Energy-saving management system and method based on Internet of Things

Abstract

The invention relates to the technical field of energy-saving management, in particular to an energy-saving management system and method based on the Internet of Things. Data, including temperature, humidity, pressure and air volume, of all key instruments in a cold and hot air unit are collected in real time and transmitted to a unified industrial master controller through the Internet of Things technology; therefore, the problem of data isolation is eliminated, and the system can comprehensively master the operation state of the fan set. The industrial master controller monitors and analyzes the operation of the fan set in real time through the supervision control system by using the integrated data; the change of the load demand is identified in time, the operation parameters of the fan set are dynamically adjusted, and the system can respond quickly when the load demand changes; on the basis of real-time data, through linkage of the Internet of Things industrial control system, the supervision control system continuously learns and adjusts various parameters, and an intelligent feedback mechanism is formed; and after the historical data and the current operation state are analyzed, the load change trend is pre-judged, and adjustment is carried out in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving management, and in particular to an energy-saving management system and method based on the Internet of Things. Background Art

[0002] HVAC systems consume a large amount of energy in commercial buildings; therefore, their performance must be optimized to reduce energy costs.

[0003] Chinese Patent Publication No.: CN108351656A discloses an HVAC management system including a space having an atmospheric condition to be controlled; also including an air manipulation component to change the atmospheric condition of the space; also including a sensor component located near the space for approximating the number of occupants present in the space based on the number of mobile devices located in the space; the sensor component includes a receiver capable of detecting communication data associated with the mobile device; the HVAC management system also includes a controller that operatively communicates with the air manipulation component and the sensor component, the controller receives information including temperature data and the number of the mobile devices from the sensor component, and the controller controls the air manipulation component based on the information received from the sensor component.

[0004] The load demand of the HVAC system often fluctuates with changes in the external environment, internal activities and production requirements. The current energy-saving management methods for HVAC systems usually rely on preset schedules or static rules; however, the load demand in the actual operating environment is often dynamic and complex. This uncertainty makes it difficult for the system to respond to load changes in real time. When the load demand is low, the system still operates at high load, resulting in unnecessary energy consumption; and when the load demand suddenly increases, the system cannot adjust quickly, resulting in insufficient cooling or heating, affecting the overall system efficiency. Therefore, there is an urgent need for an energy-saving management system and method based on the Internet of Things to solve such problems. Summary of the invention

[0005] To this end, the present invention provides an energy-saving management system and method based on the Internet of Things to overcome the problem that the prior art relies on preset schedules or static rules and cannot effectively cope with dynamic and complex load demands.

[0006] To achieve the above objectives, on the one hand, the present invention provides an energy-saving management system based on the Internet of Things, comprising:

[0007] The data collection and supervision module, as the core of the energy-saving management system, is responsible for collecting data from various subsystems and monitoring the operating status of the cooling and heating units in real time;

[0008] The data acquisition control unit is responsible for collecting and integrating the data of the hot and cold air units, including temperature, humidity, pressure, and air volume, from various subsystems, and transmitting the real-time data to the industrial main controller;

[0009] The dashboard module monitors data in real time based on sensors deployed on the hot and cold air units, including temperature, humidity, pressure, and air volume;

[0010] Industrial control module, which uses integrated data acquisition and edge computing technology to transmit data to the industrial main controller;

[0011] The industrial main controller processes and makes decisions as well as dynamically adjusts and optimizes data. The industrial main controller is the center of the system and is responsible for receiving and processing data transmitted from the industrial control module. Based on these data, the main controller performs real-time analysis and decision-making, and sends control instructions to the fan unit.

[0012] The factory control module manages the energy efficiency of the entire plant. The factory control module includes the cooling and heating air units and the equipment operation control unit. It achieves energy efficiency management of the entire plant through unified energy management. The factory control module coordinates the operation of each subsystem to minimize the overall energy consumption.

[0013] The IoT control module performs intelligent feedback and learning, and provides remote monitoring and management; IoT control enables operation and maintenance personnel to view the system status in real time, make necessary adjustments and maintenance, and improve the flexibility and operability of the system.

