CN117193019A - Intelligent building control system for building design - Google Patents
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Abstract
The invention provides an intelligent building control system for building design, which comprises a central management module, an intelligent control module and a data acquisition module, wherein the central management module consists of a graphic processing unit, a main Server and a client, and is used for managing and controlling the whole system, the network of the central management module is based on TCP/IP (transmission control protocol)/BACnet and supports client/Server (Cl/Server) and Web data access operation, the network is used for realizing data networking among different areas, the intelligent control module consists of a controller and a field control bus supported by an expansion module, and is used for monitoring scattered field electromechanical equipment, and the data acquisition module comprises an acquisition unit and an execution unit, and is used for providing measurement of a field environment and acquisition of signals by a box controller, receiving dynamic execution instructions sent by the controller and implementing the dynamic execution instructions.
Description
Technical Field
The invention relates to the technical field of building control systems, in particular to an intelligent building control system for building design.
Background
The use of intelligent building control systems is becoming increasingly important in building design. Traditional building designs focus on the structure and appearance of the building, but lack comprehensive consideration of energy utilization, comfort and operating efficiency. With the increasing importance of environmental protection and energy efficiency, building designers are beginning to pay attention to how to optimize the energy consumption of a building through intelligent technology, provide a comfortable indoor environment, and implement automated control of building equipment.
Conventional building designs and building control systems often lack comprehensive optimization considerations for energy and environment, which can lead to energy waste, inefficient equipment operation, and uncomfortable indoor environments, and furthermore, conventional building control systems lack intelligent data processing and optimization capabilities, which cannot better accommodate changing environmental conditions and demands, which limit building sustainability and energy efficiency levels.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides an intelligent building control system for building design, which comprises:
the central management module consists of a graphic processing unit, a main Server and a client, and is used for managing and controlling the whole system, wherein the network of the central management module is based on TCP/IP & BACnet and supports client/Server (Cl/Server) and Web data access operation, and is used for realizing data networking among different areas;
the intelligent control module consists of a field control bus supported by the controller and the expansion module and is used for monitoring the scattered field electromechanical equipment;
the data acquisition module comprises an acquisition unit and an execution unit, and is used for providing measurement of the field environment and acquisition of signals for the controller, receiving a dynamic execution instruction sent by the controller and implementing the dynamic execution instruction;
the intelligent control module is internally provided with a data processing and optimizing unit and a feedback unit, the data processing and optimizing unit is used for processing and optimizing data acquired by the data acquisition module, analyzing historical data, predicting future trend and optimizing energy utilization and comfortableness of indoor environment by using a mathematical model, a statistical method or a machine learning algorithm, and the feedback unit is used for collecting feedback data of the running state of the execution unit and the actual environment, comparing the feedback data with expected values and adjusting and optimizing the control algorithm in real time so as to keep the stability of system performance and parameters.
Preferably, the acquisition unit comprises a temperature sensor, a humidity sensor and an illuminance sensor for measuring the temperature, humidity and illuminance intensity inside the building.
Preferably, a temperature optimization model, a humidity optimization model and an illumination optimization model are built in the data processing and optimizing unit, the temperature optimization model corresponds to the temperature sensor, the reading of the temperature sensor is set to be Tsenor, the target temperature is set to be Tset, the temperature adjustment is optimized by using a proportional control algorithm, the humidity optimization model corresponds to the humidity sensor, the reading of the humidity sensor is set to be Hsenor, the target humidity is set to be Hset, the humidity adjustment is optimized by using a proportional control algorithm, the illumination optimization model corresponds to the illuminance sensor, the reading of the illuminance sensor is set to be Isensor, the target illumination intensity is set to be Iset, and the illumination adjustment is optimized by using a proportional control algorithm.
Preferably, the formula of the proportional control algorithm in the temperature optimization model is control command_t=k (Tset-Tsensor), where control command_t represents an output instruction of the temperature controller, and k is a control gain parameter.
Preferably, the formula of the proportional control algorithm in the humidity optimization model is control command_h=m (Hset-Hsensor), where control command_h represents an output instruction of the humidity controller, and m is a control gain parameter.
Preferably, the formula of the proportional control algorithm in the illumination optimization model is control command_i=n (Iset-Isensor), where control command_i represents an output instruction of the illumination controller, and n is a control gain parameter.
