CN114595372A - Scene recommendation method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a scene recommendation method, a scene recommendation device, computer equipment and a storage medium. The method comprises the following steps: the method comprises the steps that a server obtains environment information collected by environment collection equipment, wherein the environment information carries a user account; determining personalized information of the user from a preset historical database according to the user account, wherein the database comprises historical personalized information of a plurality of users; determining an aggregation label of the user according to the environment information and the personalized information, wherein the aggregation label is used for representing scene use information of the user; further determining a target recommendation scene from a preset scene library according to the aggregation label and pushing the target recommendation scene to a client where the user account is located; that is to say, the server in the application combines the environment information and the personalized information of the user to jointly determine the recommendation scene suitable for the real requirements of the user, and the matching degree of the recommendation scene and the real requirements of the user is greatly improved.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a scene recommendation method and apparatus, a computer device, and a storage medium.
Background
Along with the development of smart homes, more and more smart devices are used in a user's home, and the intelligent scenes realized by the user through the smart devices are more and more abundant.
Generally, some general scene functions that can be implemented by the smart device are displayed in a relevant interface of an APP (application) corresponding to the smart device, and a user can select and set a desired scene function by browsing the scene functions to meet the user demand of the user. In addition, in some cases, the APP may actively recommend some scenes to the user, for example, the APP recommends a scene of going home to the user, where the scene of going home is associated with devices such as an intelligent lamp, an intelligent air conditioner, and an intelligent refrigerator, and the user may set the functional states of the devices in the recommended scene of going home.
However, the scene recommended to the user by the existing application program is not matched with the real requirement of the user highly.
Disclosure of Invention
In view of the above, it is necessary to provide a scene recommendation method, apparatus, computer device and storage medium capable of highly matching the real requirement of the user in view of the above technical problems.
In a first aspect, a method for scene recommendation is provided, where the method includes:
acquiring environment information acquired by environment acquisition equipment, wherein the environment information carries a user account;
determining personalized information of the user from a preset historical database according to the user account; wherein the database comprises historical personalization information for a plurality of users;
determining an aggregation label of the user according to the environment information and the personalized information, wherein the aggregation label is used for representing the scene use information of the user;
determining a target recommendation scene from a preset scene library according to the aggregation label and pushing the target recommendation scene to a client where the user account is located; the scene library comprises a plurality of scenes to be recommended, and different scenes to be recommended are associated with different environmental information and different personalized information.
In one embodiment, the obtaining of the environmental information collected by the environment collection device includes: acquiring indoor environment information currently acquired by environment acquisition equipment, wherein the indoor environment information carries a user account; determining current outdoor environment information according to the position information of the user account; and carrying out fusion processing on the indoor environment information and the outdoor environment information to obtain the environment information.
In one embodiment, the fusing the indoor environment information and the outdoor environment information to obtain the environment information includes: analyzing the environmental indexes in the indoor environmental information and the outdoor environmental information to obtain the same environmental index and different environmental indexes; wherein each environmental index corresponds to a respective index value; determining the index value which belongs to the indoor environment information in the same environment index as the index value of the same environment index, and rejecting the index value which belongs to the outdoor environment information in the same environment index; and fusing the index values of the same environmental index and the index values of the different environmental indexes to obtain the environmental information.
In one embodiment, the personalized information of the user comprises the interested equipment of the user, the interested function of the interested equipment and the linkage scene interested by the user; the frequency of use of the interested device, the frequency of use of the interested function and the frequency of use of the linkage scene interested by the user are all larger than a preset threshold value.
In one embodiment, determining the aggregated tag of the user according to the environment information and the personalized information includes: extracting key bytes of the environment information to obtain first key word information; extracting key bytes of the personalized information to obtain second key word information; performing association processing on the first keyword information and the second keyword information to obtain an aggregation tag of the user; the aggregation tag includes an environment attribute, a scene attribute, and a function attribute of the device, and is specifically used to represent a probability that a user uses a scene based on an environment or a probability that a user uses a device function based on an environment.
In one embodiment, determining a target recommendation scene from a preset scene library according to the aggregation tag and pushing the target recommendation scene to a client where the user account is located includes: according to the aggregation label, determining a target aggregation label matched with the aggregation label from a preset aggregation label library; the aggregation label library comprises a plurality of aggregation labels to be matched of the user; and determining a target recommendation scene from a preset scene library according to the target aggregation label and pushing the target recommendation scene to a client where the user account is located.
In one embodiment, according to the aggregation tag, a target aggregation tag matched with the aggregation tag is determined from a preset aggregation tag library; the method comprises the following steps: and determining the aggregation label to be matched in the aggregation label library, which has the same environmental attribute as the aggregation label, as the target aggregation label.
