Self-adaptive adjustment method and system for smart home environment

Abstract

The invention relates to a self-adaptive adjustment method and system for a smart home environment, and the method comprises the following steps: obtaining a behavior habit type of a user and a corresponding behavior duration; performing matching screening on the home equipment based on the behavior habit type, and performing working parameter configuration on the screened home equipment based on the behavior duration to obtain an equipment adjustment instruction; and controlling the screened home equipment through the equipment adjustment instruction to realize dynamic adaptation between the current indoor environment parameters and the current behavior habit type of the user, and maintaining the stability and comfort of the environment parameters within the behavior duration, thereby solving the problem that the individuation and regularity of the behavior habit of the user are ignored in the traditional technology, and improving the user experience. And obvious disjunction exists between the working state of the home equipment and the actual demand of the user.

Description

Technical Field

[0001] This invention relates to the field of smart home technology, and in particular to an adaptive adjustment method and system for smart home environments. Background Technology

[0002] With the deep integration of IoT technology and home automation, smart homes have evolved from single-device control to whole-house intelligent collaboration. More and more families are deploying adjustable devices such as air conditioners, lights, and humidifiers, hoping to improve their living comfort through technology. However, most current smart home systems remain at the "passive response" level. Whether manually setting parameters through a mobile app or triggering scene modes based on fixed time points, it is difficult to accurately match the user's real-time behavior. For example, if a user habitually reads between 8 pm and 10 pm, the system cannot adjust the brightness and color temperature of the lights in advance based on this fixed behavior cycle, often requiring repeated manual intervention from the user. This increases the cumbersomeness of operation and reduces the convenience of the smart system. This "preset" adjustment mode essentially ignores the personalization and regularity of user behavior habits, resulting in a significant disconnect between the working status of home devices and the actual needs of users. Summary of the Invention

[0003] This invention aims to solve at least one of the technical problems existing in the prior art. To this end, this invention proposes an adaptive adjustment method for a smart home environment, comprising the following steps: Obtain the user's behavioral habits and the corresponding duration of the behavior; Home appliances are matched and filtered based on the behavioral habit type, and the working parameters of the filtered home appliances are configured based on the duration of the behavior to obtain device adjustment instructions. The selected home appliances are controlled by the device adjustment commands to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavioral habits, and to maintain the stability and comfort of the environmental parameters during the duration of the behavior.

[0004] Furthermore, obtain the user's behavioral habit types and the corresponding duration of the behavior, including: Collect the user's home activity data in different time periods, and divide the user's behavior time periods based on the home activity data to obtain multiple behavior time periods, and extract the user activity features in each behavior time period; Based on the user activity characteristics, user behavior is classified to obtain behavior habit types, and the duration of each behavior habit type within the corresponding behavior time period is calculated to obtain the behavior duration.

[0005] Furthermore, based on the user activity characteristics, user behavior is classified to obtain behavioral habit types, including: Key features are extracted from the user activity features to obtain core activity elements, which include key device operation actions and key changes in environmental parameters. Based on the core activity elements, the user's behavior is categorized to obtain behavior habit types.

[0006] Furthermore, home appliances are matched and filtered based on the aforementioned behavioral habit types, including: Based on the behavioral habit type, the user's environmental demand attributes are determined, and the environmental demand attributes are graded by strength to obtain a priority list for adjusting the environmental priority. Based on the priority list, the functional parameters of home appliances are compared item by item to obtain a set of functions that meet the corresponding adjustment needs. Based on the functional matching set, spatial adaptation processing is performed on the coverage space and output capabilities of home devices to obtain adaptation parameters, and home devices are matched and filtered based on the adaptation parameters.

[0007] Furthermore, based on the duration of the behavior, the working parameters of the selected home appliances are configured to obtain device adjustment instructions, including: Based on the behavioral habit type, the duration of the behavior is calculated as a percentage of the duration, resulting in a behavior duration percentage distribution table. Based on the behavior duration distribution table, the function start-up duration of the screened home devices is set to obtain the device function start-up duration configuration table. Based on the device function start-up duration configuration table and the behavior habit type, the various output powers of the screened home devices are determined. The operating modes of the selected home appliances are switched based on behavioral habits and multiple output power to obtain an operating mode switching setting table, and device adjustment instructions are generated based on the operating mode switching setting table.

