Cloud Based HVAC Monitoring and Control Systems

Remote digital cloud platform for managing and optimizing energy systems in large-scale commercial and residential buildings. The platform integrates device-level sensors and edge computing to monitor and analyze building performance data, while leveraging machine learning and AI to optimize energy distribution and consumption. The system enables real-time monitoring of building-wide energy usage patterns, automates energy management processes, and provides predictive analytics for peak demand management and energy storage optimization. The platform integrates with existing smart home systems, enables remote maintenance and operation, and provides data analytics and reporting capabilities.

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Temperature and humidity monitoring system integrating IoT and cloud computing enables real-time monitoring of switchgear conditions through a networked architecture. The system comprises a perception layer with wireless sensors and a control layer that integrates temperature and humidity data from these sensors. The perception layer collects data from multiple sensors, which are then transmitted to a gateway platform for processing and analysis. The gateway uploads data to a cloud computing platform, where it is processed and analyzed through machine learning algorithms. The system enables remote monitoring, control, and decision-making through a mobile application, ensuring real-time insights into switchgear operating conditions.

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Cloud-based intelligent control system for building HVAC systems that optimizes energy efficiency and comfort through real-time monitoring and predictive analytics. The system integrates cloud computing, AI, and IoT sensors to provide continuous environmental data, predict unmeasured parameters, and dynamically adjust HVAC parameters based on real-time conditions. The system enables intelligent control of multiple HVAC components, including air conditioning, through a user-friendly interface, while incorporating advanced waste heat recovery strategies to maximize system performance.

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Real-time power monitoring system for improved energy efficiency that enables precise energy consumption analysis across entire facilities, buildings, or equipment. The system integrates temperature sensors, microcontrollers, and advanced data processing to provide real-time energy consumption data, enabling users to pinpoint areas of high energy use and implement targeted energy-saving strategies. The system also analyzes energy consumption patterns over time, allowing users to identify trends and optimize equipment operation for more energy-efficient solutions.

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Energy management platform that integrates real-time energy usage data with predictive analytics to optimize facility operations. The system collects and analyzes energy consumption patterns through IoT sensors, generating detailed usage data that is then analyzed by AI-driven predictive models. This enables proactive facility management, energy cost optimization, and predictive maintenance through automated alerts and alerts-driven decision-making. The platform integrates with external management systems to facilitate real-time data exchange and collaboration.

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Intelligent analysis of equipment energy consumption and carbon savings using IoT sensors and big data processing to accurately monitor, analyze, and optimize energy usage for industries and equipment. The method involves deploying IoT sensors on front-end equipment to collect real-time data, processing it through a big data platform to summarize and analyze the energy consumption, and pushing the results to terminals for monitoring and management. This allows precise determination of energy usage by industry, region, equipment, etc. to help enterprises optimize energy use, detect anomalies, and achieve energy conservation and emission reduction goals.

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Cloud-based IoT platform for remote monitoring and control of industrial and agricultural environments. The system integrates environmental sensors, predictive analytics, and cloud-based processing to enable real-time monitoring and predictive maintenance across multiple parameters. It employs a distributed sensor network architecture with wireless communication, advanced analytics, and cloud-based processing to provide continuous monitoring and predictive capabilities. The system enables remote monitoring, automated anomaly detection, and historical data analysis through its user-friendly interface.

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Energy-saving optimization for HVAC systems through a carbon reduction model that leverages IoT data collection and machine learning. The system employs an intelligent body that monitors real-time HVAC operation, predicts energy consumption, and continuously updates models based on cluster analysis. The optimized models are trained using hyperparameter optimization techniques and then applied to real-time system operation. This approach enables continuous model adaptation and improves accuracy over time, enabling real-time carbon reduction optimization.

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Device for remotely monitoring energy consumption of HVAC systems like air conditioners and heating systems. The device consists of the HVAC equipment itself, with a connected capillary network. By integrating monitoring capabilities into the HVAC components themselves, it allows remote tracking of energy usage without needing separate devices or sensors.

