Intelligent home control system based on artificial intelligence

Abstract

The invention relates to the technical field of smart home, and provides a smart home control system based on artificial intelligence, an energy consumption monitoring module is used for continuously monitoring energy consumption of a smart home, monitoring use data of the smart home in real time, and obtaining historical use data of the smart home of a user; the standby analysis module judges the real standby state of the smart home according to the power consumption information of the smart home and calculates the standby time, and the prediction module predicts the use time and the use energy consumption of the smart home according to the historical monitoring data of the energy consumption monitoring module. The home control module executes related operation on the smart home according to the calculation result of the standby analysis module and the prediction result of the prediction module; according to the invention, the equipment which is really in a standby state is accurately identified through a clustering algorithm, energy waste and equipment loss can be reduced, and the time smoothing processing unit processes the time sequence data, so that the influence that the collected time data is disordered and cannot be predicted due to a complex environment is solved.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, in particular to a smart home control system based on artificial intelligence. Background Art

[0002] With the continuous development of social economy, people's pace of life is accelerating, and the requirements for quality of life are getting higher and higher. The traditional home control method requires manual operation of various electrical appliances, which is a cumbersome process. After a busy day at home, you need to turn on the lights, air conditioners, curtains and other devices separately. This operation method is gradually unable to meet people's pursuit of convenience. In this context, smart home control systems came into being, which allow users to control home appliances conveniently and quickly through simple instructions or automated settings.

[0003] The rapid progress of artificial intelligence technology has provided solid technical support for smart home control systems. Artificial intelligence algorithms such as machine learning and deep learning enable the system to understand complex information such as user behavior patterns, voice commands and environmental changes. Through the training of deep learning algorithms on a large amount of voice data, the system can accurately recognize the user's voice commands and realize voice control functions.

[0004] The existing background technology still has the following shortcomings:

[0005] 1. Ignoring the standby energy consumption of devices. The smart home system contains many smart devices, which still consume a certain amount of electricity in standby mode. However, the existing smart home control system cannot accurately adapt to the standby energy consumption characteristics of all electrical appliances when judging whether the device is in a long-term standby state, and it is easy to make misjudgments. For example, some electrical appliances may have short-term energy consumption fluctuations when they are in standby mode. The control system may misjudge this normal fluctuation as the device has entered the working state, and thus fail to perform the automatic power cut-off operation, resulting in poor energy consumption control effect; or conversely, some devices that are in normal working state but have low energy consumption are misjudged as standby state and the power is cut off, affecting the normal use of the equipment. In the long run, the accumulation of these standby energy consumption will also cause energy waste and equipment loss.

[0006] 2. Most existing smart home control systems choose to shut down the standby devices when they are in standby mode, which in turn requires users to restart the devices when they need to use them, resulting in untimely responses to user commands and affecting the user's long-term experience. Setting a timing function or predicting user behavior can solve some of the problems, but the scenes in the home environment are often complex and diverse, and the system may find it difficult to accurately identify all scene information, thus affecting the accuracy of the prediction analysis. For example, on a winter morning, the user may want to open the curtains later, but the system may only judge the user's habit of getting up based on the previous time, ignoring the seasonal factor, and still recommend opening the curtains at the usual time. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a smart home control system based on artificial intelligence, which realizes accurate judgment of the standby state of smart home devices through the clustering algorithm of the standby energy consumption monitoring unit, removes the influence of complex environment through smoothing processing of data by the prediction module, and cooperates with the home use prediction unit to realize accurate prediction of the time point when the user uses the device, so as to solve the problems in the prior art.

[0008] An intelligent home control system based on artificial intelligence, comprising:

[0009] An energy consumption monitoring module, which is used to continuously monitor the energy consumption of the smart home, monitor the smart home usage data in real time, and obtain the user's historical smart home usage data;

[0010] A standby analysis module, which is data-connected to the energy consumption monitoring module and is used to determine the real standby state of the smart home according to the power consumption information of the smart home and calculate the standby time;

[0011] A prediction module, which is data-connected to the energy consumption monitoring module and is used to predict the usage time and energy consumption of the smart home based on historical monitoring data of the energy consumption monitoring module;

[0012] A home control module, which is data-connected to the standby analysis module and the prediction module, and is used to perform relevant operations on the smart home according to the calculation results of the standby analysis module and the prediction results of the prediction module.

