Electronic information communication comprehensive service equipment

Abstract

The invention discloses integrated service equipment for electronic information communication, and the equipment comprises a data collection module which is responsible for collecting tourism-related data from multiple channels; the data storage and processing module is responsible for storing tourism-related data and carrying out cleaning, conversion and preliminary analysis; the intelligent analysis module is responsible for carrying out deep analysis on the processed data; the tourist service module is responsible for monitoring the safety condition of a scenic spot in real time and pushing personalized tourism information based on a user portrait and real-time data; the tourism enterprise management module is responsible for supporting precision marketing by using big data analysis and monitoring operation data in real time; the government supervision and service module is responsible for monitoring communication service quality and tourism resource states, establishing an emergency response mechanism and processing emergencies; according to the method, the AI monitoring probe is deployed, communication service performance index data are collected in real time, potential problems are identified, the future trend is predicted, network configuration is automatically adjusted, resource allocation is optimized, and the stability and reliability of communication service are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic information communication, and in particular to an electronic information communication integrated service device. Background Art

[0002] With the rapid development of information technology, particularly the widespread application of technologies such as big data, cloud computing, and the Internet of Things, all sectors of society are experiencing a wave of digital transformation. The tourism industry, as a vital component of the modern service industry, has been profoundly impacted by this trend. The continuous advancement of electronic information and communications technology has provided solid technical support for tourism big data management, making the collection, storage, processing, and analysis of tourism data more efficient and accurate.

[0003] After searching, the invention patent with Chinese patent number CN112150110A discloses a government-run comprehensive tourism big data management and service platform, which belongs to the technical field of comprehensive tourism big data management platforms. It includes a big data processing platform, a scenic area industry chain, a supply chain basic resource support system, and a government supervision module. The big data processing platform also includes a supply chain data resource management module, an e-commerce marketing channel data management module, an electronic ticket data system, a transaction settlement data system, and an electronic tax invoice data system. Compared with existing technologies, the invention patent with Chinese patent number CN112150110A, based on big data, is easy to use, can query the distribution of tourists at tourist attractions, and facilitate tourists to plan travel routes and time. By intervening in the big data management platform, the government can easily communicate and share information between upstream and downstream multi-channel systems, greatly reducing the phenomenon of information islands between systems, improving the government's standardization of the implementation of comprehensive data, and also improving the safety of tourists during their travels.

[0004] However, during the above-mentioned use, although it is mentioned that the data transmission and processing of each module are comprehensively analyzed and processed, there is a lack of a communication service quality monitoring system, which makes it difficult to promptly discover and solve problems that arise during the communication process. When responding to emergencies or emergencies, it is necessary to ensure the timely transmission and effective processing of information. Therefore, an electronic information communication comprehensive service device is proposed. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology, such as the lack of a communication service quality monitoring system, which makes it difficult to promptly discover and solve problems arising during the communication process, and to propose an electronic information communication integrated service device to ensure the timely transmission and effective processing of information when responding to emergencies or emergencies.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: An electronic information communication integrated service device, comprising: Data collection module: responsible for collecting tourism-related data from multiple channels such as IoT sensors, social media, user behavior records, and transaction systems; Data storage and processing module: responsible for storing massive tourism-related data and performing cleaning, conversion, and preliminary analysis to eliminate noise, unify the format, and extract useful information; Intelligent Analysis Module: Responsible for in-depth analysis of processed data, including identifying trends, predicting demand, and evaluating service quality; Tourist service module: Responsible for real-time monitoring of scenic area safety conditions through the integration of IoT sensors and intelligent analysis algorithms, and pushing personalized tourism information based on user profiles and real-time data; Tourism Enterprise Management Module: Responsible for using big data analysis to support precision marketing, improve conversion rates, monitor operational data in real time, optimize resource allocation, monitor service quality, and provide evaluation and improvement suggestions; Government supervision and service module: responsible for monitoring the quality of communication services and the status of tourism resources, ensuring the stability and reliability of the tourism environment, establishing emergency response mechanisms, and handling emergencies; Security assurance and operation and maintenance module: responsible for the system's daily operation and maintenance, data updates and function iterations, and preventing data leakage and loss; The data acquisition module transmits the collected tourism-related data to the data storage and processing module, the tourist service module and the government supervision and service module. The data storage and processing module provides the processed data to the intelligent analysis module. The AI ​​monitoring probe transmits the key data to the intelligent analysis algorithm library in real time for processing. The intelligent analysis algorithm library transmits the analysis results to the data analysis tool for visual display and sends them to the tourist service module, the tourism enterprise management module and the government supervision and service module. The tourist service module receives tourist feedback and real-time communication data, and feeds it back to the intelligent analysis module to optimize user portraits and push strategies. The tourism enterprise management module feeds back service quality monitoring data to the intelligent analysis module. When an emergency occurs, the government supervision and service module coordinates resources from all parties and feeds back relevant information to the tourist service module and the tourism enterprise management module.

