Method, system, apparatus and storage medium for smart home energy management

Abstract

The application relates to the technical field of smart home, and discloses a method, system and device for smart home energy management and a storage medium. The method comprises the following steps: obtaining a current comfort matrix of weighted information of using home appliance equipment corresponding to a current user according to daily home appliance use behavior data of the current user stored on a blockchain platform and operation data of the home appliance equipment in the smart home; obtaining home appliance equipment use parameter prediction information matched with the current comfort matrix and generating a current energy coordination strategy corresponding to the smart home under the condition of dynamic balance between user comfort and energy cost; and controlling the home appliance equipment of the smart home according to the current energy coordination strategy through a smart contract function of the blockchain platform. In this way, the user behavior data and the operation data of the home appliance equipment are stored and managed through the decentralized blockchain platform, the safety and privacy of the user behavior data are ensured, the energy management is optimized, and the household energy consumption cost is reduced.

Description

Technical Field

[0001] This application relates to the field of smart home technology, such as methods, systems, devices, and storage media for smart home energy management. Background Technology

[0002] With the rapid development of smart home technology, smart appliances are becoming increasingly common. However, most existing smart home systems adopt a centralized data management approach, where all device data is uploaded to a mobile phone or cloud server for processing via a unified interface. This approach carries the risk of data leakage and privacy violations, because if the mobile phone or cloud server is hacked, the user's smart home device data will be exposed. Furthermore, the lack of collaboration between existing smart home appliances leads to uneven energy distribution throughout the house, low efficiency, and a poor user experience.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0005] This disclosure provides methods, apparatus, systems, and storage media for energy management in smart homes, addressing the technical problem that the intelligence of energy management in smart homes needs to be improved.

[0006] In some embodiments, the method includes:

[0007] Based on the daily home appliance usage behavior data of the current user stored on the blockchain platform, as well as the operation data of home appliances in the smart home, a current comfort matrix is ​​obtained with weighted information on the use of home appliances corresponding to the current user.

[0008] Under the dynamic balance between user comfort and energy costs, the system obtains the predicted information of home appliance usage parameters that match the current comfort matrix, and generates the current energy coordination strategy corresponding to the smart home.

[0009] By leveraging the smart contract functionality of a blockchain platform, smart home appliances can be controlled based on current energy synergy strategies.

[0010] In some embodiments, it also includes:

[0011] By using IoT technology, the current registration information of smart home appliances can be obtained and registered and certified on a blockchain platform.

[0012] In some embodiments, it also includes:

[0013] Through IoT technology, data on the current user's current appliance usage behavior reported by the current home appliances in the smart home is obtained and stored in the user's daily appliance usage behavior data on the blockchain platform;

[0014] By using IoT technology, the system acquires the current operating data reported by smart home appliances and stores it on a blockchain platform.

[0015] In some embodiments, the current comfort matrix for obtaining weighted information on the use of home appliances corresponding to the current user includes:

[0016] Based on current user data on daily home appliance usage and the operating data of smart home appliances, the k-means clustering algorithm is used to form clusters based on similarity, resulting in a matrix with 3 K values. The load level corresponding to a K value of 3 is the cluster center.

[0017] The region corresponding to a K value of 3 in the matrix is ​​positioned as the region of maximum comfort, the region corresponding to a K value of 2 is positioned as the region of average comfort, and the region corresponding to a K value of 1 is positioned as the region of minimum comfort, thus obtaining the current comfort matrix.

[0018] In some embodiments, obtaining the predicted information of home appliance usage parameters based on the current comfort matrix includes:

[0019] Based on the current comfort matrix, daily home appliance usage data, and energy costs, an initial population containing multiple solutions is generated. In this population, each chromosome and gene represents a home appliance in the smart home. The gene structure on the chromosome is composed of binary vectors determined by the on / off status of the home appliances per hour. Each solution represents the usage parameter information or operating strategy of each home appliance in the smart home.

[0020] Using the fast elite multi-objective genetic algorithm NSGA-II, genetic operations are performed starting from the initial population. Once the preset number of generations is reached, predicted information on the usage parameters of home appliances is obtained.

[0021] In some embodiments, it also includes:

[0022] The operating status information of home appliances and the energy usage information of smart homes are presented through the user interaction platform;

[0023] User settings and feedback information are obtained through the user interaction platform and stored in the corresponding data on the blockchain platform.

