A smart exhibition hall control method and system based on the Internet of Everything

Abstract

This invention proposes a smart exhibition hall control method and system based on the Internet of Things (IoE). This method, which belongs to the field of Internet of Things (IoT), includes: constructing an IoE-based smart exhibition hall network architecture and wirelessly connecting it to a central control platform to create an IoE network environment; obtaining user identity information through an identity recognition device and constructing a user identity database; and analyzing user preferences using an intelligent algorithm based on this user identity information and historical user behavior data. By analyzing user identity information and historical user behavior data through an intelligent algorithm, users can be provided with personalized exhibition recommendations and exhibition routes, enabling them to more quickly find exhibits of interest and thus enhance their visiting experience.

Description

Technical Field

[0001] The present invention proposes a smart exhibition hall control method and system based on the Internet of Things, belonging to the technical field of the Internet of Things. Background Art

[0002] Currently, digital exhibition hall management mostly relies on manual labor or simple automated equipment, resulting in low management efficiency, poor user experience, and uneven resource allocation. This is especially true at large exhibitions and fairs, where dense crowds and a vast amount of information are common. Traditional management methods often struggle to cope, leading to indoor congestion and increased safety hazards. Therefore, developing a smart exhibition hall control method based on the Internet of Things (IoE) is crucial. Summary of the Invention

[0003] The present invention provides a smart exhibition hall control method and system based on the Internet of Everything to solve the problems mentioned in the above background technology:

[0004] The present invention proposes a smart exhibition hall control method based on the Internet of Everything, the method comprising:

[0005] S1. Build an IoT-based smart exhibition hall network architecture and connect it to the central control platform via wireless to form a network environment where everything is connected.

[0006] S2. Obtain user identity information through an identity recognition device and build a user identity database; analyze user preferences through an intelligent algorithm based on the user identity information and historical user behavior data;

[0007] S3. Use environmental sensors to monitor the environmental parameters in the exhibition hall in real time and adjust the equipment according to the preset environmental standards or user comfort requirements;

[0008] S4. Collect crowd flow information in the exhibition hall through image acquisition devices, and analyze the crowd density of each exhibition area in real time based on the crowd counting model; intelligently generate guidance information based on the crowd density data;

[0009] S5. The central control platform conducts comprehensive analysis of all types of collected data and provides decision support for the operation and management of the exhibition hall through data mining and machine learning algorithms.

[0010] Furthermore, the S1 includes:

[0011] S11. Design a deployment plan for IoT devices based on the exhibition hall layout and requirements, integrate various IoT devices into a unified communication protocol, and build a layered network architecture.

[0012] S12. Deploy wireless network infrastructure, implement network security measures, and conduct equipment joint debugging and testing;

[0013] S13. Simulate data transmission stress tests in different scenarios, evaluate system stability and response speed, and adjust network configuration and device parameters based on test results.

[0014] Furthermore, the S2 includes:

[0015] S21. Deploy RFID readers or facial recognition cameras at the entrance of the exhibition hall to automatically capture user identity information, compare and verify the collected identity information with the user database, and identify the user;

[0016] S22. Collect user historical behavior data and build user profiles. Use intelligent algorithms to deeply mine user preferences and predict the types of exhibits that users may be interested in.

[0017] S23. Based on the analysis results, a personalized exhibition route and exhibit recommendation list are tailored for the user; personalized exhibition information and interactive prompts are pushed to the user through multiple channels.

[0018] Furthermore, the S3 includes:

[0019] S31. Deploy environmental monitoring equipment in the exhibition hall to collect environmental parameter data in real time and upload it to the central control platform;

[0020] S32. Develop intelligent control strategies based on preset environmental standards or user comfort feedback; dynamically adjust control strategies based on historical data and real-time environmental changes through machine learning algorithms;

[0021] S33. The central control platform automatically sends control instructions to relevant equipment based on the control strategy. The relevant equipment executes the control instructions and adjusts the environmental parameters in the exhibition hall to the preset standards or user comfort requirements.

[0022] Furthermore, the S4 includes:

[0023] S41. Use high-definition cameras to capture the flow of people in the exhibition hall, use image recognition technology to extract crowd flow information, build a crowd counting model, and combine it with deep learning algorithms to analyze the crowd density of each exhibition area in real time.

[0024] S42. Based on crowd density data, assess the congestion situation in each exhibition area, predict future crowd flow trends, and develop intelligent guidance strategies;

[0025] S43. Use the LED display screens in the exhibition hall to update the crowd flow of each exhibition area and recommend visiting routes in real time, guiding users to avoid congested areas;

[0026] S44. Through multiple means, personalized tour routes and real-time crowd flow warning information are pushed to users. A voice broadcast system is set up at key locations in the exhibition hall. When the flow of people in a certain exhibition area reaches the threshold, a guiding voice is automatically played to remind users to divert traffic reasonably.

[0027] The present invention proposes an intelligent exhibition hall control system based on the Internet of Everything, the system comprising:

[0028] Architecture building module: Build an IoT-based smart exhibition hall network architecture and connect it to the central control platform via wireless to form a network environment where everything is connected;

[0029] Information acquisition module: obtains user identity information through the identity recognition device and builds a user identity database; analyzes user preferences through intelligent algorithms based on user identity information and historical user behavior data;

[0030] Real-time monitoring module: monitors the environmental parameters in the exhibition hall in real time through environmental sensors, based on preset environmental standards or user comfort requirements;

[0031] Image acquisition module: This module collects information about the flow of people in the exhibition hall through image acquisition devices and analyzes the density of people in each exhibition area in real time based on the population statistics model. It also intelligently generates guidance information based on the density data.

[0032] Comprehensive analysis module: The central control platform conducts comprehensive analysis of various types of collected data and provides decision support for the operation and management of the exhibition hall through data mining and machine learning algorithms.

[0033] Furthermore, the architecture building module includes:

[0034] Deployment Design Module: Design IoT device deployment plans based on exhibition hall layout and requirements, integrate various IoT devices into a unified communication protocol, and build a layered network architecture.

[0035] Facility deployment module: deploy wireless network infrastructure, implement network security measures, and conduct equipment joint debugging and testing;

[0036] Test simulation module: simulates data transmission stress tests in different scenarios, evaluates system stability and response speed, and adjusts network configuration and device parameters based on test results.

[0037] Furthermore, the information acquisition module includes:

[0038] Identity recognition module: Deploy RFID readers or facial recognition cameras at the entrance of the exhibition hall to automatically capture user identity information, compare and verify the collected identity information with the user database, and identify the user;

[0039] Data collection module: collects user historical behavior data and builds user portraits. It uses intelligent algorithms to deeply mine user preferences and predict the types of exhibits that users may be interested in.

[0040] Recommendation module: Based on the analysis results, personalized exhibition routes and exhibit recommendation lists are tailored for users; personalized exhibition information and interactive prompts are pushed to users through multiple channels.

[0041] Furthermore, the real-time monitoring module includes:

[0042] Data upload module: Deploy environmental monitoring equipment in the exhibition hall to collect environmental parameter data in real time and upload it to the central control platform;

[0043] User feedback module: Develops intelligent control strategies based on preset environmental standards or user comfort feedback; dynamically adjusts control strategies based on historical data and real-time environmental changes through machine learning algorithms;

[0044] Command control module: The central control platform automatically sends control commands to relevant equipment based on the control strategy. The relevant equipment executes the control commands and adjusts the environmental parameters in the exhibition hall to the preset standards or user comfort requirements.

[0045] Furthermore, the image acquisition module includes:

[0046] Crowd flow analysis module: uses high-definition cameras to capture crowd flow images in the exhibition hall, uses image recognition technology to extract crowd flow information, builds a headcount model, and combines deep learning algorithms to analyze the crowd density of each exhibition area in real time.

[0047] Situation Assessment Module: Based on crowd density data, it assesses the congestion situation in each exhibition area, predicts future crowd flow trends, and formulates intelligent guidance strategies;

[0048] User guidance module: Utilizes the LED display screens in the exhibition hall to update the crowd flow status of each exhibition area and recommend visiting routes in real time, guiding users to avoid congested areas;

[0049] User reminder module: Through multiple means, personalized tour routes and real-time crowd warning information are pushed to users. A voice broadcast system is set up at key locations in the exhibition hall. When the crowd flow in a certain exhibition area reaches the threshold, a guiding voice is automatically played to remind users to divert traffic reasonably.

[0050] The beneficial effects of the present invention include: analyzing user identity information and historical behavior data through intelligent algorithms to provide users with personalized exhibition recommendations and exhibition routes, enabling users to find exhibits of interest more quickly, thereby enhancing the visiting experience; real-time monitoring of exhibition hall environmental parameters and dynamic adjustment based on user comfort can effectively maintain the comfort and suitability of the exhibition hall environment, meet the needs of different users, and improve the overall exhibition effect; through crowd density analysis and real-time crowd flow warning, the exhibition hall can better manage and guide crowd flow, avoid congestion, improve visitor flow, and enhance the exhibition hall's operational efficiency; the central control platform conducts comprehensive analysis of various data, providing data support and decision-making basis for the exhibition hall's operational management, thereby optimizing resource allocation and exhibition arrangements, and improving the overall operational efficiency of the exhibition hall; through the design, testing, and optimization of network deployment solutions, the stability of IoT devices and the response speed of the system are ensured, the reliability and security of the system are enhanced, and the impact of potential technical problems on exhibition hall operations is reduced; combined with real-time analysis of environmental parameters and user feedback by machine learning algorithms, intelligent regulation of the exhibition hall environment is achieved, and environmental conditions are automatically adjusted to ensure the comfort and functionality of the exhibition hall. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A diagram showing the steps of the method of the present invention;

[0052] Figure 2 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0053] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein may be combined with each other.

[0054] The following description sets forth numerous specific details to facilitate a thorough understanding of the present invention. The embodiments described are merely a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0056] One embodiment of the present invention, as Figure 1 As shown, a smart exhibition hall control method based on the Internet of Everything includes:

[0057] S1. Build an IoT-based smart exhibition hall network architecture, including various IoT devices such as identity recognition devices, environmental sensors, image acquisition devices, smart access control systems, and exhibit interactive devices. These devices are wirelessly connected to a central control platform to form a fully connected network environment.

