Internet of Things intelligent linkage control method and Internet of Things intelligent linkage control device

Abstract

The invention relates to the technical field of Internet of Things, in particular to an Internet of Things intelligent linkage control method and an Internet of Things intelligent linkage control device, and the method comprises the steps: S1, obtaining the working state of home equipment and the real-time data of environment information, and transmitting the real-time data to a central control platform; s2, preprocessing the real-time data to obtain preprocessed data; s3, analyzing the preprocessed data, and generating a corresponding linkage control instruction; s4, encoding the linkage control instruction to generate encoded data, and performing lossless compression on the encoded data by adopting a ZSTD compression algorithm to obtain a compressed code; s5, searching an optimal sending communication network by adopting a flora optimization algorithm, and sending the compressed code to the home equipment; and S6, the home equipment decompresses the compressed code by using an LZMA compression algorithm, and executes the linkage control instruction. According to the invention, the ZSTD compression algorithm and the flora optimization algorithm are adopted, the delay of data transmission and processing is significantly reduced, the overall response speed of the smart home system is improved, and the user experience is better.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an Internet of Things intelligent linkage control method and an Internet of Things intelligent linkage control device. Background Art

[0002] The Internet of Things is an extension and expansion of the Internet, aiming to achieve seamless connection between the physical world and the information world. The Internet of Things integrates human society and physical systems by embedding various intelligent sensors with different functions into objects such as power grids, railways, bridges, tunnels, roads, buildings, etc., and using technologies such as wireless sensor networks, the Internet, supercomputers and cloud computing to form the Internet of Things. The introduction of Internet of Things control technology has greatly promoted the efficiency and continuity of production processes.

[0003] In traditional home environments, various devices in the home, such as lights, air conditioners, televisions and even curtains, require manual operation by users, which significantly increases the complexity of daily operations, making users feel cumbersome and inconvenient when frequently switching and adjusting devices. It is also very easy for users to forget to turn off certain devices due to negligence or memory loss, resulting in unnecessary energy waste. Furthermore, the traditional home system lacks the deployment of intelligent monitoring and early warning systems, such as vital smoke alarms, door and window sensors and other safety equipment, resulting in the inability of families to promptly and effectively detect abnormal situations when facing emergencies such as fires and thefts, thereby greatly increasing potential safety hazards and risks. In addition, the various devices in traditional homes lack the necessary intelligent linkage mechanism, and they are often isolated and helpless, and users have to manually operate each device one by one, which not only consumes a lot of time and energy, but also prevents users from experiencing the ultimate convenience and comfort brought by the seamless collaboration between devices in smart homes and automatic adaptation to environmental changes.

[0004] In order to improve the above problems, the present invention provides an Internet of Things intelligent linkage control method and an Internet of Things intelligent linkage control device. Summary of the invention

[0005] Based on this, it is necessary to propose an Internet of Things intelligent linkage control method and an Internet of Things intelligent linkage control device to address the problem that the control mode is prone to errors.

[0006] The present invention is implemented by the following technical solutions: An Internet of Things intelligent linkage control method and an Internet of Things intelligent linkage control device include the following steps: S1: acquiring real-time data of the working status and environmental information of household appliances and transmitting them to a central control platform;

[0007] S2: preprocessing the real-time data to obtain preprocessed data;

[0008] S3: Analyze the preprocessed data and generate corresponding linkage control instructions;

[0009] S4: Encode the linkage control instruction to generate encoded data, and use the ZSTD compression algorithm to losslessly compress the encoded data to obtain a compressed code;

[0010] S5: Use bacterial colony optimization algorithm to find the optimal communication network and send the compressed code to the home device;

[0011] S6: The home appliance uses the LZMA compression algorithm to decompress the compressed code and executes the linkage control instruction.

[0012] Furthermore, in step S1, a plurality of sensors are set up to monitor the working status and environmental information of household appliances in real time, and the collected real-time data is transmitted to a data acquisition device; the data acquisition device transmits the real-time data to a central control platform; and the central control platform stores the real-time data after receiving the real-time data.

[0013] Furthermore, in step S2 of removing invalid or abnormal data, preprocessing includes data cleaning, data conversion and data normalization; data cleaning is used to remove invalid data; data conversion is used to convert data into an analysis format; and data normalization is used to convert data into a unified dimension.

