Cooperative operation method and system of smart home system

Abstract

The invention discloses a cooperative operation method and system of a smart home system, and belongs to the technical field of smart home, and the cooperative operation method and system of the smart home system comprises the following specific schemes: step 1, intelligent equipment state collection: arranging a brightness sensor and a color sensor on light type intelligent equipment, a temperature sensor and a humidity sensor are arranged on the air conditioner, and the sensors collect data once every 30-60 s; 2, data format conversion: converting the original data acquired by the sensor in the step 1 into a digital signal and transmitting the digital signal to a central controller; according to the invention, the state of each device in the smart home system is modeled by establishing the mathematical model, the collaborative optimization model is constructed in combination with the user demand, and the optimal device control strategy is solved by using the optimization algorithm. Compared with a traditional smart home system, the system can effectively improve the cooperative operation efficiency between devices, reduces the energy consumption, and improves the user experience.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart home, and particularly relates to a method and system for collaborative operation of a smart home system. Background Art

[0002] With the development of Internet of Things technology, smart home systems have gradually become popular. However, the current collaborative operation efficiency among devices in smart home systems is relatively low, and the overall performance of the system cannot be fully utilized. For example, in terms of energy management, there is a lack of effective overall planning for the energy consumption of different devices, resulting in energy waste; in terms of device control, the response times of various devices are inconsistent, affecting the user experience. Therefore, a method and system that can improve the collaborative operation efficiency of smart home systems are needed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the above-mentioned disadvantages of the prior art and provide a method and system for collaborative operation of a smart home system.

[0004] The technical solution adopted to solve the above technical problem is: A method for collaborative operation of a smart home system includes the following specific solutions:

[0005] Step 1: Smart device status collection:

[0006] A brightness sensor and a color sensor are set on lighting smart devices, and a temperature sensor and a humidity sensor are equipped on the air conditioner. The sensors collect data every 30 - 60 s.

[0007] Step 2: Data format conversion:

[0008] The original data collected by the sensors in Step 1 is converted into digital signals and transmitted to the central controller.

[0009] Step 3: User demand acquisition:

[0010] The user interacts with the smart home system through the mobile APP. When the user completes the demand setting on the APP and clicks to confirm, the APP sends the user-entered data to the central controller through the wireless network.

[0011] Step 4: Collaborative optimization calculation:

[0012] The optimization calculation unit of the central controller starts to work. First, an initial population is generated. The initial population is composed of a series of randomly generated device state vector combinations. The value range of each device state vector is determined according to the physical characteristics and normal working range of the device, and then the fitness value is calculated.

[0013] Step 5: Genetic operation:

[0014] After the fitness value calculation is completed, the selection, crossover, and mutation operations of the genetic algorithm are carried out. After one round of genetic operations, the fitness values of the individuals in the new population are calculated again, and then it is judged whether the preset termination conditions are met. The termination condition is to reach the maximum number of iterations, with a value range of 500 - 600. If the termination conditions are not met, the genetic operations continue; if the termination conditions are met, the calculation stops, and the individual with the best fitness value in the current population is used as the optimal solution of the collaborative optimization model, that is, the optimal device state vector is obtained.

[0015] Step Six: Device Control:

[0016] The device control unit of the central controller generates corresponding device control instructions according to the optimal device state vector obtained from the collaborative optimization calculation. The encoded control instructions are sent to the corresponding intelligent devices in a wireless communication manner through the communication module of the central controller. After the communication module of the intelligent device receives the instructions, it decodes them and transmits the instructions to the execution module of the device, thus completing the collaborative operation process of the smart home system.

[0017] Through the above technical solution, a mathematical model is established to model the states of various devices in the smart home system, and a collaborative optimization model is constructed in combination with user requirements. The optimal device control strategy is obtained by using an optimization algorithm. Compared with the traditional smart home system, the present invention can effectively improve the collaborative operation efficiency between devices, reduce energy consumption, and enhance the user experience.