[0013] Preferably, the historical smart home usage data monitored or acquired by the energy consumption monitoring module includes smart home energy consumption information, smart home usage time point information and smart home type information.

[0014] Preferably, the standby analysis module includes a judgment unit, a standby energy consumption monitoring unit and a standby time monitoring unit;

[0015] The judgment unit classifies the smart home devices into continuous operation type and non-continuous operation type according to the historical operation time data of the smart home monitored by the energy consumption monitoring module by setting a threshold value for the operation time. The emergency type smart home devices are uniformly classified into the continuous operation type.

[0016] The standby energy consumption monitoring unit is used to analyze the real-time power consumption information of the non-continuous operation type smart home in the judgment unit to determine whether the current smart home is in a standby state;

[0017] The standby time monitoring unit determines the home appliance in the standby state based on the standby energy consumption monitoring unit, and counts the standby time for the home appliance, and transmits the standby time to the home control module.

[0018] Preferably, the standby energy consumption monitoring unit uses a clustering algorithm to eliminate the judgment errors caused by the normal energy consumption fluctuations of the household equipment in the standby state;

[0019] Collect energy consumption data samples of equipment {E1, E2, E d ,...,};

[0020] The number of clusters is determined by the elbow rule so that each data point E d To the center of the cluster F j The Euclidean distance is the smallest. Since the energy consumption data is one-dimensional data, the Euclidean distance becomes:

[0021] (E d ,F j )=|E d -F j |

[0022] Among them, d is the index of the energy consumption data sample, j is the sample index of the classified cluster center;

[0023] When judging whether the device is in standby mode, the cluster to which the energy consumption data belongs is observed. If the energy consumption data of the device always belongs to a specific cluster within a specified time and the energy consumption sample data at the center of the cluster is the smallest, the device is judged to be in standby mode.

[0024] Preferably, the prediction module includes a time smoothing processing unit, a prediction model building unit and a home use prediction unit;

[0025] The time smoothing processing unit is used to process the trend and periodic characteristics in the time series data, discover the periodic law and the overall change trend, and improve the prediction accuracy for the home use prediction unit;

[0026] The prediction model building unit builds a decision tree prediction model based on the historical smart home usage data processed by the time smoothing processing unit;

[0027] The home use prediction unit is used to predict the time point of the user's future use of the smart home based on the user's smart home usage data.

[0028] Preferably, the time smoothing processing unit first obtains historical data of a certain smart home device at a time point when the user uses the device according to the standby analysis module;

[0029] Suppose for this smart home device, the user's order of turning it on is recorded as t, the first time it is turned on is t=1, and the second time it is turned on is t=2;

[0030] Suppose a series of data points at which users start using the device, recorded in the order of behavior t, are converted into a unified numerical measurement, and the time points of each day are converted into minutes to form a minute value {y1, y2, y t ,...};

[0031] Initialization parameters: Select smoothing parameters α, β and γ, which range from 0 to 1;

[0032] At the same time, the initialization level L at t = 1 t 、Trend H t and the value of the cycle length c, for the value of the initialization level L1, the data y1 actually observed at the corresponding initial time is taken; T1 is initialized to 0; the cycle length c is set to include but not limited to the number of times the smart home device is turned on within three days, a week or half a month;

[0033] Horizontal equation:

[0034] L t =α(y t -K t-c )+(1-α)(L t-1 +H t-1 )

[0035] L t It represents the basic level estimate after removing the periodic factors after t times of smoothing. When predicting the time point of user equipment usage, it is a basic time point reference value that does not contain periodic fluctuations. The smoothing parameter α is used to control the trade-off between current data and historical trends. K t-c Indicates the previous complete cycle, the cycle factor corresponding to the cycle length c, y t is the actual recorded time point when the user used the device for the tth time;

[0036] Trend equation:

[0037] H t =β(L t -L t-1)+(1-β)H t-1

[0038] H t It represents the trend value at t, which indicates the growth or decline trend of the behavior sequence. For the time point of user device use, it reflects the trend of whether the time point is gradually advanced or postponed; β is used to control the trade-off between the current trend change and the historical trend;

[0039] Periodic equation:

[0040] K t =γ(y t -L t )+(1-γ)P t-m

[0041] Among them, K t represents the estimated value of the periodic factor at t, which is used to capture the periodic regularity in the data, including users having similar device usage time points on the same day of the week or the same time of the day. γ is used to control the trade-off between current periodic changes and historical periodic regularity;

[0042] According to the above calculation, as t increases, the final smoothed minute value {y1,y2,y t ,...} with {L1,L2,L t ,...} to represent the original time series. These smoothed level values ​​remove the interference of periodic fluctuations and some noise and serve as the basic data for the subsequent prediction model establishment unit.

