CN112556107A - Intelligent control system for indoor environment suitable for temperature, humidity and oxygen - Google Patents
Intelligent control system for indoor environment suitable for temperature, humidity and oxygen Download PDFInfo
- Publication number
- CN112556107A CN112556107A CN202011324661.1A CN202011324661A CN112556107A CN 112556107 A CN112556107 A CN 112556107A CN 202011324661 A CN202011324661 A CN 202011324661A CN 112556107 A CN112556107 A CN 112556107A
- Authority
- CN
- China
- Prior art keywords
- indoor environment
- indoor
- outdoor
- parameters
- humidity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/54—Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
Abstract
The application discloses suitable indoor environment intelligence control system that should be moist suitable oxygen of suitable temperature includes: an edge compute subsystem and a remote control subsystem communicatively coupled to the edge compute subsystem, wherein the edge compute subsystem is configured to: collecting a plurality of indoor environment parameters of an indoor environment and a plurality of outdoor environment parameters of an outdoor environment; respectively analyzing and calculating a plurality of indoor environment parameters and a plurality of outdoor environment parameters to generate an indoor environment analysis result corresponding to an indoor environment and an outdoor environment analysis result corresponding to an outdoor environment; sending the indoor environment analysis result and the outdoor environment analysis result to a remote control subsystem; and the remote control subsystem is configured to perform the following operations: receiving an indoor environment analysis result and an outdoor environment analysis result from the edge computing subsystem; determining an adjusting parameter for adjusting the indoor environment according to the indoor environment analysis result and the outdoor environment analysis result; and adjusting the indoor environment according to the adjusting parameters.
Description
Technical Field
The application relates to the technical field of indoor environment control, in particular to an intelligent control system for indoor environment which is suitable for temperature, humidity and oxygen.
Background
Most of the current indoor environment control systems are composed of data acquisition devices arranged indoors and outdoors and a server arranged remotely. Among them, the indoor and outdoor environmental parameters involved in the indoor environmental control are many, so the data acquisition equipment also includes many types and numbers of sensors, such as a temperature sensor, a humidity sensor, a PM2.5 sensor, a carbon dioxide sensor, a solar radiation sensor, a wind speed sensor, and the like. Moreover, the data acquisition equipment is only responsible for acquiring and transmitting relevant indoor and outdoor environmental parameters to a remote server, and the operations such as processing and analysis of the indoor and outdoor environmental parameters are all executed by the remote server, so that the problems of large calculation burden, low working efficiency and the like of the server are caused.
In view of the technical problems in the prior art that the indoor and outdoor environmental parameters related to indoor environmental control are very large, and operations such as processing and analysis of the indoor and outdoor environmental parameters are all executed by a remote server, which results in large computational burden and low work efficiency of the server, no effective solution is proposed at present.
Disclosure of Invention
The embodiment of the disclosure provides an intelligent control system for indoor environment suitable for temperature, humidity and oxygen, and aims to at least solve the technical problems that in the prior art, indoor and outdoor environment parameters related to indoor environment control are very many, and processing, analysis and other operations of the indoor and outdoor environment parameters are all executed by a remote server, so that the calculation burden of the server is large and the working efficiency is low.
According to an embodiment of the present disclosure, there is provided an intelligent control system for an indoor environment suitable for temperature, humidity and oxygen, including: an edge compute subsystem and a remote control subsystem communicatively coupled to the edge compute subsystem, wherein the edge compute subsystem is configured to: collecting a plurality of indoor environment parameters of an indoor environment and a plurality of outdoor environment parameters of an outdoor environment corresponding to the indoor environment; respectively analyzing and calculating a plurality of indoor environment parameters and a plurality of outdoor environment parameters to generate an indoor environment analysis result corresponding to an indoor environment and an outdoor environment analysis result corresponding to an outdoor environment; sending the indoor environment analysis result and the outdoor environment analysis result to a remote control subsystem; and the remote control subsystem is configured to perform the following operations: receiving an indoor environment analysis result and an outdoor environment analysis result from the edge computing subsystem; determining an adjusting parameter for adjusting the indoor environment according to the indoor environment analysis result and the outdoor environment analysis result; and adjusting the indoor environment according to the adjusting parameters.
In this disclosure, the intelligent control system for an indoor environment suitable for temperature, humidity and oxygen adopts a way of performing collaborative calculation between an edge terminal and a remote terminal, and sets an edge calculation subsystem with calculation capability of analyzing and calculating collected environmental parameters in an edge terminal network, and the edge calculation subsystem analyzes and calculates the collected environmental parameters to generate an indoor environment analysis result and an outdoor environment analysis result respectively and sends the generated results to a remote control subsystem connected to the remote terminal in communication. And then, determining an adjusting parameter for adjusting the indoor environment according to the received indoor environment analysis result and the outdoor environment analysis result through the remote control subsystem, and adjusting the indoor environment according to the adjusting parameter. The processing and analysis work of indoor and outdoor environment parameters is shared by the edge computing subsystem and the remote control subsystem, the computing burden of a server in the remote control subsystem is greatly reduced, and the working efficiency of the server is effectively improved. And the technical problems that in the prior art, indoor and outdoor environment parameters related to indoor environment control are very many, and operations such as processing and analysis of the indoor and outdoor environment parameters are all executed by a remote server, so that the calculation burden of the server is large and the working efficiency is low are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
FIG. 1 is a schematic diagram of a system for intelligent control of a temperature, humidity and oxygen-beneficial indoor environment according to an embodiment of the present disclosure;
FIG. 2 is a block diagram illustrating an intelligent control system for a temperature-humidity-suitable indoor environment according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart diagram illustrating a computing operation performed by the edge computing subsystem according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart diagram illustrating control operations performed by the remote control subsystem according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a network structure of an indoor environment analysis model according to an embodiment of the present disclosure; and
fig. 6 is a schematic network structure diagram of an outdoor environment analysis model according to an embodiment of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. It is to be understood that the described embodiments are merely exemplary of some, and not all, of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Examples
Fig. 1 shows a schematic diagram of the intelligent control system for an environment in a temperature-suitable-humidity room provided by this embodiment, and fig. 2 shows a block diagram of the intelligent control system for an environment in a temperature-suitable-humidity room provided by this embodiment. Referring to fig. 1 and 2, in the present embodiment, an intelligent control system for an indoor environment suitable for temperature, humidity and oxygen is provided, which includes: an edge computing subsystem 1 and a remote control subsystem 2 communicatively coupled to the edge computing subsystem 1.