Preferably, the data processing and optimizing unit is further internally provided with an overall optimizing model for optimizing energy utilization of a building and indoor environmental comfort, and the overall optimizing model is formed by combining a temperature optimizing model, a humidity optimizing model and an illumination optimizing model and optimizing parameter adjustment by using a multivariable control algorithm, wherein the overall optimizing model is as follows:
f(T,H,I)＝w1*ControlCommand_T+w2*ControlCommand_H+w3*ControlC ommand_I，
where f (T, H, I) minimizes the objective function, w1, w2, and w3 are the weight coefficients of the individual control commands, which can be adjusted according to the system requirements and optimization objectives.
Preferably, the gradient descent method is used in the overall optimization model to gradually reduce the value of the objective function by iteratively adjusting control instructions, and the update instructions of the control instructions are as follows:
where a is the learning rate, the step size for controlling each iteration,and-> For the partial derivative of the objective function, the current temperature, humidity and illumination data are collected and then transmitted to a data processing and optimizing unit for calculation.
Preferably, the central management module further comprises a fuzzy control unit, the fuzzy control unit is associated with the data processing and optimizing unit in a data intercommunication mode, fuzzy input is mapped to fuzzy output through steps of defining fuzzy aggregation, fuzzy rules, fuzzy reasoning and the like, control of a system which is inaccurate or difficult to model is achieved, and the fuzzy control unit is used for processing uncertainty and fuzzy environmental parameters in an intelligent building control system.
Preferably, the central management module further comprises a model prediction control unit, the model prediction control unit and the data processing and optimizing unit are associated in a data intercommunication mode, future behaviors of the system are modeled and optimized based on the prediction model, and the model prediction control unit is used for considering time sequence dynamic characteristics and constraint conditions of the system in the intelligent building control system through model prediction control, so that more accurate control is achieved.
Compared with the prior art, the invention has the following beneficial effects:
1. the data processing and optimizing unit analyzes and optimizes the collected data by utilizing a mathematical model, a statistical method or a machine learning algorithm, and can optimize the energy utilization and the comfort of the indoor environment by analyzing historical data and predicting future trends, thereby being beneficial to improving the energy efficiency and the comfort level of the building.
2. The feedback unit collects actual feedback data of the running state and the environment of the execution unit, compares the actual feedback data with an expected value, and can adjust and optimize in real time according to the difference of the feedback data so as to keep the stability of system performance and parameters.
3. The data processing and optimizing unit establishes an optimizing model of temperature, humidity and illumination, optimizes and adjusts parameters through a multivariable control algorithm, and the overall optimizing model comprehensively considers the weight coefficient of each control instruction to realize comprehensive optimization of energy utilization and indoor environment, so that the system can realize better control effect under comprehensive consideration of various indexes.
4. The fuzzy control unit in the central management module can process environment parameters which are inaccurate or difficult to model by defining fuzzy aggregation, fuzzy rules and fuzzy reasoning, so that the processing capacity of the system on uncertainty is improved, and the intelligent building control system has stronger adaptability and robustness.
5. The model prediction control unit in the central management module models and optimizes the future behavior of the system based on the prediction model, and the system can realize more accurate control and make reasonable adjustment and decision on the basis of predicting the future behavior by considering the time sequence dynamic characteristics and constraint conditions of the system.
Drawings
FIG. 1 is a block diagram of an intelligent building control system for architectural design in accordance with the present invention;
FIG. 2 is a block diagram of a central management module according to the present invention;
FIG. 3 is a diagram showing the construction of an intelligent control module according to the present invention;
FIG. 4 is a block diagram of a data acquisition module according to the present invention;
FIG. 5 is a diagram illustrating the operation of the mathematical model in the data processing and optimization unit of the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art.
The embodiment of the invention provides an intelligent building control system for building design, which is shown in fig. 1-4, and comprises a central management module, an intelligent control module and a data acquisition module, wherein the central management module consists of a graphic processing unit, a main Server and a client, and is used for managing and controlling the whole system;
the intelligent control module also comprises a data processing and optimizing unit and a feedback unit, wherein the data processing and optimizing unit is used for processing and optimizing the data acquired by the data acquisition module, analyzing historical data, predicting future trend by using a mathematical model, a statistical method or a machine learning algorithm, optimizing energy utilization and comfortableness of indoor environment, and the feedback unit is used for collecting feedback data of the running state of the execution unit and the actual environment, comparing the feedback data with expected values and adjusting and optimizing the control algorithm in real time so as to keep the stability of system performance and parameters.