[0024] In some embodiments, the system for smart home energy management further includes: a blockchain platform and an analytics management platform, wherein,

[0025] The analysis and management platform is configured to obtain a current comfort matrix based on the user's daily home appliance usage behavior data and the operating data of smart home appliances stored on the blockchain platform. This matrix is ​​weighted with information on the user's home appliance usage. Under the dynamic balance between user comfort and energy costs, the platform obtains predicted home appliance usage parameters matching the current comfort matrix and generates a corresponding current energy coordination strategy for the smart home. Finally, through the smart contract function of the blockchain platform, the platform controls the smart home appliances according to the current energy coordination strategy.

[0026] In some embodiments, the device for smart home energy management includes:

[0027] The matrix module is configured to obtain a current comfort matrix based on the current user's daily home appliance usage behavior data stored on the blockchain platform and the operation data of home appliances in the smart home, which is a weighted information of the current user's use of home appliances.

[0028] The prediction module is configured to obtain the predicted information of the usage parameters of home appliances that match the current comfort matrix under the condition of dynamic balance between user comfort and energy cost, and generate the current energy coordination strategy corresponding to the smart home.

[0029] The management and control module is configured to control smart home appliances based on the current energy coordination strategy through the smart contract function of the blockchain platform.

[0030] In some embodiments, the apparatus for smart home energy management includes a processor and a memory storing program instructions, the processor being configured to execute the above-described method for smart home energy management when executing the program instructions.

[0031] In some embodiments, the storage medium stores program instructions that, when executed, perform the above-described method for smart home energy management.

[0032] The method, apparatus, and system for smart home energy management provided in this disclosure can achieve the following technical effects:

[0033] By dynamically balancing user comfort and energy costs, and based on daily appliance usage data and appliance operation data stored on the blockchain platform, predictive information on appliance usage parameters is obtained. This generates a corresponding energy coordination strategy for the smart home. Through the smart contract function of the blockchain platform, the smart home appliances are controlled according to the current energy coordination strategy. In this way, by storing and managing user behavior data and home appliance operation data through a decentralized blockchain platform, the security and privacy of user behavior data are ensured, preventing data leakage and privacy violations. Furthermore, by obtaining an energy coordination strategy under the dynamic balance between user comfort and energy costs, energy management is optimized and household energy consumption costs are reduced.

[0034] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0035] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0036] Figure 1 This is a schematic diagram of an architecture for a smart home energy management system provided in an embodiment of this disclosure;

[0037] Figure 2 This is a schematic flowchart of a smart home energy management method provided in an embodiment of this disclosure;

[0038] Figure 3 This is a schematic diagram of a clustering process for obtaining a comfort matrix, provided in an embodiment of this disclosure.

[0039] Figure 4 This is a schematic diagram of a process for obtaining predicted information on the usage parameters of home appliances by matching the current comfort matrix using the NSGA-II algorithm, provided in an embodiment of this disclosure.

[0040] Figure 5 This is a schematic diagram of platform interaction for smart home energy management provided in an embodiment of the present disclosure;

[0041] Figure 6 This is a schematic diagram of information interaction for smart home energy management provided in an embodiment of this disclosure;

[0042] Figure 7 This is a schematic diagram of a smart home energy management device provided in an embodiment of this disclosure;

[0043] Figure 8This is a schematic diagram of a smart home energy management device provided in an embodiment of this disclosure. Detailed Implementation

[0044] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0045] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0046] Unless otherwise stated, the term "multiple" means two or more.

[0047] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0048] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0049] A distributed system for big data storage is a storage architecture that distributes data across multiple physical nodes. This system achieves high availability and load balancing through mechanisms such as data sharding, replication, and distribution. In this embodiment, the blockchain platform is a distributed platform for big data storage. It can store and manage user behavior data and home appliance operating data through a decentralized blockchain platform, thus ensuring the security and privacy of user behavior data, preventing data leakage and privacy violations. Furthermore, by dynamically balancing user comfort and energy costs, based on the daily appliance usage behavior data and appliance operating data stored on the blockchain platform, it obtains predicted information on appliance usage parameters and generates a corresponding current energy coordination strategy for the smart home. Through the smart contract function of the blockchain platform, it controls the smart home appliances according to the current energy coordination strategy, thereby optimizing energy management, reducing household energy costs, and providing personalized energy-saving suggestions based on user habits and preferences, increasing user awareness and participation in energy conservation, and further improving the user experience.

[0050] Figure 1 This is a schematic diagram of an architecture for a smart home energy management system provided in an embodiment of this disclosure. Figure 1 As shown, the smart home energy management system includes: a blockchain platform 100 and an analytics management platform 200. Currently, this system manages the energy of a smart home; therefore, it also includes: an Internet of Things (IoT) platform 300. The IoT platform includes: two or more home appliances, such as refrigerators, washing machines, televisions, air conditioners, etc., temperature sensors, pressure sensors, humidity sensors, etc., and the communication protocols for building the IoT platform.