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Intelligent building air conditioning energy saving system using edge computing to optimize HVAC performance and reduce energy consumption. The system has edge nodes near building equipment, like air conditioners, that collect real-time operating data and local environment conditions. Edge computing terminals in the building analyze the data and generate optimal strategies. The terminals send control signals to the equipment. This distributed edge computing approach improves automation, adaptability, and speed compared to centralized control. It leverages edge nodes close to equipment for local data collection, analysis, and control.

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Cloud-based real-time monitoring and optimization of HVAC systems to reduce energy consumption, carbon footprint, and maintenance costs. The system uses sensors in the HVAC equipment to measure parameters like pressures, temperatures, and refrigerant charge levels. This data is sent to a cloud platform where it is analyzed to monitor system health and performance. The cloud platform can also control the HVAC components autonomously based on the sensor data. This allows proactive maintenance and optimization of the HVAC system to improve efficiency and reduce failures. The cloud platform provides real-time cost monitoring, daily performance reports, and insights into energy savings potential.

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A system for real-time environmental monitoring and automated device control through cloud-based data management. The system integrates sensors, devices, and cloud infrastructure to monitor environmental conditions, while enabling remote device configuration, automatic updates, and real-time notifications. The system employs local storage for device configurations and provides cloud-based management through a user-friendly interface. This enables scalable device management across multiple locations, while ensuring device control through automated configuration and real-time monitoring.

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Intelligent environmental control terminal device and its control method that enables automatic temperature and brightness regulation through real-time monitoring of people and ambient conditions. The device employs advanced sensor systems, including human body pyroelectric detection, light intensity sensors, and traffic statistics monitoring, to dynamically adjust temperature and brightness levels. This integrated approach enables the device to automatically adjust parameters based on occupancy patterns, ambient temperature, and lighting conditions, thereby optimizing energy efficiency and occupant comfort.

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Cloud-based HVAC system that leverages AI-driven predictive optimization to achieve energy efficiency through intelligent scheduling and control. The system integrates data acquisition nodes, control nodes, and a cloud-based monitoring platform to manage building operations. The control nodes process building data and HVAC operation information, while the data acquisition nodes collect real-time building and HVAC data. The platform employs machine learning algorithms to predict building energy consumption, optimize HVAC operation, and balance energy distribution. This enables real-time energy management and predictive maintenance through AI-driven scheduling and control.

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A distributed computing system for HVAC monitoring and control that optimizes building operations through automated data collection and analysis. The system enables real-time monitoring of HVAC systems through client terminals, which are assigned virtual machines that execute container processes. These container processes collect and analyze data from relevant Building Automation System (BAS) servers, while the client terminals operate independently. The system dynamically allocates computing resources between client terminals and virtual machines based on client activity, ensuring efficient use of hardware resources.

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Cloud-based collaborative central air conditioning system that optimizes energy efficiency by leveraging cloud analysis and control. The system involves monitoring operating data and energy consumption of air conditioning equipment, uploading it to a cloud analysis device, and receiving real-time optimized strategies back. This allows coordinated control of the entire central air conditioning system to balance energy savings with comfort. The cloud device analyzes overall system data and provides optimized strategies based on principles of optimal efficiency and terminal comfort.

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Building management system (BMS) that enables remote control of HVAC systems through cloud-based environmental monitoring. The system integrates with existing HVAC infrastructure to collect real-time data on temperature, humidity, and other environmental parameters. Using machine learning algorithms, it analyzes this data to provide personalized control recommendations to building occupants and automated system operation. The system enables remote monitoring and control through a user-friendly interface, eliminating the need for local server devices.

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Cloud-side end collaborative central air conditioning global optimization energy-saving control method and system that enables real-time cloud-based optimization of central air conditioning systems through distributed computing architecture. The system comprises a cloud server, edge nodes, detection devices, and end devices. The cloud server processes complex optimization parameters, while edge nodes and end devices perform local calculations and control operations. This distributed approach optimizes energy savings by leveraging cloud computing while maintaining local control capabilities, enabling efficient and stable operation of central air conditioning systems.