[0014] Furthermore, in step S3, the specific steps of generating the linkage control instruction include:

[0015] S31: Analyze the preprocessed data using a data analysis algorithm to extract environmental information and the working status of home appliances;

[0016] S32: The central control platform determines whether the environmental information and the working status of the home appliances are within the preset range. If yes, they remain unchanged. Otherwise, the control amount of the home appliances is calculated and an adjustment instruction is generated according to the control amount.

[0017] Furthermore, in step S4, the specific steps of using the ZSTD compression algorithm to perform lossless compression include:

[0018] S41: Segment the encoded data using an LZMA compression algorithm to obtain multiple data blocks;

[0019] S42: Create a dictionary, and use the LZMA compression algorithm to find the data block with the longest prefix matching the dictionary in each data block as the optimal data block;

[0020] S43: When the optimal data block is obtained, the LZMA compression algorithm outputs a code, and the LZMA compression algorithm adds the current data block to the dictionary;

[0021] S44: The LZMA compression algorithm continues to read the input data block and repeats steps S41-S43 until all the encoded data is compressed.

[0022] Furthermore, in step S41, the LZMA compression algorithm uses the Lempel-Ziv algorithm and Markov chain coding to perform basic compression on the coded data, and uses the statistical prediction model of the Markov chain, Huffman coding and dictionary algorithm to perform secondary compression to obtain compressed data.

[0023] Further, in step S5, the step of using the bacterial colony optimization algorithm to find the optimal sending communication network includes:

[0024] S51: Select S nodes in the communication network topology;

[0025] S52: Flip each node once to determine whether the network performance of the current node is higher than the network performance at the previous moment. If yes, proceed and update the network performance evaluation function;

[0026] S53: Calculate the health function value of each node, and sort each node from small to large according to the health function value, select the last S / 2 nodes with smaller health function values ​​for reproduction, and split each node to generate two identical nodes;

[0027] S54: Each node has probability p ed The network configuration is randomly updated and adjusted;

[0028] S55: Determine whether the optimization reaches the maximum cyclic algebra, or whether the network performance evaluation function value is less than the given precision ε. If so, end the optimization and output the optimal sending communication network; otherwise, return to step S52.

[0029] Furthermore, in step S53, the formula of the health function value is expressed as:

[0030]

[0031] In the formula, J i health is the health function of node i, N c The number of steps in one iteration loop for the node.

[0032] Furthermore, in step S6, the specific steps of decompressing the compressed coded data using the LZMA compression algorithm are as follows:

[0033] S61: extracting dictionary information from the compressed coded data by using an LZMA compression algorithm;

[0034] S62: Use the decompress function in the LZMA compression algorithm to decompress the dictionary information into original bytes;

[0035] S63: All the decompressed original bytes are concatenated together to obtain the encoded data before compression.

[0036] The present invention also provides an Internet of Things intelligent linkage control device, comprising:

[0037] Data collection module: used to collect real-time data on the working status and environmental information of home appliances;

[0038] Data preprocessing module: used to preprocess real-time data to obtain preprocessed data;

[0039] Data analysis module: used to analyze environmental information and the working status of household appliances according to pre-processed data, and determine whether the environmental information and working status are within the preset range; if yes, it remains unchanged, otherwise it enters the instruction generation module;

[0040] Instruction generation module: used to calculate the control quantity of furniture equipment according to the working state, and generate linkage control instructions according to the control quantity of furniture equipment;

[0041] Coding and compression module: used to use ZSTD compression algorithm to perform lossless compression on the coded data to obtain compressed code;

[0042] Optimal network search module: used to use bacterial colony optimization algorithm to find the optimal communication network;

[0043] Encoding and decompression module: uses the LZMA compression algorithm to decompress the compressed code.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The ZSTD algorithm can achieve fast compression and decompression operations while maintaining a high compression ratio. The ZSTD algorithm supports dynamic adjustment of the compression level, allowing users to flexibly select the best compression strategy according to actual needs, thereby finding the most ideal balance between speed and efficiency. Through ZSTD compression, the storage space occupied by the original data can be significantly reduced, which is especially important for home appliances with limited storage space. The compressed data is smaller in size, and data can be transmitted faster under the same network bandwidth, improving the overall data transmission efficiency. The bacterial colony optimization algorithm can efficiently search and find the optimal communication network path to ensure that the compressed encoded data can be sent to the home appliance with the lowest latency and highest reliability. The bacterial colony optimization algorithm has good adaptability and can be dynamically adjusted and optimized according to different communication network environments and home appliance requirements.