[0058] S2. Obtain user identity information through identification devices (such as RFID tags, facial recognition technology, etc.) and build a user identity database. Based on user identity information and historical user behavior data, analyze user preferences through intelligent algorithms to provide users with personalized exhibition route planning, exhibit recommendations, and other services;

[0059] S3. Use environmental sensors (such as temperature and humidity sensors, light sensors, etc.) to monitor the environmental parameters in the exhibition hall in real time, such as temperature, humidity, and light intensity. Automatically adjust air conditioning, lighting, and other equipment based on preset environmental standards or user comfort requirements to maintain the comfort of the exhibition hall environment and the safety of exhibits.

[0060] S4. Collect crowd flow information in the exhibition hall through image acquisition devices (such as high-definition cameras) and analyze the crowd density of each exhibition area in real time based on the crowd counting model; intelligently generate guidance information based on the crowd density data; guide users to flow rationally through display screens, voice broadcasts, etc. to avoid congestion;

[0061] S5. The central control platform conducts comprehensive analysis of various types of collected data, including user behavior data, environmental data, exhibit interaction data, etc.; through data mining and machine learning algorithms, it discovers valuable information such as potential market demand and user preferences, providing decision support for the operation and management of the exhibition hall.

[0062] The working principle of the above technical solution is to integrate various IoT devices, such as identity recognition devices, environmental sensors, image acquisition devices, intelligent access control systems, and exhibit interaction devices, into the smart exhibition hall and wirelessly connect them to a central control platform, thus building a comprehensive, interconnected network environment. These devices each perform different functions and work together to support the exhibition hall's intelligent management and user experience. When a user enters the exhibition hall, an identity recognition device (such as an RFID tag or facial recognition technology) automatically captures the user's identity information, compares it with the user identity database, and records it. The system uses historical user behavior data and intelligent algorithms to analyze user preferences and provide personalized exhibition route planning, exhibit recommendation services, and other services. This not only enhances the user's visiting experience, but also increases the attractiveness of the exhibits and the interactivity of the exhibition. Environmental sensors (such as temperature and humidity sensors and light sensors) monitor the exhibition hall's environmental parameters in real time to ensure that temperature, humidity, light intensity, and other parameters are within appropriate ranges. If environmental parameters deviate from preset standards or user comfort requirements, the system automatically triggers adjustment mechanisms, such as adjusting the air conditioning temperature and lighting brightness, to maintain the comfort of the exhibition hall and the safety of the exhibits. Image acquisition devices (such as high-definition cameras) capture the flow of people in the exhibition hall in real time and analyze the density of people in each exhibition area through a population counting model. Based on the crowd density data, the system intelligently generates guidance information and conveys it to users through display screens, voice broadcasts, and other means. This helps to guide the rational flow of users, avoid congestion, and improve the efficiency and safety of the exhibition hall. As the core of the entire system, the central control platform is responsible for collecting, integrating and processing data from various IoT devices, including user behavior data, environmental data, exhibit interaction data, etc. Through data mining and machine learning algorithms, the system can discover valuable information such as potential market demand and user preferences, and provide data support and decision-making basis for the operation and management of the exhibition hall. This helps to optimize the exhibition layout, improve service quality, enhance user experience, and provide strong support for future exhibition activities.

[0063] The effects of the above technical solutions are as follows: through identity recognition and user behavior analysis, the system can provide users with personalized exhibition route planning and exhibit recommendations to meet the different interests and needs of users, thereby enhancing the user's visiting experience; environmental sensors monitor and automatically adjust the temperature, humidity and light intensity in the exhibition hall in real time to ensure that users are always in a comfortable environment, thereby improving user satisfaction; image acquisition devices and crowd counting models help the system analyze crowd density in real time, and avoid congestion through intelligent guidance information, so that users can visit the exhibition more smoothly; the central control platform conducts comprehensive analysis of various data, so that exhibition hall managers can understand the operating status of the exhibition hall in real time, thereby making more accurate and efficient management decisions; based on crowd density data, the system can intelligently adjust the resource allocation of each exhibition area, such as adding tour guides, adding interactive devices, etc., to cope with the crowd pressure in different exhibition areas; automatic adjustment of air conditioning, lighting and other equipment can It can effectively reduce energy consumption and save operating costs while ensuring environmental comfort; environmental sensors monitor the environmental parameters in the exhibition hall in real time to ensure that the exhibits are in a suitable environment and avoid damage to the exhibits due to factors such as changes in temperature and humidity or excessive light; the density of the flow of people is controlled through intelligent guidance information, reducing the risk of exhibits being excessively touched or damaged, and extending the display life of the exhibits; the central control platform uses data mining technology to extract valuable information from massive data, such as user preferences, market demand, etc., to provide data support for the future development of the exhibition hall; combined with machine learning algorithms, the system can predict future user behavior and market trends, and provide a more scientific decision-making basis for the operation and management of the exhibition hall; the smart exhibition hall enhances users' awareness and favorability of the exhibition hall and the brands it displays by providing a novel and convenient visiting experience; the smart exhibition hall itself is a high-tech display method that can reflect the brand's technical strength and innovation capabilities and enhance the brand image.

[0064] In one embodiment of the present invention, the S1 includes:

[0065] S11. Design the optimal deployment plan for IoT devices based on the exhibition hall layout and needs, integrate various IoT devices into a unified communication protocol (such as MQTT or CoAP); and build a layered network architecture; including the perception layer (IoT devices), network layer (wireless LAN, Zigbee, LoRa, etc.), platform layer (central control platform) and application layer (user interaction interface, data analysis system).

[0066] S12. Deploy wireless network infrastructure, including high-performance Wi-Fi APs and IoT gateways, to ensure stable network access for IoT devices in the exhibition hall. Implement network security measures, such as data encryption and access control list (ACL) settings, to ensure data transmission security and privacy protection. Conduct device integration testing to ensure that all IoT devices can correctly access the central control platform and transmit data stably.

[0067] S13. Simulate data transmission stress tests in different scenarios, evaluate system stability and response speed, and adjust network configuration and device parameters based on test results to optimize system performance and enhance user experience.

[0068] The working principle of the above technical solution is as follows: First, through market research, understand the advanced IoT devices currently on the market, including high-precision RFID tags, multi-modal face recognition terminals, ultra-low power multi-parameter environmental sensors, high-definition smart cameras (with night infrared, face tracking and other functions), smart access control systems and various exhibit interactive devices (such as AR augmented reality glasses, touch screen interactive screens); select the most suitable IoT devices according to the specific layout and needs of the exhibition hall; design the best deployment plan for IoT devices to ensure that the equipment can fully cover the exhibition hall, while considering the collaborative work and data interaction between devices; integrate all IoT devices into a unified communication protocol (such as MQTT or CoAP) to achieve seamless communication and data exchange between devices; build a layered network architecture, including the perception layer (IoT devices), the network layer (wireless LAN, Zigbee, LoRa and other communication technologies), the platform layer (central control platform, responsible for data processing and decision-making), and the application layer (user interaction interface, data analysis system, etc.); deploy high-performance Wi-Fi APs and IoT gateways in the exhibition hall to ensure that IoT devices can access the network stably; through cooperation The team also conducted device integration tests to ensure that all IoT devices can correctly access the central control platform and achieve stable data transmission. They also promptly identified and resolved problems during device access and communication to ensure system stability and reliability. They simulated data transmission pressures in different scenarios, such as high traffic density and intensive device use, to evaluate system stability and responsiveness. They collected data from the test, including key indicators such as transmission delay and packet loss rate, for analysis and evaluation. They adjusted the network configuration based on the test results, such as increasing the number of APs and optimizing channel allocation, to improve network performance. They also adjusted IoT device parameter settings, such as sampling frequency and transmission power, to balance data transmission quality and device energy consumption. They also optimized the system architecture based on the test results to improve overall system performance and stability. They also enhanced the user experience in the exhibition hall by optimizing data transmission and reducing latency.

[0069] The results of the above technical solutions are as follows: by researching advanced IoT devices on the market, the technological leadership and functional richness of the equipment used in the exhibition hall were ensured; the integration of various IoT devices into a unified communication protocol (such as MQTT or CoAP) improved interoperability and data exchange efficiency between devices, and reduced the difficulty of subsequent maintenance and upgrades; the constructed layered network architecture (perception layer, network layer, platform layer, application layer) makes the system structure clear, easy to manage and expand; the optimal deployment plan designed according to the exhibition hall layout and needs ensures that the IoT devices can fully function, while facilitating subsequent adjustments and upgrades; the deployment of high-performance Wi-Fi APs and IoT gateways ensures stable access to IoT devices in the exhibition hall and reduces the risk of network failures and interruptions; the implementation of security measures such as data encryption transmission and access control list (ACL) settings effectively ensures the security of data transmission and the protection of user privacy, enhancing user trust in the exhibition hall; through simulating data transmission stress tests in different scenarios, a comprehensive assessment of system stability and response speed was conducted, providing a strong basis for system optimization; based on the test results, the network configuration and device parameters were adjusted to optimize system performance and enhance user experience. This includes reducing data transmission delays, improving system response speed, etc., so that users can enjoy smoother and more convenient services during their visit; based on the intelligent recognition and analysis capabilities of IoT devices, the system can provide users with personalized exhibition route planning, exhibit recommendations and other services, improving the user's visiting experience; through real-time monitoring and automatic adjustment of environmental sensors, the system can maintain the comfort of the exhibition hall environment and the safety of exhibits, creating a more pleasant visiting environment for users; the system can collect user behavior data, environmental data, exhibit interaction data, etc. in real time, providing rich data support for the operation and management of the exhibition hall; through data mining and machine learning algorithms, the system can discover valuable information such as potential market demand and user preferences, providing scientific decision-making basis for the operation and management of the exhibition hall, and improving management efficiency and decision-making level.

[0070] In summary, the S1 phase's technical solution, in building an IoT-based smart exhibition hall network architecture, not only improved the sophistication and compatibility of equipment, but also enhanced the flexibility and scalability of the network architecture, ensuring network stability and security. Furthermore, by optimizing system performance and improving user experience, it provided strong support for exhibition hall operations and management, driving the exhibition hall's development towards intelligence and efficiency.

[0071] In one embodiment of the present invention, the S2 includes:

[0072] S21. Deploy RFID readers or facial recognition cameras at the entrance of the exhibition hall to automatically capture user identity information, compare and verify the collected identity information with the user database, and quickly identify the user;

[0073] S22. Collect historical user behavior data, such as visit records and exhibit interaction, and build user profiles. Use intelligent algorithms (such as collaborative filtering and deep learning) to deeply mine user preferences and predict the types of exhibits that users may be interested in.

[0074] S23. Based on the analysis results, a personalized exhibition route and exhibit recommendation list are tailored for the user; personalized exhibition information and interactive prompts are pushed to the user through multiple channels, including display screens in the exhibition hall, mobile phone apps or AR glasses.