[0018] Furthermore, the fitness value calculation adopts the following specific formula:

[0019] For each individual in the initial population, the optimization calculation unit calculates according to the optimization function F(S1, S2, …, S N , D) in the collaborative optimization model, where F is the optimization function, S N is the state vector, including the light brightness, air conditioner temperature, room humidity, the on / off state of the device, and the device operation time, and D is the target vector. The specific expression of the optimization function F is:

[0020]

[0021] Among them, is the difference between the device state and the user requirements, is the device energy consumption, is the device response time. Finally, the sum of these three parts is used as the fitness value;

[0022] w ij is the weight coefficient, and λ1, λ2 are penalty factors, both of which are set during system initialization.

[0023] Furthermore, the selection, crossover, and mutation operations of the genetic algorithm include:

[0024] Selection operation: The roulette wheel selection method is adopted. According to the fitness values of individuals, a selection probability is assigned to each individual. The higher the fitness value of an individual, the greater the probability of being selected. Through random selection, a certain number of individuals are selected from the initial population to form a new population.

[0025] Crossover operation: In the new population, two individuals are randomly selected as parents, and their device state vectors are subjected to a crossover operation according to a crossover probability of 80%.

[0026] Mutation operation: With a mutation probability of 5%, the individuals in the new population are subjected to a mutation operation. A certain device state vector element in the individual is randomly selected and its value is changed according to the mutation rule.

[0027] Through the above technical solution, the regulation of the device can be optimized to prevent the user from being unable to find the best regulation solution.

[0028] Furthermore, the mutation rule is specifically as follows: For the target set value of the device, it is randomly increased or decreased by 4%.

[0029] Through the above technical solution, the target value can be optimized and regulated, thus achieving the best user experience.

[0030] Furthermore, it includes a sensor module, a communication module, an execution module, a collection unit, a user demand receiving unit, an optimization calculation unit, and a device control unit. The sensor module is responsible for collecting the state information of the device itself; the communication module is responsible for data transmission with the central controller; the execution module is responsible for performing corresponding operations according to the instructions of the central controller.

[0031] Through the above technical solution, the state of the device can be accurately collected, thus facilitating the central controller to perform accurate regulation.

[0032] Furthermore, the data collection unit is responsible for collecting the state information of each intelligent device; the user demand receiving unit is responsible for obtaining the demand information input by the user; the optimization calculation unit is responsible for calculating the optimal device state using the above collaborative optimization model and optimization algorithm; the device control unit is responsible for sending control instructions to each intelligent device according to the calculation results.

[0033] Through the above technical solution, the device can be accurately controlled to meet the user's demands.

[0034] The beneficial effects of the present invention are as follows: The present invention models the states of various devices in the smart home system by establishing a mathematical model, constructs a collaborative optimization model in combination with user demands, and uses an optimization algorithm to solve for the optimal device control strategy. Compared with the traditional smart home system, the present invention can effectively improve the collaborative operation efficiency between devices, reduce energy consumption, and enhance the user experience. Brief Description of the Drawings

[0035] Figure 1 is the flowchart of the method of the present invention. Detailed Embodiment

[0036] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0037] As Figure 1 shown, a collaborative operation method and system of a smart home system in this embodiment include the following specific solutions:

[0038] Step 1: Smart device status collection:

[0039] A brightness sensor and a color sensor are set on smart devices of the lighting type, and a temperature sensor and a humidity sensor are equipped on the air conditioner. The sensors collect data every 30 - 60 s;

[0040] Step 2: Data format conversion:

[0041] The original data collected by the sensors in Step 1 is converted into digital signals and transmitted to the central controller;

[0042] Step 3: User requirement acquisition:

[0043] The user interacts with the smart home system through the mobile APP. When the user completes the requirement setting on the APP and clicks to confirm, the APP sends the data input by the user to the central controller through the wireless network;

[0044] Step 4: Collaborative optimization calculation:

[0045] The optimization calculation unit of the central controller starts to work. First, an initial population is generated. The initial population is composed of a series of randomly generated device state vectors. The value range of each device state vector is determined according to the physical characteristics and normal working range of the device, and then the fitness value is calculated;