[0051] The analysis and management platform 200 is configured to obtain a current comfort matrix based on the current user's daily home appliance usage behavior data and the operating data of home appliances in the smart home, stored on the blockchain platform 100; under the dynamic balance between user comfort and energy costs, it obtains predicted information on home appliance usage parameters matching the current comfort matrix and generates a current energy coordination strategy for the smart home; and through the smart contract function of the blockchain platform 100, it controls the home appliances in the smart home according to the current energy coordination strategy.

[0052] As can be seen, the analysis and management platform 200 is connected to the blockchain platform 100, which in turn needs to be connected not only to the analysis and management platform 200 but also to the IoT platform 300. Therefore, the home appliances in the IoT platform 300 need to be connected to the system for smart home energy management via IoT technology. In some embodiments, the blockchain platform 100 is configured to obtain the current registration information of the home appliances in the smart home through IoT technology and register and authenticate them on the blockchain platform. That is, each home appliance in the IoT platform 300 can be registered and authenticated on the blockchain platform, thus ensuring the legitimacy and security of the home appliance's identity.

[0053] The blockchain platform 100 can store and manage user behavior data and home appliance operation data. Therefore, in some embodiments, the blockchain platform 100 is configured to acquire, via IoT technology, the current user's current appliance usage behavior data reported by the current home appliances in the smart home, and store it in the current user's daily appliance usage behavior data on the blockchain platform; and to acquire, via IoT technology, the current operation data reported by the current home appliances in the smart home, and store it in the current home appliance operation data on the blockchain platform. That is, registered and certified home appliances can report corresponding data information via IoT technology, including: the current user's current appliance usage behavior data and the current operation data of the current home appliances, which are then stored and managed by the blockchain platform. In this way, blockchain technology achieves data storage and consensus among devices, ensuring data security and immutability. Although the number of home appliances may be relatively small, the distributed storage and decentralized characteristics of blockchain can significantly improve data security and prevent data leakage and tampering.

[0054] In this way, after the blockchain platform 100 stores user behavior data and the operating data of home devices, the analysis and management platform 200 can use artificial intelligence algorithms such as deep learning to predict the user's future behavior based on the data stored in the blockchain platform 100. Under the dynamic balance between user comfort and energy costs, it can formulate corresponding smart home energy coordination strategies based on the user behavior prediction results and device status. In this way, through artificial intelligence analysis, accurate prediction of user behavior can be achieved, thereby optimizing energy allocation, reducing energy consumption costs, and enabling collaborative work between devices and optimized energy utilization.

[0055] After the blockchain platform 100 formulates the current energy coordination strategy for smart homes, it can also control smart home appliances according to the current energy coordination strategy through the smart contract function of the blockchain platform. In this way, automatic scheduling and energy sharing between devices can be realized, thereby improving the overall energy efficiency of the home.

[0056] Of course, in some embodiments, as shown in the figure, the smart home energy management system may further include a user interaction platform 400. The user interaction platform 400 may include one, two, or more terminal devices, such as smartphone terminals, user interface terminals, voice terminals, visual terminals, etc. In this way, the user interaction platform 400 can present and display the working status information of home devices and the energy usage information of the smart home. It can also obtain user setting information and feedback information. Specifically, the analysis and management platform 200 is configured to present the working status information of home devices and the energy usage information of the smart home through the user interaction platform 400; and to obtain user setting information and feedback information through the user interaction platform 400 and store it in the corresponding data on the blockchain platform 100, thereby improving the interactivity and user experience of the system.

[0057] It is evident that in systems used for smart home energy management, the analysis and management platform can generate corresponding energy coordination strategies for smart homes based on data stored on the blockchain platform; and through the smart contract function of the blockchain platform, it can control home appliances to operate accordingly, thereby achieving optimized energy allocation and reducing energy costs.

[0058] Figure 2 This is a flowchart illustrating a method for smart home energy management provided in an embodiment of this disclosure. (In conjunction with...) Figure 2 The process of smart home energy management includes:

[0059] Step 201: Based on the daily home appliance usage behavior data of the current user stored on the blockchain platform, and the operation data of home appliances in the smart home, obtain the current comfort matrix of weighted information on the use of home appliances corresponding to the current user.

[0060] Of course, home appliances in smart homes need to be registered and certified on the blockchain platform to determine the legitimacy and security of the device identity in the distributed storage system. In some embodiments, IoT technology is used to obtain the current registration information of the current home appliances in the smart home and register and certify them on the blockchain platform.