[0075] The working principle of the above technical solution is as follows: RFID readers or multimodal facial recognition cameras are deployed at the exhibition hall entrance. These devices can automatically capture the identity information of users entering the exhibition hall. When users pass through the entrance, the RFID reader reads the identity information from the RFID tag they carry (such as a membership card, ticket, etc.), or the facial recognition camera captures the user's facial features and compares and verifies this information with the data pre-stored in the user database. Through efficient algorithms and database query technology, the system can quickly identify the user's identity, providing a foundation for subsequent personalized services. The system continuously collects historical user behavior data in the exhibition hall, including but not limited to visit records (such as visit time, stay time, visit path, etc.) and exhibit interaction (such as which exhibits they interacted with, how they interacted, and how long they interacted). Based on this collected data, the system uses big data and artificial intelligence technologies to build a personalized user profile. This includes multiple dimensions such as the user's age, gender, interests, preferences, and behavioral patterns. Through intelligent algorithms (such as collaborative filtering and deep learning), the system deeply explores user preferences. Collaborative filtering algorithms can predict the interests of current users based on the behavior of similar users; while deep learning algorithms can more deeply understand the complex patterns behind user behavior, thereby more accurately predicting users' points of interest; based on the results of user portraits and preference analysis, the system tailors personalized exhibition routes and exhibit recommendation lists for users. These routes and lists are designed to meet the specific interests and needs of users, improving their visiting experience and satisfaction; the system pushes personalized exhibition information and interactive prompts to users through multiple channels, including display screens in the exhibition hall (such as electronic guide screens), mobile phone apps (users can view exhibition information, make appointments for exhibit interaction, etc. through their mobile phones) or AR glasses (users can use AR glasses to get a more immersive exhibition experience); when recommending exhibits, the system also considers the interactivity and fun of the exhibits, encouraging users to interact with the exhibits and further deepen their understanding and memory of the exhibits.

[0076] The effects of the above technical solutions are as follows: through RFID readers or face recognition cameras, users can quickly complete identity authentication and enter the exhibition hall without waiting, which greatly saves time and improves the user's initial experience; based on the user's preferences and historical behavior data, the system tailors personalized exhibition routes, allowing users to go directly to the exhibition area they are most interested in, improving the pertinence and efficiency of the visit; the system predicts the types of exhibits that users may be interested in through intelligent algorithms, and pushes relevant exhibit information to help users discover more exhibits of interest, enriching the user's visiting experience; personalized exhibition information and interactive prompts are pushed to users through multiple channels such as display screens in the exhibition hall, mobile phone APPs or AR glasses, increasing the user's interaction opportunities with the exhibition content and improving the user's sense of participation and immersion; the system adjusts the exhibit display method or adds interactive links based on the user's interaction, so that users can It can provide a deeper understanding of the exhibits, enhancing the interactivity and fun of the exhibition; through personalized exhibition routes and exhibit recommendations, the system can accurately push exhibition information to the target user group, increasing the exposure and appeal of the exhibition; collecting user behavior data and building user portraits provide valuable data support for exhibition organizers, helping organizers to understand user needs, evaluate exhibition effects and continuously optimize exhibition content and services; personalized exhibition experience can improve user satisfaction and loyalty, prompting users to share exhibition information through channels such as social media, further expanding the influence and popularity of the exhibition; the system can intelligently dispatch staff and exhibit resources based on information such as user traffic and exhibit popularity to ensure order at the exhibition site and the safety of exhibits; through data analysis, the system can promptly identify problems in operations and put forward improvement suggestions to help exhibition organizers achieve efficient operation and refined management.

[0077] In summary, the S2 technical solution, through user identity recognition, historical behavior data analysis, personalized exhibition itineraries, and exhibit recommendations, not only enhances user experience and satisfaction within the exhibition hall, but also strengthens user interaction and participation, improves exhibition effectiveness and influence, and optimizes resource allocation and management. These benefits and effects collectively promote the intelligent and personalized development of the exhibition industry.

[0078] In one embodiment of the present invention, S3 includes:

[0079] S31. Deploy environmental monitoring equipment (such as temperature and humidity sensors and light sensors) in the exhibition hall to collect environmental parameter data in real time and upload it to the central control platform;

[0080] S32. Develop intelligent control strategies based on preset environmental standards (e.g., suitable temperature and humidity ranges, light intensity thresholds) or user comfort feedback; dynamically adjust control strategies based on historical data and real-time environmental changes through machine learning algorithms to improve control accuracy and efficiency;

[0081] S33. The central control platform automatically sends control instructions to relevant equipment (including air conditioning and lighting equipment) based on the control strategy. The relevant equipment executes the control instructions and adjusts the environmental parameters in the exhibition hall to the preset standards or user comfort requirements.

[0082] The working principle of the above technical solution is as follows: Environmental monitoring devices such as temperature and humidity sensors and light sensors are deployed in different areas of the exhibition hall. These devices can sense and collect environmental parameter data within the exhibition hall in real time. The collected environmental parameter data is uploaded to the central control platform in real time via wireless networks or other communication methods. As the core of data processing and decision-making, the central control platform is responsible for receiving, storing, and analyzing this data. Based on the exhibition hall's operational needs and exhibit protection requirements, it presets environmental standards such as appropriate temperature and humidity ranges and light intensity thresholds. The system also considers user comfort feedback and obtains users' subjective evaluations of environmental comfort through questionnaires and user behavior analysis. Combining preset environmental standards, user comfort feedback, historical data, and real-time environmental changes, the system uses machine learning algorithms to dynamically adjust control strategies. Machine learning algorithms can identify patterns of environmental changes, predict future environmental trends, and optimize control strategies accordingly, improving control accuracy and efficiency. Based on the established control strategies, the central control platform automatically sends control commands to relevant equipment (such as air conditioners and lighting). These control instructions include specific control targets (such as lowering the temperature, adjusting the light intensity, etc.) and parameters (such as target temperature value, light intensity value, etc.); after receiving the control instructions, the relevant equipment will perform the corresponding control operations. For example, the air-conditioning equipment will adjust the cooling or heating mode according to the control instructions to adjust the temperature in the exhibition hall; the lighting equipment will adjust the light intensity and color temperature according to the control instructions to improve the lighting environment in the exhibition hall; through the execution of the equipment, the environmental parameters in the exhibition hall are gradually adjusted to the preset standards or user comfort requirements. At the same time, the system will continuously monitor the changes in environmental parameters, and adjust the control strategy and equipment control instructions as needed to ensure the stability and comfort of the environmental parameters in the exhibition hall.

[0083] The effects of the above technical solution are as follows: by real-time monitoring of the environmental parameters in the exhibition hall and formulating control strategies based on preset standards and user comfort feedback, the system can accurately adjust environmental parameters such as temperature, humidity, and lighting in the exhibition hall to ensure that the environment is always maintained in the most suitable state and improve the user's visiting comfort; using machine learning algorithms, the system can dynamically adjust the control strategy based on historical data and real-time environmental changes, further improving the accuracy and efficiency of control, and ensuring the stability and comfort of environmental parameters; suitable temperature, humidity, and lighting conditions are crucial for the protection of exhibits. Through intelligent control, the system can ensure that the environmental parameters in the exhibition hall are always maintained within the appropriate range required by the exhibits, effectively extending the shelf life of the exhibits and reducing the risk of damage caused by environmental factors; real-time monitoring of environmental parameters can also promptly detect potential environmental problems, such as abnormal temperature and humidity, excessive light, etc., so that corresponding measures can be taken to prevent and deal with them to avoid damage to the exhibits; the system can intelligently control the operation of related equipment according to the actual environmental needs and personnel flow in the exhibition hall, avoiding unnecessary energy waste. For example, when there are fewer people in the exhibition hall, the lighting brightness and air conditioning power are automatically reduced to achieve energy conservation and emission reduction; through precise control and dynamic adjustment strategies, the system can optimize resource utilization efficiency and reduce operating costs; the entire environmental control process is automated, reducing manual intervention and operating costs. Staff can focus more on other important tasks and improve overall operational efficiency; the central control platform can monitor the environmental parameters and equipment operating status in the exhibition hall in real time, promptly detect and handle abnormal situations, and ensure the normal operation of the exhibition hall; the system can dynamically adjust environmental parameters based on user comfort feedback, providing a more personalized service experience. Users can adjust the environmental settings in the exhibition hall according to their needs and preferences, improving the satisfaction and comfort of the visit; the intelligent environmental control system can also enhance the sense of technology and modernity of the exhibition hall, attracting more users' attention and participation.

[0084] In summary, the S3 technical solution, through intelligent environmental monitoring and control, not only improves the comfort of the exhibition hall environment and the safety of exhibits, but also achieves multiple benefits such as energy conservation and emission reduction and improved operational efficiency. These benefits and effects jointly promote the sustainable development of the exhibition hall and enhance the user experience.

[0085] In one embodiment of the present invention, the step S32 includes:

[0086] Collect and analyze the exhibition hall's long-term environmental parameter data (such as temperature, humidity, light intensity, air quality, etc.) and user comfort feedback data;

[0087] Through statistical analysis and data mining technology, the correlation between environmental parameters and user comfort is identified, and an environmental comfort prediction model is constructed;

[0088] Through machine learning algorithms (such as decision trees, random forests, neural networks, etc.), the model is trained to predict the user's comfort perception under different environmental conditions;

[0089] Combined with the real-time data collected by current environmental monitoring equipment, time series analysis is used to predict environmental change trends in the future;

[0090] Based on the anomaly detection mechanism, abnormal fluctuations in environmental parameters, such as sudden temperature rise or humidity drop, are promptly detected and warned, providing a basis for rapid response;

[0091] Dynamically generate control strategies based on prediction models and user comfort preferences; strategies should take into account differentiated needs in different time periods (such as weekdays and weekends, daytime and nighttime).

[0092] Introducing a prioritization mechanism to ensure that environmental factors that have the greatest impact on user comfort (such as temperature) are regulated first when resources are limited;

[0093] Design flexible regulation strategies, such as phased regulation and gradual adjustment, to reduce discomfort during the regulation process.

[0094] Implement online learning mechanisms to continuously incorporate new environmental data and user feedback into the model and continuously optimize the control strategy; use experimental design methods such as A / B testing to compare the effects of different control strategies and select the optimal strategy for implementation;

[0095] Through the adaptive learning algorithm, the system can automatically adjust the learning rate and parameters according to the long-term operation results, thereby improving the control efficiency and accuracy;

[0096] Analyze user behavior patterns, such as visiting paths and length of stay, to predict their possible environmental needs. Combined with user portraits, provide personalized environmental control solutions for different user groups, such as providing more suitable temperature and lighting environments for elderly visitors. Design interactive interfaces to allow users to fine-tune environmental parameters according to their immediate needs, and collect feedback to further optimize control strategies.

