764results about How to "Increase engagement" patented technology

The invention discloses a mirror image type industry linkage engine system and method based on a knowledge graph, and relates to the technical field of industry linkage engines, and the system comprises a data collection module which collects multi-industry data in multiple modes, and the cleaning accuracy is 90% or above; the knowledge graph construction module is combined from bottom to top and from top to bottom, and is used for distributed storage and block chain evidence storage; the industry mirror image model generation module is used for generating a model through machine learning, and the association accuracy is 95% or above; the linkage analysis and decision module is used for finding a linkage strategy through an intelligent algorithm; and the user interaction module has a graphical interface, is fast in response, can feed back optimization, and supports multi-language and VR / AR display. The system is accurate and efficient in data processing, integrates multi-source data to construct the knowledge graph, is deep and comprehensive in industry analysis, simulates industry relations to assist decision making, is intelligent and reliable in decision making support, provides strategies and evaluation, is convenient and friendly in user experience, improves interactivity through multiple technologies, promotes accurate decision making of enterprises, and promotes collaborative development and innovation of the industry.

The invention relates to the technical field of disease follow-up visit, in particular to a health state assessment system applied to disease follow-up visit, which comprises a data acquisition module, a data preprocessing module, a health state assessment module, a personalized health management suggestion module, a dynamic health risk prediction module and a visualization module, the data acquisition module acquires multi-dimensional data of a patient and performs data fusion and integration, the health state evaluation module evaluates the health state of the patient based on multi-level feature extraction and a deep learning model, and the personalized health management suggestion module combines a health state evaluation result and historical data to obtain a personalized health management suggestion result. Customized health management suggestions are generated through a rule engine, and the dynamic health risk prediction module predicts future health risks of patients and provides real-time early warning by using a time sequence analysis model. The visualization module displays health assessment and management suggestion results through a chart and an interactive interface, and the accuracy and individuation degree of health management are improved.

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

The invention relates to the technical field of training strategy optimization, in particular to an optimization method and system based on a rehabilitation training strategy. The method comprises the following steps: carrying out real-time monitoring on physiology and training performance of a patient in a rehabilitation training process through wearable equipment, and carrying out evaluation analysis on an emotional state and athletic ability to obtain emotional feeling state data and athletic ability data of the patient; performing rehabilitation target prediction analysis according to the emotion feeling state data and the exercise ability data to obtain a prediction target of rehabilitation training of the patient; performing target feedback difference comparative analysis on the predicted target and an expected target to obtain a target difference feedback result; performing strategy optimization analysis on the current rehabilitation training strategy to generate a rehabilitation training strategy optimization scheme; and performing strategy dynamic optimization adjustment on the current rehabilitation training strategy to obtain an optimized rehabilitation training strategy. The rehabilitation training efficiency and effect of the patient can be improved.

The invention relates to the technical field of meta universe and knowledge graph, and discloses a multi-dimensional fusion meta universe and vertical AI model collaborative innovation platform, which comprises a data generation module, an interaction intention knowledge graph construction module, a vertical domain knowledge graph construction module, a knowledge graph fusion module and an intelligent interaction module. The method comprises the following steps: collecting user interaction behavior data, identifying dominant and implicit intentions, and constructing an interaction intention knowledge graph; generating a fused heterogeneous knowledge graph in combination with the vertical domain knowledge graph; constructing a vertical AI model based on the fused heterogeneous knowledge graph; according to the invention, deep understanding and prediction of user intentions are realized, so that intelligent personalized services are provided.

The invention discloses an artificial intelligence psychological assessment method and system based on multi-modal input. The method comprises the following steps that S10, a personal psychological model used for psychological assessment is established and trained; s20, receiving a psychological assessment request initiated by a user, and carrying out conversation with the user through the personal psychological model; s30, in the dialogue process with the user, multi-modal data of the user is obtained; and S40, processing and analyzing the multi-modal data of the user, and performing mental health assessment on the user. The method can acquire and process various types of data of the user, such as texts, voices, facial expressions, physiological signals and the like. The multi-modal data fusion mode can provide more comprehensive and accurate psychological state information, so that the accuracy of psychological assessment is improved. According to the method, a personalized psychological model is established and trained for each user, and a more targeted psychological assessment service can be provided.

