A method and system for controlling intelligent household appliances

Abstract

The present invention discloses a method and system for controlling smart home appliances, which relates to the field of smart home technology, including real-time collection of environmental information and user behavior data, preliminary processing, and uploading to a cloud platform; constructing a family situation model, analyzing user behavior patterns, and generating user intention instructions; taking adaptive control decisions to meet user intentions and generating control instructions based on user historical behaviors and preferences; triggering smart home appliances to perform control operations based on control instructions; monitoring the effects of control operations and dynamically adjusting device states. The present invention implements the autonomous learning and self-optimization capabilities of smart home devices by taking adaptive control decisions to meet user intentions and generating control instructions based on user historical behaviors and preferences, and can automatically adjust control strategies based on users' long-term usage habits, improve equipment operating efficiency, reduce unnecessary energy consumption, and achieve the purpose of energy conservation and emission reduction.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a method and system for controlling smart home appliances. Background Art

[0002] With the rapid development of Internet of Things technology and the popularization of smart home concepts, smart home appliances, as an important part of smart homes, have significantly improved their functionality and intelligence. Traditional home appliance control methods mainly rely on manual operation by users, which is not only inefficient, but also unable to meet users' growing demands for comfort, energy saving, etc.

[0003] In recent years, with the advancement of technologies such as cloud computing, big data, and artificial intelligence, smart home appliance control methods and systems have gradually become a research hotspot. They optimize the user experience through automated control strategies and achieve effective energy management. At present, there are some products on the market that can remotely control home appliances through mobile phone applications, or use simple sensors to monitor environmental changes to achieve basic automated control. However, these methods are often limited to the implementation of a single function, lack an in-depth understanding of user personalized needs and the ability to comprehensively analyze environmental information, resulting in problems such as low control accuracy and poor user experience. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a smart home appliance control method and system to solve the problems of inaccurate control and untimely response caused by the lack of in-depth understanding of environmental information and user behavior in existing smart home appliance control systems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for controlling a smart home appliance, which comprises:

[0008] Collect environmental information and user behavior data in real time, perform preliminary processing, and upload them to the cloud platform;

[0009] Build a family situation model, analyze user behavior patterns, and generate user intent instructions;

[0010] Adaptive control decisions are made to meet user intentions and generate control instructions based on user historical behaviors and preferences;

[0011] According to the control instructions, trigger the smart home appliances to perform control operations;

[0012] Monitor the effectiveness of execution control operations and dynamically adjust device status.

[0013] As a preferred solution of the smart home appliance control method described in the present invention, the real-time collection of environmental information and user behavior data includes using millimeter wave radar to monitor human posture and movement speed, high-definition camera to record visual images, high-sensitivity microphone to monitor environmental acoustic characteristics, and high-precision temperature and humidity sensors to measure indoor climate conditions.

[0014] As a preferred solution of the smart home appliance control method described in the present invention, the preliminary processing includes noise filtering, outlier removal and data standardization of environmental information and user behavior data, and a timestamp alignment algorithm is used to synchronize asynchronous data streams from different sensors and upload them to the cloud platform.

[0015] As a preferred solution of the intelligent home appliance control method of the present invention, wherein: constructing a family situation model, analyzing user behavior patterns, and generating user intention instructions include the following steps:

[0016] Using deep learning technology, features are extracted from environmental information and user behavior data uploaded to the cloud platform;

[0017] The K-means clustering algorithm is used to classify user behaviors, and the decision tree algorithm is used to refine the clustering results after classification to obtain the clustering results of user behaviors and more detailed behavior patterns;

[0018] Analyze user behavior clustering results and more detailed behavior patterns to identify the probability of user behavior in a specific pattern;

[0019] According to the probability of the identified user behavior in a specific mode, the user's potential intention is identified, and the expression is:

[0020]

[0021] in, The feature vector representing the user behavior, Indicates user behavior The corresponding user intention strength, Indicates adjusting the output range of the entire intent recognition. represents the angular frequency of the sine wave, Indicates time, represents the phase shift of the sine function, Indicates the base value of the adjustment location impact, Indicates the degree of influence of the adjustment location on the user's intention, Indicates the location where the numerically encoded user behavior occurs. Indicates the degree of influence of the adjustment device on the user's intention. Indicates the type of device the user is using after encoding the value.

