AI digital human-based climate prediction system and method

Abstract

The invention belongs to the technical field of artificial intelligence, and discloses an AI digital human-based climate prediction system and method, and the system comprises a meteorological data processing module which obtains domestic and overseas meteorological data through a network, comprises a numerical mode result and observation data, carries out the data cleaning and quality control, and guarantees the accuracy and integrity of the data; the artificial intelligence model training module is used for analyzing and training the meteorological data and establishing a climate prediction model, and the climate prediction model comprises the processes of feature extraction, model training and parameter optimization of the meteorological data; the digital human model rendering module is used for rendering an objective climate prediction result into a digital human image based on a digital human technology, and the digital human image comprises appearance design, speech synthesis and action design processes of a digital human; and the prediction result presentation module is used for presenting the digital human image to the user in various forms, and the user can communicate and communicate with the digital human through an interactive interface, know the climate prediction result and make a corresponding decision.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the field of artificial intelligence technology, and in particular relates to a climate prediction system and method based on AI digital human (hereinafter referred to as digital human). Background Art

[0002] Climate prediction is a scientific and technological field that predicts future climate conditions. There is an increasing demand for refined climate predictions in all walks of life, especially in agriculture, water resources management, disaster risk management and other fields. Current short-term climate predictions are usually made by climate forecasters based on the results of numerical models combined with statistical laws to make subjective judgments and finally draw comprehensive conclusions. However, the qualitative conclusions of the forecast given by this forecasting method rely heavily on the subjective judgment and experience of meteorological forecasters. This method has some limitations. For example, it is difficult to accurately highlight local characteristics in the forecast results, it is difficult to capture abnormal signals in time and highlight the extremes of the forecast, that is, there are problems such as low forecast accuracy, low extremes, and lack of objectivity.

[0003] With the development of artificial intelligence technology, digital human technology is being used more and more widely in various fields. Digital human is a virtual character or human image created based on artificial intelligence technology, with functions such as voice interaction, emotional expression, and body movements, which can provide a more intuitive and intelligent user experience.

[0004] There are significant differences between digital people and weather forecasters in weather forecasting, mainly including the following points:

[0005] Differences in technical basis: Digital humans are virtual characters or human images created based on artificial intelligence technology. Their prediction capabilities mainly rely on machine learning, deep learning and other technologies, and make objective predictions through learning and analyzing large amounts of data. Weather forecasters, on the other hand, rely on meteorological expertise and forecasting practice experience to make predictions through diagnostic analysis of observed data.

[0006] Differences in subjectivity, objectivity and refinement: Weather forecasters’ predictions are subjective and are influenced by their personal experience and judgment. Their judgment results vary from person to person, and there are limitations on objective consistency and prediction accuracy. Machine learning and other algorithms can largely avoid the subjectivity of individual forecasters’ judgments by analyzing and training large amounts of data and obtaining refined objective results. Therefore, digital human predictions are more objective and refined.

[0007] Differences in interaction methods: Digital humans can interact with users through voice, text, graphics, and other methods to provide more intelligent and personalized forecasting services. However, weather forecasters usually provide users with forecast results in the form of text, graphics, etc. according to inherent business process regulations, and the interaction method is relatively simple.

[0008] Differences in sustainability and efficiency: Digital humans can provide forecasting services 24 hours a day, with higher efficiency and better sustainability. However, the work of weather forecasters is limited by duty hours and human resources, and the frequency of forecasts and service time are relatively limited.

[0009] In general, digital people have higher objectivity, accuracy and efficiency in weather forecasting, and can provide more intelligent and personalized forecasting services. Weather forecasters have a solid foundation in meteorology and rich forecasting practice experience, and can flexibly provide more in-depth and personalized analysis and judgment according to specific circumstances.

[0010] The existing implementation scheme is a climate prediction method based on numerical model results and diagnostic analysis. It is one of the reference methods for forecasters to give forecast conclusions. This method draws a forecast conclusion on the future climate state through the simulation results and diagnostic analysis of the numerical model. However, the existing method ultimately relies on forecasters to give subjective conclusions on climate prediction based on expert experience, and its prediction accuracy and objectivity are limited. In addition, due to the limitations of the prediction method, there is a bottleneck in the forecaster's ability to predict abnormal climate trends and extreme events, which cannot meet the business service needs of effective prediction and early warning of abnormal situations.