[0046] Step 5: Genetic operation:

[0047] After the fitness value calculation is completed, the selection, crossover, and mutation operations of the genetic algorithm are carried out. After one round of genetic operations, the fitness values of the individuals in the new population are calculated again, and then it is judged whether the preset termination condition is satisfied. The termination condition is to reach the maximum number of iterations, with a value range of 500 - 600. If the termination condition is not satisfied, the genetic operations continue; if the termination condition is satisfied, the calculation stops, and the individual with the optimal fitness value in the current population is used as the optimal solution of the collaborative optimization model, that is, the optimal device state vector is obtained.

[0048] Step Six: Device Control:

[0049] The device control unit of the central controller generates corresponding device control instructions according to the optimal device state vector obtained from the collaborative optimization calculation. The encoded control instructions are sent to the corresponding intelligent device in a wireless communication manner through the communication module of the central controller. After the communication module of the intelligent device receives the instruction, it decodes it and transmits the instruction to the execution module of the device, thus completing the collaborative operation process of the smart home system. A mathematical model is established to model the states of the devices in the smart home system, and a collaborative optimization model is constructed in combination with user requirements. The optimal device control strategy is obtained by solving using an optimization algorithm. Compared with the traditional smart home system, the present invention can effectively improve the collaborative operation efficiency between devices, reduce energy consumption, and enhance the user experience.

[0050] The fitness value calculation adopts the following specific formula:

[0051] For each individual in the initial population, the optimization calculation unit calculates according to the optimization function F(S1, S2, …, S N , D) in the collaborative optimization model, where F is the optimization function, S N is the state vector, including the light brightness, air conditioner temperature, room humidity, the on / off state of the device, and the device operation time, and D is the target vector. The specific expression of the optimization function F is:

[0052]

[0053] Among them, is the difference between the device state and the user requirements, is the device energy consumption, is the device response time. Finally, the sum of these three parts is used to obtain the fitness value;

[0054] w ij is the weight coefficient, and λ1, λ2 are penalty factors, both of which are set during system initialization.

[0055] The selection, crossover, and mutation operations of the genetic algorithm include:

[0056] Selection operation: The roulette wheel selection method is adopted. According to the fitness values of individuals, a selection probability is assigned to each individual. The higher the fitness value of an individual, the greater the probability of being selected. Through random selection, a certain number of individuals are selected from the initial population to form a new population;

[0057] Crossover operation: In the new population, two individuals are randomly selected as parents, and their device state vectors are subjected to crossover operation according to a crossover probability of 80%;

[0058] Mutation operation: With a mutation probability of 5%, the individuals in the new population are subjected to mutation operation. A certain device state vector element in the individual is randomly selected and its value is changed according to the mutation rule, which can optimize the regulation of the device and prevent the user from being unable to find the optimal regulation plan.

[0059] The specific mutation rule is as follows: For the target set value of the device, it is randomly increased or decreased by 4%, which can optimize the regulation of the target value, so as to achieve the best user experience.

[0060] It includes a sensor module, a communication module, an execution module, a data acquisition unit, a user demand receiving unit, an optimization calculation unit, and a device control unit. The sensor module is responsible for collecting the state information of the device itself; the communication module is responsible for data transmission with the central controller; the execution module is responsible for performing corresponding operations according to the instructions of the central controller, and can accurately collect the state of the device, so as to facilitate the central controller to perform accurate regulation.

[0061] The data acquisition unit is responsible for collecting the state information of each intelligent device; the user demand receiving unit is responsible for obtaining the demand information input by the user; the optimization calculation unit is responsible for calculating the optimal device state by using the above-mentioned collaborative optimization model and optimization algorithm; the device control unit is responsible for sending control instructions to each intelligent device according to the calculation results, and can accurately control the device to meet the user's needs.