[0022] As a preferred solution of the intelligent home appliance control method of the present invention, wherein: according to the user's historical behavior and preferences, an adaptive control decision is taken to meet the user's intention and a control instruction is generated. The generation of the control instruction includes the following steps:

[0023] According to the result of user intention recognition, the control decision function is defined, and the system decides what to do. The expression is:

[0024]

[0025] in, For user behavior The control decision value of is the influence coefficient of user intention intensity, is the influence coefficient of energy efficiency, For user behavior Energy efficiency, is the adjustment coefficient of the effect of external temperature on energy efficiency, is the external ambient temperature, is the influence coefficient of comfort, For user behavior The comfort factor, is the adjustment coefficient of the external environment on comfort, is the sensitivity of the external environment to the comfort level, For external environmental conditions;

[0026] based on Analytical results, formulate specific control strategies, if If it is a positive value, the control is enhanced by increasing the working intensity and frequency of the equipment; If it is a negative value, the control intensity is reduced by reducing the working intensity and frequency of the equipment;

[0027] Generate specific control instructions based on the control strategy.

[0028] As a preferred solution of the smart home appliance control method of the present invention, wherein: triggering the smart home appliance to perform a control operation according to the control instruction includes the following steps:

[0029] After receiving the control command, the smart home appliance confirms the validity of the command and sends a receipt confirmation message to the control center;

[0030] Based on the received command content, the control unit of the smart home appliance will parse the command content and extract the specific operation requirements;

[0031] Smart home appliances map the parsed operational requirements into internal control systems;

[0032] Based on the mapping results, the smart home appliance generates specific control signals;

[0033] The hardware of the smart appliance performs the actions indicated by the control signal;

[0034] After executing the operation, the smart appliance reads the current status information;

[0035] The read status information and the operation completion message are packaged into a data packet, and the data packet is sent back to the control center via the wireless network.

[0036] As a preferred solution of the intelligent home appliance control method of the present invention, wherein: monitoring the effect of executing the control operation and dynamically adjusting the device state includes the following steps:

[0037] Smart home appliances continuously monitor user interaction behavior and device operating status;

[0038] Compare various indicators before and after the implementation of control operations, evaluate the actual improvement effect, and check whether the control operations have achieved the expected goals;

[0039] Evaluate the impact of control operations on energy consumption, analyze whether there are abnormal conditions in the equipment after the control operations, and identify potential problems in a timely manner;

[0040] Automatically adjust control strategies based on evaluation results;

[0041] Customize personalized control solutions based on user preferences and usage habits;

[0042] Predict potential failures based on equipment operating status and historical data analysis, and take measures in advance to reduce the probability of failures.

[0043] In a second aspect, the present invention provides a smart home appliance control system, comprising:

[0044] Data acquisition module: collects environmental information and user behavior data in real time, performs preliminary processing, and uploads it to the cloud platform; situation analysis module: builds a family situation model, analyzes user behavior patterns, and generates user intention instructions; decision generation module: takes adaptive control decisions to meet user intentions based on user historical behavior and preferences, and generates control instructions; instruction execution module: triggers smart home appliances to execute control operations based on control instructions; effect monitoring module: monitors the effect of execution control operations and dynamically adjusts device status.

[0045] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the smart home appliance control method as described in the first aspect of the present invention is implemented.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the smart home appliance control method as described in the first aspect of the present invention is implemented.