[0011] This technical solution comes from patents, journal articles or practical applications of weather forecasting systems in the field of weather forecasting. In terms of implementation, this method involves the preliminary comprehensive judgment of numerical model results and diagnostic analysis, the subjective judgment and experience correction of weather forecasters, and the correction generation of the final climate forecast results. Although multiple corrections can reduce the subjectivity of diagnostic analysis and judgment and the limitations of the model to a certain extent, the prediction accuracy and reliability are still greatly limited.

[0012] In view of the above analysis, the technical problems that need to be solved urgently in the prior art are:

[0013] Highly subjective and experience-dependent: Existing climate prediction methods mainly rely on the subjective judgment and forecasting experience of individual meteorological forecasters, which leads to the prediction results being affected by personal preferences and subjective factors and lacking objectivity.

[0014] The prediction accuracy is not high enough: Due to the subjective judgment of meteorological forecasters and the limitations of model results, existing climate prediction methods often find it difficult to accurately predict abnormal trends and extreme events in climate change, and the prediction accuracy is limited.

[0015] Poor ability to capture extreme abnormal signals: The prediction results of existing technologies tend to strengthen the ensemble average, making it difficult to capture abnormal signals in climate change in a timely manner, making it difficult to predict extreme events for a long time in advance, and unable to provide effective predictions and warnings of extreme abnormal climate events within the lead time of service needs.

[0016] If the limitations of existing technologies mentioned above can be addressed, significant technological advances will be achieved in the field of climate prediction. Summary of the invention

[0017] In view of the problems existing in the prior art, the present invention provides a climate prediction system and method based on AI digital human.

[0018] The present invention is implemented as follows: a climate prediction system based on AI digital human includes a meteorological data processing module, an artificial intelligence model training module, a digital human model rendering module and a prediction result presentation module.

[0019] Meteorological data processing module: This module obtains domestic and foreign meteorological data through the Internet, including numerical model results and observation data, and performs data cleaning and quality control to ensure the accuracy and completeness of the data.

[0020] Artificial intelligence model training module: This module uses machine learning and deep learning technologies to analyze and train meteorological data and establish a climate prediction model, which includes feature extraction of meteorological data, model training and parameter optimization process.

[0021] Digital human model rendering module: This module is based on digital human technology and renders objective climate forecast results into digital human images, including the digital human's appearance design, speech synthesis, and action design process.

[0022] Forecast result presentation module: This module presents the digital human image to the user in various forms, including numerical values, graphics, text and voice output. The user can communicate and interact with the digital human through the interactive interface, understand the climate forecast results and make corresponding decisions.

[0023] In addition, the meteorological data processing module also includes a data preprocessing unit, which is used to convert the format of the acquired meteorological data, fill in missing values, detect and remove outliers to ensure the consistency and accuracy of the data, thereby improving the training effect and prediction accuracy of the climate prediction model.

[0024] The artificial intelligence model training module also includes a feature selection unit, which is used to perform feature selection on meteorological data before model training to identify the meteorological variables that have the greatest impact on climate forecasting, thereby improving the model's forecasting efficiency and accuracy.

[0025] The prediction result presentation module also includes a user interaction unit, which is used to provide a variety of user interaction methods, including but not limited to touch screen operation, voice recognition and gesture recognition, so that users can interact with the digital human image more conveniently, obtain the required climate prediction information, and make decisions based on this information.

[0026] Furthermore, digital humans specifically include:

[0027] Data analysis and model building: Digital humans for climate forecasting use advanced data analysis techniques and climate models to analyze historical meteorological data and build corresponding forecasting models.

[0028] Presentation and explanation of forecast results: The digital human will present the forecast results to the user in an intuitive way, including trend graphs and spatial graphs of meteorological variables, and explain the meaning and impact of the forecast results.

[0029] User interaction and feedback: Digital humans interact with users through voice or text to understand their needs and preferences, and adjust prediction models or provide personalized prediction services based on user feedback.

[0030] Warning and suggestions: Based on the forecast results, the digital human provides users with corresponding weather warnings and response suggestions to help users make corresponding decisions and preparations.

[0031] Real-time updating and monitoring: Digital humans monitor changes in meteorological data in real time and update forecast results in a timely manner to maintain the accuracy and practicality of the forecast.

