Equipment recommendation method and device based on Internet of Things, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a recommendation method and device based on Internet of Things equipment, electronic equipment and a storage medium, the Internet of Things equipment is connected with distributed servers located in different regions, the Internet of Things equipment comprises local recommendation models, and data models are deployed in the distributed servers. The method comprises the following steps: acquiring model feature data output by a data model sent by at least one distributed server; wherein the model feature data is generated by the data model according to an Internet of Things data stream obtained by a distributed server; obtaining current user spatio-temporal data corresponding to the Internet of Things equipment; inputting the current user spatio-temporal data and the model feature data into a local recommendation model of the Internet of Things equipment to obtain current spatial vector feature data output by the local recommendation model; and determining current recommendation data corresponding to the Internet of Things equipment according to the current space vector feature data, and pushing the current recommendation data through the Internet of Things equipment. According to the embodiment of the invention, the use experience of the user on the Internet of Things equipment is ensured.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of Internet of Things, and in particular to a method for recommending Internet of Things devices, an apparatus for recommending Internet of Things devices, an electronic device, and a computer-readable storage medium. Background Art

[0002] In the daily use of IoT devices (such as smart home devices), many users only use basic functions and rarely pay attention to relatively complex functions that require multiple settings.

[0003] In the current use of IoT devices, some ultra-long and ultra-wide recommendation models are currently used for modeling, so as to mine the user's points of interest based on the ultra-long and ultra-wide recommendation models, and then recommend the IoT devices used by the user and the recommendation data related to the points of interest, such as relatively complex functions and functions requiring multiple settings, to improve the user's experience.

[0004] However, although the use of the ultra-long and ultra-wide recommendation model can push data that matches the user's interests to the user, since the ultra-long and ultra-wide recommendation model is a large model, and the user data collected based on the IoT device is constantly updated in real time, if you want to use the ultra-long and ultra-wide recommendation model to recommend recommended data that matches the user's current interests, you need to collect real-time user data to update the ultra-long and ultra-wide recommendation model. However, the user and the user's IoT device are in different regions, and user data in different regions are stored in different data centers. Directly obtaining data from the data center will have serious long-tail data delay problems. In other words, the ultra-long and ultra-wide recommendation model cannot be updated in real time based on user data, and thus cannot recommend recommended data about IoT devices that meet current user needs, reducing the user's experience of using IoT devices. Summary of the invention

[0005] In view of the above problems, a method and apparatus for recommending devices based on the Internet of Things is proposed to overcome the above problems or at least partially solve the above problems. The specific technical solution is as follows:

[0006] The embodiment of the present invention discloses a recommendation method based on an Internet of Things device, wherein the Internet of Things device is connected to a distribution server located in different regions, the Internet of Things device includes a local recommendation model, and the distribution server is deployed with a data model associated with the local recommendation model. The method includes:

[0007] Obtaining model feature data output by the data model sent by at least one of the distribution servers; wherein the model feature data is generated by the data model according to the IoT data stream obtained by the distribution server;

[0008] Obtaining the current user spatiotemporal data corresponding to the IoT device;

[0009] Inputting the current user spatiotemporal data and model feature data into the local recommendation model of the Internet of Things device to obtain current spatial vector feature data output by the local recommendation model;

[0010] The current recommended data corresponding to the IoT device is determined according to the current spatial vector feature data, and the current recommended data is pushed through the IoT device.

[0011] In one embodiment of the present invention, the current user spatiotemporal data and model feature data are input into the local recommendation model of the Internet of Things device to obtain the current space vector feature data output by the local recommendation model, including:

[0012] The model feature data is weighted according to preset weights to obtain weighted model feature data; the preset weights are determined according to the distributed server or the IoT data stream processed by the distributed server;

[0013] The current user spatiotemporal data and the weighted model feature data are input into the local recommendation model of the Internet of Things device to obtain the current space vector feature data output by the local recommendation model.

[0014] In an embodiment of the present invention, determining the current recommended data corresponding to the IoT device according to the current space vector feature data, and pushing the current recommended data through the IoT device includes:

[0015] Recommended data corresponding to historical space vector feature data similar to the current space vector feature data is obtained as current recommended data, and the current recommended data is pushed through the Internet of Things device.

[0016] In an embodiment of the present invention, determining the current recommended data corresponding to the IoT device according to the current space vector feature data, and pushing the current recommended data through the IoT device includes:

[0017] Get preset model conditions;

[0018] The current recommended data corresponding to the IoT device is determined according to the current spatial vector feature data, the target recommended data is determined according to the current recommended data and the preset model condition, and the target recommended data is pushed through the IoT device.

[0019] In an embodiment of the present invention, the data model of the distributed server obtains the Internet of Things data stream according to a preset update mechanism, and uses the Internet of Things data stream to update the data model.

