Information sorting method and electronic device

Abstract

The application provides an information sorting method and an electronic device, and relates to the field of terminal artificial intelligence. The method comprises the following steps: the electronic device performs searching according to a keyword in a search request of a user, and obtains labels of N candidate objects, the candidate objects can be candidate applications and / or candidate services; then, semantic similarities between the keyword and the labels of the N candidate objects are calculated; from the labels of the N candidate objects, labels of M candidate objects corresponding to the semantic similarities greater than a set threshold value are determined. Since K labels of the M candidate objects correspond to target objects of the same attribute installed on K devices, the electronic device can sort the K candidate objects according to the semantic similarities between the keyword and the K labels of the K candidate objects and device states of the K devices, and display a recommendation interface including the sorting result. In this way, the search efficiency is improved, and the user experience is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of terminals, and in particular to an information sorting method and an electronic device. BACKGROUND

[0002] With the development of intelligent terminals, it is becoming more and more common for a user to have multiple terminals, such as a user who can have three terminals of a smart phone, a tablet computer and a smart watch at the same time, and the three terminals can support different functions respectively. At present, Harmony operating system (OS) is a distributed operating system that is "future-oriented" and faces all scenarios (mobile office, sports health, social communication, media entertainment, etc.). On the basis of the traditional single-device system capability, the Harmony OS proposes a distributed concept of adapting multiple terminal forms based on the same set of system capabilities, and can support multiple electronic devices.

[0003] Among them, the application layer in the Harmony OS can include applications and services, and the services refer to feature ability (FA) services and particle ability (PA) services. Among them, the application is composed of one or more FA services and / or PA services. Among them, the FA service has a UI interface and provides the ability to interact with the user; and the PA service has no UI interface and provides the ability to run tasks in the background and unified data access abstraction. The application developed based on the FA / PA can realize specific business functions, support cross-device scheduling and distribution, and provide consistent and efficient application experience for users. The application based on the FA / PA service has the characteristics of small data volume and no need to download and install to use, and realizes the dream of "touchable" application, so it has very broad application prospects. Based on this feature of the Harmony OS, searching on one device can search for related application tags or service tags on multiple devices. If the search results are sorted based on the traditional semantic analysis method, it is likely that the user cannot find the desired application or desired service in time and accurately due to too many applications or services, which affects the user's experience. SUMMARY

[0004] The present application provides an information sorting method and an electronic device to solve the problem that the user cannot find the desired application or desired service in time and accurately due to too many applications or services.

[0005] In a first aspect, an embodiment of the present application provides an information sorting method, which can be applied to an electronic device. The method comprises: receiving a search operation of a user, the search operation comprising a keyword input by the user; displaying a recommendation interface in response to the search operation, wherein labels of K candidate objects in the recommendation interface correspond to target objects of the same attribute installed on K devices. The sorting of the K candidate objects is determined according to semantic similarity between the keyword and labels of the K candidate objects and device states of the K devices.

[0006] Compared with the prior art, the method can find multiple candidate objects from the electronic device and other devices connected to the electronic device, and sort applications or services on more relevant devices in the multiple candidate objects in advance, facilitating the user to find, improving search efficiency, and improving user experience.

[0007] In a possible design, before displaying the recommendation interface, the sorting of the K candidate objects can be implemented in the following manner. Specifically, the electronic device can acquire labels of N candidate objects according to the keyword, and calculate semantic similarity between the keyword and the labels of the N candidate objects. Then, from the labels of the N candidate objects, the electronic device determines labels of M candidate objects corresponding to semantic similarity greater than a set threshold, wherein the labels of the M candidate objects include the labels of the K candidate objects, and the labels of the M candidate objects correspond to target objects of the same attribute installed on the K devices. M≤N, and M and N are positive integers greater than or equal to 2. Therefore, the K candidate objects can be sorted according to the semantic similarity between the keyword and the labels of the K candidate objects and the device states of the K devices.

[0008] Compared with the prior art, the prior search result is usually obtained from the device locally or from the network side, while the present application can find candidate objects from the electronic device and other devices connected to the electronic device, and the method is more targeted for sorting candidate objects corresponding to target objects of the same attribute in the search result, and combines the device state in the sorting process, so that the applications or services of more relevant devices are sorted in advance, facilitating the search, improving the search efficiency, and improving the user experience.

[0009] In a possible design, the device states of the K devices can include at least one of the following states: a power supply type of the device, a screen size of the device, available computing resources of the device, and a relevance of device performance to the target object. For example, some sound boxes are plug-in type devices, smart TVs are plug-in type devices, and are large screen devices. The audio playback performance of the sound box is strongly related to music applications, and weakly related to video applications.

[0010] In the embodiments of the present application, by considering the association relationship between the device state and the target object, the application or service on the more relevant device can be sorted in the front, which facilitates the search and improves the search efficiency and user experience.

[0011] In a possible design, the target objects of the same attribute on the K devices can be target objects of the same name on the K devices, for example, the application on the mobile phone and the application on the tablet, or the target objects of the same attribute on the K devices are target objects of the same provider on the K devices, for example, the application on the mobile phone and the application on the tablet can be applications of the same provider. application on the mobile phone and the application on the tablet can be applications of the same provider. application on the mobile phone and the application on the tablet can be target objects of the same installation package, for example, the installation package names of the application on the mobile phone and the application on the tablet are the same. application on the mobile phone and the application on the tablet can be target objects of the same installation package, for example, the installation package names of the application on the mobile phone and the application on the tablet are the same.

[0012] In a possible design, the electronic device sorts the K candidate objects according to the semantic similarity between the keyword and the labels of the K candidate objects and the device states of the K devices, including: determining, by the electronic device, first weights respectively corresponding to the K candidate objects according to the semantic similarity between the keyword and the labels of the K candidate objects; and determining second weights respectively corresponding to the K candidate objects according to the matching degree between the constraint condition of the prior knowledge related to the target object and the device states of the K devices; and then sorting the K candidate objects according to the first weights and the second weights. For example, a possible implementation manner can be to sort the K candidate objects according to the product of the first weights and the second weights.

[0013] In the embodiments of the present application, on the one hand, the electronic device determines first weights respectively corresponding to the K candidate objects according to the semantic similarity between the keyword and the labels of the K candidate objects; on the other hand, the electronic device determines second weights respectively corresponding to the K candidate objects according to the matching degree between the constraint condition of the prior knowledge related to the target object and the device states of the K devices, so that the K candidate objects can be comprehensively sorted according to the first weights and the second weights. Such a sorting result can more accurately match the application or service that the user wants to search, which helps to improve the search efficiency and user experience.

[0014] In a possible design, the constraint condition of the prior knowledge related to the target object can include at least one of the following conditions: the target object is preferentially run on a large-screen device, the target object is preferentially run on a plug-in type device, the target object is preferentially run on a device with strong computing capability, and the target object is preferentially run on a device with good audio playing performance. For example, a video type FA service or a video type application is preferentially run on a large-screen device, a television type FA service or a television type application is preferentially run on a plug-in device, and a music type FA service or a music type application is preferentially run on a sound box.

[0015] In an embodiment of this application, the constraint condition of the prior knowledge can be artificially set in advance, and the association between the application or service and the device state can be established by using the above condition, thereby helping to sort the application or service by using the device state.

[0016] In a possible design, the electronic device can determine the second weight in any of the following manners:

[0017] When the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a plug-in type device, the second weight of the corresponding candidate object of the device supporting the plug-in type device is determined to be greater than the second weight of the corresponding candidate object of the device not supporting the plug-in type device.

[0018] Or, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a large-screen device, the second weight of the corresponding candidate object of the device with a larger screen is determined to be greater.

[0019] Or, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a device with strong computing capability, the second weight of the corresponding candidate object of the device with stronger computing capability is determined to be greater.

[0020] Or, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a device with good audio playing performance, the second weight of the corresponding candidate object of the device with better audio playing performance is determined to be greater.

[0021] In a possible design, the sorting manner of the K candidate objects displayed in the preset recommendation interface of the electronic device is as follows: the candidate object with a greater product of the first weight and the second weight is sorted earlier, and the candidate object with a smaller product of the first weight and the second weight is sorted later.

[0022] In the embodiments of the present application, the ranking result of the application or service recommended according to the above method is more accurate to match the application or service that the user wants to search, which helps to improve the search efficiency and improve the user experience.

[0023] In a possible design, the electronic device acquires the labels of the N candidate objects according to the keyword, including: acquiring, from a database, the detail information of the L candidate objects on the K devices; extracting original keywords from the detail information of the L candidate objects, performing semantic analysis on the original keywords, acquiring the labels of the L candidate objects from the analysis result, and acquiring the labels of the N candidate objects from the labels of the L candidate objects according to the matching degrees of the keyword and the labels of the L candidate objects. The detail information of the application includes but is not limited to: the title of the application, the description text of the application, the comment information of the application, the recommendation language of the application, the latest update characteristics of the application, and the like. The label of the application includes but is not limited to: the name of the application, the category to which the application belongs, the characteristics of the application, and the like. The detail information of the service includes but is not limited to: the title of the service, the description text of the service, the comment information of the service, the recommendation language of the service, the latest update characteristics of the service, and the like. The label of the service includes but is not limited to: the name of the service, the category to which the service belongs, the characteristics of the service, and the like.

[0024] In a second aspect, an electronic device is provided, including one or more processors and a memory, wherein the memory stores program instructions that, when executed by the device, implement the methods of any possible design related to the above aspects and the above aspects.

[0025] In a third aspect, a chip is provided, which is coupled with a memory in a device, so that the chip invokes program instructions stored in the memory when running, to implement the methods of any possible design related to the above aspects and the above aspects.

[0026] In a fourth aspect, a computer-readable storage medium is provided, which stores program instructions that, when executed on an electronic device, cause the device to perform the methods of any possible design related to the above aspects and the above aspects.