Specifically, the graphic processing unit is responsible for the display and user interaction of a graphic interface, the main server is used for storing and processing various data of the system, including acquisition data, control algorithms and the like, the client side provides a user interface, a user is allowed to access and control the system through a Web browser or other adaptive terminal equipment, the network base uses TCP/IP and BACnet protocols for data communication and networking, the controller is responsible for monitoring and controlling on-site electromechanical equipment, which can be a hardware controller or an embedded controller, the on-site control bus supported by the expansion module is a bus system connected with the controller and used for transmitting the acquisition data and executing instructions in real time, the acquisition unit comprises a temperature sensor, a humidity sensor, an illuminance sensor and the like and is used for measuring the temperature, the humidity and the illumination intensity inside the building, the execution unit is responsible for executing the control instructions, and transmits the measured data of the field environment to the controller for processing, the data processing and optimizing unit processes and optimizes the collected data by using a mathematical model, a statistical method or a machine learning algorithm, the feedback unit collects the operation state of the executing unit and the feedback data of the actual environment, compares the feedback data with expected values, and adjusts and optimizes the control algorithm in real time according to the comparison result to keep the stability of the system performance and parameters, wherein the intelligent building control system can realize the optimization of the accurate control of the building environment and the energy utilization, the central management module provides friendly user interface and network connection function, the intelligent control module communicates with the electromechanical device through the field control bus, the data collecting module acquires the environmental data and executes instructions, the data processing and optimizing unit analyzes and optimizes the data, the feedback unit adjusts the control algorithm in real time, can provide a more efficient, comfortable and sustainable building environment while providing flexibility, interconnectivity and adaptability.
In a preferred embodiment, the acquisition unit comprises a temperature sensor, a humidity sensor and an illuminance sensor for measuring the temperature, humidity and illuminance intensity inside the building.
Specifically, temperature sensor is used for measuring the inside temperature condition of building, and humidity sensor is used for measuring the inside humidity level of building, and the illuminance sensor is used for measuring the inside illumination intensity of building, through temperature sensor, humidity sensor and the illuminance sensor in the collection unit, can real-time, accurately measure the inside environmental parameter such as temperature, humidity and illumination intensity of building. The intelligent building control system can regulate the equipment such as an air conditioner, a humidifier, a dehumidifier and the like through real-time monitoring and control of temperature and humidity, so that the indoor environment is kept in a comfortable range, more pleasant living and working environments are provided, the intelligent building control system can adjust the brightness and the on-off state of indoor lighting equipment according to the external illumination condition through real-time monitoring of illuminance, so that energy conservation and energy consumption reduction are realized, collected data can be utilized by an intelligent control module, an analysis and optimization algorithm of a data processing and optimization unit is combined, an automatic control strategy is realized, the energy use and the indoor environment in a building can be managed and controlled more effectively, the collected historical data can be used by the data processing and optimization unit to analyze and predict future trends, and the energy utilization and the indoor environment comfortableness are optimized through a mathematical model, a statistical method or a machine learning algorithm, so that the energy efficiency and the building operation efficiency of the system are improved.
In a preferred embodiment, as shown in fig. 5, a temperature optimization model, a humidity optimization model and an illumination optimization model are built in the data processing and optimization unit, the temperature optimization model corresponds to a temperature sensor, the reading of the temperature sensor is set to be Tsensor, the target temperature is Tset, the temperature adjustment is optimized by using a proportional control algorithm, the humidity optimization model corresponds to the humidity sensor, the reading of the humidity sensor is set to be Hsensor, the target humidity is Hset, the humidity adjustment is optimized by using a proportional control algorithm, the illumination optimization model corresponds to an illumination sensor, the reading of the illumination sensor is set to be Isensor, the target illumination intensity is Iset, and the illumination adjustment is optimized by using a proportional control algorithm. In operation, the temperature optimization model is used for comparing the reading Tsenor of the temperature sensor with the target temperature Tset and optimizing temperature adjustment by using a proportional control algorithm, the humidity optimization model is used for comparing the reading Hsenor of the humidity sensor with the target humidity Hset and optimizing humidity adjustment by using a proportional control algorithm, the illumination optimization model is used for comparing the reading Isensor of the illumination sensor with the target illumination intensity Iset and optimizing illumination adjustment by using a proportional control algorithm, the temperature, humidity and illumination optimization model is established and combined with real-time data of the sensor, the temperature, the humidity and the illumination intensity of an indoor environment can be monitored and optimized in real time by combining the real-time data of the sensor, the temperature, the humidity and the illumination intensity of the indoor environment are enabled to be close to target set values as much as possible, the system can adjust the set values of the temperature, the humidity and the illumination according to actual environment demands, the comfortable indoor environment can simultaneously reduce energy consumption, accordingly, the effect of energy conservation and emission reduction can be achieved, the indoor temperature, the humidity and the illumination are continuously adjusted and optimized, the system can provide more suitable for the indoor environment, the user can be provided with the indoor environment, the humidity and the illumination intensity is better than the user, the user can obtain the intelligent decision-making system and the intelligent control system can be used for making and optimizing the performance and the system.