[0008] Furthermore, based on the behavioral habit type, the duration of the behavior is calculated as a percentage, resulting in a behavior duration percentage distribution table, including: Based on the behavioral habit type, the duration of the behavior is extracted to obtain the high-frequency activity period corresponding to each behavioral habit type. Based on the high-frequency activity period, the duration of the behavior is matched and labeled to obtain the time period matching and labeling results. The time period matching and labeling results are subjected to duration statistics processing. The total occurrence duration of each behavior habit type within the duration of the behavior is calculated to obtain the behavior duration distribution table of each behavior habit type within the duration of the behavior.

[0009] Furthermore, the selected home appliances are controlled via the device adjustment commands to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavioral habits, and to maintain the stability and comfort of the environmental parameters during the duration of the behavior, including: The device adjustment command is parsed to obtain a device execution parameter table including device start sequence, power adjustment value and runtime parameter, and the selected home devices are started in batches based on the device execution parameter table; The system collects environmental feedback data from selected home appliances that are activated in batches using environmental sensors, and determines whether the environmental feedback data falls within the range of target environmental parameters corresponding to the behavioral habit type. If not, the current operating parameters of the selected home devices that are started in batches are iteratively adjusted based on the target environmental parameters and the environmental feedback data until the environmental feedback data is within the range of the target environmental parameters corresponding to the behavior habit type, so as to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavior habit type, and maintain the stability and comfort of the environmental parameters during the duration of the behavior.

[0010] The present invention also provides an adaptive adjustment system for a smart home environment, comprising: The acquisition module is used to acquire the user's behavioral habit types and the corresponding duration of the behavior. The filtering module is used to match and filter home appliances based on the behavioral habit type, and configure the working parameters of the filtered home appliances based on the duration of the behavior to obtain device adjustment instructions; The control module is used to control the selected home appliances through the device adjustment commands, so as to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavior habits, and maintain the stability and comfort of the environmental parameters during the duration of the behavior.

[0011] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above methods.

[0012] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the above methods.

[0013] This invention provides an adaptive adjustment method for a smart home environment, comprising the following steps: acquiring the user's behavioral habit type and the corresponding duration of the behavior; matching and filtering home devices based on the behavioral habit type, and configuring the working parameters of the filtered home devices based on the duration of the behavior to obtain device adjustment instructions; controlling the filtered home devices through the device adjustment instructions to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavioral habit type, and maintaining the stability and comfort of the environmental parameters within the duration of the behavior. This solves the technical problem that traditional technologies ignore the personalization and regularity of user behavioral habits, resulting in a significant disconnect between the working state of home devices and the actual needs of users. It achieves the technical effect of ensuring that the indoor temperature and humidity are always maintained within a suitable range for office work through continuous monitoring and fine-tuning, without the situation of fluctuating temperature due to untimely adjustment, which affects the comfort of office work. Attached Figure Description

[0014] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart illustrating the steps of the adaptive adjustment method for a smart home environment in an embodiment of the present invention. Figure 2 This is a structural block diagram of the adaptive adjustment system for the smart home environment in an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0015] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0017] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0018] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0019] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0020] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0021] Reference Figure 1 This invention provides an adaptive adjustment method for a smart home environment, comprising the following steps: Step S1: Obtain the user's behavioral habit type and the corresponding duration of the behavior.

[0022] Specifically, when acquiring user behavior patterns and their corresponding duration, the system first collects historical environmental parameters for the past 1-2 months using sensors for indoor temperature, humidity, light, and device operating status. For example, it records the brightness of living room lights, air conditioner temperature, and operating time at different times of the day. Next, it analyzes the patterns of these parameters. If it is found that from 7:30 PM to 9:00 PM, Monday to Friday, the living room lights consistently maintain 300 lux, the air conditioner remains stable at 24℃, and the human activity detector shows the user staying in a fixed area of ​​the living room during this period, the corresponding user behavior pattern can be determined as "weekday evening living room reading." Then, the system calculates the continuous duration of this behavior from when the lights are turned on and the air conditioner is started until the devices are turned off, calculating the average duration of this period over the past month. If it consistently remains around 1.5 hours, the duration of the "weekday evening living room reading" behavior pattern can be determined to be 1.5 hours.