[0014] Furthermore, the data acquisition and supervision module conducts real-time monitoring and analysis based on the data acquired by IoT sensors, identifies abnormal conditions in equipment operation, and issues warnings when abnormalities are found;

[0015] The industrial control module uses edge computing technology to perform preliminary processing and analysis before data transmission, reducing the computing load of the main controller;

[0016] The industrial control module collects and integrates data from different sensors in a unified manner;

[0017] The industrial master controller dynamically adjusts based on real-time data to optimize the operating parameters of the cooling and heating units, including air volume and temperature set points;

[0018] Through collaboration with the data acquisition and supervision module, the main controller can continuously optimize energy efficiency;

[0019] The plant control module works in conjunction with other plant subsystems including lighting, ventilation, and security systems;

[0020] The IoT control module continuously learns and optimizes system operating parameters through integrated data and intelligent algorithms; analyzes historical data and predicts future load demand.

[0021] On the other hand, the present invention also provides an energy-saving management method based on the Internet of Things, comprising:

[0022] Step S1, data integration, collects all key instrument data in the hot and cold air units through the Internet of Things technology and transmits it to the industrial main controller; eliminates the problem of data isolation through the integrated data acquisition system, so that the system can fully grasp the operating status of the fan unit;

[0023] Step S2, real-time monitoring, the industrial main controller uses the real-time collected data to monitor and analyze the operating status of the fan unit in real time through the supervisory control system; the monitoring here is used to ensure system stability and timely discover potential efficiency reduction or abnormal conditions. Real-time monitoring provides necessary real-time information for dynamic adjustment, so that the system can respond quickly according to the current operating status;

[0024] Step S3, dynamic adjustment, based on the data and analysis provided by the real-time monitoring in step S2, instantly identifies changes in load demand and dynamically adjusts the fan unit operating parameters, including air volume and temperature set points; the dynamic adjustment function enables the system to quickly adapt to environmental changes, reduce energy waste, and improve operating efficiency, and closely integrates with real-time data to achieve optimization of energy use;

[0025] Step S4, intelligent feedback, introduces an intelligent feedback mechanism based on dynamic adjustment, and continuously learns and optimizes operating parameters through the Internet of Things industrial control system; the intelligent feedback system not only makes adjustments based on the current operating status, but also predicts future load demand by analyzing historical data, and makes optimization adjustments in advance, thereby further improving energy efficiency; the adaptive mechanism ensures that the system is continuously optimized during operation to improve energy saving effects.

[0026] Furthermore, the data integration method in step S1 includes:

[0027] Collect data from the key instruments of the cooling and heating units. Let the collected data of each sensor be xi(t), where i represents different instrument data types, including temperature, humidity, pressure, and air volume, and t represents time;

[0028] The collected data is calibrated and fused to obtain the fused data X'i(t):

[0029] in is the instrument data collected by the jth sensor at time t, N is the number of sensors for each instrument data, wj is the weight factor of the jth sensor, satisfying

[0030]

[0031] The data transmission delay is modeled and minimized. The transmission delay τ is expressed as:

[0032] Where τi is the transmission delay of the i-th instrument data, Li is the data packet size, B is the transmission bandwidth, αi is the transmission noise coefficient, and Pi(t) is the network load

[0033] By adjusting the transmission bandwidth B and optimizing the packet size Li, the transmission delay τi is minimized.

[0034] Furthermore, in step S1, all different types of sensor data are integrated into a comprehensive operating status view through a data fusion algorithm. For each time point t, a comprehensive state vector S(t) is defined:

[0035] S(t)=[wT·XT(t),wH·XH(t),wP·XP(t),wF·XF(t)]Τ, where S(t) is the comprehensive state vector of the system at time t, XT(t), XH(t), XP(t), XF(t) represent the calibration fusion data of temperature, humidity, pressure and air volume respectively, wT, wH, wP, wF represent the weight factors of temperature, humidity, pressure and air volume respectively; Τ represents the transposition operation;

[0036] The Bayesian network-based fusion method is used to take into account the dependencies of multi-source data and perform data integration:

[0037] Where P(S(t)) represents the comprehensive state probability of the system at time t, Xi(t) represents each type of data, and Xparents(i)(t) represents other data associated with Xi(t) in the Bayesian network. Various types of data are integrated through the Bayesian network model to eliminate isolation and form a consistent operating state model.

[0038] All data is transmitted to the industrial main controller through the Internet of Things network for processing and storage. Adaptive transmission protocol is used to dynamically adjust data transmission parameters when network conditions change, and system status information is generated by weighted accumulation:

[0039] Where Sfinal(t) represents the final system status information after integration, and wi represents the weight of each instrument data;

[0040] The final system status information Sfinal(t) is transmitted to the industrial main controller in real time for subsequent monitoring and adjustment. Through data calibration, transmission delay optimization, construction of comprehensive state vector and fusion of Bayesian network, the operating status of the wind turbine group can be fully grasped, data isolation problem can be eliminated, and efficient energy-saving management can be achieved.

