CN116594313B - Smart home equipment management method, system, equipment and medium - Google Patents
Smart home equipment management method, system, equipment and medium Download PDFInfo
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The application discloses a method, a system, equipment and a medium for managing intelligent household equipment, which relate to the technical field of intelligent household systems and comprise the following steps: setting up an equipment management platform, and interacting with all types of intelligent home equipment in the equipment management platform; collecting indoor environment parameter data, preprocessing, establishing an environment parameter data set, and acquiring an indoor environment evaluation value Pr in a target building based on the environment parameter data set; the technical key points are as follows: the fuzzy neural network control model is established to regulate and control the intelligent household equipment, the joint regulation and control of the intelligent household equipment is realized through the fuzzy rule, the comfort of a user is improved, early warning can be timely sent out when the target intelligent household equipment is in a disconnection or fault condition, and the overhaul work of the target intelligent household equipment can be accurately and efficiently completed by combining the position information of the target intelligent household equipment.
Description
Technical Field
The application relates to the technical field of intelligent home systems, in particular to an intelligent home equipment management method, an intelligent home equipment management system, intelligent home equipment management equipment and an intelligent home equipment management medium.
Background
The intelligent home system organically combines all subsystems related to home life, such as security protection, light control, curtain control, gas valve control, information home appliances, scene linkage, floor heating, health care, health epidemic prevention, security protection and the like, according to the human engineering principle by utilizing advanced computer technology, network communication technology, intelligent cloud control, comprehensive wiring technology and medical electronic technology and integrating individual requirements, and realizes the brand new home life experience of 'people' through networked comprehensive intelligent control and management.
In the chinese application of application publication No. CN115708390a, a device management system, a method and a master device are disclosed, where the system includes a master device, and a first device and a second device that are respectively connected wirelessly to the master device, where the master device is configured to, after obtaining a first user operation, send a connection instruction to the first device and/or the second device when the first device and the second device are not connected, where the connection instruction is used to instruct the first device and the second device to establish a wireless connection, and where the first device and the second device are configured to establish a wireless connection according to the connection instruction.
In the above application, although the connection relationship between two intelligent home devices is increased, the comfort of the indoor environment cannot be effectively and autonomously improved in the application of the intelligent home devices, the traditional intelligent home device management mode can only regulate and control a single environment parameter, the overall environment condition cannot be comprehensively mastered, and if a certain intelligent home device fails, a user cannot acquire the intelligent home device in time, so that the working efficiency in subsequent overhaul is affected.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides the intelligent household equipment management method, the system, the equipment and the medium, a fuzzy neural network control model is established, intelligent household equipment is regulated and controlled, linkage regulation and control can be carried out aiming at different application scenes, after interaction and influence among a plurality of environment parameters are considered, joint regulation and control on the intelligent household equipment is realized through fuzzy rules, the comfort of a user is improved, early warning can be timely sent out in case of disconnection or failure of target intelligent household equipment, and the overhaul work on the target intelligent household equipment can be accurately and efficiently completed by combining the position information of the target intelligent household equipment, so that the problems in the background technology are solved.
In order to achieve the above purpose, the application is realized by the following technical scheme:
an intelligent home equipment management method comprises the following steps:
setting up an equipment management platform, interacting with all types of intelligent home equipment in the equipment management platform, providing a visual window for the equipment management platform, and acquiring the positions and states of all types of intelligent home equipment;
collecting indoor environment parameter data, preprocessing, establishing an environment parameter data set, acquiring an indoor environment evaluation value Pr in a target building based on the environment parameter data set, inputting the environment parameter data set and the indoor environment evaluation value Pr into an established fuzzy neural network control model, and if the indoor environment evaluation value Pr is smaller than a standard threshold, completing regulation and control on intelligent household equipment through the fuzzy neural network control model until the indoor environment evaluation value Pr is larger than or equal to the standard threshold;
after the fuzzy neural network control model sends a regulation and control instruction to the target intelligent household equipment, the equipment management platform obtains the state of the target intelligent household equipment, and if the target intelligent household equipment does not respond, the state is fed back to the mobile terminal through the equipment management platform, and a repair report instruction is triggered.