[0007] The above technical solution further includes: Furthermore, the data storage and processing module includes a distributed database, a big data processing framework, a data warehouse and an ETL tool. The distributed database is responsible for the storage and management of large-scale data. The big data processing framework is used to process and analyze large-scale data sets. The data warehouse supports data query and analysis operations. The ETL tool is responsible for data extraction, conversion and loading processes. The data acquisition module writes the collected data directly into the distributed database. The big data processing framework reads data from the distributed database for processing and analysis. The data processed by the big data processing framework can be loaded into the data warehouse. The ETL tool extracts data from the data source and loads it into the distributed database or data warehouse after cleaning and conversion.

[0008] Furthermore, the intelligent analysis module includes AI monitoring probes, intelligent analysis algorithm libraries and data analysis tools. The AI ​​monitoring probes are responsible for real-time collection and transmission of key data, such as scenic area traffic, tourist behavior trajectories, service quality indicators, etc., to provide a real-time data source for intelligent analysis. The intelligent analysis algorithm library contains a variety of machine learning and deep learning algorithms for in-depth mining and analysis of data. The data analysis tool provides data visualization and report generation functions to help users intuitively understand the analysis results. The AI ​​monitoring probe serves as the front end of real-time data collection, and transmits key data to the intelligent analysis algorithm library in real time for processing. The intelligent analysis algorithm library then conducts in-depth mining and analysis of the data, and transmits the analysis results to the data analysis tool for visual display, and sends them to the tourist service module, tourism enterprise management module and government supervision and service module.

[0009] Furthermore, the intelligent analysis algorithm library conducts in-depth mining and analysis of data, including trend identification, demand forecasting, service quality assessment and anomaly detection. The trend identification is to identify trends such as seasonal changes in the tourism market and tourist behavior patterns by analyzing historical data and real-time data. The demand forecasting is to use a prediction model to combine current data and historical trends to predict key indicators such as tourist demand and scenic spot traffic in the future. The service quality assessment is to collect tourist feedback and monitor service process data to comprehensively evaluate the quality of tourism services, put forward improvement suggestions, and improve tourist satisfaction. The anomaly detection is to use AI monitoring probes to monitor data changes in real time, promptly discover and warn of abnormal situations, such as a sudden increase in scenic spot traffic, a decline in service quality, etc., so as to take quick measures to respond.

[0010] Furthermore, the tourist service module includes a user portrait construction unit, a data integration and processing unit, a content generation and push unit, an alarm and notification unit, and an emergency rescue dispatch unit. The user portrait construction unit collects and analyzes tourist data to construct a user portrait. The data integration and processing unit integrates real-time communication data (such as GPS location), user portrait data, and scenic spot operation data. The content generation and push unit generates personalized push content based on the integrated data and pushes it to tourists through mobile phone APP, text messages, etc. The alarm and notification unit triggers an alarm and notifies relevant personnel or tourists based on the analysis results of the intelligent analysis module. The emergency rescue dispatch unit is responsible for receiving emergency help information and dispatching rescue resources. The user portrait construction unit sends the user portrait data to the data integration and processing unit. The data integration and processing unit receives real-time communication data (such as GPS location), user portrait data, and scenic spot operation data for comprehensive analysis and processing. The content generation and push unit generates personalized push content based on the output results of the data integration and processing unit and pushes it to tourists. The alarm and notification unit sends alarms or prompt information to relevant personnel or tourists through the content generation and push unit.

[0011] Furthermore, the tourism enterprise management module includes a marketing information push unit, an effect evaluation unit, a tourist evaluation collection unit, a service data analysis unit and a quality improvement suggestion unit. The marketing information push unit formulates and pushes personalized marketing information based on the results of the intelligent analysis module. The effect evaluation unit collects marketing activity data, performs effect evaluation, and feeds back to the marketing information push unit and the intelligent analysis module. The resource allocation suggestion unit provides resource allocation optimization suggestions for tourism enterprises based on the data analysis results. The tourist evaluation collection unit is responsible for collecting tourists' evaluation information on tourism services, including communication service quality. The service data analysis unit conducts a comprehensive analysis of tourist evaluations and service process data. The quality improvement suggestion unit makes service quality improvement suggestions based on the analysis results. The tourist evaluation collection unit transmits the evaluation information to the service data analysis unit. The service data analysis unit analyzes in combination with other service process data and transmits the analysis results to the quality improvement suggestion unit.

[0012] Furthermore, the government supervision and service module includes a communication service quality monitoring and optimization platform, which is responsible for real-time monitoring of communication service performance indicators and dynamic adjustment and optimization. The communication service quality monitoring and optimization platform uses AI monitoring probes deployed at key nodes of the communication network to collect key performance indicator data including network delay, packet loss rate, bandwidth utilization, signal strength, etc. in real time, and predicts possible service quality problems or fault points through an intelligent analysis algorithm library. The communication service quality monitoring and optimization platform automatically adjusts network parameters or configurations based on the analysis results, such as load balancing, resource scheduling, signal enhancement, etc.