[0027] In a fifth aspect, a computer program product is provided, which, when executed on an electronic device, causes the electronic device to perform the methods of any possible design related to the above aspects and the above aspects.

[0028] In addition, the technical effects brought by any possible design in the second aspect to the fifth aspect can be referred to the technical effects brought by different design manners in the method part, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 A schematic diagram of a mobile phone structure is provided for the embodiments of the present application.

[0030] Figure 2 A schematic diagram of an Android operating system structure is provided for the embodiments of the present application.

[0031] Figure 3 A schematic diagram of an application scenario is provided for the embodiments of the present application.

[0032] Figures 4A-4E Another set of interface schematic diagrams is provided for the embodiments of the present application.

[0033] Figure 5A Another schematic diagram of an application scenario is provided for the embodiments of the present application.

[0034] Figure 5B Another set of interface schematic diagrams is provided for the embodiments of the present application.

[0035] Figures 6A-6B An interface schematic diagram is provided for the embodiments of the present application.

[0036] Figure 7 Another information sorting method flowchart is provided for the embodiments of the present application.

[0037] Figure 8 Another information sorting method flowchart is provided for the embodiments of the present application.

[0038] Figure 9 Another information sorting method flowchart is provided for the embodiments of the present application.

[0039] Figure 10 Another information sorting method flowchart is provided for the embodiments of the present application.

[0040] Figure 11 A schematic diagram of an electronic device structure is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0041] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings and specific implementation manners.

[0042] Currently, an electronic device can perform semantic matching on a keyword input by a user to obtain a search result of a related application, and sort the search result according to semantic similarity. However, the search result does not involve application information on other devices, because the electronic device is limited by an Android operating system installed thereon. Therefore, only application information related to the input keyword on the electronic device is displayed on a search result interface of the electronic device. It can be seen that the current application search method of the electronic device does not involve searching and sorting of application information or service information on multiple devices.

[0043] It is considered that if the search result is still sorted based on the traditional semantic analysis method on a Harmony operating system (OS), the user may not be able to find the desired application or service in time and accurately due to too many applications or services, which affects the user experience. Therefore, an embodiment of the present application provides an information sorting method and an electronic device. The method can accurately sort applications and / or services in search results of the device itself and other devices connected to the device, effectively improve the information search efficiency, and thus facilitate the user to find the desired application or service in time and accurately.

[0044] The information sorting method provided by the embodiment of the present application can be applied to an electronic device. In some embodiments, the electronic device can be a portable terminal including functions such as a personal digital assistant and / or a music player, such as a mobile phone, a tablet computer, a wearable device (such as a smart watch) with wireless communication function, a vehicle-mounted device, and the like. Exemplary embodiments of the portable terminal include, but are not limited to, a portable terminal equipped with a Harmony OS or other operating system. The portable terminal described above can also be a laptop computer (Laptop) with a touch-sensitive surface (for example, a touch panel), and the like. It should also be understood that in other embodiments, the terminal described above can also be a desktop computer with a touch-sensitive surface (for example, a touch panel).

[0045] Figure 1 A structural schematic diagram of an electronic device 100 is shown.

[0046] ​The electronic device 100 can include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone jack 170D, a sensor module 180, a key 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc. The sensor module 180 can include a pressure sensor 180A, a gyro sensor 180B, a barometric pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, etc.

[0047] It can be understood that the structure shown in the embodiments of the present application does not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 can include more or fewer components than shown, or combine certain components, or split certain components, or different component arrangements. The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0048] The processor 110 can include one or more processing units, for example: the processor 110 can include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units can be independent devices, or can be integrated into one or more processors.

[0049] The electronic device 100 implements a display function through a GPU, a display 194, and an application processor, etc. The GPU is a microprocessor for image processing, connected to the display 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor 110 can include one or more GPUs that execute program instructions to generate or change display information.

[0050] The electronic device 100 can implement a photographing function through an ISP, a camera 193, a video codec, a GPU, a display 194, and an application processor, etc.

[0051] The SIM card interface 195 is used to connect a SIM card. The SIM card can be inserted into or pulled out of the SIM card interface 195 to achieve contact and separation with the electronic device 100. The electronic device 100 can support one or N SIM card interfaces, and N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. The same SIM card interface 195 can simultaneously insert multiple cards. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 can also be compatible with external storage cards. The electronic device 100 interacts with the network through the SIM card to implement functions such as calling and data communication. In some embodiments, the electronic device 100 uses an eSIM, i.e., an embedded SIM card.

[0052] The wireless communication function of the electronic device 100 can be implemented through an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, a modem processor, and a baseband processor, etc. The antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the electronic device 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be multiplexed to improve the utilization rate of the antennas. For example, the antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0053] The mobile communication module 150 can provide a solution for wireless communication including 2G / 3G / 4G / 5G, etc. applied to the electronic device 100. The mobile communication module 150 can include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive an electromagnetic wave by the antenna 1, and perform filtering, amplification, etc. on the received electromagnetic wave, and transfer the processed signal to the modem processor to be demodulated. The mobile communication module 150 can also amplify a signal modulated by the modem processor, and radiate the signal as an electromagnetic wave through the antenna 1. In some embodiments, at least part of the function modules of the mobile communication module 150 can be disposed in the processor 110. In some embodiments, at least part of the function modules of the mobile communication module 150 can be disposed in the same device as at least part of the modules of the processor 110.

[0054] The wireless communication module 160 can provide a solution for wireless communication including wireless local area networks (WLAN) (e.g., wireless fidelity (Wi-Fi) network), bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared radiation (IR) technology, etc. applied to the electronic device 100. The wireless communication module 160 can be one or more devices integrated with at least one communication processing module. The wireless communication module 160 receives an electromagnetic wave via the antenna 2, performs frequency modulation and filtering on the electromagnetic wave signal, and transmits the processed signal to the processor 110. The wireless communication module 160 can also receive a signal to be transmitted from the processor 110, perform frequency modulation and amplification on the signal, and radiate the signal as an electromagnetic wave through the antenna 2.

[0055] In some embodiments, the antenna 1 and the mobile communication module 150 of the electronic device 100 are coupled, and the antenna 2 and the wireless communication module 160 are coupled, so that the electronic device 100 can communicate with a network and other devices through wireless communication technologies. The wireless communication technologies can include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technologies, etc.

[0056] It can be understood that, Figure 1 The components shown do not constitute a specific limitation on the electronic device 100, and the electronic device 100 can also include more or fewer components than shown, or combine certain components, or split certain components, or different component arrangements. In addition, Figure 1 The combination / connection relationship between the components in

[0057] The software system of the electronic device can adopt a layered architecture, an event-driven architecture, a micro-kernel architecture, a micro-service architecture, or a cloud architecture. The embodiments of the present application take the layered architecture of Harmony OS as an example to exemplarily illustrate the software structure of the electronic device. Figure 2 is a software structure block diagram of the electronic device of the embodiments of the present application.

[0058] The layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Layers communicate with each other through software interfaces. In some embodiments, the Android system is divided into four layers, from top to bottom, the application layer, the application framework layer, the system service layer, and the kernel layer.

[0059] The application layer includes applications and services, where the applications include system applications and third-party applications, and the services include FA services and PA services, for example, the system applications include applications, FA services, and PA services, exemplarily, the applications are, for example, desktop, control bar, phone, settings, etc., the FA services are, for example, soybean milk machine services, and the PA services are, for example, video conference services, the third-party applications are, for example, WeChat, QQ, etc. Application. An application of Harmony OS is composed of one or more feature ability (FA) services and / or particle ability (PA) services. In other words, when an application is composed of one FA service, the application is equivalent to the FA service, and when an application is composed of one PA service, the application is equivalent to the PA service.

[0060] Among them, the FA service has a UI interface and provides the ability to interact with the user; and the PA service has no UI interface and provides the ability to run tasks in the background and unified data access abstraction. The application developed based on FA / PA can realize specific business functions, support cross-device scheduling and distribution, and provide users with consistent and efficient application experience.

[0061] The application framework layer provides a user program framework and an ability framework for applications of Harmony OS in multiple languages such as Java / C / C++ / JS, and a multi-language framework application programming interface (API) opened by various software and hardware services; and provides a framework API in multiple languages such as C / C++ / JS for devices using Harmony OS, and the API supported by different devices is related to the degree of componentization of the system.

[0062] The system service layer is a core ability set of Harmony OS, which provides services to applications through the framework layer. As shown in Figure 2 The system service layer can include a system basic ability subsystem set, a basic software service subsystem set, an enhanced software service subsystem set, a hardware service subsystem set, and the like.

[0063] The system basic ability subsystem set provides basic abilities for distributed applications to run, schedule, and migrate on multiple devices of Harmony OS, and is composed of a distributed software bus, a distributed data management, a distributed task scheduling, a boat multi-language runtime, a common basic library, a multi-mode input, a graphics, a security, an AI, and the like. Among them, the boat runtime provides a C / C++ / JS multi-language runtime and a basic system library, and also provides a runtime for a Java program (i.e., an application program or a part developed in Java language in the framework layer) that is statically compiled using the boat compiler.

[0064] The basic software service subsystem set provides common and universal software services for Harmony OS, and is composed of an event notification, a telephone, a multimedia, a design for X (DFX), and the like.

[0065] Enhanced software service subsystem set: provides differentiated capability-enhanced software services for different devices for Harmony OS, composed of smart TV exclusive business, wearable exclusive business, IoT exclusive business, and other subsystems.

[0066] Hardware service subsystem set: provides hardware services for Harmony OS, composed of location services, biometrics, wearable exclusive hardware services, IoT exclusive hardware services, and other subsystems.