The invention relates to the technical field of health monitoring, in particular to a health monitoring system based on wearable equipment, which comprises a data acquisition module, a health data analysis module, an emotion and sound analysis module and a health prediction feedback module. According to the health monitoring system, the prediction accuracy of the health monitoring system is effectively improved by combining time sequence analysis with a processing strategy of environmental factors, and more accurate health condition early warning, disease prevention and management strategy optimization and emotion and sound analysis integration are provided by comprehensively analyzing long-term health data and real-time environmental changes of the user, so that the user experience is improved. The emotion change is clarified through acoustic feature analysis, the understanding of the emotion state of the user is improved, the health monitoring dimension is enhanced, the participation degree and satisfaction degree of the user are improved through personalized health suggestion and real-time feedback, the practicability and effectiveness of the system are enhanced, and health management is more accurate and has interactivity.

The invention discloses an AI auxiliary limb rehabilitation system, and belongs to the field of communication control systems, the AI auxiliary limb rehabilitation system combines a sensing module, an AI analysis module, a VR interaction module, a feedback module and the like, comprehensively collects limb movement and physiological data, introduces gamification elements, improves the patient participation degree through target setting, an award mechanism and interesting tasks, and improves the rehabilitation effect. A personalized rehabilitation plan is generated by using a deep learning algorithm, an emotion recognition unit can adjust the plan according to a psychological state, environment self-adaption and real-time adjustment of training difficulty to ensure training safety and is challenging, data cloud storage facilitates remote monitoring, third-party equipment is compatible with extended monitoring ability, and the training efficiency is improved. A brand-new, efficient and personalized solution system scheme is provided for limb rehabilitation.

The invention, which relates to the technical field of artificial intelligence and skill training examination, discloses a multi-crowd adaptive training evaluation system comprising the following components: a user data management module, an RAG module, an answer processing module, a score analysis module, an intensive training module and a question bank updating module. According to the method, comprehensive and accurate assessment of the user ability is realized by constructing a multi-crowd adaptive training assessment system, the system constructs the user ability portrait by collecting historical data and real-time performance of the user in multiple dimensions and utilizing advanced data processing and analysis technologies, and dynamically adjusts the training content and difficulty, so that the user ability assessment efficiency is improved. According to the method and the system, the ability level and the learning requirement of different users are adapted, the personalized training mode not only improves the participation degree and the learning efficiency of the users, but also helps the users to quickly and accurately identify the ability weakness of the users, and powerful support is provided for subsequent improvement.

The invention belongs to the technical field of information recommendation, and discloses a financial information recommendation method and system based on multi-channel distribution, and the method comprises the steps: collecting behavior data of multiple channels of a user, carrying out the cross-platform data integration, and forming a complete user behavior data set; constructing a multi-dimensional user portrait, establishing a financial information database, and marking a keyword tag for each piece of financial information; according to the keyword matching degree of the user portrait and the information content, using content recommendation and financial information conforming to user preferences, and based on historical behavior data of the user and behavior habits of similar users, using collaborative filtering to recommend financial information in which the user is interested to obtain a financial information recommendation list; according to the use habits of the user at different terminals, cross-platform information distribution is performed on the financial information recommendation list through multiple channels, so that the accurate recommendation capability of financial information is improved, and the user experience is improved.

The invention discloses an electric power system climbing demand evaluation and optimization method considering load side resources, and the method comprises the steps: providing a calculation method for maximum upward and downward climbing demands in different time periods, including a calculation method for deterministic and uncertain climbing demands, in order to effectively cope with the fluctuation and uncertainty of loads or new energy; a climbing demand optimization model which takes minimization of the uncertainty climbing demand as an objective function and takes constraint conditions such as load adjusting quantity and demand response into consideration is provided; a gradeability optimization model which takes minimization of comprehensive consumables of the power system as an optimization target and considers constraints such as node balance and line power flow is provided; modeling is carried out on AMPL software, a solver GUROBI is called for solving, and an electric power system climbing demand evaluation and optimization scheme is obtained. The comprehensive operation consumables of the power system can be effectively reduced, and the net load peak load shifting effect on the power system is achieved, so that the operation reliability of the power system is improved, and the dispatching pressure of the power system is relieved.