[0097] The working principle of the above technical solution is as follows: the system first collects real-time environmental parameter data (such as temperature, humidity, light intensity, and air quality) through environmental monitoring equipment deployed in the exhibition hall. At the same time, the system collects user comfort feedback data, which may come from questionnaires, user reviews, or direct comfort adjustment requests. Statistical analysis and data mining techniques are used to process the collected data to identify potential correlations between environmental parameters and user comfort. Based on the data analysis results, an environmental comfort prediction model is constructed. This model can predict user comfort perception based on current or future environmental parameters. Machine learning algorithms (such as decision trees, random forests, and neural networks) are used to train the model to improve the accuracy and robustness of the predictions. After the model is trained, it can predict user comfort under different environmental conditions in real time or periodically, providing a basis for the formulation of control strategies. Time series analysis techniques are combined with current environmental monitoring data to predict environmental change trends over a period of time. An anomaly detection mechanism is implemented to monitor fluctuations in environmental parameters in real time. Once an anomaly is detected (such as a sudden temperature increase or humidity decrease), an early warning mechanism is immediately triggered. Control strategies are dynamically generated based on the prediction model and user comfort preferences. Strategies need to take into account differentiated needs in different time periods, such as the focus of control may be different on weekdays and weekends, and during the day and at night; introduce a priority sorting mechanism to ensure that, when resources are limited, the control needs of environmental factors that have the greatest impact on user comfort are met first; design flexible control strategies, such as phased regulation and gradual adjustment, to reduce discomfort during the control process; implement online learning mechanisms to continuously incorporate new environmental data and user feedback into the model to continuously optimize the control strategy; use experimental design methods such as A / B testing to compare the effects of different control strategies and select the optimal strategy for implementation; use adaptive learning algorithms to automatically adjust the learning rate and parameters based on the long-term operation of the system to improve control efficiency and accuracy; analyze user behavior patterns, such as visiting paths, length of stay, etc., to predict their possible environmental needs; combine user portrait technology to provide personalized environmental control solutions for different user groups. For example, a more suitable temperature and lighting environment is provided for elderly visitors; an interactive interface is designed to allow users to fine-tune environmental parameters according to their immediate needs, and this feedback is collected to further optimize the control strategy; the central control platform automatically sends control instructions to relevant equipment based on the generated control strategy; the relevant equipment executes the control instructions and adjusts the environmental parameters in the exhibition hall to preset standards or user comfort requirements; the system continuously monitors environmental changes and user feedback, and adjusts the control strategy based on the feedback results to form a closed-loop control.

[0098] The benefits of this technical solution are as follows: Using a precise environmental comfort prediction model, the system can proactively predict and adjust environmental parameters to ensure the exhibition hall environment consistently remains within the user's desired comfort range. This not only enhances the user experience but also strengthens customer satisfaction and loyalty. Intelligent control strategies precisely adjust based on real-time environmental data and user needs, avoiding excessive resource waste. Furthermore, by incorporating energy-saving technologies and green energy utilization solutions, the system reduces energy consumption while ensuring comfort, achieving energy conservation, emission reduction, and sustainable development. The control strategy is flexible and can be adjusted promptly to rapidly changing environmental conditions and user needs. Whether using phased or gradual adjustments, it effectively reduces discomfort during the control process and enhances user comfort. Anomaly detection mechanisms promptly detect and warn of unusual fluctuations in environmental parameters, providing exhibition hall management with ample time to react. This real-time warning capability helps reduce the inconvenience and losses caused by environmental issues and improves exhibition hall operational efficiency and safety. By analyzing user behavior patterns and building user profiles, the system can provide personalized environmental control solutions for different user groups. This customized service can meet users' specific needs, enhance their sense of belonging and satisfaction, and thus strengthen user stickiness. Online learning mechanisms and adaptive learning algorithms enable the system to continuously incorporate new environmental data and user feedback into the model and continuously optimize the control strategy. This continuous optimization capability helps improve the system's control efficiency and accuracy, enabling it to adapt to more complex and changing environments and user needs. Through comprehensive analysis of historical and real-time data, the system can provide exhibition hall managers with detailed data support and decision-making basis. This helps managers more accurately grasp the exhibition hall's operating conditions and formulate more scientific and reasonable decision-making plans. The design of an interactive interface allows users to fine-tune environmental parameters according to their immediate needs, enhancing the interactivity between users and the system. This sense of participation not only improves user satisfaction but also helps to collect more user feedback to further optimize the control strategy.

[0099] In one embodiment of the present invention, the S4 includes:

[0100] S41. Use high-definition cameras to capture the flow of people in the exhibition hall, use image recognition technology to extract crowd flow information, build a crowd counting model, and combine it with deep learning algorithms to analyze the crowd density of each exhibition area in real time.

[0101] S42. Based on crowd density data, assess congestion in each exhibition area, predict future crowd flow trends, and develop intelligent guidance strategies. This includes dynamically adjusting the order in which exhibition areas are opened, setting up temporary diversion channels, and activating guide signs in specific exhibition areas.

[0102] The congestion situation in each exhibition area is evaluated using the following formula:

[0103]

[0104] Among them, CI represents the congestion coefficient. The larger the value, the more serious the congestion. i represents the actual crowd density of the ith exhibition area, RC i represents the capacity of the i-th exhibition hall, SF i represents the speed factor of the i-th exhibition area, α represents the density weight exponent, which is used to adjust the impact of pedestrian density on congestion; β represents the speed weight exponent, which is used to adjust the impact of pedestrian speed on congestion.

[0105] S43. Use the LED display screens in the exhibition hall to update the crowd flow of each exhibition area and recommend visiting routes in real time, guiding users to avoid congested areas;

[0106] S44. Through multiple means (such as smart guide APP), personalized tour routes and real-time crowd warning information are pushed to users. A voice broadcast system is set up at key locations in the exhibition hall. When the flow of people in a certain exhibition area reaches the threshold, a guiding voice is automatically played to remind users to divert traffic reasonably.

[0107] The working principle of the above technical solution is as follows: high-definition cameras are deployed in the exhibition hall to capture real-time images of crowd flow within the exhibition hall. Advanced image recognition technology is used to extract crowd flow information, such as the number of people and movement direction, from the captured images. Based on the extracted crowd flow information, a crowd counting model is constructed to conduct real-time analysis of the crowd density in each exhibition area within the exhibition hall. Deep learning algorithms are combined to improve the accuracy and real-time performance of crowd density analysis, providing data support for subsequent congestion assessment and guidance strategy formulation. Based on the crowd density data, congestion conditions in each exhibition area are assessed in real time to identify potential congested areas. Historical data and real-time crowd flow information are used to predict future crowd flow trends through prediction algorithms, providing a basis for developing more accurate and effective guidance strategies. Based on the congestion assessment and crowd flow trend prediction results, intelligent guidance strategies are formulated. These strategies may include dynamically adjusting the opening order of exhibition areas, setting up temporary diversion channels, and activating guide signs in specific exhibition areas to effectively alleviate congestion and improve the user experience. LED displays in the exhibition hall are used to update the crowd flow status of each exhibition area and recommend visiting routes in real time. Users can check the information on the display screen to understand the congestion situation of each exhibition area and adjust their visit plans accordingly; according to the intelligent guidance strategy, adjust the guidance signs in the exhibition hall, such as setting temporary arrow signs, changing the exhibition area entrance signs, etc., to guide users to divert reasonably; push personalized tour routes to users through multiple means such as the smart guide APP. These routes will be customized according to the user's interests and preferences and the current congestion situation of the exhibition hall to help users better plan their visit itineraries; set up a voice broadcast system at key locations in the exhibition hall. When the flow of people in a certain exhibition area reaches the preset threshold, the system automatically plays a guiding voice to remind users that the exhibition area is congested and recommends visiting other exhibition areas. In addition, channels such as the smart guide APP will also push crowd warning information in real time to ensure that users can obtain and respond in a timely manner.

[0108] The effects of the above technical solutions are: through high-definition cameras and image recognition technology, the crowd density of each exhibition area is analyzed in real time, so that users can understand the degree of congestion in each exhibition area in a timely manner, and make more reasonable visiting decisions; the smart guide APP pushes personalized visiting routes according to user interests and current crowd conditions, reducing user waiting time, improving visiting efficiency and satisfaction; the LED display screen and voice broadcast system update the crowd conditions and provide guidance in real time, helping users avoid congested areas and enjoy a smoother visiting experience; according to crowd density data and predicted trends, the exhibition area opening order is dynamically adjusted, temporary diversion channels are set, etc., effectively alleviating exhibition hall congestion and improving the overall operational efficiency of the exhibition hall; through real-time monitoring and data analysis, exhibition hall managers can allocate human and material resources more accurately to ensure the efficient operation of each exhibition area; when the crowd flow in a certain exhibition area reaches the threshold, , automatically plays guidance voice messages to remind users to divert traffic appropriately, effectively preventing safety accidents caused by overcrowding. In emergency situations, the intelligent guidance system can quickly adjust strategies and provide evacuation guidance to ensure the safety of those within the exhibition hall. By collecting and analyzing large amounts of crowd flow data, exhibition hall managers can gain a deeper understanding of visitor behavior patterns and preferences, providing data support for future exhibition planning and operations management. Based on user visitor behavior and preference data, exhibition halls can carry out more targeted marketing campaigns to enhance the exhibition's appeal and impact. The application of advanced technologies such as high-definition cameras, image recognition, and deep learning algorithms imbues the exhibition hall with a sense of technology, enhancing visitor interest. The combination of intelligent guide apps, LED displays, and voice announcement systems provides users with a richer interactive experience, enhancing the fun and participation of the exhibition. The above formula provides a quantitative indicator to measure the degree of congestion in the exhibition area, allowing managers to objectively understand and compare congestion conditions across different exhibition areas. By calculating the congestion index in real time, managers can quickly respond and take measures, such as adjusting the opening sequence and setting up temporary diversion channels, to improve the visitor experience. The congestion index can help managers identify which exhibition areas require more resources (such as security, tour guides, etc.), thereby allocating resources more efficiently. Combining historical and real-time data, the congestion index can be used to predict future congestion trends, helping managers plan exhibition layouts and tour routes in advance. By monitoring and controlling congestion, potential safety risks caused by overcrowding, such as stampedes, can be reduced. The congestion index can be used to provide visitors with personalized tour routes and early warning information, improving the level of personalized service. The calculation of the congestion index relies on data analysis, which encourages data-based decision-making and increases the scientific and effective nature of decision-making. By guiding visitors to avoid congested areas, the overall visiting experience can be improved, waiting times can be reduced, and visitor satisfaction can be increased. Responding promptly to congestion can reduce potential losses caused by congestion, such as reducing additional operating costs caused by poor crowd management.Effective congestion management can improve the organization level and professional image of the exhibition, attract more visitors and enhance brand reputation.