[0023] Step S2: Match and filter home appliances based on the behavior habit type, and configure the working parameters of the filtered home appliances based on the duration of the behavior to obtain device adjustment instructions.

[0024] Specifically, in the step of "matching and filtering home appliances based on the behavior habit type, and configuring the working parameters of the filtered home appliances based on the duration of the behavior to obtain device adjustment instructions," the process first involves filtering home appliances directly related to the behavior from all home appliances in the house, based on the determined behavior habit type. For example, when the behavior habit type is "nighttime bedroom sleep," the bedroom air conditioner, bedside lamp, and bedroom humidifier will be matched first, while irrelevant appliances such as the living room TV and kitchen range hood will be excluded. After the matching and filtering is completed, the working parameters of the filtered devices are configured according to the duration of the behavior corresponding to "nighttime bedroom sleep" (assuming it is 8 hours). For example, the bedroom air conditioner temperature is set to 22℃ and the fan speed is adjusted to low, the bedside lamp is set to nighttime off mode, and the humidifier humidity is maintained at 50%-55%. These parameters will have preset dynamic fine-tuning thresholds based on the 8-hour duration. Finally, the parameter configurations of these devices are integrated into a unified device adjustment instruction to ensure that the instruction can be directly used for subsequent device control.

[0025] Step S3: Control the selected home appliances through the device adjustment command to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavioral habits, and maintain the stability and comfort of the environmental parameters during the duration of the behavior.

[0026] Specifically, in the step of "controlling the selected home appliances through the device adjustment commands to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavioral habits, and maintaining the stability and comfort of the environmental parameters during the duration of the behavior," the previously obtained device adjustment commands are first transmitted to the control system of the selected home appliances. For example, for the current behavioral habit type of "daytime study office," the commands will be accurately sent to the study air conditioner, desk lamp, and air purifier. After receiving the commands, the devices immediately start the corresponding working mode. The air conditioner is adjusted to 24°C according to the configuration parameters, the desk lamp is switched to 4000K cool white light, and the air purifier is turned on at medium fan speed. The current environmental parameters such as indoor temperature, light intensity, and air quality are adjusted in real time to a state suitable for the office scenario. During the 4 hours of this office behavior, the system will collect the current environmental parameters every 15 minutes. If the indoor temperature rises to 25°C due to changes in outdoor temperature, a fine-tuning command will be automatically triggered to increase the cooling capacity of the air conditioner and pull the temperature back to 24°C, always keeping the environmental parameters stable within the comfortable range without significant fluctuations.

[0027] In a specific embodiment, obtaining the user's behavioral habit type and the corresponding duration of the behavior includes: Collect the user's home activity data in different time periods, and divide the user's behavior time periods based on the home activity data to obtain multiple behavior time periods, and extract the user activity features in each behavior time period; Based on the user activity characteristics, user behavior is classified to obtain behavior habit types, and the duration of each behavior habit type within the corresponding behavior time period is calculated to obtain the behavior duration.