[0020] In one embodiment of the present invention, the model feature data is generated by inputting the incremental IoT data stream obtained by the distributed server into the data model after being weighted according to preset weights.

[0021] In an embodiment of the present invention, determining the current recommended data corresponding to the IoT device according to the current space vector feature data, and pushing the current recommended data through the IoT device includes:

[0022] Determine the current recommended data corresponding to the IoT device according to the current spatial vector feature data;

[0023] Obtain current device data of other IoT devices associated with the IoT device;

[0024] Update the current recommended data according to the current device data;

[0025] The updated current recommendation data is pushed through the IoT device.

[0026] In an embodiment of the present invention, after pushing the updated current recommended data through the Internet of Things device, the method further includes:

[0027] The updated current recommended data is sent to the other IoT devices, so as to push the updated current recommended data in the other IoT devices.

[0028] In one embodiment of the present invention, the recommendation data at least includes the recommended scene, recommended action and recommended setting corresponding to the IoT device; the IoT data stream at least includes the device category, user category, spatiotemporal data, device status and user behavior data of the IoT device used by the user.

[0029] The embodiment of the present invention further discloses a device for recommending based on an Internet of Things device, wherein the Internet of Things device is connected to a distribution server located in different regions, the Internet of Things device includes a local recommendation model, and the distribution server is deployed with a data model associated with the local recommendation model, and the device includes:

[0030] A model feature data acquisition module, used to acquire model feature data output by the data model sent by at least one of the distribution servers; wherein the model feature data is generated by the data model according to the IoT data stream acquired by the distribution server;

[0031] A user spatiotemporal data acquisition module, used to acquire the current user spatiotemporal data corresponding to the IoT device;

[0032] A spatial vector feature data acquisition module, used to input the current user spatiotemporal data and model feature data into the local recommendation model of the Internet of Things device to obtain the current spatial vector feature data output by the local recommendation model;

[0033] The recommended data push module is used to determine the current recommended data corresponding to the Internet of Things device according to the current space vector feature data, and push the current recommended data through the Internet of Things device.

[0034] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0035] The memory is used to store computer programs;

[0036] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0037] The embodiment of the present invention further discloses a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the method described in the embodiment of the present invention.

[0038] The embodiment of the present invention further discloses a computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, enables the processors to execute the method described in the embodiment of the present invention.

[0039] The embodiments of the present invention include the following advantages:

[0040] In an embodiment of the present invention, an IoT device is connected to a distribution server located in different regions, the IoT device includes a local recommendation model, and a data model associated with the local recommendation model is deployed in the distribution server. When recommending recommended data corresponding to a certain IoT device to a user, model feature data output by the data model sent by at least one distribution server is obtained, wherein the model feature data is generated by the data model according to the IoT data stream obtained by the distributed server, the current user spatiotemporal data corresponding to the IoT device is obtained, the current user spatiotemporal data and the model feature data are input into the local recommendation model of the IoT device, the current space vector feature data output by the local recommendation model is obtained, the current recommended data corresponding to the IoT device is determined according to the current space vector feature data, and the current recommended data is pushed through the IoT device. The IoT device of the embodiment of the present invention does not need to transmit a large amount of IoT data streams to distributed servers in different regions, but only needs to transmit a small amount of model feature data. Therefore, the local recommendation model of the IoT device can timely combine the model feature data and the current user spatiotemporal data collected in real time to generate recommended data about the IoT device that meets the current user needs, thereby ensuring the user's experience of using the IoT device.

[0041] In addition, the data model of the distributed server can generate model feature data based on incremental IoT data streams rather than full IoT data streams. The data processing volume is small, so the model feature data can be quickly generated and sent to the IoT device, further ensuring the user experience of the IoT device. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flowchart of a method for recommending IoT devices provided in an embodiment of the present invention;

[0043] Figure 2 is a schematic diagram of an application environment provided in an embodiment of the present invention;

[0044] Figure 3 It is a structural block diagram of a device for recommending Internet of Things devices provided in an embodiment of the present invention;

[0045] Figure 4 It is a schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] Reference Figure 1, shows a flowchart of a method for recommending an IoT device provided in an embodiment of the present invention, wherein the IoT device is connected to a distribution server located in a different region, the IoT device includes a local recommendation model, and the distribution server is deployed with a data model associated with the local recommendation model. The method may specifically include the following steps:

[0048] Step 101: Obtain model feature data output by the data model sent by at least one of the distributed servers; wherein the model feature data is generated by the data model according to the IoT data stream obtained by the distributed server.