[0032] One object of the present invention is to provide a climate prediction system that implements the above-mentioned AI digital human prediction method, including: obtaining domestic and foreign numerical model results through a meteorological data processing module, and performing quality control and data cleaning; using an artificial intelligence model training module to analyze and train meteorological data to obtain objective climate prediction conclusions; rendering the prediction results into a digital human image, including appearance, voice, and action, through a digital human model rendering module; and using a prediction result presentation module to present the digital human image to the user in a variety of forms, including numerical, graphical, text, and voice output, to improve the intuitiveness and comprehensibility of the prediction results.

[0033] Another object of the present invention is to provide a computer device, a computer-readable storage medium and an information data processing terminal. The computer device includes a memory and a processor, the memory stores a computer program, the processor processes the computer program, and when the computer program is executed by the processor, the processor executes the steps of the climate prediction method based on AI digital human; the computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the climate prediction method based on AI digital human; the information data processing terminal includes the climate prediction system based on AI digital human.

[0034] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0035] (1) Improve prediction accuracy:

[0036] The present invention uses artificial intelligence technology to analyze and train meteorological data and establish an objective climate prediction model. Compared with traditional methods that rely on expert experience, the prediction accuracy is higher.

[0037] (2) Reduce subjectivity:

[0038] The present invention uses digital human technology to present the prediction results to users in various forms, reducing the influence of subjective judgment of weather forecasters and improving the objectivity and credibility of the prediction results.

[0039] (3) Improve user experience:

[0040] Through the interactive interface with the digital human, users can intuitively understand the climate forecast results and make corresponding decisions based on their personal needs, which improves user experience and participation.

[0041] (4) Personalized prediction:

[0042] The present invention realizes personalized driving through digital human technology, and can provide customized prediction services according to user preferences and needs to better meet user needs.

[0043] At the same time, as auxiliary evidence of the inventiveness of the claims of the present invention, it is also reflected in the following aspects:

[0044] The technical solution of the present invention overcomes technical prejudice: the present application provides a digital human method, device, storage medium and program product for climate prediction. In the present application, artificial intelligence technology is used to correct the results of domestic and foreign numerical models to obtain objective numerical model prediction conclusions. In addition, by conducting diagnostic analysis on climate background, circulation and sea temperature, the numerical model conclusions are further corrected, and the digital human model is rendered to generate objective prediction data, including output in the form of numerical values, graphics, text and voice.

[0045] The implementation of this method ensures that the digital human for climate forecasting has higher accuracy and objectivity. Driven by personalization, the accuracy of the digital human in numerical values, images, sounds, and text images is improved, further improving the user experience. The application of this technology has significantly improved the intelligence, refinement, and immersion of the digital human for climate forecasting. In summary, this application solves the problems of high subjectivity and insufficient accuracy in the prior art, and AI digital humans have brought important breakthroughs in climate forecasting. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of a climate prediction method based on AI digital human provided by an embodiment of the present invention. 1. Meteorological data processing module; 2. Artificial intelligence model training module; 3. Digital human model rendering module; 4. Prediction result presentation module.

[0047] Figure 2 It is a structural diagram of a climate prediction system based on AI digital human provided by an embodiment of the present invention.

[0048] Figure 3 It is an overall structural diagram of the system provided by an embodiment of the present invention.

[0049] Figure 4 It is a flow chart of a meteorological data processing module provided by an embodiment of the present invention.

[0050] Figure 5 It is a flow chart of the artificial intelligence model training module provided by an embodiment of the present invention.

[0051] Figure 6 Schematic diagram of a digital human model rendering module provided in an embodiment of the present invention.

[0052] Figure 7 This is an interface diagram of a digital human prediction system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] like Figure 1 As shown, the climate prediction system based on AI digital human provided by an embodiment of the present invention includes a meteorological data processing module 1, an artificial intelligence model training module 2, a digital human model rendering module 3 and a prediction result presentation module 4.

[0055] Meteorological data processing module 1: This module obtains domestic and foreign meteorological data through the Internet, including numerical model results and observation data, and performs data cleaning and quality control to ensure the accuracy and completeness of the data.

[0056] Artificial Intelligence Model Training Module 2: This module uses machine learning and deep learning technologies to analyze and train meteorological data and establish a climate prediction model, which includes feature extraction of meteorological data, model training and parameter optimization processes.