[0028] Specifically, to achieve "obtaining user behavior patterns and corresponding durations," the system first collects user activity data at different times through indoor human body sensors, device status monitors, and environmental parameter collectors. For example, it records the status of bedroom lights (on / off), kitchen appliance (rice cooker, range hood) operation time, living room TV playback time, indoor temperature and humidity changes, and user movement patterns in different areas from 6:00 AM to 12:00 AM daily for a week. Then, based on this home activity data, the system divides user behavior into time periods. For instance, if it's found that from 6:30 AM to 7:30 AM, bedroom lights are on, the user moves between the bathroom and kitchen, and the rice cooker is running, this period is designated as the "morning preparation period." Similarly, based on the frequent use of the kitchen range hood and the user's longer time spent in the dining room from 12:00 PM to 1:00 PM, this period is designated as the "lunchtime period." And based on the time from 8:00 PM to 10:00 PM... The living room TV is running and the lights are set to a soft mode, which is divided into "evening leisure time". This gives us multiple behavioral time periods, and we extract the user activity characteristics within each behavioral time period. The characteristics of "morning preparation time" are "bedroom lights on + kitchen rice cooker running + user activity in the kitchen and bathroom area", the characteristics of "lunchtime meal time" are "kitchen range hood running + user stay in the dining room for ≥40 minutes", and the characteristics of "evening leisure time" are "living room TV running + light brightness ≤100 lux". Then, based on these user activity characteristics, user behavior is categorized. Behaviors matching the "morning preparation period" characteristics are classified as "morning routine" behavior, those matching the "lunchtime" characteristics are classified as "lunchtime" behavior, and those matching the "evening leisure period" characteristics are classified as "evening entertainment" behavior. Finally, the duration of each behavior habit type within the corresponding behavior period is calculated. By recording the actual start and end times of "morning routine" within the "morning preparation period" (6:30-7:30) over a week, the daily duration is calculated (e.g., Monday 6:32-7:28, 56 minutes; Tuesday 6:35-7:30, 55 minutes). The average is taken to obtain the duration of "morning routine" behavior as approximately 55 minutes. Similarly, "lunchtime" is calculated to be approximately 50 minutes and "evening entertainment" to be approximately 110 minutes. Finally, the user behavior habit types and their corresponding durations are obtained.

[0029] In a specific embodiment, user behavior is classified based on the user activity characteristics to obtain behavior habit types, including: Key features are extracted from the user activity features to obtain core activity elements, which include key device operation actions and key changes in environmental parameters. Based on the core activity elements, the user's behavior is categorized to obtain behavior habit types.

[0030] Specifically, when implementing the "classifying user behavior based on the user activity characteristics to obtain behavior habit types", the first step is to extract key features from the extracted user activity characteristics. For example, for the user activity characteristics of the "morning preparation period" of "bedroom lights on + rice cooker running + user activity in the kitchen and bathroom area from 6:30 to 7:30 every day", non-critical information such as "occasionally spending a short time on the balcony" in "user activity in the kitchen and bathroom area" is removed, and the focus is on extracting content that reflects the essence of the behavior. From the perspective of device operation, key device operation actions such as "starting the rice cooker (pressing the power button) and switching the bedroom lights on and off (from off to on)" are extracted. From the perspective of environmental parameters, key environmental parameter changes such as "the kitchen temperature rises by 2-3℃ after the rice cooker runs and the light intensity in the bedroom increases from 10 lux to 200 lux after the bedroom lights are turned on" are extracted. These extracted contents together constitute the core activity elements corresponding to the user activity characteristics. For example, regarding the user activity characteristics of "daily 20:00-21:30, desk lamp in study room turned on + air conditioner running + user staying at desk", the core activity elements obtained after key feature extraction include the key device operation actions of "student lamp turned on (brightness adjusted to 300 lux) and air conditioner started (temperature set to 24℃)" and the key environmental parameter changes of "student room light intensity increased from 50 lux to 300 lux after desk lamp turned on and indoor temperature decreased from 26℃ to 24℃ after air conditioner ran". Subsequently, user behavior is categorized based on these core activity elements. If the core activity elements extracted from user activity characteristics at different times all include content related to food preparation such as "starting the rice cooker and a slight increase in kitchen temperature," and are concentrated in the morning or noon, this type of behavior is classified as the "food preparation" behavior habit type. If the core activity elements include "turning on the desk lamp (high brightness setting), stable lighting in the desk area, and the air conditioner maintaining a comfortable temperature," and are concentrated in the evening, accompanied by the indirect characteristic of users spending a long time at their desks, this type of behavior is classified as the "evening reading" behavior habit type. In this way, different core activity elements are associated with corresponding behavioral scenarios, ultimately resulting in a clear classification of behavioral habit types.

[0031] In a specific embodiment, matching and filtering home appliances based on the behavioral habit type includes: Based on the behavioral habit type, the user's environmental demand attributes are determined, and the environmental demand attributes are graded by strength to obtain a priority list for adjusting the environmental priority. Based on the priority list, the functional parameters of home appliances are compared item by item to obtain a set of functions that meet the corresponding adjustment needs. Based on the functional matching set, spatial adaptation processing is performed on the coverage space and output capabilities of home devices to obtain adaptation parameters, and home devices are matched and filtered based on the adaptation parameters.