[0041] Furthermore, the real-time monitoring method in step S2 includes:

[0042] Based on the data integration in step S1, the comprehensive state vector S(t) is obtained, and the system state vector S(t) is defined as:

[0043] S(t)=[XT(t),XH(t),XP(t),XF(t)]Τ, where XT(t), XH(t), XP(t), XF(t) represent the fusion data of temperature, humidity, pressure and air volume respectively;

[0044] An adaptive state prediction model is introduced, which uses historical data S(t-Δt) and current state S(t) combined with time series prediction technology for prediction;

[0045] The prediction formula is:

[0046] in represents the predicted system state vector, A(t) represents the time-dependent system dynamic matrix, B(t) represents the historical state weight matrix, S(t-Δt) represents the system state vector at the past time point, and ε(t) represents the Gaussian noise term;

[0047] By comparing the current state S(t) with the predicted state Detect whether there is an anomaly in the system. Here, the anomaly detection function D(t) is introduced to quantify the deviation:

[0048] Where D(t) is the abnormal measurement detected, reflecting the difference between the current state of the system and the predicted state, Si(t) represents the i-th parameter in the current state vector, represents the i-th parameter in the predicted state vector, σi(t) represents the standard deviation of the parameter Si(t), reflecting its normal fluctuation range;

[0049] When D(t) exceeds a certain set threshold τ, the system determines that there is an abnormality; at this time, the system triggers an alarm or initiates preventive operations.

[0050] Furthermore, the health status score H(t) is introduced into the real-time supervision method in step S2:

[0051] H(t) is based on the parameters in the state vector and their deviations from the ideal state:

[0052] Where H(t) represents the health status score of the system at time t, ranging from [0,1]. The closer the value is to 1, the healthier the status is. represents the target value of parameter Si(t) under ideal conditions, σi(t) represents the standard deviation of parameter Si(t);

[0053] The H(t) score is used to monitor the overall health of the system in real time and provide a basis for dynamic adjustments.

[0054] Furthermore, the real-time monitoring method in step S2 also includes:

[0055] Based on real-time monitoring data and health status scores, the industrial master controller immediately decides whether adjustments are needed. Here, the adjustment decision function R(t) is used to determine whether immediate action is needed;

[0056] If the abnormality metric D(t) is greater than the threshold τ, or the health status score H(t) is less than the minimum tolerance value Hmin, then R(t) = 1, indicating that adjustment is required, otherwise R(t) = 0, indicating that the status quo is maintained;

[0057] This enables accurate monitoring of the operating status of the hot and cold air units and timely identification of potential problems; it enables the system to quickly respond to changes in operating status and continue to maintain efficient operation.

[0058] Furthermore, the dynamic adjustment method in step S3 includes:

[0059] Continuously receive data from the real-time monitoring module, including the real-time status of key parameters such as temperature, humidity, pressure, and air volume;

[0060] Identify changes in current load demand based on real-time status. When significant fluctuations in temperature or humidity, or changes in the external environment (such as seasonal changes or sudden weather changes) are detected, evaluate whether current operating parameters (such as air volume, temperature set point) meet current load demand and determine whether adjustments are needed;

[0061] Adjust the target based on the data analysis results: when the indoor temperature rises, the system will lower the set temperature and increase the air volume;

[0062] According to the magnitude of the load demand change, the range of adjustment parameters is determined, that is, the percentage of air volume increase or the magnitude of temperature set point reduction;

[0063] After determining the adjustment target and range, the corresponding operating parameters are automatically modified, including the air volume output or temperature set point of the fan unit; directly sent to the control device and take effect immediately;

[0064] The dynamic adjustment here is not limited to the adjustment of the operating parameters of a single fan group. By working in coordination with other building automation systems (lighting, ventilation), global energy efficiency optimization can be achieved. When multiple systems work together, the operating status and energy consumption of each subsystem are comprehensively considered to perform global optimization adjustments.

[0065] Furthermore, the intelligent feedback method in step S4 includes:

[0066] Store the real-time data collected during operation in a central database for pattern recognition and prediction;

[0067] Identify common operating modes and load demand variation patterns through pattern recognition algorithms, including typical load demand characteristics in different seasons and time periods;

[0068] Predict possible future load demand changes based on the identified patterns.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention adopts the Internet of Things technology to collect and transmit the data of all key instruments in the cold and hot air units, including temperature, humidity, pressure, and air volume, in real time to a unified industrial main controller; thereby eliminating the problem of data isolation and enabling the system to fully grasp the operating status of the fan unit.