[0049] In the specific implementation, refer to Figure 2 , is a schematic diagram of an application environment provided in an embodiment of the present invention. The application may be an Internet of Things environment. The Internet of Things environment may include Internet of Things devices (wherein the user node is the Internet of Things device used by the current user), and the Internet of Things device may be connected to one or more distributed servers (data center 1, data center 2, data center 3). Specifically, the Internet of Things device may also be referred to as an edge node. Exemplarily, the Internet of Things device may include, but is not limited to, ovens, rice cookers, lighting equipment, dehumidifiers, switches, sockets, sensor devices, smoke alarms, smart door locks, and security cameras. Distributed servers (data centers) are usually distributed in different regions. The distributed servers may collect Internet of Things data streams (user data) of Internet of Things devices of users in the region, where the Internet of Things data streams may include data about users and Internet of Things devices, such as device categories, user categories, spatiotemporal data, device status, and user behavior data of Internet of Things devices used by users. Among them, spatiotemporal data may include data on the timeline and the spaceline. Specifically, the data on the timeline may refer to the data of users and IoT devices collected at different time points, such as the user's operation records of the oven in the morning and evening. The data on the spaceline may refer to the data of users and IoT devices collected at different spatial locations, such as the user's operation records of the oven at work and at home.

[0050] In an embodiment of the present invention, the data model of the distributed server obtains the Internet of Things data stream according to a preset update mechanism, and uses the Internet of Things data stream to update the data model.

[0051] In an embodiment of the present invention, a distributed server can obtain relevant IoT data streams during the user's daily use of IoT devices, wherein the IoT data streams can come from different IoT devices, as well as APPs (applications) related to IoT devices operated by users. The IoT data streams that the distributed server itself can obtain can be based on the introduction of basic data, environmental data, user behavior and other data to improve the diversity and relative controllability of the sources of the overall IoT data streams. The distributed server can pre-establish a real-time update mechanism, based on which the distributed server can regularly use the latest acquired IoT data streams to update the data model, so that the data model can reflect the latest device status and interests of IoT devices, and thus can better recommend data related to IoT devices to users.

[0052] In an embodiment of the present invention, a local recommendation model can be deployed in an IoT device, a data model associated with the local recommendation model is deployed in a distribution server, and the distributed server can collect IoT data streams, and then the IoT data streams can be input into the data model, so that the data model can output model feature data accordingly. When it is necessary to recommend recommended data related to IoT devices to users, for example, when it is detected that a user uses a certain IoT device or the user automatically triggers a recommendation, the IoT device currently used by the user can obtain the model feature data output by the data model sent by at least one distribution server, and the local recommendation model of the IoT device will determine the user's push data to the IoT device based on the model feature data output by the data models of multiple different regions. In an optional embodiment of the present invention, the local recommendation model can be the original spatiotemporal model of the IoT device itself or the ultra-long and ultra-wide recommendation model, wherein ultra-long refers to a large span of data volume, a large concept of spatiotemporal domain, a long time span and a large space span, and ultra-wide refers to multiple data sources, multiple fields and extremely low correlation, such as a mixture of weather data, device status data, and human health data. Among them, both the spatiotemporal model and the ultra-long and ultra-wide recommendation model can generate recommendation data for the user's IoT devices. The difference is that the amount and type of data used to update the spatiotemporal model are lower than those of the ultra-long and ultra-wide recommendation model. Then, the data model can be deployed separately in the distributed servers in different regions of the spatiotemporal model or the ultra-long and ultra-wide recommendation model, so that the data model can send the model feature data generated according to the IoT data stream to them respectively. In this way, only the data model needs to be added, which is simple and easy to implement, and easy to promote and use. Of course, the local recommendation model can be other modules or models that can integrate the model feature data sent by the distributed server to output the recommendation data, and the embodiments of the present invention do not need to be limited to this.

[0053] In one embodiment of the present invention, the model feature data is generated by inputting the incremental IoT data stream obtained by the distributed server into the data model after being weighted according to preset weights.

[0054] Among them, the incremental IoT data stream (basic layer data) is the newly added or changed IoT data stream obtained by the distributed server. The data types of different IoT data streams may be different. For example, the IoT data stream may include but is not limited to data such as device category, user category, region, and usage method. Different data types also have different priorities or importance when recommending data to users. Therefore, after the distributed server obtains the incremental IoT data stream, it can add corresponding preset weights to the incremental IoT data stream, and then input the incremental IoT data stream with the preset weight value added into the data model, so that the data model outputs the corresponding model feature data (weighted feature value).

[0055] Step 102: Obtain the current user spatiotemporal data corresponding to the IoT device.

[0056] In a specific implementation, the IoT device can collect the current user's spatiotemporal data corresponding to the user using the IoT device. The current user's spatiotemporal data is the data on the user's current timeline and spaceline. In the IoT scenario, user behavior patterns on different time segments of the timeline can be extracted, and the user's behavior and points of interest will also change significantly on different spacelines. Therefore, the IoT device of an embodiment of the present invention can collect at least data including the user and the IoT device in the current time and space as the current user's spatiotemporal data, such as the device category, user category, device status, and user behavior data in the current time and space, and can then recommend recommended data associated with the IoT device to the user based on the current user's spatiotemporal data.