[0013] Furthermore, the visitor service module pushes personalized information, specifically in the following steps: User profile construction: Collect historical behavior data (such as browsing history, click behavior, and purchase history) and preference data (such as questionnaire results and user reviews), perform data processing and feature extraction, use machine learning to train the extracted features, build a user profile model, and evaluate the accuracy and effectiveness of the model through methods such as cross-validation to ensure the accuracy of the user profile; Data integration: Using GPS positioning, sensors and other technologies to collect real-time data on tourists' current location and behavior, integrating real-time data with user profile data and scenic spot operation data to form a tourist information database, and performing pre-processing such as cleaning and conversion on the integrated data; Content generation: Based on the integrated data, a combination recommendation algorithm is used to calculate the current needs and potential interests of tourists and generate a recommendation list; Push execution: Select appropriate push channels (such as mobile app push, SMS push, etc.) based on tourists' preferences and current scenarios, send the generated push content to tourists through the selected channels, collect user feedback and behavior data after the push, evaluate the push effect and optimize subsequent push strategies.

[0014] Furthermore, in content generation, the combined recommendation algorithm is used to calculate the current needs and potential interests of tourists and generate a recommendation list. The specific steps are: Data collection: Obtain user profiles and scenic spot operation data. The user profiles include tourists' real-time communication data (such as GPS location), historical travel behavior, preferences, etc. The scenic spot operation data includes the flow of visitors to each attraction, event arrangements, facility status, etc. Data cleaning: Identify and remove data irrelevant to the pushed content, reasonably infer or fill in missing tourist behavior data, and integrate real-time communication data, user profile data, and scenic spot operation data to form a comprehensive data set; Feature extraction: Extract behavioral features from tourists' touring paths, duration of stay, and interactive behaviors, and combine them with information such as age, gender, and interests in user profiles to construct tourist feature vectors. Collaborative filtering: Construct a tourist-attraction / activity matrix: Based on the tourists’ travel history and interest preferences, construct a user-attraction / activity interaction matrix. Assume that Tourists and Attractions / Activities, Visitor-Attractions / Activities Matrix It can represent: in, Indicates tourists About attractions / activities If a tourist has no interest or participation, the corresponding element can be set to 0 or left blank; Calculate similarity: Calculate the similarity between tourists with similar travel preferences, or the similarity between attractions / activities: in, are tourists and tourists A collection of attractions / activities that have been interacted with. are tourists About attractions / activities A rating or some kind of metric, are tourists About attractions / activities Rating or measurement value of Generate recommendation list: Generate personalized tour recommendation list for current tourists based on the travel history and similarity of attractions / activities of similar tourists; Content base recommendation: Feature matching: Match the tourist's travel preference feature vector with the feature vector of the attraction / activity and calculate the similarity: in, is the characteristic vector of tourists’ travel preferences, is the feature vector of attractions / activities; Rating prediction: Based on content similarity, predict tourists’ potential interest in attractions they have not visited or activities they have not participated in; Mixed recommendations: Weight allocation: Assign different weights to collaborative filtering and content-based recommendation based on actual application scenarios and data characteristics; Comprehensive score: The score results of collaborative filtering and content-based recommendation are weighted and summed to obtain the final recommendation score; Generate recommendations: Based on the final ratings, select the highest-rated attractions / activities as recommendations; Push execution: Push personalized push content to tourists in real time through mobile APP, SMS, etc. Tourist feedback: collect tourists’ feedback on recommendation results; Model adjustment: Based on visitor feedback, dynamically adjust the parameters and weights of the recommendation model to optimize the recommendation effect.

[0015] Furthermore, the intelligent analysis module identifies potential service quality issues and predicts future trends, and the government supervision and service module adjusts network parameters or configurations based on the analysis results. Specifically, the following steps are performed: Real-time monitoring of communication service performance indicators: Deploy AI monitoring probes at key nodes of the communication network (such as base stations, routers, switches, etc.). These AI monitoring probes collect key performance indicator data in real time, such as network latency, packet loss rate, bandwidth utilization, and signal strength. The monitoring probe collects data from each node every second to form time series data; Data preprocessing: Clean the collected raw data, remove noise and outliers, and aggregate or sample the data to reduce the data volume and improve processing efficiency; Intelligent analysis and prediction of service quality issues: Use LSTM to conduct in-depth analysis of pre-processed data to predict possible service quality issues or failure points; If the predicted delay exceeds the threshold, an alert is issued; Automatically adjust and optimize network configuration: Automatically adjust network parameters or configuration based on analysis and prediction results. Adjustment strategies may include load balancing, resource scheduling, signal enhancement, etc.