[0109] In summary, the S4 technical solution, through intelligent crowd management and guidance, not only improves user experience and showroom operational efficiency, but also enhances safety and data-driven decision-making capabilities, while also giving the showroom a stronger sense of technology and interactivity. These benefits and effects collectively promote the sustainable development and competitiveness of the showroom.

[0110] In one embodiment of the present invention, the S5 includes:

[0111] S51. Integrate multi-source data (such as user behavior data, environmental data, and exhibit interaction data) through the data collection platform and pre-process the integrated multi-source data, including cleaning, deduplication, and formatting.

[0112] S52. Based on big data analysis algorithms, conduct in-depth analysis of user behavior patterns, exhibit popularity, and environmental change trends. Use machine learning algorithms to identify the potential needs and market trends behind user behavior and predict future visitor trends and exhibit popularity.

[0113] S53. Evaluate the effectiveness of existing strategies and activities in accordance with the exhibition hall's operational objectives, propose improvement suggestions, and provide scientific decision-making support to the exhibition hall management based on data analysis results; including exhibit layout adjustment, event planning, marketing strategy, etc.

[0114] S54. Continue to optimize the control system and service processes of the smart exhibition hall, introduce new technologies and equipment, and improve user experience and exhibition hall operation efficiency; establish a feedback mechanism to collect feedback from users and management on the smart exhibition hall control system, and continuously iterate and optimize system functions and performance.

[0115] The above technical solution works as follows: A data collection platform integrates multi-source data (such as user behavior data, environmental data, and exhibit interaction data) from various channels and systems. This data may originate from sensors, cameras, smart devices, and user interaction systems within the exhibition hall. This integrated multi-source data undergoes pre-processing, including cleansing, deduplication, and formatting. Cleansing removes invalid, erroneous, or duplicate data; deduplication ensures data uniqueness; and formatting converts the data into a uniform format suitable for subsequent analysis and processing. Based on big data analytics algorithms, in-depth analysis is conducted on user behavior patterns, exhibit popularity, and environmental trends. This analysis helps understand user behavior habits and preferences, as well as the market performance of exhibits. Machine learning algorithms are used to deeply mine the pre-processed data to identify the underlying needs and market trends behind user behavior. By training models, future visitor trends and exhibit popularity can be predicted, providing forward-looking guidance for exhibition hall operations and management. Existing strategies and activities are evaluated in conjunction with the exhibition hall's operational objectives. By comparing actual operational results with expected goals, existing issues and shortcomings are identified. Based on the data analysis results, exhibition hall management is provided with scientific decision-making support. These suggestions may involve adjustments to exhibit layouts, event planning, marketing strategies, and other aspects, aiming to optimize the showroom's operations and enhance the user experience. Based on data analysis results and operational feedback, the smart showroom's control system and service processes will be continuously optimized. This includes introducing new technologies and equipment to enhance the system's intelligence and operational efficiency. Simultaneously, system functionality and performance will be iteratively optimized to ensure the system remains in optimal condition. A feedback mechanism for user and management feedback on the smart showroom's control system will be established. By collecting and analyzing user and management opinions and suggestions, system problems and deficiencies will be promptly identified and addressed. Furthermore, feedback will be incorporated into subsequent optimization and improvement plans, forming a closed-loop continuous optimization process.

[0116] In summary, the S5 technical solution achieves comprehensive management and optimization of smart exhibition halls through steps such as data integration and preprocessing, big data analysis and prediction, decision support and strategy optimization, and continuous optimization and feedback mechanisms. This process not only improves the exhibition hall's operational efficiency and user experience, but also provides strong data support and decision-making basis for its long-term development.

[0117] The above technical solution ensures data accuracy and reliability by integrating and preprocessing data from multiple sources. Based on this data, big data analysis algorithms and machine learning techniques enable in-depth analysis of user behavior, exhibit popularity, and environmental trends, providing management with precise decision-making support. Furthermore, the effectiveness of existing strategies and activities can be evaluated in conjunction with exhibition hall operational objectives, and improvement recommendations based on data analysis results can be made. This scientific decision-making approach helps management make more rational and effective decisions, improving the overall operational effectiveness of the exhibition hall. By identifying the underlying needs and market trends behind user behavior, users can be provided with more personalized services. For example, exhibits or activities can be recommended based on user interests and preferences to enhance the visitor experience and satisfaction. In-depth analysis of environmental trends can help timely adjust exhibition hall settings, such as temperature, humidity, and lighting, to provide a more comfortable and pleasant environment. Based on data analysis results, exhibit layout, human resources, and material resources can be optimized to ensure efficient utilization of all resources within the exhibition hall. The effectiveness of event planning and marketing strategies can be evaluated in real time, allowing timely adjustments to respond to market changes and increase event participation and impact. New technologies and equipment are continuously introduced to enhance the intelligence and operational efficiency of smart exhibition halls. For example, technologies such as artificial intelligence and the Internet of Things can be used to improve the automation and interactivity of exhibition halls. Feedback mechanisms can be established to collect feedback from users and management, and continuously iteratively optimize system functions and performance. This continuous improvement approach helps maintain the competitiveness and attractiveness of exhibition halls. By predicting future visitor trends and exhibit popularity, insights into market changes can be gained, allowing for proactive planning and strategic adjustments to address market challenges and opportunities. By providing high-quality visitor experiences and services, exhibition halls can enhance their brand image and reputation, strengthening their market competitiveness.

[0118] In summary, the S5 technical solution has brought significant benefits and results to the operation and management of smart exhibition halls through data-driven decision support, enhanced user experience, improved operational efficiency, continuous innovation and optimization, and enhanced market competitiveness. These benefits and results have jointly promoted the sustainable development and competitiveness of exhibition halls.

[0119] One embodiment of the present invention, as Figure 2 As shown, a smart exhibition hall control system based on the Internet of Everything, the system includes:

[0120] Architecture building module: Build an IoT-based smart exhibition hall network architecture, including various IoT devices such as identity recognition devices, environmental sensors, image acquisition devices, smart access control systems, and exhibit interactive devices. These devices are wirelessly connected to the central control platform to form a fully connected network environment.

[0121] Information Acquisition Module: This module acquires user identity information through identification devices (such as RFID tags, facial recognition technology, etc.) and builds a user identity database. Based on this information and historical user behavior data, it analyzes user preferences through intelligent algorithms. It also provides users with personalized exhibition route planning, exhibit recommendations, and other services.

[0122] Real-time monitoring module: This module uses environmental sensors (such as temperature and humidity sensors, light sensors, etc.) to monitor the environmental parameters in the exhibition hall in real time, such as temperature, humidity, and light intensity. Based on preset environmental standards or user comfort requirements, it automatically adjusts air conditioning, lighting, and other equipment to maintain the comfort of the exhibition hall environment and the safety of exhibits.

[0123] Image acquisition module: This module collects information about the flow of people in the exhibition hall through image acquisition devices (such as high-definition cameras) and analyzes the density of people in each exhibition area in real time based on a population statistics model. It also intelligently generates guidance information based on the density data and uses display screens, voice announcements, and other means to guide users to flow rationally and avoid congestion.

[0124] Comprehensive analysis module: The central control platform conducts comprehensive analysis of various types of collected data, including user behavior data, environmental data, exhibit interaction data, etc.; through data mining and machine learning algorithms, it discovers valuable information such as potential market demand and user preferences, providing decision support for the operation and management of the exhibition hall.

[0125] The working principle of the above technical solution is to integrate various IoT devices, such as identity recognition devices, environmental sensors, image acquisition devices, intelligent access control systems, and exhibit interaction devices, into the smart exhibition hall and wirelessly connect them to a central control platform, thus building a comprehensive, interconnected network environment. These devices each perform different functions and work together to support the exhibition hall's intelligent management and user experience. When a user enters the exhibition hall, an identity recognition device (such as an RFID tag or facial recognition technology) automatically captures the user's identity information, compares it with the user identity database, and records it. The system uses historical user behavior data and intelligent algorithms to analyze user preferences and provide personalized exhibition route planning, exhibit recommendation services, and other services. This not only enhances the user's visiting experience, but also increases the attractiveness of the exhibits and the interactivity of the exhibition. Environmental sensors (such as temperature and humidity sensors and light sensors) monitor the exhibition hall's environmental parameters in real time to ensure that temperature, humidity, light intensity, and other parameters are within appropriate ranges. If environmental parameters deviate from preset standards or user comfort requirements, the system automatically triggers adjustment mechanisms, such as adjusting the air conditioning temperature and lighting brightness, to maintain the comfort of the exhibition hall and the safety of the exhibits. Image acquisition devices (such as high-definition cameras) capture the flow of people in the exhibition hall in real time and analyze the density of people in each exhibition area through a population counting model. Based on the crowd density data, the system intelligently generates guidance information and conveys it to users through display screens, voice broadcasts, and other means. This helps to guide the rational flow of users, avoid congestion, and improve the efficiency and safety of the exhibition hall. As the core of the entire system, the central control platform is responsible for collecting, integrating and processing data from various IoT devices, including user behavior data, environmental data, exhibit interaction data, etc. Through data mining and machine learning algorithms, the system can discover valuable information such as potential market demand and user preferences, and provide data support and decision-making basis for the operation and management of the exhibition hall. This helps to optimize the exhibition layout, improve service quality, enhance user experience, and provide strong support for future exhibition activities.