The invention relates to an interactive projection optimization method for an exhibition hall, and the method comprises the following steps: before projection is started, sensing the change of ambient light in the exhibition hall through a sensor, and evaluating the illumination conditions of different regions; acquiring a spatial layout in the exhibition hall by using a spatial modeling technology, and identifying wall surface and object shielding information of a projection area; gestures, actions and watching directions of audiences are captured in real time through a camera or infrared induction equipment; dynamically adjusting the projection content according to the action and position of the audience; comprising the following steps: when an audience approaches a certain exhibit, amplifying related projection content or switching to a detailed display mode; collecting behavior data of each audience including stopping time and interaction times of the audience in front of an exhibit; interest points of audiences are predicted through a machine learning model, and projection content related to audience interests is recommended; and adjusting the brightness and color parameters of each projector according to the actual projection area and content of each projection device.

The invention discloses an energy industry intelligent algorithm optimization method based on federal learning. The method comprises the following steps: S1, collecting and preprocessing local energy data; s2, acquiring and preprocessing expert example data; s3, constructing a comprehensive data set by applying a multi-modal data fusion technology; s4, each participant performs local model training on the comprehensive data set by using a federal adaptive optimization algorithm to generate local model update; s5, the central server aggregates local model updating to generate a global model; s6, applying a soft actor-commentator algorithm in the global model, and combining an inverse reinforcement learning technology to optimize an energy distribution strategy; s7, creating a virtual simulation environment by using a digital twinning technology, and testing and optimizing the performance of the global model; and S8, providing a user-friendly interface and a natural language processing interface, and realizing personalized energy management suggestions. According to the method, federated learning, a soft actor-commentator algorithm and an inverse reinforcement learning technology are combined, and an energy distribution strategy is optimized.

According to the intelligent energy-saving analysis method based on the building information model (BIM), building thermal performance parameters and an intelligent simulation algorithm are integrated, building heat conduction, a heat bridge effect and solar heat gain are accurately simulated, and a scientific basis is provided for design. Meanwhile, an energy-saving optimization strategy is automatically generated, such as wall thickness adjustment and window design, so that energy consumption is reduced. Besides, a multidisciplinary cooperative working platform is established, effective implementation of energy-saving measures is ensured, and energy use and maintenance tracking of the whole life cycle of the building are realized and energy-saving performance is continuously optimized in combination with an energy management system. The innovation of the method lies in the automatic and intelligent analysis process, and a comprehensive solution is provided for the field of building energy conservation.

The invention discloses a personalized rehabilitation plan generation method and system based on multi-modal data, and relates to the technical field of medical rehabilitation, and the method comprises the steps: obtaining physiological data, motion data and emotion and cognition data of a patient; preprocessing the physiological data, the motion data and the emotion and cognition data to obtain multi-modal fusion data; analyzing the multi-modal fusion data by adopting a pre-trained machine learning model, and determining the rehabilitation potential and demand of the patient to obtain an evaluation result; a personalized rehabilitation plan of a rehabilitation training type is automatically generated according to an evaluation result, and training parameters, psychological counseling and nutrition suggestions are generated according to the conditions of the patient. According to the personalized rehabilitation plan generation method and system based on the multi-modal data, accurate evaluation can be carried out according to the conditions of the patient, the personalized rehabilitation plan is generated, the rehabilitation efficiency of the patient can be improved, and it is ensured that the rehabilitation process is more scientific and efficient.

The invention discloses an emotional pressure relieving and monitoring method combined with a virtual reality technology, and the method comprises the following steps: S1, providing a virtual environment for a user through a virtual reality device, and enabling the user to interact with the environment through wearing a virtual reality head-mounted device and using a controller; s2, collecting physiological data of the user in real time through a biosensor, wherein the physiological data comprises heart rate, brain wave, electrodermal response and oxyhemoglobin saturation; s3, inputting the physiological data in the S2 into a long-short-term memory network model for processing, and predicting emotional changes; s4, adjusting the virtual reality environment in real time to adapt to the current emotional state of the user based on the emotional state analysis and prediction result of the long-short-term memory network model; and S5, repeating the steps S2 to S4 to monitor the emotion of the user. According to the method, solid technical support is provided for emotion pressure management and monitoring based on the virtual reality technology, and a traditional static emotion regulation method can be effectively replaced.

