CN111275313A - Method and device for triggering home-returning mode of household equipment and computer equipment - Google Patents

Abstract

The application relates to a method, an apparatus, a computer device and a storage medium for triggering a home mode of a home appliance. The method comprises the following steps: acquiring current activity information of a target user uploaded by community equipment in a community; acquiring historical behavior track information of a target user in a community; inputting current activity information and historical behavior track information into a machine learning model; obtaining a prediction result output by a machine learning model; when the prediction result judges that the target user is about to go home, the household equipment of the target user is triggered to be in a home-returning mode, in the scheme, the conditions are fewer, and the triggering action of the home-returning mode can be accurately completed under most conditions, so that the energy waste caused by misjudgment of the home-returning mode can be avoided, the intelligent home scheme can be implemented more conveniently by accurately triggering the home-returning mode, the life quality and efficiency of the user are improved, and the comfort level and the life quality of the user are further improved.

Description

Method and device for triggering home-returning mode of household equipment and computer equipment

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for triggering a home mode of a home appliance, a computer device, and a storage medium.

Background

In the home-returning mode of the smart home, the home appliances usually start to work after people arrive at home. In the traditional technology, aiming at the problem, the technical scheme is adopted to calculate the time of the target user arriving at home according to the mode of mobile phone GPS positioning or automobile GPS positioning reporting of the target user so as to trigger a home-returning mode and turn on an air conditioner, a water heater, a lamp and the like in advance. However, the trigger modes are limited by conditions such as a 3G/4G network environment, a GPS signal, the electric quantity of a mobile phone, an App process of the mobile phone, a flow switch of the mobile phone and the like; if people forget to take the mobile phone or use other vehicles to go home, the mobile phone cannot be triggered; in addition, a certain misjudgment problem exists, if people carry mobile phones or drive vehicles to pass through roads around a cell, the mobile phones are often misjudged to be in a home mode, and energy waste is caused.

Disclosure of Invention

In view of the above, it is necessary to provide a method, an apparatus, a computer device, and a storage medium for triggering a home mode of a home appliance, which are capable of improving accuracy of triggering the home mode, without depending on a device such as a user's mobile phone or a network of a vehicle, contents such as GPS, power, App process, and setting.

A method of triggering a home mode of a home appliance, the method comprising:

acquiring current activity information of a target user uploaded by community equipment;

acquiring historical behavior track information of a target user in the community;

inputting the current activity information and the historical behavior track information into a machine learning model;

obtaining a prediction result output by the machine learning model;

and when the prediction result judges that the target user is going to go home, triggering the household equipment of the target user to be in a home-returning mode.

An apparatus for triggering a home mode of a home appliance, the apparatus comprising:

the information acquisition module is used for acquiring current activity information of a target user uploaded by community equipment in a community; acquiring historical behavior track information of a target user in the community;

the prediction module is used for inputting the current activity information and the historical behavior track information into a machine learning model; obtaining a prediction result output by the machine learning model;

and the mode confirmation module is used for triggering the household equipment of the target user to be in a home-returning mode when the prediction result judges that the target user is going to return home.

A computer device comprising a memory, the memory storing a computer program, a processor implementing the following steps when the processor executes the computer program:

acquiring current activity information of a target user uploaded by community equipment;

acquiring historical behavior track information of a target user in the community;

inputting the current activity information and the historical behavior track information into a machine learning model;

obtaining a prediction result output by the machine learning model;

and when the prediction result judges that the target user is going to go home, triggering the household equipment of the target user to be in a home-returning mode.

A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:

acquiring current activity information of a target user uploaded by community equipment;

acquiring historical behavior track information of a target user in the community;

inputting the current activity information and the historical behavior track information into a machine learning model;

obtaining a prediction result output by the machine learning model;

and when the prediction result judges that the target user is going to go home, triggering the household equipment of the target user to be in a home-returning mode.

According to the method, the device, the computer equipment and the storage medium for triggering the home returning mode of the household equipment, the current activity information of the target user in the community is acquired by acquiring the current activity information of the target user uploaded by the community equipment in the community, the historical behavior track information of the target user in the community is input into the machine learning model, the prediction result output by the machine learning model is acquired, when the prediction result judges that the target user is going to return home, the household equipment in the home of the target user is triggered into the home returning mode, in the technical scheme, the intelligent community equipment is linked with the home returning mode of the intelligent home, the server can judge whether the target user is going to return home to enter the home according to the machine learning method, so that the home returning mode of the intelligent home can be triggered accurately, compared with the traditional technical scheme, the method has fewer conditions, the intelligent home system has the advantages that the triggering action of the home returning mode can be accurately completed under most conditions, so that energy waste caused by misjudgment of the home returning mode can be avoided, the intelligent home scheme can be implemented more conveniently by the accurate home returning triggering mode, the life quality and efficiency of a user are improved, and the comfort level and the life quality of the user are further improved.