Further, each type of smart home device at least includes: the intelligent air conditioner, the intelligent humidifier and the intelligent ventilation fan share the same local area network, and the equipment management platform and each intelligent household equipment are in communication connection through a WIFI protocol; for intelligent household equipment such as intelligent air conditioners, intelligent humidifiers and intelligent ventilating fans, networking units are arranged in all the equipment and used for sending position and state information of the intelligent household equipment to an equipment management platform, remote control of all the intelligent household equipment can be achieved through the equipment management platform, unified regulation and control processing is conveniently carried out on all the intelligent household equipment, and the intelligent degree is embodied.
Further, a GPS locator is built in each intelligent home device for locating the position of the intelligent home device, and the state of the intelligent home device at least includes: real-time operating state and energy consumption.
Further, the indoor environment parameter data at least includes: indoor temperature T, indoor humidity H and indoor carbon dioxide content C, wherein, indoor temperature T's acquisition mode is: the temperature sensor is used for direct measurement, and the indoor humidity H is obtained by the following steps: the humidity sensor is used for direct measurement, and the acquisition mode of the indoor carbon dioxide content C is as follows: directly measuring by using a carbon dioxide concentration sensor; the indoor temperature T, the indoor humidity H and the indoor carbon dioxide content C which are acquired in the indoor environment evaluation value Pr are all in the same floor, and the indoor environment evaluation value Pr can be acquired respectively through different floors, so that the partition type judgment is realized, and the accuracy of the measurement result can be ensured to a certain extent;
the indoor environment evaluation value Pr in the target floor is obtained as follows: acquiring indoor temperature T, indoor humidity H and indoor carbon dioxide content C in a target building layer, and correlating to form an indoor environment evaluation value Pr after dimensionless treatment;
；
wherein the meaning of the parameter is 0.，/>And->，/>For the weight, its specific value is set by the user adjustment, +.>Is a constant correction coefficient.
Further, in the fuzzy neural network control model, linkage type regulation is adopted for intelligent household equipment regulation, and the method comprises the following steps:
the first step is to define linkage relation: when the indoor temperature T exceeds a set threshold, the intelligent air conditioner and the intelligent ventilating fan are synchronously started, when the indoor humidity H is lower than a preset threshold, the intelligent humidifier and the intelligent ventilating fan are synchronously started, and when the indoor carbon dioxide content C is lower than a preset threshold, the intelligent ventilating fan and the intelligent air conditioner are synchronously started;
secondly, constructing a CNN convolutional neural network model in the fuzzy neural network model: constructing a CNN convolutional neural network model according to the linkage relation;
third step, defining input variables and membership functions: determining indoor environment parameter data and membership functions of each parameter data according to the linkage relation, wherein the indoor environment parameter data is an input variable, and the membership functions are used for blurring the input variable so as to map continuous input to a blurred language description;
fourth, making fuzzy rules and reasoning rules: based on the linkage relation and the input variable, expressing the linkage relation of the input variable and the output equipment by using a fuzzy IF-THEN rule, wherein the output equipment is intelligent household equipment of each type;
it should be noted that: the fuzzy IF-THEN rule is a form of rule based on fuzzy logic, consisting of two parts: front (fuzzy IF) and back (fuzzy THEN);
the front-end (fuzzy IF) section of which describes the condition of the input variables, is typically expressed using fuzzy sets and fuzzy logic operations; for example, "high temperature AND low humidity", where "high temperature" AND "low humidity" are fuzzy sets, are represented in fuzzy logic using membership functions AND logical operations (e.g., AND, OR);
the part of the back part (fuzzy THEN) describes corresponding output equipment or control actions, in this example, the back part can be an intelligent air conditioner and an intelligent ventilating fan which are also fuzzy sets or fuzzy actions, and membership functions of the fuzzy sets can be further defined according to specific requirements and control modes of the equipment to express the output intensity or degree of the equipment;
the inference rule is to perform inference calculation through a fuzzy IF-THEN rule according to the linkage relation and the relation between input variables, in the inference process, the fuzzy engine performs fuzzy inference operation on the input fuzzy set and the fuzzy IF-THEN rule of the front piece to obtain a fuzzy output result, and THEN the fuzzy output result is converted into a corresponding specific output value through a defuzzification method to be used for controlling intelligent household equipment.
Fifth step, training CNN convolutional neural network model: training the learning and adjustment linkage regulation relation by adjusting the weight and the bias of the CNN convolutional neural network model by using a back propagation algorithm;
sixth step, linkage regulation: the CNN convolutional neural network model is applied to the actual linkage regulation of the intelligent household equipment, and is input into the CNN convolutional neural network model according to the current input variable value, and corresponding output signals are obtained through fuzzy reasoning and fuzzy output processing, so that the intelligent household equipment is controlled to carry out linkage regulation.