The invention relates to the technical field of energy management and environmental protection, in particular to an energy efficiency optimization system based on electric power and carbon emission market coupling, which comprises an electric power demand prediction module, a carbon emission calculation module, a market coupling module, a strategy generation module, an execution feedback module and an interaction interface module. Wherein the power demand prediction module is used for predicting future power demand; the carbon emission calculation module is used for calculating the corresponding carbon emission; the market coupling module is used for generating a supply and demand report of an electric power and carbon emission market; the strategy generation module is used for formulating a specific power dispatching and carbon emission reduction strategy; the execution feedback module is used for adjusting future strategy generation according to market feedback; and the interactive interface module provides an operation interface for a user. According to the invention, by integrating electric power and carbon emission markets, optimizing resource allocation and realizing dynamic strategy adjustment, the energy use efficiency and the environmental protection effect are effectively improved, and the adaptability of the system and the user interactivity are enhanced at the same time.

The invention discloses a light-weight government affair questionnaire intelligent touch and analysis system and method based on a fusion message, and the system comprises an integrated message application platform operation interface which is used for the submission, application, strategy definition and transmission of a customer message; the questionnaire engine is used for generating questionnaires, issuing strategies and performing multi-dimensional statistical analysis on questionnaire results; the intelligent message scheduling engine is used for realizing AI generation of rich media messages, multi-channel intelligent reach distribution, optimal reach mode selection and summary analysis of reach mode results; and the electronic fence data engine is used for processing operator real-time signaling big data and capturing operator numbers and accurate touch short messages in a specified range. An efficient and convenient short message creation and auditing mechanism, flexible and diversified questionnaire construction, intelligent rich media message generation, accurate regional orientation and audience access, intelligent distribution, high interactivity, comprehensive and deep data analysis and report generation can be realized.

The invention discloses a real-time cultural tourism path optimization and adjustment system based on big data analysis, and relates to the technical field of cultural tourism path optimizing.The system comprises an acquisition and analysis module, the acquisition and analysis module is connected with a combination and matching module, the combination and matching module is connected with a path optimization module, and the path optimization module is connected with a data processing module. The path optimization module is connected with an interaction feedback module, and the collection and analysis module is used for collecting tourism behavior characteristic information of tourists in real time by using a big data analysis technology. According to the real-time cultural tourism path optimization and adjustment system based on big data analysis, a personalized model is established, tourism paths can be customized for tourists, unique tourism requirements and preferences of the tourists are met, the satisfaction degree and participation degree of the tourists in the tourism process are improved, the tourism paths can be adjusted in time according to real-time external data, and the tourism efficiency is improved. The tourists are prevented from being caught in unfavorable conditions such as congestion and severe weather, and smooth and efficient travel itinerary is ensured.

The invention relates to the technical field of user data processing, in particular to a user portrait enhancement method based on a dynamic tag, which is applied to a system method for designing data acquisition, data processing, data analysis and data classification of a user information and user portrait data use platform. By analyzing the user behavior in real time and dynamically adjusting the label in the user portrait, the more accurate and comprehensive user portrait can be constructed, and the real-time interest and long-term preference of the user can be captured. Based on the dynamically updated user portraits, the system can provide highly personalized recommendation services, commodity recommendation, content recommendation or service recommendation can be closer to the real demands of the users, and the satisfaction degree and participation degree of the users are improved.

The invention provides an AIGC-based (advanced integrated gasification control-based) aisle deduction system and a use method thereof, and belongs to the technical field of non-abandoned inheritance, and the AIGC-based aisle deduction system and the use method thereof are characterized in that the AIGC-based aisle deduction system and the use method thereof are provided; comprising a creativity input and planning module, a music and sound effect generation module, a visual design and rendering module, a performance synthesis and director module, an interactive experience and user feedback module and an education and culture transmission module, and the whole system comprises creativity excitation, content generation, user interaction and culture transmission. According to the method, a closed loop is formed, creation efficiency and viewing experience of the aisle art are improved, content is continuously iterated and optimized through intelligent analysis and an instant feedback mechanism, tight fit between works and audience requirements is ensured, the problems of low efficiency and market competitiveness in a traditional supply chain are effectively solved, and the method is suitable for popularization and application. And the live inheritance and development of the traditional cultural form of the aisle in the modern society are promoted.