Drawings

FIG. 1 is a diagram of an application environment for a method of triggering a home mode of a home appliance in one embodiment;

FIG. 2 is a flow diagram illustrating a method for triggering a home mode of a home appliance in one embodiment;

FIG. 3 is a diagram illustrating a method for triggering a home mode of a home appliance in one embodiment;

FIG. 4 is a block diagram showing an example of a device for triggering a home mode of a home appliance;

FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

The method for triggering the home mode of the household device provided by the application can be applied to the application environment shown in fig. 1. The plurality ofcommunity devices 102 communicate with theserver 104 through the network, and can collect and upload activity information of target users in the community to theserver 104. When the target user meets the home-returning mode condition, theserver 104 may send an instruction to a plurality of home devices included in thehome 106 of the target user through network communication, so that the home devices may perform corresponding on, off, or state adjustment according to the instruction issued by theserver 104. Thecommunity device 102 may be but not limited to a community device including but not limited to a face recognition access control, a mobile phone NFC access control, a two-dimensional code access control, a hardware induction access control, a community parking lot license plate recognition device, a camera deployed in a community, an unmanned commuter car operated in a smart community, a community delivery robot, a device in other community commercial establishments accessed in the smart community, and the like, theserver 104 may be implemented by an independent server or a server cluster composed of a plurality of servers, the household device contained in thetarget user home 106 may be a water heater, an air conditioner, a sound box, a lamp, a curtain, a television, a floor sweeping robot, an intelligent switch, and the like.

In one embodiment, as shown in fig. 2, there is provided a method for triggering a home-returning mode of a home appliance, which is illustrated by applying the method to a server in fig. 1, and includes the following steps:

step 201, obtaining current activity information of a target user uploaded by community equipment in a community.

Step 202, obtaining historical behavior track information of a target user in a community.

The community device refers to various devices which are installed in a community and can collect and upload data through network communication. The community equipment comprises but is not limited to face recognition access control, cell-phone NFC access control, two-dimensional code access control, hardware induction access control, community parking lot license plate recognition equipment, cameras deployed in the community, unmanned commuter vehicles operated in the smart community, community delivery robots, equipment accessed into other community commercial institutions of the smart community and the like. The community equipment can continuously collect the activity information of each user appearing in the community, and when the community equipment detects that the target user appears in the community, the community equipment can report the current activity information of the target user in the community to the server.

The historical behavior track information of the target user in the community is acquired by the community device before the current state and is the activity information of the target user in the community uploaded to the server, and the server can also acquire the historical behavior track information of the target user in the community.

Step 203, inputting the current activity information and the historical behavior track information into the machine learning model.

Andstep 204, acquiring a prediction result output by the machine learning model.

Andstep 205, when the prediction result judges that the target user is going to go home, triggering the household equipment of the target user to be in a home-returning mode.

After the current activity information of the target user in the community and the historical behavior track information of the target user in the community are acquired, the current activity information and the historical behavior track information can be input into the machine learning model, and whether the user goes home or not can be judged through the machine learning model.

In one embodiment, the method further comprises: acquiring a general knowledge base corresponding to a target user; inputting current activity information, historical behavior track information and a general knowledge base into a machine learning model; obtaining a prediction result output by a machine learning model; and when the prediction result indicates that the target user is going to go home, triggering the home equipment of the target user to be in a home-returning mode.

The method can judge whether the target user is coming home according to the current activity information of the target user in the community and the historical behavior track information of the target user in the community in the past, and can also judge whether the target user is coming home by introducing data in a general knowledge base. The server can acquire a universal knowledge base corresponding to the target user, input the current activity information, the historical behavior trajectory information and the universal knowledge base into the machine learning model, and acquire a prediction result output by the machine learning model. And when the server shows that the target user meets the home returning mode according to the prediction result, the home returning mode is triggered for the household equipment in the home of the target user. The universal knowledge base can include proper time periods of human daily activities, climate and weather data of various regions, local customs habits, religious beliefs of users and the like, and can be gradually sorted and enriched in actual operation, so that the data contained in the universal knowledge base is more comprehensive and accurate.