[0061] The blockchain platform needs to store users' daily home appliance usage behavior data and the operational data of home appliances. Therefore, registered and certified home appliances can report the following data: the current user's current home appliance usage behavior data and the current operational data of the current home appliances. Specifically, in some embodiments, IoT technology is used to acquire the current user's current home appliance usage behavior data reported by the current home appliances in the smart home and store it in the current user's daily home appliance usage behavior data on the blockchain platform; IoT technology is also used to acquire the current operational data reported by the current home appliances in the smart home and store it in the current home appliance operational data on the blockchain platform. The current user's current home appliance usage behavior data includes: electricity consumption, water consumption, home appliance usage time, and home appliance usage duration. The operational data of the home appliances includes: on / off status, operating parameter values, etc.

[0062] Therefore, based on the data stored on the blockchain platform, artificial intelligence algorithms such as deep learning can be used to predict users' future behavior. Among these, data analysis can be performed on the data stored on the blockchain platform to obtain data such as home appliance load data and energy costs, and these data can be stored accordingly. Thus, based on the data stored on the blockchain platform, a current comfort matrix can be obtained with weighted information on the current user's use of home appliances.

[0063] There are various ways to predict future user behavior using artificial intelligence algorithms, such as machine learning-based prediction algorithms, time series analysis-based prediction algorithms, clustering and classification-based prediction algorithms, reinforcement learning-based prediction algorithms, and so on. The K-means algorithm is a clustering algorithm primarily used to divide a dataset into K clusters, ensuring that data points within each cluster are as similar as possible, while data points between different clusters are as different as possible. The core idea of ​​the K-means algorithm is to find the optimal cluster partitioning through iterative optimization. Therefore, in some embodiments, obtaining the current comfort matrix based on the weighted information of the user's use of home appliances includes: based on the user's daily home appliance usage behavior data and the operating data of home appliances in the smart home, using the k-means clustering algorithm to form clusters based on similarity, resulting in a matrix with 3 K values, where the load level corresponding to a K value of 3 is the cluster center; the region corresponding to a K value of 3 in the matrix is ​​positioned as the maximum comfort region, the region corresponding to a K value of 2 is positioned as the average comfort region, and the region corresponding to a K value of 1 is positioned as the minimum comfort region, thus obtaining the current comfort matrix.

[0064] The comfort matrix represents a weighted average of how users use appliances throughout the day, including weighted information on appliance usage. Generating the comfort matrix using artificial intelligence allows for a more accurate representation of user comfort. To quantify user comfort, some embodiments use three levels of indicators: maximum, average, and minimum comfort. Maximum comfort, represented by the number 3, defines the periods when users frequently use smart home appliances. Average comfort, represented by the number 2, represents the periods when users occasionally use some appliances, while minimum comfort, represented by the number 1, defines the periods when users rarely use appliances. To obtain the comfort matrix, a k-means algorithm is applied to historical smart home appliance data to form clusters based on similarity. This historical data includes: daily appliance usage behavior data stored on a blockchain platform, appliance operation data, and load data of the appliances obtained through analysis of this data.

[0065] like Figure 3 As shown, the steps for obtaining the comfort matrix through clustering may include:

[0066] Step 301: Determine the number of groups, k, to be 3.

[0067] That is, K = 3 load levels.

[0068] Step 302: Determine the load level corresponding to K value 3 as the cluster center.

[0069] The load level corresponding to K=3 is determined as the cluster center. In this case, the center of the group is the arithmetic mean of the load thresholds associated with that group.

[0070] Step 303: Associate the remaining nk load levels in the historical data with the most recent center and recalculate the center value of the added group.

[0071] Historical data includes: analysis of users' daily home appliance usage behavior data and home appliance operation data stored on the blockchain platform to obtain home appliance load data.

[0072] Step 304: Traverse the entire historical data corresponding to the current user stored on the blockchain platform and associate each load level with the nearest central cluster.

[0073] When associating the load level with a group that is different from the previously associated group, it is necessary to recalculate the addition and reduction of cluster centers.

[0074] Step 305: Observe whether there are any further clustering changes in the load level of the historical data? If yes, proceed to step 306; otherwise, return to step 304.

[0075] Step 306: Obtain the corresponding first matrix, and in the first matrix, locate the region corresponding to K value 3 as the maximum comfort region, the region corresponding to K value 2 as the average comfort region, and the region corresponding to K value 1 as the minimum comfort region, to obtain the current comfort matrix.

[0076] Therefore, by using the k-means algorithm and the data stored on the blockchain platform, the current comfort matrix corresponding to the current user can be obtained.