[0067] According to the deployment environment of different device forms, the basic software service subsystem set, the enhanced software service subsystem set, and the hardware service subsystem set can be tailored at the subsystem granularity, and each subsystem can be tailored at the function granularity.

[0068] The core library includes two parts: one part is the kernel subsystem, Linux kernel (open source computer operating system kernel), and Lite OS (lightweight operating system), etc.; the other part is the driver subsystem.

[0069] Kernel subsystem: Harmony OS adopts a multi-kernel design, supporting the selection of suitable OS kernels for different resource-constrained devices. The kernel abstract layer (KAL) provides basic kernel capabilities to the upper layer by shielding the differences between multiple kernels, including process / thread management, memory management, file system, network management, and peripheral management.

[0070] Driver subsystem: the Harmony OS driver framework (HDF) is the basis for the opening of the Harmony OS hardware ecosystem, providing unified peripheral access capabilities and driver development and management framework.

[0071] The kernel layer is the layer between hardware and software. The kernel layer at least includes display drivers, camera drivers, audio drivers, and sensor drivers. Among them, hardware can refer to various sensors, such as the acceleration sensor, the gyroscope sensor, the touch sensor, and the pressure sensor involved in the embodiments of the present application.

[0072] The above Figure 1 and Figure 2 are the hardware structure and software structure of the electronic device to which the embodiments of the present application are applicable, to solve the problems raised in the background art, the embodiments of the present application provide an information sorting method, which can search and sort the applications or FA services on the device itself and other devices connected to the device, thereby facilitating the user to timely and accurately find the desired application or service.

[0073] The method provided by the embodiments of the present application is exemplarily introduced below in scenes, each of which is exemplarily introduced below taking the smart home shown in Figure 3 as an example, Figure 3 The smart home device includes a smart speaker, a mobile phone, a tablet, a smart screen in a living room, etc. The smart screen is a large-screen product in a home terminal. Compared with a traditional TV, the smart screen can not only watch TV programs, but also watch network videos online, and even supports voice control to control smart home appliances, etc.

[0074] Scene One

[0075] The interface of the mobile phone displays a negative one screen interface 400 as shown in (a) of Figure 4A The search box 401 of the negative one screen of the mobile phone can receive the keyword input by the user. Since the user can have multiple devices, for example, the user has a mobile phone, a smart screen and a tablet, etc., the multiple devices can respectively install an application and a service. Therefore, when the user inputs a keyword in the search box of the negative one screen of the mobile phone, the application and the FA service information related to the keyword can be searched from the mobile phone of the user and other devices connected to the mobile phone of the user.

[0076] It is assumed that the smart screen of the user installs a Welink application, which is composed of one or more FA services and PA services. For example, the FA service can include a Welink-video FA service and / or a Welink-chat FA service, and the PA service can include a Welink-video PA service and / or a Welink-chat PA service. The Welink-video FA service and the Welink-video PA service can be independently run from the Welink application, and similarly, the Welink-chat FA service and the Welink-chat PA service can also be independently run from the Welink application. In one possible case, exemplarily, when the user touches the search box (i.e., the focus falls into the search box) and inputs a keyword "Welink" in the search box, the mobile phone displays an interface 410 as shown in (b) of Figure 4A When the user completes the input operation of the keyword, the mobile phone receives the operation of the user acting on the search control 411, and in response to the operation, the mobile phone performs a query operation. Specifically, the mobile phone first queries whether there is another device in a connection state with the mobile phone through an interconnection protocol, and if there is, then according to the search keyword, the mobile phone searches the application and the service information related to the keyword from the mobile phone and the other device, and displays the related search results on the interface 410.

[0077] Exemplarily, as shown in Figure 4AAs shown in (b), interface 410 includes the "Welink" application related to "Welink" on the mobile phone, as well as the "Welink-Video FA" service and the "Welink-Chat FA" service related to "Welink" on the smart screen in the living room, and the "Welink-Jetta" application related to "Welink" on the mobile phone, etc. It should be noted that interface 410 may also include other applications or services related to "Welink," which are not shown in the figure. Because the smart screen is a plugged-in device and has a large screen, the "Welink-Video" service related to "video call" on the smart screen is also shown. FA "The service has the highest priority, so it is listed first."

[0078] Additionally, it should be noted that if the phone does not find any other connected devices via the interoperability protocol's near-field query, the phone can also display only the FA service information related to the keyword on interface 410. For example, it can only display the "FunDrive Welink-Jetta" application related to "Welink" on the phone. Alternatively, if the phone does not find any other connected devices via the interoperability protocol's near-field query, but the phone determines that other connectable devices exist, the phone can actively establish a connection with those other devices and then search for FA service information related to the keyword from both the phone and the connected devices.

[0079] In one possible scenario, suppose the user selects to touch "Welink - Video". FA "When the service is opened via control 412, the target object is the 'Welink-Video' app on the smart screen in the living room." FA "The service allows the phone to run the program locally, enabling control of the 'Welink-Video' display on the smart screen in the living room." FA The purpose of the service is to allow users to access "Welink-Video" on their phones. FA The service's control interface allows users to remotely operate the "Welink-Video" feature on their living room smart screen by using the interface on their mobile phones. FA "The effectiveness of the service enables the smart screen in the living room to respond accordingly. For example, such as..." Figure 4B As shown in (a), assuming the user acts on the video control 421 in interface 420, in response to this operation, the phone displays as follows: Figure 4B Interface 430, shown in (b) above, allows users to pull up this "Welink-Video" app on their phones. FA "The service control interface allows you to make video conference calls. When the video session is connected, your phone will display something like..." Figure 4BInterface 440 is shown in (c) above. Additionally, in response to the operation of the video control 421 on interface 420, the smart screen in the living room also invokes relevant hardware (such as a camera, microphone, etc.) to activate the "Welink-Video" feature on the smart screen. PA "Service to establish a video session connection, ultimately displaying as..." Figure 4C The interface shown. This allows users to operate the "Welink-Video" feature on the smart screen in their living room from their mobile phones. FA "Services enable users to initiate video conversations using the smart screen in their living room."

[0080] In another possible scenario, suppose the user selects to touch "Welink - Chat". FA "When the service opens via control 413, the target is the 'Welink-Chat' app on the smart screen in the living room." FA The service allows the phone to run the program locally, enabling control of the "Welink-Chat" feature on the smart screen in the living room. FA The purpose of the service is to allow users to launch "Welink-Chat" on their mobile phones. FA The service's control interface allows users to remotely operate the "Welink-Chat" feature on their living room smart screen by using the interface on their mobile phones. FA "The effectiveness of the service enables the smart screen in the living room to respond accordingly. For example, such as..." Figure 4D As shown in (a), assuming the user acts on the contact control 451 in interface 450, in response to this operation, the phone displays as follows: Figure 4D The interface shown in (b) 460 indicates that users can pull up this "Welink-Chat" on their mobile phones. FA "In addition to the service control interface, in response to the operation of the contact control 451 on interface 450, the smart screen in the living room also calls relevant hardware (such as camera, microphone, etc.) to launch the "Welink-Chat" feature on the smart screen." PA "Service to establish a short message session connection and display as follows" Figure 4E The interface shown. This allows users to operate the "Welink-Chat" feature on the smart screen in the living room from their mobile phones. FA "Services enable users to initiate short message conversations using the smart screen in their living room."

[0081] Assume the user's smart screen has the Welink app installed, which includes Welink-Video FA and Welink-Chat FA services. Additionally, the smart screen also has the WeChat app installed, which includes WeChat-Video FA and WeChat-Chat FA services. In another possible scenario, for example, when the user touches the search box (i.e., the touch focus falls on the search box) and enters the keyword "video call" in the search box, the phone displays as follows: Figure 4A Interface 410 is shown in (c). After the user completes the keyword input operation, the mobile phone receives the user's operation on the search control 411 and, in response to this operation, performs a query operation. Specifically, the mobile phone first queries the near field to see if there are other devices connected to the mobile phone. If so, it then searches for application and FA service information related to the keyword from the mobile phone and other devices, and displays the more relevant search results on interface 410. For example, as shown in (c)... Figure 4A As shown in (c), this interface 410 includes, in addition to the "Welink-Video" feature related to "video calls" on the smart screen, FA The service is also displayed on the smart screen in the living room. -FA service, as well as the "Welink" and "WeChat" apps on the phone. It should be noted that interface 410 may also include other applications or services related to "video calling," which are not shown in the diagram. Because the smart screen is a plug-in device with a large screen, the "Welink-video" service related to "video calling" on the smart screen is... FA "The service has the highest priority, so it is listed first."

[0082] It should be noted that, as mentioned above, the Welink application is a conferencing application. The Welink application can consist of one or more FA (Financial Advisor) and PA (Personal Advisor) services. For example, the FA service may include Welink-video FA service and / or Welink-chat FA service. Similarly, The application is a social networking application. An application can consist of one or more FA services and PA services. For example, FA services may include... - Video FA services and / or -Chat FA service components, PA service may include - Video PA service and / or -Chat PA service. Video FA services and Video PA services can be independent The application runs independently, and similarly, Chat FA service and The chat PA service can also be independent of application and run separately. Since a user can have multiple devices, such as a mobile phone, a smart TV, and a tablet, the application and the service can be installed on the multiple devices respectively. Therefore, when the user inputs a keyword in the search box on the home screen of the mobile phone, the application and the service information related to the keyword can be searched from the mobile phone and other devices connected to the mobile phone.

[0083] In a possible case, it is assumed that the user selects the opening control 414 corresponding to the "Welink-Video FA " control, i.e., the target object is the "Welink-Video FA " service on the smart TV in the living room, and the mobile phone runs the program locally on the mobile phone to achieve the purpose of controlling the "Welink-Video FA " service on the smart TV in the living room. That is, the user can pull up the control interface of the "Welink-Video FA " service on the mobile phone, and the user can achieve the effect of remotely operating the "Welink-Video FA " service on the smart TV in the living room by operating the control interface on the mobile phone, so that the smart TV in the living room responds correspondingly. For specific examples, refer to the examples shown in the above Figures 4B-4D , which will not be repeated here.