[0057] Digital human model rendering module 3: Based on digital human technology, this module renders the objective climate forecast results into digital human images, and designs the digital human appearance design, speech synthesis, and action design process according to the forecast period and forecast results. This module involves the appearance design of digital humans, which means that the image of digital humans may change according to the results of climate forecasts, such as clothing, expressions, or body shapes, to reflect the predicted climate conditions. At the same time, speech synthesis is also part of this process. Digital humans will convey climate forecast information through speech, making the presentation of information more vivid and intuitive. Finally, action design is also a key link. Digital humans will further emphasize and explain the results of climate forecasts through specific actions. In summary, this process is indeed to anthropomorphize the climate forecast results into digital humans for presentation, rather than simply interactively calling them. Such a design can make the results of climate forecasts easier to understand and accept, and also increase the fun and interactivity of the information.

[0058] Forecast result presentation module 4: This module presents the digital human image to the user in various forms, including numerical values, graphics, text and voice output. The user can communicate and interact with the digital human through the interactive interface, understand the climate forecast results and make corresponding decisions.

[0059] like Figure 2 As shown, the climate prediction method based on AI digital human provided by the embodiment of the present invention includes:

[0060] S1, obtain domestic and foreign numerical model results through the meteorological data processing module, and perform quality control and data cleaning;

[0061] S2, using the artificial intelligence model training module to analyze and train meteorological data to draw objective climate prediction conclusions;

[0062] S3, the prediction results are rendered into a digital human image through the digital human model rendering module, including appearance, voice, and action; the process of rendering the prediction results into a digital human image through the digital human model rendering module includes multiple detailed steps. First, select a suitable digital human model framework, such as Unity 3D or Unreal Engine, and initialize the model parameters, including basic attributes such as gender, height, body shape, skin color, and clothing, to ensure that the model is in a standard posture when rendering begins.

[0063] Users can interact with digital humans, who use artificial intelligence technology to make climate forecasts. Specifically, digital humans use advanced artificial intelligence algorithms to make forecasts and correct forecast results by analyzing climate factors such as circulation and sea temperature. Then, digital humans present the forecast results to users in the form of pictures, text, voice, etc. to ensure the accuracy and intuitiveness of information transmission. In the process of user interaction with digital humans, virtual characters not only "think" through artificial intelligence technology, but also adjust appearance parameters such as facial features, hairstyle, eye color and clothing style according to the forecast results of different time periods. Through 3D modeling tools, digital humans are able to adjust the details of the model in real time so that its appearance meets the expected effect of the forecast scenario. This approach not only improves the interactivity of climate forecasts, but also enhances users' understanding and participation.

[0064] Next, high-resolution textures and material maps are applied to make the model's skin, clothing, and other surfaces more realistic, and the model's three-dimensionality and realism are enhanced through lighting and shadow effects. In terms of speech rendering, the text information in the prediction results is converted into speech using text-to-speech (TTS) technology, and an appropriate speech synthesis engine is selected and appropriate speech samples are selected based on the model's gender and age. Then, the generated speech is synchronized with the lip movements of the digital human model to ensure that the model's mouth shape matches the speech pronunciation. In the motion rendering part, by inputting specific motion parameters such as gait, gestures, and facial expressions, dynamic movements are added to the model using motion capture technology or predefined animation libraries, and the model's joints, postures, and motion trajectories are adjusted to ensure natural and smooth movements. Finally, the appearance, speech, and motion rendering results are combined for real-time rendering to generate the final digital human image, and the rendering process is optimized to ensure high frame rate and low latency, achieving a smooth user experience. At the same time, the user's interaction with the digital human model is designed, such as touch, voice commands, and gesture control, to ensure that the interaction process is natural and smooth, and the model's appearance, speech, and movements are adjusted in real time based on user feedback. Through these steps, the digital human model rendering module can efficiently and realistically render the prediction results into a digital human image with appearance, voice and movements, achieving a highly realistic and natural display effect.

[0065] S4, using the prediction result presentation module to present the digital human image to the user in various forms, including numerical values, graphics, text and voice output, to improve the intuitiveness and comprehensibility of the prediction results.

[0066] The digital human provided by the embodiment of the present invention has the following functions and features:

[0067] Data analysis and model building: Digital humans for climate forecasting can use advanced data analysis techniques and climate models to analyze historical meteorological data and build corresponding forecasting models.