[0077] Step 202: Under the dynamic balance between user comfort and energy cost, obtain the predicted information of home appliance usage parameters matching the current comfort matrix, and generate the current energy coordination strategy corresponding to the smart home.

[0078] Based on the current comfort matrix, prediction is a dynamic balance problem between smart home user comfort and energy costs. It determines the optimal time periods and usage patterns for each appliance under ideal conditions, thus providing predicted usage parameters. Furthermore, in real-world scenarios, users do not always use appliances according to the optimal strategy analyzed by artificial intelligence. Therefore, in some implementations, the strategy is continuously adjusted and optimized by considering user behavior, the comfort matrix, and energy costs. Solving the dynamic balance between user comfort and energy costs in smart homes is a multi-objective optimization problem. NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a classic multi-objective optimization genetic algorithm. Therefore, in some embodiments, the NSGA-II algorithm can be used to obtain the predicted information of home appliance usage parameters matching the current comfort matrix under the dynamic balance between user comfort and energy costs. This can include: generating an initial population containing multiple solutions based on the current comfort matrix, daily home appliance usage behavior data, and energy costs. In this population, each chromosome and gene represents a home appliance in the smart home. The gene structure on the chromosome is composed of binary vectors determined by the on / off status of home appliances per hour. Each solution represents the usage parameter information or operating strategy of each home appliance in the smart home. Using the fast elite multi-objective genetic algorithm NSGA-II, genetic operations are performed starting from the initial population. After reaching a preset number of generations, the predicted information of home appliance usage parameters is obtained.

[0079] like Figure 4 As shown, the predicted information of home appliance usage parameters obtained by matching the current comfort matrix using the fast elite multi-objective genetic algorithm NSGA-II includes:

[0080] Step 401: Based on the current comfort matrix, daily home appliance usage data, and energy costs, randomly generate an initial population containing multiple solutions.

[0081] Each chromosome in the population represents a space, and a gene on that chromosome represents a home appliance in the smart home. The gene structure on the chromosome can be composed of binary vectors of length 24, where each position describes the hour of the day, with 1 indicating that the appliance is running at the corresponding time and 0 indicating that the appliance is off. Each solution represents the usage parameter information or operating strategy of a particular home appliance in the smart home.

[0082] Step 402: Sort the individuals in the population using the fast non-dominated sorting algorithm and divide the individuals into different non-dominated levels; calculate the crowding degree of each individual to obtain the distribution density of the individuals in the target space.

[0083] Step 403: Select individuals from the population using a tournament selection strategy to perform crossover or mutation operations to generate a new offspring population.

[0084] Crossover generates new individuals by exchanging some genes of parent individuals, while mutation introduces new diversity by randomly changing the gene values ​​of individuals.

[0085] Step 404: When generating the new generation of population, an elite strategy is used to retain the best individuals from the parent and offspring generations to obtain the first population.

[0086] Step 405: Determine if the preset generation number has been reached? If yes, proceed to step 406; otherwise, return to step 402.

[0087] Step 406: Based on the first group, obtain the predicted information of the usage parameters of home appliances.

[0088] Since each solution in the population represents the usage parameter information or operating strategy of various home appliances in the smart home, after multiple genetic iterations, the usage parameter prediction information of home appliances can be obtained based on the solutions corresponding to the population.

[0089] It is evident that, under the dynamic balance between user comfort and energy costs, the predicted information of home appliance usage parameters can be obtained, that is, the usage parameter information or operation strategy of each home appliance in the smart home can be obtained. Therefore, the predicted information of home appliance usage parameters matched with the current comfort matrix can be used to generate the current energy coordination strategy corresponding to the smart home.

[0090] Step 203: Control smart home appliances based on the current energy coordination strategy through the smart contract function of the blockchain platform.

[0091] The smart contract function of the blockchain platform can be used to automatically execute the current energy coordination strategy, thereby enabling collaborative work between devices and optimized energy utilization.

[0092] As can be seen, in this embodiment, under the dynamic balance between user comfort and energy costs, the daily home appliance usage behavior data and the operation data of home appliances stored on the blockchain platform are used to obtain home appliance usage parameter prediction information, generate the current energy coordination strategy corresponding to the smart home, and control the home appliances of the smart home according to the current energy coordination strategy through the smart contract function of the blockchain platform. In this way, energy management is optimized and household energy consumption costs are reduced.

[0093] Of course, the system for smart home energy management also includes a user interaction platform. Therefore, in some embodiments, the method for smart home energy management further includes: presenting the working status information of home devices and the energy usage information of the smart home through the user interaction platform; obtaining user settings and feedback information through the user interaction platform and storing them in the corresponding data on the blockchain platform. This allows for personalized energy-saving suggestions based on user habits and preferences, increasing user awareness and participation in energy conservation, and further improving the user experience. In addition, the blockchain platform ensures data security and immutability, preventing data leakage and tampering.