[0071] The present invention utilizes integrated data, and the industrial main controller monitors and analyzes the operation of the fan group in real time through a supervisory control system; instantly identifies changes in load demand, and dynamically adjusts the operating parameters of the fan group. When the load demand changes, the system can respond quickly, reducing energy waste and response lag problems.

[0072] The present invention, based on real-time data, is linked to the Internet of Things industrial control system, and the supervisory control system continuously learns and adjusts various parameters to form an intelligent feedback mechanism; and after analyzing historical data and current operating status, the system predicts load change trends, makes adjustments in advance, optimizes energy efficiency, and avoids unnecessary energy consumption.

[0073] The present invention, through the coordinated work of data integration, real-time supervisory control and intelligent feedback, the system can speed up the response speed to external environmental changes and internal demand fluctuations; the linkage of the Internet of Things industrial control system further improves the efficiency of data processing and decision-making, so that the hot and cold air units can always maintain an efficient and energy-saving operating state when facing complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a schematic diagram of the energy-saving management method based on the Internet of Things of the present invention;

[0075] Figure 2 It is a schematic diagram of the structure of the energy-saving management system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0076] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0077] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0078] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0079] Example 1

[0080] See also Figure 1-Figure 2 The present invention provides an energy-saving management system based on the Internet of Things, comprising:

[0081] The data collection and supervision module, as the core of the energy-saving management system, is responsible for collecting data from various subsystems and monitoring the operating status of the cooling and heating units in real time;

[0082] The data acquisition control unit is responsible for collecting and integrating the data of the hot and cold air units, including temperature, humidity, pressure, and air volume, from various subsystems, and transmitting the real-time data to the industrial main controller;

[0083] The dashboard module monitors data in real time based on sensors deployed on the hot and cold air units, including temperature, humidity, pressure, and air volume;

[0084] Industrial control module, which uses integrated data acquisition and edge computing technology to transmit data to the industrial main controller;

[0085] The industrial main controller processes and makes decisions as well as dynamically adjusts and optimizes data. The industrial main controller is the center of the system and is responsible for receiving and processing data transmitted from the industrial control module. Based on these data, the main controller performs real-time analysis and decision-making, and sends control instructions to the fan unit.

[0086] The factory control module manages the energy efficiency of the entire plant. The factory control module includes the cooling and heating air units and the equipment operation control unit. It achieves energy efficiency management of the entire plant through unified energy management. The factory control module coordinates the operation of each subsystem to minimize the overall energy consumption.

[0087] The IoT control module provides intelligent feedback and learning, and remote monitoring and management. IoT control enables operation and maintenance personnel to view system status in real time, make necessary adjustments and maintenance, and improve system flexibility and operability.

[0088] The data acquisition and supervision module conducts real-time monitoring and analysis based on the data obtained by IoT sensors, identifies abnormal conditions in equipment operation, and issues warnings when abnormalities are found;

[0089] The industrial control module uses edge computing technology to perform preliminary processing and analysis before data transmission, reducing the computing load of the main controller;

[0090] The industrial control module collects and integrates data from different sensors in a unified manner;

[0091] The industrial master controller dynamically adjusts based on real-time data to optimize the operating parameters of the cooling and heating units, including air volume and temperature set points;

[0092] Through collaboration with the data acquisition and supervision module, the main controller can continuously optimize energy efficiency;

[0093] The plant control module works in conjunction with other plant subsystems including lighting, ventilation, and security systems;

[0094] The IoT control module continuously learns and optimizes system operating parameters through integrated data and intelligent algorithms; analyzes historical data and predicts future load demand.

[0095] In the current HVAC system, data from various instruments are collected and processed independently. The isolated data collection method leads to the lack of a global perspective of the system, and it is impossible to effectively conduct comprehensive analysis and real-time optimization. In addition, the integration between the industrial control system of the HVAC system and the Internet of Things platform is low, and the data processing efficiency is not high, making it difficult to achieve rapid response and deep optimization. Information is difficult to integrate, and the overall operating status is not well understood. At the same time, due to the lack of real-time data integration and analysis, it is difficult for the system to deeply optimize various operating parameters, resulting in limited energy efficiency management effects. For this reason, the present invention adopts the following solutions:

[0096] The present invention also provides an energy-saving management method based on the Internet of Things, comprising:

[0097] Step S1, data integration, collects all key instrument data in the hot and cold air units through the Internet of Things technology and transmits it to the industrial main controller; eliminates the problem of data isolation through the integrated data acquisition system, so that the system can fully grasp the operating status of the fan unit;

[0098] The data integration method in step S1 includes:

[0099] Collect data from the key instruments of the cooling and heating units. Let the collected data of each sensor be xi(t), where i represents different instrument data types, including temperature, humidity, pressure, and air volume, and t represents time;

[0100] The collected data is calibrated and fused to obtain the fused data X'i(t):

[0101] in is the instrument data collected by the jth sensor at time t, N is the number of sensors for each instrument data, wj is the weight factor of the jth sensor, satisfying

[0102]

[0103] The data transmission delay is modeled and minimized. The transmission delay τ is expressed as:

[0104] Where τi is the transmission delay of the i-th instrument data, Li is the data packet size, B is the transmission bandwidth, αi is the transmission noise coefficient, and Pi(t) is the network load

[0105] Minimize the transmission delay τi by adjusting the transmission bandwidth B and optimizing the packet size Li;

[0106] In step S1, all different types of sensor data are integrated into a comprehensive operating status view through a data fusion algorithm. For each time point t, a comprehensive state vector S(t) is defined:

[0107] S(t)=[wT·XT(t),wH·XH(t),wP·XP(t),wF·XF(t)]Τ, where S(t) is the comprehensive state vector of the system at time t, XT(t), XH(t), XP(t), XF(t) represent the calibration fusion data of temperature, humidity, pressure and air volume respectively, wT, wH, wP, wF represent the weight factors of temperature, humidity, pressure and air volume respectively; Τ represents the transposition operation;

[0108] The Bayesian network-based fusion method is used to take into account the dependencies of multi-source data and perform data integration:

[0109] Where P(S(t)) represents the comprehensive state probability of the system at time t, Xi(t) represents each type of data, and Xparents(i)(t) represents other data associated with Xi(t) in the Bayesian network. Various types of data are integrated through the Bayesian network model to eliminate isolation and form a consistent operating state model.

[0110] All data is transmitted to the industrial main controller through the Internet of Things network for processing and storage. Adaptive transmission protocol is used to dynamically adjust data transmission parameters when network conditions change, and system status information is generated by weighted accumulation:

[0111] Where Sfinal(t) represents the final system status information after integration, and wi represents the weight of each instrument data;

[0112] The final system status information Sfinal(t) is transmitted to the industrial main controller in real time for subsequent monitoring and adjustment. Through data calibration, transmission delay optimization, construction of comprehensive state vector and fusion of Bayesian network, the operation status of the wind turbine group can be fully grasped, data isolation problem can be eliminated, and efficient energy-saving management can be achieved.

[0113] Specifically, the data integration module uses the Internet of Things technology to achieve comprehensive monitoring of various key parameters of the cooling and heating units, eliminating the problem of data isolation in traditional systems and providing a data basis for subsequent monitoring and optimization;

[0114] Step S2, real-time monitoring, the industrial main controller uses the real-time collected data to monitor and analyze the operating status of the fan unit in real time through the supervisory control system; the monitoring here is used to ensure system stability and timely discover potential efficiency reduction or abnormal conditions. Real-time monitoring provides necessary real-time information for dynamic adjustment, so that the system can respond quickly according to the current operating status;

[0115] Real-time monitoring methods include:

[0116] Based on the data integration in step S1, the comprehensive state vector S(t) is obtained, and the system state vector S(t) is defined as:

[0117] S(t)=[XT(t),XH(t),XP(t),XF(t)]Τ, where XT(t), XH(t), XP(t), XF(t) represent the fusion data of temperature, humidity, pressure and air volume respectively;

[0118] An adaptive state prediction model is introduced, which uses historical data S(t-Δt) and current state S(t) combined with time series prediction technology for prediction;

[0119] The prediction formula is:

[0120] in represents the predicted system state vector, A(t) represents the time-dependent system dynamic matrix, B(t) represents the historical state weight matrix, S(t-Δt) represents the system state vector at the past time point, and ε(t) represents the Gaussian noise term;

[0121] By comparing the current state S(t) with the predicted state Detect whether there is an anomaly in the system. Here, the anomaly detection function D(t) is introduced to quantify the deviation:

[0122] Where D(t) is the abnormal measurement detected, reflecting the difference between the current state of the system and the predicted state, Si(t) represents the i-th parameter in the current state vector, represents the i-th parameter in the predicted state vector, σi(t) represents the standard deviation of the parameter Si(t), reflecting its normal fluctuation range;