[0057] Step 103: Input the current user spatiotemporal data and model feature data into the local recommendation model of the Internet of Things device to obtain current spatial vector feature data output by the local recommendation model.

[0058] Step 104: determine the current recommended data corresponding to the IoT device according to the current spatial vector feature data, and push the current recommended data through the IoT device.

[0059] In one embodiment of the present invention, the recommendation data may include at least the recommended scene, recommended action and recommended setting corresponding to the IoT device. For example, for an IoT device such as an oven, the recommended scene may be "breakfast scene", "lunch scene" and "dinner scene", the recommended action may be the current recommendation to turn the oven on or off, and the recommended setting may be the specific values ​​to which the various parameters of the oven are set.

[0060] In an embodiment of the present invention, the IoT device can input the acquired spatiotemporal data of the current user and the model feature data generated by the data models of different regions into the local recommendation model of the IoT device, so that the local recommendation model can output the corresponding current spatial vector feature data. Subsequently, the current recommendation data corresponding to the IoT device can be determined based on the current spatial vector feature data, and the current recommendation data can be pushed to the user through the display screen, speaker and other front ends of the IoT device. The user can adjust the user behavior of the IoT device or adjust various parameters of the IoT device according to the current recommendation data. In some embodiments, the IoT device can directly set the IoT device according to the current recommendation data and provide a one-click confirmation option on the IoT device, so that the IoT device can be quickly set to a state that meets the user's current needs without the user having to perform complex operations or settings, thereby improving the user's experience of using the IoT device.

[0061] In an embodiment of the present invention, an IoT device is connected to a distribution server located in different regions, the IoT device includes a local recommendation model, and a data model associated with the local recommendation model is deployed in the distribution server. When recommending recommended data corresponding to a certain IoT device to a user, model feature data output by the data model sent by at least one distribution server is obtained, wherein the model feature data is generated by the data model according to the IoT data stream obtained by the distributed server, the current user spatiotemporal data corresponding to the IoT device is obtained, the current user spatiotemporal data and the model feature data are input into the local recommendation model of the IoT device, the current space vector feature data output by the local recommendation model is obtained, the current recommended data corresponding to the IoT device is determined according to the current space vector feature data, and the current recommended data is pushed through the IoT device. The IoT device of the embodiment of the present invention does not need to transmit a large amount of IoT data streams to distributed servers in different regions, but only needs to transmit a small amount of model feature data. Therefore, the local recommendation model of the IoT device can timely combine the model feature data and the current user spatiotemporal data collected in real time to generate recommended data about the IoT device that meets the current user needs, thereby ensuring the user's experience of using the IoT device.

[0062] In one embodiment of the present invention, inputting the current user spatiotemporal data and the model feature data into the local recommendation model of the Internet of Things device to obtain the current spatial vector feature data output by the local recommendation model may include:

[0063] The model feature data is weighted according to preset weights to obtain weighted model feature data; the preset weights are determined according to the distributed server or the IoT data stream processed by the distributed server;

[0064] The current user spatiotemporal data and the weighted model feature data are input into the local recommendation model of the Internet of Things device to obtain the current space vector feature data output by the local recommendation model.

[0065] In a specific implementation, the data volume and data type of the IoT device streams processed by distributed servers (data centers) in different regions may be different, and different data types may have different priorities or importance when recommending data to users. Therefore, different preset weights can be added to the model feature data output by the data models of different distributed servers.

[0066] In an embodiment of the present invention, after obtaining the model feature data sent by each distributed server, the IoT device can perform weighted processing according to the preset weights corresponding to the distributed servers to obtain weighted model feature data, and then the current user spatiotemporal data and the weighted model feature data can be input into the local recommendation model of the IoT device, so that the local recommendation model can output the current spatial vector feature data accordingly, and then the current recommendation data corresponding to the IoT device can be determined based on the current spatial vector feature data. According to the model feature data with different preset weights added in combination with the current user spatiotemporal data, the accuracy and comprehensiveness of the recommended data can be improved.

[0067] In an embodiment of the present invention, determining the current recommended data corresponding to the IoT device according to the current spatial vector feature data, and pushing the current recommended data through the IoT device may include:

[0068] Recommended data corresponding to historical space vector feature data similar to the current space vector feature data is obtained as current recommended data, and the current recommended data is pushed through the Internet of Things device.