[0094] The following describes the operational process in a specific embodiment, illustrating the energy management process for smart homes provided by the embodiments of the present invention.

[0095] In one embodiment of this disclosure, the system for smart home energy management can be as follows: Figure 1 As shown, the system includes: a blockchain platform, a sharing management platform, an IoT platform, and a user interaction platform. The IoT platform includes home appliances such as washing machines, refrigerators, air conditioners, televisions, kettles, and robot vacuum cleaners. These devices are already registered and certified on the blockchain platform. Thus, the platform interaction diagram in this system can be shown as follows: Figure 5 As shown, the home appliances in a smart home can be smart appliances, and the blockchain network combines the functions of a blockchain platform and an analysis and management platform.

[0096] Figure 6 This is a schematic diagram of information interaction for smart home energy management provided in an embodiment of this disclosure; combined with Figure 5 , Figure 6 The process of smart home energy management includes:

[0097] Step 601: The blockchain platform monitors the IoT platform in real time.

[0098] By using IoT technology, the system obtains the current operating data reported by smart home appliances and stores it in the current operating data of smart home appliances on the blockchain platform.

[0099] Step 602: Through IoT technology, the blockchain platform obtains the current user's current appliance usage behavior data reported by the current home appliances in the smart home and stores it in the current user's daily appliance usage behavior data.

[0100] Step 603: Through IoT technology, the blockchain platform obtains the current operating data reported by the current home appliances in the smart home and stores it in the current home appliance operating data.

[0101] Steps 602 and 603 can be performed simultaneously. Any home appliance in the Internet of Things can be the current home appliance and report data.

[0102] Step 604: The analysis and management platform processes the data based on the current user's daily home appliance usage behavior data and the operation data of home appliances in the smart home stored on the blockchain platform to obtain historical data including home appliance load data.

[0103] Step 605: The analysis and management platform uses the k-means algorithm to obtain the corresponding current comfort matrix based on historical data.

[0104] Step 606: The analysis and management platform uses the NSGA-II algorithm to solve for the predicted parameters of home appliances by dynamically balancing user comfort and energy costs, based on the current comfort matrix, daily home appliance usage data, and energy costs.

[0105] Step 607: The analysis and management platform obtains the current energy coordination strategy based on the predicted information of home appliance usage parameters.

[0106] Step 608: The analysis and management platform sends the current energy coordination strategy to the smart contract of the blockchain platform, so that the smart contract function can automatically execute the current energy coordination strategy and control the corresponding home appliances.

[0107] Step 609: The analysis and management platform can present the working status information of home devices and the energy usage information of smart homes through the user interaction platform.

[0108] Step 610: Through the user interaction platform, analyze the management platform to obtain user settings and feedback information, and store them in the corresponding data on the blockchain platform.

[0109] As can be seen, in this embodiment, a decentralized blockchain platform is used to store and manage user behavior data and home appliance operation data. This ensures the security and privacy of user behavior data, preventing data leakage and privacy violations. Furthermore, by dynamically balancing user comfort and energy costs, the platform obtains predicted information on home appliance usage parameters based on the daily appliance usage data and appliance operation data stored on the blockchain platform. This generates a corresponding current energy coordination strategy for the smart home, and through the smart contract function of the blockchain platform, the smart home appliances are controlled according to the current energy coordination strategy. This optimizes energy management, reduces household energy costs, and provides personalized energy-saving suggestions based on user habits and preferences, increasing user awareness and participation in energy conservation, and further improving the user experience.

[0110] Based on the above process for smart home energy management, a device for smart home energy management can be constructed.

[0111] Figure 7 This is a schematic diagram of a smart home energy management device provided in an embodiment of this disclosure. Figure 7 As shown, the smart home energy management device 700 includes: a matrix module 710, a prediction module 720, and a management control module 730.

[0112] Matrix module 710 is configured to obtain a current comfort matrix based on the current user's daily home appliance usage behavior data stored on the blockchain platform and the operation data of home appliances in the smart home, thereby obtaining a weighted information of the home appliances used by the current user.

[0113] The prediction module 720 is configured to obtain the predicted information of the usage parameters of home appliances that match the current comfort matrix under the condition of dynamic balance between user comfort and energy cost, and generate the current energy coordination strategy corresponding to the smart home.

[0114] The management control module 730 is configured to control smart home appliances based on the current energy coordination strategy through the smart contract function of the blockchain platform.