In one embodiment, each scene to be recommended in the scene library corresponds to at least one scene tag, where the scene tag includes: an environmental attribute and a functional attribute of the device; determining a target recommendation scene from a preset scene library according to the target aggregation label, wherein the target recommendation scene comprises the following steps: according to the attribute of the target aggregation label and the attribute of each scene label, performing relevance matching on the target aggregation label and each scene label, and determining a target scene label with the highest relevance; and determining the scene to be recommended corresponding to the target scene label as the target recommended scene.
In one embodiment, the method further comprises: acquiring first equipment information associated with the target recommendation scene; determining second equipment information associated under the user account according to the user account; and if the equipment in the first equipment information comprises other equipment except the second equipment information, recommending the purchase information of the other equipment to the user.
In a second aspect, a scene recommendation apparatus is provided, the apparatus comprising:
the acquisition module is used for acquiring environment information acquired by the environment acquisition equipment, wherein the environment information carries a user account;
the first determining module is used for determining the personalized information of the user from a preset historical database according to the user account; wherein the database comprises historical personalization information for a plurality of users;
the second determining module is used for determining an aggregation label of the user according to the environment information and the personalized information, wherein the aggregation label is used for representing the scene use information of the user;
the pushing module is used for determining a target recommendation scene from a preset scene library according to the aggregation label and pushing the target recommendation scene to a client where the user account is located; the scene library comprises a plurality of scenes to be recommended, and different scenes to be recommended are associated with different environmental information and different personalized information.
In a third aspect, a computer device is provided, comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring environment information acquired by environment acquisition equipment, wherein the environment information carries a user account;
determining personalized information of the user from a preset historical database according to the user account; wherein the database comprises historical personalization information for a plurality of users;
determining an aggregation label of the user according to the environment information and the personalized information, wherein the aggregation label is used for representing the scene use information of the user;
determining a target recommendation scene from a preset scene library according to the aggregation tag and pushing the target recommendation scene to a client where the user account is located; the scene library comprises a plurality of scenes to be recommended, and different scenes to be recommended are associated with different environmental information and different personalized information.
In a fourth aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring environment information acquired by environment acquisition equipment, wherein the environment information carries a user account;
determining personalized information of the user from a preset historical database according to the user account; wherein the database comprises historical personalization information for a plurality of users;
determining an aggregation label of the user according to the environment information and the personalized information, wherein the aggregation label is used for representing the scene use information of the user;
determining a target recommendation scene from a preset scene library according to the aggregation label and pushing the target recommendation scene to a client where the user account is located; the scene library comprises a plurality of scenes to be recommended, and different scenes to be recommended are associated with different environmental information and different personalized information.
According to the scene recommendation method, the scene recommendation device, the computer equipment and the storage medium, the server acquires the environment information acquired by the environment acquisition equipment, determines the personalized information of the user from a preset historical database according to the user account carried in the environment information, determines the aggregation tag of the user according to the environment information and the personalized information, and further determines the target recommendation scene from the preset scene library according to the aggregation tag and pushes the target recommendation scene to the client side where the user account is located; that is to say, the server in the application combines the environment information and the personalized information of the user to jointly determine the recommendation scene suitable for the real requirements of the user, and the matching degree of the recommendation scene and the real requirements of the user is greatly improved.
Drawings
FIG. 1 is a diagram of an application environment of a scene recommendation method in one embodiment;
FIG. 2 is a flowchart illustrating a method for scene recommendation in one embodiment;
FIG. 3 is a flowchart illustrating a scene recommendation method according to another embodiment;
FIG. 4 is a flowchart illustrating a method for scene recommendation in another embodiment;
FIG. 5 is a flowchart illustrating a method for scene recommendation in another embodiment;
FIG. 6 is a block diagram showing the structure of a scene recommendation apparatus according to an embodiment;
FIG. 7 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 scene recommendation method provided by the application can be applied to the application environment shown in fig. 1. The application environment comprises a terminal 101, an environment acquisition device 102 and a server 103; the terminal 101, the environment collection device 102, and the server 103 communicate via a network, respectively. The terminal 101 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices; the environment acquisition device 102 may be, but is not limited to, various acquisition devices with sensor functions, may be an independent sensor acquisition device, such as a temperature sensor, a humidity sensor, or the like, or may be an intelligent network device with sensor functions, such as an intelligent air conditioner, an intelligent refrigerator, an intelligent television, or the like; the server 103 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a scene recommendation method is provided, which is described by taking the application of the method to the server in fig. 1 as an example, and includes the following steps:
When scene recommendation is performed for a user according to the real requirement of the user, the server needs to know the real requirement of the user, and the real requirement of the user can be the real requirement under the current actual environment. Under the condition that a user does not send own real requirements to the server through the client side, the server can acquire actual environment information of the user at the current moment through the environment acquisition equipment; this actual environmental information may include temperature, humidity, air quality, haze conditions, lighting conditions, weather conditions, and the like.