[0032] Specifically, when conducting the "matching and screening of home appliances based on the aforementioned behavioral habit types," the user's environmental needs attributes are first clarified based on the identified behavioral habit types. For example, when the behavioral habit type is "nighttime bedroom sleep," the environmental needs attributes include "stable temperature," "low light," "humid air," and "no noise interference." These attributes are then categorized by strength—"stable temperature" and "low light" directly affect sleep quality and are given the highest priority, followed by "humid air," with "no noise interference" being the basic priority. This creates a clear priority list for adjusting environmental settings. Subsequently, based on this priority list, the functional parameters of all home appliances are compared item by item. First, the functional parameters corresponding to the highest priority needs are compared. For example, "stable temperature" requires the appliance to have precise temperature control (error ±0.5℃) and continuous operation capability, which can filter out bedroom air conditioners; "low light" requires the appliance to support low brightness (≤5 lux) or an off mode, which can filter out bedroom ceiling lights and bedside lamps. Then, the next priority, "humid air," is compared. Based on the corresponding humidification function parameters, bedroom humidifiers were selected. The basic priority, "no noise interference," required the device's operating noise to be ≤30 decibels. The noise parameters of the initially selected air conditioners, lights, and humidifiers were further compared to obtain a set of functions that met all adjustment needs. Finally, based on this set of functions, the coverage area and output capacity of the home appliances were adapted to the space. For a bedroom of approximately 15 square meters, it was necessary to determine whether the air conditioner's cooling / heating area covered 15 square meters (e.g., a 1.5 horsepower air conditioner is suitable for 10-20 square meters, meeting the requirement) and whether the humidifier's humidification capacity was suitable for a 15 square meter space (e.g., 200ml / h). The system assesses the compatibility of various home appliances, including the air conditioner's humidification capacity (suitable for 10-18㎡, meeting requirements) and the lighting range of the lamps (e.g., ceiling lights with a radius ≥2.5m, meeting requirements). These criteria determine the compatibility parameters for the air conditioner, humidifier, and lighting range. Based on these parameters, the system performs a final matching and screening process, eliminating devices with insufficient coverage or incompatible output capabilities. Ultimately, the system determines the bedroom air conditioner, bedroom ceiling light, bedside lamp, and bedroom humidifier as the best home appliances.

[0033] In a specific embodiment, the working parameters of the screened home appliances are configured based on the duration of the behavior to obtain device adjustment instructions, including: The duration of the behavior is divided into time segments to obtain multiple duration segments. The duration of each duration segment is calculated based on the behavior habit type to obtain a behavior duration distribution table. Based on the behavior duration distribution table, the function start-up duration of the screened home devices is set to obtain the device function start-up duration configuration table. Based on the device function start-up duration configuration table and the behavior habit type, the various output powers of the screened home devices are determined. The operating modes of the selected home appliances are switched based on behavioral habits and multiple output power to obtain an operating mode switching setting table, and device adjustment instructions are generated based on the operating mode switching setting table.