[0069] In an embodiment of the present invention, the current space vector feature data is a vector generated based on data such as current user behavior, device status, and environmental data, and can describe the status and needs of the current user. The historical space vector feature data is a vector generated based on data such as historical user behavior, device status, and environmental data, and is used to describe the status and needs of historical users. The historical space vector feature data may include recommendation data such as recommended scenes, recommended actions, and recommended settings corresponding to the IoT device. The current space vector feature data may be matched with the historical space vector feature data, and then the recommendation data corresponding to the historical space vector feature data with a similarity reaching a preset similarity may be used as the current recommendation data, or the recommendation data corresponding to the historical space vector feature data with the highest similarity may be used as the current recommendation data, and then the current recommendation data may be pushed to the user through the IoT device. Among them, the similarity calculation may be calculated using a similarity algorithm such as cosine similarity and Euclidean distance, and the embodiment of the present invention does not need to be limited to this.

[0070] In an embodiment of the present invention, determining the current recommended data corresponding to the IoT device according to the current space vector feature data, and pushing the current recommended data through the IoT device includes:

[0071] Get preset model conditions;

[0072] The current recommended data corresponding to the IoT device is determined according to the current spatial vector feature data, the target recommended data is determined according to the current recommended data and the preset model condition, and the target recommended data is pushed through the IoT device.

[0073] In a specific implementation, the local recommendation model of the IoT device can determine the current recommendation data based on the input space vector feature data, and then push the current recommendation data to the user through the IoT device. In addition, the embodiment of the present invention can also combine the preset model conditions to further generate the final target recommendation data based on the current recommendation data. Specifically, refer to Figure 2, the preset model conditions may include the existing preset recommendation content (preset recommendation) preset by the IoT device. The preset model conditions may be rules or conditions predefined by the IoT device itself or by the user, which are used to assist the local recommendation model in generating recommendation data. For example, for an IoT device that is an oven, the preset model conditions may be set to "breakfast mode" in the morning. If the recommended scenes of the oven output by the local recommendation model are "breakfast mode" and "lunch mode", then the target recommendation data "breakfast mode" may be generated by combining the preset model conditions and the current recommendation data. The IoT device recommends data related to the IoT device to the user by combining simple preset model conditions and the current recommendation data output by the local recommendation model, which supplements the deficiencies of the local recommendation model, can generate more comprehensive recommendation data, and further improves the user experience of the IoT device.

[0074] In an embodiment of the present invention, determining the current recommended data corresponding to the IoT device according to the current space vector feature data, and pushing the current recommended data through the IoT device includes:

[0075] Determine the current recommended data corresponding to the IoT device according to the current spatial vector feature data;

[0076] Obtain current device data of other IoT devices associated with the IoT device;

[0077] Update the current recommended data according to the current device data;

[0078] The updated current recommendation data is pushed through the IoT device.

[0079] In a specific implementation, IoT devices can work together. For example, an IoT device may include an associated oven and a smart fan. When the oven is baking food at high temperature, the smart fan can automatically adjust the wind speed according to the working status of the oven to avoid overheating in the room and maintain air circulation.

[0080] In an embodiment of the present invention, the Internet of Things device can determine the current recommended data corresponding to the Internet of Things device based on the current spatial vector feature data output by the local recommendation model, and then determine other Internet of Things devices associated with the Internet of Things device. For example, if the Internet of Things device currently used by the user is an oven, then the other associated Internet of Things devices may include a smart fan. The current device data of other Internet of Things devices can be obtained, such as whether other Internet of Things devices are turned on, currently set related device parameters, etc., and then the current recommended data can be updated according to the current device data of other Internet of Things devices, and then the updated current recommended data can be pushed to the user through the Internet of Things device. For example, assuming that the current recommended data is that the oven is 200 degrees and the current smart fan is not in an turned-on state, the updated current recommended data may be "the oven is 200 degrees" and "turn on the smart fan", or, assuming that the current recommended data is that the oven is 200 degrees and the current smart fan is in an turned-on state, then the updated current recommended data may be "the oven is 190 degrees".

[0081] In an embodiment of the present invention, after pushing the updated current recommendation data through the Internet of Things device, the method may further include:

[0082] The updated current recommended data is sent to the other IoT devices, so as to push the updated current recommended data in the other IoT devices.

[0083] In an embodiment of the present invention, after updating the current recommended data according to the current device data of other IoT devices associated with the IoT device, the updated current recommended data can also be sent to other IoT devices, so that the updated current recommended data can also be pushed in other IoT devices. Users can also set the recommended scenarios, recommended actions and recommended settings corresponding to other associated IoT devices according to the current recommended data of other IoT devices, avoiding the need to check in the IoT device, thereby improving the user experience of the IoT device.

[0084] The embodiments of the present invention solve the huge time and computing power costs required for calculating and pushing data in the current ultra-long and ultra-wide implementation by mining the needs and behavior patterns of users of IoT devices, optimize the recommendation path, and allow users of IoT devices to obtain recommendation data pushed by recommendation models in real time, such as recommended settings and recommended setting scenarios.