In one embodiment, inputting current activity information, historical behavior trace information, and a general knowledge base into a machine learning model includes: extracting the characteristics of the current activity information, the historical behavior track information and the data in the general knowledge base; the extracted features are input into a machine learning model.

After acquiring current activity information and historical behavior track information of a target user in a community and a general knowledge base corresponding to the target user, the server can perform feature extraction on the data to obtain data features of each data, and input the data features corresponding to each data into a machine learning model, wherein the machine learning model can output corresponding prediction results according to the input data features. The server can obtain all the prediction results output by the machine learning model and the probability value corresponding to each prediction result, and takes the prediction result with the highest probability value as the final prediction result of the machine learning model. The machine learning model used here is a machine model trained in advance, and during training, the same training mode as other classification models can be adopted, which is not described here again.

After obtaining the prediction result output by the machine learning model, the server can confirm whether the target user is about to arrive at home according to the prediction result. When the prediction result indicates that the target user satisfies the home-returning mode, the home-returning mode may be triggered for the home device in the home of the target user.

In one embodiment, triggering the home device of the target user to go home mode comprises: acquiring preset household equipment setting information; changing the state of the home appliance according to the setting information.

The target user can set the starting configuration of each household device in the home in advance according to the requirement of the target user, for example, the water heater needs to be started 20 minutes before returning home, the air conditioner needs to be started 10 minutes before returning home, and the like. The starting configuration information of the household equipment pairs is preset household equipment setting information, so that the server can send equipment starting, equipment closing or equipment state adjusting instructions to the corresponding household equipment according to the preset setting information of the target user.

In one embodiment, the home device configuration information includes a state change time of each home device; the states of the home appliances are respectively changed according to the state change times.

The configuration information of the home devices may further include start time of each home device, where the start time may include specific start, stop, or adjustment operation state time of the device, and duration of the start, stop, or adjustment operation state. For example, the device a needs to be started at 8 o 'clock and 10 min, and the starting time is 30 min, that is, the device a is automatically turned off at 8 o' clock and 40 min. Or the starting condition of the device B is that the device B is started immediately when detecting that the target user is coming home, and the starting time is 1 hour, or the starting condition of the device C is that the device B is started after detecting that the target user is coming home for 10 minutes, and the starting time is 10 minutes, and so on. The specific configuration information can be determined according to the self requirement of the target user, and the server can send a corresponding starting instruction to the household equipment according to the setting information, so that the household equipment can be started, closed or adjusted in running state according to the preset configuration information.

In one embodiment, after the state of the home appliance is respectively changed according to the state change time, further comprising: and sending the state change information of each household device to a target user according to a preset mode.

After the server sends the corresponding starting instruction to the household equipment according to the setting information, the starting information of each household equipment can be sent to the target user according to a preset mode, the preset mode can be a short message notification mode, a WeChat mode, a QQ mode or a specific application mode, a pushing mode and the like, and therefore the user can timely know the working condition of each household equipment in a home.

In one embodiment, the method further comprises: acquiring the behavior track information of the current activity of a target user; and storing the behavior track information into a database, and updating the historical behavior track information of the target user.

As shown in fig. 3, the target user can set in advance the specific actions of the respective home appliances in the return-to-home mode. The target user can personally define the home returning mode in advance through a mobile phone or other equipment, and specific content is that home equipment controlled by an intelligent home system, such as a water heater, an air conditioner, a sound box, a lamp, a curtain, a television, a floor sweeping robot, an intelligent switch and the like, starts to work or stops working according to a specified mode. The defined action of each device can be instantly generated when the device is switched to the home mode, or can be delayed for a period of time after the device is switched to the home mode.

After the current activity information of the target user in the community is found by the community equipment and uploaded to the server, the server can acquire historical behavior track information of the target user in the community and the universal knowledge base corresponding to the target user, and calculate whether the target user reaches the condition of the mode of returning home according to the data, namely, the current activity information, the historical behavior track information and the universal knowledge base corresponding to the target user of the target user are input into a machine learning model trained in advance, and whether the target user is about to arrive home is judged. If the target user is confirmed to meet the mode of returning home according to the prediction result of the machine learning model, corresponding instructions for starting, closing or adjusting the running state can be issued to the household equipment according to the preset setting of the user. Optionally, a corresponding notification may also be sent to the target user, and the target user may confirm whether to intervene in starting, closing, or adjusting the operation state of the household appliance through the received notification, for example, to turn off an already-turned-on water heater.