[0123] When D(t) exceeds a certain set threshold τ, the system determines that there is an abnormality; at this time, the system triggers an alarm or initiates preventive operations;

[0124] The health status score H(t) is introduced into the real-time supervision method in step S2:

[0125] H(t) is based on the parameters in the state vector and their deviations from the ideal state:

[0126] Where H(t) represents the health status score of the system at time t, ranging from [0,1]. The closer the value is to 1, the healthier the status is. represents the target value of parameter Si(t) under ideal conditions, σi(t) represents the standard deviation of parameter Si(t);

[0127] The H(t) score is used to monitor the overall health of the system in real time and provide a basis for dynamic adjustments;

[0128] The real-time supervision method in step S2 also includes:

[0129] Based on real-time monitoring data and health status scores, the industrial master controller immediately decides whether adjustments are needed. Here, the adjustment decision function R(t) is used to determine whether immediate action is needed;

[0130] If the abnormality metric D(t) is greater than the threshold τ, or the health status score H(t) is less than the minimum tolerance value Hmin, then R(t) = 1, indicating that adjustment is required, otherwise R(t) = 0, indicating that the status quo is maintained;

[0131] This enables accurate monitoring of the operating status of the cooling and heating units and timely identification of potential problems; enables the system to quickly respond to changes in operating status and maintain efficient operation;

[0132] Specifically, the real-time monitoring system uses the integrated data to comprehensively monitor and analyze the operating status of the wind turbine group; introduces an adaptive state prediction model and health status score to promptly identify potential efficiency declines or abnormal conditions, so that preventive actions can be taken when problems first occur; and can respond quickly and accurately to changes in load demand;

[0133] Step S3, dynamic adjustment, based on the data and analysis provided by the real-time monitoring in step S2, instantly identifies changes in load demand and dynamically adjusts the fan unit operating parameters, including air volume and temperature set points; the dynamic adjustment function enables the system to quickly adapt to environmental changes, reduce energy waste, and improve operating efficiency, and closely integrates with real-time data to achieve optimization of energy use;

[0134] The dynamic adjustment method in step S3 includes:

[0135] Continuously receive data from the real-time monitoring module, including the real-time status of key parameters such as temperature, humidity, pressure, and air volume;

[0136] Identify changes in current load demand based on real-time status. When significant fluctuations in temperature or humidity, or changes in the external environment (such as seasonal changes or sudden weather changes) are detected, evaluate whether current operating parameters (such as air volume, temperature set point) meet current load demand and determine whether adjustments are needed;

[0137] Adjust the target based on the data analysis results: when the indoor temperature rises, the system will lower the set temperature and increase the air volume;

[0138] According to the magnitude of the load demand change, the range of adjustment parameters is determined, that is, the percentage of air volume increase or the magnitude of temperature set point reduction;

[0139] After determining the adjustment target and range, the corresponding operating parameters are automatically modified, including the air volume output or temperature set point of the fan unit; directly sent to the control device and take effect immediately;

[0140] The dynamic adjustment here is not limited to the adjustment of the operating parameters of a single fan unit. It can achieve global energy efficiency optimization by working in coordination with other building automation systems (lighting, ventilation). In the case of multiple systems working together, the operating status and energy consumption of each subsystem are comprehensively considered to make global optimization adjustments.

[0141] Specifically, the dynamic adjustment module further enhances the system's adaptability and energy efficiency management capabilities; based on the data and analysis results provided by the real-time monitoring system, it automatically adjusts the fan unit's operating parameters (air volume and temperature set points) to ensure that the system always maintains efficient operation under different environmental conditions; the dynamic adjustment mechanism significantly reduces energy waste while improving overall operating efficiency;

[0142] Step S4, intelligent feedback, introduces an intelligent feedback mechanism based on dynamic adjustment, and continuously learns and optimizes operating parameters through the IoT industrial control system; the intelligent feedback system not only makes adjustments based on the current operating status, but also predicts future load demand by analyzing historical data, and makes optimization adjustments in advance, thereby further improving energy efficiency; the adaptive mechanism ensures that the system is continuously optimized during operation to improve energy-saving effects;

[0143] The intelligent feedback method in step S4 includes:

[0144] Store the real-time data collected during operation in a central database for pattern recognition and prediction;

[0145] Identify common operating modes and load demand variation patterns through pattern recognition algorithms, including typical load demand characteristics in different seasons and time periods;

[0146] Predicting possible future load demand changes based on the identified patterns;

[0147] Specifically, the intelligent feedback mechanism enables the system to have the ability of self-learning and continuous optimization; by analyzing historical data and identifying operating modes, it can predict future load demand and make optimization adjustments in advance;

[0148] The present invention greatly improves the operating efficiency of the cold and hot air units, reduces energy consumption, and enhances the system's ability to respond to environmental changes; the close combination of the intelligent feedback mechanism with real-time supervision and dynamic adjustment enables the system to not only cope with current operating needs, but also achieve long-term energy-saving effects through an adaptive mechanism.