Wherein the edge computing subsystem 1 is configured to perform the following operations:
s301: collecting a plurality of indoor environment parameters of an indoor environment and a plurality of outdoor environment parameters of an outdoor environment corresponding to the indoor environment;
s302: respectively analyzing and calculating a plurality of indoor environment parameters and a plurality of outdoor environment parameters to generate an indoor environment analysis result corresponding to an indoor environment and an outdoor environment analysis result corresponding to an outdoor environment;
s303: and sending the indoor environment analysis result and the outdoor environment analysis result to the remote control subsystem.
Wherein the remote control subsystem 2 is configured to perform the following operations:
s401: receiving an indoor environment analysis result and an outdoor environment analysis result from the edge computing subsystem;
s402: determining an adjusting parameter for adjusting the indoor environment according to the indoor environment analysis result and the outdoor environment analysis result; and
s403: and adjusting the indoor environment according to the adjusting parameters.
Specifically, as described in the background art, most of the current indoor environment control systems are composed of data acquisition devices disposed indoors and outdoors and a server disposed remotely. Among them, the indoor and outdoor environmental parameters involved in the indoor environmental control are many, so the data acquisition equipment also includes many types and numbers of sensors, such as a temperature sensor, a humidity sensor, a PM2.5 sensor, a carbon dioxide sensor, a solar radiation sensor, a wind speed sensor, and the like. Moreover, the data acquisition equipment is only responsible for acquiring and transmitting relevant indoor and outdoor environmental parameters to a remote server, and the operations such as processing and analysis of the indoor and outdoor environmental parameters are all executed by the remote server, so that the problems of large calculation burden, low working efficiency and the like of the server are caused.
In view of this, referring to fig. 1 and fig. 2, the present embodiment provides the edge computing subsystem 1 on the edge end network, provides the remote control subsystem 2 on the remote network, and enables the remote control subsystem 2 to be connected to the edge computing subsystem 1 in a communication manner. Therefore, in the process of adjusting and controlling the indoor environment, the intelligent control system for the indoor environment suitable for temperature, humidity and oxygen firstly acquires a plurality of indoor environment parameters of the indoor environment to be adjusted and controlled and a plurality of outdoor environment parameters of the outdoor environment corresponding to the indoor environment by the edge computing subsystem 1 arranged on the edge end network. The edge computing subsystem 1 may collect different indoor environment parameters through each sensor disposed indoors, where the plurality of indoor environment parameters may be, for example and without limitation, an indoor temperature parameter, an indoor humidity parameter, an indoor carbon dioxide concentration, and the like. Similarly, the edge computing subsystem 1 may further collect different outdoor environment parameters through various sensors disposed outdoors, and the outdoor environment parameters may be, for example and without limitation, an outdoor temperature parameter, an outdoor humidity parameter, a PM2.5 concentration, and the like.
Further, the edge computing subsystem 1 needs to analyze and calculate a plurality of indoor environment parameters and a plurality of outdoor environment parameters, respectively, and generate an indoor environment analysis result corresponding to the indoor environment and an outdoor environment analysis result corresponding to the outdoor environment. For example: the edge calculation subsystem 1 analyzes and calculates the collected indoor temperature parameters to judge whether the temperature of the indoor environment is too high or too low, analyzes and calculates the collected indoor humidity parameters to judge whether the indoor environment is dry or wet, and analyzes and calculates the collected indoor carbon dioxide concentration to judge whether the ventilation of the indoor environment is good. Similarly, the edge computing subsystem 1 analyzes and computes the collected outdoor temperature parameters to determine the temperature condition of the outdoor environment, analyzes and computes the collected solar irradiance parameters to determine the illumination intensity condition of the outdoor environment, and the like. The indoor environment analysis result generated by the edge computing subsystem 1 may be, for example: the temperature of the indoor environment is lower than the temperature which is comfortable and healthy for human body, and the indoor environment is dry and has poor ventilation. The outdoor environment analysis result generated by the edge computing subsystem 1 may be, for example: the temperature of outdoor environment is far lower than the comfortable temperature for human health, and the outdoor environment is dry, low in illumination intensity and strong in wind.
Further, the edge calculation subsystem 1 transmits the generated indoor environment analysis result and the outdoor environment analysis result to the remote control subsystem 2. In this application scenario, the remote control subsystem 2 receives the indoor environment analysis result and the outdoor environment analysis result from the edge computing subsystem 1, and then determines an adjustment parameter for adjusting the indoor environment according to the received indoor environment analysis result and outdoor environment analysis result. The adjusting parameters for adjusting the indoor environment include, for example, a temperature adjusting parameter, a humidity adjusting parameter, a ventilation adjusting parameter, and the like. Finally, the remote control subsystem 2 adjusts the indoor environment according to the determined adjustment parameters. For example, the respective adjusting parameters (e.g., temperature adjusting parameter, humidity adjusting parameter, and ventilation adjusting parameter) are respectively sent to the respective adjusting devices (e.g., temperature adjusting device, humidity adjusting device, fresh air adjusting device, and purification adjusting device) for adjusting the indoor environment, and the indoor environment parameters are adjusted by the respective adjusting devices according to the adjusting parameters.
Therefore, in this way, the intelligent control system for the indoor environment suitable for temperature, humidity and oxygen adopts a way of performing collaborative calculation by the edge end and the remote end, the edge calculation subsystem 1 with the calculation capability of analyzing and calculating the collected environmental parameters is arranged in the edge end network, the edge calculation subsystem 1 analyzes and calculates the collected environmental parameters, and the indoor environment analysis result and the outdoor environment analysis result are respectively generated and sent to the remote control subsystem 2 which is connected with the edge calculation subsystem 1 in communication and is arranged at the remote end. And then, determining an adjusting parameter for adjusting the indoor environment according to the received indoor environment analysis result and the outdoor environment analysis result through the remote control subsystem 2, and adjusting the indoor environment according to the adjusting parameter. The processing and analysis work of indoor and outdoor environment parameters is shared by the edge computing subsystem 1 and the remote control subsystem 2, the computing burden of a server in the remote control subsystem 2 is greatly reduced, and the working efficiency of the server is effectively improved. And the technical problems that in the prior art, indoor and outdoor environment parameters related to indoor environment control are very many, and operations such as processing and analysis of the indoor and outdoor environment parameters are all executed by a remote server, so that the calculation burden of the server is large and the working efficiency is low are solved.