[0043] Preferably, the prediction model building unit firstly calculates {L1, L2, L t ,...}Build a decision tree;

[0044] For {L1,L2,L t ,...}, that is, the time point when the user starts to use the device, first calculate its information entropy S(L t ), information entropy formula:

[0045]

[0046] Where n represents L t The total number of values ​​in category i. If a day is divided into intervals of 10 minutes, the maximum value of n is 143. YesL t The probability of belonging to the i-th category is obtained by counting the frequency of time points falling into each interval in the past data. This information entropy value reflects the degree of uncertainty of the target variable before feature segmentation;

[0047] Extract L tThe derived features are used as input features, including the value of t and the period K in which t is located. t and trend value H t ; Take these three data as L t The calculation formula for the feature X in is:

[0048]

[0049] Among them, m is the number of values ​​of feature X, and r is the index of feature X;

[0050]

[0051] Then calculate the information gain IG(L t ,X):

[0052] IG(L t ,X)=S(L t )-S(L t │X)

[0053] Select the feature with the largest information gain as the best feature for the current partition, partition at the root node, divide the data set into different subsets, repeat this process, calculate all historical data to build a decision tree, and the last subset is the leaf node for storing L t The corresponding time point y t .

[0054] Preferably, the home use prediction unit uses the constructed decision tree to make predictions;

[0055] Calculate the input feature vector X for a specific future time point, input the feature vector into the already constructed decision tree, start from the root node, select the corresponding branch according to the different subsets stored in the root node, and so on, until reaching a leaf node;

[0056] The leaf nodes are stored at the time point, expressed in minutes, which is the predicted time point when the user used the device at that specific time point.

[0057] Preferably, the home control module includes an energy consumption calculation confirmation unit and an execution unit;

[0058] The energy consumption calculation confirmation unit calculates the energy consumption of the non-continuously running smart home based on the standby time calculated by the standby analysis module, and provides an operation interface for the user to set the standby energy consumption threshold. When the threshold is exceeded, the smart home in the non-standby state is automatically shut down;

[0059] The execution unit responds in advance according to the prediction result of the time point of the smart home that the user will use in the future by the prediction module, and turns on the non-continuous operation smart home that is turned off by the energy consumption calculation confirmation unit.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. The present invention adopts a clustering algorithm through an energy consumption monitoring module and a standby energy consumption monitoring unit, collects equipment energy consumption data samples, and determines whether the equipment is in standby mode by judging the cluster center and cluster type, thereby effectively avoiding judgment errors caused by normal energy consumption fluctuations of household equipment in standby mode, thereby accurately identifying equipment that is truly in standby mode, which is beneficial to reducing energy waste and equipment loss.

[0062] 2. The present invention solves the problem of chaotic and unpredictable effects on collected time data in complex environments by processing time series data with a time smoothing processing unit, extracts relevant features and establishes a decision tree prediction model, and uses the model to predict specific time points in the future. The execution unit responds in advance according to the prediction results, and promptly turns on the non-continuously running smart home that was previously turned off due to energy consumption control when the user needs to use the device, avoiding the inconvenience caused by restarting the device due to premature shutdown, better meeting the user's usage needs in different scenarios, and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of the system module structure of the present invention;

[0064] Figure 2 It is a working diagram of the standby analysis module of the present invention;

[0065] Figure 3 It is a flow chart of establishing the prediction module model of the present invention. DETAILED DESCRIPTION

[0066] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0067] The present invention provides an intelligent home control system based on artificial intelligence, comprising:

[0068] An energy consumption monitoring module, which is used to continuously monitor the energy consumption of the smart home, monitor the smart home usage data in real time, and obtain the user's historical smart home usage data;

[0069] A standby analysis module, which is data-connected to the energy consumption monitoring module and is used to determine the real standby state of the smart home according to the power consumption information of the smart home and calculate the standby time;

[0070] A prediction module, which is data-connected to the energy consumption monitoring module and is used to predict the usage time and energy consumption of the smart home based on historical monitoring data of the energy consumption monitoring module;

[0071] A home control module, which is data-connected to the standby analysis module and the prediction module, and is used to perform relevant operations on the smart home according to the calculation results of the standby analysis module and the prediction results of the prediction module.