[0016] The present invention has the following beneficial effects: 11. In the present invention, AI monitoring probes are deployed to collect communication service performance indicator data in real time, conduct in-depth analysis of the data, identify potential problems and predict future trends, and automatically adjust network configuration and optimize resource allocation based on the analysis results to ensure the stability and reliability of communication services.

[0017] 12. In the present invention, big data is used to analyze user behavior, and mixed recommendations are used to accurately push tourism information, providing tourists with a more personalized and accurate service experience.

[0018] 13. In the present invention, by integrating IoT sensors and intelligent analysis algorithms, an intelligent real-time communication system is constructed that can monitor the safety status of scenic spots in real time and quickly respond to emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a system block diagram of an electronic information communication integrated service device proposed by the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] See also Figure 1 As shown, the present invention is an electronic information communication integrated service device, comprising: Data collection module: responsible for collecting tourism-related data from multiple channels such as IoT sensors, social media, user behavior records, and transaction systems; Data storage and processing module: responsible for storing massive tourism-related data and performing cleaning, conversion, and preliminary analysis to eliminate noise, unify the format, and extract useful information; Intelligent analysis module: responsible for in-depth analysis of processed data, including identifying trends, predicting demand, and evaluating service quality; Tourist service module: Responsible for real-time monitoring of scenic area safety conditions through the integration of IoT sensors and intelligent analysis algorithms, and pushing personalized tourism information based on user profiles and real-time data; Tourism Enterprise Management Module: Responsible for using big data analysis to support precision marketing, improve conversion rates, monitor operational data in real time, optimize resource allocation, monitor service quality, and provide evaluation and improvement suggestions; Government supervision and service module: responsible for monitoring the quality of communication services and the status of tourism resources, ensuring the stability and reliability of the tourism environment, establishing emergency response mechanisms, and handling emergencies; Security assurance and operation and maintenance module: responsible for the system's daily operation and maintenance, data updates and function iterations, and preventing data leakage and loss; The data collection module transmits the collected tourism-related data to the data storage and processing module, the tourist service module and the government supervision and service module. The data storage and processing module provides the processed data to the intelligent analysis module. The AI ​​monitoring probe transmits the key data to the intelligent analysis algorithm library in real time for processing. The intelligent analysis algorithm library transmits the analysis results to the data analysis tool for visualization and sends them to the tourist service module, the tourism enterprise management module and the government supervision and service module. The tourist service module receives tourist feedback and real-time communication data, and feeds it back to the intelligent analysis module to optimize user portraits and push strategies. The tourism enterprise management module feeds back service quality monitoring data to the intelligent analysis module. When an emergency occurs, the government supervision and service module coordinates resources from all parties and feeds back relevant information to the tourist service module and the tourism enterprise management module.

[0022] In one embodiment, for the above-mentioned data storage and processing module, the data storage and processing module includes a distributed database, a big data processing framework, a data warehouse and an ETL tool. The distributed database is responsible for the storage and management of large-scale data. The big data processing framework is used to process and analyze large-scale data sets. The data warehouse supports data query and analysis operations. The ETL tool is responsible for the data extraction, conversion and loading process. The data acquisition module writes the collected data directly into the distributed database. The big data processing framework reads data from the distributed database for processing and analysis. The data processed by the big data processing framework can be loaded into the data warehouse. The ETL tool extracts data from the data source and loads it into the distributed database or data warehouse after cleaning and conversion.

[0023] In one embodiment, for the above-mentioned intelligent analysis module, the intelligent analysis module includes an AI monitoring probe, an intelligent analysis algorithm library, and a data analysis tool. The AI ​​monitoring probe is responsible for real-time collection and transmission of key data, such as scenic area traffic, tourist behavior trajectories, service quality indicators, etc., to provide a real-time data source for intelligent analysis. The intelligent analysis algorithm library contains a variety of machine learning and deep learning algorithms for in-depth mining and analysis of data. The data analysis tool provides data visualization and report generation functions to help users intuitively understand the analysis results. The AI ​​monitoring probe serves as the front end of real-time data collection and transmits key data to the intelligent analysis algorithm library for processing in real time. The intelligent analysis algorithm library performs in-depth mining and analysis on the data, and transmits the analysis results to the data analysis tool for visual display, and sends them to the tourist service module, tourism enterprise management module, and government supervision and service module.

[0024] In one embodiment, for the above-mentioned intelligent analysis algorithm library, the intelligent analysis algorithm library conducts in-depth mining and analysis of data, including trend identification, demand forecasting, service quality assessment and anomaly detection. Trend identification is to identify seasonal changes in the tourism market, tourist behavior patterns and other trends by analyzing historical data and real-time data. Demand forecasting is to use a prediction model to combine current data and historical trends to predict key indicators such as tourist demand and scenic spot traffic in the future. Service quality assessment is to collect tourist feedback and monitor service process data to comprehensively evaluate the quality of tourism services, put forward improvement suggestions, and improve tourist satisfaction. Anomaly detection is to use AI monitoring probes to monitor data changes in real time, promptly discover and warn of abnormal situations, such as a sudden increase in scenic spot traffic, a decline in service quality, etc., so as to take quick measures to respond.