[0085] By applying the embodiment of the present invention, on the basis of the original spatiotemporal model or the ultra-long and ultra-wide recommendation model, a multi-layer data model is added to the distributed servers in different regions, and the data model of each distributed server is updated in real time in the form of incremental IoT data stream to output the model data. The local recommendation model of the IoT device replaces the actual IoT data stream with the model data at each level, and performs weighted calculation on the model data. Then, the result of the weighted calculation and the preset model conditions are mixed and calculated, and the recommended data of the output IoT device is recommended to the user through the IoT device or APP, thereby improving the user experience of the IoT device.

[0086] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0087] Reference Figure 3 , shows a structural block diagram of a device for recommending based on an Internet of Things device provided in an embodiment of the present invention, wherein the Internet of Things device is connected to a distribution server located in different regions, the Internet of Things device includes a local recommendation model, and the distribution server is deployed with a data model associated with the local recommendation model, and the device may specifically include the following modules:

[0088] The model feature data acquisition module 301 is used to acquire the model feature data output by the data model sent by at least one of the distribution servers; wherein the model feature data is generated by the data model according to the IoT data stream acquired by the distribution server;

[0089] A user spatiotemporal data acquisition module 302 is used to acquire the current user spatiotemporal data corresponding to the IoT device;

[0090] A spatial vector feature data acquisition module 303 is used to input the current user spatiotemporal data and model feature data into the local recommendation model of the Internet of Things device to obtain the current spatial vector feature data output by the local recommendation model;

[0091] The recommended data pushing module 304 is used to determine the current recommended data corresponding to the IoT device according to the current spatial vector feature data, and push the current recommended data through the IoT device.

[0092] In one embodiment of the present invention, the spatial vector feature data acquisition module 303 is used to:

[0093] The model feature data is weighted according to preset weights to obtain weighted model feature data; the preset weights are determined according to the distributed server or the IoT data stream processed by the distributed server;

[0094] The current user spatiotemporal data and the weighted model feature data are input into the local recommendation model of the Internet of Things device to obtain the current space vector feature data output by the local recommendation model.

[0095] In one embodiment of the present invention, the recommendation data push module 304 is used to:

[0096] Recommended data corresponding to historical space vector feature data similar to the current space vector feature data is obtained as current recommended data, and the current recommended data is pushed through the Internet of Things device.

[0097] In one embodiment of the present invention, the recommendation data push module 304 is used to:

[0098] Get preset model conditions;

[0099] The current recommended data corresponding to the IoT device is determined according to the current spatial vector feature data, the target recommended data is determined according to the current recommended data and the preset model condition, and the target recommended data is pushed through the IoT device.

[0100] In an embodiment of the present invention, the data model of the distributed server obtains the Internet of Things data stream according to a preset update mechanism, and uses the Internet of Things data stream to update the data model.

[0101] In one embodiment of the present invention, the model feature data is generated by inputting the incremental IoT data stream obtained by the distributed server into the data model after being weighted according to preset weights.

[0102] In one embodiment of the present invention, the recommendation data push module 304 is used to:

[0103] Determine the current recommended data corresponding to the IoT device according to the current spatial vector feature data;

[0104] Obtain current device data of other IoT devices associated with the IoT device;

[0105] Update the current recommended data according to the current device data;

[0106] The updated current recommendation data is pushed through the IoT device.

[0107] In one embodiment of the present invention, the recommendation data push module 304 is used to:

[0108] The updated current recommended data is sent to the other IoT devices, so as to push the updated current recommended data in the other IoT devices.

[0109] In one embodiment of the present invention, the recommendation data includes at least recommended scenarios, recommended actions and recommended settings corresponding to the IoT device; the IoT data stream includes at least device category, user category, spatiotemporal data, device status, and user behavior data of the IoT device used by the user.

[0110] In an embodiment of the present invention, an IoT device is connected to a distribution server located in different regions, the IoT device includes a local recommendation model, and a data model associated with the local recommendation model is deployed in the distribution server. When recommending recommended data corresponding to a certain IoT device to a user, model feature data output by the data model sent by at least one distribution server is obtained, wherein the model feature data is generated by the data model according to the IoT data stream obtained by the distributed server, the current user spatiotemporal data corresponding to the IoT device is obtained, the current user spatiotemporal data and the model feature data are input into the local recommendation model of the IoT device, the current space vector feature data output by the local recommendation model is obtained, the current recommended data corresponding to the IoT device is determined according to the current space vector feature data, and the current recommended data is pushed through the IoT device. The IoT device of the embodiment of the present invention does not need to transmit a large amount of IoT data streams to distributed servers in different regions, but only needs to transmit a small amount of model feature data. Therefore, the local recommendation model of the IoT device can timely combine the model feature data and the current user spatiotemporal data collected in real time to generate recommended data about the IoT device that meets the current user needs, thereby ensuring the user's experience of using the IoT device.