[0034] Specifically, when configuring the working parameters of the selected home appliances based on the duration of the behavior to obtain device adjustment instructions, the process first divides the determined duration of the behavior (e.g., 4 hours for "daytime study office") into time segments, with each hour divided into four segments: "0-1 hour, 1-2 hours, 2-3 hours, and 3-4 hours." Then, considering the behavior habit type of "daytime study office," the intensity of user demand for the environment within each segment is analyzed—for example, 0-1 hours represents the initial stage of work, where users need to quickly get into the zone and have a high demand for environmental stability; 1-2 hours and 2-3 hours represent the core work period, where demand remains stable; and 3-4 hours represent the final stage of work, where demand decreases slightly. Based on this, the duration percentage of each segment is calculated (all 25%), forming a behavior duration percentage distribution table. Next, based on this distribution table, the function activation time of the selected home appliances (e.g., study air conditioner, desk lamp, air purifier) ​​is set. The air conditioner needs to activate within the entire 4-hour timeframe. The system operates continuously for 4 hours, with the desk lamp also set to 4 hours. The air purifier can stop for 10 minutes every hour (adapting to different needs), resulting in a total startup time of 3 hours and 20 minutes. This leads to a configuration table for device startup times. Considering the specific environmental requirements of a "daytime study / office," the output power of the air conditioner (1000W for cooling, 1200W for heating), the desk lamp (30W to ensure sufficient but not glaring light), and the air purifier (400W for medium airflow) are determined. Finally, based on user habits and these output powers, the operating modes of the selected home appliances are switched: the air conditioner starts in "rapid temperature control mode" (1200W) for 0-1 hours, switches to "stable operation mode" (1000W) for 1-2 and 2-3 hours, and then switches to "stable operation mode" (1000W) for 3-4 hours. The system maintains a "stable operation mode" for hours, the desk lamp maintains an "office lighting mode" (30W power) throughout, the air purifier starts a "medium purification mode" (400W power) during operation, and switches to "standby mode" during shutdown. These settings are compiled into an operation mode switching setting table, and then the parameters in the table are converted into instruction codes that the device can recognize, ultimately generating device adjustment instructions.

[0035] In a specific embodiment, the duration of the behavior is calculated as a percentage based on the type of behavioral habit, resulting in a behavior duration percentage distribution table, including: Based on the behavioral habit type, the duration of the behavior is extracted to obtain the high-frequency activity period corresponding to each behavioral habit type. Based on the high-frequency activity period, the duration of the behavior is matched and labeled to obtain the time period matching and labeling results. The time period matching and labeling results are subjected to duration statistics processing. The total occurrence duration of each behavior habit type within the duration of the behavior is calculated to obtain the behavior duration distribution table of each behavior habit type within the duration of the behavior.

[0036] Specifically, in implementing this step, firstly, based on the behavioral habit type of "reading in the study on weekday evenings," the duration of this behavior (assumed to be 19:00-21:00) is extracted to extract behavioral time period features. By analyzing historical environmental parameters (such as study room lighting and desk lamp operation status) over the past month, it was found that this behavior occurs most frequently between 19:10 and 20:50, thus identifying this interval as the corresponding high-frequency activity period. Next, based on this high-frequency activity period, the duration of the behavior between 19:00 and 21:00 is matched and labeled, with 19:10-20:50 labeled as "matching high-frequency period" and the remaining periods labeled as "non-high-frequency period," resulting in the time period matching labeling results. Then, the results are statistically processed to calculate that the total duration of the high-frequency period for "reading in the study on weekday evenings" within the behavior duration is 100 minutes, and the non-high-frequency period is 20 minutes. Based on this, the proportion of each period is compiled to form a behavioral duration distribution table for this behavioral habit type within the corresponding behavior duration.

[0037] In a specific embodiment, the selected home appliances are controlled via the device adjustment commands to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavioral habits, and to maintain the stability and comfort of the environmental parameters during the duration of the behavior, including: The device adjustment command is parsed to obtain a device execution parameter table including device start sequence, power adjustment value and runtime parameter, and the selected home devices are started in batches based on the device execution parameter table; The system collects environmental feedback data corresponding to home appliances that are activated in batches using environmental sensors, and determines whether the environmental feedback data is within the range of target environmental parameters corresponding to the behavioral habit type. If not, the current operating parameters of the home devices started in batches are iteratively adjusted based on the target environmental parameters and the environmental feedback data until the environmental feedback data is within the range of the target environmental parameters corresponding to the behavior habit type, so as to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavior habit type, and maintain the stability and comfort of the environmental parameters during the duration of the behavior.