The invention relates to the field of cerebral apoplexy rehabilitation management, in particular to a cerebral apoplexy patient rehabilitation training management system and method based on an internet platform. The stroke patient rehabilitation training management system based on the internet platform comprises a training plan generation module, a training time dynamic adjustment module and a training intensity dynamic adjustment module. According to the method, personalized exercise priority indexes and cognitive priority indexes are calculated according to the specific exercise function recovery degree and cognitive function recovery degree of each cerebral apoplexy patient, the fields in which the patient needs more attention and training are determined, a targeted exercise function training plan and a targeted cognitive function training plan are generated according to the fields, and the training efficiency of the cerebral apoplexy patient is improved. Therefore, the allocation of rehabilitation resources is optimized; the personalized rehabilitation training plan can better meet individual differences of patients, the rehabilitation efficiency is improved, and the recovery progress is accelerated.

The invention relates to the technical field of drinking water safety monitoring, and discloses a rural drinking water safety monitoring method, which comprises the following steps: S1, water sample collection; s2, monitoring a water sample; s3, data acquisition and transmission; s4, data processing and analysis; s5, early warning; s6, water quality reporting, water quality safety of rural drinking water sources and water delivery pipe networks is ensured by monitoring and evaluating water quality parameters in real time, potential water quality problems can be found and dealt with in time through regular and instant monitoring, health risks caused by water quality pollution are reduced, automatic and continuous water quality monitoring and data transmission are achieved, and the water quality monitoring efficiency is improved. The frequency of manual sampling and inspection is reduced, the monitoring efficiency is greatly improved, real-time collection and analysis of data also shorten the problem discovery and response time, the response speed is improved, and a scientific basis is provided for water quality management and decision making through comprehensive water quality index CWQI calculation and water quality report generation. A decision maker can analyze according to data and trends in the report.

The invention provides a student attention assessment method and system based on big data analysis, and the method comprises the steps: capturing a class image of a student through a camera, collecting the facial expression of the student from the class image of the student, and recognizing the expression type of the student according to the facial expression of the student; acquiring student posture images from the student class images, and judging whether the students raise hands to answer questions or not according to the student posture images; attention scores are calculated from changes in the facial expression types and the body features of the students, and whether the attention of the students is concentrated is determined based on the attention scores. Through the method and the corresponding system, the attention of the student can be evaluated by using a plurality of sequences recorded by the video, and a more real result can be obtained.

The invention relates to the technical field of digital marketing, in particular to a digital advertisement putting method based on big data, and the method comprises the following steps: initializing an advertisement model, importing advertisement request time sequence data, carrying out time sequence analysis, adjusting an enqueue rule and a processing rate, calculating request distribution in a differentiated time period, and generating model parameter configuration. According to the invention, through a queuing theory and time sequence data analysis, advertisement time sequence and processing efficiency optimization, time sequence analysis and parameter adjustment, advertisement timeliness is enhanced, user activity peak synchronization is ensured, user participation degree and advertisement effect are improved, and recalculation of service time and estimation of waiting time support real-time monitoring of service efficiency. Accurate matching of advertisement resources and user requirements is achieved, real-time adjustment of strategies and simulation testing rapidly adapt to market changes, the resource utilization efficiency and ROI are improved, and precision and economic benefits of advertisement putting are remarkably improved through fine management and data driving optimization.

The invention relates to the technical field of director and picture synthesis methods, in particular to an intelligent director and picture synthesis method of a panoramic live broadcast system, which comprises the following steps: acquiring panoramic video data shot by a panoramic camera; obtaining user feedback data; based on the panoramic video data, generating a shot selection strategy by using a deep reinforcement learning algorithm; detecting a live broadcast key event in real time by using a target detection and tracking algorithm based on the panoramic video data; generating a picture shooting plan sequence according to the shot selection strategy and the live broadcast key event; performing intelligent editing and synthesis on the panoramic video data based on the picture shooting plan sequence; and outputting the synthesized live video. And updating parameters of the deep reinforcement learning algorithm based on the user feedback data, thereby realizing full automation of the program directing process, greatly improving the efficiency, and enabling the system to continuously learn from live broadcast experience through the deep reinforcement learning algorithm and gradually improve the decision-making ability of the system.