[0046] By adopting the ZSTD compression algorithm and the bacterial colony optimization algorithm, the delay of data transmission and processing can be significantly reduced, thereby improving the overall response speed of the smart home system. The compressed data takes up less storage space, reducing the investment and maintenance costs of storage devices. At the same time, the efficient communication network also reduces the energy consumption and cost of data transmission. The smart home system can respond to user instructions faster and provide a smoother and smarter user experience. For smart home systems that need to process large amounts of data, the combination of the ZSTD compression algorithm and the bacterial colony optimization algorithm can support more efficient data storage, transmission and processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a step diagram of an Internet of Things intelligent linkage control method in Embodiment 1 of the present invention;

[0048] Figure 2 This is a flow chart of an Internet of Things intelligent linkage control method in Embodiment 1 of the present invention;

[0049] Figure 3 This is a module diagram of an Internet of Things intelligent linkage control device in Example 1 of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] It should be noted that when a component is referred to as being "mounted on" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component at the same time. When a component is considered to be "fixed to" another component, it may be directly fixed on the other component or there may be a central component at the same time.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related listed items.

[0053] Example 1: Please refer to Figure 1 , this embodiment provides an Internet of Things intelligent linkage control method, comprising the following steps:

[0054] S1: Obtain real-time data on the working status and environmental information of home appliances and transmit it to the central control platform.

[0055] S11: Multiple sensors are set in the Internet of Things system to monitor the working status and environmental information of household appliances in real time. The sensors convert the real-time monitored data into electrical signals through their built-in sensing elements, and transmit them to the data acquisition device in a wired or wireless manner. In this embodiment, the multiple sensors include temperature sensors, humidity sensors, light sensors, etc., which are respectively used to monitor environmental temperature, humidity, light intensity, etc.

[0056] S12: The data acquisition device transmits the collected sensor data to the central control platform through the communication network. The data is encrypted and verified during the transmission process to ensure the integrity and security of the data.

[0057] S2: Preprocess the real-time data to obtain preprocessed data.

[0058] S21: After receiving the real-time data output by the sensor, the central control platform stores the real-time data.

[0059] S22: The central control platform preprocesses the real-time data to obtain preprocessed data, where the preprocessing includes data cleaning, data conversion and data normalization.

[0060] S221: Data cleaning is used to identify and handle errors, missing values, duplicate values, outliers, and other issues in the data to ensure data quality and consistency. If there are many missing values ​​and the impact on the model is small, you can directly delete the records with missing values. Fill missing values ​​with methods such as mean, median, mode, previous value, or next value. For categorical variables, use the most frequently occurring category to fill. For time series data, interpolation methods can be used to fill missing values. Check and delete duplicate records to avoid bias during model training. Identify and handle outliers in the data.

[0061] S222: Data conversion aims to convert raw data into a form suitable for model training, including: data type conversion, feature encoding, feature construction, etc. Data type conversion is to convert the data type into a form suitable for subsequent processing and analysis, such as converting a string to a numeric type. One-hot encoding is to convert a categorical variable into a binary vector, which is suitable for processing non-numeric data. Label encoding is to convert a categorical variable into an integer, but it is necessary to pay attention to maintaining the order relationship. Target encoding is to replace each category of a categorical variable with the average value of the category in the target variable to deal with the problem of category imbalance. Feature construction is to create new features by combining existing features or applying mathematical functions to capture complex relationships in the data. Discretization divides continuous variables into several intervals for converting continuous variables into categorical variables.

[0062] S223: Data normalization is to scale feature values ​​of different dimensions to the same dimension, making it easier for the model to learn and compare the importance of different features.

[0063] Minimum-maximum normalization: linearly scale the eigenvalues ​​to the interval [0,1]. The formula for minimum-maximum normalization is:

[0064]

[0065] Where, X min is the minimum value, X max is the maximum value.