Further, the standard that the target intelligent home equipment does not respond is that the energy consumption of the target intelligent home equipment is always 0, then the equipment management platform feeds back the acquired state information of the target intelligent home equipment to the mobile terminal in a wireless signal transmission mode, and a repair instruction is triggered on the user mobile phone APP to realize vibration prompt.
An intelligent home equipment management system comprises an equipment connection module, an equipment management module and an equipment maintenance module;
the device connection module is used for interacting with all types of intelligent home devices in the device management platform, providing a visual window for the device management platform and acquiring the positions and states of all types of intelligent home devices;
the device management module is used for collecting indoor environment parameter data, preprocessing the indoor environment parameter data, establishing an environment parameter data set, acquiring an indoor environment evaluation value Pr in a target floor based on the environment parameter data set, inputting the environment parameter data set and the indoor environment evaluation value Pr into the established fuzzy neural network control model, and if the indoor environment evaluation value Pr is smaller than a standard threshold value, completing regulation and control on intelligent household devices through the fuzzy neural network control model until the indoor environment evaluation value Pr is more than or equal to the standard threshold value;
the equipment maintenance module is used for acquiring the state of the target intelligent household equipment by the equipment management platform after the fuzzy neural network control model sends a regulation and control instruction to the target intelligent household equipment, and if the target intelligent household equipment does not respond, the equipment maintenance module feeds back the regulation and control instruction to the mobile terminal through the equipment management platform to trigger a repair report instruction;
specifically, a fuzzy neural network control model is established by using the cooperation among all modules, and intelligent household equipment is regulated and controlled, and the used fuzzy neural network has learning capacity, so that the performance of the model can be gradually regulated and improved through training and optimization, and the linkage regulation and control can be adaptively regulated according to actual feedback and data, so that the regulation and control strategy and effect are gradually improved.
The intelligent household equipment management equipment comprises a management equipment body and a controller, wherein the controller comprises a memory, a processor and a computer program stored in the memory and running on the processor, and the steps of the intelligent household equipment management method are realized when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a smart home device management method.
The application provides an intelligent household equipment management method, system, equipment and medium, which have the following beneficial effects:
1. the intelligent household equipment is regulated and controlled by establishing a fuzzy neural network control model, linkage regulation and control can be carried out aiming at different application scenes, and after interaction and influence among a plurality of environment parameters are considered, the joint regulation and control on the intelligent household equipment is realized through fuzzy rules, so that the complex relation and coupling problems among multiple variables possibly faced by the traditional control method are solved, meanwhile, the judgment of an indoor environment evaluation value Pr is increased, one-layer more judgment standard is provided for the linkage regulation and control process, intelligent management is realized, the indoor environment is always kept at extremely high comfort level, and the comfort level of users is improved;
2. the running state of each intelligent household device can be observed in real time on the visual window provided by the device management platform, the established fuzzy neural network control model is used for adjusting and controlling the target intelligent household device in order to ensure the comfort of the indoor environment, if the condition that the target intelligent household device is in disconnection or fails is met in the adjusting and controlling process, early warning can be timely sent out, and the overhaul work of the target intelligent household device can be accurately and efficiently completed by combining the position information of the target intelligent household device.
Drawings
FIG. 1 is a flow chart of a method of smart home device management of the present application;
fig. 2 is a block diagram of a smart home device management system of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1: referring to fig. 1, the application provides a method for managing intelligent home equipment, which comprises the following steps:
firstly, building an equipment management platform, interacting with all types of intelligent home equipment in the equipment management platform, providing a visual window for the equipment management platform, and acquiring the positions and states of all types of intelligent home equipment;
the first step comprises the following steps:
step 101, each type of smart home device at least includes: the intelligent air conditioner, the intelligent humidifier and the intelligent ventilation fan, and the intelligent household equipment and the equipment management platform share the same local area network, the equipment management platform and each intelligent household equipment are in communication connection through a WIFI protocol, networking units are arranged in each equipment for sending position and state information to the equipment management platform, remote control can be achieved on each intelligent household equipment through the equipment management platform, and repeated description is omitted for the models of each intelligent household, and each equipment is of a structure common in the market.