[0115] In some embodiments, it also includes:

[0116] The registration and authentication module is configured to obtain the current registration information of smart home appliances through IoT technology and register and authenticate them on the blockchain platform.

[0117] In some embodiments, it also includes:

[0118] The first acquisition module is configured to acquire, through IoT technology, the current user's current appliance usage behavior data reported by the current home appliances in the smart home, and store it in the current user's daily appliance usage behavior data on the blockchain platform.

[0119] The second acquisition module is configured to acquire the current operating data reported by the current home appliances in the smart home through Internet of Things (IoT) technology, and store it in the current operating data of the home appliances on the blockchain platform.

[0120] In some embodiments, the matrix module 710 includes:

[0121] The clustering unit is configured to form clusters based on similarity using the k-means clustering algorithm, based on the current user's daily home appliance usage behavior data and the operation data of home appliances in the smart home, resulting in a matrix with 3 K values, where the load level corresponding to a K value of 3 is the cluster center.

[0122] The positioning unit is configured to position the region corresponding to a K value of 3 in the matrix as the maximum comfort region, the region corresponding to a K value of 2 as the average comfort region, and the region corresponding to a K value of 1 as the minimum comfort region, thereby obtaining the current comfort matrix.

[0123] In some embodiments, the prediction module 720 includes:

[0124] The generation unit is configured to generate an initial population containing multiple solutions based on the current comfort matrix, daily home appliance usage data, and energy costs. Each chromosome in the population represents a home appliance in the smart home, and the gene structure on the chromosome is composed of binary vectors determined by the on / off status of the home appliances per hour. Each solution represents the usage parameter information or operating strategy of each home appliance in the smart home.

[0125] The genetic iteration unit is configured to perform genetic operations from the initial population using the fast elite multi-objective genetic algorithm NSGA-II, and obtain the predicted information of the usage parameters of home appliances when the preset number of generations is reached.

[0126] In some embodiments, it also includes:

[0127] The interaction module is configured to present the working status information of home devices and the energy usage information of smart homes through the user interaction platform; through the user interaction platform, it obtains user settings and feedback information and stores them in the corresponding data on the blockchain platform.

[0128] As can be seen, in this embodiment, a decentralized blockchain platform is used to store and manage user behavior data and home appliance operation data. This ensures the security and privacy of user behavior data, preventing data leakage and privacy violations. Furthermore, under the dynamic balance between user comfort and energy costs, the smart home energy management device can obtain predicted information on home appliance usage parameters based on the daily appliance usage behavior data and home appliance operation data stored on the blockchain platform. It can then generate a corresponding current energy coordination strategy for the smart home and control the smart home appliances according to the current energy coordination strategy through the smart contract function of the blockchain platform. This optimizes energy management, reduces household energy consumption costs, and can also provide personalized energy-saving suggestions based on user habits and preferences, improving user energy-saving awareness and participation, and further enhancing the user experience.

[0129] Combination Figure 8 This disclosure provides an apparatus 800 for smart home energy management, comprising:

[0130] The processor 1000 and memory 1001 may further include a communication interface 1002 and a bus 1003. The processor 1000, communication interface 1002, and memory 1001 can communicate with each other via the bus 1003. The communication interface 1002 can be used for information transmission. The processor 1000 can call logical instructions stored in the memory 1001 to execute the method for smart home energy management described in the above embodiments.

[0131] Furthermore, the logic instructions in the aforementioned memory 1001 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0132] The memory 1001, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 1000 executes functional applications and data processing by running the program instructions / modules stored in the memory 1001, thereby implementing the method for smart home energy management in the above method embodiments.

[0133] The memory 1001 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 1001 may include high-speed random access memory and may also include non-volatile memory.

[0134] This disclosure provides a smart home energy management device, including: a processor and a memory storing program instructions, wherein the processor is configured to execute a smart home energy management method when executing the program instructions.

[0135] This disclosure provides a storage medium storing program instructions that, when executed, perform the method for smart home energy management as described above.

[0136] This disclosure provides a computer program product, which includes a computer program stored on a storage medium. The computer program includes program instructions, which, when executed by a computer, cause the computer to perform the above-described method for smart home energy management.

[0137] The aforementioned storage medium can be a transient computer-readable storage medium or a non-transitory computer-readable storage medium.

[0138] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, including: a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and other media capable of storing program code; it can also be a transient storage medium.