[0111] In addition, the data model of the distributed server can generate model feature data based on incremental IoT data streams rather than full IoT data streams. The data processing volume is small, so the model feature data can be quickly generated and sent to the IoT device, further ensuring the user experience of the IoT device.

[0112] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0113] In addition, an embodiment of the present invention further provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, each process of the above-mentioned embodiment of the method for recommending an IoT device is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0114] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned method for recommending an IoT device is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0115] An embodiment of the present invention also provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned embodiment of the method for recommending an IoT device, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0116] Figure 4 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0117] The electronic device 400 includes but is not limited to: a radio frequency unit 401, a network module 402, an audio output unit 403, an input unit 404, a sensor 405, a display unit 406, a user input unit 407, an interface unit 408, a memory 409, a processor 410, and a power supply 411. Those skilled in the art will appreciate that Figure 4 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. In the embodiments of the present invention, the electronic device includes but is not limited to a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted terminal, a wearable device, and a pedometer.

[0118] It should be understood that in the embodiment of the present invention, the radio frequency unit 401 can be used for receiving and sending signals during information transmission or communication. Specifically, after receiving downlink data from the base station, it is sent to the processor 410 for processing; in addition, uplink data is sent to the base station. Generally, the radio frequency unit 401 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc. In addition, the radio frequency unit 401 can also communicate with the network and other devices through a wireless communication system.

[0119] The electronic device provides users with wireless broadband Internet access through the network module 402, such as helping users to send and receive emails, browse web pages, and access streaming media.

[0120] The audio output unit 403 can convert the audio data received by the RF unit 401 or the network module 402 or stored in the memory 409 into an audio signal and output it as sound. Moreover, the audio output unit 403 can also provide audio output related to a specific function performed by the electronic device 400 (for example, a call signal reception sound, a message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, a receiver, etc.

[0121] The input unit 404 is used to receive audio or video signals. The input unit 404 may include a graphics processor (GPU) 4041 and a microphone 4042, and the graphics processor 4041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The processed image frame can be displayed on the display unit 406. The image frame processed by the graphics processor 4041 can be stored in the memory 409 (or other storage medium) or sent via the radio frequency unit 401 or the network module 402. The microphone 4042 can receive sound and can process such sound into audio data. The processed audio data can be converted into a format output that can be sent to a mobile communication base station via the radio frequency unit 401 in the case of a telephone call mode.

[0122] The electronic device 400 also includes at least one sensor 405, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor and a proximity sensor, wherein the ambient light sensor can adjust the brightness of the display panel 4061 according to the brightness of the ambient light, and the proximity sensor can turn off the display panel 4061 and / or the backlight when the electronic device 400 is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used to identify the posture of the electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; the sensor 405 can also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which will not be repeated here.

[0123] The display unit 406 is used to display information input by the user or information provided to the user. The display unit 406 may include a display panel 4061, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), or the like.

[0124] The user input unit 407 can be used to receive input digital or character information, and to generate key signal input related to user settings and function control of the electronic device. Specifically, the user input unit 407 includes a touch panel 4071 and other input devices 4072. The touch panel 4071, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 4071 or near the touch panel 4071 using any suitable object or accessory such as a finger, stylus, etc.). The touch panel 4071 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the contact point coordinates, and then sends it to the processor 410, receives the command sent by the processor 410 and executes it. In addition, the touch panel 4071 can be implemented using various types such as resistive, capacitive, infrared and surface acoustic waves. In addition to the touch panel 4071, the user input unit 407 may also include other input devices 4072. Specifically, other input devices 4072 may include but are not limited to a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which are not described in detail here.

[0125] Furthermore, the touch panel 4071 may be overlaid on the display panel 4061. When the touch panel 4071 detects a touch operation on or near it, it is transmitted to the processor 410 to determine the type of the touch event. Then, the processor 410 provides a corresponding visual output on the display panel 4061 according to the type of the touch event. Figure 4 In the figure, the touch panel 4071 and the display panel 4061 are used as two independent components to realize the input and output functions of the electronic device. However, in some embodiments, the touch panel 4071 and the display panel 4061 can be integrated to realize the input and output functions of the electronic device, which is not limited here.

[0126] The interface unit 408 is an interface for connecting an external device to the electronic device 400. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, a headphone port, etc. The interface unit 408 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the electronic device 400 or may be used to transmit data between the electronic device 400 and an external device.

[0127] The memory 409 can be used to store software programs and various data. The memory 409 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 409 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0128] The processor 410 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 409, and calling data stored in the memory 409, so as to monitor the electronic device as a whole. The processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 410.