[0047] The beneficial effects of the present invention are as follows: by collecting environmental information and user behavior data in real time and performing preliminary processing, and uploading them to the cloud platform, all-round perception and data management of the user's living environment are achieved, the response speed and accuracy of the system are enhanced, a family situation model is constructed, user behavior patterns are analyzed, and user intention instructions are generated, thereby achieving a deep understanding and prediction of user personalized needs, more intelligently matching users' actual needs, and improving user experience. According to users' historical behaviors and preferences, adaptive control decisions are made to meet user intentions, control instructions are generated, equipment operation efficiency is improved, and unnecessary energy consumption is reduced. According to control instructions, smart home appliances are triggered to execute control operations, and the effects of executing control operations are monitored, and the equipment status is dynamically adjusted, thereby ensuring the stable operation and efficient performance of smart home appliances, improving the reliability and security of the system, and further improving the quality of life and intelligent experience level of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0049] Figure 1 This is a flow chart of the smart home appliance control method in Example 1.

[0050] Figure 2 This is a flow chart of the control operation executed by the smart home appliance in Example 1. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0052] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0053] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0054] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for controlling a smart home appliance, comprising the following steps:

[0055] S1. Collect environmental information and user behavior data in real time, perform preliminary processing, and upload them to the cloud platform.

[0056] Real-time collection of environmental information and user behavior data includes using millimeter-wave radar to monitor human posture and movement speed, high-definition cameras to record visual images, high-sensitivity microphones to monitor environmental acoustic characteristics, and high-precision temperature and humidity sensors to measure indoor climate conditions; preliminary processing includes noise filtering, outlier removal, and data standardization of environmental information and user behavior data, and the use of timestamp alignment algorithms to synchronize asynchronous data streams from different sensors and upload them to the cloud platform.

[0057] S2, building a family situation model, analyzing user behavior patterns, and generating user intention instructions include the following steps:

[0058] Using deep learning technology, features are extracted from environmental information and user behavior data uploaded to the cloud platform; the K-means clustering algorithm is used to classify user behaviors, and the decision tree algorithm is used to refine the clustering results after classification to obtain clustering results of user behaviors and more detailed behavior patterns; the user behavior clustering results and more detailed behavior patterns are analyzed to identify the probability of user behaviors in specific patterns; based on the identified probability of user behaviors in specific patterns, the user's potential intentions are identified, expressed as:

[0059]

[0060] in, The feature vector representing the user behavior, Indicates user behavior The corresponding user intention strength. The higher the value, the stronger the user intention, and vice versa. Indicates adjusting the output range of the entire intent recognition. Will amplify differences in user intent, smaller This will reduce the difference, Represents the angular frequency of the sine wave, which determines how fast the sine function changes over time. Indicates time, Represents the phase shift of the sine function, which is used to adjust the starting position of the sine function to adapt to different daily habits. Indicates the base value of the adjustment location impact, Indicates the degree of influence of the adjustment location on the user's intention, with a larger This means that location changes have a greater impact on user intent. Indicates the location where the user behavior occurs. It is a room or area number represented by a numerical code. Indicates the degree of influence of the adjustment device on the user's intention, with a larger This means that the device used has a greater impact on user intent. Indicates the type of device the user is using, which is a device category represented by a numerical code.

[0061] Furthermore, the K-means clustering process is as follows:

[0062] Assume that user behavior data has been collected ,in , A behavioral feature vector representing a user, including but not limited to activity time, location, device type used, etc. First, the number of clusters needs to be determined , assuming It has been determined by the elbow rule or silhouette coefficient, and then the objective function of K-means clustering is defined as minimizing the sum of the squares of the distances from all points in each cluster to the centroid, expressed as:

[0063]

[0064] in, It represents the value of the clustering objective function, that is, the sum of the squares of the distances from all data points in the cluster to the centers of their respective clusters. The smaller the value, the better the clustering effect. represents the number of clusters, and k represents the index variable, that is, the number of the cluster currently considered, from 1 to , Indicates The set of all data points in a cluster, represents the data point currently considered, Represents data points The characteristic vector of Indicates The feature vector of the center point of each cluster, Represents data points The eigenvector of With cluster center The Euclidean distance between

[0065] In order to refine the clustering results, the decision tree algorithm is further used. Assume that the input feature set of the decision tree is , is the total number of features, Represents user behavior The objective function of the decision tree is expressed as:

[0066]

[0067] in, Represents the total value of the objective function of the decision tree, that is, the sum of the information gain of the entire decision tree. It is an indicator to measure the overall quality of the decision tree. The larger the value, the better the division effect of the decision tree on the data set. is an index variable used to identify different features. It is a feature The weight reflects the importance of the feature in the decision tree. The larger the weight, the greater the influence of the feature in the decision process.