Optionally, the edge calculation subsystem comprises: the system comprises a plurality of indoor sensors, a plurality of outdoor sensors, a first intelligent computing node and a second intelligent computing node, wherein the plurality of indoor sensors are configured to acquire a plurality of indoor environment parameters and send the indoor environment parameters to the first intelligent computing node in communication connection; the outdoor sensors are configured to acquire outdoor environment parameters and send the outdoor environment parameters to the second intelligent computing node in communication connection; the first intelligent computing node is configured to receive a plurality of indoor environment parameters from the plurality of indoor sensors, analyze and compute the plurality of indoor environment parameters, generate an indoor environment analysis result related to an indoor environment, and send the indoor environment analysis result to the remote control subsystem; and the second intelligent computing node is used for receiving the outdoor environment parameters from the outdoor sensors, analyzing and calculating the outdoor environment parameters, generating an outdoor environment analysis result related to the outdoor environment, and sending the outdoor environment analysis result to the remote control subsystem.
Specifically, referring to fig. 1 and 2, the edge computing subsystem 1 includes a plurality of indoor sensors 110 a-110 n, a plurality of outdoor sensors 120 a-120 n, a first intelligent computing node 210, and a second intelligent computing node 220. The first intelligent computing node 210 is in communication connection with the plurality of indoor sensors 110a to 110n, the plurality of indoor sensors 110a to 110n are used for collecting a plurality of indoor environment parameters (such as an indoor temperature parameter, an indoor humidity parameter, an indoor ventilation parameter, and the like), the first intelligent computing node 210 is used for analyzing and calculating the plurality of indoor environment parameters, and generating an indoor environment analysis result related to an indoor environment, wherein the indoor environment analysis result is, for example, that the temperature of the indoor environment is lower than a comfortable temperature for human health, and the indoor environment is dry and has poor ventilation.
Further, the second intelligent computing node 220 is communicatively connected to the plurality of outdoor sensors 120a to 120n, the plurality of outdoor sensors 120a to 120n are configured to collect a plurality of outdoor environment parameters (e.g., an outdoor temperature parameter, an outdoor humidity parameter, and an outdoor PM2.5 parameter), and the second intelligent computing node 220 is configured to perform analysis and computation on the plurality of outdoor environment parameters, and generate an outdoor environment analysis result related to the outdoor environment, where the outdoor environment analysis result is, for example, that the temperature of the outdoor environment is far lower than a comfortable temperature for human health, dryness, low illumination intensity, and strong wind. Therefore, the present embodiment is configured with not only the plurality of indoor sensors 110a to 110n dedicated to collecting various indoor environment parameters and the first intelligent computing node 210 dedicated to analyzing and calculating the collected various indoor environment parameters, but also the plurality of outdoor sensors 120a to 120n dedicated to collecting various outdoor environment parameters and the second intelligent computing node 220 dedicated to analyzing and calculating the collected various outdoor environment parameters. By the method, the collection, analysis and calculation efficiency of various indoor and outdoor environment parameters is improved, and the accuracy of the generated outdoor environment analysis result and the accuracy of the outdoor environment analysis result are effectively guaranteed.
Optionally, the operation of the first intelligent computing node, where an indoor environment analysis model based on deep learning is deployed in advance, and the first intelligent computing node performs analysis and computation on a plurality of indoor environment parameters to generate an indoor environment analysis result related to an indoor environment, includes: the first intelligent computing node analyzes and computes a plurality of indoor environment parameters by using the indoor environment analysis model, and outputs an indoor environment analysis result related to the indoor environment.
Specifically, in order to quickly and accurately perform analysis and calculation on a plurality of indoor environment parameters, the first intelligent computing node 210 may be pre-deployed with an indoor environment analysis model based on deep learning. The first intelligent computing node 210 trains the indoor environment analysis model in advance by using a large number of indoor environment parameter samples. Therefore, in the process of analyzing and calculating the plurality of indoor environment parameters, the first intelligent computing node 210 may analyze and calculate the plurality of indoor environment parameters by using a pre-trained indoor environment analysis model, and output an indoor environment analysis result related to the indoor environment. By the method, the purpose of rapidly and accurately analyzing and calculating the plurality of indoor environment parameters is achieved, and the indoor environment analysis result generated by the indoor environment analysis model based on deep learning is more accurate and comprehensive.
Optionally, the indoor environment analysis model includes a first feature extraction network, a second feature extraction network, a third feature extraction network, a fourth feature extraction network, and a first full connection layer, a plurality of indoor environment parameters collected by the plurality of indoor sensors include an indoor temperature parameter, an indoor humidity parameter, an indoor carbon dioxide concentration parameter, and an indoor cleanliness parameter, and the first intelligent computing node performs analysis and computation on the plurality of indoor environment parameters by using the indoor environment analysis model, and outputs an operation of an indoor environment analysis result related to an indoor environment, including: the first intelligent computing node inputs the indoor temperature parameter into a first feature extraction network and outputs first feature information related to the temperature of the indoor environment; the first intelligent computing node inputs the indoor humidity parameter and the first characteristic information into a second characteristic extraction network and outputs second characteristic information related to the temperature and the humidity of the indoor environment; the first intelligent computing node inputs the indoor carbon dioxide concentration parameter and the second characteristic information into a third characteristic extraction network and outputs third characteristic information related to the temperature, the humidity and the carbon dioxide concentration of the indoor environment; the first intelligent computing node inputs the indoor cleanliness parameter and the third characteristic information into a fourth characteristic extraction network, and outputs fourth characteristic information related to the temperature, the humidity, the carbon dioxide concentration and the cleanliness of the indoor environment; and the first intelligent computing node inputs the fourth characteristic information into the first full connection layer and outputs an indoor environment analysis result.
Specifically, fig. 5 exemplarily shows a network structure of an indoor environment analysis model, and referring to fig. 5, the indoor environment analysis model includes a first feature extraction network, a second feature extraction network, a third feature extraction network, a fourth feature extraction network, and a first full connection layer. The plurality of indoor sensors include, for example, a temperature sensor, a humidity sensor, a carbon dioxide sensor, and a cleanliness sensor provided indoors, and the plurality of indoor environment parameters acquired by these sensors include an indoor temperature parameter, an indoor humidity parameter, an indoor carbon dioxide concentration parameter, and an indoor cleanliness parameter.