Specifically, different environment acquisition devices acquire respective corresponding environment information and respectively send the respective acquired environment information to a server, wherein the environment information may include an environment index, a corresponding environment index value, and a user account; after receiving the environment information sent by different environment acquisition devices, the server can correspondingly store the environment information under the user account; for example: after the server receives the environmental information collected by the temperature sensor, the environmental information may include: "environmental index: temperature "," environmental index value: 20 ℃ ",": 123 ", the server may correspondingly store the temperature information in the user account 123.
Generally, a server pre-stores historical usage records of each intelligent network device in a home from a user, that is, each time the user uses the intelligent network device in the home, the server stores the use conditions of the user such as the control operation, each function used, and the linkage scene used by the user on each intelligent network device in a database to form the historical usage records of each user; wherein, this linkage scene can be that the user sets up with a scene of many equipment concurrent control, under this scene, the user can associate many equipment to realize triggering simultaneously of many equipment, for example: the user can set a 'go home' linkage scene, and under the scene, the user can set a go home back, turn on a lamp, turn on music, start an air conditioner, turn on a water dispenser and the like. Further, the server can also obtain personalized information of each user by analyzing the historical use record of each user; optionally, the usage frequency of each intelligent network device, the usage frequency of each function of each intelligent network device, and the usage frequency of each linkage scene by the user in a period of time may be counted, and the device of interest, the function of interest of the device of interest, and the linkage scene of interest to the user in the period of time may be obtained according to the relationship between the usage frequencies and the preset threshold, where the device of interest is a frequently-used device of the user, the function of interest of the device of interest is a frequently-used device of the user, and the linkage scene of interest to the user is a frequently-used linkage scene of the user, and the linkage scene is stored in the database as the personalized information of the user, so as to form a historical database including historical personalized information of a plurality of users.
Specifically, after acquiring the environmental information acquired by the environment acquisition device, the server may acquire the personalized information of the user from the database through a user account carried in the environmental information, that is, determine an interested device of the user, an interested function of the interested device, and an interested linkage scene of the user; for example: the user often uses interested devices such as an intelligent air conditioner, an intelligent sound box and an intelligent curtain, often uses a dehumidification function of the intelligent air conditioner, a timing alarm clock function of the intelligent sound box, a timing opening and timing closing function of the intelligent curtain, and often uses a home-returning scene and the like, wherein the home-returning scene comprises automatic light turning on, air conditioning opening, water dispenser opening, music playing and the like.
Specifically, after obtaining the environment information and the personalized information of the user, the server may associate the environment information with the personalized information of the user to form an aggregation tag of the user; alternatively, the corresponding relationship between the environment information and the personalized information of the user may be directly established to form an aggregation tag of the user, and the aggregation tag may be used to characterize the scene usage information of the user, that is, what scene or device function the user is likely to use in what environment.
And 204, determining a target recommendation scene from a preset scene library according to the aggregation label and pushing the target recommendation scene to a client where the user account is located.
The server is provided with a scene library in advance, the scene library comprises a plurality of scenes to be recommended, and different scenes to be recommended are associated with different environmental information and different personalized information. Optionally, for each scene to be recommended in the scene library, a plurality of pieces of environment information and a plurality of pieces of personalized information may be corresponded, so as to form a one-to-many correspondence relationship between the scene and the environment information and the personalized information; for example: for scenario A, there may be corresponding environmental information B1And personalized information C1Form AB1C1The corresponding relationship of (a); may also correspond to the environmental information B2And personalized information C2Form AB2C2The correspondence of (c), etc.
Specifically, the server determines a target recommendation scene from a preset scene library according to the obtained aggregation tag and pushes the target recommendation scene to a client where the user account is located; optionally, the aggregation tag is a corresponding relationship between the environment information and the personalized information of the user, and the server may determine the target recommended scene from a preset scene library according to the one-to-many corresponding relationship between the aggregation tag and the scene, the environment information, and the personalized information, that is, may determine the target recommended scene a according to the environment information B and the personalized information C of the user.
In the scene recommendation method, a server acquires environment information acquired by environment acquisition equipment, determines personalized information of a user from a preset historical database according to a user account carried in the environment information, determines a polymerization tag of the user according to the environment information and the personalized information, determines a target recommendation scene from a preset scene library according to the polymerization tag, and pushes the target recommendation scene to a client where the user account is located; that is to say, the server in the application combines the environment information and the personalized information of the user to jointly determine the recommendation scene suitable for the real requirements of the user, and the matching degree of the recommendation scene and the real requirements of the user is greatly improved.
Fig. 3 is a flowchart illustrating a scene recommendation method in another embodiment, where this embodiment relates to an optional implementation process in which a server obtains environment information collected by an environment collection device. On the basis of the above embodiment, as shown in fig. 3, the step 201 includes:
When the optimal recommended scene is matched for the user according to the real requirements of the user, the current actual environment information of the user collected by the embodiment is actually the indoor environment information in the user family and the outdoor environment information outside the user family.