Step 102, after the device management platform is in communication connection with each intelligent home device, because a GPS (global positioning system) locator is built in each intelligent home device and is used for locating the position of the intelligent home device, the position, the real-time running state and the energy consumption of each type of intelligent home device are obtained;
in use, the contents of steps 101 and 102 are combined:
the intelligent household equipment management system can regulate and control the target intelligent household equipment, if the condition of losing connection or faults of the target intelligent household equipment is met in the regulation and control process, early warning can be timely sent out, and the overhaul work of the target intelligent household equipment can be accurately and efficiently completed by combining the position information of the target intelligent household equipment.
Step two, acquiring indoor environment parameter data, preprocessing, establishing an environment parameter data set, acquiring an indoor environment evaluation value Pr in a target floor based on the environment parameter data set, inputting the environment parameter data set and the indoor environment evaluation value Pr into an established fuzzy neural network control model, and if the indoor environment evaluation value Pr is smaller than a standard threshold, completing regulation and control on intelligent household equipment through the fuzzy neural network control model until the indoor environment evaluation value Pr is more than or equal to the standard threshold;
the standard threshold is also manually selected, and in practice, the standard threshold needs to be adjusted and set according to building characteristics, environmental conditions and user requirements, and when the indoor environment evaluation value pr=the standard threshold, it indicates that the indoor environment reaches a minimum comfort level.
The second step comprises the following steps:
step 201, indoor environment parameter data at least includes: indoor temperature T, indoor humidity H and indoor carbon dioxide content C, wherein, indoor temperature T's acquisition mode is: the temperature sensor is used for direct measurement, and the indoor humidity H is obtained by the following steps: the humidity sensor is used for direct measurement, and the acquisition mode of the indoor carbon dioxide content C is as follows: directly measuring by using a carbon dioxide concentration sensor; the indoor temperature T, the indoor humidity H and the indoor carbon dioxide content C which are acquired in the indoor environment evaluation value Pr are all in the same floor, and the indoor environment evaluation value Pr can be acquired respectively through different floors, so that partition type judgment is realized.
The indoor environment evaluation value Pr in the target floor is obtained as follows: acquiring indoor temperature T, indoor humidity H and indoor carbon dioxide content C in a target building layer, and correlating to form an indoor environment evaluation value Pr after dimensionless treatment;
；
wherein the meaning of the parameter is 0.，/>And->，/>For the weight, its specific value is set by the user adjustment, +.>Is a constant correction coefficient;
step 202, in a fuzzy neural network control model, adopting linkage type regulation for intelligent household equipment regulation, comprising the following steps:
the first step is to define linkage relation: when the indoor temperature T exceeds a set threshold, the intelligent air conditioner and the intelligent ventilating fan are synchronously started, when the indoor humidity H is lower than a preset threshold, the intelligent humidifier and the intelligent ventilating fan are synchronously started, and when the indoor carbon dioxide content C is lower than a preset threshold, the intelligent ventilating fan and the intelligent air conditioner are synchronously started;
the above-mentioned set threshold, predetermined threshold and preset threshold are all manually set thresholds, and are set according to the actual needs of the user and the conditions required for the human body to keep comfortable, for example: under winter, the set threshold value corresponding to the indoor humidity H is 25 ℃, and the preset threshold value corresponding to the indoor humidity H is 80%; in summer, the set threshold value corresponding to the indoor humidity H becomes 28 ℃, and the predetermined threshold value corresponding to the indoor humidity H is 60%.