[0139] The foregoing description and accompanying drawings fully illustrate embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included or replace parts and features of other embodiments. The scope of the embodiments of this disclosure includes the entire scope of the claims and all available equivalents of the claims. While the terms “first,” “second,” etc., may be used in this application to describe elements, these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first element may be called a second element without changing the meaning of the description, and similarly, a second element may be called a first element, provided that all occurrences of “first element” are consistently renamed and all occurrences of “second element” are consistently renamed. First and second elements are both elements, but may not be the same element. Moreover, the terminology used in this application is only for describing embodiments and is not intended to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the” are intended to also include the plural forms. Similarly, the term “and / or” as used herein means including one or more of the associated listed elements and all possible combinations thereof. Additionally, when used herein, the terms “comprise” and its variations “comprises” and / or “comprising” refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase “comprising an…” does not exclude the presence of additional identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0140] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0141] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0142] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for energy management in smart homes, characterized in that, include: Based on the daily home appliance usage behavior data of the current user stored on the blockchain platform, as well as the operation data of home appliances in the smart home, a current comfort matrix is ​​obtained with weighted information on the use of home appliances corresponding to the current user. Under the dynamic balance between user comfort and energy costs, the system obtains the predicted information of home appliance usage parameters that match the current comfort matrix, and generates the current energy coordination strategy corresponding to the smart home. By leveraging the smart contract functionality of a blockchain platform, smart home appliances can be controlled based on current energy synergy strategies.

2. The method according to claim 1, characterized in that, Also includes: By using IoT technology, the current registration information of smart home appliances can be obtained and registered and certified on a blockchain platform.

3. The method according to claim 1, characterized in that, Also includes: Through IoT technology, data on the current user's current appliance usage behavior reported by the current home appliances in the smart home is obtained and stored in the user's daily appliance usage behavior data on the blockchain platform; By using IoT technology, the system acquires the current operating data reported by smart home appliances and stores it on a blockchain platform.

4. The method according to claim 1, characterized in that, The current comfort matrix, which obtains the weighted information on the use of home appliances corresponding to the current user, includes: Based on current user data on daily home appliance usage and the operating data of smart home appliances, the k-means clustering algorithm is used to form clusters based on similarity, resulting in a matrix with 3 K values. The load level corresponding to a K value of 3 is the cluster center. The region corresponding to a K value of 3 in the matrix is ​​positioned as the region of maximum comfort, the region corresponding to a K value of 2 is positioned as the region of average comfort, and the region corresponding to a K value of 1 is positioned as the region of minimum comfort, thus obtaining the current comfort matrix.

5. The method according to claim 4, characterized in that, The predicted information of home appliance usage parameters obtained by matching the current comfort matrix includes: Based on the current comfort matrix, daily home appliance usage data, and energy costs, an initial population containing multiple solutions is generated. In this population, each chromosome and gene represents a home appliance in the smart home. The gene structure on the chromosome is composed of binary vectors determined by the on / off status of the home appliances per hour. Each solution represents the usage parameter information or operating strategy of each home appliance in the smart home. Using the fast elite multi-objective genetic algorithm NSGA-II, genetic operations are performed starting from the initial population. Once the preset number of generations is reached, predicted information on the usage parameters of home appliances is obtained.

6. The method according to any one of claims 1-5, characterized in that, Also includes: The operating status information of home appliances and the energy usage information of smart homes are presented through the user interaction platform; User settings and feedback information are obtained through the user interaction platform and stored in the corresponding data on the blockchain platform.

7. A system for smart home energy management, characterized in that, include: Blockchain platform, analytics and management platform, among which, The analysis and management platform is configured to obtain a current comfort matrix based on the current user's daily home appliance usage behavior data stored on the blockchain platform and the operation data of home appliances in the smart home. Under the dynamic balance between user comfort and energy costs, it obtains the predicted information of home appliance usage parameters matched with the current comfort matrix and generates the current energy coordination strategy corresponding to the smart home. By leveraging the smart contract functionality of a blockchain platform, smart home appliances can be controlled based on current energy synergy strategies.

8. A device for smart home energy management, characterized in that, include: The matrix module is configured to obtain a current comfort matrix based on the current user's daily home appliance usage behavior data stored on the blockchain platform and the operation data of home appliances in the smart home, which is a weighted information of the current user's use of home appliances. The prediction module is configured to obtain the predicted information of the usage parameters of home appliances that match the current comfort matrix under the condition of dynamic balance between user comfort and energy cost, and generate the current energy coordination strategy corresponding to the smart home. The management and control module is configured to control smart home appliances based on the current energy coordination strategy through the smart contract function of the blockchain platform.

9. A device for smart home energy management, the device comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to perform, when executing the program instructions, the method for smart home energy management as described in any one of claims 1 to 6.

10. A storage medium storing program instructions, characterized in that, When the program instructions are executed, they perform the method for smart home energy management as described in any one of claims 1 to 6.

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