Specifically, indoor environment information in the home of the user at the current moment may be collected by each environment collection device in the home of the user, for example: the temperature, the humidity, the air quality and the like in the family of the user send the collected indoor environment information to the server through each environment collection device, and the server needs to carry a user account in the sent indoor environment information, so that the server can store the indoor environment information under the user account according to the user account.
Specifically, when the server obtains outdoor environment information outside the user's home, the server may determine, according to the home location of the user account stored in the server, current outdoor environment information of the location where the user account is located by calling a data interface of the internet end, for example: outdoor haze conditions, lighting conditions, weather conditions, sunrise and sunset times, and the like.
Specifically, after obtaining the indoor environment information and the outdoor environment information of the user, the server needs to perform fusion processing on the indoor environment information and the outdoor environment information to obtain the environment information.
Optionally, the environment indexes in the indoor environment information and the outdoor environment information may be analyzed, and in a case that the environment indexes in the indoor environment information are different from the environment indexes in the outdoor environment information, the environment indexes and the index values of the indoor environment information and the outdoor environment information may be directly fused to obtain the environment information. The fusion here can be understood as "a union operation of a set of environment indicators in indoor environment information and a set of environment indicators in outdoor environment information".
Alternatively, in the case that the environmental index in the indoor environmental information is partially the same as the environmental index in the outdoor environmental information, the same environmental index and different environmental indexes may be obtained, and the index value belonging to the indoor environmental information in the same environmental index is determined as the index value of the same environmental index, the index value belonging to the outdoor environmental information in the same environmental index is eliminated, and then the index value of the same environmental index and the index value of the different environmental index are fused to obtain the environmental information. For example: when the environment indexes of the indoor environment information and the outdoor environment information both include the temperature index, the index value corresponding to the temperature index in the indoor environment information may be used as the index value corresponding to the temperature index in the merged environment information. And acquiring fused environment information by performing union set operation on different environment indexes in the indoor environment information and the outdoor environment information.
In the embodiment, the server acquires indoor environment information inside a user family and outdoor environment information outside the user family, and fuses the indoor environment information and the outdoor environment information to obtain environment information; indoor and outdoor environmental information can be comprehensively considered, and the accuracy of determining the real requirements of the users is improved.
Fig. 4 is a flowchart of a scene recommendation method in another embodiment, which relates to an alternative implementation process of how to determine an aggregation tag of a user according to environment information and personalized information and how to determine a target recommended scene from a scene library according to the aggregation tag of the user. On the basis of the above embodiment, the step 203 may include the following steps 401-403, and the step 204 specifically includes the steps 404 and 405. As shown in fig. 4:
Specifically, the server may extract a key byte from the collected environment information to obtain first key word information; for example, the actual value of the temperature in the collected environment information may be converted into the corresponding high-low degree value when the temperature is in different ranges, as the key byte of the temperature, such as: when the temperature value is 20 ℃, the key byte corresponding to the temperature is lower in temperature; for other environment indexes in the environment information, extraction can be performed according to a definition rule of the key bytes of the temperature, and other extraction methods of the key bytes can also be selected.
Specifically, the server may extract key bytes from the acquired personalized information of the user to obtain second keyword information; optionally, the interesting function of the user can be used as a key byte of the user personalized information, such as: when the interesting function of the user is the dehumidification function of the intelligent air conditioner, the second keyword information can be obtained and is the dehumidification function of the frequently-used air conditioner, and the linkage scene interesting to the user can be used as the key byte of the user personalized information.
And step 403, performing association processing on the first keyword information and the second keyword information to obtain an aggregation tag of the user.
The aggregation tag includes an environment attribute, a scene attribute, and a function attribute of the device, and is specifically used to characterize a probability that a user uses a scene under an environment or a probability that the user uses a device function under the scene under an environment, in other words, what scene the user is most likely to use under what environment or what function of what device under the scene is used under.
The preset aggregation label library of the server comprises a plurality of aggregation labels to be matched of the user; optionally, the aggregation tags to be matched may be all aggregation tags obtained by the server through a correlation algorithm according to historical environment information stored in the database and historical usage records of the user, or may be at least one high-frequency tag that is selected from all aggregation tags and has a high frequency of use by the user within a preset period of time, and the high-frequency tag is used as the aggregation tag to be matched.
Specifically, the server determines a target aggregation tag matched with the aggregation tag from a plurality of aggregation tags to be matched in a preset aggregation tag library according to the currently acquired aggregation tag; optionally, the aggregation tag to be matched, which is the same as the environmental attribute of the aggregation tag in the aggregation tag library, may be determined as the target aggregation tag; for example: the aggregation labels to be matched include an aggregation label at a very high temperature, an aggregation label at a medium temperature or the like, an aggregation label at a low temperature, an aggregation label at a very low temperature, and the like.
The preset scene library comprises a plurality of scenes to be recommended, each scene to be recommended corresponds to at least one scene label, and the scene labels comprise environment attributes and function attributes of equipment.