[0038] Specifically, when executing the instruction to "control the selected home appliances through the device adjustment command to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavioral habits, and maintain the stability and comfort of the environmental parameters during the duration of the behavior", the device adjustment command is first parsed and processed to extract the device start sequence (start the living room air conditioner first, and start the living room lights and projector 1 minute later), power adjustment value (air conditioner cooling power 1100W, light brightness adjusted to the lowest level, projector running at standard power) and running time parameter (air conditioner and projector running continuously for 2 hours, and lights running synchronously for 2 hours). This information is organized into a device execution parameter table, and then the selected home appliances are started and controlled in batches according to the start sequence in the table - first, a start command is sent to the living room air conditioner, and after the air conditioner runs for 1 minute and enters a stable state, a start command is sent to the living room lights and projector. Next, the environmental feedback data corresponding to these activated devices is collected in real time through indoor temperature and light sensors. The living room temperature and light value are recorded every 2 minutes. For example, the temperature is 24.2℃ and the light is 38 lux when collected for the first time. Then, these environmental feedback data are compared with the target environmental parameters (temperature 23℃±0.5℃, light ≤30 lux) corresponding to "watching movies in the living room on a weekend afternoon". It is found that the temperature is higher than the upper limit of the target range and the light exceeds the target range, so it is determined that it is not within the target parameter range. At this point, based on the target environmental parameters and environmental feedback data, the current operating parameters of the home appliances are iteratively adjusted. The air conditioner's cooling power is increased from 1100W to 1300W to accelerate cooling, and the light brightness is further reduced from the lowest setting to a "dark setting for movie watching." After adjustment, environmental feedback data is collected again through sensors. The second collection shows a temperature of 23.5℃ and light intensity of 28 lux. The temperature is still slightly higher than the target upper limit, so the air conditioner power is fine-tuned again to 1200W. During the third collection, the temperature has dropped to 22.8℃ and the light intensity remains at 28 lux, both within the target environmental parameter range, and the iterative adjustment stops. During the subsequent 2-hour duration of the behavior, environmental feedback data is collected every 10 minutes. If the temperature rises to 23.6℃, ​​the air conditioner power is fine-tuned again to 1250W to ensure that the environmental feedback data is always within the target range. This achieves dynamic adaptation between the current environmental parameters and the user's current behavior habits, and maintains the stability and comfort of the environmental parameters.

[0039] The adaptive adjustment method for the smart home environment in the embodiments of the present invention has been described above. The adaptive adjustment system for the smart home environment in the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 2 One embodiment of the adaptive adjustment system for smart home environments in this invention includes: Module 21 is used to obtain the user's behavioral habit type and the corresponding duration of the behavior; The filtering module 22 is used to match and filter home appliances based on the behavior habit type, and configure the working parameters of the filtered home appliances based on the duration of the behavior to obtain device adjustment instructions; The control module 23 is used to control the selected home appliances through the device adjustment commands, so as to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavior habits, and maintain the stability and comfort of the environmental parameters during the duration of the behavior.

[0040] In this embodiment, the specific implementation of each unit in the above system embodiment is the same as that in the above method embodiment, and will not be repeated here.

[0041] like Figure 3 As shown in the diagram, this embodiment of the invention provides a structural schematic block diagram of a computer device, including: At least one processor; At least one memory for storing at least one program; When at least one program is executed by at least one processor, the at least one processor implements the above-described adaptive adjustment method for the smart home environment.

[0042] It is evident that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented in this device embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0043] Furthermore, this application also discloses a computer program product or computer program stored in a computer-readable storage medium. A processor of a computer device can read the computer program from the computer-readable storage medium and execute the computer program, causing the computer device to perform the aforementioned adaptive adjustment method for a smart home environment. Similarly, the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0044] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. An adaptive adjustment method for a smart home environment, characterized in that, Includes the following steps: Obtain the user's behavioral habits and the corresponding duration of the behavior; Home appliances are matched and filtered based on the behavioral habit type, and the working parameters of the filtered home appliances are configured based on the duration of the behavior to obtain device adjustment instructions. The selected home appliances are controlled by the device adjustment commands to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavioral habits, and to maintain the stability and comfort of the environmental parameters during the duration of the behavior.

2. The adaptive adjustment method for a smart home environment according to claim 1, characterized in that, Obtain the user's behavioral habit types and the corresponding duration of the behavior, including: Collect the user's home activity data in different time periods, and divide the user's behavior time periods based on the home activity data to obtain multiple behavior time periods, and extract the user activity features in each behavior time period; Based on the user activity characteristics, user behavior is classified to obtain behavior habit types, and the duration of each behavior habit type within the corresponding behavior time period is calculated to obtain the behavior duration.