[0068] is the information gain function, which measures the feature Contribution to the splitting node of the decision tree;

[0069] Using information gain as the classification criterion, the expression of the information gain function is:

[0070]

[0071] in, It is behavioral data The information entropy of It is in the characteristics Conditional entropy under conditions;

[0072] The definitions of information entropy and conditional entropy are as follows:

[0073]

[0074] in, is a category set, c represents the category to which the sample in the behavior dataset belongs, Yes Category The probability of occurrence, It is a feature The set of all possible values ​​of Belongs to The value is The number of samples, It is in the characteristics The value is Time Category The probability of Representation characteristics A specific value of .

[0075] Going further, we define a function to measure the probability of user behavior in a specific mode, expressed as:

[0076]

[0077] in, Indicates user behavior In a given mode The probability under which M represents a specific pattern in the clustering result of user behavior in the pattern set. Representing a collection of patterns Another pattern in , used to calculate user behavior The relative probability among all modes, is a positive real number used to adjust the width of the distribution so that behaviors closer to the mode have higher probability, Indicates user behavior With mode The distance between Indicates user behavior With mode The distance between.

[0078] S3. Adaptive control decisions are made to meet user intentions based on user historical behaviors and preferences, and control instructions are generated. Generating control instructions includes the following steps:

[0079] According to the result of user intention recognition, the control decision function is defined. The system decides what control measures to take to meet the user intention. The expression is:

[0080]

[0081] in, For user behavior The control decision value of is the influence coefficient of user intention intensity, is the influence coefficient of energy efficiency, For user behavior Energy efficiency, is the adjustment coefficient of the effect of external temperature on energy efficiency, is the external ambient temperature, is the influence coefficient of comfort, For user behavior The comfort factor, is the adjustment coefficient of the external environment on comfort, is the sensitivity of the external environment to the comfort level, External environmental conditions (such as humidity, light, etc.);

[0082] based on Analytical results, formulate specific control strategies, if If it is a positive value, the control is enhanced by increasing the working intensity and frequency of the equipment; If it is a negative value, the control intensity is reduced by reducing the working intensity and frequency of the equipment; according to the control strategy, specific control instructions are generated.

[0083] S4, triggering the smart home appliance to execute the control operation according to the control instruction includes the following steps:

[0084] After receiving the control command, the smart home appliance confirms the validity of the command and sends a receipt confirmation message to the control center; based on the received command content, the control unit of the smart home appliance will parse the command content and extract specific operation requirements; the smart home appliance maps the parsed operation requirements to the internal control system. For example, if the command received is to increase the cooling intensity, the smart home appliance needs to convert this requirement into an internal control signal, such as increasing the operating frequency of the compressor or increasing the fan speed; based on the mapping result, the smart home appliance generates a specific control signal. For example, for an air conditioner, it may be necessary to generate a PWM (pulse width modulation) signal to adjust the operating frequency of the compressor, or change the voltage of the fan motor to adjust the fan speed; the hardware of the smart home appliance executes the action indicated by the control signal. For example, if the control signal indicates to increase the operating frequency of the compressor, the compressor will increase the operating speed accordingly; if it indicates to increase the fan speed, the fan will rotate faster; after executing the operation, the smart home appliance reads the current status information; the read status information is packaged into a data packet together with the operation completion message, and the data packet is sent back to the control center via the wireless network.