[0072] Embodiment 1:

[0073] like Figure 1-3 As shown, in this embodiment, in an ordinary family, a smart home system is installed and operated, which includes a variety of smart devices such as a smart TV, a smart speaker, a smart microwave oven, a smart desk lamp and a smart smoke alarm.

[0074] The energy consumption monitoring module works continuously to collect the energy consumption information, usage time information and device type information of each smart device. For example, if a smart TV is turned on at 7 pm for viewing, the energy consumption monitoring module will record the energy consumption of the TV during this period.

[0075] The standby analysis module includes a judgment unit, a standby energy consumption monitoring unit and a standby time monitoring unit;

[0076] The judgment unit classifies the smart home devices into continuous operation and non-continuous operation categories based on the historical operation time data of the smart home monitored by the energy consumption monitoring module, by setting a threshold for the operation time. Emergency-type smart home devices are uniformly classified into the continuous operation category; for example, smoke alarms are classified into the continuous operation category.

[0077] The standby energy consumption monitoring unit performs real-time power consumption analysis on non-continuously running devices such as smart TVs. It collects energy consumption data samples when smart TVs are in standby mode, and determines the number of clusters through clustering algorithms and elbow rule.

[0078] Collect energy consumption data samples of equipment {E1, E2, E d ,...,};

[0079] The number of clusters is determined by the elbow rule so that each data point E d To the center of the cluster F j The Euclidean distance is the smallest. Since the energy consumption data is one-dimensional data, the Euclidean distance becomes:

[0080] (E d ,F j )=|E d -F j |

[0081] Among them, d is the index of the energy consumption data sample, j is the sample index of the classified cluster center;

[0082] When judging whether the device is in standby mode, the cluster to which the energy consumption data belongs is observed. If the energy consumption data of the device always belongs to a specific cluster within a specified time and the energy consumption sample data at the center of the cluster is the smallest, the device is judged to be in standby mode.

[0083] For example, in the continuous monitoring of ten minutes, it was found that the energy consumption of the smart TV in standby mode had a small fluctuation, and the energy consumption range was always in the center of the lowest cluster. The algorithm judged that these fluctuations belonged to the normal standby energy consumption range. When the smart TV is in standby mode for a long time and the energy consumption is stable in the standby energy consumption cluster, the standby time monitoring unit will time it and transmit the standby time to the home control module.

[0084] The energy consumption calculation and confirmation unit of the home control module calculates the energy consumption according to the standby time of the smart TV, and sets a reasonable standby energy consumption threshold through the operation interface. When the standby time of the smart TV is too long and exceeds the threshold, the energy consumption calculation and confirmation unit automatically turns it off, avoiding unnecessary energy waste of the smart TV in standby mode and achieving the purpose of energy saving.

[0085] Embodiment 2:

[0086] like Figure 1-3 As shown, in this embodiment, the smart TV in the family of Embodiment 1 is taken as an example.

[0087] The prediction module includes a time smoothing processing unit, a prediction model building unit and a home use prediction unit;

[0088] The time smoothing processing unit obtains the historical usage data of the smart TV, including the time information when the user turns on the TV to watch different programs. For example, the user usually watches the news broadcast at 7:30 p.m. every weekday and variety shows at 8 p.m. on weekends, etc. These different time points every day are converted into minutes, which are 450 and 480 respectively.