Specifically, the server may determine at least one target recommendation scene from a preset scene library according to the target aggregation tag; optionally, the server may perform relevance matching on the target aggregation tag and each scene tag according to the attribute of the target aggregation tag and the attribute of each scene tag, and determine a target scene tag with the highest relevance; that is to say, the server may select a scene tag with a higher overlap degree between the attribute of the target aggregation tag and the attribute of the scene tag as the target scene tag by comparing the environmental attribute in the target aggregation tag with the environmental attribute of each scene tag, and/or comparing the functional attribute of the device in the target aggregation tag with the functional attribute of the device of each scene tag, and determine the to-be-recommended scene corresponding to the target scene tag as the target recommended scene.
In the embodiment, the server respectively extracts the key bytes from the environment information and the personalized information of the user to obtain first key word information corresponding to the environment information and second key word information corresponding to the personalized information of the user, and associates the first key word information and the second key word information to obtain the aggregation tag of the user, wherein the aggregation tag obtained through the key word information can more clearly represent the association relationship between the environment information and the personalized information of the user, so that the association accuracy is improved; furthermore, the server determines a target aggregation label matched with the aggregation label from a preset aggregation label library, determines a target recommendation scene from a preset scene library according to the target aggregation label and pushes the target recommendation scene to a client where the user account is located; the target recommendation scene is more in line with the real requirements of the user, and the accuracy of recommending the target recommendation scene for the user is improved.
Fig. 5 is a flowchart illustrating a scene recommendation method in another embodiment, where this embodiment relates to an optional implementation process of how to recommend a product to a user when a device under a target recommendation scene does not exist in devices associated with a user account. On the basis of the above embodiment, as shown in fig. 5, the method further includes:
The target recommendation scene comprises at least one intelligent network device associated with the user account and device functions which can be realized by the intelligent network device, and certainly can also comprise some intelligent network device devices which are not available for the user so as to realize a more intelligent linkage scene effect, and for products which are not available for the user in the target recommendation scene, the products can also be recommended to the user.
Specifically, the server may obtain first device information associated with the target recommendation scenario, where the first device information may include device names of all devices associated with the target recommendation scenario, and may also include functions of each device.
Specifically, the server may determine, according to the user account, second device information associated under the user account, where the second device information may be device names of all devices associated under the user account.
In step 503, if the device in the first device information includes another device except the second device information, the purchase information of the other device is recommended to the user.
Optionally, the server may determine, one by one, whether the device name in the first device information is the same as each device name in the second device information, and if it is determined that the device names are the same, it may be described that the device in the target recommendation scene is associated with the user account; if the same device name is not judged, it can be shown that the device in the target recommendation scene is not associated under the user account, and then the server can recommend the device in the target recommendation scene which is not associated under the user account to the user; optionally, when recommending the device to the user, the device name of the device and the function of the device may be recommended to the user together.
In this embodiment, the server recommends the purchase information of the other device to the user by acquiring first device information associated with the target recommendation scene and second device information associated with the user account, and in a case that the device in the first device information includes the other device except the second device information; the method has the advantages that the corresponding products can be recommended for the user while the linkage scene suitable for the user is recommended for the user, so that the effect of the intelligent linkage scene in the family of the user can be improved according to the recommended products for the user.
It should be understood that although the various steps in the flow charts of fig. 2-5 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. 2-5 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least some of the other steps or stages.
In one embodiment, as shown in fig. 6, there is provided a scene recommendation apparatus including: an obtaining module 601, a first determining module 602, a second determining module 603, and a pushing module 604, wherein:
the obtaining module 601 is configured to obtain environment information collected by an environment collection device, where the environment information carries a user account.
A first determining module 602, configured to determine personalized information of a user from a preset history database according to the user account; wherein the database includes historical personalization information for a plurality of users.
A second determining module 603, configured to determine an aggregation tag of the user according to the environment information and the personalized information, where the aggregation tag is used to characterize the scene usage information of the user.
The pushing module 604 is configured to determine a target recommended scene from a preset scene library according to the aggregation tag and push the target recommended scene to a client where the user account is located; the scene library comprises a plurality of scenes to be recommended, and different scenes to be recommended are associated with different environmental information and different personalized information.
In one embodiment, the obtaining module 601 includes an obtaining unit, a first determining unit, and a fusing unit; the acquisition unit is used for acquiring indoor environment information currently acquired by the environment acquisition equipment, and the indoor environment information carries a user account; the first determining unit is used for determining the current outdoor environment information according to the position information of the user account; the fusion unit is used for performing fusion processing on the indoor environment information and the outdoor environment information to obtain the environment information.
In one embodiment, the fusion unit is specifically configured to analyze the environmental indexes in the indoor environmental information and the outdoor environmental information to obtain the same environmental index and different environmental indexes; wherein each environmental index corresponds to a respective index value; determining the index value which belongs to the indoor environment information in the same environment index as the index value of the same environment index, and rejecting the index value which belongs to the outdoor environment information in the same environment index; and fusing the index values of the same environmental index and the index values of the different environmental indexes to obtain the environmental information.