In a preferred embodiment, the proportional control algorithm in the temperature optimization model is formulated as control command_t=k (Tset-Tsensor), where control command_t represents the output command of the temperature controller and k is the control gain parameter.
Preferably, the formula of the proportional control algorithm in the humidity optimization model is control command_h=m (Hset-Hsensor), where control command_h represents an output command of the humidity controller, and m is a control gain parameter.
Preferably, the proportional control algorithm formula in the illumination optimization model is control command_i=n (Iset-Isensor), where control command_i represents an output instruction of the illumination controller and n is a control gain parameter.
Preferably, the data processing and optimizing unit is internally provided with an integral optimizing model for optimizing energy utilization of a building and indoor environmental comfort, and the parameter is optimized by using a multivariable control algorithm through combination of a temperature optimizing model, a humidity optimizing model and an illumination optimizing model, wherein the integral optimizing model is as follows:
f(T,H,I)＝w1*ControlCommand_T+w2*ControlCommand_H+w3*ControlC ommand_I，
where f (T, H, I) minimizes the objective function, w1, w2, and w3 are the weight coefficients of the individual control commands, which can be adjusted according to the system requirements and optimization objectives.
Specifically, the overall optimization model aims at minimizing the objective function f (T, H, I) through coordinating the adjustment of temperature, humidity and illumination, effectively optimizing the energy utilization efficiency of the building, reducing the energy waste, reducing the operation cost, through optimizing the control algorithm and providing accurate target set values, the overall optimization model can realize more comfortable and beneficial indoor environment, the living quality and the working efficiency of staff or residents are improved, through combining the optimization model with sensor data and control instructions, the overall optimization implementation can realize automatic temperature, humidity and illumination control, the artificial intervention is reduced, the intelligent degree and the operation efficiency of the system are improved, the data trend and the performance index of the internal environment of the building can be obtained through the overall optimization model, and reliable data are provided for building managers and decision makers, thereby being beneficial to making more intelligent decisions and strategy adjustment.
In a preferred embodiment, the gradient descent method is used within the overall optimization model to gradually reduce the value of the objective function by iteratively adjusting the control instructions, the update instructions of which are:
where a is the learning rate, the step size for controlling each iteration,and-> For the partial derivative of the objective function, the current temperature, humidity and illumination data are collected and then transmitted to a data processing and optimizing unit for calculation.
Specifically, according to the gradient descent method, the control command (control command_ T, controlCommand _ H, controlCommand _i) is updated through iteration. At each iteration, the control command subtracts the learning rate (α) times the partial derivative of the objective function with respect to the corresponding variable (T, H, I)In this way, the control command is adjusted according to the gradient information of the objective function to approach or achieve the objective of minimizing the objective function, and in order to calculate the partial derivative of the objective function, the current temperature, humidity, illumination and other data are collected and transmitted to the data processing and optimizing unit for calculation. The data processing and optimization unit will use these data to calculate the gradient of the objective function>And transmits it back to the overall optimization model for updating the control instructions by using a gradient descent methodThe method comprises the steps of iteratively adjusting, wherein an overall optimization model aims at minimizing an objective function f (T, H, I), so that energy utilization of a building is more effective, a more comfortable indoor environment is provided, an iterative process of a gradient descent method allows a system to adjust control instructions in real time so as to respond to environment changes and changes of an optimization target, the system can quickly adapt to new conditions and continuously optimize the control instructions through continuous iterative updating, the selection of a learning rate alpha has an influence on convergence speed and stability, the system can converge to an optimal solution by properly selecting the learning rate, oscillation or divergence is avoided in the iterative process, the system can control updating step length while guaranteeing the optimization effect through adjusting the learning rate, and the overall optimization model can calculate partial derivatives of the objective function according to collected real-time data.