[0066] Z-score normalization: convert the eigenvalues ​​to a distribution with a mean of 0 and a standard deviation of 1. The Z-score normalization formula is expressed as:

[0067]

[0068] Where μ is the mean and σ is the standard deviation.

[0069] Maximum absolute value normalization: Scale the eigenvalues ​​to the interval [-1,1] by dividing by the maximum absolute value of the eigenvalues. The formula for maximum absolute value normalization is:

[0070]

[0071] In the formula, |X| max is the maximum absolute value.

[0072] S3: Analyze the preprocessed data, formulate control strategies and generate corresponding linkage control instructions based on the status judgment results.

[0073] S31: Analyze the preprocessed data using a data analysis algorithm to extract environmental information and the working status of home appliances;

[0074] S32: The central control platform determines whether the environmental information and the working status of the home appliances are within the preset range. If yes, they remain unchanged. Otherwise, the control amount of the home appliances is calculated and an adjustment instruction is generated according to the control amount.

[0075] S4: The specific steps of using the ZSTD compression algorithm to directly perform lossless compression on data are as follows:

[0076] S41: Segment the encoded data using an LZMA compression algorithm to obtain multiple data blocks;

[0077] S42: Create a dictionary, and use the LZMA compression algorithm to find the data block with the longest prefix matching the dictionary in each data block as the optimal data block;

[0078] S43: When the optimal data block is obtained, the LZMA compression algorithm outputs a code, and the LZMA compression algorithm adds the current data block to the dictionary;

[0079] S44: The LZMA compression algorithm continues to read the input data block and repeats steps S41-S43 until all the encoded data is compressed.

[0080] S5: Adopt the bacterial colony optimization algorithm to find the optimal communication network and send the compressed code to the smart home.

[0081] S51: The communication network topology has S nodes, and the maximum number of steps to move in the same direction is N s , the number of chemotaxis behavior steps in a chemotaxis cycle of a node is N c (Where N c >N s ), the number of generations in the reproduction cycle N re , the number of migration events N ed , dissipation probability p ed , chemotaxis step length C(i), inter-node attraction depth d attract and width Repulsion height h repellant and width At the same time, the position and fitness of each node in the initial communication network topology are initialized.

[0082] S52: Perform a flip for each node and compare the node's network performance evaluation function with the previous network performance evaluation function. If the current value is better than the previous value and the number of forward movements in the same direction has not reached the limit, then move forward and update the network performance evaluation function.

[0083] S53: For a given k,l,i=1,2,...,S, define J i healthis the health function of node i, which is used to measure the energy obtained by the node. Arrange the nodes in ascending order. Select the last S / 2 nodes with smaller health function values ​​for reproduction, and each node splits to generate two identical nodes. The formula for the health function value is:

[0084]

[0085] In the formula, J i health is the health function of node i, N c The number of steps in one iteration loop for the node.

[0086] S54: Each node has probability p ed The network configuration is randomly updated and adjusted again.

[0087] S55: Determine whether the optimization reaches the maximum cyclic algebra, or the network performance evaluation function value is less than the given precision ε. If so, end the optimization and output the optimal sending communication network; otherwise, go to step S52.

[0088] S6: Decompress the compressed coded data using the LZMA compression algorithm.

[0089] S61: extracting dictionary information from the compressed coded data by using an LZMA compression algorithm;

[0090] S62: Using the decompress function in the LZMA compression algorithm, the specific steps of decompressing the dictionary information into original bytes are:

[0091] S621: Convert the dictionary to a JSON string and encode it to UTF-8 bytes.

[0092] S622: Compress the byte data using the decompress function in the LZMA compression algorithm.

[0093] S623: Decompress the byte data using the decompress function in the LZMA compression algorithm. The decompress function accepts a compressed byte data as input and returns a decompressed byte data.

[0094] S624: The decompressed data is byte data. Use the .decode('utf-8') method of the byte data to decode the byte data into a string in UTF-8 format. The decoded string is in JSON format and needs to be converted back to a Python dictionary. Use the json.loads() method, which accepts a JSON format string as input and returns a dictionary.

[0095] S63: All the decompressed original bytes are concatenated together to obtain the encoded data before compression.