[0085] S5. Monitoring the effect of execution control operation and dynamically adjusting the equipment status include the following steps:

[0086] Smart home appliances continuously monitor user interaction behaviors (such as the number of remote control button presses, voice commands, etc.) and device operating status (such as indoor temperature, humidity, energy consumption, etc.); compare various indicators before and after the control operation is executed to evaluate the actual improvement effect; check whether the control operation has achieved the expected goals, such as the set temperature and humidity levels; evaluate the impact of the control operation on energy consumption, analyze whether there are any abnormal conditions in the equipment after the control operation, and discover potential problems in a timely manner; automatically adjust the control strategy based on the evaluation results, such as dynamically adjusting the cooling or heating intensity according to changes in indoor temperature; customize personalized control plans based on user preferences and usage habits, such as learning the user's daily routine to preset the most suitable home environment; predict potential failures based on the device operating status and historical data analysis, and take measures in advance to reduce the probability of failures.

[0087] This embodiment also provides a smart home appliance control system, including:

[0088] Data acquisition module: collects environmental information and user behavior data in real time, performs preliminary processing, and uploads it to the cloud platform; situation analysis module: builds a family situation model, analyzes user behavior patterns, and generates user intention instructions; decision generation module: takes adaptive control decisions to meet user intentions based on user historical behavior and preferences, and generates control instructions; instruction execution module: triggers smart home appliances to execute control operations based on control instructions; effect monitoring module: monitors the effect of execution control operations and dynamically adjusts device status.

[0089] This embodiment also provides a computer device, which is suitable for the case of a smart home appliance control method, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the smart home appliance control method proposed in the above embodiment.

[0090] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0091] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for controlling a smart home appliance proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0092] In summary, the present invention realizes all-round perception and data management of the user's living environment by collecting environmental information and user behavior data in real time, performing preliminary processing, and uploading them to the cloud platform, thereby enhancing the response speed and accuracy of the system, building a family situation model, analyzing user behavior patterns, and generating user intention instructions, thereby realizing a deep understanding and prediction of user's personalized needs, more intelligently matching users' actual needs, and improving user experience. According to users' historical behaviors and preferences, adaptive control decisions are made to meet users' intentions, control instructions are generated, equipment operation efficiency is improved, and unnecessary energy consumption is reduced. According to control instructions, smart home appliances are triggered to execute control operations, and the effects of executing control operations are monitored, and equipment status is dynamically adjusted, thereby ensuring the stable operation and efficient performance of smart home appliances, improving the reliability and security of the system, and further improving users' quality of life and intelligent experience level.

[0093] Example 2

[0094] Referring to Table 1, which is the second embodiment of the present invention, in order to further verify the advancement of the present invention, experimental simulation data of the intelligent home appliance control method are provided.

[0095] First, 10 households were selected as research subjects, with the number of family members in each household ranging from 2 to 5. The experiment was divided into two phases. The first phase was a one-week baseline data collection period, which was used to collect users' daily behavior data when the system of the present invention was not used; the second phase was a one-week application test period, during which the smart home appliance control system of the present invention was deployed and used.

[0096] In the first phase of the experiment, traditional methods were used to collect home environment information and user behavior data, including but not limited to manually recording the user's activity time, location, type of equipment used, etc., while also recording the energy consumption and operating status of smart home appliances. In this way, the user's natural behavior pattern was obtained without the intervention of the smart home appliance system.

[0097] In the second stage of the experiment, the following steps are performed according to the content of the present invention:

[0098] S1. Use millimeter-wave radar to monitor human posture and movement speed; high-definition camera to record visual images; high-sensitivity microphone to monitor environmental acoustic characteristics; high-precision temperature and humidity sensors to measure indoor climate conditions.

[0099] S2. Build a family context model, analyze user behavior patterns, and generate user intent instructions.

[0100] Use deep learning technology to extract features from environmental information and user behavior data on the cloud platform; use the K-means clustering algorithm to classify user behavior, and use the decision tree algorithm to refine the clustering results after classification; analyze user behavior clustering results and more detailed behavior patterns to identify the probability of user behavior in a specific pattern; identify the user's potential intentions based on the probability of user behavior in a specific pattern.