In one embodiment, the personalized information of the user comprises the interested equipment of the user, the interested function of the interested equipment and the linkage scene interested by the user; the use frequency of the interested device, the use frequency of the interested function and the use frequency of the linkage scene interested by the user are all larger than a preset threshold value.
In one embodiment, the second determining module 603 is specifically configured to perform key byte extraction on the environment information to obtain first key information; extracting key bytes of the personalized information to obtain second key word information; performing association processing on the first keyword information and the second keyword information to obtain an aggregation tag of the user; the aggregation tag includes an environment attribute, a scene attribute, and a function attribute of the device, and is specifically used to represent a probability that a user uses a scene based on an environment or a probability that a user uses a device function based on an environment.
In one embodiment, the pushing module 604 includes a second determining unit, a third determining unit, and a pushing unit; the second determining unit is used for determining a target aggregation label matched with the aggregation label from a preset aggregation label library according to the aggregation label; the aggregation label library comprises a plurality of aggregation labels to be matched of the user; the third determining unit is used for determining a target recommendation scene from a preset scene library according to the target aggregation label; the pushing unit is used for pushing the target recommendation scene to a client where the user account is located.
In one embodiment, the second determining unit is specifically configured to determine, as the target aggregated tag, an aggregated tag to be matched in the aggregated tag library, where the aggregated tag has the same environmental attribute as the aggregated tag.
In one embodiment, each scene to be recommended in the scene library corresponds to at least one scene tag, where the scene tag includes: an environmental attribute and a functional attribute of the device; the third determining unit is specifically configured to perform correlation matching on the target aggregation tag and each scene tag according to the attribute of the target aggregation tag and the attribute of each scene tag, and determine a target scene tag with a highest correlation; and determining the scene to be recommended corresponding to the target scene label as the target recommended scene.
In one embodiment, the pushing module 604 is further configured to obtain first device information associated with the target recommendation scenario; determining second equipment information associated under the user account according to the user account; and if the equipment in the first equipment information comprises other equipment except the second equipment information, recommending the purchase information of the other equipment to the user.
For the specific definition of the scene recommendation device, reference may be made to the above definition of the scene recommendation method, which is not described herein again. The modules in the scene recommendation device can 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. 7. The computer device includes a processor, a memory, and a network interface 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 historical integrated environment information and historical use record data of each device, device function and linkage scene used by a user. 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 scene recommendation method.
It will be appreciated by those skilled in the art that the configuration shown in fig. 7 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or fewer components than 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 environment information acquired by environment acquisition equipment, wherein the environment information carries a user account;
determining personalized information of the user from a preset historical database according to the user account; wherein the database comprises historical personalization information for a plurality of users;
determining an aggregation label of the user according to the environment information and the personalized information, wherein the aggregation label is used for representing the scene use information of the user;
determining a target recommendation scene from a preset scene library according to the aggregation label and pushing the target recommendation scene to a client where the user account is located; the scene library comprises a plurality of scenes to be recommended, and different scenes to be recommended are associated with different environmental information and different personalized information.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the environmental information that obtains environment collection equipment collection includes: acquiring indoor environment information currently acquired by environment acquisition equipment, wherein the indoor environment information carries a user account; determining current outdoor environment information according to the position information of the user account; and carrying out fusion processing on the indoor environment information and the outdoor environment information to obtain the environment information.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the fusion processing is performed on the indoor environment information and the outdoor environment information to obtain the environment information, and the fusion processing comprises the following steps: analyzing the environmental indexes in the indoor environmental information and the outdoor environmental information to obtain the same environmental index and different environmental indexes; wherein each environmental index corresponds to a respective index value; determining the index value which belongs to the indoor environment information in the same environment index as the index value of the same environment index, and rejecting the index value which belongs to the outdoor environment information in the same environment index; and fusing the index values of the same environmental index and the index values of the different environmental indexes to obtain the environmental information.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the personalized information of the user comprises the interested equipment of the user, the interested function of the interested equipment and the interested linkage scene of the user; the frequency of use of the interested device, the frequency of use of the interested function and the frequency of use of the linkage scene interested by the user are all larger than a preset threshold value.
In one embodiment, the processor when executing the computer program further performs the steps of: determining an aggregated tag of the user according to the environment information and the personalized information, comprising: extracting key bytes of the environment information to obtain first key word information; extracting key bytes of the personalized information to obtain second key word information; performing association processing on the first keyword information and the second keyword information to obtain an aggregation tag of the user; the aggregation tag includes an environment attribute, a scene attribute, and a function attribute of the device, and is specifically used to represent a probability that a user uses a scene based on an environment or a probability that a user uses a device function based on an environment.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining a target recommendation scene from a preset scene library according to the aggregation tag and pushing the target recommendation scene to a client where the user account is located, wherein the steps of: according to the aggregation label, determining a target aggregation label matched with the aggregation label from a preset aggregation label library; the aggregation label library comprises a plurality of aggregation labels to be matched of the user; and determining a target recommendation scene from a preset scene library according to the target aggregation label and pushing the target recommendation scene to a client where the user account is located.