[0084] In this embodiment, it is considered that the label of the FA service includes not only the name of the FA service, but also the device attribute, the device type, the function description, and the FA service package name. For example, the device attribute can be a large-screen device or a small-screen device, the device type can be a smart TV type or a smart TV device number, the function description of the FA service can be a video service or a music service, and the like. Generally, when the FA service is put on the application market, the FA service will be artificially labeled with the labels related to the device attribute, the device type, the function description, and the FA service package name. Based on this, in addition to searching by inputting the name of the FA service in the search box, the user can also input the attribute of the FA service in the search box. For example, the attribute of the FA service can be the function feature of the FA service. It is assumed that "video call" is input in the search box, and "Welink-Video FA ", "WeChat-Video FA ", and the like related to "video call" can be searched; or the attribute of the FA service can be the service type of the FA service. It is assumed that "music" is input in the search box, and "Music FA ", "QQ Music", and the like related to "music" can be searched. or, the attribute of the FA service can be a device name strongly related to the FA service, assuming that "Zhihui screen" is input in the search box, "Welink-video FA ", "WeChat-video FA " and the like related to "Zhihui screen" can be searched. When the user selects to open the FA service on other devices in the search result of the mobile phone, the mobile phone runs the process of the FA service, displays the control interface, and the process of how to pull up the corresponding PA service of other devices is similar to the process described above Figures 4B-4E , and will not be exemplified one by one here.

[0085] In the embodiment, the sorting method of the mobile phone for the search result can be any one or a combination of multiple ways as follows.

[0086] The first way, the mobile phone can obtain the tags of N candidate FA services related to the keyword according to the keyword, and then calculate the semantic similarity between the keyword and the tags of the N candidate FA services; according to the size of the semantic similarity, determine the FA service corresponding to the semantic similarity greater than the set threshold, wherein the FA service with greater semantic similarity is ranked more in front. For example, the interface 410 displays 4 FA services with semantic similarity greater than the set threshold.

[0087] The second way, the mobile phone can obtain the tags of N candidate FA services related to the keyword according to the keyword, and then calculate the word frequency distribution of the keyword in the tags of the N candidate FA services, on the one hand, if there are more FA services related to the keyword in the mobile phone, the search result of the mobile phone is more in front than the search result of other devices in the search result under all devices in the interface 310. That is, the more FA services related to the keyword, the more in front of the sorting of the device. On the other hand, if the FA services related to the keyword are evenly distributed in the mobile phone and other devices, the sorting of the FA service is relatively later, and vice versa, if the FA services related to the keyword are only high-frequency distributed in part of the devices (such as the Zhihui screen in the living room), the sorting of the FA service of the device is more in front.

[0088] Thirdly, the phone can obtain the device status of the phone and other devices, such as the power supply type of the device, the screen size of the device, the available computing resources of the device, the correlation between the device performance and the target FA service, and the priori knowledge constraint condition related to the FA service, and then sort the FA services in the search result according to the device status and the priori knowledge constraint condition related to the FA service. The priori knowledge constraint condition related to the FA service can include that the FA service is preferentially run on a large-screen device, the FA service is preferentially run on a plug-in type device, the FA service is preferentially run on a device with strong computing power, or the FA service is preferentially run on a device with good audio playback performance. For example, the priori knowledge constraint condition of the "Welink-Video FA " service is to preferentially run on a large-screen device and preferentially run on a plug-in device. Since the device status of the living room smart screen is a large-screen device and the power supply type is a plug-in type, the matching degree between the living room smart screen and the "Welink-Video FA " service is high, so the "Welink-Video FA " service on the living room smart screen is the most forward.

[0089] Scenario two

[0090] This scenario is described by taking the driving scenario shown in Figure 5A as an example. The devices shown in FIG. 5 include a vehicle terminal, a phone, a smart watch, and the like. It is assumed that a family member A sitting in the back row of the vehicle needs to perform navigation. Then, the family member A can operate his own phone to control the vehicle terminal to perform map navigation.

[0091] For example, as shown in Figure 5B , the interface of the phone displays a negative one screen interface 500 as shown in (a) of Figure 5B . When the user touches the search box (i.e., the focus falls into the search box) to prepare to input a keyword, the phone displays an interface 510 as shown in (b) of Figure 5B . It is assumed that the user inputs "navigation" in the interface 510. After the user completes the input operation of the keyword, the phone receives an operation of the user acting on a search control 511. In response to this operation, the phone searches for application information related to the keyword "navigation" from the phone and other devices (such as a smart watch and a vehicle terminal in the vehicle). Then, according to the query result, the phone displays the search result related to the search keyword "navigation" on the interface 510. For example, as shown in (b) of FIG. 5, the interface 510 includes not only the "navigation" related application on the phone, but also the "navigation" related application on the smart watch and the "navigation" related application on the vehicle terminal.

[0092] ​​​Let's say the user decides to select the touchscreen phone... The application, the mobile phone receives the user's actions The application control 512 operates by running the program locally on the mobile phone to control the vehicle terminal. The purpose of the application is to allow users to control the in-vehicle terminal via their mobile phones. Navigation services.

[0093] In this embodiment, the method by which the mobile phone sorts search results can specifically be as follows: After establishing a connection with the in-vehicle terminal and the smartwatch, in addition to obtaining the device's own status, the mobile phone can also obtain the device status of the in-vehicle terminal and the smartwatch. For example, the in-vehicle terminal's device status is "powered on," "plugged in," and "sufficient computing resources"; the smartwatch's device status includes "battery-powered" and "insufficient computing resources." In addition, the mobile phone also obtains... Application-related prior knowledge constraints, such as the phone retrieving data from a cloud server. The application's prior knowledge constraints prioritize plug-in devices and operation on devices with high computing power. Applying relevant prior knowledge constraints and the device status of each device, the mobile phone interprets the search results... Applications are sorted. Because the vehicle-mounted terminal's device status is that computing resources are sufficient and its power supply type is plug-in, the vehicle-mounted terminal and... The application has a high degree of compatibility, therefore the vehicle terminal... Apps ranked first, followed by mobile devices. Applications, and then smartwatches. application.

[0094] It should be noted that the example shown in Scenario 2 above also applies to the search and sorting of navigation-related FA services. That is, if a user enters "navigation" in the search box, the search results may include FA services related to "navigation" on smartwatches and on in-vehicle terminals. Users can also operate the FA service on the in-vehicle terminal from their mobile phones to activate navigation-related PA services on the in-vehicle terminal. For details, please refer to the example shown in Scenario 1 above; further illustrations will not be provided here.

[0095] Scene 3

[0096] The scene is still based on Figure 3 The following explanation uses a smart home example.

[0097] For example, the phone's interface displays as follows:Figure 6A In the interface 600 shown in (a) of FIG. 6, the search box 601 of the home screen of the mobile phone can receive the keyword input by the user. When the user is ready to input the keyword by touching the search box (i.e., the focus is on the search box), the mobile phone displays the interface 610 shown in (b) of FIG. 6. Figure 6A In the interface 610 shown in (b) of FIG. 6, it is assumed that the user inputs “music” in the interface 610. After the user completes the input operation of the keyword, the mobile phone receives the operation of the user on the search control 611, and in response to the operation, the mobile phone performs a query operation. Specifically, the mobile phone first queries whether there is another device in a connected state with the mobile phone through an interconnection protocol, and if there is, then the mobile phone searches for application information related to the keyword “music” from the mobile phone and the other device according to the search keyword, and displays the search results related to the keyword “music” in the interface 610. Exemplarily, as shown in (b) of FIG. 6, in addition to the control of the “music” application on the mobile phone, the interface 610 also displays the control of the “music” application on the tablet and the control of the “music” application on the smart speaker. Figure 6A In the interface 610 shown in (b) of FIG. 6, it is assumed that the user inputs “music” in the interface 610. After the user completes the input operation of the keyword, the mobile phone receives the operation of the user on the search control 611, and in response to the operation, the mobile phone performs a query operation. Specifically, the mobile phone first queries whether there is another device in a connected state with the mobile phone through an interconnection protocol, and if there is, then the mobile phone searches for application information related to the keyword “music” from the mobile phone and the other device according to the search keyword, and displays the search results related to the keyword “music” in the interface 610. Exemplarily, as shown in (b) of FIG. 6, in addition to the control of the “music” application on the mobile phone, the interface 610 also displays the control of the “music” application on the tablet and the control of the “music” application on the smart speaker.

[0098] In this embodiment, the sorting method of the mobile phone for the search results in the interface 610 can be as follows: After the mobile phone establishes a connection with the smart speaker and the tablet, in addition to obtaining the device state of the device itself, the mobile phone can also obtain the device state of the tablet and the device state of the smart speaker. For example, the device state of the smart speaker is a plug-in type device with good audio playback performance, and the device state of the tablet includes a battery-powered device with good video playback performance. In addition, the mobile phone also obtains the priori knowledge constraint condition related to the “music” application, for example, the mobile phone obtains the priori knowledge constraint condition related to the “music” application from the cloud server, which is to prefer a plug-in type device and to prefer running on a device with good audio playback performance. Based on the priori knowledge constraint condition related to the “music” application and the device state of each device, the mobile phone sorts the “music” application in the search results. Since the device state of the smart speaker is a plug-in type device with good audio playback performance, the matching degree of the smart speaker and the “music” application is high, so the “music” application on the smart speaker is the first, followed by the “music” application on the mobile phone, and then the “music” application on the tablet.

[0099] Further, it is assumed that the user selects the corresponding opening control 612 of the “music” application, i.e., the target object is the “music” application on the smart speaker. The mobile phone runs the program locally on the mobile phone to achieve the purpose of controlling the “music” application on the smart speaker. That is, the user can pull up the control interface of the “music” application on the mobile phone, and the user can operate the control interface on the mobile phone to achieve the effect of remotely operating the “music” application on the smart speaker, so that the smart speaker responds accordingly.

[0100] Exemplarily, as shown in (b) of FIG. 6, in addition to the control of the “music” application on the mobile phone, the interface 610 also displays the control of the “music” application on the tablet and the control of the “music” application on the smart speaker. Figure 6BAs shown in (a) of FIG. 6, assuming that the user acts on the play control 621 in the interface 620, the phone displays the interface 630 as shown in (b) of FIG. 6 in response to the operation, that is, the user can pull up the control interface of the "music" application on the phone to control the smart speaker to start playing music. In addition, in response to the operation of acting on the play control 621 in the interface 620, the relevant hardware (such as a loudspeaker, etc.) of the smart speaker is also invoked to pull up the "music" application to play music. In this way, the user can operate the "music" application on the smart speaker on the phone to achieve the purpose of controlling the smart speaker to play music. Figure 6B

[0101] It should be noted that the example shown in the above scenario three is also applicable to the search and sorting of the music FA service, that is, if the user inputs "music" in the search box, the search results can also include the FA service related to "music" on the smart speaker and the FA service related to "music" on the tablet, and the user can also operate the FA service on the smart speaker on the phone to pull up the PA service related to music on the smart speaker. For details, please refer to the example shown in the above scenario one, which will not be illustrated one by one here.

[0102] In combination with the above scenarios one to three, specifically, the specific method of the phone searching for application information or service information related to the keyword from the phone and other devices according to the keyword can be any one or a combination of multiple ways as follows.

[0103] The first way, the phone can obtain the tags of N candidate objects according to the keyword, and then calculate the semantic similarity between the keyword and the tags of the N candidate objects; according to the size of the semantic similarity, determine the application or service corresponding to the semantic similarity greater than a set threshold, wherein the more the semantic similarity of the application or service, the higher the sorting.

[0104] The second way, the phone can obtain the tags of N candidate objects according to the keyword, and then calculate the term frequency distribution of the keyword in the tags of the N candidate objects. On the one hand, if there are more applications or services related to the keyword in the phone, the search results of the phone under all devices in the interface 410 are more in front of the search results of other devices. That is, the more the applications or services related to the keyword, the higher the sorting of the device. On the other hand, if the application or service related to the keyword appears in the phone and other devices, the sorting of the application or service is relatively later, and vice versa, if the application or service related to the keyword only appears in part of the devices (such as the smart screen in the living room), the sorting of the application or service is more in front.

[0105] ​In the third mode, the mobile phone can acquire the device states of the mobile phone and other devices, such as the power supply type of the device, the screen size of the device, the available computing resources of the device, the correlation between the device performance and the target object, and the prior knowledge constraint conditions related to the application or service, and then sort the applications or services in the search results according to the device states and the prior knowledge constraint conditions related to the application or service. The prior knowledge constraint conditions related to the application or service can include that the application or service is preferentially run on a large-screen device, the application or service is preferentially run on a device of a plugged-in type, the application or service is preferentially run on a device with strong computing capability, or the application or service is preferentially run on a device with good audio playback performance.

[0106] In this way, the search results sorted according to the third mode described above can have the following features:

[0107] Feature 1: If a device is in a state of being used by a user, the application or service related to the keyword on the device is ranked more forward. For example, the user is browsing video information on a smart screen in the living room, and at this time, if the user searches for some application or service related to video information through the mobile phone, the application or service related to the large screen is ranked more forward in the search results. In this way, it helps to save the starting time of the application or service on the device, and the large screen is more convenient for the user to view information.

[0108] Feature 2: If the use experience of an application or service is better in a device A, the search information of the device A is ranked more forward. Taking the application of music playback as an example, in the case where there are applications of sound box, large screen, mobile phone, and tablet in the search results, the application on the sound box is ranked more forward.

[0109] Feature 3: If the use of an application or service consumes more power, the related application or service on a plugged-in device that can support high power consumption is ranked more forward. Taking games as an example, in the case where a mobile phone, a tablet, and a large screen are simultaneously supported, the game application on the large screen is ranked more forward, because the large screen is usually a plugged-in device, and there is no need to consider the power consumption problem.

[0110] Feature 4: If the use of an application or service needs to occupy high computing resources, the related application or service on a device with relatively sufficient computing resources is ranked more forward.

[0111] In the fourth mode, the mobile phone can obtain the device status of the mobile phone and other devices, and obtain the priori knowledge constraint condition related to the application or service, and then according to the device status and the priori knowledge constraint condition related to the application or service, on the one hand, according to the semantic similarity or word frequency distribution between the keyword and the label of the application or service, determine the first weight corresponding to the application or service respectively. On the other hand, according to the matching degree between the constraint condition of the priori knowledge related to the application or service and the device status, determine the second weight corresponding to the application or service respectively; then according to the first weight and the second weight, sort the searched application or service. Specifically, if the search result includes the application label and / or service label of multiple devices corresponding to the same application or service, it is necessary to sort the application label and / or service label of multiple devices according to the matching degree between the constraint condition of the priori knowledge related to the application or service and the device status. For example, the mobile phone, the tablet and the smart screen all search for the "Welink-Video FA " service, and the constraint condition of the priori knowledge related to the "Welink-Video FA " application or service is that the large screen is preferred and the plugged-in device is preferred, so the "Welink-Video FA " application or service label on the smart screen is earlier in the search result.

[0112] In combination with the example of the above scenario one, in summary, the embodiment of the present application provides an information sorting method, which can be applied to the devices in a distributed system. Specifically, as shown in Figure 7 , the method comprises the following steps.

[0113] Step 701, the electronic device receives a search request of a user, the search request including a keyword input by the user.

[0114] Exemplarily, the search request can be the content input by the user through touch or the content input by the user through voice instruction. Wherein, in the process of inputting the content by the user, the electronic device can use the information input by the user to perform extensive matching of the keyword, and when the user completes the input of the keyword, the electronic device can perform accurate matching of the keyword based on the keyword input by the user.

[0115] Step 702, the electronic device obtains the label of N candidate objects according to the keyword, and calculates the semantic similarity between the keyword and the label of the N candidate objects.

[0116] Specifically, in a possible embodiment, the electronic device can calculate the semantic similarity between the search word and the plurality of candidate object programs according to the deep neural network model respectively. Wherein, the deep neural network model can be a machine model generated by pre-training on the training corpus obtained from the candidate objects.

[0117] In a possible embodiment, before step 702 is performed, the electronic device obtains the detail information of the L candidate objects from the database, and then extracts the original keywords from the detail information of the L candidate objects, performs semantic analysis on the original keywords, and obtains the labels of the L candidate objects from the analysis result. Exemplarily, the detail information of the application includes but is not limited to: the title of the application, the description text of the application, the comment information of the application, the recommendation language of the application, the latest updated features of the application, and the like. The label of the application includes but is not limited to: the name of the application, the category to which the application belongs, the features of the application, and the like. The detail information of the service includes but is not limited to: the title of the service, the description text of the service, the comment information of the service, the recommendation language of the service, the latest updated features of the service, and the like. The label of the service includes but is not limited to: the name of the service, the category to which the service belongs, the features of the service, and the like.

[0118] Specifically, first, after obtaining the detail information of the L candidate objects from the database, the detail information of the candidate objects can be segmented. For example: a custom dictionary can be constructed, and the to-be-analyzed Chinese character string is matched with the entries in the custom dictionary according to a preset strategy. If a certain character string can be found in the custom dictionary, the matching is successful (that is, a word is recognized). According to the difference in scanning direction, the string matching segmentation method can be divided into forward matching and reverse matching. According to the different length priority matching conditions, the string matching segmentation method can be divided into maximum (longest) matching and minimum (shortest) matching. In actual application, a specific segmentation method can be selected according to the demand. Second, after the segmentation is completed, stop words, invalid words and the like can be filtered out. For example: information irrelevant to the object described in the object description text can be filtered out, such as the self-introduction of the developer, the contact information left by the developer; advertising or promotional information can be filtered out, such as promotional advertisements in shopping applications, game currency marketing information in game applications; in addition, numbers, pinyin and the like can also be filtered out. The specific filtering method, for example: a regular filtering rule can be constructed, and words matching the regular filtering rule are filtered out. For example, a regular filtering rule is constructed: “contact information”, “email”, “telephone”, and the like, and then the contact information, email, telephone and the like in the detail information of the object can be filtered out. Third, after the stop words and invalid words are filtered out, the obtained words can be screened, for example, the parts of speech are screened, and verbs, nouns and the like are selected to obtain at least one keyword.

[0119] In another possible implementation, if the original keywords obtained through screening are relatively large in number, the term frequency (TF) of each original keyword, which represents the frequency of occurrence of a word in a document, and the inverse document frequency (IDF) of each keyword, which is obtained by dividing the total number of documents in the database by the number of documents containing the word and then taking the logarithm of the quotient, can be calculated. The product of the term frequency and the inverse document frequency of each keyword is taken as the TF-IDF value of the corresponding keyword. The keywords with a TF-IDF value greater than a preset threshold value are selected as the original keywords. The preset threshold value can be customized according to actual needs. In addition, if the keywords obtained through screening are not large in number, only similarity matching can be performed. Then, the electronic device performs semantic analysis on the original keywords by using a semantic analysis model to generate the labels of the objects.

[0120] In step 703, the electronic device determines, from the labels of the N candidate objects, the labels of M candidate objects corresponding to a semantic similarity greater than a set threshold.

[0121] The labels of the M candidate objects include the labels of K candidate objects corresponding to the same attribute target objects installed on K devices. M is less than N, and M, N, and K are positive integers greater than or equal to 2. The same attribute target objects on the K devices can be the same name target objects on the K devices, for example, the application on the mobile phone and the application on the tablet are the same name target objects. The same attribute target objects on the K devices can be the same supplier target objects on the K devices, for example, the application on the mobile phone and the application on the tablet are the same supplier target objects. The same attribute target objects on the K devices can be the same function target objects on the K devices, for example, the application on the mobile phone and the application on the tablet both have the function of sharing short videos.

[0122] For example, the labels of the M candidate objects include the label of the “Welink” object on the mobile phone, the label of the “Welink” object on the smart screen, and the label of the “Welink” object on the tablet.

[0123] In step 704, the electronic device sorts the K candidate objects according to the semantic similarity between the keywords and the labels of the K candidate objects and the device states of the K devices.

[0124] In a possible embodiment, the electronic device can determine a first weight and a second weight corresponding to each of the K candidate objects respectively, and rank the K candidate objects according to the first weight and the second weight.

[0125] Specifically, in one aspect, the greater the semantic similarity between the keyword and the label of the K candidate objects, the greater the first weight; on the contrary, the smaller the semantic similarity, the smaller the first weight. For example, the mobile phone determines the first weight of the "Welink" service on the mobile phone as K1 according to the semantic similarity between the keyword and the label of the "Welink" service on the mobile phone; the mobile phone determines the first weight of the "Welink" service on the smart screen as K2 according to the semantic similarity between the keyword and the label of the "Welink" service on the smart screen; and the mobile phone determines the first weight of the "Welink" service on the tablet as K3 according to the semantic similarity between the keyword and the label of the "Welink" service on the tablet.

[0126] In another aspect, the electronic device determines a second weight corresponding to each of the K candidate objects according to the constraint condition of the prior knowledge related to the target object and the matching degree between the device state of the K devices.

[0127] The constraint condition of the prior knowledge related to the target object includes at least one of the following conditions: the target object is preferentially run on a large-screen device, the target object is preferentially run on a plug-in type device, the target object is preferentially run on a device with strong computing power, and the target object is preferentially run on a device with good audio playback performance. For example, it is assumed that the target object is the "Welink-video FA " service in the above scenario one, and the "Welink-video FA " service is an office conference software, and thus the constraint condition of the prior knowledge related to the "Welink-video FA " service includes preferentially running on a large-screen device. For another example, it is assumed that the target object is "music" in scenario three, and "music" is a music software, and thus the constraint condition of the prior knowledge related to the "music" service includes preferentially running on a device with good audio playback performance.

[0128] It should be noted that the constraint condition of the prior knowledge related to the target object can be a condition preset by a developer, and the electronic device can download the application or service from the server to obtain the constraint condition, or the electronic device can periodically obtain the constraint condition from the cloud server, and the embodiments of the present application are not limited thereto.

[0129] Furthermore, the device's status includes at least one of the following: the device's power supply type, the device's screen size, the device's available computing resources, and the relevance of the device's performance to the target object. For example, a mobile phone is typically powered by a battery, a smart screen in the living room is powered by a plug-in connection, and a speaker's audio playback performance is more relevant to music applications or services.

[0130] For example, suppose Welink-video FA "The prior knowledge constraint for the service prioritizes operation on large screens and plugged-in devices. The mobile phone's device status is battery-powered with a 7-inch screen; the smart screen in the living room is plugged-in with a 55-inch screen; and the tablet is battery-powered with a 7.9-inch screen. Thus, the mobile phone, based on its device status and Welink-video..." FA "The degree of matching of the prior knowledge constraints of the service determines the 'Welink-video' function on the mobile phone." FA "The second weight of the service is L1; the mobile phone determines the device status of the smart screen in the living room and the Welink-video..." FA "The degree of matching of the prior knowledge constraints of the service determines the 'Welink-video' function on the mobile phone." FA "The second weight of the service is L2; ​​the mobile phone is based on the device status of the tablet and Welink-video." FA "The degree of matching of the prior knowledge constraints of the service determines the 'Welink-video' function on the mobile phone." FA "The second weight of the service is L3."

[0131] In one possible embodiment, the electronic device determines the second weights corresponding to the K candidate objects in any one or a combination of the following methods.

[0132] Method 1: When the prior knowledge constraint related to the target object is that the target object preferentially runs on plug-in devices, determine that the second weight of the candidate object corresponding to the K devices corresponding to the K candidate objects is greater than the second weight of the candidate object corresponding to the device that does not support plug-in devices. For example, if the search results of the aforementioned mobile phone include "Welink-video" from the smart screen in the living room... FA "Services include the 'Welink-Video' feature on the smart screen in the living room." FA "The sorting in the 'All Devices' section is higher, while 'Welink - Video' on phones or tablets..." FA "Service comes later. Because the smart screen in the living room can support high-power plugged-in devices, there is basically no problem with insufficient power."

[0133] In a second way, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a large-screen device, the second weight of the candidate object corresponding to the device with a larger screen among the K devices corresponding to the K candidate objects is determined to be larger. For example, if the search result includes the "Welink-Video FA " service of the living room smart screen, the "Welink-Video FA " on the living room smart screen is ranked more forward in the column of all devices, and the "Welink-Video FA " service on the mobile phone or tablet is ranked backward. This is because the living room smart screen belongs to a large-screen device, and it is more convenient for multiple users to view the information on the large screen at the same time.

[0134] In a third way, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a device with strong computing power, the second weight of the candidate object corresponding to the device with stronger computing power among the K devices corresponding to the K candidate objects is determined to be larger. For example, if the search result includes the "AI photo shooting" service of the living room smart screen, the "AI photo shooting" on the living room smart screen is ranked more forward in the column of all devices, and the "AI photo shooting" service on the mobile phone or tablet is ranked backward. This is because the living room smart screen belongs to a device with strong processing power, and the data processing efficiency is higher.

[0135] In a fourth way, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a device with good audio playing performance, the second weight of the candidate object corresponding to the device with better audio playing performance among the K devices corresponding to the K candidate objects is determined to be larger. For example, if the search result includes the "music" service of the smart speaker, the "music" on the smart speaker is ranked more forward in the column of all devices, and the "music" service on the mobile phone or tablet is ranked backward. This is because the smart speaker has good audio playing performance and belongs to a device strongly related to the "music" service.

[0136] It should be noted that, with the expansion of application types in the application market and the expansion of service types in the application market, other types of applications or services may exist in the future market, or the constraint condition of the prior knowledge related to the application or service may also be updated with the update of the application or service. Therefore, the embodiments of the present application do not limit the way in which the electronic device determines the second weight of the K candidate objects, and other ways of determining the second weight using other constraint conditions of prior knowledge may exist in the future.

[0137] In a possible case, the electronic device can calculate the product of the first weight and the second weight, and sort the K candidate objects according to the size of the product, for example, the larger the product, the more forward the ranking, or the smaller the product, the more forward the ranking.

[0138] In another possible scenario, the electronic device can also combine the recommendation results of contextual intelligence in the device with information such as user habits to adjust the product of the first weight and the second weight, and use the adjusted product of the second weight and the first weight to rank the K candidate objects.

[0139] Optionally, after sorting the K candidate objects, this embodiment may further include step 705, whereby the electronic device displays the sorted K candidate objects in a preset recommendation interface, wherein the candidate object with a larger product of the first weight and the second weight is ranked higher. For example, as... Figure 4A As shown in (b) of the image, the "Welink-Video" option of the smart screen in the living room is displayed in the recommended results of the "All Devices" section on the negative one screen 600. FA "Services and Welink Chat" FA "Service is prioritized first, followed by tablet's 'Welink-Video'." FA "The service, finally, is the mobile phone's 'Welink-video'..." FA "Serve.

[0140] In another possible scenario, the electronic device can also retrieve device tags from a database or server, then calculate the similarity between keywords and device tags, and determine the third weight of the K candidate objects corresponding to the K devices based on the similarity score. Further, the electronic device can rank the K candidate objects according to the first, second, and third weights. For example, the tags for a smart screen in the living room include video, conferencing, etc., and the tags for a mobile phone include making calls, internet access, etc. The mobile phone can retrieve the tags of the aforementioned devices from a cloud server. These tags can be content preset by the developer or actively marked by the user and uploaded to the cloud server; this application does not limit this.

[0141] Specifically, the embodiments of this application provide as follows: Figure 8 The flowchart shown describes a method that includes the following steps.

[0142] Step 801: The electronic device receives a search request from the user, which includes keywords entered by the user.

[0143] For details, please refer to step 701 above.

[0144] Step 802: During the process of receiving input words, the electronic device can determine in real time whether the user has finished inputting. If not, proceed to step 803a; otherwise, proceed to step 803b.

[0145] For example, when a user is typing "Welink", the phone determines whether the input has ended as it receives each character.

[0146] Step 803a, if no, the electronic device can perform a broad match on the received keyword, and calculate the similarity between the keyword and the label of the candidate object.

[0147] For example, the electronic device performs a broad match on the received keyword "We", and calculates the similarity between the keyword "We" and the label of the candidate object.

[0148] Step 803b, if yes, the electronic device can perform a precise match on the received keyword, and calculate the similarity between the keyword and the label of the candidate object, and calculate the similarity between the constraint condition of the prior knowledge related to the candidate object and the state of the device.

[0149] Step 804, the electronic device determines K candidate objects from the M candidate objects whose labels have a semantic similarity greater than a set threshold, the K candidate objects corresponding to the target object of the same attribute.

[0150] Step 805, for any one of the K candidate objects, the electronic device determines a first weight according to the similarity between the keyword and the label of the candidate object, determines a second weight according to the similarity between the constraint condition of the prior knowledge related to the candidate object and the state of the device, and determines a third weight according to the similarity between the keyword and the label of the device.

[0151] Step 806, the electronic device sorts the candidate objects according to the first weight, the second weight and the third weight.

[0152] In this embodiment, the search results after sorting have the following characteristics: the application or service with a label closer in semantics to the keyword is ranked higher; the search result of the device with a label closer in semantics to the keyword is ranked higher; the search result of the device with a state closer to the constraint condition of the prior knowledge related to the candidate object is ranked higher.

[0153] In summary, the embodiments of the present application also provide an information sorting method, specifically, as shown in Figure 9 the method includes the following steps.

[0154] Step 901, the electronic device receives a search request of a user, the search request including a keyword input by the user.

[0155] Exemplarily, the search request can be content input by the user through touch input, or content input through voice instruction. In the process of inputting the content by the user, the electronic device can perform broad matching of the keyword by using the information input by the user, and when the user completes the input of the keyword, the electronic device can perform accurate matching of the keyword based on the keyword input by the user.

[0156] At step 902, the electronic device acquires labels of N candidate objects according to the keyword, and calculates semantic similarity between the keyword and the labels of the N candidate objects.

[0157] For details, refer to step 702 described above, which will not be repeated here.

[0158] At step 903, the electronic device determines, from the labels of the N candidate objects, labels of M candidate objects corresponding to semantic similarity greater than a set threshold. Among the labels of the M candidate objects, K labels of the candidate objects correspond to target objects of different attributes installed on K devices; M≤N, M, N and K are positive integers greater than or equal to 2.

[0159] Specifically, the target objects of different attributes can be target objects of different names, for example application and application, or the target objects of different attributes can be target objects of different suppliers, for example application and application of different suppliers; or the target objects of different attributes can be target objects of different installation packages, for example, the installation package name of the application and the installation package name of the Alipay application are different. Or the target objects of different attributes can be target objects of different functions, for example, application and application have different functions.

[0160] At step 904, the electronic device sorts the M candidate objects according to the semantic similarity between the keyword and the labels of the M candidate objects, and the device states of the M devices.

[0161] In one possible embodiment, the electronic device can determine a first weight and a second weight corresponding to each of the M candidate objects respectively, and sort the M candidate objects according to the first weight and the second weight.

[0162] Specifically, on the one hand, the greater the semantic similarity between the keyword and the labels of the M candidate objects, the greater the first weight; on the contrary, the smaller the semantic similarity, the smaller the first weight.

[0163] On the other hand, the electronic device determines the second weight corresponding to each of the M candidate objects according to a constraint condition of prior knowledge related to the M candidate objects and a matching degree between the device state of the K devices.

[0164] The constraint condition of prior knowledge related to the M candidate objects includes at least one of the following conditions: the candidate object is preferentially run on a large-screen device, the candidate object is preferentially run on a plug-in type device, the candidate object is preferentially run on a device with strong computing power, and the candidate object is preferentially run on a device with good audio playback performance. For example, assuming that the candidate object is the "Baidu Map" service in the above-mentioned scenario two, the "Baidu Map" service is a kind of navigation software, and therefore, the constraint condition of prior knowledge related to the "Baidu Map" service includes preferentially running on a vehicle-mounted device, and therefore, the "Baidu Map" service has the highest matching degree with the device state of the vehicle-mounted device.

[0165] In a possible embodiment, the electronic device can determine the second weight corresponding to each of the M candidate objects in any one of the following manners or a combination of multiple manners.

[0166] In the first manner, for any one of the M candidate objects, when the constraint condition of prior knowledge related to the candidate object is preferentially running on a plug-in type device, the electronic device determines the second weight according to whether the device where the candidate object is located is a plug-in type device, wherein the second weight in the case where the device where the candidate object is located is a plug-in type device is higher than the second weight in the case where the device where the candidate object is located is a battery type device.

[0167] For example, if the search result of the mobile phone in the above-mentioned scenario two includes the "Baidu Map" service of the vehicle-mounted terminal, the "Baidu Map" on the vehicle-mounted terminal is ranked more forward in the all devices column, and the "Baidu Map" service on the mobile phone is ranked backward. Because the vehicle-mounted terminal is a plug-in device that can support high power consumption, there is basically no problem of insufficient power.

[0168] In the second manner, for any one of the M candidate objects, when the constraint condition of prior knowledge related to the candidate object is that the candidate object is preferentially run on a large-screen device, the electronic device determines the second weight according to the screen size of the device where the candidate object is located, wherein the second weight in the case where the device where the candidate object is located is a large-screen device is higher than the second weight in the case where the device where the candidate object is located is a small-screen device.

[0169] In a third mode, for any one of the M candidate objects, when the constraint condition of the prior knowledge related to the candidate object is that the candidate object is preferentially run on a device with strong computing capability, the electronic device determines the second weight according to whether the computing resource of the device where the candidate object is located is sufficient, and the second weight in the case that the device where the candidate object is located is sufficient in computing resource is higher than the second weight in the case that the device where the candidate object is located is insufficient in computing resource.

[0170] In a fourth mode, for any one of the M candidate objects, when the constraint condition of the prior knowledge related to the candidate object is that the candidate object is preferentially run on a device with good audio playing performance, the electronic device determines the second weight according to the audio playing performance of the device where the candidate object is located, and the second weight in the case that the device where the candidate object is located is good in audio playing performance is higher than the second weight in the case that the device where the candidate object is located is poor in audio playing performance.

[0171] In another possible case, the electronic device can also obtain the label of the device from a database or a server, and then calculate the similarity between the keyword and the label of the device, so as to determine the third weight of the K candidate objects corresponding to the K devices according to the similarity. Further, the electronic device can sort the K candidate objects according to the first weight, the second weight and the third weight. For example, the label of the smart TV in the living room includes video, meeting, etc., and the label of the mobile phone includes making a call, surfing the Internet, etc. The mobile phone can obtain the labels of the above devices from the cloud server, and the label can be the content pre-set by the developer or the label marked by the user and uploaded to the cloud server. The application is not limited in this regard.

[0172] Specifically, the embodiment of the application provides a method flowchart as shown in Figure 10 The method includes the following steps.

[0173] In step 1001, the electronic device receives a search request of a user, and the search request includes a keyword input by the user.

[0174] The specific implementation can refer to step 701.

[0175] In step 1002, during the process that the electronic device receives the input keyword, the electronic device can determine whether the user has finished inputting in real time. If not, step 1003a is performed, otherwise, step 1003b is performed.

[0176] For example, during the process that the user inputs "navigation", the mobile phone determines whether the user has finished inputting in the process of receiving each character.

[0177] Step 1003a, if no, the electronic device can perform a broad match on the received keyword, and calculate the similarity between the keyword and the label of the candidate object.

[0178] For example, the electronic device performs a broad match on the received keyword "guide", and calculates the similarity between the keyword "guide" and the label of the application, and the similarity between the keyword "guide" and the label of the device.

[0179] Step 1003b, if yes, the electronic device can perform a precise match on the received keyword, and calculate the similarity between the keyword and the label of the candidate object, and the similarity between the constraint condition of the prior knowledge related to the candidate object and the state of the device.

[0180] For example, the electronic device performs a precise match on the received keyword "navigation", and calculates the similarity between the keyword "navigation" and the label of the application, and the similarity between the keyword "navigation" and the label of the device.

[0181] Step 1004, the electronic device determines the labels of M candidate objects corresponding to the semantic similarity greater than the set threshold.

[0182] Step 1005, for any one of the M candidate objects, the electronic device determines a first weight according to the similarity between the keyword and the label of the candidate object, determines a second weight according to the similarity between the constraint condition of the prior knowledge related to the candidate object and the state of the device, and determines a third weight according to the similarity between the keyword and the label of the device.

[0183] Step 1006, the electronic device sorts the candidate objects according to the first weight, the second weight and the third weight.

[0184] In this embodiment, the search result after the sorting has the following characteristics: the application or service with the label of the application or service more similar in semantics to the keyword is ranked more in front; the search result of the device with the label of the device more similar in semantics to the keyword is ranked more in front; the search result of the device with the state more matching the constraint condition of the prior knowledge related to the candidate object is ranked more in front.

[0185] In conclusion, the embodiment of the present application can comprehensively rank the application or service according to the constraint condition of the prior verification knowledge related to the device state and the application or service and the relevance of the content word meaning of the user input, so as to realize accurate ranking of the application or service. When the search result includes the application label or service label of the corresponding same target object on different devices, the constraint condition of the prior verification knowledge related to the target object and the matching of the device state can be used to realize the purpose of accurate ranking of the application or service of the corresponding same target object on different devices. Such ranking result can more accurately match the application or service that the user wants to search, which helps to improve the search efficiency and improve the user experience.

[0186] In some embodiments of the present application, an electronic device is disclosed, as shown in FIG. 1, Figure 11 The electronic device can include a touch screen 1101, one or more processors 1102, a memory 1103, one or more application programs (not shown), and one or more computer programs 1104, which can be connected through one or more communication buses 1105. The one or more computer programs 1104 are stored in the memory 1103 and configured to be executed by the one or more processors 1102, and the one or more computer programs 1104 include instructions for performing the steps of the embodiments of FIG. 5, Figure 7 or Figure 8 the steps of the corresponding embodiments.

[0187] The embodiments of the present application also provide a computer readable storage medium, which stores computer instructions, when the computer instructions are run on an electronic device, the electronic device executes the related method steps to realize the information ranking method in the above embodiments.

[0188] The embodiments of the present application also provide a computer program product, when the computer program product is run on a computer, the computer executes the related steps to realize the information ranking method in the above embodiments.

[0189] In addition, the embodiments of the present application also provide an apparatus, which can be a chip, a component or a module, and the apparatus can include a processor and a memory connected thereto. The memory is configured to store computer execution instructions, and when the apparatus is running, the processor can execute the computer execution instructions stored in the memory to enable the chip to execute the information ranking method in the above method embodiments.

[0190] The electronic device, the computer storage medium, the computer program product or the chip provided in the embodiments of the present application are all used for executing the corresponding method provided above, and thus the beneficial effects achieved thereby can refer to the beneficial effects in the corresponding method provided above, which will not be repeated here.

[0191] Through the above description of the embodiments, those skilled in the art can understand that, for the convenience and brevity of description, only the division of the above functional modules is taken as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0192] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner. For example, a plurality of units or components can be combined or integrated into another device, or some features can be discarded or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0193] The units described as separate components can or can not be physically separate, and the components shown as units can be one physical unit or multiple physical units, that is, can be located in one place or distributed in multiple different places. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0194] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0195] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The software product is stored in a storage medium, including a plurality of instructions to make a device (which can be a single-chip microcomputer, a chip, etc.) or a processor execute all or part of the steps of the various embodiments of the method of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0196] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of ranking information, characterized by, The method comprises: receiving a search request of a user, the search request comprising a keyword input by the user; in response to the search request, displaying a recommendation interface, wherein the labels of K candidate objects in the recommendation interface correspond to target objects of the same attribute installed on K devices, the candidate objects being candidate applications and / or candidate services, and K being a positive integer greater than or equal to 2; wherein the ranking of the K candidate objects is determined according to first weights respectively corresponding to the K candidate objects and second weights respectively corresponding to the K candidate objects, the first weights respectively corresponding to the K candidate objects being determined according to semantic similarities between the keyword and the labels of the K candidate objects, and the second weights respectively corresponding to the K candidate objects being determined according to matching degrees between constraint conditions of prior knowledge related to the target objects and device states of the K devices; the constraint conditions of the prior knowledge related to the target objects being used to indicate device states of devices to be used preferentially.

2. The method of claim 1, wherein, Before displaying the recommendation interface, the method further comprises: obtaining labels of N candidate objects and calculating semantic similarities between the keyword and the labels of the N candidate objects according to the keyword; determining, from the labels of the N candidate objects, labels of M candidate objects corresponding to semantic similarities greater than a set threshold, wherein the labels of the M candidate objects include the labels of the K candidate objects corresponding to the target objects of the same attribute installed on the K devices; M≤N, and M and N are positive integers greater than or equal to 2; ranking the K candidate objects according to the semantic similarities between the keyword and the labels of the K candidate objects and the device states of the K devices.

3. The method of claim 1, wherein, The device states of the K devices include at least one of the following states: a power supply type of a device, a screen size of a device, available computing resources of a device, and a correlation between a device performance and the target object.

4. The method according to any one of claims 1 to 3, characterized in that, The target objects of the same attribute on the K devices are target objects of the same name on the K devices; or the target objects of the same attribute on the K devices are target objects of the same supplier; or the target objects of the same attribute on the K devices are target objects of the same installation package on the K devices; or the target objects of the same attribute on the K devices are target objects of the same function on the K devices.

5. The method of claim 2, wherein, The ranking of the K candidate objects according to the semantic similarities between the keyword and the labels of the K candidate objects and the device states of the K devices comprises: determining the first weights respectively corresponding to the K candidate objects according to the semantic similarities between the keyword and the labels of the K candidate objects; determining the second weights respectively corresponding to the K candidate objects according to the matching degrees between the constraint conditions of the prior knowledge related to the target objects and the device states of the K devices; ranking the K candidate objects according to the first weights and the second weights, wherein the ranking of a candidate object is earlier if the product of the first weight and the second weight of the candidate object is greater.

6. The method of claim 5, wherein, The constraint condition of the prior knowledge related to the target object comprises at least one of the following conditions: The target object is preferentially run on a large-screen device, the target object is preferentially run on a plug-in type device, the target object is preferentially run on a device with strong computing capability, and the target object is preferentially run on a device with good audio playback performance.

7. The method of claim 5, wherein, The matching degree between the constraint condition of the prior knowledge related to the target object and the state of the K devices is used to determine the second weight corresponding to the K candidate objects respectively. When the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a plug-in type device, it is determined that the second weight of the corresponding candidate object of the device supporting the plug-in type device is greater than the second weight of the corresponding candidate object of the device not supporting the plug-in type device among the K devices corresponding to the K candidate objects. Or, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a large-screen device, it is determined that the second weight of the corresponding candidate object of the device with a larger screen is greater among the K devices corresponding to the K candidate objects. Or, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a device with strong computing capability, it is determined that the second weight of the corresponding candidate object of the device with stronger computing capability is greater among the K devices corresponding to the K candidate objects. Or, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a device with good audio playback performance, it is determined that the second weight of the corresponding candidate object of the device with better audio playback performance is greater among the K devices corresponding to the K candidate objects.

8. The method of claim 2, wherein, The N candidate objects are obtained according to the keyword, comprising: Obtaining the detail information of the L candidate objects on the K devices from the database; Extracting the original keyword from the detail information of the L candidate objects, performing semantic analysis on the original keyword, and obtaining the label of the L candidate objects from the analysis result, According to the matching degree between the keyword and the label of the L candidate objects, the label of the N candidate objects is obtained from the label of the L candidate objects.

9. An electronic device, comprising: The electronic device comprises a processor and a memory; The memory stores program instructions; The processor is configured to run the program instructions stored in the memory, so that the electronic device performs: Receiving a search request of a user, the search request comprising a keyword input by the user; In response to the search request, a recommendation interface is displayed, wherein the labels of K candidate objects in the candidate objects in the recommendation interface correspond to the same attribute target object installed on K devices, and the candidate objects are candidate applications and / or candidate services, K is a positive integer greater than or equal to 2; The sorting of the K candidate objects is determined according to first weights corresponding to the K candidate objects respectively and second weights corresponding to the K candidate objects respectively, the first weight corresponding to each of the K candidate objects is determined according to semantic similarity between the keyword and a label of the K candidate object, and the second weight corresponding to each of the K candidate objects is determined according to matching degrees between a constraint condition of prior knowledge related to the target object and device states of the K devices; the constraint condition of the prior knowledge related to the target object is used to indicate device states of a device that is preferentially used.

10. The electronic device of claim 9, wherein, Before the recommended interface is displayed, the processor is configured to execute the program instruction stored in the memory, so that the electronic device further performs: According to the keyword, obtaining labels of N candidate objects, and calculating semantic similarity between the keyword and the labels of the N candidate objects; From the labels of the N candidate objects, determining labels of M candidate objects corresponding to semantic similarity greater than a set threshold, wherein the labels of the M candidate objects include the labels of the K candidate objects corresponding to the target objects of the same attribute installed on the K devices; M≤N, M and N are positive integers greater than or equal to 2; According to the semantic similarity between the keyword and the labels of the K candidate objects and the device states of the K devices, sorting the K candidate objects.

11. The electronic device of claim 9, wherein, The device states of the K devices include at least one of the following states: The power supply type of the device, the screen size of the device, the available computing resources of the device, and the correlation between the device performance and the target object.

12. The electronic device of any of claims 9 to 11, wherein, The target objects of the same attribute on the K devices are target objects of the same name on the K devices; or the target objects of the same attribute on the K devices are target objects of the same supplier; or the target objects of the same attribute on the K devices are target objects of the same installation package on the K devices; or the target objects of the same attribute on the K devices are target objects of the same function on the K devices.

13. The electronic device of claim 10, wherein, The processor is configured to execute the program instruction stored in the memory, so that the electronic device specifically performs: According to the semantic similarity between the keyword and the labels of the K candidate objects, determining first weights corresponding to the K candidate objects respectively; According to the matching degrees between the constraint condition of the prior knowledge related to the target object and the device states of the K devices, determining second weights corresponding to the K candidate objects respectively; According to the first weights and the second weights, sorting the K candidate objects, wherein the more the product of the first weight and the second weight is, the higher the sorting of the candidate object is.

14. The electronic device of claim 13, wherein, The constraint condition of the prior knowledge related to the target object includes at least one of the following conditions: The target object is preferentially run on a large-screen device, the target object is preferentially run on a device of a plug-in type, the target object is preferentially run on a device with strong computing power, and the target object is preferentially run on a device with good audio playback performance.

15. The electronic device of claim 13, wherein, The processor is configured to run the program instructions stored in the memory, so that the electronic device specifically performs the following when determining the second weights corresponding to the K candidate objects respectively: When the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a plug-in type device, it is determined that the second weight of the corresponding candidate object of the device supporting the plug-in type among the K devices corresponding to the K candidate objects is greater than the second weight of the corresponding candidate object of the device not supporting the plug-in type; Or, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a large-screen device, it is determined that the second weight of the corresponding candidate object of the device with a larger screen among the K devices corresponding to the K candidate objects is greater; Or, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a device with strong computing power, it is determined that the second weight of the corresponding candidate object of the device with stronger computing power among the K devices corresponding to the K candidate objects is greater; Or, when the constraint condition of the prior knowledge related to the target object is that the target object is preferentially run on a device with good audio playback performance, it is determined that the second weight of the corresponding candidate object of the device with better audio playback performance among the K devices corresponding to the K candidate objects is greater.

16. The electronic device of claim 10, wherein, The processor is configured to run the program instructions stored in the memory, so that the electronic device specifically performs the following: Obtain the detail information of L candidate objects on the K devices from a database; Extract original keywords from the detail information of the L candidate objects, and perform semantic analysis on the original keywords to obtain the labels of the L candidate objects from the analysis results, According to the matching degree of the keywords and the labels of the L candidate objects, obtain the labels of N candidate objects from the labels of the L candidate objects.

17. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises program instructions, when the program instructions are run on the electronic device, the electronic device performs the method as claimed in any one of claims 1 to 8.

Images