In a preferred embodiment, the central management module further comprises a fuzzy control unit, the fuzzy control unit and the data processing and optimizing unit are related in a data intercommunication mode, fuzzy inputs are mapped to fuzzy outputs through the steps of defining fuzzy sets, fuzzy rules, fuzzy reasoning and the like, and control of a system which is inaccurate or difficult to model is achieved, and the fuzzy control unit is used for processing uncertainty and fuzzy environmental parameters in the intelligent building control system.
Specifically, the central management module comprises a fuzzy control unit, which processes an inaccurate or difficultly modeled system by adopting fuzzy logic, maps fuzzy input to fuzzy output by defining fuzzy aggregation, fuzzy rules, fuzzy reasoning and the like, so as to realize control of environment parameters, associates the fuzzy control unit with the data processing and optimizing unit in a data intercommunication mode, and enables the fuzzy control unit to receive and send data to the data processing and optimizing unit so as to acquire real-time environment parameter data and send control instructions, wherein the fuzzy control unit can process the inaccurate or fuzzy environment parameters, and because some systems are difficult to describe by using an accurate mathematical model, the fuzzy control can realize control of the systems by defining fuzzy aggregation, fuzzy rules, fuzzy reasoning and the like. The intelligent building control system can flexibly control according to real-time data and changes of requirements and adapt to the changes of different environment parameters, the intelligent building control system can process the environment parameters more comprehensively through introducing the fuzzy control unit, including inaccurate or fuzzy conditions, can improve the control capability and adaptability of the system, can better meet the control requirements of different requirements in a building environment, can process the environment parameters of uncertainty and ambiguity through introducing the fuzzy control unit and communicating with the data processing and optimizing unit, can realize the control of the parameters through fuzzy logic, and provides flexibility, adaptability and enhanced control capability, thereby being beneficial to optimizing the operation efficiency and the environment comfort of the intelligent building.
In a preferred embodiment, the central management module further comprises a model prediction control unit, the model prediction control unit and the data processing and optimizing unit are associated in a data intercommunication mode, future behaviors of the system are modeled and optimized based on the prediction model, and the model prediction control unit is used for considering time sequence dynamic characteristics and constraint conditions of the system in the intelligent building control system, so that more accurate control is achieved.
In particular, the central management module comprises a model predictive control unit that models future behavior of the system by building predictive models. The prediction models can consider time sequence dynamic characteristics and various constraint conditions of the system, the model prediction control unit and the data processing and optimizing unit are associated in a data intercommunication mode, so that the model prediction control unit can receive real-time environment data from the data processing and optimizing unit and send control instructions to the data processing and optimizing unit, the model prediction control can accurately model future behaviors of the system by establishing the prediction model, the system can consider the time sequence dynamic characteristics and the constraint conditions and make more accurate control decisions, the response speed and the control precision of the system are improved, and the model prediction control can comprehensively consider the current environment state and the predicted future behaviors, so that the time sequence dynamic characteristics of the system are effectively modeled. This allows the system to better adapt to and respond to environmental changes and make adjustments accordingly, in intelligent building control systems, there are often various constraints, such as energy limitations, comfort requirements, etc. The model predictive control can consider the constraint conditions and optimize in the control process, so that the system achieves better control effect on the premise of meeting the constraint conditions, and the model predictive control has certain adaptability and flexibility based on the established predictive model. The system can adjust the control strategy in time through real-time data intercommunication and update of the prediction model, adapt to new environmental change and optimization targets, and realize more accurate control by introducing a model prediction control unit and carrying out data intercommunication with a data processing and optimizing unit, wherein the intelligent building control system can consider the time sequence dynamic characteristics and constraint conditions of the system according to the established prediction model. This helps to improve the responsiveness of the system, control accuracy, and the ability to meet constraints, further optimizing the operating efficiency and environmental comfort of the intelligent building.