Secondly, constructing a CNN convolutional neural network model in the fuzzy neural network model: constructing a CNN convolutional neural network model according to the linkage relation;
third step, defining input variables and membership functions: determining indoor environment parameter data and membership functions of each parameter data according to the linkage relation, wherein the indoor environment parameter data is an input variable, and the membership functions are used for blurring the input variable so as to map continuous input to a blurred language description;
fourth, making fuzzy rules and reasoning rules: based on the linkage relation and the input variable, expressing the linkage relation of the input variable and the output equipment by using a fuzzy IF-THEN rule, wherein the output equipment is intelligent household equipment of each type;
it should be noted that: the fuzzy IF-THEN rule is a form of rule based on fuzzy logic, consisting of two parts: front (fuzzy IF) and back (fuzzy THEN);
the front-end (fuzzy IF) section of which describes the condition of the input variables, is typically expressed using fuzzy sets and fuzzy logic operations; for example, "high temperature AND low humidity", where "high temperature" AND "low humidity" are fuzzy sets, are represented in fuzzy logic using membership functions AND logical operations (e.g., AND, OR);
the part of the back part (fuzzy THEN) describes corresponding output equipment or control actions, in this example, the back part can be an intelligent air conditioner and an intelligent ventilating fan which are also fuzzy sets or fuzzy actions, and membership functions of the fuzzy sets can be further defined according to specific requirements and control modes of the equipment to express the output intensity or degree of the equipment;
the inference rule is to perform inference calculation through a fuzzy IF-THEN rule according to the linkage relation and the relation between input variables, in the inference process, the fuzzy engine performs fuzzy inference operation on the input fuzzy set and the fuzzy IF-THEN rule of the front piece to obtain a fuzzy output result, and THEN the fuzzy output result is converted into a corresponding specific output value through a defuzzification method to be used for controlling intelligent household equipment.
Fifth step, training CNN convolutional neural network model: training the learning and adjustment linkage regulation relation by adjusting the weight and the bias of the CNN convolutional neural network model by using a back propagation algorithm;
sixth step, linkage regulation: the CNN convolutional neural network model is applied to the actual linkage regulation of the intelligent household equipment, and is input into the CNN convolutional neural network model according to the current input variable value, and corresponding output signals are obtained through fuzzy reasoning and fuzzy output processing, so that the intelligent household equipment is controlled to carry out linkage regulation;
in use, the contents of steps 201 and 202 are combined:
the intelligent household equipment is regulated and controlled by establishing the fuzzy neural network control model, linkage regulation and control can be carried out aiming at different application scenes, and after interaction and influence among a plurality of environment parameters are considered, the joint regulation and control on the intelligent household equipment is realized through fuzzy rules, so that the complex relation and coupling problems among multiple variables possibly faced by the traditional control method are solved, meanwhile, the judgment of an indoor environment evaluation value Pr is increased, one-layer more judgment standard is provided for the linkage regulation and control process, intelligent management is realized, the indoor environment always keeps extremely high comfort level, and the comfort level of users is improved.
Step three, after the fuzzy neural network control model sends a regulation and control instruction to the target intelligent household equipment, the equipment management platform obtains the state of the target intelligent household equipment, and if the target intelligent household equipment does not respond, the state is fed back to the mobile terminal through the equipment management platform to trigger a repair report instruction;
specifically, the standard that the target intelligent home equipment does not respond is that the energy consumption of the target intelligent home equipment is always 0, then the equipment management platform feeds back the acquired state information of the target intelligent home equipment to the mobile terminal in a wireless signal transmission mode, and a repair instruction is triggered on a user mobile phone APP to realize vibration prompt;
the running state of each intelligent household device can be observed in real time on the visual window provided by the device management platform, the established fuzzy neural network control model is used for adjusting and controlling the target intelligent household device in order to ensure the comfort of the indoor environment, if the condition that the target intelligent household device is in disconnection or fails is met in the adjusting and controlling process, early warning can be timely sent out, and the overhaul work of the target intelligent household device can be accurately and efficiently completed by combining the position information of the target intelligent household device.
Example 2: referring to fig. 2, the application provides an intelligent home equipment management system, which comprises an equipment connection module, an equipment management module and an equipment maintenance module;
the device connection module is used for interacting with all types of intelligent home devices in the device management platform, providing a visual window for the device management platform and acquiring the positions and states of all types of intelligent home devices;
the device management module is used for collecting indoor environment parameter data, preprocessing the indoor environment parameter data, establishing an environment parameter data set, acquiring an indoor environment evaluation value Pr in a target floor based on the environment parameter data set, inputting the environment parameter data set and the indoor environment evaluation value Pr into the established fuzzy neural network control model, and if the indoor environment evaluation value Pr is smaller than a standard threshold value, completing regulation and control on intelligent household devices through the fuzzy neural network control model until the indoor environment evaluation value Pr is more than or equal to the standard threshold value;
the equipment maintenance module is used for acquiring the state of the target intelligent household equipment by the equipment management platform after the fuzzy neural network control model sends a regulation and control instruction to the target intelligent household equipment, and if the target intelligent household equipment does not respond, the equipment maintenance module feeds back the regulation and control instruction to the mobile terminal through the equipment management platform to trigger a repair report instruction;
by using the cooperation among the modules, a fuzzy neural network control model is established to regulate and control the intelligent household equipment, and the used fuzzy neural network has learning capacity, so that the performance of the model can be gradually regulated and improved through training and optimization, and the linkage regulation and control can be adaptively regulated according to actual feedback and data, so that the regulation and control strategy and effect are gradually improved.