[0025] In one embodiment, for the above-mentioned tourist service module, the tourist service module includes a user portrait construction unit, a data integration and processing unit, a content generation and push unit, an alarm and notification unit, and an emergency rescue dispatch unit. The user portrait construction unit collects and analyzes tourist data to construct a user portrait. The data integration and processing unit integrates real-time communication data (such as GPS location), user portrait data, and scenic spot operation data. The content generation and push unit generates personalized push content based on the integrated data and pushes it to tourists through mobile phone APP, text messages, etc. The alarm and notification unit triggers an alarm and notifies relevant personnel or tourists based on the analysis results of the intelligent analysis module. The emergency rescue dispatch unit is responsible for receiving emergency help information and dispatching rescue resources. The user portrait construction unit sends the user portrait data to the data integration and processing unit. The data integration and processing unit receives real-time communication data (such as GPS location), user portrait data, and scenic spot operation data for comprehensive analysis and processing. The content generation and push unit generates personalized push content based on the output results of the data integration and processing unit and pushes it to tourists. The alarm and notification unit sends alarms or prompt information to relevant personnel or tourists through the content generation and push unit.

[0026] In one embodiment, for the above-mentioned tourism enterprise management module, the tourism enterprise management module includes a marketing information push unit, an effect evaluation unit, a tourist evaluation collection unit, a service data analysis unit and a quality improvement suggestion unit. The marketing information push unit formulates and pushes personalized marketing information based on the results of the intelligent analysis module. The effect evaluation unit collects marketing activity data, performs effect evaluation, and feeds back to the marketing information push unit and the intelligent analysis module. The resource allocation suggestion unit provides resource allocation optimization suggestions for tourism enterprises based on the data analysis results. The tourist evaluation collection unit is responsible for collecting tourists' evaluation information on tourism services, including communication service quality. The service data analysis unit conducts a comprehensive analysis of tourist evaluations and service process data. The quality improvement suggestion unit proposes service quality improvement suggestions based on the analysis results. The tourist evaluation collection unit transmits the evaluation information to the service data analysis unit. The service data analysis unit analyzes in combination with other service process data and transmits the analysis results to the quality improvement suggestion unit.

[0027] In one embodiment, for the above-mentioned government supervision and service module, the government supervision and service module includes a communication service quality monitoring and optimization platform. The communication service quality monitoring and optimization platform is responsible for real-time monitoring of communication service performance indicators and dynamic adjustment and optimization. The communication service quality monitoring and optimization platform uses AI monitoring probes deployed at key nodes of the communication network to collect key performance indicator data including network delay, packet loss rate, bandwidth utilization, signal strength, etc. in real time, and predicts possible service quality problems or fault points through an intelligent analysis algorithm library. The communication service quality monitoring and optimization platform automatically adjusts network parameters or configurations based on the analysis results, such as load balancing, resource scheduling, signal enhancement, etc.

[0028] In one embodiment, for the above-mentioned tourist service module, the tourist service module pushes personalized information, specifically in the following steps: User profile construction: Collect historical behavior data (such as browsing history, click behavior, and purchase history) and preference data (such as questionnaire results and user reviews), perform data processing and feature extraction, use machine learning to train the extracted features, build a user profile model, and evaluate the accuracy and effectiveness of the model through methods such as cross-validation to ensure the accuracy of the user profile; Imagine a travel app where user A browses multiple natural scenery spots and likes several of them. We can use this behavioral data as features and input it into a user profile model. After training, the model might conclude that user A has a high interest in natural scenery spots and use this as part of their profile.

[0029] Data integration: Using GPS positioning, sensors and other technologies to collect real-time data on tourists' current location and behavior, integrating real-time data with user profile data and scenic spot operation data to form a tourist information database, and performing pre-processing such as cleaning and conversion on the integrated data; When tourist A enters a scenic spot, the system obtains his current location through GPS positioning and integrates it with tourist A's user profile data (such as interest preferences) and scenic spot operation data (such as current visitor flow, popular attractions, etc.). In this way, the system can more accurately understand tourist A's needs and preferences within the scenic spot;

[0030] Content generation: Based on the integrated data, a combination recommendation algorithm is used to calculate the current needs and potential interests of tourists and generate a recommendation list; The system analyzes that Tourist A has a high interest in natural scenery and is currently located near a popular natural scenery spot. The system then selects a detailed description, images, and videos of the attraction from the content library and generates a personalized push message: "Found a popular natural scenery spot near you - XX Mountain. It's picturesque and not to be missed! Click to view details and plan your trip!"