[0089] At the same time, the smoothing parameters and trend parameters are initialized, and a week is set as the cycle according to the user's viewing habits. For this smart home device, the user's order of opening it is recorded as t. On the first day of the cycle, the first 450 minutes start, which is t = 1;

[0090] The data were processed by means of level equations, trend equations, and periodic equations;

[0091] Suppose a series of data points at which users start using the device, recorded in the order of behavior t, are converted into a unified numerical measurement, and the time points of each day are converted into minutes to form a minute value {y1, y2, y t,...};

[0092] Initialization parameters: Select smoothing parameters α, β and γ, which range from 0 to 1;

[0093] At the same time, the initialization level L at t = 1 t 、Trend H t and the value of the cycle length c, for the value of the initialization level L1, the data y1 actually observed at the corresponding initial time is taken; T1 is initialized to 0; the cycle length c is set to include but not limited to the number of times the smart home device is turned on within three days, a week or half a month;

[0094] Horizontal equation:

[0095] L t =α(y t -K t-c )+(1-α)(L t-1 +H t-1 )

[0096] L t It represents the basic level estimate after removing the periodic factors after t times of smoothing. When predicting the time point of user equipment usage, it is a basic time point reference value that does not contain periodic fluctuations. The smoothing parameter α is used to control the trade-off between current data and historical trends. K t-c Indicates the previous complete cycle, the cycle factor corresponding to the cycle length c, y t is the actual recorded time point when the user used the device for the tth time;

[0097] Trend equation:

[0098] H t =β(L t -L t-1 )+(1-β)H t-1

[0099] H t It represents the trend value at t, which indicates the growth or decline trend of the behavior sequence. For the time point of user device use, it reflects the trend of whether the time point is gradually advanced or postponed; β is used to control the trade-off between the current trend change and the historical trend;

[0100] Periodic equation:

[0101] K t =γ(y t -L t )+(1-γ)P t-m

[0102] Among them, K trepresents the estimated value of the periodic factor at t, which is used to capture the periodic regularity in the data, including users having similar device usage time points on the same day of the week or the same time of the day. γ is used to control the trade-off between current periodic changes and historical periodic regularity;

[0103] According to the above calculation, as t increases, the final smoothed minute value {y1,y2,y t ,...} with {L1,L2,L t ,...} to represent the original time series. These smoothed level values ​​remove the interference of periodic fluctuations and some noise and serve as the basic data for the subsequent prediction model establishment unit.

[0104] In this way, the periodic fluctuations and some noise interference in the original time series can be removed.

[0105] This is very useful for complex user behavior time series data by processing trends, periodicity, and irregular fluctuations in time series at the same time. For example, a user may use the device late in the evening on weekdays and early in the afternoon on weekends. This periodic pattern (on a weekly basis) and the possible trend of gradually delaying or advancing the use time point can be well captured by this method.

[0106] The prediction model building unit builds a decision tree based on the processed historical data. The information entropy of the TV on time point data is calculated, and the derived features including the on sequence, cycle and trend value are extracted as input features. The conditional entropy and information gain are calculated, and the best features are selected for division. The complete decision tree is gradually built. The prediction model building unit first calculates the {L1, L2, L t ,...}Build a decision tree;

[0107] For {L1,L2,L t ,...}, that is, the time point when the user starts to use the device, first calculate its information entropy S(L t ), information entropy formula:

[0108]

[0109] Where n represents L t The total number of values ​​in category i. If a day is divided into intervals of 10 minutes, the maximum value of n is 143. YesL t The probability of belonging to the i-th category is obtained by counting the frequency of time points falling into each interval in the past data. This information entropy value reflects the degree of uncertainty of the target variable before feature segmentation;

[0110] Extract L tThe derived features are used as input features, including the value of t and the period K in which t is located. t and trend value H t ; Take these three data as L t The calculation formula for the feature X in is:

[0111]

[0112] Among them, m is the number of values ​​of feature X, and r is the index of feature X;

[0113]

[0114] Then calculate the information gain IG(L t ,X):

[0115] IG(L t ,X)=S(L t )-S(L t │X)

[0116] Select the feature with the largest information gain as the best feature for the current partition, partition at the root node, divide the data set into different subsets, repeat this process, calculate all historical data to build a decision tree, and the last subset is the leaf node for storing L t The corresponding time point y t .

[0117] Once, a popular variety show that usually aired at 8 p.m. was changed to 9 p.m. due to special arrangements. The home use prediction unit calculates the input feature vector for this new time point based on the new situation, combined with the current time, cycle and other information, and inputs it into the constructed decision tree.

