CN112269853B

CN112269853B - Retrieval processing method, device and storage medium

Info

Publication number: CN112269853B
Application number: CN202011283948.4A
Authority: CN
Inventors: 毕浩; 杨俊拯; 钟卫东
Original assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Current assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date: 2020-11-16
Filing date: 2020-11-16
Publication date: 2023-06-13
Anticipated expiration: 2040-11-16
Also published as: WO2022100221A1; CN112269853A

Abstract

The application discloses a retrieval processing method, a retrieval processing device and a storage medium, which are applied to electronic equipment, wherein the method comprises the following steps: acquiring an input search term; processing the search term to obtain a query expression; collecting data according to the search term to obtain target data; updating a preset index table according to the target data to obtain a first index table; and inquiring the first index table according to the inquiry expression to obtain at least one search result. By adopting the embodiment of the application, the retrieval function can be realized, and the user experience is improved.

Description

Retrieval processing method, device and storage medium

Technical Field

The present invention relates to the field of information processing technologies, and in particular, to a search processing method, a search processing device, and a storage medium.

Background

Along with the wide popularization and application of electronic products (such as mobile phones, tablet computers and the like), the electronic products have more and more applications and more powerful functions, the electronic products develop towards diversification and individuation, the importance of the electronic products to users is conceivable, and obviously the electronic products become indispensable electronic products in the life of the users. Taking a mobile phone as an example, the existing search function is single, and when a plurality of search results are aimed at, intelligent display cannot be achieved.

Disclosure of Invention

The embodiment of the application provides a retrieval processing method, a retrieval processing device and a storage medium, which can realize a retrieval function and improve user experience.

In a first aspect, an embodiment of the present application provides a search processing method, which is applied to an electronic device, where the method includes:

acquiring an input search term;

processing the search term to obtain a query expression;

collecting data according to the search term to obtain target data;

updating a preset index table according to the target data to obtain a first index table;

and inquiring the first index table according to the inquiry expression to obtain at least one search result.

In a second aspect, an embodiment of the present application provides a search processing apparatus, applied to an electronic device, where the apparatus includes: an acquisition unit, a processing unit, a collection unit, an updating unit and a search unit, wherein,

the acquisition unit is used for acquiring the input search term;

the processing unit is used for processing the search term to obtain a query expression;

the collecting unit is used for collecting data according to the search word to obtain target data;

The updating unit is used for updating a preset index table according to the target data to obtain a first index table;

and the searching unit is used for inquiring the first index table according to the inquiring expression to obtain at least one searching result.

In a third aspect, embodiments of the present application provide an electronic device comprising a processor, a memory for storing one or more programs and configured for execution by the processor, the programs comprising instructions for performing the steps in the method of any of the first aspects.

In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program causes a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application.

In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

By implementing the embodiment of the application, the following beneficial effects are achieved:

it can be seen that, in the embodiments of the present application, the search processing method, the device and the storage medium described in the embodiments of the present application are applied to an electronic device, obtain an input search term, process the search term to obtain a query expression, collect data according to the search term to obtain target data, update a preset index table according to the target data to obtain a first index table, and query the first index table according to the query expression to obtain at least one search result, so, on one hand, the data to be searched can be updated, and on the other hand, the search term can be converted into the query expression, and further, the search function can be realized, and the user experience is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

fig. 2 is a schematic software structure of an electronic device according to an embodiment of the present application;

fig. 3A is a schematic flow chart of a search processing method according to an embodiment of the present application;

FIG. 3B is a flowchart illustrating another search processing method according to an embodiment of the present disclosure;

FIG. 3C is a schematic illustration of a word segmentation process provided in an embodiment of the present application;

FIG. 3D is a flowchart illustrating another search processing method according to an embodiment of the present disclosure;

FIG. 3E is a schematic illustration of a merging tree structure provided in an embodiment of the present application;

FIG. 4 is a schematic flow chart of another search processing method according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

FIG. 6A is a functional block diagram of a retrieval processing device according to an embodiment of the present application;

fig. 6B is a functional unit composition block diagram of a retrieval processing device according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings.

For a better understanding of aspects of embodiments of the present application, related terms and concepts that may be related to embodiments of the present application are described below.

The electronic devices may include devices of various Ultra Wide Band (UWB) modules, such as smartphones, car-mounted devices (navigator, automobile data recorder, radar rangefinder, etc. payment loader, etc.), wearable devices, smart watches, base Station devices, tag devices, interphones, smart glasses, wireless bluetooth headsets, computing devices or other processing devices connected to wireless modems, as well as various forms of User Equipment (UE), mobile Station (MS), virtual reality/augmented reality devices, terminal devices (terminal devices), ETC.

In the first part, the software and hardware operation environment of the technical scheme disclosed in the application is introduced as follows.

As shown, fig. 1 shows a schematic structural diagram of an electronic device 100. Electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (universal serial bus, USB) interface 130, a charge 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, an earphone interface 170D, a sensor module 180, a compass 190, a motor 191, an indicator 192, a camera 193, a display 194, a subscriber identity module (subscriber identification module, SIM) card interface 195, and the like.

It is to be understood that the structure illustrated 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, electronic device 100 may include more or fewer components than shown, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units, such as: the processor 110 may include an application processor AP, a modem processor, a graphics processor GPU, an image signal processor (image signal processor, ISP), a controller, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and/or a neural network processor NPU, etc. Wherein the different processing units may be separate components or may be integrated in one or more processors. In some embodiments, the electronic device 101 may also include one or more processors 110. The controller can generate operation control signals according to the instruction operation codes and the time sequence signals to finish the control of instruction fetching and instruction execution. In other embodiments, memory may also be provided in the processor 110 for storing instructions and data. Illustratively, the memory in the processor 110 may be a cache memory. The memory may hold instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to reuse the instruction or data, it may be called directly from memory. This avoids repeated accesses and reduces the latency of the processor 110, thereby improving the efficiency of the electronic device 101 in processing data or executing instructions.

In some embodiments, the processor 110 may include one or more interfaces. The interfaces may include inter-integrated circuit (inter-integrated circuit, I2C) interfaces, inter-integrated circuit audio (inter-integrated circuit sound, I2S) interfaces, pulse code modulation (pulse code modulation, PCM) interfaces, universal asynchronous receiver transmitter (universal asynchronous receiver/transmitter, UART) interfaces, mobile industry processor interfaces (mobile industry processor interface, MIPI), general-purpose input/output (GPIO) interfaces, SIM card interfaces, and/or USB interfaces, among others. The USB interface 130 is an interface conforming to the USB standard, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface 130 may be used to connect a charger to charge the electronic device 101, or may be used to transfer data between the electronic device 101 and a peripheral device. The USB interface 130 may also be used to connect headphones through which audio is played.

It should be understood that the interfacing relationship between the modules illustrated in the embodiments of the present application is only illustrative, and does not limit the structure of the electronic device 100. In other embodiments of the present application, the electronic device 100 may also use different interfacing manners, or a combination of multiple interfacing manners in the foregoing embodiments.

The charge management module 140 is configured to receive a charge input from a charger. The charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charge management module 140 may receive a charging input of a wired charger through the USB interface 130. In some wireless charging embodiments, the charge management module 140 may receive wireless charging input through a wireless charging coil of the electronic device 100. The charging management module 140 may also supply power to the electronic device through the power management module 141 while charging the battery 142.

The power management module 141 is used for connecting the battery 142, and the charge management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charge management module 140 and provides power to the processor 110, the internal memory 121, the external memory, the display 194, the camera 193, the wireless communication module 160, and the like. The power management module 141 may also be configured to monitor battery capacity, battery cycle times, battery health (leakage, impedance), and other parameters. In other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charge management module 140 may be disposed in the same device.

The wireless communication function of the electronic device 100 may be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, a modem processor, a baseband processor, and the like.

The

antennas

1 and 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the electronic device 100 may be used to cover a single or multiple communication bands. Different antennas may also be multiplexed to improve the utilization of the antennas. For example: the antenna 1 may be multiplexed into a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.

The mobile communication module 150 may provide a solution for wireless communication including 2G/3G/4G/5G/6G, etc. applied on the electronic device 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (low noise amplifier, LNA), etc. The mobile communication module 150 may receive electromagnetic waves from the antenna 1, perform processes such as filtering, amplifying, and the like on the received electromagnetic waves, and transmit the processed electromagnetic waves to the modem processor for demodulation. The mobile communication module 150 can amplify the signal modulated by the modem processor, and convert the signal into electromagnetic waves through the antenna 1 to radiate. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be disposed in the processor 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 may be provided in the same device as at least some of the modules of the processor 110.

The wireless communication module 160 may provide solutions for wireless communication including wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (wireless fidelity, wi-Fi) network), bluetooth (BT), global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field wireless communication technology (near field communication, NFC), infrared technology (IR), UWB module, etc., applied to the electronic device 100. The wireless communication module 160 may be one or more devices that integrate at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the electromagnetic wave signals, filters the electromagnetic wave signals, and transmits the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be transmitted from the processor 110, frequency modulate it, amplify it, and convert it to electromagnetic waves for radiation via the antenna 2.

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

The display screen 194 is used to display images, videos, and the like. The display 194 includes a display panel. The display panel may employ a liquid crystal display (liquid crystal display, LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED) or an active-matrix organic light-emitting diode (matrix organic light emitting diode), a flexible light-emitting diode (FLED), a mini light-emitting diode (mini light-emitting diode), microLed, micro-OLED, a quantum dot light-emitting diode (quantum dot light emitting diodes, QLED), or the like. In some embodiments, the electronic device 100 may include 1 or more display screens 194.

The electronic device 100 may implement a photographing function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.

The ISP is used to process data fed back by the camera 193. For example, when photographing, the shutter is opened, light is transmitted to the camera photosensitive element through the lens, the optical signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to the ISP for processing, so that the electrical signal is converted into an image visible to naked eyes. ISP can also perform algorithm optimization on noise, brightness and skin color of the image. The ISP can also optimize parameters such as exposure, color temperature, etc. of the photographed scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or video. The object generates an optical image through the lens and projects the optical image onto the photosensitive element. The photosensitive element may be a charge coupled device (charge coupled device, CCD) or a Complementary Metal Oxide Semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, which is then transferred to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV, or the like format. In some embodiments, electronic device 100 may include 1 or more cameras 193.

The digital signal processor is used for processing digital signals, and can process other digital signals besides digital image signals. For example, when the electronic device 100 selects a frequency bin, the digital signal processor is used to fourier transform the frequency bin energy, or the like.

Video codecs are used to compress or decompress digital video. The electronic device 100 may support one or more video codecs. In this way, the electronic device 100 may play or record video in a variety of encoding formats, such as: moving picture experts group (moving picture experts group, MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

The NPU is a neural-network (NN) computing processor, and can rapidly process input information by referencing a biological neural network structure, for example, referencing a transmission mode between human brain neurons, and can also continuously perform self-learning. Applications such as intelligent awareness of the electronic device 100 may be implemented through the NPU, for example: image recognition, face recognition, speech recognition, text understanding, etc.

The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to enable expansion of the memory capabilities of the electronic device 100. The external memory card communicates with the processor 110 through an external memory interface 120 to implement data storage functions. For example, files such as music, video, etc. are stored in an external memory card.

The internal memory 121 may be used to store one or more computer programs, including instructions. The processor 110 may cause the electronic device 101 to execute the methods of displaying page elements, and various applications, data processing, and the like provided in some embodiments of the present application by executing the above-described instructions stored in the internal memory 121. The internal memory 121 may include a storage program area and a storage data area. The storage program area can store an operating system; the storage program area may also store one or more applications (such as gallery, contacts, etc.), etc. The storage data area may store data created during use of the electronic device 101 (e.g., photos, contacts, etc.), and so on. In addition, the internal memory 121 may include high-speed random access memory, and may also include nonvolatile memory, such as one or more disk storage units, flash memory units, universal flash memory (universal flash storage, UFS), and the like. In some embodiments, processor 110 may cause electronic device 101 to perform the methods of displaying page elements provided in embodiments of the present application, as well as other applications and data processing, by executing instructions stored in internal memory 121, and/or instructions stored in a memory provided in processor 110. The electronic device 100 may implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, an application processor, and the like. Such as music playing, recording, etc.

The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, and the like.

The pressure sensor 180A is used for sensing a pressure signal, and can convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be disposed on the display screen 194. The pressure sensor 180A is of various types, such as a resistive pressure sensor, an inductive pressure sensor, a capacitive pressure sensor, and the like. The capacitive pressure sensor may be a capacitive pressure sensor comprising at least two parallel plates with conductive material. The capacitance between the electrodes changes when a force is applied to the pressure sensor 180A. The electronic device 100 determines the strength of the pressure from the change in capacitance. When a touch operation is applied to the display screen 194, the electronic apparatus 100 detects the touch operation intensity according to the pressure sensor 180A. The electronic device 100 may also calculate the location of the touch based on the detection signal of the pressure sensor 180A. In some embodiments, touch operations that act on the same touch location, but at different touch operation strengths, may correspond to different operation instructions. For example: and executing an instruction for checking the short message when the touch operation with the touch operation intensity smaller than the first pressure threshold acts on the short message application icon. And executing an instruction for newly creating the short message when the touch operation with the touch operation intensity being greater than or equal to the first pressure threshold acts on the short message application icon.

The gyro sensor 180B may be used to determine a motion gesture of the electronic device 100. In some embodiments, the angular velocity of electronic device 100 about three axes (i.e., X, Y and Z axis) may be determined by gyro sensor 180B. The gyro sensor 180B may be used for photographing anti-shake. For example, when the shutter is pressed, the gyro sensor 180B detects the shake angle of the electronic device 100, calculates the distance to be compensated by the lens module according to the angle, and makes the lens counteract the shake of the electronic device 100 through the reverse motion, so as to realize anti-shake. The gyro sensor 180B may also be used for navigating, somatosensory game scenes.

The acceleration sensor 180E may detect the magnitude of acceleration of the electronic device 100 in various directions (typically three axes). The magnitude and direction of gravity may be detected when the electronic device 100 is stationary. The electronic equipment gesture recognition method can also be used for recognizing the gesture of the electronic equipment, and is applied to horizontal and vertical screen switching, pedometers and other applications.

The ambient light sensor 180L is used to sense ambient light level. The electronic device 100 may adaptively adjust the brightness of the display 194 based on the perceived ambient light level. The ambient light sensor 180L may also be used to automatically adjust white balance when taking a photograph. Ambient light sensor 180L may also cooperate with proximity light sensor 180G to detect whether electronic device 100 is in a pocket to prevent false touches.

The fingerprint sensor 180H is used to collect a fingerprint. The electronic device 100 may utilize the collected fingerprint feature to unlock the fingerprint, access the application lock, photograph the fingerprint, answer the incoming call, etc.

The temperature sensor 180J is for detecting temperature. In some embodiments, the electronic device 100 performs a temperature processing strategy using the temperature detected by the temperature sensor 180J. For example, when the temperature reported by temperature sensor 180J exceeds a threshold, electronic device 100 performs a reduction in the performance of a processor located in the vicinity of temperature sensor 180J in order to reduce power consumption to implement thermal protection. In other embodiments, when the temperature is below another threshold, the electronic device 100 heats the battery 142 to avoid the low temperature causing the electronic device 100 to be abnormally shut down. In other embodiments, when the temperature is below a further threshold, the electronic device 100 performs boosting of the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperatures.

The touch sensor 180K, also referred to as a "touch panel". The touch sensor 180K may be disposed on the display screen 194, and the touch sensor 180K and the display screen 194 form a touch screen, which is also called a "touch screen". The touch sensor 180K is for detecting a touch operation acting thereon or thereabout. The touch sensor may communicate the detected touch operation to the application processor to determine the touch event type. Visual output related to touch operations may be provided through the display 194. In other embodiments, the touch sensor 180K may also be disposed on the surface of the electronic device 100 at a different location than the display 194.

By way of example, fig. 2 shows a block diagram of the software architecture of the electronic device 100. The layered architecture divides the software into several layers, each with distinct roles and branches. The layers communicate with each other through a software interface. In some embodiments, the Android system is divided into four layers, from top to bottom, an application layer, an application framework layer, an Zhuoyun row (Android run) and system libraries, and a kernel layer, respectively. The application layer may include a series of application packages.

As shown in fig. 2, the application layer may include applications for cameras, gallery, calendar, phone calls, maps, navigation, WLAN, bluetooth, music, video, short messages, etc.

The application framework layer provides an application programming interface (application programming interface, API) and programming framework for application programs of the application layer. The application framework layer includes a number of predefined functions.

As shown in FIG. 2, the application framework layer may include a window manager, a content provider, a view system, a telephony manager, a resource manager, a notification manager, and the like.

The window manager is used for managing window programs. The window manager can acquire the size of the display screen, judge whether a status bar exists, lock the screen, intercept the screen and the like.

The content provider is used to store and retrieve data and make such data accessible to applications. The data may include video, images, audio, calls made and received, browsing history and bookmarks, phonebooks, etc.

The view system includes visual controls, such as controls to display text, controls to display pictures, and the like. The view system may be used to build applications. The display interface may be composed of one or more views. For example, a display interface including a text message notification icon may include a view displaying text and a view displaying a picture.

The telephony manager is used to provide the communication functions of the electronic device 100. Such as the management of call status (including on, hung-up, etc.).

The resource manager provides various resources for the application program, such as localization strings, icons, pictures, layout files, video files, and the like.

The notification manager allows the application to display notification information in a status bar, can be used to communicate notification type messages, can automatically disappear after a short dwell, and does not require user interaction. Such as notification manager is used to inform that the download is complete, message alerts, etc. The notification manager may also be a notification in the form of a chart or scroll bar text that appears on the system top status bar, such as a notification of a background running application, or a notification that appears on the screen in the form of a dialog window. For example, a text message is prompted in a status bar, a prompt tone is emitted, the electronic device vibrates, and an indicator light blinks, etc.

Android run time includes a core library and virtual machines. Android run time is responsible for scheduling and management of the Android system.

The core library consists of two parts: one part is a function which needs to be called by java language, and the other part is a core library of android.

The application layer and the application framework layer run in a virtual machine. The virtual machine executes java files of the application program layer and the application program framework layer as binary files. The virtual machine is used for executing the functions of object life cycle management, stack management, thread management, security and exception management, garbage collection and the like.

The system library may include a plurality of functional modules. For example: surface manager (surface manager), media library (media library), three-dimensional graphics processing library (e.g., openGL ES), 2D graphics engine (e.g., SGL), etc.

The surface manager is used to manage the display subsystem and provides a fusion of 2D and 3D layers for multiple applications.

Media libraries support a variety of commonly used audio, video format playback and recording, still image files, and the like. The media library may support a variety of audio video encoding formats, such as: MPEG4, h.264, MP3, AAC, AMR, JPG, PNG, etc.

The three-dimensional graphic processing library is used for realizing three-dimensional graphic drawing, image rendering, synthesis, layer processing and the like.

The 2D graphics engine is a drawing engine for 2D drawing.

The kernel layer is a layer between hardware and software. The inner core layer at least comprises a display driver, a camera driver, an audio driver and a sensor driver.

The second part, the retrieval processing method and the device disclosed in the embodiments of the present application are described below.

Fig. 3A is a flow chart of a search processing method provided in an embodiment of the present application, and the search processing method is applied to an electronic device, as shown in the drawing, and includes:

301. and acquiring the input search term.

Wherein the search term can be input by the user or pushed by the system. The search term may be at least one of the following: chinese characters, english sentences, character strings, german sentences, french sentences, dialect sentences, japanese sentences, korean sentences, images, videos, documents, and the like, are not limited herein. In a specific implementation, the electronic device may input the search term through the touch display screen. When the input search term is not text content, the search term can be converted into text content, and then subsequent search is carried out.

In one possible example, the step 301 of obtaining the input search term may include the following steps:

a31, obtaining a target voice fragment;

a32, analyzing the target voice fragment to obtain the search term.

The electronic equipment can collect target voice fragments input by a user, convert the target voice fragments into texts, and perform semantic recognition on the texts to obtain search words.

b31, obtaining input content;

b32, when the input content is not text content, converting the input content into target text content;

and B33, extracting keywords from the target text content to obtain the search term.

In a specific implementation, the electronic device may acquire the input content through an input circuit, where the input circuit may be at least one of: microphones, touch-sensitive display screens, touch-sensitive pens, mice, keyboards, etc., are not limited herein.

In a specific implementation, the electronic device may acquire the input content, and when the input content is not text content, the input content may be converted into the target text content by means of character recognition, voice recognition, image recognition, and the like, and keyword extraction is performed on the target text content to obtain a search term, so that subsequent search may be facilitated.

302. And processing the search term to obtain a query expression.

The electronic device may perform lexical analysis and grammatical analysis on the search term, and convert the search term into a corresponding query expression, where the query expression may be a suffix expression (e.g., an inverse polish expression).

In one possible example, the step 302 of processing the term to obtain the query expression may include the following steps:

21. performing lexical analysis on the search term to obtain a plurality of first term lists;

22. and carrying out grammar analysis on the plurality of first term lists to obtain the query expression.

The electronic device may perform lexical analysis on a search term (query) of the user, that is, perform at least one process on the search term through a lexical analyzer: normalization, synonym conversion, word segmentation, stop word removal, and the like, and further, a plurality of first term lists (term lists) after word segmentation can be obtained, and further, grammar analysis can be performed. First, the relation between the first term list is analyzed, and finally a query expression is generated. Since it is developed on electronic equipment, a simple configuration mode is adopted. The relationship between different term lists (term) may be that all, one term is hit. The data formulas are converted into supporting OR operation (or) and AND operation (and) with the same priority.

The following are illustrative: query = global box office ranking list, term = global, term = box office, term = ranking list after lexical analysis, and query expressions 1&2&3 are analyzed if the term is in a relationship with each other, wherein the number is term identification, for example, 1 represents term of global term; and 2 represents term of the box office.

For this query expression, it is a suffix expression that is more consistent with human thought logic and difficult for a computer to process, so it is also possible to convert the suffix expression into a suffix expression that is easier for a computer to process (reverse Polish expression, combined stack can easily accomplish arithmetic summation). For example, the query expressions 1&2&3 are converted to 12&3&.

303. And collecting data according to the search term to obtain target data.

In this embodiment of the present application, the electronic device may collect local data or push data applied by a third party according to a search term to obtain target data, where the local data may be at least one of the following: local media data, address book data, usage record data for third party applications. The third party application may agree in advance with the electronic device to allow the retrieved application. The third party application may be at least one of: game applications, video applications, audio applications, instant messaging applications, shopping applications, weather applications, payment applications, and the like, are not limited herein.

In the specific implementation, for example, a mobile phone is taken as an example, when a search engine is started for the first time, data in a corresponding module is obtained in a full amount, and the data is stored in a search engine device, and on the other hand, the data of a user is updated and changed, so that a monitor can be registered on the module, and when the data of the user changes, a data collecting module can be notified in time and perform response processing.

The

steps

302 and 303 may be executed synchronously or asynchronously, and when the

steps

302 and 303 are executed synchronously, a process or a thread may be adopted to execute respectively.

In a possible example, the step 303, performing data collection according to the term to obtain target data may be implemented as follows:

acquiring local media data according to the search term to obtain the target data;

and/or the number of the groups of groups,

acquiring local address book data according to the search term to obtain the target data;

and/or the number of the groups of groups,

and obtaining pushing or using record data of the third party application according to the search term to obtain the target data.

The electronic device obtains the local media data in a preset time period according to the search term, and then the target data related to the search term can be obtained. The local media data may be at least one of: images, audio, video, files (word text, PDF text, PPT, etc.), without limitation, the preset time period may be set by the user himself or by default; and/or, the electronic equipment can also acquire the local address book data according to the search word to acquire target data; and/or the electronic device can acquire the pushing or using record data of the third party application according to the search term to obtain the target data, namely in the embodiment of the application, the electronic device can provide an open API for the third party application, and after authentication is completed, the third party application can push one piece of data to the electronic device through a corresponding API interface to perform data processing, and the updating action of the data can be marked as adding, deleting and updating.

In a possible example, the step 303, performing data collection according to the term to obtain target data may include the following steps:

31. acquiring historical data of a preset time period;

32. Screening the historical data according to the search term to obtain screened historical data;

33. and determining the priority order of the screened historical data, and carrying out queue processing on the screened historical data according to the priority order to obtain the target data.

The preset time period can be set by a user or default by the system.

In a specific implementation, the electronic device may acquire historical data in a preset time period, screen the historical data according to the search term to obtain screened historical data, and aims to screen data with high association degree with the search term as much as possible, further determine a priority order of the screened historical data, and perform queue processing on the screened historical data according to the priority order to obtain target data, where the priority order may be a high-low association order, or the priority order may also be determined by the generation time of the data. Therefore, the mode of passing through the priority queue can also be different in response to different data, and the data to be updated in time can be processed by adopting the high priority queue to complete the update of the data in time, so that the subsequent data retrieval efficiency is improved.

304. And updating a preset index table according to the target data to obtain a first index table.

The preset index table may be pre-stored in a Database (DB), and may be an inverted table, in which at least one of the following information may be pre-stored: the term list name, the number of documents to which the term list corresponds, document identification, document size, document usage frequency, and the like are not limited herein.

In a possible example, the step 304 of updating the preset index table according to the target data to obtain the first index table may include the following steps:

41. acquiring reference data corresponding to the preset index table;

42. comparing the target data with the reference data to obtain data to be updated;

43. performing lexical analysis according to the data to be updated to obtain at least one second term list;

44. and updating the preset index table according to the at least one second term list to obtain the first index table.

The node in the preset index table stores the identifiers corresponding to the data, and according to the identifiers, the reference data corresponding to the preset index table can be obtained, further, the target data and the reference data can be compared to obtain data to be updated, which does not exist in the reference data, in the target data, further, lexical analysis can be performed on the data to be updated, and specifically, at least one process is performed on the data to be updated through a lexical analyzer: normalization, synonym conversion, word segmentation and stop word removal, wherein the method is not limited herein, at least one second term list is obtained, a preset index table is updated according to the at least one second term list, namely, indexes of updated data are spliced in the preset index table, and further, a first index table can be obtained.

305. And inquiring the first index table according to the inquiry expression to obtain at least one search result.

The first index table may be an inverted table, the query expression may be processed into a tree structure, such as a binary tree, and the electronic device may query the first index table according to the binary tree to obtain at least one search result. Further, the fuzzy search or the accurate search can be realized, specifically, according to the accuracy of the search term or the update degree of the index table, for example, when the search term is a fuzzy term, the fuzzy search can be realized, for example, when the search term is an accurate term, the accurate search can be realized, for example, when the index table is not updated in time or has less update data, the fuzzy search can be realized, for example, when the index table is updated in time or has sufficient update data, and the accurate search can be realized.

In one possible example, the step 305, according to the query expression, of querying the first index table to obtain at least one search result may include the following steps:

51. generating a merging tree structure by the query expression;

52. and acquiring inverted zipper data corresponding to each node in the first index table according to the merging tree structure, and inquiring based on the inverted zipper data to obtain the at least one search result.

Wherein, the first index table is an inverted table because it is pre-stored in the database. The electronic device may generate a merging tree structure based on the query expression, and the inverted list may store at least one of the following information in advance: the term list name, the number of documents to which the term list corresponds, document identification, document size, document usage frequency, and the like are not limited herein. Furthermore, the electronic device may obtain inverted zipper data corresponding to each node in the first index table according to the merging tree structure, and query based on the inverted zipper data, to obtain at least one search result.

In a possible example, following the above step 305, the following steps 306-307 may be further included, specifically as follows:

306. and determining the relevance between each search result in the at least one search result and the search term by adopting a preset algorithm to obtain at least one relevance.

The preset algorithm may be set by the user or default by the system, the preset algorithm may be at least one algorithm, and the preset algorithm may be at least one of the following: the terms frequency-reverse document frequency (term frequency-inverse document frequency, TF/IDF), pomegranate algorithm, scindapsus algorithm, hurricane algorithm, original star fire plan, aspen algorithm, light boat algorithm, surprise algorithm, space net algorithm, beacon fire algorithm, thin rain algorithm, pomegranate algorithm, aurora algorithm, lightning algorithm, blue sky algorithm, ice bucket algorithm, panda algorithm, penguin algorithm, and the like are not limited herein. In a specific implementation, the electronic device may determine a relevance between each search result in the at least one search result and the search term by using a preset algorithm, so as to obtain at least one relevance.

In one possible example, when the preset algorithm is a plurality of algorithms, the step 306 of determining, using the preset algorithm, a relevance between each of the at least one search result and the search term, to obtain at least one relevance may include the following steps:

61. determining a target retrieval word type corresponding to the retrieval word;

62. according to a mapping relation between a preset algorithm and a search word type, determining a target algorithm corresponding to the target search word type;

63. and determining the relevance between each search result in the at least one search result and the search term according to the target algorithm to obtain at least one relevance.

In this embodiment of the present application, for example, the type of the search term may be determined according to the format of the search term, and the type of the search term may be at least one of the following: the audio type, the video type, the image type, the text type, and the like are not limited herein, and for example, the search term type may be determined according to the number of search terms, for example, the search term type may be determined according to the complexity of the search term, for example, the search term type may be at least one of the following search terms: hotwords, low frequency words, general hotwords, etc., are not limited herein.

In a specific implementation, when the preset algorithm is a plurality of algorithms, the search efficiency and the search precision of different algorithms are different for different search words, and then, the mapping relation between the preset algorithm and the search word type can be stored in the electronic equipment in advance, and then, after the electronic equipment determines the target search word type corresponding to the search word, the target algorithm corresponding to the target search word type is determined according to the mapping relation between the preset algorithm and the search word type, and then, the relevance between each search result in at least one search result and the search word is determined according to the target algorithm, so that at least one relevance is obtained, and further, the relevance of the search result is facilitated to be determined.

307. And displaying the at least one search result according to a high-priority display principle of the relevance.

The electronic device may display at least one search result according to a principle of high-priority display of relevance, so that the search result with high relevance may be displayed preferentially, and the search result with low relevance may be hidden or displayed after the display of the search result with high relevance is completed.

Based on the above-mentioned search processing method, take the mobile phone as an example, it is a brand new search that carries on full text search on the mobile phone, can be used for meeting the contradictory demand between user data file and quick accurate location of the user that grows increasingly, and under such terminal search engine, can meet the user and can't accomplish the content search, fuzzy search, according to the pain point that search term and file relativity reveal in the present contest article, in the design, consider the development stability requirement on the mobile phone, therefore, the pioneer uses DB as the inverted storage, meanwhile, utilize index of DB to reach and locate to a certain particular inverted zipper fast, therefore search can get the response fast under the large-scale data of user too, in the aspect of data collection, through the way of priority queue, can also be different to the response of different data, can adopt the high priority queue to process in the face of the data that need to update in time, finish the timely update of data.

For example, as shown in fig. 3B, the search engine is divided into two data flows by taking the mobile phone as an example, one data flow is a processing flow for data on the mobile phone of the user, for example, an oval identification part in the figure, media data, address book data, application data or collaborative third party application data can be analyzed according to customized configuration, the data is processed and then stored in the search system, the other data flow is a square identification part, the processing process after the search system is entered by the search term input by the user is performed, and finally, the data of documents, applications, audios, videos and the like matched with the search term is returned to the user.

The data processing flow is a collection and warehousing process of user data. Taking the mobile phone as an example, development has very high requirements on the reliability and stability of software, and timely repair of a server cannot be achieved. The stability requirement results in some complex logic not being suitable for placing on the mobile phone as an example, but the search engine is a very complex project, which needs to be implemented in a better way, and in order to meet the stability requirement, a lightweight DB can be used to store the inverted list. Key information such as a term queue (term) name, a corresponding document number, a list of document ids, and the like is stored in the inverted list.

The first step of the data processing flow is to collect data, mainly two types of data can be accessed into a search system, one type is data which is authorized to be accessed by a user on the basis of a mobile phone, and the other type is user data pushed by a cooperative third party application and pushed from the outside.

In the specific implementation, for media data, address book and application data on the mobile phone, the data in the corresponding module can be obtained in a full quantity when the search engine is started for the first time, and the data is stored in the search engine device, on the other hand, the data of the user is updated and changed, so that a monitor is registered on the modules, and when the data of the user changes, the data collection module can be timely notified and responds.

For collaborative third party applications, an open API is provided. After authentication is completed, the third party application can push one piece of data to the data collection module through the API to process the data, and the data updating action needs to be marked as adding, deleting and updating.

In the process of processing the data, there is a problem of queuing, and since the data volume of a user may be very large, it is proposed to allocate different priorities to different types of data, and data with high priority is preferentially processed.

After completing the data collection, the index is updated according to each piece of data through the processing of the index engine, in the process, the lexical analyzer is called to normalize, convert synonyms, divide words, remove stop words, finally form a term list, keep term frequencies, position information appearing in documents and the like, then update the term list in the inverted list storage, and complete the data update, as shown in fig. 3C, the term analyzer is a sample data processing process in the processing flow, specifically, for example, the term is a "ranking list of global movies", the term is normalized in terms of complex characters and cases, so as to obtain a ranking list of global movies, "the ranking list of global movies" is obtained, the term cutting process is performed, and finally the stop words are removed, so as to obtain the ranking list of global movies.

Further, as shown in fig. 3D, the processing process of another data flow, that is, the processing process of the search word of the user, the processed input is the search word of the user, and the output is all the matched data result sets recorded in the search system, which may include the following steps 1-6:

1. firstly, performing lexical analysis on the query of the user, wherein the process can be basically consistent with a lexical analyzer in a data processing flow, and a term list after word segmentation is required to be obtained.

2. And performing grammar analysis. First, the relationship between term lists is analyzed, and finally a query expression is generated. Since the mobile phone is developed as an example, a simple configuration mode is adopted. The relationship between different term may be that all, one term is hit. The data formulas are converted into supporting OR operation (or) and AND operation (and) with the same priority. For example: query = global box gate ranking list, term = global after lexical analysis, term = box gate ranking list, and query expressions 1&2&3 are analyzed if the term is in a relationship with each other, wherein the number is term identification, for example, 1 represents term of global term; and 2 represents term of the box office. Among other things, for this query expression, this is a suffix expression that is more consistent with human thought logic, and is difficult for computers to process, so it is necessary to convert the suffix expression into a suffix expression that is easier for computers to process (reverse Polish expression, combined stack is easy to accomplish arithmetic summation). For example, query expressions 1&2&3 translate to 12&3&.

3. And the merging engine processes to generate a merging tree structure by using the structural suffix expression after the grammar analysis. The query expression is converted into a binary tree structure for storage, so that subsequent merging operation is facilitated. The nodes of the binary tree comprise operator nodes and term nodes, wherein the term nodes are leaf nodes, and id corresponding to term and some key information of term are recorded; the operator node has left and right child nodes, and is an arithmetic node for operating the left and right child nodes. An index query engine is needed to acquire the inverted zipper data corresponding to each term node. All index information which is put in storage in the current system is stored in the index engine, a piece of inverted zipper information is stored, and the inverted table of db is inquired according to the name of term to obtain corresponding inverted data. The index of the term field is established in advance in the db, the corresponding inverted zipper can be queried in a short time by utilizing the index mechanism of the B+ tree of the db, the time complexity is only O (log 2N), and even the number of millions of terms can be loaded and inverted into the memory in a very short time.

4. And (3) completing arithmetic operation through the operation of the merging tree, and obtaining a merged final result zipper. In the process, the operation of the operator nodes is finished one by one from bottom to top, and finally the merging calculation of the root nodes is finished.

Where the or (|) calculation is to find the union between the two zippers and the and (+) calculation is to find the intersection of the two zippers, as shown in fig. 3E. In the merging, intersection process, a term set of hits for each document needs to be recorded as part of the input of the correlation calculation in the next step.

5. And scoring the relevance between each document and the search term in the final zipper result according to the TF/IDF algorithm. The TF/IDF is an algorithm for evaluating relevance commonly used in the industry, and the core idea is that the more a term is included in a document, the higher the relevance between the document and the term is considered, the more important the term is considered if the frequency of the term is about high in all documents, the less important the term is considered, the relevance between a search term and a hit document is scored according to the core idea, and a relevance score is obtained.

6. And sorting according to the result of the relevance scoring, and displaying the documents with the high scores to the user in a way of arranging the documents in the front.

In one possible example, before the step 301 of obtaining the input search term, the following steps may be further included:

c1, acquiring target identity information of a user;

c2, verifying the target identity information;

and C3, executing the step of acquiring the input search term when the target identity information is verified.

In this embodiment of the present application, the target identity information may be at least one of the following: character strings, touch parameters, face images, fingerprint images, palm print images, vein images, brain waves, voiceprints, and the like, without limitation.

In a specific implementation, the electronic device may acquire the target identity information of the user, and may verify the target identity information, and execute step 301 when the target identity information is verified, or not execute the subsequent steps.

Further, when the target identity information is a target face image, the method may further include the following steps between step C2 and step C3:

c4, determining a target image quality evaluation value of the target face image;

c5, when the target image quality evaluation value is larger than a preset image quality evaluation value, matching the target face image with a preset face template to obtain a target matching value;

And C6, when the target matching value is larger than a preset matching threshold value, confirming that the target identity information is verified.

The preset face template can be pre-stored in the electronic equipment, and the preset image quality evaluation value and the preset matching threshold value can be set by a user or default by the system. In a specific implementation, the electronic device may perform image quality evaluation on the target face image by using at least one image quality evaluation index to obtain a target image quality evaluation value, where the image quality evaluation index may be at least one of the following: information entropy, average gradient, average gray, contrast, etc., are not limited herein. When the target image quality evaluation value is larger than the preset image quality evaluation value, matching the target face image with the preset face template can be performed to obtain a target matching value, otherwise, identity authentication can be required to be performed again.

Further, the step C4 of determining the target image quality evaluation value of the target face image may include the following steps:

c41, determining the distribution density of target feature points and the target signal-to-noise ratio of the target face image;

c42, determining a first image quality evaluation value corresponding to the target feature point distribution density according to a mapping relation between the preset feature point distribution density and the image quality evaluation value;

C43, determining a target image quality deviation value corresponding to the target signal-to-noise ratio according to a mapping relation between a preset signal-to-noise ratio and the image quality deviation value;

c44, acquiring first shooting parameters of the target face image;

c45, determining a target optimization coefficient corresponding to the first shooting parameter according to a mapping relation between a preset shooting parameter and the optimization coefficient;

and C46, adjusting the first image quality evaluation value according to the target optimization coefficient and the target image quality deviation value to obtain the target image quality evaluation value.

In a specific implementation, a memory in the electronic device may store a mapping relationship between a preset feature point distribution density and an image quality evaluation value, a mapping relationship between a preset signal-to-noise ratio and an image quality deviation value, and a mapping relationship between a preset shooting parameter and an optimization coefficient in advance, where a value range of the image quality evaluation value may be 0-1, or may be 0-100. The image quality deviation value may be a positive real number, for example, 0 to 1, or may be greater than 1. The value range of the optimization coefficient can be between-1 and 1, for example, the optimization coefficient can be between-0.1 and 0.1. In this embodiment of the present application, the shooting parameter may be at least one of the following: exposure time, photographing mode, sensitivity ISO, white balance parameter, focal length, focus, region of interest, and the like, are not limited herein.

In a specific implementation, the electronic device may determine a target feature point distribution density and a target signal-to-noise ratio of the target face image, and determine a first image quality evaluation value corresponding to the target feature point distribution density according to a mapping relationship between the preset feature point distribution density and the image quality evaluation value, where the feature point distribution density reflects image quality to a certain extent, and the feature point distribution density may be understood as a ratio between the total number of feature points of the target face image and the image area of the target face image. Furthermore, the electronic device may determine the target image quality deviation value corresponding to the target signal-to-noise ratio according to the mapping relationship between the preset signal-to-noise ratio and the image quality deviation value, and when generating the image, due to some noise generated by external (weather, light, angle, jitter, etc.) or internal (system, GPU) reasons, the noise may have some influence on the image quality, so that the image quality may be adjusted to some extent to ensure objective evaluation of the image quality.

Further, the electronic device may further obtain a first shooting parameter of the target face image, where the first shooting parameter may also be carried in the communication connection request, further determine, according to a mapping relationship between a preset shooting parameter and an optimization coefficient, a target optimization coefficient corresponding to the first shooting parameter, where the setting of the shooting parameter may also bring a certain influence to the image quality evaluation, so that it is required to determine an influence component of the shooting parameter on the image quality, and finally adjust the first image quality evaluation value according to the target optimization coefficient and the target image quality deviation value to obtain a target image quality evaluation value, where the target image quality evaluation value may be obtained according to the following formula:

In the case where the image quality evaluation value is a percentile, the specific calculation formula is as follows:

target image quality evaluation value= (first image quality evaluation value+target image quality deviation value) × (1+target optimization coefficient)

In the case where the image quality evaluation value is a percentage, a specific calculation formula is as follows:

target image quality evaluation value=first image quality evaluation value (1+target image quality deviation value) ×1+target optimization coefficient

Therefore, the image quality can be objectively evaluated by combining the influences of internal and external environment factors, shooting setting factors and the like, and the image quality evaluation accuracy is improved.

It can be seen that, in the search processing method described in the embodiment of the present application, the search processing method is applied to an electronic device, an input search term is obtained, the search term is processed, a query expression is obtained, data collection is performed according to the search term, target data is obtained, a preset index table is updated according to the target data, a first index table is obtained, the first index table is queried according to the query expression, at least one search result is obtained, a preset algorithm is adopted, the relevance between each search result in the at least one search result and the search term is determined, at least one relevance is obtained, and at least one search result is displayed according to a relevance high-priority display principle, so that on one hand, data to be searched can be updated, on the other hand, the search term can be converted into the query expression, and further, accurate search for the search term can be achieved, and the search result can be displayed according to the relevance, intelligent display of the search result is achieved, and user experience is improved.

Fig. 4 is a schematic flow chart of a search processing method provided in an embodiment of the present application, which is applied to an electronic device; as shown in the figure, the retrieval processing method comprises the following steps:

401. input content is acquired.

402. When the input content is not text content, converting the input content into target text content.

403. And extracting keywords from the target text content to obtain a search term.

404. And processing the search term to obtain a query expression.

405. And collecting data according to the search term to obtain target data.

406. And updating a preset index table according to the target data to obtain a first index table.

407. And inquiring the first index table according to the inquiry expression to obtain at least one search result.

408. And determining the relevance between each search result in the at least one search result and the search term by adopting a preset algorithm to obtain at least one relevance.

409. And displaying the at least one search result according to a high-priority display principle of the relevance.

The specific description of the steps 401 to 409 may refer to the corresponding steps of the search processing method described in fig. 3A, and will not be repeated herein.

It can be seen that the search processing method described in the embodiment of the present application is applied to an electronic device, and can update data to be searched on the one hand, and can extract a search term through inputting content and convert the search term into a query expression on the other hand, so that accurate search for the search term can be achieved, and search results can be displayed according to the relevance, thereby achieving intelligent display of search results and improving user experience.

In accordance with the above embodiments, referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of the present application, as shown in the fig. 5, the electronic device includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and in the embodiment of the present application, the programs include instructions for executing the following steps:

acquiring an input search term;

processing the search term to obtain a query expression;

collecting data according to the search term to obtain target data;

And inquiring the first index table according to the inquiry expression to obtain at least one search result. It can be seen that, in the electronic device described in the embodiments of the present application, an input search term is obtained, the search term is processed to obtain a query expression, data collection is performed according to the search term to obtain target data, a preset index table is updated according to the target data to obtain a first index table, and the first index table is queried according to the query expression to obtain at least one search result, on one hand, the data to be searched can be updated, on the other hand, the search term can be converted into the query expression, and further, fuzzy search or accurate search according to the search term can be achieved, so that user experience is improved.

In one possible example, in terms of said collecting data according to said search term, obtaining target data, the above-mentioned program comprises instructions for performing the steps of:

and/or the number of the groups of groups,

acquiring historical data of a preset time period;

screening the historical data according to the search term to obtain screened historical data;

and determining the priority order of the screened historical data, and carrying out queue processing on the screened historical data according to the priority order to obtain the target data.

In one possible example, in said processing of said terms to obtain a query expression, the above-mentioned program comprises instructions for:

performing lexical analysis on the search term to obtain at least one first term list;

and analyzing the at least one first term list in a grammar manner to obtain the query expression.

In one possible example, in said updating of the preset index table according to said target data, a first index table is obtained, the above-mentioned program comprises instructions for performing the following steps:

acquiring reference data corresponding to the preset index table;

comparing the target data with the reference data to obtain data to be updated;

Performing lexical analysis according to the data to be updated to obtain at least one second term list;

and updating the preset index table according to the at least one second term list to obtain the first index table.

In one possible example, in terms of said querying said first index table according to said query expression resulting in at least one search result, the above-mentioned program comprises instructions for performing the steps of:

generating a merging tree structure by the query expression;

and acquiring inverted zipper data corresponding to each node in the first index table according to the merging tree structure, and inquiring based on the inverted zipper data to obtain the at least one search result.

In one possible example, after said querying said first index table according to said query expression for at least one search result, the above-mentioned program comprises instructions for performing the following steps:

determining the relevance between each search result in the at least one search result and the search term by adopting a preset algorithm to obtain at least one relevance;

and displaying the at least one search result according to a high-priority display principle of the relevance.

In one possible example, when the preset algorithm is a plurality of algorithms, determining a relevance between each of the at least one search result and the search term to obtain at least one relevance when the preset algorithm is adopted, the program includes instructions for executing the following steps:

determining a target retrieval word type corresponding to the retrieval word;

according to a mapping relation between a preset algorithm and a search word type, determining a target algorithm corresponding to the target search word type;

and determining the relevance between each search result in the at least one search result and the search term according to the target algorithm to obtain at least one relevance.

In one possible example, in terms of the retrieval word to be input, the above program includes instructions for performing the steps of:

acquiring input content;

when the input content is not text content, converting the input content into target text content;

and extracting keywords from the target text content to obtain the search term.

The foregoing description of the embodiments of the present application has been presented primarily in terms of a method-side implementation. It will be appreciated that the electronic device, in order to achieve the above-described functions, includes corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied as hardware or a combination of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The embodiment of the application may divide the functional units of the electronic device according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated in one processing unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice.

Fig. 6A is a functional unit block diagram of the retrieval processing device 600 according to the embodiment of the present application. The retrieval processing apparatus 600 is applied to an electronic device, and the apparatus 600 includes: an acquisition unit 601, a processing unit 602, a collection unit 603, an update unit 604, and a search unit 605, wherein,

the acquiring unit 601 is configured to acquire an input search term;

the processing unit 602 is configured to process the search term to obtain a query expression;

the collecting unit 603 is configured to collect data according to the search term, so as to obtain target data;

the updating unit 604 is configured to update a preset index table according to the target data to obtain a first index table;

The searching unit 605 is configured to query the first index table according to the query expression, so as to obtain at least one search result.

It can be seen that, the search processing device described in the embodiments of the present application is applied to an electronic device, obtains an input search term, processes the search term, obtains a query expression, performs data collection according to the search term, obtains target data, updates a preset index table according to the target data, obtains a first index table, and queries the first index table according to the query expression to obtain at least one search result, so that, on one hand, data to be searched can be updated, and on the other hand, the search term can be converted into the query expression, and further, fuzzy search or accurate search on the search term can be realized, and user experience is improved.

In one possible example, in terms of the data collection according to the term, the search unit 603 is specifically configured to:

and/or the number of the groups of groups,

And/or the number of the groups of groups,

acquiring historical data of a preset time period;

In one possible example, in terms of the processing the term to obtain a query expression, the processing unit 602 is specifically configured to:

In one possible example, in the aspect of updating the preset index table according to the target data to obtain the first index table, the updating unit 605 is specifically configured to:

Acquiring reference data corresponding to the preset index table;

comparing the target data with the reference data to obtain data to be updated;

In one possible example, in terms of said querying the first index table according to the query expression to obtain at least one search result, the search unit 605 is specifically configured to:

generating a merging tree structure by the query expression;

In one possible example, as shown in fig. 6B, fig. 6B is a further modified structure of the apparatus shown in fig. 6A, which may further include: the determining unit 606 and the displaying unit 607 are specifically as follows:

the determining unit 606 is configured to determine a relevance between each search result in the at least one search result and the search term by using a preset algorithm, so as to obtain at least one relevance;

The display unit 607 is configured to display the at least one search result according to a high-priority display principle of relevance.

Further, in one possible example, when the preset algorithm is a plurality of algorithms, the determining unit 606 is specifically configured to, when the adopting the preset algorithm, determine a relevance between each of the at least one search result and the term, and obtain at least one relevance:

determining a target retrieval word type corresponding to the retrieval word;

In one possible example, in terms of the acquiring the inputted search term, the acquiring unit 601 is specifically configured to:

acquiring input content;

and extracting keywords from the target text content to obtain the search term.

It should be noted that the tag device described in the embodiments of the present application is presented in the form of a functional unit. The term "unit" as used herein should be understood in the broadest possible sense, and the objects used to implement the functions described by the various "units" may be, for example, an integrated circuit ASIC, a single circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.

The acquiring unit 601, the processing unit 602, the collecting unit 603, the updating unit 604, the searching unit 605, the determining unit 606 and the displaying unit 607 may be one or more of a control circuit or a processor, and the functions or steps of any of the methods described above can be implemented based on the above unit modules.

The present embodiment also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the above computer program causes a computer to execute the embodiments as the present application for implementing any of the methods of the embodiments.

The present embodiment also provides a computer program product which, when run on a computer, causes the computer to perform the above-described relevant steps to implement any of the methods of the above-described embodiments.

In addition, embodiments of the present application also provide an apparatus, which may be specifically a chip, a component, or a module, and may include a processor and a memory connected to each other; the memory is configured to store computer-executable instructions that, when the device is operated, are executable by the processor to cause the chip to perform any one of the method embodiments described above.

The electronic device, the computer storage medium, the computer program product, or the chip provided in this embodiment are used to execute the corresponding methods provided above, so that the beneficial effects thereof can be referred to the beneficial effects in the corresponding methods provided above, and will not be described herein.

It will be appreciated by those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another apparatus, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and the parts shown as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be essentially or a part contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions to cause a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A retrieval processing method, characterized by being applied to an electronic device, the method comprising:

acquiring an input search term;

processing the search term to obtain a query expression, specifically: performing lexical analysis and grammar analysis on the search term to convert the search term into a corresponding query expression, wherein the query expression is a suffix expression;

collecting data according to the search term to obtain target data;

updating a preset index table according to the target data to obtain a first index table, wherein the preset index table comprises an inverted table, and at least one of the following information is stored in the inverted table in advance: the name of the term list, the number of documents corresponding to the term list, the document identification, the document size and the document use frequency;

querying the first index table according to the query expression to obtain at least one search result;

the method for updating the preset index table according to the target data to obtain a first index table includes:

acquiring reference data corresponding to the preset index table according to the identification;

comparing the target data with the reference data to obtain data to be updated;

Performing lexical analysis according to the data to be updated to obtain at least one second term list, wherein the second term list specifically comprises: at least one process is performed on the data to be updated through a lexical analyzer: normalization, synonym conversion, word segmentation and stop word removal;

updating the preset index table according to the at least one second term list, specifically: splicing the index of the update data in the preset index table to obtain the first index table;

wherein, the querying the first index table according to the query expression to obtain at least one search result includes:

generating a merging tree structure by the query expression;

acquiring inverted zipper data corresponding to each node in the first index table according to the merging tree structure, and inquiring based on the inverted zipper data to obtain the at least one search result;

before the input search term is acquired, the method further comprises the following steps:

acquiring target identity information of a user;

verifying the target identity information;

executing the step of acquiring the input search term when the target identity information is verified;

when the target identity information is a target face image, the method further comprises the following steps:

Determining a target image quality evaluation value of the target face image;

when the target image quality evaluation value is larger than a preset image quality evaluation value, matching the target face image with a preset face template to obtain a target matching value;

when the target matching value is larger than a preset matching threshold value, confirming that the target identity information is verified;

wherein the determining the target image quality evaluation value of the target face image includes:

determining the distribution density of target feature points and the target signal-to-noise ratio of the target face image;

determining a first image quality evaluation value corresponding to the target feature point distribution density according to a mapping relation between the preset feature point distribution density and the image quality evaluation value;

determining a target image quality deviation value corresponding to the target signal-to-noise ratio according to a mapping relation between a preset signal-to-noise ratio and the image quality deviation value;

acquiring a first shooting parameter of the target face image;

determining a target optimization coefficient corresponding to the first shooting parameter according to a mapping relation between a preset shooting parameter and the optimization coefficient;

and adjusting the first image quality evaluation value according to the target optimization coefficient and the target image quality deviation value to obtain the target image quality evaluation value.

2. The method according to claim 1, wherein the step of collecting data according to the search term to obtain target data includes:

and/or the number of the groups of groups,

3. The method according to claim 1, wherein the step of collecting data according to the search term to obtain target data includes:

acquiring historical data of a preset time period;

4. A method according to any one of claims 1-3, wherein said processing said term to obtain a query expression comprises:

5. A method according to any one of claims 1-3, wherein after said querying said first index table according to said query expression for at least one search result, the method further comprises:

6. The method of claim 5, wherein when the preset algorithm is a plurality of algorithms, the determining the relevance between each of the at least one search result and the term using the preset algorithm, to obtain at least one relevance comprises:

determining a target retrieval word type corresponding to the retrieval word;

7. A method according to any one of claims 1-3, wherein the obtaining the entered term comprises:

acquiring input content;

and extracting keywords from the target text content to obtain the search term.

8. A retrieval processing apparatus, characterized by being applied to an electronic device, comprising: an acquisition unit, a processing unit, a collection unit, an updating unit and a search unit, wherein,

the acquisition unit is used for acquiring the input search term;

the processing unit is configured to process the search term to obtain a query expression, specifically: performing lexical analysis and grammar analysis on the search term to convert the search term into a corresponding query expression, wherein the query expression is a suffix expression;

the updating unit is configured to update a preset index table according to the target data to obtain a first index table, where the preset index table includes an inverted table, and at least one of the following information is stored in the inverted table in advance: the name of the term list, the number of documents corresponding to the term list, the document identification, the document size and the document use frequency;

The searching unit is used for inquiring the first index table according to the inquiring expression to obtain at least one searching result;

comparing the target data with the reference data to obtain data to be updated;

generating a merging tree structure by the query expression;

Before the input search term is obtained, the device is further specifically configured to:

acquiring target identity information of a user;

verifying the target identity information;

wherein, when the target identity information is a target face image, the device is further specifically configured to:

determining a target image quality evaluation value of the target face image;

Acquiring a first shooting parameter of the target face image;

9. An electronic device comprising a processor, a memory for storing one or more programs and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-7.

10. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-7.