[0062] The above is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A collaborative operation method of a smart home system, characterized in that: The following specific plans are included: Step 1: Smart device status collection: Brightness sensors and color sensors are installed on lighting smart devices, and temperature sensors and humidity sensors are installed on air conditioners. The sensors collect data every 30-60 seconds. Step 2: Data format conversion: Convert the raw data collected by the sensor in step 1 into digital signals and transmit them to the central controller; Step 3: Obtaining user needs: Users interact with the smart home system through mobile phone APP. When users complete the required settings on the APP and click to confirm, the APP sends the user input data to the central controller via the wireless network; Step 4: Collaborative optimization calculation: The optimization calculation unit of the central controller starts working. First, the initial population is generated. The initial population is composed of a series of randomly generated device state vectors. The value range of each device state vector is determined according to the physical characteristics of the device and the normal working range. Then the fitness value is calculated. Step 5: Genetic operation: After completing the fitness value calculation, the genetic algorithm selection, crossover and mutation operations are performed. After a round of genetic operations, the fitness values ​​of individuals in the new population are calculated again, and then it is determined whether the preset termination condition is met. The termination condition is to reach the maximum number of iterations, which is 500-600. If the termination condition is not met, the genetic operation continues; if the termination condition is met, the calculation is stopped, and the individual with the best fitness value in the current population is used as the optimal solution of the collaborative optimization model, that is, the optimal device state vector is obtained; Step 6: Device Control: The device control unit of the central controller generates corresponding device control instructions based on the optimal device state vector obtained by collaborative optimization calculation. The encoded control instructions are sent to the corresponding smart devices through the communication module of the central controller in the form of wireless communication. After receiving the instructions, the communication module of the smart device decodes them and transmits them to the execution module of the device, thereby completing the collaborative operation process of the smart home system.

2. The method for cooperative operation of a smart home system according to claim 1, characterized in that: The fitness value is calculated using the following specific formula: For each individual in the initial population, the optimization calculation unit calculates the optimal function F(S1, S2, ..., S N ,D), where F is the optimization function, S N is the state vector, including light brightness, air conditioning temperature, room humidity, equipment switch and equipment running time, D is the target vector, and the specific expression of the optimization function F is: in, is the difference between the device status and the user's needs, is the energy consumption of the equipment, is the device response time, and finally these three parts are added together to get the fitness value; w ij is the weight coefficient, λ1 and λ2 are penalty factors, all of which are set when the system is initialized.

3. The collaborative operation method of a smart home system according to claim 2, characterized in that: The selection, crossover and mutation operations of the genetic algorithm include: Selection operation: Roulette selection method is used to assign a selection probability to each individual according to the individual's fitness value. The higher the fitness value of the individual, the greater the probability of being selected. Through random selection, a certain number of individuals are selected from the initial population to form a new population; Crossover operation: In the new population, two individuals are randomly selected as parents, and their device state vectors are crossovered with a crossover probability of 80%. Mutation operation: With a mutation probability of 5%, mutation operation is performed on individuals in the new population. An element of the device state vector in the individual is randomly selected and its value is changed according to the mutation rule.

4. The method for cooperative operation of a smart home system according to claim 3, characterized in that: The specific variation rule is: for the target setting value of the equipment, randomly increase or decrease it by 4%.

5. The collaborative operation system of a smart home system according to claim 4, characterized in that: It includes a sensor module, a communication module, an execution module, a collection unit, a user demand receiving unit, an optimization calculation unit and a device control unit. The sensor module is responsible for collecting the device's own status information; The communication module is responsible for data transmission with the central controller; the execution module is responsible for executing corresponding operations according to the instructions of the central controller.

6. The collaborative operation system of a smart home system according to claim 5, characterized in that: The data acquisition unit is responsible for collecting the status information of each intelligent device; The user demand receiving unit is responsible for obtaining the demand information input by the user; the optimization calculation unit is responsible for calculating the optimal device state using the above-mentioned collaborative optimization model and optimization algorithm; and the device control unit is responsible for sending control instructions to each intelligent device based on the calculation results.

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