In one embodiment, the processor, when executing the computer program, further performs the steps of: according to the aggregation label, determining a target aggregation label matched with the aggregation label from a preset aggregation label library; the method comprises the following steps: and determining the aggregation label to be matched in the aggregation label library, which has the same environmental attribute as the aggregation label, as the target aggregation label.
In one embodiment, the processor, when executing the computer program, further performs the steps of: each scene to be recommended in the scene library corresponds to at least one scene tag, and the scene tag comprises: environmental attributes and functional attributes of the device; determining a target recommendation scene from a preset scene library according to the target aggregation label, wherein the target recommendation scene comprises the following steps: according to the attribute of the target aggregation label and the attribute of each scene label, performing relevance matching on the target aggregation label and each scene label, and determining a target scene label with the highest relevance; and determining the scene to be recommended corresponding to the target scene label as the target recommended scene.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring first equipment information associated with the target recommendation scene; determining second equipment information associated under the user account according to the user account; and if the equipment in the first equipment information comprises other equipment except the second equipment information, recommending the purchase information of the other equipment to the 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 environment information acquired by environment acquisition equipment, wherein the environment information carries a user account;
determining personalized information of the user from a preset historical database according to the user account; wherein the database comprises historical personalization information for a plurality of users;
determining an aggregation label of the user according to the environment information and the personalized information, wherein the aggregation label is used for representing the scene use information of the user;
determining a target recommendation scene from a preset scene library according to the aggregation label and pushing the target recommendation scene to a client where the user account is located; the scene library comprises a plurality of scenes to be recommended, and different scenes to be recommended are associated with different environmental information and different personalized information.
In one embodiment, the computer program when executed by the processor further performs the steps of: the environmental information that obtains environment collection equipment collection includes: acquiring indoor environment information currently acquired by environment acquisition equipment, wherein the indoor environment information carries a user account; determining current outdoor environment information according to the position information of the user account; and carrying out fusion processing on the indoor environment information and the outdoor environment information to obtain the environment information.
In one embodiment, the computer program when executed by the processor further performs the steps of: the fusion processing is performed on the indoor environment information and the outdoor environment information to obtain the environment information, and the fusion processing comprises the following steps: analyzing the environmental indexes in the indoor environmental information and the outdoor environmental information to obtain the same environmental index and different environmental indexes; wherein each environmental index corresponds to a respective index value; determining the index value which belongs to the indoor environment information in the same environment index as the index value of the same environment index, and rejecting the index value which belongs to the outdoor environment information in the same environment index; and fusing the index values of the same environmental index and the index values of the different environmental indexes to obtain the environmental information.
In one embodiment, the computer program when executed by the processor further performs the steps of: the personalized information of the user comprises the interested equipment of the user, the interested function of the interested equipment and the interested linkage scene of the user; the frequency of use of the interested device, the frequency of use of the interested function and the frequency of use of the linkage scene interested by the user are all larger than a preset threshold value.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining an aggregated tag of the user according to the environment information and the personalized information, comprising: extracting key bytes of the environment information to obtain first key word information; extracting key bytes of the personalized information to obtain second key word information; performing association processing on the first keyword information and the second keyword information to obtain an aggregation tag of the user; the aggregation tag includes an environment attribute, a scene attribute, and a function attribute of the device, and is specifically used to represent a probability that a user uses a scene based on an environment or a probability that a user uses a device function based on an environment.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a target recommendation scene from a preset scene library according to the aggregation tag and pushing the target recommendation scene to a client where the user account is located, wherein the method comprises the following steps: according to the aggregation label, determining a target aggregation label matched with the aggregation label from a preset aggregation label library; the aggregation label library comprises a plurality of aggregation labels to be matched of the user; and determining a target recommendation scene from a preset scene library according to the target aggregation label and pushing the target recommendation scene to a client where the user account is located.
In one embodiment, the computer program when executed by the processor further performs the steps of: according to the aggregation label, determining a target aggregation label matched with the aggregation label from a preset aggregation label library; the method comprises the following steps:
and determining the aggregation label to be matched in the aggregation label library, which has the same environmental attribute as the aggregation label, as the target aggregation label.