[0118] Calculate the input feature vector X for a specific future time point, input the feature vector into the already constructed decision tree, start from the root node, select the corresponding branch according to the different subsets stored in the root node, and so on, until reaching a leaf node;

[0119] The leaf nodes are stored at the time point, expressed in minutes, which is the predicted time point when the user used the device at that specific time point.

[0120] Since the system has learned the user's habit of watching the variety show and related surrounding information, the prediction results show that the user is likely to turn on the TV around 9 o'clock in the evening.

[0121] Based on the prediction result, the execution unit of the home control module turns on the power of the smart TV which was previously turned off by the energy consumption calculation confirmation unit for energy saving at around 9 o'clock, avoiding the inconvenience caused by restarting the device due to premature shutdown, better meeting the user's usage needs in different scenarios, and improving the user experience. At the same time, it avoids the energy consumption of the smart TV due to long-term standby, improves the user experience, and the TV's startup time can better adapt to complex situations such as changes in program time.

[0122] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An intelligent home control system based on artificial intelligence, characterized in that: include: An energy consumption monitoring module, which is used to continuously monitor the energy consumption of the smart home, monitor the smart home usage data in real time, and obtain the user's historical smart home usage data; A standby analysis module, which is data-connected to the energy consumption monitoring module and is used to determine the real standby state of the smart home according to the power consumption information of the smart home and calculate the standby time; A prediction module, which is data-connected to the energy consumption monitoring module and is used to predict the usage time and energy consumption of the smart home based on historical monitoring data of the energy consumption monitoring module; A home control module, which is data-connected to the standby analysis module and the prediction module, and is used to perform relevant operations on the smart home according to the calculation results of the standby analysis module and the prediction results of the prediction module.

2. The smart home control system based on artificial intelligence as claimed in claim 1, characterized in that: The historical smart home usage data monitored or acquired by the energy consumption monitoring module includes smart home energy consumption information, smart home usage time point information and smart home type information.

3. The smart home control system based on artificial intelligence as claimed in claim 1, characterized in that: The standby analysis module includes a judgment unit, a standby energy consumption monitoring unit and a standby time monitoring unit; The judgment unit classifies the smart home devices into continuous operation type and non-continuous operation type according to the historical operation time data of the smart home monitored by the energy consumption monitoring module by setting a threshold value for the operation time. The emergency type smart home devices are uniformly classified into the continuous operation type. The standby energy consumption monitoring unit is used to analyze the real-time power consumption information of the non-continuous operation type smart home in the judgment unit to determine whether the current smart home is in a standby state; The standby time monitoring unit determines the home appliance in the standby state based on the standby energy consumption monitoring unit, and counts the standby time for the home appliance, and transmits the standby time to the home control module.

4. The smart home control system based on artificial intelligence as claimed in claim 3, characterized in that: The standby energy consumption monitoring unit uses a clustering algorithm to eliminate the judgment errors caused by the normal energy consumption fluctuations of the household equipment in the standby state; Collect energy consumption data samples of equipment {E1, E2, E d ,...,}; The number of clusters is determined by the elbow rule so that each data point E d To the center of the cluster F j The Euclidean distance is the smallest. Since the energy consumption data is one-dimensional data, the Euclidean distance becomes: (E d ,F j )=|E d -F j | Among them, d is the index of the energy consumption data sample, j is the sample index of the classified cluster center; When judging whether the device is in standby mode, the cluster to which the energy consumption data belongs is observed. If the energy consumption data of the device always belongs to a specific cluster within a specified time and the energy consumption sample data at the center of the cluster is the smallest, the device is judged to be in standby mode.

5. The smart home control system based on artificial intelligence as claimed in claim 1, characterized in that: The prediction module includes a time smoothing processing unit, a prediction model building unit and a home use prediction unit; The time smoothing processing unit is used to process the trend and periodic characteristics in the time series data, discover the periodic law and the overall change trend, and improve the prediction accuracy for the home use prediction unit; The prediction model building unit builds a decision tree prediction model based on the historical smart home usage data processed by the time smoothing processing unit; The home use prediction unit is used to predict the time point of the user's future use of the smart home based on the user's smart home usage data.