Specific embodiments of the invention: the system consists of a central management module, an intelligent control module and a data acquisition module, wherein the central management module consists of a graphic processing unit, a main server and a client, and is responsible for managing and controlling the whole system, the system supports client/server and Web data access operation based on TCP/IP and BACnet network protocols, realizes data networking among different areas, and the intelligent control module comprises a controller and a field control bus supported by an expansion module and is used for monitoring scattered field electromechanical devices. The intelligent control module comprises a data acquisition module, a data processing and optimizing unit and a feedback unit, wherein the data acquisition module comprises an acquisition unit and an execution unit, the acquisition unit is used for measuring the temperature, the humidity and the illumination intensity in a building, receiving an execution instruction sent by a controller and implementing the execution instruction, the data processing and optimizing unit is used for processing and optimizing acquired data by using a mathematical model, a statistical method or a machine learning algorithm, analyzing historical data and predicting future trend so as to optimize energy utilization and comfort of indoor environment. The feedback unit collects the feedback data of the running state and the actual environment of the execution unit, compares the feedback data with expected values, and maintains the stability of system performance and parameters through real-time adjustment and optimization control algorithm, the acquisition unit comprises a temperature sensor, a humidity sensor and an illuminance sensor, which are used for measuring the temperature, humidity and illuminance in the building, a temperature optimization model, a humidity optimization model and an illuminance optimization model are built in the data processing and optimization unit, which correspond to the temperature sensor, the humidity sensor and the illuminance sensor respectively, a control instruction is generated according to the difference between the target value and the sensor reading through a proportional control algorithm, the optimization adjustment of the temperature, the humidity and the illuminance is performed, the overall optimization model is used for adjusting the importance degree of each control instruction through combining the temperature optimization model, the humidity optimization model and the illuminance optimization model, the adjustment of the parameters is performed through the utilization of the temperature optimization control algorithm, the weight coefficients w1, w2 and w3 are used for adjusting the importance degree of each control instruction, thereby realizing the overall optimization of energy utilization and indoor environment comfortableness, the value of a target function is reduced through iterative adjustment control instruction, the learning rate alpha control is performed, the update of the control instruction is performed through the iterative step length, the update of the control instruction is performed through the fuzzy control module, the fuzzy control module is used for updating the fuzzy control system, the fuzzy control system is further based on the current fuzzy control system, the fuzzy control system is calculated based on the fuzzy control module, the fuzzy control system is based on the fuzzy control system, and the fuzzy control system is used in a fuzzy control system, and fuzzy control system is based on a fuzzy control system, and a fuzzy control system is used for a fuzzy control system, the intelligent building control system utilizes a data acquisition, optimization model and feedback mechanism, realizes the accurate control of building environment and the optimization of energy utilization through intelligent control and optimization algorithm, can provide more efficient, comfortable and sustainable building environment, brings energy conservation, emission reduction and improvement of user satisfaction, and simultaneously, the client/server of the central management module and the application of Web data access operation and fuzzy control and model prediction control enable the system to have flexibility, interconnectivity and adaptability.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the foregoing embodiments, but rather, the foregoing embodiments and description are merely illustrative of the principles of the present invention, and various modifications and improvements may be made without departing from the spirit and scope of the invention, which is defined in the appended claims and their equivalents.
Claims (10)
1. An intelligent building control system for architectural design, comprising:
the central management module consists of a graphic processing unit, a main Server and a Client, and is used for managing and controlling the whole system, wherein the network of the central management module is based on TCP/IP & BACnet and supports Client/Server and Web data access operation, and is used for realizing data networking among different areas;
the intelligent control module consists of a field control bus supported by the controller and the expansion module and is used for monitoring the scattered field electromechanical equipment;
the data acquisition module comprises an acquisition unit and an execution unit, and is used for providing measurement of the field environment and acquisition of signals for the controller, receiving a dynamic execution instruction sent by the controller and implementing the dynamic execution instruction;
the intelligent control module is internally provided with a data processing and optimizing unit and a feedback unit, the data processing and optimizing unit is used for processing and optimizing data acquired by the data acquisition module, analyzing historical data, predicting future trend and optimizing energy utilization and comfortableness of indoor environment by using a mathematical model, a statistical method and a machine learning algorithm, and the feedback unit is used for collecting feedback data of the running state of the execution unit and the actual environment, comparing the feedback data with expected values and adjusting and optimizing the control algorithm in real time so as to keep the stability of system performance and parameters.
2. An intelligent building control system for architectural design according to claim 1, wherein the acquisition unit comprises a temperature sensor, a humidity sensor and an illuminance sensor for measuring the temperature, humidity and illuminance intensity inside the building.