Further, referring to fig. 5, during the operation of analyzing and calculating the plurality of indoor environment parameters by using the indoor environment analysis model, the first intelligent computing node 210 firstly inputs the indoor temperature parameters into the first feature extraction network, and outputs first feature information related to the temperature of the indoor environment. And then inputting the indoor humidity parameter and the first characteristic information into a second characteristic extraction network together, and outputting second characteristic information related to the temperature and the humidity of the indoor environment. And secondly, inputting the indoor carbon dioxide concentration parameter and the second characteristic information into a third characteristic extraction network, and outputting third characteristic information related to the temperature, the humidity and the carbon dioxide concentration of the indoor environment. And secondly, inputting the indoor cleanliness parameter and the third characteristic information into a fourth characteristic extraction network, and outputting fourth characteristic information related to the temperature, the humidity, the carbon dioxide concentration and the cleanliness of the indoor environment. And finally, inputting fourth feature information output by the fourth feature extraction network into the first full connection layer, and outputting an indoor environment analysis result. Therefore, in the embodiment, by setting the multiple layers of feature extraction networks, in the process of respectively performing feature extraction on each indoor environment parameter, the result output by the previous layer of feature extraction network is continuously utilized, so that the feature information output by the last layer of feature extraction network deeply covers the feature information of all indoor environment parameters, and the comprehensiveness and accuracy of the finally output indoor environment analysis result are ensured.
Optionally, the operation of the second intelligent computing node, where an outdoor environment analysis model based on deep learning is deployed in advance, and the second intelligent computing node performs analysis calculation on the plurality of outdoor environment parameters to generate an outdoor environment analysis result related to the outdoor environment, includes: and the second intelligent computing node analyzes and computes the plurality of outdoor environment parameters by using the outdoor environment analysis model and outputs an outdoor environment analysis result related to the outdoor environment.
Specifically, in order to quickly and accurately analyze and calculate a plurality of outdoor environment parameters, the second intelligent computing node 220 may also be pre-deployed with an outdoor environment analysis model based on deep learning. The second intelligent computing node 220 trains the outdoor environment analysis model by using a large number of outdoor environment parameter samples in advance. Therefore, in the process of analyzing and calculating the outdoor environment parameters, the second intelligent computing node 220 may analyze and calculate the outdoor environment parameters by using the outdoor environment analysis model trained in advance, and output an outdoor environment analysis result related to the outdoor environment. By the method, the purpose of rapidly and accurately analyzing and calculating the outdoor environment parameters is achieved, and the outdoor environment analysis result generated by the outdoor environment analysis model based on deep learning is more accurate and comprehensive.
Optionally, the outdoor environment analysis model includes a fifth feature extraction network, a sixth feature extraction network and a second full connection layer, the plurality of outdoor environment parameters collected by the plurality of outdoor sensors include an outdoor temperature parameter, an outdoor humidity parameter, an outdoor PM2.5 parameter, a solar irradiance parameter, a wind speed parameter, a wind pressure parameter and a rainfall parameter, and the second intelligent computing node performs analysis and computation on the plurality of outdoor environment parameters by using the outdoor environment analysis model, and outputs an outdoor environment analysis result related to the outdoor environment, including: the second intelligent computing node inputs the outdoor temperature parameter and the outdoor humidity parameter into a fifth feature extraction network and outputs fifth feature information related to the temperature and the humidity of the outdoor environment; the second intelligent computing node inputs the outdoor PM2.5 parameter, the solar irradiance parameter, the wind speed parameter, the wind pressure parameter, the rainfall parameter and the fifth characteristic information into a sixth characteristic extraction network, and outputs sixth characteristic information related to the temperature, the humidity, the PM2.5 concentration, the solar irradiance, the wind speed, the wind pressure and the rainfall of the outdoor environment; and the second intelligent computing node inputs the sixth characteristic information into the second full connection layer and outputs an outdoor environment analysis result.
Specifically, fig. 6 exemplarily shows a network structure of the outdoor environment analysis model, and referring to fig. 6, the outdoor environment analysis model includes a fifth feature extraction network, a sixth feature extraction network, and a second fully connected layer. The plurality of outdoor sensors include, for example, a temperature sensor, a humidity sensor, a PM2.5 sensor, a solar radiation degree sensor, an air speed sensor, a wind pressure sensor, a rainfall sensor, and the like, which are installed outdoors. The outdoor environment parameters collected by the outdoor sensors comprise an outdoor temperature parameter, an outdoor humidity parameter, an outdoor PM2.5 parameter, a solar radiation degree parameter, a wind speed parameter, a wind pressure parameter and a rainfall parameter.
Further, during the operation process of analyzing and calculating the plurality of outdoor environment parameters by the second intelligent computing node 220 using the outdoor environment analysis model, the outdoor temperature parameter and the outdoor humidity parameter may be input into the fifth feature extraction network together, and the fifth feature information related to the temperature and the humidity of the outdoor environment is output. And then, fifth characteristic information output by the fifth characteristic extraction network is utilized, the fifth characteristic information, the outdoor PM2.5 parameter, the solar radiation degree parameter, the wind speed parameter, the wind pressure parameter and the rainfall parameter are input into a sixth characteristic extraction network together, and sixth characteristic information related to the temperature, the humidity, the PM2.5 concentration, the solar radiation degree, the wind speed, the wind pressure and the rainfall of the outdoor environment is output. And finally, inputting sixth feature information output by the sixth feature extraction network into the second full connection layer, and outputting an outdoor environment analysis result. Therefore, in the present embodiment, by setting a double-layer feature extraction network, and combining the relevance of each outdoor environment parameter, the multiple indoor environment parameters are divided into two types, for example, the outdoor temperature parameter and the outdoor humidity parameter with strong relevance are divided into the same type, and the other outdoor environment parameters are divided into the other type. And then inputting the outdoor environment parameters of the same type into the corresponding feature extraction network, and utilizing the result output by the feature network of the previous layer to enable the feature information output by the feature extraction network of the last layer to deeply cover the feature information of all the outdoor environment parameters, thereby ensuring the comprehensiveness and accuracy of the finally output outdoor environment analysis result.
Optionally, the remote control subsystem is preconfigured with an indoor environment adjusting model based on deep learning, and determines an operation for adjusting an adjusting parameter of the indoor environment according to the indoor environment analysis result and the outdoor environment analysis result, including: the remote control subsystem acquires the house type graph information corresponding to the indoor environment; and the remote control subsystem inputs the house type graph information, the indoor environment analysis result and the outdoor environment analysis result into the indoor environment regulation model and outputs regulation parameters for regulating the indoor environment.
Specifically, in order to ensure that the remote control subsystem 2 can determine a comprehensive and accurate adjusting parameter for adjusting the indoor environment according to the indoor environment analysis result and the outdoor environment analysis result. In the embodiment, a pre-trained indoor environment regulation model based on deep learning is configured in the remote control subsystem 2, and in the operation process of determining the regulation parameters, the remote control subsystem 2 firstly acquires a house type image corresponding to the indoor environment. For example, an image acquisition device which is in communication connection with the remote control subsystem 2 is arranged on the edge terminal network, and a house type image corresponding to the indoor environment is acquired through the image acquisition device. In addition, the remote control subsystem 2 may also search and acquire a house pattern image corresponding to the indoor environment from the database. And is not particularly limited herein.