The invention discloses a transcutaneous electrical stimulation device based on a large language model, and relates to the technical field of wearable devices. The transcutaneous electrical stimulation equipment based on the large language model comprises a data acquisition and preprocessing module, a feature extraction and fusion module, a model training and prediction module and a real-time information processing and response module, real-time data processing and intervention capabilities are integrated, human body data can be analyzed in real time, electrical stimulation parameters can be automatically adjusted, an instant treatment or adjustment scheme is provided for the problems of arrhythmia, chronic pain and the like, and the timeliness and effectiveness of treatment are greatly improved. And secondly, by fusing the physiological signal features and the symptom text features, the equipment can more comprehensively understand the health condition of the user, so that a more accurate treatment scheme is provided. Besides, the equipment also has the functions of anomaly detection and trend analysis, can predict potential health risks, and triggers an alarm in time when a serious anomaly is detected, thereby effectively guaranteeing the life safety of the user.

The invention relates to a virtual power plant regulation and control method and system for realizing new energy consumption. The method comprises the steps of performing statistics according to a wind and light power generation module to obtain predicted power generation amount E1 in each time period; according to an electric vehicle scheduling module, predicting the actual charging demand electric quantity QS1 of the electric vehicle in each time period; predicting actual load demand electric quantity QS2 in each time period according to a load prediction module; according to the energy storage module, calculating the storable electric quantity C and the standby residual capacity CS of each time period in real time; and according to the virtual power plant global scheduling module, the electric quantity difference value E difference required by each time period is calculated according to the predicted electric quantity E1 of each time period, the actual charging demand electric quantity QS1, the actual load demand electric quantity QS2 and the storable electric quantity C, and according to the electric quantity difference value E difference, a scheduling power generation instruction is issued to the heat power unit module for time-phased power generation. The distributed new energy consumption can be realized, and the stability and economy of grid-connected scheduling operation of the distributed new energy equipment can be improved at the same time.

The invention provides a user multi-feature intelligent recommendation method and system, and the method comprises the steps: obtaining the historical behavior feature data of a user, and constructing a multi-interest user portrait based on a MIND model; based on the behavior interaction frequency and behavior interaction time of the user on the candidate commodities of each category, calculating the comprehensive weight of the candidate commodities of each category; and based on the comprehensive weight, selecting candidate commodities and recommending the candidate commodities to the user. According to the method, the multi-interest user portraits are constructed based on the MIND model, through combination of the weighted model based on the behavior frequency, the time decay model and the weighted voting mechanism, the commodities of proper categories are accurately selected and recommended to the users, and diversified behaviors and demand changes of the users can be processed more flexibly. Through combination of the models and mechanisms, the accuracy and correlation of recommended contents are improved, the satisfaction degree and participation degree of users are further improved, and it is ensured that a recommendation system can still keep efficient and accurate recommendation ability when facing complex user behaviors.

The invention relates to the technical field of carbon emission evaluation, in particular to a BIM-based carbon emission dynamic evaluation and prediction system, which comprises a data extraction and matching module, a carbon emission calculation module, a full life cycle analysis module and a result visualization module. According to the method, data of the building materials are automatically extracted and are in butt joint with the carbon emission database, so that the capacity of accurately matching the attributes and the number of the materials is achieved, calculation of the carbon emission is more detailed, the method further has the personalized analysis capacity, each material and each construction activity are independently calculated, more detailed data support is provided, and the calculation efficiency is improved. Carbon emission of the whole life cycle is monitored and predicted in real time, the practicability of data is enhanced, environmental changes can be responded more quickly, effective carbon emission reduction measures are implemented, a data visualization technology is applied, carbon emission information becomes more intuitive and understandable, information propagation and application efficiency is enhanced, and the cognition and participation degree of the public and professionals to environmental protection problems is improved.