[0031] Push execution: Select appropriate push channels (e.g., mobile app push, SMS push, etc.) based on visitor preferences and current scenarios, send generated push content to visitors via the selected channels, collect user feedback and behavior data after push, evaluate push effectiveness, and optimize subsequent push strategies; The system sends this personalized message to Tourist A via a mobile app. After receiving the message, Tourist A clicks on it to view the details and may further plan their itinerary or share it with friends. The system collects this behavioral data to evaluate the effectiveness of the message and continuously optimizes subsequent push strategies based on this feedback.

[0032] In one embodiment, for the above content generation, a combined recommendation algorithm is used to calculate the current needs and potential interests of tourists and generate a recommendation list. The specific steps are as follows: Data collection: Obtain user profiles and scenic spot operation data. User profiles include tourists' real-time communication data (such as GPS location), historical travel behavior, preferences, etc. Scenic spot operation data includes visitor flow, activity arrangements, facility status, etc. at each attraction; Data cleaning: Identify and remove data irrelevant to the pushed content, reasonably infer or fill in missing tourist behavior data, and integrate real-time communication data, user profile data, and scenic spot operation data to form a comprehensive data set; Feature extraction: Extract behavioral features from tourists' touring paths, duration of stay, and interactive behaviors, and combine them with information such as age, gender, and interests in user profiles to construct tourist feature vectors. Collaborative filtering: Construct a tourist-attraction / activity matrix: Based on the tourists’ travel history and interest preferences, construct a user-attraction / activity interaction matrix. Assume that Tourists and Attractions / Activities, Visitor-Attractions / Activities Matrix It can represent: in, Indicates tourists About attractions / activities If a tourist has no interest or participation, the corresponding element can be set to 0 or left blank; Calculate similarity: Calculate the similarity between tourists with similar travel preferences, or the similarity between attractions / activities: in, are tourists and tourists A collection of attractions / activities that have been interacted with. are tourists About attractions / activities A rating or some kind of metric, are tourists About attractions / activities Rating or measurement value of Generate recommendation list: Generate personalized tour recommendation list for current tourists based on the travel history and similarity of attractions / activities of similar tourists; Content base recommendation: Feature matching: Match the tourist's travel preference feature vector with the feature vector of the attraction / activity and calculate the similarity: in, is the characteristic vector of tourists’ travel preferences, is the feature vector of attractions / activities; Rating prediction: Based on content similarity, predict tourists’ potential interest in attractions they have not visited or activities they have not participated in; Mixed recommendations: Weight allocation: Assign different weights to collaborative filtering and content-based recommendation based on actual application scenarios and data characteristics; Comprehensive score: The score results of collaborative filtering and content-based recommendation are weighted and summed to obtain the final recommendation score; Generate recommendations: Based on the final ratings, select the highest-rated attractions / activities as recommendations; Push execution: Push personalized push content to tourists in real time through mobile APP, SMS, etc. Tourist feedback: collect tourists’ feedback on recommendation results; Model adjustment: Based on visitor feedback, dynamically adjust the parameters and weights of the recommendation model to optimize the recommendation effect.

[0033] In one embodiment, the intelligent analysis module identifies potential service quality issues and predicts future trends, and the government supervision and service module adjusts network parameters or configurations based on the analysis results. Specifically, the following steps are performed: Real-time monitoring of communication service performance indicators: Deploy AI monitoring probes at key nodes of the communication network (such as base stations, routers, switches, etc.). The AI ​​monitoring probes collect key performance indicator data in real time, such as network latency, packet loss rate, bandwidth utilization, and signal strength. The monitoring probe collects data from each node every second to form time series data; Data preprocessing: Clean the collected raw data, remove noise and outliers, and aggregate or sample the data to reduce the data volume and improve processing efficiency; Intelligent analysis and prediction of service quality issues: Use LSTM to conduct in-depth analysis of pre-processed data to predict possible service quality issues or failure points; If the predicted delay exceeds the threshold, an alert is issued; Automatically adjust and optimize network configuration: Automatically adjust network parameters or configuration based on analysis and prediction results. Adjustment strategies may include load balancing, resource scheduling, signal enhancement, etc.