Further, the remote control subsystem 2 inputs the acquired house type image, the indoor environment analysis result and the outdoor environment analysis result into the indoor environment adjustment model together, and outputs adjustment parameters for adjusting the indoor environment. The indoor environment adjustment model may be, for example, an existing CNN + RNN + Attention-based network model capable of implementing intelligent analysis, or may be another type of neural network model. Through the mode, the indoor environment analysis result and the outdoor environment analysis result which are received from the edge end network are combined with the house type image corresponding to the indoor environment to be adjusted, and the indoor environment adjustment model which is trained in advance is utilized to carry out intelligent analysis, so that a comprehensive and accurate adjustment parameter for adjusting the indoor environment is determined, and more intelligent and comfortable indoor environment adjustment service is provided for the user.
Optionally, the remote control subsystem with set up in indoor environment's temperature regulation equipment, humidity control equipment and new trend clarification plant communication connection to according to adjusting parameter, carry out the operation adjusted to indoor environment, include: the remote control subsystem respectively determines a temperature adjusting instruction, a humidity adjusting instruction and a ventilation adjusting instruction for controlling the temperature adjusting equipment, the humidity adjusting equipment and the fresh air purifying equipment according to the adjusting parameters; and the remote control subsystem sends the temperature regulation instruction, the humidity regulation instruction and the ventilation regulation instruction to the temperature regulation equipment, the humidity regulation equipment and the fresh air purification equipment respectively.
Specifically, this embodiment can preselect and set up the temperature regulation equipment who is used for adjusting indoor temperature, the humidity control equipment who is used for adjusting indoor humidity and be used for adjusting the new trend clarification plant of indoor ventilation degree in indoor environment to set up in remote control subsystem 2 of remote network all with temperature regulation equipment, humidity control equipment and new trend clarification plant communication connection. Wherein, new trend clarification plant includes new trend adjusting device and purification and regulation equipment. Therefore, in the operation process of adjusting the indoor environment by the remote control subsystem 2 according to the adjusting parameters, the remote control subsystem 2 needs to determine a temperature adjusting instruction for controlling the temperature adjusting device, a humidity adjusting instruction for controlling the humidity adjusting device and a ventilation adjusting instruction for controlling the fresh air purifying device according to the adjusting parameters output by the model. And the temperature adjusting instruction, the humidity adjusting instruction and the ventilation adjusting instruction are respectively sent to the temperature adjusting device, the humidity adjusting device and the fresh air purifying device, the temperature adjusting device adjusts the temperature of the indoor environment according to the received temperature adjusting instruction, the humidity adjusting device adjusts the humidity of the indoor environment according to the received humidity adjusting instruction, and the fresh air purifying device adjusts the ventilation of the indoor environment according to the received ventilation adjusting instruction. By the method, the aim of independently adjusting various parameters of the indoor environment is fulfilled, so that an optimal and comfortable indoor environment is provided for a user.
Optionally, the remote control subsystem is also pre-learned and configured with a correction model based on deep learning, and before the remote control subsystem sends the temperature regulation instruction, the humidity regulation instruction and the ventilation regulation instruction to the operation of temperature regulation equipment, humidity regulation equipment and fresh air purification equipment respectively, the remote control subsystem further comprises: the remote control subsystem acquires human body comfort evaluation data corresponding to a user in an indoor environment; the remote control subsystem acquires various indexes of a human body of a user, wherein the various indexes of the human body comprise the body surface temperature, the heart rate and the respiratory rate of the human body; the remote control subsystem will obtain historical adjustment data associated with the user; the remote control subsystem inputs the human body comfort evaluation data, various indexes of the human body and historical adjustment data into the correction model and outputs correction parameters; and the remote control subsystem corrects the temperature regulation instruction, the humidity regulation instruction and the ventilation regulation instruction according to the correction parameters.
Specifically, in the process of adjusting the indoor environment, various parameters of the indoor environment and the outdoor environment need to be combined, and particularly comfort evaluation data of a user and various indexes of a human body need to be combined, so that more intelligent, personalized and precise indoor environment control services are provided for the user. In this embodiment, the remote control subsystem 2 first obtains human comfort evaluation data corresponding to a user in an indoor environment, for example, the remote control subsystem 2 is provided with a human-computer interaction interface for interacting with the user, the user may access the human-computer interaction interface through a mobile terminal, and the comfort evaluation data is input in the human-computer interaction interface, so that the remote control subsystem 2 may obtain the comfort evaluation data of the user through the human-computer interaction interface.
Further, the remote control subsystem 2 needs to obtain various indexes of the human body of the user, wherein the various indexes of the human body include the body surface temperature, the heart rate and the respiratory rate of the human body. For example, remote control subsystem 2 can be with human body sensor communication connection, and the user can wear human body sensor, detects and sends to remote control subsystem 2 through various indexes such as human body surface temperature, rhythm of the heart and respiratory frequency of human body sensor to remote control subsystem 2 can acquire each index of user's human body through human body sensor.
In addition, in order to further combine the adjustment preference of the user to perform adjustment control of the indoor environment, the remote control subsystem 2 needs to acquire historical adjustment data of the user. And the remote control subsystem 2 is pre-configured with a trained correction model based on deep learning, and outputs a correction parameter by inputting the obtained human body comfort evaluation data, various indexes of the human body and historical adjustment data into the correction model together. Because the input data of the correction model covers the comfort evaluation data, various indexes of the human body and historical adjustment data of the user, the correction parameters obtained by correcting the model can fully embody the environmental parameters of the indoor environment preferred by the user. In this application scenario, the remote control subsystem 2 corrects the temperature adjustment instruction, the humidity adjustment instruction, and the ventilation adjustment instruction according to the correction parameter, so as to send the corrected temperature adjustment instruction, humidity adjustment instruction, and ventilation adjustment instruction to the corresponding adjustment devices, respectively. Through the mode, in the control process of the indoor environment, the control method and the control device not only combine various parameters of the indoor environment and the outdoor environment, but also combine comfort evaluation data of the user and various indexes of the human body, so that more intelligent, personalized and accurate indoor environment control service is provided for the user.
Optionally, before the operation of analyzing and calculating the plurality of indoor environment parameters and the plurality of outdoor environment parameters by the edge computing subsystem, the method further includes: the edge computing subsystem respectively preprocesses the plurality of indoor environment parameters and the plurality of outdoor environment parameters.