3. The intelligent building control system for building design according to claim 2, wherein a temperature optimization model, a humidity optimization model and an illumination optimization model are built in the data processing and optimization unit, the temperature optimization model corresponds to the temperature sensor, the reading of the temperature sensor is set to be Tsensor, the target temperature is Tset, the temperature adjustment is optimized by using a proportional control algorithm, the humidity optimization model corresponds to the humidity sensor, the reading of the humidity sensor is set to be Hsensor, the target humidity is Hset, the humidity adjustment is optimized by using a proportional control algorithm, the illumination optimization model corresponds to the illuminance sensor, the reading of the illuminance sensor is set to be Isensor, the target illumination intensity is Iset, and the illumination adjustment is optimized by using a proportional control algorithm.
4. An intelligent building control system for architectural design according to claim 3, wherein the proportional control algorithm formula in the temperature optimization model is control command_t=k (Tset-Tsensor), wherein control command_t represents an output command of the temperature controller, and k is a control gain parameter.
5. The intelligent building control system according to claim 4, wherein the formula of the proportional control algorithm in the humidity optimization model is control command_h=m (Hset-Hsensor), wherein control command_h represents an output command of the humidity controller, and m is a control gain parameter.
6. The intelligent building control system for architectural design according to claim 5, wherein the proportional control algorithm formula in the illumination optimization model is control command_i = n (Iset-Isensor), wherein control command_i represents an output command of the illumination controller, and n is a control gain parameter.
7. The intelligent building control system for building design according to claim 6, wherein the data processing and optimizing unit is further provided with an overall optimizing model inside for optimizing energy utilization of the building and indoor environmental comfort, and the parameter adjustment is optimized by combining a temperature optimizing model, a humidity optimizing model and an illumination optimizing model and using a multivariable control algorithm, and the overall optimizing model is:
f(T,H,I)＝w1*ControlCommand_T+w2*ControlCommand_H+w3*ControlC ommand_I，
wherein f (T, H, I) minimizes the objective function, w1, w2, and w3 are the weight coefficients of the respective control instructions, and are adjusted according to the system requirements and the optimization objectives.
8. An intelligent building control system for architectural design according to claim 7, wherein gradient descent method is used in the overall optimization model to gradually decrease the value of the objective function by iteratively adjusting control instructions, the update instructions of which are:
where a is the learning rate, the step size for controlling each iteration,and-> For the partial derivative of the objective function, the current temperature, humidity and illumination data are collected and then transmitted to a data processing and optimizing unit for calculation.
9. The intelligent building control system for building design according to claim 8, wherein the central management module further comprises a fuzzy control unit, the fuzzy control unit and the data processing and optimizing unit are related in a data intercommunication mode, fuzzy input is mapped to fuzzy output through definition of fuzzy aggregation, fuzzy rules and fuzzy reasoning, and control of a system which is inaccurate and difficult to model is achieved, and the fuzzy control unit is used for processing uncertainty and fuzzy environment parameters in the intelligent building control system.
10. The intelligent building control system for building design according to claim 8, wherein the central management module further comprises a model prediction control unit, the model prediction control unit and the data processing and optimizing unit are associated in a data intercommunication mode, future behaviors of the system are modeled and optimized based on a prediction model, and time sequence dynamic characteristics and constraint conditions of the system are considered through model prediction control in the intelligent building control system, so that more accurate control is achieved.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117555225A (en) * | 2024-01-10 | 2024-02-13 | 万桥信息技术有限公司 | Green building energy management control system |
| CN117742166A (en) * | 2023-12-21 | 2024-03-22 | 南京市生态环境保护科学研究院 | Intelligent energy-saving building system |
| CN118041968A (en) * | 2024-04-09 | 2024-05-14 | 无锡锐泰节能系统科学有限公司 | Detection method and system of router of Internet of things |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117742166A (en) * | 2023-12-21 | 2024-03-22 | 南京市生态环境保护科学研究院 | Intelligent energy-saving building system |
| CN117555225A (en) * | 2024-01-10 | 2024-02-13 | 万桥信息技术有限公司 | Green building energy management control system |
| CN117555225B (en) * | 2024-01-10 | 2024-04-26 | 万桥信息技术有限公司 | Green building energy management control system |
| CN118041968A (en) * | 2024-04-09 | 2024-05-14 | 无锡锐泰节能系统科学有限公司 | Detection method and system of router of Internet of things |
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