[0149] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An energy-saving management system based on the Internet of Things, characterized in that: include: The data collection and supervision module, as the core of the energy-saving management system, is responsible for collecting data from various subsystems and monitoring the operating status of the cooling and heating units in real time; The data acquisition control unit is responsible for collecting and integrating the data of the hot and cold air units, including temperature, humidity, pressure, and air volume, from various subsystems, and transmitting the real-time data to the industrial main controller; The dashboard module monitors data in real time based on sensors deployed on the hot and cold air units, including temperature, humidity, pressure, and air volume; Industrial control module, which uses integrated data acquisition and edge computing technology to transmit data to the industrial main controller; The industrial main controller processes and makes decisions as well as dynamically adjusts and optimizes data. The industrial main controller is the center of the system and is responsible for receiving and processing data transmitted from the industrial control module. The factory control module manages the energy efficiency of the entire factory. The factory control module includes the cooling and heating air units and the equipment operation control unit; The IoT control module performs intelligent feedback and learning, and provides remote monitoring and management.

2. According to the energy-saving management system based on the Internet of Things according to claim 1, it is characterized in that: The data acquisition and supervision module conducts real-time monitoring and analysis based on the data obtained by IoT sensors, identifies abnormal conditions in equipment operation, and issues warnings when abnormalities are found; The industrial control module uses edge computing technology to perform preliminary processing and analysis before data transmission; The industrial control module collects and integrates data from different sensors in a unified manner; The industrial master controller dynamically adjusts based on real-time data to optimize the operating parameters of the cooling and heating units, including air volume and temperature set points; The plant control module works in conjunction with other plant subsystems including lighting, ventilation, and security systems; The IoT control module continuously learns and optimizes system operating parameters through integrated data and intelligent algorithms; analyzes historical data and predicts future load demand.

3. An energy-saving management method based on the Internet of Things, characterized in that: An energy-saving management system based on the Internet of Things as described in any one of claims 1-2 is used, comprising the following steps: Step S1, data integration, collect all key instrument data in the cold and hot air units through the Internet of Things technology and transmit them to the industrial main controller; Step S2, real-time monitoring, the industrial main controller uses the real-time collected data to monitor and analyze the operating status of the fan unit in real time through the supervisory control system; Step S3, dynamic adjustment, based on the data and analysis provided by the real-time monitoring in step S2, instantly identifies changes in load demand and dynamically adjusts the fan unit operating parameters, including air volume and temperature set points; Step S4, intelligent feedback, introduces an intelligent feedback mechanism based on dynamic adjustment to continuously learn and optimize operating parameters through the IoT industrial control system.

4. The energy-saving management method based on the Internet of Things according to claim 3 is characterized in that: The data integration method in step S1 includes: Collect data from the key instruments of the cooling and heating units. Let the collected data of each sensor be xi(t), where i represents different instrument data types, including temperature, humidity, pressure, and air volume, and t represents time; The collected data is calibrated and fused to obtain the fused data X'i(t): in is the instrument data collected by the jth sensor at time t, N is the number of sensors for each instrument data, wj is the weight factor of the jth sensor, satisfying The data transmission delay is modeled and minimized. The transmission delay τ is expressed as: Where τi is the transmission delay of the i-th type of instrument data, Li is the data packet size, B is the transmission bandwidth, αi is the transmission noise coefficient, and Pi(t) is the network load; By adjusting the transmission bandwidth B and optimizing the packet size Li, the transmission delay τi is minimized.