[0129] The electronic device 400 may also include a power supply 411 (such as a battery) for supplying power to each component. Preferably, the power supply 411 may be logically connected to the processor 410 through a power management system, thereby managing functions such as charging, discharging, and power consumption through the power management system.

[0130] In addition, the electronic device 400 includes some functional modules not shown, which will not be described in detail here.

[0131] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0132] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0133] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

[0134] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the embodiments of the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0136] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0137] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0138] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0139] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0140] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for recommending devices based on the Internet of Things, characterized in that: The Internet of Things device is connected to a distribution server located in a different region, the Internet of Things device includes a local recommendation model, and the distribution server is deployed with a data model associated with the local recommendation model. The method includes: Obtaining model feature data output by the data model sent by at least one of the distribution servers; wherein the model feature data is generated by the data model according to the IoT data stream obtained by the distribution server; Obtaining the current user spatiotemporal data corresponding to the IoT device; Inputting the current user spatiotemporal data and model feature data into the local recommendation model of the Internet of Things device to obtain current spatial vector feature data output by the local recommendation model; The current recommended data corresponding to the IoT device is determined according to the current spatial vector feature data, and the current recommended data is pushed through the IoT device.

2. The method according to claim 1, characterized in that Inputting the current user spatiotemporal data and the model feature data into the local recommendation model of the IoT device to obtain the current spatial vector feature data output by the local recommendation model, including: The model feature data is weighted according to preset weights to obtain weighted model feature data; the preset weights are determined according to the distributed server or the IoT data stream processed by the distributed server; The current user spatiotemporal data and the weighted model feature data are input into the local recommendation model of the Internet of Things device to obtain the current space vector feature data output by the local recommendation model.

3. The method according to claim 1, characterized in that Determining current recommended data corresponding to the IoT device according to the current spatial vector feature data, and pushing the current recommended data through the IoT device, including: Recommended data corresponding to historical space vector feature data similar to the current space vector feature data is obtained as current recommended data, and the current recommended data is pushed through the Internet of Things device.

4. The method according to claim 1, characterized in that: Determining current recommended data corresponding to the IoT device according to the current spatial vector feature data, and pushing the current recommended data through the IoT device, including: Get preset model conditions; The current recommended data corresponding to the IoT device is determined according to the current spatial vector feature data, the target recommended data is determined according to the current recommended data and the preset model condition, and the target recommended data is pushed through the IoT device.

5. The method according to claim 1, characterized in that The data model of the distributed server obtains the Internet of Things data stream according to a preset update mechanism, and uses the Internet of Things data stream to update the data model.

6. The method according to claim 5, characterized in that The model feature data is generated by obtaining incremental IoT data streams from the distributed server, weighted according to preset weights, and then input into the data model.

7. The method according to claim 1, characterized in that Determining current recommended data corresponding to the IoT device according to the current spatial vector feature data, and pushing the current recommended data through the IoT device, including: Determine the current recommended data corresponding to the IoT device according to the current spatial vector feature data; Obtain current device data of other IoT devices associated with the IoT device; Update the current recommended data according to the current device data; The updated current recommendation data is pushed through the IoT device.

8. The method according to claim 7, characterized in that After pushing the updated current recommendation data through the Internet of Things device, the method further includes: The updated current recommended data is sent to the other IoT devices, so as to push the updated current recommended data in the other IoT devices.

9. The method according to claim 1, characterized in that: The recommendation data includes at least the recommended scenarios, recommended actions and recommended settings corresponding to the IoT device; the IoT data stream includes at least the device category, user category, spatiotemporal data, device status, and user behavior data of the IoT device used by the user.

10. A device for recommending Internet of Things devices, characterized in that: The Internet of Things device is connected to a distribution server located in a different region, the Internet of Things device includes a local recommendation model, and the distribution server is deployed with a data model associated with the local recommendation model, and the apparatus includes: A model feature data acquisition module, used to acquire model feature data output by the data model sent by at least one of the distribution servers; wherein the model feature data is generated by the data model according to the IoT data stream acquired by the distribution server; A user spatiotemporal data acquisition module, used to acquire the current user spatiotemporal data corresponding to the IoT device; A spatial vector feature data acquisition module, used to input the current user spatiotemporal data and model feature data into the local recommendation model of the Internet of Things device to obtain the current spatial vector feature data output by the local recommendation model; The recommended data push module is used to determine the current recommended data corresponding to the Internet of Things device according to the current space vector feature data, and push the current recommended data through the Internet of Things device.

11. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the method according to any one of claims 1 to 9 when executing the program stored in the memory.

12. A computer-readable storage medium having instructions stored thereon, which, when executed by one or more processors, cause the processors to perform the method according to any one of claims 1 to 9.

Images