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Remote monitoring system for automated air conditioning equipment through the Internet of Things (IoT) that enables remote control and monitoring of air conditioning systems. The system comprises cloud-based modules that collect and transmit equipment parameters to a central server, while a remote terminal connects to the server for real-time monitoring and control. A field monitoring module at the site of the equipment provides environmental data, enabling remote intervention through the on-site terminal. This integrated approach streamlines maintenance operations by eliminating the need for on-site personnel while maintaining operational reliability.

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A remote monitoring platform for automated air conditioning equipment that enables real-time monitoring and control through a network of field terminals connected to the equipment. The platform integrates environmental sensors and IoT modules with a cloud-based server, enabling remote monitoring and control through a user-friendly interface. The system also incorporates voice communication capabilities to provide immediate assistance to on-site operators when system alarms occur.

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Cloud-based data visualization control for HVAC systems that enables real-time monitoring and unified management through a cloud platform. The system integrates with HVAC control modules through instant messaging, allowing them to share data and receive control signals. The cloud platform analyzes the HVAC system's operational status and performs control actions based on the data received from the control module. This enables comprehensive monitoring and automation of HVAC systems in real-time, improving user experience and system performance.

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A system for improving energy efficiency of building HVAC systems using AI and sensors. The system involves collecting environmental, energy consumption, and operational parameter data from distributed sensors in a building. This data is transmitted via a network to a remote server where it's processed by an AI platform to compute control strategies for the HVAC equipment. These strategies are then output and executed by the HVAC devices to optimize energy usage while maintaining comfortable indoor conditions.

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Remote monitoring split-body temperature controller that enables real-time monitoring of the temperature controller through a cloud-based platform. The controller features a wireless connection between the main control board and Alibaba Cloud server, as well as a WeChat-enabled terminal for remote monitoring. The system comprises a microprocessor-based main control board with an output terminal connected to the indicator light, and an input terminal connected to the thermostat's sensor. The cloud-based platform enables two-way communication between the thermostat and the cloud, allowing remote monitoring and control through the WeChat app.

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Central air-conditioning system monitoring and control system that eliminates the lag in temperature measurement between the outside and inside of the system. The system employs a unique temperature probe configuration where the outside temperature probe is positioned at the outside wall of the system's circulating pipeline, while the inside temperature probe is positioned in the chilled water line. This configuration ensures that the outside temperature probe captures the system's actual outdoor temperature, while the inside temperature probe measures the actual chilled water temperature. This configuration eliminates the traditional time lag between outside and inside temperature measurements, enabling accurate real-time monitoring of the system's operating conditions.

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Cognitive energy assessments in thermal energy systems that identify waste events through real-time monitoring of system performance indicators. The method employs non-intrusive IoT sensors strategically deployed throughout the system, including temperature sensors, to learn operational patterns and detect energy inefficiencies. By analyzing historical data and system behavior, the system identifies and validates identified energy waste events against predefined thresholds, enabling proactive energy management and capacity planning.

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Remote and proactive HVAC system diagnostics through connected sensors and advanced data analysis. The system enables continuous monitoring of HVAC equipment through sensor networks, using data from multiple sensors to evaluate system performance and detect potential issues before they become critical. The system integrates with a monitoring device on-site, allowing technicians to access real-time data and perform proactive maintenance. This approach enables early detection of system degradation, reduces maintenance costs, and ensures optimal system performance.

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An intelligent central air-conditioning monitoring system that enables remote monitoring, real-time control, and data sharing through a cloud platform. The system integrates advanced monitoring capabilities with smart control functions, enabling residents to remotely monitor air quality and temperature parameters, receive alerts, and adjust system settings through a user-friendly interface. The system supports remote data transmission, enables real-time data sharing between building management systems and remote monitoring platforms, and provides advanced analytics for predictive maintenance and energy efficiency optimization.