[0101] S3. Based on the results of user intent recognition, a control decision function is defined, and the system decides what control measures to take to meet the user intent; based on the analysis results of the control decision function, a specific control strategy is formulated and specific control instructions are generated.

[0102] S4. After receiving the control command, the smart home appliance confirms the validity of the command and sends a receipt confirmation message to the control center; the control unit of the smart home appliance parses the command content, extracts the specific operation requirements, and maps them to the internal control system; the smart home appliance generates a specific control signal and executes the action indicated by the control signal.

[0103] S5. Smart home appliances continuously monitor user interaction behaviors and device operating status; compare various indicators before and after executing control operations to evaluate the actual improvement effects; and automatically adjust control strategies based on evaluation results, such as dynamically adjusting cooling or heating intensity based on changes in indoor temperature.

[0104] The details are shown in Table 1:

[0105] Table 1 Experimental table of intelligent home appliance control system

[0106]

[0107] By analyzing the table data, after using the intelligent home appliance control system of the present invention, the average energy consumption reduction rate of the household is significantly improved. From the baseline data, the energy consumption reduction rate under the traditional method is usually around 5%, while in the embodiment of the present invention, the energy consumption reduction rate reaches 12.8%~18.1%, and the average is about 15.3%. This means that through intelligent control, energy consumption is effectively reduced, and household expenses are also reduced.

[0108] In terms of user satisfaction rating, all participating households scored above 4.0 (out of 5), with an average score of 4.45, indicating that users have a high degree of acceptance of the smart home appliance system. In particular, households C, G, and J scored as high as 4.7 points in user satisfaction, which shows that the present invention not only effectively reduces energy consumption, but also greatly improves user experience.

[0109] In terms of control accuracy, the control accuracy of all households exceeded 88%, with an average of 91.6%, showing the high accuracy of the system of the present invention. In addition, the average device response time was 125ms, which is about 20% faster than the traditional method, which shows that the system of the present invention has a faster response speed.

[0110] In terms of fault prediction accuracy, the fault prediction accuracy of all households exceeded 84%, with an average of 89.5%, which shows that the system of the present invention can effectively predict possible problems with the equipment, so as to take measures in advance to avoid the occurrence of faults.

[0111] In terms of the number of dynamic adjustments, the average number of adjustments for all households is 19.1 times, which reflects that the system of the present invention can be flexibly adjusted according to actual conditions to meet the different needs of users.

[0112] In summary, the intelligent home appliance control system proposed in the present invention has significant advantages over traditional methods, not only excelling in energy saving, but also showing outstanding effects in improving user experience, control accuracy and response speed, etc. These data fully demonstrate the practical value and innovation of the present invention.

[0113] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling a smart home appliance, characterized in that: include, Collect environmental information and user behavior data in real time, perform preliminary processing, and upload them to the cloud platform; Build a family situation model, analyze user behavior patterns, and generate user intent instructions; Adaptive control decisions are made to meet user intentions and generate control instructions based on user historical behaviors and preferences; According to the control instructions, trigger the smart home appliances to perform control operations; Monitor the effects of execution control operations and dynamically adjust equipment status; The preliminary processing includes noise filtering, outlier removal and data standardization of environmental information and user behavior data, and using a timestamp alignment algorithm to synchronize asynchronous data streams from different sensors and upload them to the cloud platform; Building a family situation model, analyzing user behavior patterns, and generating user intent instructions include the following steps: Using deep learning technology, features are extracted from environmental information and user behavior data uploaded to the cloud platform; The K-means clustering algorithm is used to classify user behaviors, and the decision tree algorithm is used to refine the clustering results after classification to obtain the clustering results of user behaviors and more detailed behavior patterns; Analyze user behavior clustering results and more detailed behavior patterns to identify the probability of user behavior in a specific pattern; According to the probability of the identified user behavior in a specific mode, the user's potential intention is identified, and the expression is: in, The feature vector representing the user behavior, Indicates user behavior The corresponding user intention strength, Indicates adjusting the output range of the entire intent recognition. represents the angular frequency of the sine wave, Indicates time, represents the phase shift of the sine function, Indicates the base value of the adjustment location impact, Indicates the degree of influence of the adjustment location on the user's intention, Indicates the location where the numerically encoded user behavior occurs. Indicates the degree of influence of the adjustment device on the user's intention. Indicates the type of device the user is using after encoding the value.