[0068] Presentation and explanation of forecast results: Digital humans can present forecast results to users in an intuitive way, including trend graphs and spatial graphs of meteorological variables, while explaining the meaning and impact of the forecast results.

[0069] User interaction and feedback: Digital humans can interact with users through voice or text, understand their needs and preferences, and adjust prediction models or provide personalized prediction services based on user feedback.

[0070] Warnings and suggestions: Based on the forecast results, digital humans can provide users with corresponding weather warnings and response suggestions to help users make corresponding decisions and preparations.

[0071] Real-time updating and monitoring: Digital humans can monitor changes in meteorological data in real time and update forecast results in a timely manner to maintain the accuracy and practicality of the forecast.

[0072] By utilizing digital human technology, climate forecasts can be more intuitive and intelligent, providing users with better weather forecast services and helping people better adapt to climate change and respond to natural disasters.

[0073] The embodiment of the present application provides a method, device, storage medium and program product for video generation and interaction based on digital human. In the embodiment of the present application, text-to-speech processing is performed based on the voice characteristics and emotion tags of the user, and speech-to-expression processing is performed based on the mapping relationship between the voice characteristics and expression coefficients of the user, and the digital human model is rendered based on the voice signal and expression coefficient to obtain the video data of the digital human model. Thus, the voice characteristics of the user are accurately simulated, ensuring that the voice output of the digital human not only sounds natural, but also has a high degree of personalization, realizing the personalized drive of the digital human, improving the fidelity of the digital human in terms of sound and dynamic image, and thus improving the user experience, and enhancing the interactivity, realism and immersion of the digital human.

[0074] There are some notable differences between number crunchers and weather forecasters when it comes to climate prediction:

[0075] Differences in technical basis: Digital humans are virtual characters or human images created based on artificial intelligence technology. Their prediction ability mainly relies on machine learning, deep learning and other technologies, and predictions are made through learning and analyzing large amounts of data. Weather forecasters, on the other hand, rely on meteorological knowledge, professional experience and observation data to make predictions, and their prediction ability is affected by their personal skills and experience.

[0076] Differences in subjectivity, objectivity and refinement: Digital human predictions are more objective and accurate because they analyze and train large amounts of data based on machine learning algorithms and are not affected by personal subjective consciousness and experience. However, the predictions of weather forecasters are somewhat subjective and are affected by personal experience and judgment, so the accuracy and objectivity of predictions are limited.

[0077] Differences in interaction methods: Digital humans can interact with users through voice, text, graphics, and other methods to provide more intelligent and personalized forecasting services. However, weather forecasters usually provide users with forecast results in the form of text, charts, etc., and the interaction method is relatively simple.

[0078] Differences in sustainability and efficiency: Digital humans can provide forecasting services 24 hours a day, with higher sustainability and efficiency. However, the work of weather forecasters is limited by time and human resources, and the forecast coverage and service time are relatively limited.

[0079] In general, digital humans have higher objectivity, accuracy and efficiency in climate forecasting and can provide more intelligent and personalized forecasting services, while weather forecasters have rich meteorological knowledge and professional experience and can conduct more flexible and in-depth analysis and judgment based on specific circumstances.

[0080] Current short-term climate predictions are usually based on subjective judgments by climate forecasters based on numerical model results, diagnostic analysis and other techniques, and expert experience to draw comprehensive conclusions. However, this method has some limitations, such as the prediction results tend to be average and it is difficult to capture abnormal trends.

[0081] The present application provides a digital human method, device, storage medium and program product for climate prediction. In the present application, artificial intelligence technology is used to correct the results of domestic and foreign numerical models to obtain objective numerical model prediction conclusions. In addition, by conducting diagnostic analysis on climate background, circulation and sea temperature, the numerical model conclusions are further corrected, and the digital human model is rendered to generate objective prediction data, including output in the form of numerical values, graphics, text and voice.

[0082] The implementation of this method ensures that the digital human for climate forecasting has higher accuracy and objectivity. Through personalized drive, the accuracy of the digital human in terms of numerical values, images, sounds and text images is improved, further improving the user experience. The application of this technology has significantly improved the intelligence, refinement and immersion of the digital human for climate forecasting.

[0083] The specific climate prediction method based on AI digital human is as follows:

[0084] (1) Data processing method: In addition to using artificial intelligence technology to analyze and train meteorological data, the present invention can also use traditional statistical analysis methods or other machine learning algorithms to process data. For example, support vector machines (SVM), decision trees and other algorithms can be used for model training.