6. An artificial intelligence-based smart home control system as claimed in claim 5, characterized in that: The time smoothing processing unit first obtains the historical data of a certain smart home device and the time point at which the user uses the device according to the standby analysis module; Suppose for this smart home device, the user's order of turning it on is recorded as t, the first time it is turned on is t=1, and the second time it is turned on is t=2; Assume a series of data points at the time when users start using the device, recorded in the order of behavior t, and convert these time points into a unified numerical measurement. Convert the time points of each day into minutes to form a minute value {y1, y2, y t , ...}; Initialization parameters: Select smoothing parameters α, β and γ, which range from 0 to 1; At the same time, the initialization level L at t = 1 t 、Trend H t and the value of the cycle length c, for the value of the initialization level L1, the data y1 actually observed at the corresponding initial time is taken; T1 is initialized to 0; the cycle length c is set to include but not limited to the number of times the smart home device is turned on within three days, a week or half a month; Horizontal equation: L t =α(y t -K t-c )+(1-α)(L t-1 +H t-1 ) L t It represents the basic level estimate after removing the periodic factors after t times of smoothing. When predicting the time point of user equipment usage, it is a basic time point reference value that does not contain periodic fluctuations. The smoothing parameter α is used to control the trade-off between current data and historical trends. K t-c Indicates the previous complete cycle, the cycle factor corresponding to the cycle length c, y t is the actual recorded time point when the user used the device for the tth time; Trend equation: H t =β(L t -L t-1 )+(1-β)H t-1 H t It represents the trend value at t, which indicates the growth or decline trend of the behavior sequence. For the time point of user device use, it reflects the trend of whether the time point is gradually advanced or postponed. β is used to control the trade-off between the current trend change and the historical trend. Periodic equation: K t =γ(y t -L t )+(1-γ)P t-m Among them, K t represents the estimated value of the periodic factor at t, which is used to capture the periodic regularity in the data, including users having similar device usage time points on the same day of the week or the same time of the day. γ is used to control the trade-off between current periodic changes and historical periodic regularity; According to the above calculation, as t continues to increase, the final smoothed minute value {y1, y2, y t , ...} with {L1, L2, L t , ...} to represent the original time series. These smoothed level values ​​remove the interference of periodic fluctuations and some noises and serve as the basic data for the subsequent prediction model establishment unit.

7. The smart home control system based on artificial intelligence as claimed in claim 5, characterized in that: The prediction model building unit firstly calculates {L1, L2, L t , ...}Build a decision tree; For {L1, L2, L t , ...}, that is, the time point when the user starts to use the device, first calculate its information entropy S(L t ), information entropy formula: Where n represents L t The total number of values ​​in category i. If a day is divided into intervals of 10 minutes, the maximum value of n is 143. YesL t The probability of belonging to the i-th category is obtained by counting the frequency of time points falling into each interval in the past data. This information entropy value reflects the degree of uncertainty of the target variable before feature segmentation; Extract L t The derived features are used as input features, including the value of t and the period K in which t is located. t and trend value H t ; Take these three data as L t The calculation formula for the feature X in is: Among them, m is the number of values ​​of feature X, and r is the index of feature X; Then calculate the information gain IG(L t ,X): IG(L t ,X)=S(L t )-S(L t |X) Select the feature with the largest information gain as the best feature for the current partition, partition at the root node, divide the data set into different subsets, repeat this process, calculate all historical data to build a decision tree, and the last subset is the leaf node for storing L t The corresponding time point y t .

8. The smart home control system based on artificial intelligence as claimed in claim 5, characterized in that: The home use prediction unit uses the constructed decision tree to make predictions; Calculate the input feature vector X for a specific future time point, input the feature vector into the already constructed decision tree, start from the root node, select the corresponding branch according to the different subsets stored in the root node, and so on, until reaching a leaf node; The leaf nodes are stored at the time point, expressed in minutes, which is the predicted time point when the user used the device at that specific time point.

9. The smart home control system based on artificial intelligence as claimed in claim 1, characterized in that: The home control module includes an energy consumption calculation confirmation unit and an execution unit; The energy consumption calculation confirmation unit calculates the energy consumption of the non-continuously running smart home based on the standby time calculated by the standby analysis module, and provides an operation interface for the user to set the standby energy consumption threshold. When the threshold is exceeded, the smart home in the non-standby state is automatically shut down; The execution unit responds in advance according to the prediction result of the time point of the smart home that the user will use in the future by the prediction module, and turns on the non-continuous operation smart home that is turned off by the energy consumption calculation confirmation unit.

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