2. The smart home appliance control method according to claim 1, characterized in that: The real-time collection of environmental information and user behavior data includes using millimeter wave radar to monitor human posture and movement speed, high-definition cameras to record visual images, high-sensitivity microphones to monitor environmental acoustic characteristics, and high-precision temperature and humidity sensors to measure indoor climate conditions.

3. The intelligent home appliance control method according to claim 2, characterized in that: According to the user's historical behavior and preferences, adaptive control decisions are taken to meet the user's intentions and generate control instructions. Generating control instructions includes the following steps: According to the result of user intention recognition, the control decision function is defined. The system decides what control measures to take to meet the user intention. The expression is: in, For user behavior The control decision value of is the influence coefficient of user intention intensity, is the influence coefficient of energy efficiency, For user behavior Energy efficiency, is the adjustment coefficient of the effect of external temperature on energy efficiency, is the external ambient temperature, is the influence coefficient of comfort, For user behavior The comfort factor, is the adjustment coefficient of the external environment on comfort, is the sensitivity of the external environment to the comfort level, For external environmental conditions; based on Analytical results, formulate specific control strategies, if If it is a positive value, the control is enhanced by increasing the working intensity and frequency of the equipment; If it is a negative value, the control intensity is reduced by reducing the working intensity and frequency of the equipment; Generate specific control instructions based on the control strategy.

4. The intelligent home appliance control method according to claim 3, characterized in that: According to the control instruction, triggering the smart home appliance to perform the control operation includes the following steps: After receiving the control command, the smart home appliance confirms the validity of the command and sends a receipt confirmation message to the control center; Based on the received command content, the control unit of the smart home appliance will parse the command content and extract the specific operation requirements; Smart home appliances map the parsed operational requirements into internal control systems; Based on the mapping results, the smart home appliance generates specific control signals; The hardware of the smart appliance performs the actions indicated by the control signal; After executing the operation, the smart appliance reads the current status information; The read status information and the operation completion message are packaged into a data packet, and the data packet is sent back to the control center via the wireless network.

5. The intelligent home appliance control method according to claim 4, characterized in that: Monitoring the effect of execution control operations and dynamically adjusting the device status includes the following steps: Smart home appliances continuously monitor user interaction behavior and device operating status; Compare various indicators before and after the implementation of control operations, evaluate the actual improvement effect, and check whether the control operations have achieved the expected goals; Evaluate the impact of control operations on energy consumption, analyze whether there are abnormal conditions in the equipment after the control operations, and identify potential problems in a timely manner; Automatically adjust control strategies based on evaluation results; Customize personalized control solutions based on user preferences and usage habits; Predict potential failures based on equipment operating status and historical data analysis, and take measures in advance to reduce the probability of failures.

6. A smart home appliance control system, based on the smart home appliance control method according to any one of claims 1 to 5, characterized in that: include, Data collection module: collects environmental information and user behavior data in real time, performs preliminary processing, and uploads it to the cloud platform; Situation analysis module: builds a family situation model, analyzes user behavior patterns, and generates user intent instructions; Decision generation module: Adaptive control decisions are made to meet user intentions and generate control instructions based on user historical behaviors and preferences; Instruction execution module: triggers smart home appliances to execute control operations according to control instructions; Effect monitoring module: monitors the effect of execution control operations and dynamically adjusts the device status.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the smart home appliance control method described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the smart home appliance control method described in any one of claims 1 to 5 are implemented.

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