Specifically, in order to further increase the speed of analyzing and calculating the plurality of indoor environment parameters by the first intelligent node 210 and the speed of analyzing and calculating the plurality of outdoor environment parameters by the second intelligent node 220, the edge computing subsystem 1 may perform a preprocessing operation on the plurality of indoor environment parameters and the plurality of outdoor environment parameters, respectively, in advance, for example, preprocessing the plurality of indoor environment parameters, so as to filter out some invalid parameters belonging to the interference signal.
The edge computing subsystem 1 may further compute the human comfort index according to a plurality of indoor environment parameters and a plurality of outdoor environment parameters by using the following formula:
ssd＝(1.818Δt+18.18)(0.88+0.002Δf)+(T-32)/(45-t)-3.2v+3.3p+3.6h+1.3i+18.2。
wherein ssd is a human comfort index, T is an indoor temperature, T is an outdoor temperature, Δ T is a difference between the indoor temperature and the outdoor temperature, Δ f is a difference between an indoor humidity and an outdoor humidity, v is an outdoor wind speed, p is an outdoor wind pressure, h is a solar irradiance, and i is a rainfall.
The relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". (the device may also be oriented 90 degrees or at other orientations in different ways), and the spatially relative descriptors used herein interpreted accordingly.
In the description of the present disclosure, it is to be understood that the orientation or positional relationship indicated by the directional terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal" and "top, bottom", etc., are generally based on the orientation or positional relationship shown in the drawings, and are presented only for the convenience of describing and simplifying the disclosure, and in the absence of a contrary indication, these directional terms are not intended to indicate and imply that the device or element being referred to must have a particular orientation or be constructed and operated in a particular orientation, and therefore, should not be taken as limiting the scope of the disclosure; the terms "inner and outer" refer to the inner and outer relative to the profile of the respective component itself.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An intelligent control system for an indoor environment suitable for temperature, humidity and oxygen, which is characterized by comprising: an edge computing subsystem and a remote control subsystem in communication with the edge computing subsystem, wherein
The edge computation subsystem is configured to perform the following operations:
collecting a plurality of indoor environment parameters of an indoor environment and a plurality of outdoor environment parameters of an outdoor environment corresponding to the indoor environment;
respectively analyzing and calculating the plurality of indoor environment parameters and the plurality of outdoor environment parameters to generate an indoor environment analysis result corresponding to the indoor environment and an outdoor environment analysis result corresponding to the outdoor environment;
sending the indoor environment analysis result and the outdoor environment analysis result to the remote control subsystem; and is
The remote control subsystem is configured to perform the following operations:
receiving the indoor environment analysis results and the outdoor environment analysis results from the edge computing subsystem;
determining an adjusting parameter for adjusting the indoor environment according to the indoor environment analysis result and the outdoor environment analysis result; and
and adjusting the indoor environment according to the adjusting parameters.
2. The intelligent temperature, humidity and oxygen-eligible indoor environment control system of claim 1 wherein the margin calculation subsystem comprises: a plurality of indoor sensors, a plurality of outdoor sensors, a first intelligent computing node and a second intelligent computing node, and
the plurality of indoor sensors are configured to acquire the plurality of indoor environment parameters and send the indoor environment parameters to the first intelligent computing node in communication connection;
the plurality of outdoor sensors are configured to collect the plurality of outdoor environment parameters and send the outdoor environment parameters to the second intelligent computing node in communication connection;
the first intelligent computing node is configured to receive the plurality of indoor environment parameters from the plurality of indoor sensors, perform analysis calculation on the plurality of indoor environment parameters, generate an indoor environment analysis result related to the indoor environment, and send the indoor environment analysis result to the remote control subsystem; and
the second intelligent computing node is used for receiving the outdoor environment parameters from the outdoor sensors, analyzing and calculating the outdoor environment parameters, generating an outdoor environment analysis result related to the outdoor environment, and sending the outdoor environment analysis result to the remote control subsystem.
3. The intelligent temperature and humidity sensible indoor environment control system of claim 2, wherein said first intelligent computing node is pre-deployed with an indoor environment analysis model based on deep learning, and wherein said operation of said first intelligent computing node performing analytical computation on said plurality of indoor environment parameters to generate indoor environment analysis results associated with said indoor environment comprises:
and the first intelligent computing node analyzes and calculates the plurality of indoor environment parameters by using the indoor environment analysis model and outputs an indoor environment analysis result related to the indoor environment.
4. The intelligent temperature and humidity suitable indoor environment control system according to claim 3, wherein the indoor environment analysis model includes a first feature extraction network, a second feature extraction network, a third feature extraction network, a fourth feature extraction network, and a first fully-connected layer, the plurality of indoor environment parameters collected by the plurality of indoor sensors include an indoor temperature parameter, an indoor humidity parameter, an indoor carbon dioxide concentration parameter, and an indoor cleanliness parameter, and the first intelligent computing node performs the analysis calculation on the plurality of indoor environment parameters using the indoor environment analysis model, and outputs the indoor environment analysis result related to the indoor environment, including:
the first intelligent computing node inputs the indoor temperature parameter into the first feature extraction network and outputs first feature information related to the temperature of the indoor environment;
the first intelligent computing node inputs the indoor humidity parameter and the first characteristic information into the second characteristic extraction network and outputs second characteristic information related to the temperature and the humidity of the indoor environment;
the first intelligent computing node inputs an indoor carbon dioxide concentration parameter and the second characteristic information into the third characteristic extraction network, and outputs third characteristic information related to the temperature, the humidity and the carbon dioxide concentration of the indoor environment;
the first intelligent computing node inputs the indoor cleanliness parameter and the third feature information into the fourth feature extraction network, and outputs fourth feature information related to the temperature, the humidity, the carbon dioxide concentration and the cleanliness of the indoor environment; and
and the first intelligent computing node inputs the fourth characteristic information into the first full-connection layer and outputs the indoor environment analysis result.
5. The intelligent temperature, humidity and oxygen-beneficial indoor environment control system according to claim 2, wherein the operation of the second intelligent computing node pre-deploying an outdoor environment analysis model based on deep learning and performing the analysis calculation on the plurality of outdoor environment parameters by the second intelligent computing node to generate the outdoor environment analysis result related to the outdoor environment comprises:
and the second intelligent computing node analyzes and computes the outdoor environment parameters by using the outdoor environment analysis model and outputs an outdoor environment analysis result related to the outdoor environment.