For example, when a user A drives a registered automobile to enter a community parking lot during the off-duty period of a working day, the parking lot photographing and rod lifting device reports the identified data to the cloud; the historical behavior track of the user A shows that the user goes to the gymnasium of the community for exercise and then returns home after leaving work, so that the mode of returning home is not triggered temporarily, and the user waits for the user to finish gymnasium exercise and report the card swiping record of the gymnasium member card to the cloud side, and then the mode of returning home is triggered. Or when the user A drives the registered automobile to enter the cell in the early morning of the working day, the user A knows that the gymnasium and other commercial facilities of the cell are closed at the moment through the universal knowledge base, so that the user can be judged to return home directly, and the mode of returning home can be triggered directly at the moment without waiting for other data to be reported.

For another example, when a user B drives a registered automobile to enter a community parking lot during the off-duty period of a working day, the parking lot photographing and rod lifting device reports the identified data to the cloud; the historical behavior track of the user B indicates that the user can go home directly after work, so that the home-returning mode can be triggered directly without waiting for other data to be reported. After receiving the instruction, the smart home device starts or stops working according to the state expected by the user, for example, the air conditioner starts working, the sound box starts playing light music, the sweeping robot in operation stops working and returns to the charging position, and the user can enjoy the expected comfortable home environment after returning home. At this time, the time of the user arriving at home is recorded into the historical behavior track of the user, and the recorded time and the historical behavior track are used as one of the basis sources for subsequent cloud judgment.

In the method for triggering the home returning mode of the household equipment, the current activity information of the target user uploaded by the community equipment in the community is acquired, the historical behavior track information of the target user in the community is acquired, the current activity information and the historical behavior track information are input into the machine learning model, the prediction result output by the machine learning model is acquired, and when the prediction result judges that the target user is going to return home, the household equipment in the home of the target user is triggered into the home returning mode. Therefore, energy waste caused by misjudgment of the home returning mode can be avoided, the intelligent home scheme can be implemented more conveniently by accurately triggering the home returning mode, the life quality and efficiency of the user are improved, and the comfort level and the life quality of the user are further improved.

It should be understood that although the various steps in the flow charts of fig. 2-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.

In one embodiment, as shown in fig. 4, there is provided an apparatus for triggering a home-returning mode of a home appliance, including: the device comprises an information acquisition module, a prediction module and a mode confirmation module, wherein:

theinformation acquisition module 401 is configured to acquire current activity information of a target user uploaded by a community device in a community; acquiring historical behavior track information of a target user in a community;

aprediction module 402, configured to input current activity information and historical behavior trajectory information into a machine learning model; obtaining a prediction result output by a machine learning model;

and amode confirmation module 403, configured to trigger the home device of the target user to be in a home mode when the prediction result determines that the target user is going to go home.

In one embodiment, theprediction module 402 is further configured to obtain a general knowledge base corresponding to the target user; inputting current activity information, historical behavior track information and a general knowledge base into a machine learning model; obtaining a prediction result output by a machine learning model; and when the prediction result indicates that the target user is going to go home, triggering the home equipment of the target user to be in a home-returning mode.

In one embodiment, theprediction module 402 is further configured to perform feature extraction on the current activity information, the historical behavior trajectory information, and data in the general knowledge base; the extracted features are input into a machine learning model.

In one embodiment, themode confirmation module 403 is further configured to obtain preset home device setting information; changing the state of the home appliance according to the setting information

In one embodiment, the home device configuration information includes a state change time of each home device. Themode confirmation module 403 is also used to change the states of the home appliances according to the state change times, respectively.

In one embodiment, themode confirmation module 403 is further configured to send the status change information of each household appliance to the target user in a preset manner.

In one embodiment, the apparatus further includes a data acquisition module (not shown in the figure) configured to acquire behavior trace information of a current activity of the target user; and storing the behavior track information into a database, and updating the historical behavior track information of the target user.

For specific limitations of the means for triggering the home mode of the home appliance, reference may be made to the above limitations of the method for triggering the home mode of the home appliance, which are not described herein again. The respective modules in the above-described apparatus for triggering a home mode of a home appliance may be wholly or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing relevant data confirming that the mode of returning home is triggered. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of triggering a home mode of a home appliance.

Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program: acquiring current activity information of a target user uploaded by community equipment in a community; acquiring historical behavior track information of a target user in a community; inputting current activity information and historical behavior track information into a machine learning model; obtaining a prediction result output by a machine learning model; and when the prediction result judges that the target user is going to go home, triggering the household equipment of the target user to be in a home-returning mode.