[0096] In other embodiments, users can also actively adjust home appliances through terminal devices according to their own needs. The central control platform provides a user portal, allowing users to issue commands to the central control platform to adjust home appliances according to their own needs. Users can view the operating status and environmental data of the equipment through mobile phone applications or other terminal devices, and provide feedback on unsatisfactory control effects. For example, if a user feels that the temperature setting of a room is not appropriate, he can directly modify the set temperature of the air conditioner on his mobile phone, and the system will adjust the relevant linkage rules based on the user's feedback.

[0097] In addition, the central control platform has adaptive learning capabilities, automatically optimizing linkage rules by analyzing long-term environmental data and equipment operation data. For example, if a certain environmental parameter change pattern frequently occurs during a specific time period in a certain season, the system will automatically learn and adjust the corresponding linkage strategy to improve control accuracy and comfort. In addition, the system can also dynamically adjust control instructions according to changes in equipment performance to ensure effective operation of the equipment.

[0098] Embodiment 2: This embodiment provides another kind of Internet of Things intelligent linkage control device, which is a key equipment for realizing the above-mentioned control method. It may include a data acquisition module, a data preprocessing module, a data analysis module, an instruction generation module, a coding compression module, an optimal network module and a coding decompression module.

[0099] The data acquisition module is used to collect real-time data on the working status and environmental information of home appliances.

[0100] The data preprocessing module is used to preprocess the real-time data to obtain preprocessed data.

[0101] The data analysis module is used to analyze the environmental information and the working status of the home appliances according to the pre-processed data, and determine whether the environmental information and the working status are within the preset range. If yes, it remains unchanged, otherwise it enters the instruction generation module.

[0102] The instruction generation module is used to calculate the control quantity of the furniture equipment according to the working status, and generate linkage control instructions according to the control quantity of the furniture equipment.

[0103] The coding compression module is used to use the ZSTD compression algorithm to perform lossless compression on the coded data to obtain a compressed code.

[0104] The optimal network search module is used to use the bacterial colony optimization algorithm to find the optimal sending communication network.

[0105] The encoding and decompression module uses the LZMA compression algorithm to decompress the compressed code.

[0106] The IoT intelligent linkage control device of this embodiment compresses the encoded data by adopting the ZSTD algorithm, and can realize fast compression and decompression operations while maintaining a high compression ratio. The ZSTD algorithm supports dynamic adjustment of the compression level, allowing users to flexibly select the best compression strategy according to actual needs, so as to find the most ideal balance between speed and efficiency. Through ZSTD compression, the storage space occupied by the original data can be significantly reduced, which is particularly important for home appliances with limited storage space. The compressed data volume is smaller, and data can be transmitted faster under the same network bandwidth, improving the overall data transmission efficiency. The bacterial colony optimization algorithm can efficiently search and find the optimal communication network path to ensure that the compressed encoded data can be sent to the home appliance with the lowest delay and highest reliability. The bacterial colony optimization algorithm has good adaptability and can be dynamically adjusted and optimized according to different communication network environments and home appliance requirements.

[0107] By adopting the ZSTD compression algorithm and the bacterial colony optimization algorithm, the delay of data transmission and processing can be significantly reduced, thereby improving the overall response speed of the smart home system. The compressed data takes up less storage space, reducing the investment and maintenance costs of storage devices. At the same time, the efficient communication network also reduces the energy consumption and cost of data transmission. The smart home system can respond to user instructions faster and provide a smoother and smarter user experience. For smart home systems that need to process large amounts of data, the combination of the ZSTD compression algorithm and the bacterial colony optimization algorithm can support more efficient data storage, transmission and processing.

[0108] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. An Internet of Things intelligent linkage control method for linkage control of home appliances, characterized in that: The steps include: S1: Obtain real-time data on the working status and environmental information of home appliances and transmit it to the central control platform; S2: preprocessing the real-time data to obtain preprocessed data; S3: Analyze the preprocessed data and generate corresponding linkage control instructions; S4: Encode the linkage control instruction to generate coded data, and use a ZSTD compression algorithm to losslessly compress the coded data to obtain a compressed code; S5: using a bacterial colony optimization algorithm to find an optimal transmission communication network, and sending the compressed code to the home device; S6: The home appliance uses the LZMA compression algorithm to decompress the compressed code and executes the linkage control instruction.