[0126] The effects of the above technical solutions are as follows: through identity recognition and user behavior analysis, the system can provide users with personalized exhibition route planning and exhibit recommendations to meet the different interests and needs of users, thereby enhancing the user's visiting experience; environmental sensors monitor and automatically adjust the temperature, humidity and light intensity in the exhibition hall in real time to ensure that users are always in a comfortable environment, thereby improving user satisfaction; image acquisition devices and crowd counting models help the system analyze crowd density in real time, and avoid congestion through intelligent guidance information, so that users can visit the exhibition more smoothly; the central control platform conducts comprehensive analysis of various data, so that exhibition hall managers can understand the operating status of the exhibition hall in real time, thereby making more accurate and efficient management decisions; based on crowd density data, the system can intelligently adjust the resource allocation of each exhibition area, such as adding tour guides, adding interactive devices, etc., to cope with the crowd pressure in different exhibition areas; automatic adjustment of air conditioning, lighting and other equipment can It can effectively reduce energy consumption and save operating costs while ensuring environmental comfort; environmental sensors monitor the environmental parameters in the exhibition hall in real time to ensure that the exhibits are in a suitable environment and avoid damage to the exhibits due to factors such as changes in temperature and humidity or excessive light; the density of the flow of people is controlled through intelligent guidance information, reducing the risk of exhibits being excessively touched or damaged, and extending the display life of the exhibits; the central control platform uses data mining technology to extract valuable information from massive data, such as user preferences, market demand, etc., to provide data support for the future development of the exhibition hall; combined with machine learning algorithms, the system can predict future user behavior and market trends, and provide a more scientific decision-making basis for the operation and management of the exhibition hall; the smart exhibition hall enhances users' awareness and favorability of the exhibition hall and the brands it displays by providing a novel and convenient visiting experience; the smart exhibition hall itself is a high-tech display method that can reflect the brand's technical strength and innovation capabilities and enhance the brand image.

[0127] In one embodiment of the present invention, the architecture building module includes:

[0128] Research advanced IoT devices on the market, including high-precision RFID tags, multimodal facial recognition terminals, ultra-low power environmental sensors (supporting multi-parameter monitoring such as temperature, humidity, and CO2 concentration), high-definition smart cameras (supporting night infrared, face tracking, and other functions), smart access control systems, and interactive exhibit devices (such as AR augmented reality glasses and touch screen interactive screens).

[0129] Deployment Design Module: Design the optimal deployment plan for IoT devices based on the exhibition hall layout and requirements, integrate various IoT devices into a unified communication protocol (such as MQTT or CoAP), and build a layered network architecture; including the perception layer (IoT devices), network layer (wireless LAN, Zigbee, LoRa, etc.), platform layer (central control platform), and application layer (user interface, data analysis system).

[0130] Facility Deployment Module: Deploy wireless network infrastructure, including high-performance Wi-Fi APs and IoT gateways, to ensure stable network access for IoT devices within the exhibition hall. Implement network security measures, such as data encryption and access control list (ACL) settings, to ensure data transmission security and privacy protection. Conduct device integration testing to ensure all IoT devices can correctly access the central control platform and stably transmit data.

[0131] Test Simulation Module: Simulates data transmission stress tests in different scenarios, evaluates system stability and response speed, and adjusts network configuration and device parameters based on test results to optimize system performance and enhance user experience.

[0132] The working principle of the above technical solution is as follows: First, through market research, understand the advanced IoT devices currently on the market, including high-precision RFID tags, multi-modal face recognition terminals, ultra-low power multi-parameter environmental sensors, high-definition smart cameras (with night infrared, face tracking and other functions), smart access control systems and various exhibit interactive devices (such as AR augmented reality glasses, touch screen interactive screens); select the most suitable IoT devices according to the specific layout and needs of the exhibition hall; design the best deployment plan for IoT devices to ensure that the equipment can fully cover the exhibition hall, while considering the collaborative work and data interaction between devices; integrate all IoT devices into a unified communication protocol (such as MQTT or CoAP) to achieve seamless communication and data exchange between devices; build a layered network architecture, including the perception layer (IoT devices), the network layer (wireless LAN, Zigbee, LoRa and other communication technologies), the platform layer (central control platform, responsible for data processing and decision-making), and the application layer (user interaction interface, data analysis system, etc.); deploy high-performance Wi-Fi APs and IoT gateways in the exhibition hall to ensure that IoT devices can access the network stably; through cooperation The team also conducted device integration tests to ensure that all IoT devices can correctly access the central control platform and achieve stable data transmission. They also promptly identified and resolved problems during device access and communication to ensure system stability and reliability. They simulated data transmission pressures in different scenarios, such as high traffic density and intensive device use, to evaluate system stability and responsiveness. They collected data from the test, including key indicators such as transmission delay and packet loss rate, for analysis and evaluation. They adjusted the network configuration based on the test results, such as increasing the number of APs and optimizing channel allocation, to improve network performance. They also adjusted IoT device parameter settings, such as sampling frequency and transmission power, to balance data transmission quality and device energy consumption. They also optimized the system architecture based on the test results to improve overall system performance and stability. They also enhanced the user experience in the exhibition hall by optimizing data transmission and reducing latency.

[0133] The results of the above technical solutions are as follows: by researching advanced IoT devices on the market, the technological leadership and functional richness of the equipment used in the exhibition hall were ensured; the integration of various IoT devices into a unified communication protocol (such as MQTT or CoAP) improved interoperability and data exchange efficiency between devices, and reduced the difficulty of subsequent maintenance and upgrades; the constructed layered network architecture (perception layer, network layer, platform layer, application layer) makes the system structure clear, easy to manage and expand; the optimal deployment plan designed according to the exhibition hall layout and needs ensures that the IoT devices can fully function, while facilitating subsequent adjustments and upgrades; the deployment of high-performance Wi-Fi APs and IoT gateways ensures stable access to IoT devices in the exhibition hall and reduces the risk of network failures and interruptions; the implementation of security measures such as data encryption transmission and access control list (ACL) settings effectively ensures the security of data transmission and the protection of user privacy, enhancing user trust in the exhibition hall; through simulating data transmission stress tests in different scenarios, a comprehensive assessment of system stability and response speed was conducted, providing a strong basis for system optimization; based on the test results, the network configuration and device parameters were adjusted to optimize system performance and enhance user experience. This includes reducing data transmission delays, improving system response speed, etc., so that users can enjoy smoother and more convenient services during their visit; based on the intelligent recognition and analysis capabilities of IoT devices, the system can provide users with personalized exhibition route planning, exhibit recommendations and other services, improving the user's visiting experience; through real-time monitoring and automatic adjustment of environmental sensors, the system can maintain the comfort of the exhibition hall environment and the safety of exhibits, creating a more pleasant visiting environment for users; the system can collect user behavior data, environmental data, exhibit interaction data, etc. in real time, providing rich data support for the operation and management of the exhibition hall; through data mining and machine learning algorithms, the system can discover valuable information such as potential market demand and user preferences, providing scientific decision-making basis for the operation and management of the exhibition hall, and improving management efficiency and decision-making level.

[0134] In summary, the S1 phase's technical solution, in building an IoT-based smart exhibition hall network architecture, not only improved the sophistication and compatibility of equipment, but also enhanced the flexibility and scalability of the network architecture, ensuring network stability and security. Furthermore, by optimizing system performance and improving user experience, it provided strong support for exhibition hall operations and management, driving the exhibition hall's development towards intelligence and efficiency.

[0135] In one embodiment of the present invention, the information acquisition module includes:

[0136] Identity recognition module: Deploy RFID readers or facial recognition cameras at the entrance of the exhibition hall to automatically capture user identity information, compare and verify the collected identity information with the user database, and quickly identify the user's identity;

[0137] Data collection module: collects user historical behavior data, such as visit records and exhibit interaction, and builds user profiles. It then uses intelligent algorithms (such as collaborative filtering and deep learning) to deeply mine user preferences and predict the types of exhibits that users may be interested in.

[0138] Recommendation module: Based on the analysis results, personalized exhibition routes and exhibit recommendation lists are tailored for users; personalized exhibition information and interactive prompts are pushed to users through multiple channels, including display screens in the exhibition hall, mobile apps or AR glasses.

[0139] The working principle of the above technical solution is as follows: RFID readers or multimodal facial recognition cameras are deployed at the exhibition hall entrance. These devices can automatically capture the identity information of users entering the exhibition hall. When users pass through the entrance, the RFID reader reads the identity information from the RFID tag they carry (such as a membership card, ticket, etc.), or the facial recognition camera captures the user's facial features and compares and verifies this information with the data pre-stored in the user database. Through efficient algorithms and database query technology, the system can quickly identify the user's identity, providing a foundation for subsequent personalized services. The system continuously collects historical user behavior data in the exhibition hall, including but not limited to visit records (such as visit time, stay time, visit path, etc.) and exhibit interaction (such as which exhibits they interacted with, how they interacted, and how long they interacted). Based on this collected data, the system uses big data and artificial intelligence technologies to build a personalized user profile. This includes multiple dimensions such as the user's age, gender, interests, preferences, and behavioral patterns. Through intelligent algorithms (such as collaborative filtering and deep learning), the system deeply explores user preferences. Collaborative filtering algorithms can predict the interests of current users based on the behavior of similar users; while deep learning algorithms can more deeply understand the complex patterns behind user behavior, thereby more accurately predicting users' points of interest; based on the results of user portraits and preference analysis, the system tailors personalized exhibition routes and exhibit recommendation lists for users. These routes and lists are designed to meet the specific interests and needs of users, improving their visiting experience and satisfaction; the system pushes personalized exhibition information and interactive prompts to users through multiple channels, including display screens in the exhibition hall (such as electronic guide screens), mobile phone apps (users can view exhibition information, make appointments for exhibit interaction, etc. through their mobile phones) or AR glasses (users can use AR glasses to get a more immersive exhibition experience); when recommending exhibits, the system also considers the interactivity and fun of the exhibits, encouraging users to interact with the exhibits and further deepen their understanding and memory of the exhibits.

[0140] The effects of the above technical solutions are as follows: through RFID readers or face recognition cameras, users can quickly complete identity authentication and enter the exhibition hall without waiting, which greatly saves time and improves the user's initial experience; based on the user's preferences and historical behavior data, the system tailors personalized exhibition routes, allowing users to go directly to the exhibition area they are most interested in, improving the pertinence and efficiency of the visit; the system predicts the types of exhibits that users may be interested in through intelligent algorithms, and pushes relevant exhibit information to help users discover more exhibits of interest, enriching the user's visiting experience; personalized exhibition information and interactive prompts are pushed to users through multiple channels such as display screens in the exhibition hall, mobile phone APPs or AR glasses, increasing the user's interaction opportunities with the exhibition content and improving the user's sense of participation and immersion; the system adjusts the exhibit display method or adds interactive links based on the user's interaction, so that users can It can provide a deeper understanding of the exhibits, enhancing the interactivity and fun of the exhibition; through personalized exhibition routes and exhibit recommendations, the system can accurately push exhibition information to the target user group, increasing the exposure and appeal of the exhibition; collecting user behavior data and building user portraits provide valuable data support for exhibition organizers, helping organizers to understand user needs, evaluate exhibition effects and continuously optimize exhibition content and services; personalized exhibition experience can improve user satisfaction and loyalty, prompting users to share exhibition information through channels such as social media, further expanding the influence and popularity of the exhibition; the system can intelligently dispatch staff and exhibit resources based on information such as user traffic and exhibit popularity to ensure order at the exhibition site and the safety of exhibits; through data analysis, the system can promptly identify problems in operations and put forward improvement suggestions to help exhibition organizers achieve efficient operation and refined management.