6. The intelligent temperature, humidity and oxygen-beneficial indoor environment control system according to claim 5, wherein the outdoor environment analysis model comprises a fifth feature extraction network, a sixth feature extraction network and a second full connection layer, the outdoor environment parameters collected by the outdoor sensors comprise an outdoor temperature parameter, an outdoor humidity parameter, an outdoor PM2.5 parameter, a solar irradiance parameter, a wind speed parameter, a wind pressure parameter and a rainfall parameter, and the second intelligent computing node performs analysis and computation on the outdoor environment parameters by using the outdoor environment analysis model, and outputs an outdoor environment analysis result related to the outdoor environment, comprising:
the second intelligent computing node inputs the outdoor temperature parameter and the outdoor humidity parameter into the fifth feature extraction network and outputs fifth feature information related to the temperature and the humidity of the outdoor environment;
the second intelligent computing node inputs the outdoor PM2.5 parameter, the solar irradiance parameter, the wind speed parameter, the wind pressure parameter, the rainfall parameter and the fifth characteristic information into the sixth characteristic extraction network, and outputs sixth characteristic information related to the temperature, the humidity, the PM2.5 concentration, the solar irradiance, the wind speed, the wind pressure and the rainfall of the outdoor environment; and
and the second intelligent computing node inputs the sixth feature information into the second full-connection layer and outputs the outdoor environment analysis result.
7. The intelligent temperature and humidity suitable indoor environment control system according to claim 1, wherein the remote control subsystem is preconfigured with an indoor environment adjustment model based on deep learning, and determines an operation for adjusting an adjustment parameter of the indoor environment according to the indoor environment analysis result and the outdoor environment analysis result, including:
the remote control subsystem acquires the house type graph information corresponding to the indoor environment; and
and the remote control subsystem inputs the house type graph information, the indoor environment analysis result and the outdoor environment analysis result into the indoor environment regulation model and outputs the regulation parameters for regulating the indoor environment.
8. The intelligent temperature and humidity and oxygen available indoor environment control system according to claim 7, wherein the remote control subsystem is in communication connection with a temperature adjusting device, a humidity adjusting device and a fresh air purifying device which are arranged in the indoor environment, and performs operation of adjusting the indoor environment according to the adjusting parameters, comprising:
the remote control subsystem respectively determines a temperature adjusting instruction, a humidity adjusting instruction and a ventilation adjusting instruction for controlling the temperature adjusting device, the humidity adjusting device and the fresh air purifying device according to the adjusting parameters; and
the remote control subsystem will temperature regulation instruction, humidity control instruction with the ventilation control instruction send to respectively temperature regulation equipment humidity regulation equipment and new trend clarification plant.
9. The intelligent temperature and humidity aerobic indoor environment control system according to claim 8, wherein the remote control subsystem is further pre-configured with a deep learning based correction model, and the remote control subsystem sends the temperature adjustment command, the humidity adjustment command and the ventilation adjustment command to the temperature adjustment device, the humidity adjustment device and the fresh air purification device respectively before operation, further comprising:
the remote control subsystem acquires human comfort evaluation data corresponding to a user in the indoor environment;
the remote control subsystem acquires various indexes of a human body of the user, wherein the various indexes of the human body comprise the body surface temperature, the heart rate and the respiratory rate of the human body;
the remote control subsystem will obtain historical adjustment data relating to the user;
the remote control subsystem inputs the human body comfort evaluation data, the human body indexes and the historical adjustment data into the correction model and outputs correction parameters; and
and the remote control subsystem corrects the temperature regulation instruction, the humidity regulation instruction and the ventilation regulation instruction according to the correction parameters.
10. The intelligent temperature, humidity and oxygen-beneficial indoor environment control system according to claim 2, wherein prior to the operation of analyzing and calculating the plurality of indoor environmental parameters and the plurality of outdoor environmental parameters, respectively, the edge calculation subsystem further comprises: the edge computing subsystem respectively preprocesses the plurality of indoor environment parameters and the plurality of outdoor environment parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011324661.1A CN112556107B (en) | 2020-11-23 | 2020-11-23 | Intelligent control system for indoor environment suitable for temperature, humidity and oxygen |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011324661.1A CN112556107B (en) | 2020-11-23 | 2020-11-23 | Intelligent control system for indoor environment suitable for temperature, humidity and oxygen |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112556107A true CN112556107A (en) | 2021-03-26 |
| CN112556107B CN112556107B (en) | 2022-10-28 |
Family
ID=75044928
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011324661.1A Active CN112556107B (en) | 2020-11-23 | 2020-11-23 | Intelligent control system for indoor environment suitable for temperature, humidity and oxygen |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112556107B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113268098A (en) * | 2021-06-23 | 2021-08-17 | 上海市建筑科学研究院有限公司 | Indoor environment regulation and control method and system |
| CN113503596A (en) * | 2021-06-25 | 2021-10-15 | 沃逸新能源科技（江苏）有限公司 | Human body self-adaptive adjustment system and method in penta-constant environment |
| CN113847714A (en) * | 2021-06-29 | 2021-12-28 | 浪潮软件科技有限公司 | Household edge computing-based automatic indoor environment adjusting method and device |
| CN113932300A (en) * | 2021-10-08 | 2022-01-14 | 青岛海尔空调器有限总公司 | Fresh air conditioner control method and device and fresh air conditioner |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN205334209U (en) * | 2015-10-29 | 2016-06-22 | 陈亚政 | Indoor environmental management system of intelligence based on server |
| US20170093594A1 (en) * | 2015-04-03 | 2017-03-30 | Deawang System Co., Ltd. | Smart home system adapted to indoor and outdoor environmental changes |
| CN108008666A (en) * | 2017-10-31 | 2018-05-08 | 上海雅直科技有限公司 | One kind building neutral net and its method of work |