[0034] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An electronic information communication integrated service device, characterized in that: include: Data collection module: responsible for collecting tourism-related data from multiple channels; Data storage and processing module: responsible for storing tourism-related data and performing cleaning, conversion and preliminary analysis; Intelligent Analysis Module: Responsible for in-depth analysis of processed data, including identifying trends, predicting demand, and evaluating service quality. The intelligent analysis module includes AI monitoring probes, an intelligent analysis algorithm library, and data analysis tools. Tourist service module: responsible for real-time monitoring of scenic area safety conditions and pushing personalized tourism information based on user portraits and real-time data; Tourism Enterprise Management Module: Responsible for using big data analysis to support precision marketing, real-time monitoring of operational data, optimizing resource allocation, monitoring service quality, and providing evaluation and improvement suggestions; Government supervision and service module: responsible for monitoring the quality of communication services and the status of tourism resources, establishing emergency response mechanisms, and handling emergencies; Security assurance and operation and maintenance module: responsible for the system's daily operation and maintenance, data updates and function iterations, and preventing data leakage and loss; The data acquisition module transmits the collected tourism-related data to the data storage and processing module, the tourist service module and the government supervision and service module. The data storage and processing module provides the processed data to the intelligent analysis module. The AI ​​monitoring probe transmits the key data to the intelligent analysis algorithm library in real time for processing. The intelligent analysis algorithm library transmits the analysis results to the data analysis tool for visual display and sends them to the tourist service module, the tourism enterprise management module and the government supervision and service module. The tourist service module receives tourist feedback and real-time communication data, and feeds it back to the intelligent analysis module to optimize user portraits and push strategies. The tourism enterprise management module feeds back service quality monitoring data to the intelligent analysis module. When an emergency occurs, the government supervision and service module coordinates resources from all parties and feeds back relevant information to the tourist service module and the tourism enterprise management module.

2. The electronic information communication integrated service device according to claim 1, characterized in that: The data storage and processing module includes a distributed database, a big data processing framework, a data warehouse and an ETL tool. The distributed database is responsible for the storage and management of large-scale data. The big data processing framework is used to process and analyze large-scale data sets. The data warehouse supports data query and analysis operations. The ETL tool is responsible for data extraction, conversion and loading. The data acquisition module writes the collected data directly into the distributed database. The big data processing framework reads data from the distributed database for processing and analysis. The data processed by the big data processing framework can be loaded into the data warehouse. The ETL tool extracts data from the data source and loads it into the distributed database or data warehouse after cleaning and conversion.

3. The electronic information communication integrated service device according to claim 2, characterized in that: The AI ​​monitoring probe is responsible for collecting and transmitting key data in real time, the intelligent analysis algorithm library is used to conduct in-depth mining and analysis of the data, and the data analysis tool provides data visualization and report generation functions. The AI ​​monitoring probe serves as the front end of real-time data collection, and transmits key data to the intelligent analysis algorithm library in real time for processing. The intelligent analysis algorithm library conducts in-depth mining and analysis of the data, and transmits the analysis results to the data analysis tool for visual display, and sends them to the tourist service module, tourism enterprise management module and government supervision and service module.

4. The electronic information communication integrated service device according to claim 3, characterized in that: The intelligent analysis algorithm library conducts in-depth mining and analysis of data, including trend identification, demand forecasting, service quality assessment and anomaly detection. Trend identification is to identify trends in the tourism market by analyzing historical data and real-time data. Demand forecasting is to use prediction models to combine current data and historical trends to predict key indicators in the future. Service quality assessment is to conduct a comprehensive assessment of tourism service quality by collecting tourist feedback and monitoring service process data, and to put forward improvement suggestions. Anomaly detection is to use AI monitoring probes to monitor data changes in real time, and promptly discover and warn of abnormal situations.

5. The electronic information communication integrated service device according to claim 4, characterized in that: The tourist service module includes a user portrait construction unit, a data integration and processing unit, a content generation and push unit, an alarm and notification unit, and an emergency rescue dispatch unit. The user portrait construction unit collects and analyzes tourist data to construct a user portrait. The data integration and processing unit integrates real-time communication data, user portrait data, and scenic spot operation data. The content generation and push unit generates personalized push content based on the integrated data and pushes it to tourists. The alarm and notification unit triggers an alarm and notifies relevant personnel or tourists based on the analysis results of the intelligent analysis module. The emergency rescue dispatch unit is responsible for receiving emergency help information and dispatching rescue resources. The user portrait construction unit sends the user portrait data to the data integration and processing unit. The data integration and processing unit receives real-time communication data, user portrait data, and scenic spot operation data for comprehensive analysis and processing. The content generation and push unit generates personalized push content based on the output results of the data integration and processing unit and pushes it to tourists. The alarm and notification unit sends alarms or prompt information to relevant personnel or tourists through the content generation and push unit.

6. The electronic information communication integrated service device according to claim 4, characterized in that: The tourism enterprise management module includes a marketing information push unit, an effect evaluation unit, a tourist evaluation collection unit, a service data analysis unit and a quality improvement suggestion unit. The marketing information push unit formulates and pushes personalized marketing information based on the results of the intelligent analysis module. The effect evaluation unit collects marketing activity data, conducts effect evaluation, and feeds back to the marketing information push unit and the intelligent analysis module. The resource allocation suggestion unit provides resource allocation optimization suggestions for tourism enterprises based on the data analysis results. The tourist evaluation collection unit is responsible for collecting tourists' evaluation information on tourism services, including communication service quality. The service data analysis unit conducts a comprehensive analysis of tourist evaluations and service process data. The quality improvement suggestion unit makes service quality improvement suggestions based on the analysis results. The tourist evaluation collection unit transmits the evaluation information to the service data analysis unit. The service data analysis unit analyzes in combination with other service process data and transmits the analysis results to the quality improvement suggestion unit.