3. The adaptive adjustment method for a smart home environment according to claim 2, characterized in that, Based on the aforementioned user activity characteristics, user behavior is categorized to obtain behavioral habit types, including: Key features are extracted from the user activity features to obtain core activity elements, which include key device operation actions and key changes in environmental parameters. Based on the core activity elements, the user's behavior is categorized to obtain behavior habit types.

4. The adaptive adjustment method for a smart home environment according to claim 1, characterized in that, Home appliances are matched and filtered based on the aforementioned behavioral habit types, including: Based on the behavioral habit type, the user's environmental demand attributes are determined, and the environmental demand attributes are graded by strength to obtain a priority list for adjusting the environmental priority. Based on the priority list, the functional parameters of home appliances are compared item by item to obtain a set of functions that meet the corresponding adjustment needs. Based on the functional matching set, spatial adaptation processing is performed on the coverage space and output capabilities of home devices to obtain adaptation parameters, and home devices are matched and filtered based on the adaptation parameters.

5. The adaptive adjustment method for a smart home environment according to claim 1, characterized in that, Based on the duration of the aforementioned behavior, the operating parameters of the selected home appliances are configured to obtain device adjustment instructions, including: Based on the behavioral habit type, the duration of the behavior is calculated as a percentage of the duration, resulting in a behavior duration percentage distribution table. Based on the behavior duration distribution table, the function start-up duration of the screened home devices is set to obtain the device function start-up duration configuration table. Based on the device function start-up duration configuration table and the behavior habit type, the various output powers of the screened home devices are determined. The operating modes of the selected home appliances are switched based on behavioral habits and multiple output power to obtain an operating mode switching setting table, and device adjustment instructions are generated based on the operating mode switching setting table.

6. The adaptive adjustment method for a smart home environment according to claim 5, characterized in that, Based on the behavioral habit type, the duration of the behavior is calculated as a percentage, resulting in a behavior duration percentage distribution table, including: Based on the behavioral habit type, the duration of the behavior is extracted to obtain the high-frequency activity period corresponding to each behavioral habit type. Based on the high-frequency activity period, the duration of the behavior is matched and labeled to obtain the time period matching and labeling results. The time period matching and labeling results are subjected to duration statistics processing. The total occurrence duration of each behavior habit type within the duration of the behavior is calculated to obtain the behavior duration distribution table of each behavior habit type within the duration of the behavior.

7. The adaptive adjustment method for a smart home environment according to claim 1, characterized in that, The selected home appliances are controlled via the device adjustment commands to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavioral habits, and to maintain the stability and comfort of the environmental parameters during the duration of the behavior, including: The device adjustment command is parsed to obtain a device execution parameter table including device start sequence, power adjustment value and runtime parameter, and the selected home devices are started in batches based on the device execution parameter table; The system collects environmental feedback data corresponding to home appliances that are activated in batches using environmental sensors, and determines whether the environmental feedback data is within the range of target environmental parameters corresponding to the behavioral habit type. If not, the current operating parameters of the home devices started in batches are iteratively adjusted based on the target environmental parameters and the environmental feedback data until the environmental feedback data is within the range of the target environmental parameters corresponding to the behavior habit type, so as to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavior habit type, and maintain the stability and comfort of the environmental parameters during the duration of the behavior.

8. An adaptive adjustment system for a smart home environment, characterized in that, include: The acquisition module is used to acquire the user's behavioral habit types and the corresponding duration of the behavior. The filtering module is used to match and filter home appliances based on the behavioral habit type, and configure the working parameters of the filtered home appliances based on the duration of the behavior to obtain device adjustment instructions; The control module is used to control the selected home appliances through the device adjustment commands, so as to achieve dynamic adaptation between the current indoor environmental parameters and the user's current behavior habits, and maintain the stability and comfort of the environmental parameters during the duration of the behavior.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, The processor executes the steps of any one of claims 1 to 7 when executing a computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When a computer program is executed by a processor, it implements the steps of the method of any one of claims 1 to 7.

Images