In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring a general knowledge base corresponding to a target user; inputting current activity information, historical behavior track information and a general knowledge base into a machine learning model; obtaining a prediction result output by a machine learning model; and when the prediction result indicates that the target user is going to go home, triggering the home equipment of the target user to be in a home-returning mode.

In one embodiment, inputting current activity information, historical behavior trace information, and a general knowledge base into a machine learning model includes: extracting the characteristics of the current activity information, the historical behavior track information and the data in the general knowledge base; the extracted features are input into a machine learning model.

In one embodiment, triggering the home device of the target user to go home mode comprises: acquiring preset household equipment setting information; changing the state of the home appliance according to the setting information.

In one embodiment, the home device configuration information includes a state change time of each home device; the states of the home appliances are respectively changed according to the state change times.

In one embodiment, after the state of the home appliance is respectively changed according to the state change time, further comprising: and sending the state change information of each household device to a target user according to a preset mode.

In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring the behavior track information of the current activity of a target user; and storing the behavior track information into a database, and updating the historical behavior track information of the target user.

In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring current activity information of a target user uploaded by community equipment in a community; acquiring historical behavior track information of a target user in a community; inputting current activity information and historical behavior track information into a machine learning model; obtaining a prediction result output by a machine learning model; and when the prediction result judges that the target user is going to go home, triggering the household equipment of the target user to be in a home-returning mode.

In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a general knowledge base corresponding to a target user; inputting current activity information, historical behavior track information and a general knowledge base into a machine learning model; obtaining a prediction result output by a machine learning model; and when the prediction result indicates that the target user is going to go home, triggering the home equipment of the target user to be in a home-returning mode.

In one embodiment, inputting current activity information, historical behavior trace information, and a general knowledge base into a machine learning model includes: extracting the characteristics of the current activity information, the historical behavior track information and the data in the general knowledge base; the extracted features are input into a machine learning model.

In one embodiment, triggering the home device of the target user to go home mode comprises: acquiring preset household equipment setting information; changing the state of the home appliance according to the setting information.

In one embodiment, the home device configuration information includes a state change time of each home device; the states of the home appliances are respectively changed according to the state change times.

In one embodiment, after the state of the home appliance is respectively changed according to the state change time, further comprising: and sending the state change information of each household device to a target user according to a preset mode.

In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring the behavior track information of the current activity of a target user; and storing the behavior track information into a database, and updating the historical behavior track information of the target user.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method of triggering a home mode of a home appliance, the method comprising:

acquiring current activity information of a target user uploaded by community equipment;

acquiring historical behavior track information of a target user in the community;

inputting the current activity information and the historical behavior track information into a machine learning model;

obtaining a prediction result output by the machine learning model;

and when the prediction result judges that the target user is going to go home, triggering the household equipment of the target user to be in a home-returning mode.

2. The method of claim 1, further comprising:

acquiring a general knowledge base corresponding to the target user;

inputting the current activity information, the historical behavior track information and the general knowledge base into a machine learning model;

obtaining a prediction result output by the machine learning model;

and when the prediction result indicates that the target user is about to go home, triggering the home equipment of the target user to be in a home-returning mode.

3. The method of claim 2, wherein inputting the current activity information, the historical behavior trace information, and the universal knowledge base into a machine learning model comprises:

extracting the characteristics of the current activity information, the historical behavior track information and the data in the general knowledge base;

the extracted features are input into a machine learning model.

4. The method of claim 1, wherein triggering the target user's home device to go home comprises:

acquiring preset household equipment setting information;

changing a state of the home appliance according to the setting information.

5. The method according to claim 4, wherein the home device configuration information includes a state change time of each home device;

and respectively changing the states of the household appliances according to the state change time.

6. The method according to claim 5, further comprising, after said changing the states of the home appliances according to the state change times, respectively:

and sending the state change information of each household device to the target user according to a preset mode.

7. The method of claim 1, further comprising:

acquiring the behavior track information of the current activity of the target user;

and storing the behavior track information to a database, and updating the historical behavior track information of the target user.

8. An apparatus for triggering a home mode of a home appliance, the apparatus comprising:

the information acquisition module is used for acquiring current activity information of a target user uploaded by community equipment; acquiring historical behavior track information of a target user in the community;

the prediction module is used for inputting the current activity information and the historical behavior track information into a machine learning model; obtaining a prediction result output by the machine learning model;

and the mode confirmation module is used for triggering the household equipment of the target user to be in a home-returning mode when the prediction result judges that the target user is going to return home.

9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.

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