5. The energy-saving management method based on the Internet of Things according to claim 4 is characterized in that: In step S1, all different types of sensor data are integrated into a comprehensive operating status view through a data fusion algorithm. For each time point t, a comprehensive state vector S(t) is defined: Where S(t) is the comprehensive state vector of the system at time t, XT(t), XH(t), XP(t), XF(t) represent the calibrated fusion data of temperature, humidity, pressure, and air volume, respectively, wT, wH, wP, wF represent the weight factors of temperature, humidity, pressure, and air volume, respectively; Τ represents the transposition operation; The Bayesian network-based fusion method is used to take into account the dependencies of multi-source data and perform data integration: Where P(S(t)) represents the comprehensive state probability of the system at time t, Xi(t) represents each type of data, and Xparents(i)(t) represents other data associated with Xi(t) in the Bayesian network; All data is transmitted to the industrial main controller through the Internet of Things network for processing and storage. Adaptive transmission protocol is used to dynamically adjust data transmission parameters when network conditions change, and system status information is generated by weighted accumulation: Where Sfinal(t) represents the final system status information after integration, and wi represents the weight of each instrument data; The final system status information Sfinal(t) is transmitted to the industrial main controller in real time for subsequent monitoring and adjustment.

6. The energy-saving management method based on the Internet of Things according to claim 5 is characterized in that: The real-time supervision method in step S2 includes: Based on the data integration in step S1, the comprehensive state vector S(t) is obtained, and the system state vector S(t) is defined as: Here, XT(t), XH(t), XP(t), and XF(t) represent the fused data of temperature, humidity, pressure, and air volume, respectively; An adaptive state prediction model is introduced, which uses historical data S(t-Δt) and current state S(t) combined with time series prediction technology for prediction; The prediction formula is: in represents the predicted system state vector, A(t) represents the time-dependent system dynamic matrix, B(t) represents the historical state weight matrix, S(t-Δt) represents the system state vector at the past time point, and ε(t) represents the Gaussian noise term; By comparing the current state S(t) with the predicted state Detect whether there is an anomaly in the system. Here, the anomaly detection function D(t) is introduced to quantify the deviation: Where D(t) is the abnormal measurement detected, reflecting the difference between the current state of the system and the predicted state, Si(t) represents the i-th parameter in the current state vector, represents the i-th parameter in the predicted state vector, σi(t) represents the standard deviation of the parameter Si(t), reflecting its normal fluctuation range; When D(t) exceeds a certain set threshold τ, the system determines that there is an abnormality; at this time, the system triggers an alarm or initiates preventive operations.

7. The energy-saving management method based on the Internet of Things according to claim 6 is characterized in that: The health status score H(t) is introduced into the real-time supervision method in step S2: H(t) is based on the parameters in the state vector and their deviations from the ideal state: Where H(t) represents the health status score of the system at time t, ranging from [0,1]. The closer the value is to 1, the healthier the status is. represents the target value of parameter Si(t) under ideal conditions, σi(t) represents the standard deviation of parameter Si(t); The H(t) score is used to monitor the overall health of the system in real time and provide a basis for dynamic adjustments.

8. The energy-saving management method based on the Internet of Things according to claim 7 is characterized in that: The real-time supervision method in step S2 also includes: Based on real-time monitoring data and health status scores, the industrial master controller immediately decides whether adjustments are needed. Here, the adjustment decision function R(t) is used to determine whether immediate action is needed; If the abnormality metric D(t) is greater than the threshold τ, or the health status score H(t) is less than the minimum tolerance value Hmin, then R(t) = 1, indicating that adjustment is required, otherwise R(t) = 0, indicating that the status quo is maintained.

9. The energy-saving management method based on the Internet of Things according to claim 8 is characterized in that: The dynamic adjustment method in step S3 includes: Continuously receive data from the real-time monitoring module, including the real-time status of key parameters such as temperature, humidity, pressure, and air volume; Identify changes in current load demand based on real-time status. When significant fluctuations in temperature or humidity, or changes in the external environment are detected, evaluate whether current operating parameters meet current load demand and determine whether adjustments are needed. Adjust the target based on the data analysis results: when the indoor temperature rises, the system will lower the set temperature and increase the air volume; According to the magnitude of the load demand change, the range of adjustment parameters is determined, that is, the percentage of air volume increase or the magnitude of temperature set point reduction; After determining the adjustment target and range, the corresponding operating parameters are automatically modified, including the air volume output or temperature set point of the fan unit; they are directly sent to the control device and take effect immediately.

10. The energy-saving management method based on the Internet of Things according to claim 9, characterized in that: The intelligent feedback method in step S4 includes: Store the real-time data collected during operation in a central database for pattern recognition and prediction; Identify common operating modes and load demand variation patterns through pattern recognition algorithms, including typical load demand characteristics in different seasons and time periods; Predict possible future load demand changes based on the identified patterns.

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