The intelligent household equipment management device comprises a management equipment body and a controller, wherein the controller comprises a memory, a processor and a computer program which is stored in the memory and runs on the processor, and the processor realizes the steps of the intelligent household equipment management method when executing the computer program; a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a smart home device management method.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (8)
1. The intelligent household equipment management method is characterized by comprising the following steps of: the method comprises the following steps:
setting up an equipment management platform, interacting with all types of intelligent home equipment in the equipment management platform, providing a visual window for the equipment management platform, and acquiring the positions and states of all types of intelligent home equipment;
collecting indoor environment parameter data, preprocessing, establishing an environment parameter data set, acquiring an indoor environment evaluation value Pr in a target building based on the environment parameter data set, inputting the environment parameter data set and the indoor environment evaluation value Pr into an established fuzzy neural network control model, and if the indoor environment evaluation value Pr is smaller than a standard threshold, completing regulation and control on intelligent household equipment through the fuzzy neural network control model until the indoor environment evaluation value Pr is larger than or equal to the standard threshold;
the indoor environment evaluation value Pr in the target floor is obtained as follows: acquiring indoor temperature T, indoor humidity H and indoor carbon dioxide content C in a target building layer, and correlating to form an indoor environment evaluation value Pr after dimensionless treatment;
；
wherein the meaning of the parameter is 0.，/>And->，/>For the weight, its specific value is set by the user adjustment, +.>Is a constant correction coefficient;
in the fuzzy neural network control model, linkage type regulation is adopted for intelligent household equipment regulation, and the method comprises the following steps:
the first step is to define linkage relation: when the indoor temperature T exceeds a set threshold, the intelligent air conditioner and the intelligent ventilating fan are synchronously started, when the indoor humidity H is lower than a preset threshold, the intelligent humidifier and the intelligent ventilating fan are synchronously started, and when the indoor carbon dioxide content C is lower than a preset threshold, the intelligent ventilating fan and the intelligent air conditioner are synchronously started;
secondly, constructing a CNN convolutional neural network model in the fuzzy neural network model: constructing a CNN convolutional neural network model according to the linkage relation;
third step, defining input variables and membership functions: determining indoor environment parameter data and membership functions of each parameter data according to the linkage relation, wherein the indoor environment parameter data is an input variable, and the membership functions are used for blurring the input variable so as to map continuous input to a blurred language description;
fourth, making fuzzy rules and reasoning rules: based on the linkage relation and the input variable, expressing the linkage relation of the input variable and the output equipment by using a fuzzy IF-THEN rule, wherein the output equipment is intelligent household equipment of each type;
fifth step, training CNN convolutional neural network model: training the learning and adjustment linkage regulation relation by adjusting the weight and the bias of the CNN convolutional neural network model by using a back propagation algorithm;
sixth step, linkage regulation: the CNN convolutional neural network model is applied to the actual linkage regulation of the intelligent household equipment, and is input into the CNN convolutional neural network model according to the current input variable value, and corresponding output signals are obtained through fuzzy reasoning and fuzzy output processing, so that the intelligent household equipment is controlled to carry out linkage regulation;
after the fuzzy neural network control model sends a regulation and control instruction to the target intelligent household equipment, the equipment management platform obtains the state of the target intelligent household equipment, and if the target intelligent household equipment does not respond, the state is fed back to the mobile terminal through the equipment management platform, and a repair report instruction is triggered.
2. The smart home device management method according to claim 1, wherein: each type of smart home device includes at least: the intelligent air conditioner, the intelligent humidifier and the intelligent ventilation fan share the same local area network with the intelligent household equipment and the equipment management platform, and the equipment management platform and each intelligent household equipment are in communication connection through a WIFI protocol.
3. The smart home device management method according to claim 1, wherein: each intelligent household device is internally provided with a GPS (global positioning system) positioner for positioning the position of the intelligent household device, and the state of the intelligent household device at least comprises: real-time operating state and energy consumption.