[0085] (2) Digital human technology replacement: In addition to digital human technology, the present invention can also use other forms of user interfaces, such as charts, text descriptions, etc., to present prediction results to users. For example, interactive charts and text descriptions can be designed to allow users to understand the prediction results more intuitively.

[0086] (3) Prediction result presentation method: In addition to the digital human image, the present invention can also present the prediction results to the user in the form of voice prompts, push notifications, etc. For example, the prediction results can be converted into voice broadcasts through speech synthesis technology to improve the convenience of users in obtaining information.

[0087] (4) Prediction model training method: In addition to using machine learning and deep learning techniques, the present invention can also try other prediction model training methods, such as genetic algorithms, fuzzy logic, etc. Different training methods will bring different prediction effects and performances.

[0088] The specific application fields or related products of the present invention.

[0089] Improve prediction accuracy: This invention uses artificial intelligence technology to analyze and train meteorological data and establish an objective climate prediction model. Compared with traditional methods that rely on expert experience, the prediction accuracy is higher.

[0090] Reducing subjectivity: The present invention uses digital human technology to present the forecast results to users in various forms, reducing the influence of the subjective judgment of weather forecasters and improving the objectivity and credibility of the forecast results.

[0091] Improve user experience: Through the interactive interface with the digital human, users can intuitively understand the climate forecast results and make corresponding decisions based on their personal needs, which improves user experience and participation.

[0092] Realize personalized prediction: The present invention realizes personalized drive through digital human technology, and can provide customized prediction services according to user preferences and needs to better meet user needs.

[0093] Relevant evidence of the technical effects achieved by the embodiments of the present invention.

[0094] Specific technical implementation plan:

[0095] Meteorological data processing module: This module obtains domestic and foreign meteorological data through the Internet, including numerical model results, observation data, etc., and performs data cleaning and quality control to ensure the accuracy and completeness of the data.

[0096] Artificial intelligence model training module: This module uses machine learning and deep learning technologies to analyze and train meteorological data and build a climate prediction model. This includes processes such as feature extraction of meteorological data, model training, and parameter optimization.

[0097] Digital human model rendering module: Based on digital human technology, this module renders objective climate forecast results into digital human images, including the digital human appearance design, speech synthesis, action design and other processes.

[0098] Prediction result presentation module: This module presents the digital human image to the user in various forms, including numerical values, graphics, text, voice and other outputs. Users can communicate and interact with the digital human through the interactive interface, understand the climate prediction results and make corresponding decisions.

[0099] Please refer to the attached Figure 3 To Attachment Figure 6 ,in:

[0100] Attached Figure 3 This is the overall structure diagram of the system, including the meteorological data processing module, the artificial intelligence model training module, the digital human model rendering module and the prediction result presentation module;

[0101] Attached Figure 4 This is the flow chart of the meteorological data processing module, including steps such as data acquisition, quality control and cleaning;

[0102] Attached Figure 5 A flowchart of the AI ​​model training module, including steps such as feature extraction, model training, and parameter optimization;

[0103] Attached Figure 6 This is a schematic diagram of the digital human model rendering module, including quantification of model conclusions, adjustment of numerical model forecast results based on background analysis and diagnostic synthesis, and output of text and voice results.

[0104] Attached Figure 7 This is an interface diagram of a digital human prediction system provided by an embodiment of the present invention.

[0105] 1. Quality control and cleaning of meteorological data processing modules to ensure the accuracy and completeness of acquired meteorological data.

[0106] 2. The machine learning and deep learning technologies of the artificial intelligence model training module enable the analysis and training of meteorological data and the establishment of an objective climate prediction model.

[0107] 3. The digital human technology of the digital human model rendering module renders the objective prediction results into a digital human image and presents it to the user in various forms.

[0108] 4. The interactive interface of the prediction result presentation module allows users to understand climate prediction results and make corresponding decisions through interaction with the digital human.

[0109] Data processing methods: In addition to using artificial intelligence technology to analyze and train meteorological data, traditional statistical analysis methods or other machine learning algorithms can also be used for data processing. For example, algorithms such as support vector machines (SVM) and decision trees can be used for model training.