[0141] In summary, the S2 technical solution, through user identity recognition, historical behavior data analysis, personalized exhibition itineraries, and exhibit recommendations, not only enhances user experience and satisfaction within the exhibition hall, but also strengthens user interaction and participation, improves exhibition effectiveness and influence, and optimizes resource allocation and management. These benefits and effects collectively promote the intelligent and personalized development of the exhibition industry.

[0142] In one embodiment of the present invention, the real-time monitoring module includes:

[0143] Data upload module: Deploy environmental monitoring equipment (such as temperature and humidity sensors and light sensors) in the exhibition hall to collect environmental parameter data in real time and upload it to the central control platform;

[0144] User feedback module: Develops intelligent control strategies based on preset environmental standards (such as suitable temperature and humidity ranges, light intensity thresholds) or user comfort feedback. Through machine learning algorithms, dynamically adjusts control strategies based on historical data and real-time environmental changes, improving control accuracy and efficiency.

[0145] Command control module: The central control platform automatically sends control commands to relevant equipment (including air conditioning and lighting equipment) based on the control strategy. The relevant equipment executes the control commands and adjusts the environmental parameters in the exhibition hall to the preset standards or user comfort requirements.

[0146] The working principle of the above technical solution is as follows: Environmental monitoring devices such as temperature and humidity sensors and light sensors are deployed in different areas of the exhibition hall. These devices can sense and collect environmental parameter data within the exhibition hall in real time. The collected environmental parameter data is uploaded to the central control platform in real time via wireless networks or other communication methods. As the core of data processing and decision-making, the central control platform is responsible for receiving, storing, and analyzing this data. Based on the exhibition hall's operational needs and exhibit protection requirements, it presets environmental standards such as appropriate temperature and humidity ranges and light intensity thresholds. The system also considers user comfort feedback and obtains users' subjective evaluations of environmental comfort through questionnaires and user behavior analysis. Combining preset environmental standards, user comfort feedback, historical data, and real-time environmental changes, the system uses machine learning algorithms to dynamically adjust control strategies. Machine learning algorithms can identify patterns of environmental changes, predict future environmental trends, and optimize control strategies accordingly, improving control accuracy and efficiency. Based on the established control strategies, the central control platform automatically sends control commands to relevant equipment (such as air conditioners and lighting). These control instructions include specific control targets (such as lowering the temperature, adjusting the light intensity, etc.) and parameters (such as target temperature value, light intensity value, etc.); after receiving the control instructions, the relevant equipment will perform the corresponding control operations. For example, the air-conditioning equipment will adjust the cooling or heating mode according to the control instructions to adjust the temperature in the exhibition hall; the lighting equipment will adjust the light intensity and color temperature according to the control instructions to improve the lighting environment in the exhibition hall; through the execution of the equipment, the environmental parameters in the exhibition hall are gradually adjusted to the preset standards or user comfort requirements. At the same time, the system will continuously monitor the changes in environmental parameters, and adjust the control strategy and equipment control instructions as needed to ensure the stability and comfort of the environmental parameters in the exhibition hall.

[0147] The effects of the above technical solution are as follows: by real-time monitoring of the environmental parameters in the exhibition hall and formulating control strategies based on preset standards and user comfort feedback, the system can accurately adjust environmental parameters such as temperature, humidity, and lighting in the exhibition hall to ensure that the environment is always maintained in the most suitable state and improve the user's visiting comfort; using machine learning algorithms, the system can dynamically adjust the control strategy based on historical data and real-time environmental changes, further improving the accuracy and efficiency of control, and ensuring the stability and comfort of environmental parameters; suitable temperature, humidity, and lighting conditions are crucial for the protection of exhibits. Through intelligent control, the system can ensure that the environmental parameters in the exhibition hall are always maintained within the appropriate range required by the exhibits, effectively extending the shelf life of the exhibits and reducing the risk of damage caused by environmental factors; real-time monitoring of environmental parameters can also promptly detect potential environmental problems, such as abnormal temperature and humidity, excessive light, etc., so that corresponding measures can be taken to prevent and deal with them to avoid damage to the exhibits; the system can intelligently control the operation of related equipment according to the actual environmental needs and personnel flow in the exhibition hall, avoiding unnecessary energy waste. For example, when there are fewer people in the exhibition hall, the lighting brightness and air conditioning power are automatically reduced to achieve energy conservation and emission reduction; through precise control and dynamic adjustment strategies, the system can optimize resource utilization efficiency and reduce operating costs; the entire environmental control process is automated, reducing manual intervention and operating costs. Staff can focus more on other important tasks and improve overall operational efficiency; the central control platform can monitor the environmental parameters and equipment operating status in the exhibition hall in real time, promptly detect and handle abnormal situations, and ensure the normal operation of the exhibition hall; the system can dynamically adjust environmental parameters based on user comfort feedback, providing a more personalized service experience. Users can adjust the environmental settings in the exhibition hall according to their needs and preferences, improving the satisfaction and comfort of the visit; the intelligent environmental control system can also enhance the sense of technology and modernity of the exhibition hall, attracting more users' attention and participation.

[0148] In summary, the S3 technical solution, through intelligent environmental monitoring and control, not only improves the comfort of the exhibition hall environment and the safety of exhibits, but also achieves multiple benefits such as energy conservation and emission reduction and improved operational efficiency. These benefits and effects jointly promote the sustainable development of the exhibition hall and enhance the user experience.

[0149] In one embodiment of the present invention, the image acquisition module includes:

[0150] Crowd flow analysis module: uses high-definition cameras to capture crowd flow images in the exhibition hall, uses image recognition technology to extract crowd flow information, builds a headcount model, and combines deep learning algorithms to analyze the crowd density of each exhibition area in real time.

[0151] Situation Assessment Module: Based on crowd density data, it assesses the congestion situation in each exhibition area, predicts future crowd flow trends, and formulates intelligent guidance strategies. This includes dynamically adjusting the order of exhibition area openings, setting up temporary diversion channels, and activating guidance signs in specific exhibition areas.

[0152] User guidance module: Utilizes the LED display screens in the exhibition hall to update the crowd flow status of each exhibition area and recommend visiting routes in real time, guiding users to avoid congested areas;

[0153] User reminder module: Through multiple means (such as smart guide APP), personalized tour routes and real-time crowd warning information are pushed to users. A voice broadcast system is set up at key locations in the exhibition hall. When the flow of people in a certain exhibition area reaches the threshold, a guiding voice is automatically played to remind users to make reasonable diversions.

[0154] The working principle of the above technical solution is as follows: high-definition cameras are deployed in the exhibition hall to capture real-time images of crowd flow within the exhibition hall. Advanced image recognition technology is used to extract crowd flow information, such as the number of people and movement direction, from the captured images. Based on the extracted crowd flow information, a crowd counting model is constructed to conduct real-time analysis of the crowd density in each exhibition area within the exhibition hall. Deep learning algorithms are combined to improve the accuracy and real-time performance of crowd density analysis, providing data support for subsequent congestion assessment and guidance strategy formulation. Based on the crowd density data, congestion conditions in each exhibition area are assessed in real time to identify potential congested areas. Historical data and real-time crowd flow information are used to predict future crowd flow trends through prediction algorithms, providing a basis for developing more accurate and effective guidance strategies. Based on the congestion assessment and crowd flow trend prediction results, intelligent guidance strategies are formulated. These strategies may include dynamically adjusting the opening order of exhibition areas, setting up temporary diversion channels, and activating guide signs in specific exhibition areas to effectively alleviate congestion and improve the user experience. LED displays in the exhibition hall are used to update the crowd flow status of each exhibition area and recommend visiting routes in real time. Users can check the information on the display screen to understand the congestion situation of each exhibition area and adjust their visit plans accordingly; according to the intelligent guidance strategy, adjust the guidance signs in the exhibition hall, such as setting temporary arrow signs, changing the exhibition area entrance signs, etc., to guide users to divert reasonably; push personalized tour routes to users through multiple means such as the smart guide APP. These routes will be customized according to the user's interests and preferences and the current congestion situation of the exhibition hall to help users better plan their visit itineraries; set up a voice broadcast system at key locations in the exhibition hall. When the flow of people in a certain exhibition area reaches the preset threshold, the system automatically plays a guiding voice to remind users that the exhibition area is congested and recommends visiting other exhibition areas. In addition, channels such as the smart guide APP will also push crowd warning information in real time to ensure that users can obtain and respond in a timely manner.

[0155] The effects of the above technical solutions are: through high-definition cameras and image recognition technology, the crowd density of each exhibition area is analyzed in real time, so that users can understand the degree of congestion in each exhibition area in a timely manner, and make more reasonable visiting decisions; the smart guide APP pushes personalized visiting routes according to user interests and current crowd conditions, reducing user waiting time, improving visiting efficiency and satisfaction; the LED display screen and voice broadcast system update the crowd conditions and provide guidance in real time, helping users avoid congested areas and enjoy a smoother visiting experience; according to crowd density data and predicted trends, the exhibition area opening order is dynamically adjusted, temporary diversion channels are set, etc., effectively alleviating exhibition hall congestion and improving the overall operational efficiency of the exhibition hall; through real-time monitoring and data analysis, exhibition hall managers can allocate human and material resources more accurately to ensure the efficient operation of each exhibition area; when the crowd flow in a certain exhibition area reaches the threshold, , automatically plays guiding voice to remind users to divert reasonably, and effectively prevent safety accidents caused by excessive crowds; in an emergency, the intelligent guidance system can quickly adjust its strategy, provide guidance for personnel evacuation, and ensure the safety of people in the exhibition hall; by collecting and analyzing a large amount of crowd flow data, exhibition hall managers can gain an in-depth understanding of visitors' behavior patterns and preferences, and provide data support for future exhibition planning and operation management; based on user visiting behavior and preference data, the exhibition hall can carry out more precise marketing activities to enhance the attractiveness and influence of the exhibition; the application of advanced technologies such as high-definition cameras, image recognition, and deep learning algorithms makes the exhibition hall full of technology and enhances users' interest in visiting; the combined use of smart guide APP, LED display screens, voice broadcast systems and other means provides users with a richer interactive experience, enhancing the fun and sense of participation in the exhibition.