4. The smart home device management method according to claim 2, wherein: the indoor environment parameter data at least comprises: indoor temperature T, indoor humidity H and indoor carbon dioxide content C, wherein, indoor temperature T's acquisition mode is: the temperature sensor is used for direct measurement, and the indoor humidity H is obtained by the following steps: the humidity sensor is used for direct measurement, and the acquisition mode of the indoor carbon dioxide content C is as follows: direct measurement was performed using a carbon dioxide concentration sensor.
5. A method for managing smart home devices according to claim 3, wherein: the standard that the target intelligent home equipment does not respond is that the energy consumption of the target intelligent home equipment is 0 all the time, then the equipment management platform feeds back the acquired state information of the target intelligent home equipment to the mobile terminal in a wireless signal transmission mode, and a repair instruction is triggered on a user mobile phone APP to realize vibration prompt.
6. An intelligent home equipment management system which is characterized in that: the device comprises a device connection module, a device management module and a device maintenance module;
the device connection module is used for interacting with all types of intelligent home devices in the device management platform, providing a visual window for the device management platform and acquiring the positions and states of all types of intelligent home devices;
the device management module is used for collecting indoor environment parameter data, preprocessing the indoor environment parameter data, establishing an environment parameter data set, acquiring an indoor environment evaluation value Pr in a target floor based on the environment parameter data set, inputting the environment parameter data set and the indoor environment evaluation value Pr into the established fuzzy neural network control model, and if the indoor environment evaluation value Pr is smaller than a standard threshold value, completing regulation and control on intelligent household devices through the fuzzy neural network control model until the indoor environment evaluation value Pr is more than or equal to the standard threshold value;
the indoor environment evaluation value Pr in the target floor is obtained as follows: acquiring indoor temperature T, indoor humidity H and indoor carbon dioxide content C in a target building layer, and correlating to form an indoor environment evaluation value Pr after dimensionless treatment;
；
wherein the meaning of the parameter is 0.，/>And->，/>For the weight, its specific value is set by the user adjustment, +.>Is a constant correction coefficient;
in the fuzzy neural network control model, linkage type regulation is adopted for intelligent household equipment regulation, and the method comprises the following steps:
the first step is to define linkage relation: when the indoor temperature T exceeds a set threshold, the intelligent air conditioner and the intelligent ventilating fan are synchronously started, when the indoor humidity H is lower than a preset threshold, the intelligent humidifier and the intelligent ventilating fan are synchronously started, and when the indoor carbon dioxide content C is lower than a preset threshold, the intelligent ventilating fan and the intelligent air conditioner are synchronously started;
secondly, constructing a CNN convolutional neural network model in the fuzzy neural network model: constructing a CNN convolutional neural network model according to the linkage relation;
third step, defining input variables and membership functions: determining indoor environment parameter data and membership functions of each parameter data according to the linkage relation, wherein the indoor environment parameter data is an input variable, and the membership functions are used for blurring the input variable so as to map continuous input to a blurred language description;
fourth, making fuzzy rules and reasoning rules: based on the linkage relation and the input variable, expressing the linkage relation of the input variable and the output equipment by using a fuzzy IF-THEN rule, wherein the output equipment is intelligent household equipment of each type;
fifth step, training CNN convolutional neural network model: training the learning and adjustment linkage regulation relation by adjusting the weight and the bias of the CNN convolutional neural network model by using a back propagation algorithm;
sixth step, linkage regulation: the CNN convolutional neural network model is applied to the actual linkage regulation of the intelligent household equipment, and is input into the CNN convolutional neural network model according to the current input variable value, and corresponding output signals are obtained through fuzzy reasoning and fuzzy output processing, so that the intelligent household equipment is controlled to carry out linkage regulation;
and the equipment maintenance module is used for acquiring the state of the target intelligent household equipment by the equipment management platform after the fuzzy neural network control model sends the regulation and control instruction to the target intelligent household equipment, and if the target intelligent household equipment does not respond, the equipment maintenance module feeds back the regulation and control instruction to the mobile terminal through the equipment management platform to trigger the repair instruction.
7. An intelligent household equipment management device is characterized in that: comprising a management device body and a controller comprising a memory, a processor and a computer program stored in the memory and running on the processor, the processor implementing the steps of the method according to any one of claims 1-5 when the computer program is executed.
8. A computer-readable storage medium having stored thereon a computer program, characterized by: computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1-5.
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