[0110] Digital human technology alternative: In addition to digital human technology, other forms of user interfaces, such as charts, text descriptions, etc., can be used to present prediction results to users. For example, interactive charts and text descriptions can be designed to allow users to understand the prediction results more intuitively.

[0111] Prediction result presentation method: In addition to digital human images, prediction results can also be presented to users through voice prompts, push notifications, etc. For example, the prediction results can be converted into voice broadcasts through speech synthesis technology to improve the convenience of users in obtaining information.

[0112] Prediction model training methods: In addition to using machine learning and deep learning techniques, you can also try other prediction model training methods, such as genetic algorithms, fuzzy logic, etc. Different training methods will bring different prediction results and performance.

[0113] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. A person of ordinary skill in the art will understand that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a data carrier provided on a disk, CD or DVD-ROM carrier medium, a programmable memory of a read-only memory (firmware), or an optical or electronic signal carrier. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0114] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A climate prediction system based on AI digital human, characterized in that: include: Meteorological data processing module: This module obtains domestic and foreign meteorological data through the Internet, including numerical model results and observation data, and performs data cleaning and quality control to ensure the accuracy and integrity of the data; Artificial intelligence model training module: This module uses machine learning and deep learning technologies to analyze and train meteorological data and establish a climate prediction model, which includes feature extraction of meteorological data, model training and parameter optimization processes; Digital human model rendering module: Based on digital human technology, this module renders objective climate forecast results into digital human images, including the digital human appearance design, speech synthesis, and action design processes; Forecast result presentation module: This module presents the digital human image to the user in various forms, including numerical values, graphics, text and voice output. The user can communicate and interact with the digital human through the interactive interface, understand the climate forecast results and make corresponding decisions.

2. The climate prediction system based on AI digital human as claimed in claim 1, wherein the characteristics are as follows: include: Data analysis and model building: Digital humans for climate prediction use advanced data analysis techniques and climate models to analyze historical meteorological data and build corresponding prediction models; Prediction result display and explanation: The digital human will present the prediction results to the user in an intuitive way, including trend graphs and spatial graphs of meteorological variables, and explain the meaning and impact of the prediction results; User interaction and feedback: Digital humans interact with users through voice or text to understand their needs and preferences, and adjust prediction models or provide personalized prediction services based on user feedback; Warning and suggestions: Based on the forecast results, the digital human provides users with corresponding weather warnings and response suggestions to help users make corresponding decisions and preparations; Real-time updating and monitoring: Digital humans monitor changes in meteorological data in real time and update forecast results in a timely manner to maintain the accuracy and practicality of the forecast.

3. The climate prediction system based on AI digital human according to claim 1 is characterized in that The meteorological data processing module also includes a data preprocessing unit, which is used to perform format conversion, missing value filling, and outlier detection and removal on the acquired meteorological data to ensure data consistency and accuracy, thereby improving the training effect and prediction accuracy of the climate prediction model.

4. The climate prediction system based on AI digital human according to claim 1 is characterized in that: The artificial intelligence model training module also includes a feature selection unit, which is used to perform feature selection on meteorological data before model training to identify the meteorological variables that have the greatest impact on climate forecasting, thereby improving the model's forecasting efficiency and accuracy.

5. The climate prediction system based on AI digital human according to claim 1 is characterized in that: The prediction result presentation module also includes a user interaction unit, which is used to provide a variety of user interaction methods, including but not limited to touch screen operation, voice recognition and gesture recognition, so that users can interact with the digital human image more conveniently, obtain the required climate prediction information, and make decisions based on this information.

6. A climate prediction system for implementing the climate prediction method based on AI digital human as claimed in any one of claims 1 to 5, comprising: S1, obtain domestic and foreign numerical model results through the meteorological data processing module, and perform quality control and data cleaning; S2, using the artificial intelligence model training module to analyze and train meteorological data to draw objective climate prediction conclusions; S3, rendering the prediction results into a digital human image, including appearance, voice, and action, through the digital human model rendering module; S4, using the prediction result presentation module to present the digital human image to the user in various forms, including numerical values, graphics, text and voice output, to improve the intuitiveness and comprehensibility of the prediction results.

7. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the climate prediction method based on AI digital human as claimed in claim 6.

8. A computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to execute the steps of the climate prediction method based on AI digital human as claimed in claim 3.

9. An information data processing terminal, comprising the climate prediction system and method based on AI digital human as described in any one of claims 1 to 5.

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