2. The method for controlling the Internet of Things through intelligent linkage according to claim 1, characterized in that: In step S1, multiple sensors are set up to monitor the working status and environmental information of household appliances in real time, and the collected real-time data is transmitted to a data acquisition device; the data acquisition device transmits the real-time data to a central control platform; the central control platform stores the real-time data after receiving the real-time data.

3. The Internet of Things intelligent linkage control method according to claim 1 is characterized in that: In step S2, the preprocessing includes data cleaning, data conversion and data normalization; the data cleaning is used to remove invalid data in the real-time data; the data conversion is used to convert the real-time data into a preset analysis format; the data normalization is used to normalize the real-time data.

4. The Internet of Things intelligent linkage control method according to claim 1, characterized in that: In step S3, the specific steps of generating the linkage control instruction include: S31: Analyze the pre-processed data using a data analysis algorithm to extract environmental information and the working status of home appliances; S32: The central control platform determines whether the environmental information and the working status of the home appliance are within a preset range. If yes, they remain unchanged. Otherwise, the control amount of the home appliance is calculated, and a linkage control instruction is generated according to the control amount.

5. The Internet of Things intelligent linkage control method according to claim 1 is characterized in that: In step S4, the specific steps of using the ZSTD compression algorithm to perform lossless compression include: S41: Segment the encoded data using an LZMA compression algorithm to obtain multiple data blocks; S42: creating a dictionary, and searching for a data block with the longest prefix matching the dictionary in each data block by using the LZMA compression algorithm as the optimal data block; S43: the LZMA compression algorithm outputs a code, and the optimal data block is added to the dictionary; S44: Repeat steps S41-S43 until all the encoded data are compressed.

6. The Internet of Things intelligent linkage control method according to claim 1, characterized in that: In step S41, the LZMA compression algorithm uses the Lempel-Ziv algorithm and Markov chain coding to perform basic compression on the coded data, and uses the statistical prediction model of the Markov chain, Huffman coding and dictionary algorithm to perform secondary compression to obtain compressed data.

7. The Internet of Things intelligent linkage control method according to claim 1, characterized in that: In step S5, the steps of using the bacterial colony optimization algorithm to find the optimal sending communication network include: S51: Select S nodes in the communication network topology; S52: Flip each node once to determine whether the network performance of the current node is higher than the network performance at the previous moment. If yes, proceed and update the network performance evaluation function; S53: Calculate the health function value of each node, and arrange each node from small to large according to the health function value, select the nodes with smaller health function values ​​after S / 2 nodes for reproduction, and split each node to generate two identical nodes; S54: Each node has probability p ed The network configuration is randomly updated and adjusted; S55: Determine whether the optimization reaches the maximum cyclic algebra, or whether the network performance evaluation function value is less than the given precision ε. If so, end the optimization and output the optimal sending communication network; otherwise, return to step S52.

8. The method for intelligent linkage control of the Internet of Things according to claim 7, characterized in that: In step S53, the formula of the health function value is expressed as: In the formula, J i health is the health function of node i, N c The number of steps in one iteration loop for the node.

9. The Internet of Things intelligent linkage control method according to claim 1, characterized in that: In step S6, the specific steps of decompressing the compressed coded data using the LZMA compression algorithm are: S61: extracting dictionary information from the compressed coded data by using an LZMA compression algorithm; S62: using the decompress function in the LZMA compression algorithm to decompress the dictionary information into original bytes; S63: splice all the decompressed original bytes together to obtain the encoded data before compression.

10. An Internet of Things intelligent linkage control device, which adopts an Internet of Things intelligent linkage control method as claimed in any one of claims 1 to 9, characterized in that: include: Data collection module: used to collect real-time data on the working status and environmental information of home appliances; Data preprocessing module: used for preprocessing the real-time data to obtain preprocessed data; Data analysis module: used to analyze environmental information and the working status of household appliances according to the pre-processed data, and determine whether the environmental information and the working status are within a preset range; if yes, keep unchanged, otherwise enter the instruction generation module; Instruction generation module: used for calculating the control amount of the furniture device according to the working state, and generating linkage control instructions according to the control amount of the furniture device; Coding and compression module: used for performing lossless compression on the coded data by using ZSTD compression algorithm to obtain compressed code; Optimal network search module: used to use bacterial colony optimization algorithm to find the optimal communication network; Coding and decompression module: uses LZMA compression algorithm to decompress the compressed code.

Images