[0156] In summary, the S4 technical solution, through intelligent crowd management and guidance, not only improves user experience and showroom operational efficiency, but also enhances safety and data-driven decision-making capabilities, while also giving the showroom a stronger sense of technology and interactivity. These benefits and effects collectively promote the sustainable development and competitiveness of the showroom.

[0157] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A smart exhibition hall control method based on the Internet of Everything, characterized in that: The method comprises: S1. Build an IoT-based smart exhibition hall network architecture and connect it to the central control platform via wireless to form a network environment where everything is connected. S2. Obtain user identity information through an identity recognition device and build a user identity database; analyze user preferences through an intelligent algorithm based on the user identity information and historical user behavior data; S3. Use environmental sensors to monitor the environmental parameters in the exhibition hall in real time and adjust the equipment according to the preset environmental standards or user comfort requirements; S4. Collect crowd flow information in the exhibition hall through image acquisition devices, and analyze the crowd density of each exhibition area in real time based on the crowd counting model; intelligently generate guidance information based on the crowd density data; S5. The central control platform conducts comprehensive analysis of all collected data and provides decision support for the operation and management of the exhibition hall through data mining and machine learning algorithms; Said S5 comprises: Integrate multi-source data and pre-process the integrated multi-source data; Identify potential needs and market trends behind user behavior, and predict future visitor trends and exhibit popularity; Evaluate the effectiveness of existing strategies and activities and provide decision support for the showroom; Continuously iterate and optimize system functions and performance; The S3 includes: S31. Deploy environmental monitoring equipment in the exhibition hall to collect environmental parameter data in real time and upload it to the central control platform; S32. Formulate a control strategy based on preset environmental standards or user comfort feedback; dynamically adjust the control strategy based on historical data and real-time environmental changes through machine learning algorithms; S33. The central control platform automatically sends control instructions to relevant equipment based on the control strategy. The relevant equipment executes the control instructions and adjusts the environmental parameters in the exhibition hall to the preset standards or user comfort requirements; The S32 includes: Collect and analyze the exhibition hall's long-term environmental parameter data and user comfort feedback data; Through statistical analysis and data mining technology, the correlation between environmental parameters and user comfort is identified, and an environmental comfort prediction model is constructed; Through machine learning algorithms, the model is trained to predict the user's comfort level under different environmental conditions; Combined with the real-time data collected by current environmental monitoring equipment, time series analysis is used to predict environmental change trends in the future; Based on the anomaly detection mechanism, abnormal fluctuations in environmental parameters are discovered and warned; Dynamically generate control strategies based on prediction models and user comfort preferences; strategies should take into account differentiated needs in different time periods; Introducing a priority sorting mechanism to prioritize the environmental factors that have the greatest impact on user comfort when resources are limited; through regulatory strategies, reducing discomfort during the regulation process; Implement online learning mechanisms to continuously incorporate new environmental data and user feedback into the model and continuously optimize the control strategy; use A / B testing experimental design methods to compare the effects of different control strategies and select the optimal strategy for implementation; Through the adaptive learning algorithm, the system automatically adjusts the learning rate and parameters according to the long-term operation results; Analyze user behavior patterns and predict environmental needs; combine user portraits to provide personalized environmental control solutions for different user groups.

2. The intelligent exhibition hall control method based on the Internet of Everything according to claim 1 is characterized in that: Said S1 comprises: S11. Design a deployment plan for IoT devices based on the exhibition hall layout and requirements, integrate various IoT devices into a unified communication protocol, and build a layered network architecture. S12. Deploy wireless network infrastructure, implement network security measures, and conduct equipment joint debugging and testing; S13. Simulate data transmission stress tests in different scenarios, evaluate system stability and response speed, and adjust network configuration and device parameters based on test results.

3. The intelligent exhibition hall control method based on the Internet of Everything according to claim 1 is characterized in that: Said S2 comprises: S21. Deploy RFID readers or facial recognition cameras at the entrance of the exhibition hall to automatically capture user identity information, compare and verify the collected identity information with the user database, and identify the user; S22. Collect user historical behavior data and build user profiles. Use intelligent algorithms to deeply mine user preferences and predict the types of exhibits that users are interested in. S23. Based on the analysis results, a personalized exhibition route and exhibit recommendation list are tailored for the user; personalized exhibition information and interactive prompts are pushed to the user through multiple channels.

4. The intelligent exhibition hall control method based on the Internet of Everything according to claim 1 is characterized in that: Said S4 comprises: S41. Use high-definition cameras to capture the flow of people in the exhibition hall, use image recognition technology to extract crowd flow information, build a crowd counting model, and combine it with deep learning algorithms to analyze the crowd density of each exhibition area in real time. S42. Based on crowd density data, assess the congestion situation in each exhibition area, predict future crowd flow trends, and develop intelligent guidance strategies; S43. Use the LED display screens in the exhibition hall to update the crowd flow of each exhibition area and recommend visiting routes in real time, guiding users to avoid congested areas; S44. Through multiple means, personalized tour routes and real-time crowd flow warning information are pushed to users. A voice broadcast system is set up at key locations in the exhibition hall. When the flow of people in a certain exhibition area reaches the threshold, a guiding voice is automatically played to remind users to divert traffic reasonably.

5. An intelligent exhibition hall control system based on the Internet of Everything, characterized by: The system comprises: Architecture building module: Build an IoT-based smart exhibition hall network architecture and connect it to the central control platform via wireless to form a network environment where everything is connected; Information acquisition module: obtains user identity information through the identity recognition device and builds a user identity database; analyzes user preferences through intelligent algorithms based on user identity information and historical user behavior data; Real-time monitoring module: monitors the environmental parameters in the exhibition hall in real time through environmental sensors, based on preset environmental standards or user comfort requirements; Image acquisition module: This module collects information about the flow of people in the exhibition hall through image acquisition devices and analyzes the density of people in each exhibition area in real time based on the population statistics model. It also intelligently generates guidance information based on the density data. Comprehensive analysis module: The central control platform conducts comprehensive analysis of all types of collected data and provides decision support for exhibition hall operations and management through data mining and machine learning algorithms; The comprehensive analysis method of the comprehensive analysis module includes: Integrate multi-source data and pre-process the integrated multi-source data; Identify potential needs and market trends behind user behavior, and predict future visitor trends and exhibit popularity; Evaluate the effectiveness of existing strategies and activities and provide decision support for the showroom; Continuously iterate and optimize system functions and performance; The real-time monitoring module includes: Data upload module: Deploy environmental monitoring equipment in the exhibition hall to collect environmental parameter data in real time and upload it to the central control platform; User feedback module: Develops control strategies based on preset environmental standards or user comfort feedback; dynamically adjusts control strategies based on historical data and real-time environmental changes through machine learning algorithms; Command control module: The central control platform automatically sends control commands to relevant equipment based on the control strategy. The relevant equipment executes the control commands and adjusts the environmental parameters in the exhibition hall to the preset standards or user comfort requirements; The user feedback method of the user feedback module includes: Collect and analyze the exhibition hall's long-term environmental parameter data and user comfort feedback data; Through statistical analysis and data mining technology, the correlation between environmental parameters and user comfort is identified, and an environmental comfort prediction model is constructed; Through machine learning algorithms, the model is trained to predict the user's comfort level under different environmental conditions; Combined with the real-time data collected by current environmental monitoring equipment, time series analysis is used to predict environmental change trends in the future; Based on the anomaly detection mechanism, abnormal fluctuations in environmental parameters are discovered and warned; Dynamically generate control strategies based on prediction models and user comfort preferences; strategies should take into account differentiated needs in different time periods; Introducing a priority sorting mechanism to prioritize the environmental factors that have the greatest impact on user comfort when resources are limited; through regulatory strategies, reducing discomfort during the regulation process; Implement online learning mechanisms to continuously incorporate new environmental data and user feedback into the model and continuously optimize the control strategy; use A / B testing experimental design methods to compare the effects of different control strategies and select the optimal strategy for implementation; Through the adaptive learning algorithm, the system automatically adjusts the learning rate and parameters according to the long-term operation results; Analyze user behavior patterns and predict environmental needs; combine user portraits to provide personalized environmental control solutions for different user groups.

6. The intelligent exhibition hall control system based on the Internet of Everything according to claim 5 is characterized in that: The architecture building blocks include: Deployment Design Module: Design IoT device deployment plans based on exhibition hall layout and requirements, integrate various IoT devices into a unified communication protocol, and build a layered network architecture. Facility deployment module: deploy wireless network infrastructure, implement network security measures, and conduct equipment joint debugging and testing; Test simulation module: simulates data transmission stress tests in different scenarios, evaluates system stability and response speed, and adjusts network configuration and device parameters based on test results.

7. The intelligent exhibition hall control system based on the Internet of Everything according to claim 5 is characterized in that: The information acquisition module includes: Identity recognition module: Deploy RFID readers or facial recognition cameras at the entrance of the exhibition hall to automatically capture user identity information, compare and verify the collected identity information with the user database, and identify the user; Data collection module: collects user historical behavior data and builds user portraits. It uses intelligent algorithms to deeply mine user preferences and predict the types of exhibits that users are interested in. Recommendation module: Based on the analysis results, personalized exhibition routes and exhibit recommendation lists are tailored for users; personalized exhibition information and interactive prompts are pushed to users through multiple channels.

8. The intelligent exhibition hall control system based on the Internet of Everything according to claim 5 is characterized in that: The image acquisition module includes: Crowd flow analysis module: uses high-definition cameras to capture crowd flow images in the exhibition hall, uses image recognition technology to extract crowd flow information, builds a crowd counting model, and combines deep learning algorithms to analyze the crowd density of each exhibition area in real time. Situation Assessment Module: Based on crowd density data, it assesses the congestion situation in each exhibition area, predicts future crowd flow trends, and formulates intelligent guidance strategies; User guidance module: Utilizes the LED display screens in the exhibition hall to update the crowd flow status of each exhibition area and recommend visiting routes in real time, guiding users to avoid congested areas; User reminder module: Through multiple means, personalized tour routes and real-time crowd warning information are pushed to users. A voice broadcast system is set up at key locations in the exhibition hall. When the crowd flow in a certain exhibition area reaches the threshold, a guiding voice is automatically played to remind users to divert traffic reasonably.

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