7. The electronic information communication integrated service device according to claim 4, characterized in that: The government supervision and service module includes a communication service quality monitoring and optimization platform, which is responsible for real-time monitoring of communication service performance indicators and dynamic adjustment and optimization. The communication service quality monitoring and optimization platform uses AI monitoring probes deployed at key nodes of the communication network to collect key performance indicator data in real time, and predicts possible service quality problems or failure points through an intelligent analysis algorithm library. The communication service quality monitoring and optimization platform automatically adjusts network parameters or configurations based on the analysis results.

8. The electronic information communication integrated service device according to claim 1, characterized in that: The visitor service module pushes personalized information, specifically: User portrait construction: Collect historical behavior data and preference data of tourists, perform data processing, feature extraction, use machine learning to train the extracted features, and build a user portrait model; Data integration: Collect tourists' current location and behavior data in real time, integrate the real-time data with user portrait data and scenic spot operation data to form a tourist information database, and pre-process the integrated data; Content generation: Based on the integrated data, a combination recommendation algorithm is used to calculate the current needs and potential interests of tourists and generate a recommendation list; Push execution: Select appropriate push channels based on visitor preferences and current scenarios, send generated push content to visitors through the selected channels, collect user feedback and behavior data after the push, evaluate the push effect and optimize subsequent push strategies.

9. The electronic information communication integrated service device according to claim 8, characterized in that: In content generation, the combined recommendation algorithm is used to calculate the current needs and potential interests of tourists and generate a recommendation list. The specific steps are as follows: Collect data: obtain user portraits and scenic spot operation data; Data cleaning: Identify and remove data irrelevant to the pushed content, reasonably infer or fill in missing tourist behavior data, and integrate real-time communication data, user profile data, and scenic spot operation data to form a comprehensive data set; Feature extraction: extract behavioral features from tourists' tour paths, stay time, and interactive behaviors, and combine them with information from user portraits to construct tourist feature vectors; Collaborative filtering: Construct a tourist-attraction / activity matrix: Based on the tourists’ travel history and interest preferences, construct a user-attraction / activity interaction matrix. Assume that Tourists and Attractions / Activities, Visitor-Attractions / Activities Matrix It can represent: in, Indicates tourists About attractions / activities If a tourist has no interest or participation, the corresponding element can be set to 0 or left blank; Calculate similarity: Calculate the similarity between tourists with similar travel preferences, or the similarity between attractions / activities: in, are tourists and tourists A collection of attractions / activities that have been interacted with. are tourists About attractions / activities A rating or some metric, are tourists About attractions / activities Rating or measurement value of Generate recommendation list: Generate personalized tour recommendation list for current tourists based on the travel history and similarity of attractions / activities of similar tourists; Content base recommendation: Feature matching: Match the tourist's travel preference feature vector with the feature vector of the attraction / activity and calculate the similarity: in, is the characteristic vector of tourists’ travel preferences, is the feature vector of attractions / activities; Rating prediction: Based on content similarity, predict tourists' potential interest in attractions they have not visited or activities they have not participated in; Mixed recommendations: Weight allocation: Assign different weights to collaborative filtering and content-based recommendation based on actual application scenarios and data characteristics; Comprehensive score: The score results of collaborative filtering and content-based recommendation are weighted and summed to obtain the final recommendation score; Generate recommendations: Based on the final ratings, select the highest-rated attractions / activities as recommendations; Push execution: Push personalized push content to visitors in real time Tourist feedback: collect tourists’ feedback on recommendation results; Model adjustment: Based on visitor feedback, dynamically adjust the parameters and weights of the recommendation model to optimize the recommendation effect.

10. The electronic information communication integrated service device according to claim 4, characterized in that: The intelligent analysis module identifies potential service quality issues and predicts future trends, and the government supervision and service module adjusts network parameters or configurations based on the analysis results. Specific steps are: Real-time monitoring of communication service performance indicators: Deploy AI monitoring probes at key nodes of the communication network to collect key performance indicator data in real time; Data preprocessing: Clean the collected raw data, remove noise and outliers, and aggregate or sample the data; Intelligent analysis and prediction of service quality issues: Use LSTM to conduct in-depth analysis of pre-processed data to predict possible service quality issues or failure points; If the predicted delay exceeds the threshold, an alert is issued; Automatically adjust and optimize network configuration: Automatically adjust network parameters or configuration based on analysis and prediction results.

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