In one embodiment, the computer program when executed by the processor further performs the steps of: each scene to be recommended in the scene library corresponds to at least one scene tag, and the scene tag comprises: an environmental attribute and a functional attribute of the device; determining a target recommendation scene from a preset scene library according to the target aggregation label, wherein the target recommendation scene comprises the following steps: according to the attribute of the target aggregation label and the attribute of each scene label, performing relevance matching on the target aggregation label and each scene label, and determining a target scene label with the highest relevance; and determining the scene to be recommended corresponding to the target scene label as the target recommended scene.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring first equipment information associated with the target recommendation scene; determining second equipment information associated under the user account according to the user account; and if the equipment in the first equipment information comprises other equipment except the second equipment information, recommending the purchase information of the other equipment to the 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 can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
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 (12)
1. A method for scene recommendation, the method comprising:
acquiring environment information acquired by environment acquisition equipment, wherein the environment information carries a user account;
determining personalized information of the user from a preset historical database according to the user account; wherein the database comprises historical personalization information for a plurality of users;
determining an aggregation label of a user according to the environment information and the personalized information, wherein the aggregation label is used for representing scene use information of the user;
determining a target recommendation scene from a preset scene library according to the aggregation label and pushing the target recommendation scene to a client where the user account is located; the scene library comprises a plurality of scenes to be recommended, and different scenes to be recommended are associated with different environmental information and different personalized information.
2. The method of claim 1, wherein the obtaining environmental information collected by the environment collection device comprises:
acquiring indoor environment information currently acquired by environment acquisition equipment, wherein the indoor environment information carries a user account;
determining current outdoor environment information according to the position information of the user account;
and carrying out fusion processing on the indoor environment information and the outdoor environment information to obtain the environment information.
3. The method according to claim 2, wherein the fusing the indoor environment information and the outdoor environment information to obtain the environment information comprises:
analyzing the environmental indexes in the indoor environmental information and the outdoor environmental information to obtain the same environmental index and different environmental indexes; wherein each environmental index corresponds to a respective index value;
determining the index values which belong to the indoor environment information in the same environment indexes as the index values of the same environment indexes, and rejecting the index values which belong to the outdoor environment information in the same environment indexes;
and fusing the index values of the same environmental index and the index values of the different environmental indexes to obtain the environmental information.
4. The method of claim 2, wherein the personalized information of the user comprises a device of interest of the user, a function of interest of the device of interest, and a linkage scene of interest to the user; the frequency of use of the interested device, the frequency of use of the interested function and the frequency of use of the linkage scene interested by the user are all larger than a preset threshold value.
5. The method of claim 4, wherein determining the aggregated tag of the user based on the environmental information and the personalized information comprises:
extracting key bytes of the environment information to obtain first key word information;
extracting key bytes of the personalized information to obtain second key word information;
performing association processing on the first keyword information and the second keyword information to obtain an aggregation tag of the user; the aggregation tag includes an environment attribute, a scene attribute, and a function attribute of the device, and is specifically used to represent a probability that a user uses a scene based on an environment or a probability that a user uses a device function based on an environment.
6. The method according to claim 5, wherein the determining a target recommended scene from a preset scene library according to the aggregation tag and pushing the target recommended scene to a client where the user account is located includes:
determining a target aggregation label matched with the aggregation label from a preset aggregation label library according to the aggregation label; the aggregation label library comprises a plurality of aggregation labels to be matched of the user;
and determining a target recommendation scene from a preset scene library according to the target aggregation label and pushing the target recommendation scene to a client where the user account is located.
7. The method according to claim 6, wherein the target aggregation tag matching the aggregation tag is determined from a preset aggregation tag library according to the aggregation tag; the method comprises the following steps:
and determining the aggregation label to be matched in the aggregation label library, which has the same environmental attribute as the aggregation label, as the target aggregation label.
8. The method according to claim 7, wherein each scene to be recommended in the scene library corresponds to at least one scene tag, and the scene tag comprises: an environmental attribute and a functional attribute of the device; the determining a target recommendation scene from a preset scene library according to the target aggregation tag comprises the following steps:
according to the attributes of the target aggregation labels and the attributes of each scene label, performing relevance matching on the target aggregation labels and each scene label, and determining the target scene label with the highest relevance;
and determining the scene to be recommended corresponding to the target scene label as the target recommended scene.
9. The method of claim 8, further comprising:
acquiring first equipment information associated with the target recommendation scene;
determining second equipment information associated under the user account according to the user account;
and if the equipment in the first equipment information comprises other equipment except the second equipment information, recommending the purchase information of the other equipment to the user.
10. A scene recommendation apparatus, characterized in that the apparatus comprises:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring environment information acquired by environment acquisition equipment, and the environment information carries a user account;
the first determining module is used for determining the personalized information of the user from a preset historical database according to the user account; wherein the database comprises historical personalization information for a plurality of users;
the second determination module is used for determining an aggregation label of the user according to the environment information and the personalized information, wherein the aggregation label is used for representing the scene use information of the user;
the pushing module is used for determining a target recommendation scene from a preset scene library according to the aggregation label and pushing the target recommendation scene to a client where the user account is located; the scene library comprises a plurality of scenes to be recommended, and different scenes to be recommended are associated with different environmental information and different personalized information.
11. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method of any one of claims 1 to 9 when executing the computer program.
12. 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 9.
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