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Smart home system that optimizes energy efficiency through cloud-based monitoring and control. The system integrates environmental sensors, home appliances, and a cloud server to monitor and manage indoor conditions. When parameters exceed thresholds, the system automatically sends control commands to appliances to adjust their operation. The system continuously collects environmental data from sensors and communicates with the cloud server, enabling real-time monitoring and automated adjustments to maintain optimal conditions.

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An indoor environment monitoring system that integrates cloud-based data analysis with IoT sensors to provide personalized comfort control. The system comprises an environmental monitoring sensor, a data acquisition module, and a data transmission module. The sensor collects environmental data, which is then transmitted to a cloud-based processing system. The processing system analyzes the environmental data, generates comfort indicators, and provides personalized comfort recommendations through a user interface. The system enables real-time monitoring, data analysis, and expert-driven comfort control through a cloud-based platform.

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A mobile platform for real-time monitoring and analysis of industrial equipment, facilities, and resources through a single, self-contained architecture. The platform integrates wireless sensor data from multiple sources, including internal and external sensors, into a unified monitoring system. It enables real-time data aggregation, processing, and visualization through a single, self-contained node architecture, eliminating the need for multiple nodes and reducing data transmission latency. The platform also incorporates advanced analytics capabilities, including pattern detection and machine learning, to provide predictive maintenance insights and detect anomalies.

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Remote monitoring system for temperature and humidity controllers that enables continuous real-time monitoring and control of equipment while eliminating the need for human presence. The system integrates the controller with a cloud-based monitoring platform, where recorded data is stored and managed through a unique password-protected address index. The system connects to the cloud through a built-in network interface, enabling remote access and control of the equipment. This eliminates the need for on-site personnel, reduces maintenance costs, and enables proactive issue detection and resolution.

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A cloud-based smart air conditioning control system that enables real-time monitoring and control of HVAC systems across multiple locations. The system integrates with existing HVAC devices through standardized interfaces, converts them to a common communication protocol, and stores monitoring data in a cloud-based database. The cloud platform processes and analyzes this data using machine learning algorithms to identify patterns and anomalies, enabling proactive maintenance and optimization across sites. The system provides a centralized interface for managing HVAC systems, with real-time monitoring and alerts through web-based interfaces.

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A centralized HVAC control system for mobile cloud environments that maximizes energy efficiency and reduces operating costs through remote monitoring and control using smart terminals. The system has a central control unit connected to converters for converting proprietary protocols of individual HVAC devices to TCP/IP. The central unit provides optimal operating parameters to the devices based on predetermined algorithms. Users access the system via smart terminals to view and set parameters. This allows remote optimization of all devices rather than periodic on-site checks.

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Building controller that enables remote control of building systems while conserving power. The controller has a local energy storage device to power itself and components like HVAC. It determines available power and communicates a delay until next transmission based on the storage level. This allows remote control but reduces network power usage by prolonging communication intervals when battery is low.

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A cloud-based environmental monitoring and analysis system for real-time data management and decision support. The system enables efficient monitoring, analysis, and decision-making for environmental data through a networked server architecture. It organizes data into a structured query interface, enabling public service government agencies to monitor environmental conditions through a single interface. The system optimizes sensor data processing and evaluation, providing predictive analytics and forecasting capabilities for environmental management.

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Centralized heat supply station remote monitoring system for automated operation and predictive maintenance of heating systems. The system enables real-time monitoring and predictive analytics of heating system performance, allowing operators to detect potential issues before they cause disruptions. The system integrates with existing heating infrastructure, provides comprehensive data on system performance, and enables proactive maintenance to prevent equipment failures.

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A monitoring system that enables diverse data types to be monitored across multiple platforms through a unified virtual station architecture. The system integrates environmental data, sensor status, and device-related data into a single virtual station, allowing users to define and manage monitoring configurations through a user-friendly interface. This architecture enables the system to handle multiple data types through a single monitoring platform, eliminating the need for separate cloud platforms for different data types.

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