| CN108828409A (en) * | 2018-08-03 | 2018-11-16 | 南方电网科学研究院有限责任公司 | A kind of fault detection system based on edge calculations |
| CN109730656A (en) * | 2019-01-09 | 2019-05-10 | 中国科学院苏州纳米技术与纳米仿生研究所 | Nerve network system, computer equipment for pulse wave signal classification |
| CN110190605A (en) * | 2019-04-22 | 2019-08-30 | 杭州电子科技大学 | A kind of distribution network voltage regulation and control system and method based on edge calculations and air conditioning requirements response |
| EP3609205A1 (en) * | 2018-08-10 | 2020-02-12 | Otis Elevator Company | Wireless data communication in a system |
| CN110836509A (en) * | 2018-08-17 | 2020-02-25 | 珠海格力电器股份有限公司 | Household equipment control method and device, control equipment and readable storage medium |
| CN111062651A (en) * | 2020-03-18 | 2020-04-24 | 南京中电科能技术有限公司 | Safe power utilization management system and method based on edge calculation and big data analysis |
| CN111077791A (en) * | 2020-01-09 | 2020-04-28 | 珠海格力电器股份有限公司 | Control method and device of intelligent household equipment and storage medium |
| CN111352349A (en) * | 2020-01-27 | 2020-06-30 | 东北石油大学 | System and method for collecting and adjusting information of old people living environment |
| WO2020159471A1 (en) * | 2019-01-28 | 2020-08-06 | Johnson Controls Technology Company | Building management system with hybrid edge-cloud processing |
| WO2020161935A1 (en) * | 2019-02-05 | 2020-08-13 | 日本電気株式会社 | Learning device, learning method, and program |
| CN111880430A (en) * | 2020-08-27 | 2020-11-03 | 珠海格力电器股份有限公司 | Control method and device for intelligent household equipment |
| CN111947284A (en) * | 2020-08-10 | 2020-11-17 | 海信（山东）空调有限公司 | Air conditioner and multi-temperature-zone setting method |
-
2020
- 2020-11-23 CN CN202011324661.1A patent/CN112556107B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170093594A1 (en) * | 2015-04-03 | 2017-03-30 | Deawang System Co., Ltd. | Smart home system adapted to indoor and outdoor environmental changes |
| CN205334209U (en) * | 2015-10-29 | 2016-06-22 | 陈亚政 | Indoor environmental management system of intelligence based on server |
| CN108008666A (en) * | 2017-10-31 | 2018-05-08 | 上海雅直科技有限公司 | One kind building neutral net and its method of work |
| CN108828409A (en) * | 2018-08-03 | 2018-11-16 | 南方电网科学研究院有限责任公司 | A kind of fault detection system based on edge calculations |
| EP3609205A1 (en) * | 2018-08-10 | 2020-02-12 | Otis Elevator Company | Wireless data communication in a system |
| CN110836509A (en) * | 2018-08-17 | 2020-02-25 | 珠海格力电器股份有限公司 | Household equipment control method and device, control equipment and readable storage medium |
| CN109730656A (en) * | 2019-01-09 | 2019-05-10 | 中国科学院苏州纳米技术与纳米仿生研究所 | Nerve network system, computer equipment for pulse wave signal classification |
| WO2020159471A1 (en) * | 2019-01-28 | 2020-08-06 | Johnson Controls Technology Company | Building management system with hybrid edge-cloud processing |
| WO2020161935A1 (en) * | 2019-02-05 | 2020-08-13 | 日本電気株式会社 | Learning device, learning method, and program |
| CN110190605A (en) * | 2019-04-22 | 2019-08-30 | 杭州电子科技大学 | A kind of distribution network voltage regulation and control system and method based on edge calculations and air conditioning requirements response |
| CN111077791A (en) * | 2020-01-09 | 2020-04-28 | 珠海格力电器股份有限公司 | Control method and device of intelligent household equipment and storage medium |
| CN111352349A (en) * | 2020-01-27 | 2020-06-30 | 东北石油大学 | System and method for collecting and adjusting information of old people living environment |
| CN111062651A (en) * | 2020-03-18 | 2020-04-24 | 南京中电科能技术有限公司 | Safe power utilization management system and method based on edge calculation and big data analysis |
| CN111947284A (en) * | 2020-08-10 | 2020-11-17 | 海信（山东）空调有限公司 | Air conditioner and multi-temperature-zone setting method |
| CN111880430A (en) * | 2020-08-27 | 2020-11-03 | 珠海格力电器股份有限公司 | Control method and device for intelligent household equipment |
Non-Patent Citations (1)
| Title |
|---|
| 王震宇等: "基于深度卷积和门控循环神经网络的传感器运动识别", 《电子测量与仪器学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113268098A (en) * | 2021-06-23 | 2021-08-17 | 上海市建筑科学研究院有限公司 | Indoor environment regulation and control method and system |
| CN113503596A (en) * | 2021-06-25 | 2021-10-15 | 沃逸新能源科技（江苏）有限公司 | Human body self-adaptive adjustment system and method in penta-constant environment |
| CN113847714A (en) * | 2021-06-29 | 2021-12-28 | 浪潮软件科技有限公司 | Household edge computing-based automatic indoor environment adjusting method and device |
| CN113932300A (en) * | 2021-10-08 | 2022-01-14 | 青岛海尔空调器有限总公司 | Fresh air conditioner control method and device and fresh air conditioner |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112556107B (en) | 2022-10-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112556107B (en) | Intelligent control system for indoor environment suitable for temperature, humidity and oxygen | |
| CN104033988B (en) | Air conditioner control system and control method of air conditioner control system | |
| CN105628091B (en) | The detection method of environmental data and the detecting system of environmental data | |
| CN110955212A (en) | Wisdom agricultural information processing system based on thing networking | |
| Choudhary et al. | Autonomous crop irrigation system using artificial intelligence | |
| CN106060174A (en) | Data analysis based agricultural guidance system | |
| CN114564058B (en) | Intelligent regulation and control management system for monitoring house indoor environment based on Internet of things | |
| CN110207324B (en) | Dynamic ventilation control method and system | |
| CN110940034B (en) | Split air conditioner self-adaptive control system and method based on human body perception | |
| CN109489212A (en) | A kind of air conditioner intelligent sleep control method, adjustment system and equipment | |
| CN107219759B (en) | Greenhouse environment control method and device | |
| CN110822616A (en) | Automatic air conditioner adjusting method and device | |
| CN109974242A (en) | Air-conditioning system intelligent thermoregulating method and system based on thermal imaging | |
| CN112178785A (en) | Dehumidification control method and dehumidification control equipment for air conditioner | |
| CN104808723B (en) | A kind of office's air-conditioning semi-intelligent regulator control system based on wireless sensor network | |
| CN205247189U (en) | Wisdom farming system based on thing networking | |
| CN112923525A (en) | Machine learning type comfortable energy-saving air conditioner intelligent control method | |
| CN109782832A (en) | A kind of breeding layer chicken environmental monitoring system and method | |
| CN114742987A (en) | Automatic positioning control method and system for cutting of non-metallic materials | |
| CN108803737A (en) | A kind of intelligent building air monitoring system based on big data | |
| CN208859789U (en) | A kind of indoor environment intelligent control system based on study user behavior | |
| CN109163422A (en) | Air conditioner and its control method, device and computer readable storage medium | |
| CN205279345U (en) | Intelligent air conditioner | |
| CN114063572A (en) | Non-sensing intelligent device control method, electronic device and control system | |
| CN212259966U (en) | Large-span steel construction green house |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |