of the original materials. The design of the pipeline template aims to improve efficiency, reduce manual intervention, and at the same time ensure the consistency and quality of the output. It allows users to combine different processing steps as needed to form a customized pipeline. Each processing step supports detailed parameter configuration to meet specific requirements. The pipeline is easy to expand and adjust, and can be quickly modified or new processing steps can be added according to business needs. Built-in dependency management ensures that the dependencies between processing steps are properly managed. For example, only when a certain step is completed, the next related step will be triggered. The built-in error handling mechanism can pause the pipeline when an error occurs and provide an error report for easy troubleshooting. Detailed logs of the pipeline execution process are recorded, including information such as the status and time consumption of each step, which is convenient for subsequent analysis and auditing.

[0057] The solution provided by the embodiments of this application provides scalability through rich plug-in scripts. Specifically, the rich plug-in scripts, as reusable basic building blocks, are divided into two categories: preset and user-defined. They can be configured and extended as needed. For example, the material selector supports users to browse and select multimedia files such as images, videos, audios, and texts; the data parser is responsible for converting the original data into structured information; the chart component is used to visually present data such as bar charts, pie charts, and line charts; the map component provides a geographic information display function; the picture component focuses on picture processing and display, covering editing and cropping; the text component is used for text input, formatting, and automated processing; the voice component involves speech recognition and synthesis technologies to process audio; the video component is responsible for video capture and upload, editing, content customization, and security and privacy protection; users can also develop custom components. And the script is the program code to execute specific tasks, which will run at different stages of the system to automatically carry out a series of operations. Among them, the data source script extracts data from data sources such as external databases, the rule script defines business logics such as content review, permission control, and data filtering rules, the data parsing script cleans, transforms, and standardizes data, the publishing path script determines the target location and method of content publishing, the batch processing script processes a large amount of data or content items, the automated workflow script advances tasks according to the preset process, the data processing script performs complex data analysis or aggregation, the API integration script interacts with third-party services or APIs, and the automated test script ensures the quality and stability of content and system functions.

[0058] In some embodiments, after identifying the warning release object, warning time, affected spatial range, warning type, and warning level in the warning data in the above step S110, the method may further include the following steps: Perform data cleaning on the warning data to screen out the warning data of the specified level as the specified high-level warning data, calculate and exclude the duplicate data in the specified high-level warning data to obtain the first warning data; Group the first warning data according to the level of the warning release object, determine the storage array data for storing the first warning data, and sort the storage array data according to the warning level and warning type; Synchronize historical warning data through automatic task scheduling, and use the Redis cache mechanism to synchronize the warning release object, warning type, and warning time into Redis array data. Compare the effective warning data corresponding to the specified moment with the Redis array data, and determine the continuous days of the effective warning data according to the comparison result.

[0059] As an example, the process of intelligent conversion of a high-level warning into a long picture is taken as an example below.

[0060] Step 1: Data collection: Use the warning interface to obtain the currently effective national warning data; Step 2: Data cleaning: From the currently effective national warning data, filter out the warning data of red and orange levels as high-level warning data; Step 3: Generate daily warning information overview data: 3.1 Calculate duplicate data: For the same type of red and orange level warning data currently in effect at the Central Meteorological Observatory, take the latest release (release time field). If it has been downgraded to yellow and blue levels, it will not be displayed; 3.2 Store the warning data with the warning release area name of "national level" in the storage array data; Sort the storage array data according to the warning level and warning type (give priority to displaying 14 types of warnings); 3.3 Group by province and count the warning data under the province; Display the corresponding fields according to each region divided by region, such as: "Province (Autonomous Region, Municipality)"; Sort by province in a custom order; 3.4 Determine that the warning data with the warning level (severity field) being "Red" and the first two digits of the warning release administrative code (senderCode field) not being "00" and the third to sixth digits being "0000" is provincial warning data, and store it in the storage array data; At 0:00 every day, the automatic task scheduling will synchronize the red and orange warning data of the previous day, and at the same time, use the Redis cache mechanism to synchronize the province, warning type, and warning time of the provincial warning data into Redis array data; At 8:00 in the morning every day, the effective warning data will be directly used to calculate whether it is the first time and the continuous days by comparing with the Redis data; 3.5 The map rendering uses the district and county level warning data, and the legend of the map uses the first 14 conventional warning types; Step 4: Generate national actual situation data: 4.1 The precipitation real-time map (past 24 hours) and ranking are fixed to be displayed; sometimes the precipitation map has not been generated at 08:30, then neither of them will be displayed; 4.2 When a national high temperature warning occurs, the highest temperature real-time map (past 24 hours) and ranking will be displayed; when a national cold wave warning occurs, the lowest temperature real-time map (past 24 hours) and ranking will be displayed; 4.3 National precipitation and temperature real-time section, the title of "National precipitation and temperature real-time" should determine whether there is a national-level high temperature and cold wave warning. If not, it will display "National precipitation real-time"; 4.4 Add a typhoon section below the precipitation live section, display the title "Typhoon Path" and display the picture of the typhoon path; 4.5 If a region breaks a historical extreme value on the ranking list, it will be stamped with "Breaking Extreme Value"; Step 5: Generate typhoon warning data: 5.1 If there is a typhoon warning in the fourth part, display the typhoon module; 5.2 Typhoon module includes typhoon description, typhoon path map, gale forecast map, precipitation forecast Figure 4 Part, the data are from the official typhoon warning of the Central Meteorological Observatory; Step 6: Generate weather trend data: 6.1 The content of future weather copywriting shall be extracted from the weather bulletin of the Meteorological Bureau on the day, and the key weather forecast shall be intercepted; if the content of the copywriting contains "the above-mentioned areas", it shall automatically replace the specific province, city, county or area name before "etc.", "local", and "appear" in the previous content; 6.2 If a national-level warning occurs on the day and weather warning graphic data exists, the weather warning will be displayed; otherwise, the future weather forecast - precipitation will be displayed; 6.3 Weather warning graphics and weather forecast graphics; Step 7: Generate risk alert data: 7.1 Early warning risk reminders, with the content providing impact and prevention reminders based on the warning category; 7.2 Based on the warning data at 8:00 on the same day, all provinces statistically distributed by warning type are marked and displayed with the corresponding wind direction reminder list; 7.3 Template content: Provincial warning label reference, with corresponding defense guidelines displayed on the label; 7.4 The risk reminder of high temperature warning is changed to: high temperature influence, do a good job in heatstroke prevention and cooling, water and electricity supply, and food safety; 7.5 National-level warnings: typhoons, heavy rain, strong winds, mountain torrents, heavy rain, and only those in the same area will be covered; 7.6 If there is a warning from the Central Meteorological Observatory in the warning data, the same type of warning label will not be displayed on the provincial warning. 7.7 If there is no national-level warning, the top 3 provincial warnings will be displayed. If there is a national-level warning, the national-level warning and the top 3 provincial warnings will be displayed (sorted by the number of warning cities, districts, and counties). 7.8 The national-level warning is displayed according to the warning level (highest) and the release time (latest). 7.9 Warnings for a single province are not displayed (except for typhoons, in which case the typhoon warning for a single province is displayed). 7.10 If there is a national-level typhoon warning with a "concern level (red, orange)" on the same day, the province corresponding to the typhoon warning + the typhoon warning description will be extracted as the typhoon warning. 7.11 For national-level warnings, typhoon warnings have the highest priority and will override other national-level warnings.

[0061] In the embodiments of the present application, as Figure 2 shown, a full-link intelligent production line architecture is constructed, covering the entire process from data collection, cleaning, processing to the synthesis of audio-video long images, realizing end-to-end automated processing and the intelligent synthesis driven by the production line architecture. Through modular design and distributed computing, the architecture improves the processing efficiency, and the daily processing capacity reaches the PB level. The core of the production line architecture is a multi-modal data fusion engine, which supports the real-time access and cleaning of heterogeneous data in multiple industries such as meteorology, geography, transportation, and agriculture, improving the accuracy of data cleaning. In addition, the system integrates a natural language large model (NLP), a deep algorithm large model, and a video graphical template engine, supporting functions such as multi-language speech synthesis, intelligent typesetting, and dynamic chart generation, realizing one-key switching of product styles and meeting the needs of different scenarios.

[0062] Figure 3 A structural schematic diagram of a meteorological disaster warning service data generation device is provided. As Figure 3 shown, the meteorological disaster warning service data generation device 300 includes: An acquisition module 301, configured to obtain warning data through a warning interface and identify the warning release object, warning time, affected spatial range, warning type, and warning level in the warning data; The retrieval module 302 is used to retrieve the target meteorological data matching the warning data from the weather data through an automatic matching algorithm of multiple data sources based on the warning issuing object, the warning time, the affected spatial range, the warning type and the warning level, and fuse the target meteorological data with the warning data according to the warning level and the warning type to obtain the fused meteorological disaster warning data; the weather data includes at least one of meteorological observation history data, weather actual data, weather forecast data, typhoon data and risk reminder data; the target meteorological data includes meteorological data associated with the warning type within a target geographical range, and the target geographical range is a geographical range corresponding to the affected spatial range determined by the spatiotemporal alignment engine; The generation module 303 is used to establish a componentized rendering pipeline based on the integrated meteorological disaster warning data, and generate visual meteorological disaster warning service data based on the componentized rendering pipeline through a specified image visualization production tool; wherein the visual meteorological disaster warning service data contains service data in text form, picture form and video form.

[0063] The meteorological disaster warning service data generation device provided in the embodiment of the present application has the same technical features as the meteorological disaster warning service data generation method provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.

[0064] An electronic device provided in an embodiment of the present application is Figure 4 As shown, the electronic device 400 includes a processor 402 and a memory 401, wherein the memory stores a computer program that can be run on the processor, and the processor implements the steps of the method provided in the above embodiment when executing the computer program.

[0065] See also Figure 4 The electronic device further includes: a bus 403 and a communication interface 404, a processor 402, a communication interface 404 and a memory 401 are connected via the bus 403; the processor 402 is used to execute an executable module stored in the memory 401, such as a computer program.

[0066] The memory 401 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 404 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0067] The bus 403 can be an ISA bus, a PCI bus, an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 4 only a bidirectional arrow is used in Figure 4 , but it does not mean that there is only one bus or one type of bus.

[0068] Among them, the memory 401 is used to store a program. After receiving an execution instruction, the processor 402 executes the program. The method executed by the device defined by any of the processes disclosed in any embodiment of the present application can be applied to or implemented by the processor 402.

[0069] The processor 402 may be an integrated circuit chip with the ability to process signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor 402 or the instructions in the form of software. The above-mentioned processor 402 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field programmable gate array (FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by the combination of the hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 401, and the processor 402 reads the information in the memory 401 and combines its hardware to complete the steps of the above method.

[0070] Corresponding to the above method for generating meteorological disaster warning service data, an embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to run the steps of the above method for generating meteorological disaster warning service data.

[0071] The meteorological disaster warning service data generation device provided by the embodiments of the present application can be specific hardware on the device, or software or firmware installed on the device, etc. For the device provided by the embodiments of the present application, its implementation principle and the technical effects produced are the same as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference can be made to the corresponding content in the foregoing method embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the foregoing described systems, devices, and units can all refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0072] In the embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical or other form.

[0073] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of the devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementation manners, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0074] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0075] In addition, each functional unit in the embodiments provided in the present application may be integrated into one processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit.

[0076] If the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the meteorological disaster warning service data generation method described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM for short), random access memories (RAM for short), magnetic disks, or optical discs that can store program codes.

[0077] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0078] Finally, it should be noted that: the above-mentioned embodiments are only specific implementation manners of the present application, used to illustrate the technical solutions of the present application, rather than limiting them. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or make equivalent replacements for some of the technical features; and these modifications, changes, or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application. All should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for generating meteorological disaster warning service data, characterized in that: The method comprises: Acquire warning data through the warning interface, and identify the warning issuing object, warning time, affected spatial scope, warning type and warning level in the warning data; Based on the warning issuing object, the warning time, the affected spatial range, the warning type and the warning level, the target meteorological data matching the warning data is retrieved from the weather data through an automatic matching algorithm of multiple data sources, and the target meteorological data and the warning data are fused according to the warning level and the warning type to obtain fused meteorological disaster warning data; the weather data includes at least one of meteorological observation historical data, weather actual data, weather forecast data, typhoon data and risk reminder data; the target meteorological data includes meteorological data associated with the warning type within a target geographical range, and the target geographical range is a geographical range corresponding to the affected spatial range determined by a spatiotemporal alignment engine; A componentized rendering pipeline is established based on the integrated meteorological disaster warning data, and visual meteorological disaster warning service data is generated based on the componentized rendering pipeline through a designated image visualization production tool; wherein the visual meteorological disaster warning service data contains service data in text, picture and video forms.

2. The method according to claim 1, characterized in that The establishing of a componentized rendering pipeline based on the fused meteorological disaster warning data, and generating visualized meteorological disaster warning service data through a designated image visualization production tool based on the componentized rendering pipeline, includes: Generate an early warning graphic drawing template according to the early warning type, the early warning level and the early warning issuing object; the early warning graphic drawing template includes Echarts statistical chart configuration options and customization options for various aspects of the chart; Based on the fused meteorological disaster warning data, a componentized rendering pipeline and an integrated AI typesetting engine are established, and based on the componentized rendering pipeline and the AI ​​typesetting engine, the warning graphic drawing template is used to generate visual meteorological disaster warning service data through a designated image visualization production tool; wherein, in the process of establishing the componentized rendering pipeline and the integrated AI typesetting engine, the component adaptive arrangement is performed using the constraint satisfaction algorithm CSP, and the typesetting priority rules corresponding to the component adaptive arrangement include at least one of the following: the typesetting priority is that the warning mark takes precedence over the real-time data and the real-time data takes precedence over the forecast curve, the color block contrast rule in visual balance, and the minimum click area rule in mobile terminal adaptation; the designated image visualization production tool uses Matplotlib to draw multiple graphics on the same coordinate axis, and / or uses OpenCV to perform pixel-level operations on the image to implement layer overlay operations of different images.

3. The method according to claim 2, characterized in that The generating of the warning graphic drawing template according to the warning type, the warning level and the warning issuing object comprises: A corresponding visual warning graphic display template is constructed according to the warning type, the warning level and the warning issuing object, and based on the association rule mining model of the FP-Growth algorithm, the warning graphic display template is spliced ​​and combined through the combination of historical selection recommendation components to generate various forms of warning graphic drawing templates.

4. The method according to claim 1, characterized in that: After identifying the warning issuing object, warning time, affected spatial range, warning type and warning level in the warning data, the following steps are further included: Performing data cleaning on the warning data to filter warning data of a specified level from the warning data as designated high-level warning data, calculating and excluding duplicate data in the designated high-level warning data, and obtaining first warning data; Grouping the first warning data according to the level of the warning issuing object, determining storage array data storing the first warning data, and sorting the storage array data according to the warning level and the warning type; Historical warning data is synchronized through automatic task scheduling, and the warning release object, the warning type and the warning time are synchronized into Redis array data using the Redis cache mechanism. The effective warning data corresponding to the specified time is compared with the Redis array data, and the duration of the effective warning data is determined based on the comparison result.

5. The method according to claim 1, characterized in that The step of retrieving target meteorological data matching the warning data from the weather data includes: Send multiple crawling requests to multiple servers simultaneously by asynchronous network request processing, and use the built-in middleware mechanism to process the crawling requests and responses; wherein the data in the multiple servers include real-time monitoring data in the meteorological department system and topographic data in the geographic information system; The proxy IP mechanism is used to automatically identify and process verification codes, analyze the website rules of the target websites corresponding to the multiple server ends, and crawl and integrate multi-source heterogeneous data from the multiple target websites through a distributed crawler framework based on the crawling request and the website rules to obtain the meteorological data in the target website, and retrieve the target meteorological data matching the warning data from the meteorological data; wherein the distributed crawler framework implements the anti-crawling strategy by including setting the request header User-Agent and controlling the request frequency random mechanism; An initial meteorological data cube storage system is generated by means of distributed storage optimization, and the spatial resolution of the initial meteorological data cube storage system is dynamically adapted from 1km to 100m by means of improved GeoHash encoding to obtain a meteorological big data cube storage system, and the captured meteorological data is stored in the meteorological big data cube storage system by means of persistent storage; the meteorological big data cube storage system includes databases in various formats.

6. The method according to claim 1, characterized in that Before retrieving target meteorological data matching the warning data from weather data through an automatic matching algorithm of multiple data sources based on the warning issuing object, the warning time, the affected spatial range, the warning type and the warning level, the method further includes: Through the multimodal fusion architecture, a multi-source perception layer with spatiotemporal alignment function is constructed, and the engine corresponding to the multi-source perception layer is vectorized by unstructured data to obtain the spatiotemporal alignment engine, so as to realize the standardized analysis of radar-based data, sensor time series data, and geographic grid heterogeneous data.

7. The method according to claim 1, characterized in that The step of fusing the target meteorological data and the warning data according to the warning level and the warning type to obtain the fused meteorological disaster warning data includes: Construct a multi-dimensional credibility evaluation model based on the attention mechanism, and use the multi-dimensional credibility evaluation model to remove abnormal data from the target meteorological data and the warning data in real time by means of feature-level cross-validation, so as to realize a dynamic quality evaluation mechanism through a dynamic weight allocation strategy, and obtain the target meteorological data and warning data after the abnormal data are removed; According to the warning level and the warning type, the target meteorological data after the abnormal data is eliminated and the warning data are fused to obtain fused meteorological disaster warning data.

8. The method according to claim 1, characterized in that The generating of visual meteorological disaster warning service data based on the componentized rendering pipeline through a designated image visualization production tool includes: Determine the target scene requirements of the media to be disseminated corresponding to the visualized meteorological disaster warning service data, and generate the visualized meteorological disaster warning service data suitable for the media to be disseminated by means of video stream synthesis and / or picture synthesis using a designated image visualization production tool based on the componentized rendering pipeline and the target scene requirements; Based on the text information in the visualized meteorological disaster warning service data, the natural language large model NLP is used to perform audio synthesis of multilingual speech, and based on the audio synthesis results, speech scene simulation is performed to obtain the meteorological disaster warning service scene simulation results.

9. A meteorological disaster warning service data generation device, characterized in that: include: An acquisition module is used to acquire warning data through the warning interface and identify the warning issuing object, warning time, affected spatial range, warning type and warning level in the warning data; A retrieval module is used to retrieve target meteorological data matching the warning data from weather data based on the warning issuing object, the warning time, the affected spatial range, the warning type and the warning level through an automatic matching algorithm of multiple data sources, and fuse the target meteorological data with the warning data according to the warning level and the warning type to obtain fused meteorological disaster warning data; the weather data includes at least one of meteorological observation history data, weather actual data, weather forecast data, typhoon data and risk reminder data; the target meteorological data includes meteorological data associated with the warning type within a target geographical range, and the target geographical range is a geographical range corresponding to the affected spatial range determined by a spatiotemporal alignment engine; A generation module is used to establish a componentized rendering pipeline based on the integrated meteorological disaster warning data, and generate visual meteorological disaster warning service data based on the componentized rendering pipeline through a specified image visualization production tool; wherein the visual meteorological disaster warning service data contains service data in text form, picture form and video form.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to execute the method according to any one of claims 1 to 8.

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