Search-based object recognition method, device, equipment, and storage medium

Abstract

The present disclosure provides a search-based object recognition method, apparatus, device and storage medium, which relates to the field of computer technology, and in particular to the field of search-based object recognition. The specific implementation scheme is: obtaining all search requests associated with the target object; determining the proportion of the target search request in all the above search requests, wherein the above target search request is determined by judging the emotional tendency of all search requests; determining the credibility value corresponding to the target web page, wherein the above target web page is a web page containing search content associated with the above target search request, and the above credibility value is used to characterize the credibility of the above target web page in supporting the above target search request; based on the above proportion value and the above credibility value, determining the object category to which the above target object belongs. The present disclosure solves the technical problem that the search-based object recognition method in the prior art uses click-through rate to measure credibility, resulting in deviations in the results.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to the field of search-based object recognition. Background Art

[0002] Internet encyclopedias contain a significant proportion of entries about people, and these entries are diverse and mixed. This often leads to entries about suspicious individuals. For various reasons, these entries often lack warning information within the text, leaving users with only partial information, which compromises the comprehensiveness and neutrality of encyclopedias. Therefore, it's necessary to identify suspicious individuals and provide user warnings on the page.

[0003] However, the search-based object recognition method used in the existing technology has no gold standard for identifying suspicious persons. Even if it is judged manually, it can only be based on personal experience, which makes the mining results unable to be accurately verified and there are certain deviations.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The present disclosure provides a method, apparatus, device, and storage medium for search-based object recognition.

[0006] According to one aspect of the present disclosure, a search-based object recognition method is provided, including: obtaining all search requests associated with a target object; determining a proportion of the target search request in all the above search requests, wherein the above target search request is determined by judging the sentiment tendency of all the search requests; determining a credibility value corresponding to a target web page, wherein the above target web page is a web page containing search content associated with the above target search request, and the above credibility value is used to characterize the degree of credibility of the above target web page in supporting the above target search request; based on the above proportion value and the above credibility value, determining the object category to which the above target object belongs.

[0007] Optionally, the above-mentioned acquisition of all search requests associated with the target object includes: detecting whether there is descriptive information associated with the above-mentioned target object in the received search request; if the above-mentioned descriptive information exists in the received search request, aggregating the received search request based on the above-mentioned descriptive information to obtain all the above-mentioned search requests associated with the above-mentioned target object.

[0008] Optionally, the target search request is determined in the following manner: an initial search request is obtained from all the search requests using a preset sample template; in the process of retracing the initial search requests, an emotional tendency model is used to determine the emotional tendency of each of the initial search requests; the sample template is expanded based on the target emotional tendency, and the expanded sample template is used to obtain the initial search request again from all the search requests, and the target search request is obtained after multiple iterations.

[0009] Optionally, the above-mentioned determination of the proportion of the target search request in all the above-mentioned search requests includes: determining the first search traffic value of all the search requests, and the second search traffic value of the above-mentioned target search request; and calculating the proportion of the above-mentioned target search request in all the above-mentioned search requests based on the above-mentioned first search traffic value and the above-mentioned second search traffic value.

[0010] Optionally, the above-mentioned determination of the first search traffic value of all search requests and the second search traffic value of the above-mentioned target search request includes: calculating the total traffic value of all the above-mentioned search requests to obtain the above-mentioned first search traffic value; calculating the search traffic value corresponding to the above-mentioned target search request to obtain the above-mentioned second search traffic value.

[0011] Optionally, the above-mentioned determination of the credibility value corresponding to the target webpage includes: obtaining the number of clicks and the number of impressions related to the above-mentioned target webpage; calculating the ratio of the above-mentioned number of clicks and the number of impressions; and determining the above-mentioned ratio as the above-mentioned credibility value.

[0012] Optionally, the above-mentioned determining the object category to which the above-mentioned target object belongs based on the above-mentioned proportion value and the above-mentioned credibility value includes: comparing a preset credibility threshold with the above-mentioned credibility value to obtain a comparison result; screening all web pages according to the above-mentioned comparison result to obtain an evidence list, wherein each evidence in the above-mentioned evidence list includes: the above-mentioned target web page and the credibility value of the above-mentioned target web page; determining the object category to which the above-mentioned target object belongs based on the above-mentioned proportion value and the above-mentioned evidence list.

[0013] According to another aspect of the present disclosure, a search-based object recognition device is provided, including: an acquisition module for acquiring all search requests associated with a target object; a first determination module for determining a proportion of the target search request in all the above search requests, wherein the above target search request is determined by judging the sentiment tendency of all the search requests; a second determination module for determining a credibility value corresponding to a target web page, wherein the above target web page is a web page containing search content associated with the above target search request, and the above credibility value is used to characterize the degree of credibility of the above target web page in supporting the above target search request; a third determination module for determining the object category to which the above target object belongs based on the above proportion value and the above credibility value.

[0014] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the above-mentioned search-based object recognition methods.

[0015] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any one of the above-mentioned search-based object recognition methods.

[0016] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, which implements any one of the above-mentioned search-based object recognition methods when executed by a processor.

[0017] In an embodiment of the present disclosure, all search requests associated with a target object are obtained; the proportion of the target search request in all the above search requests is determined, wherein the above target search request is determined by judging the sentiment tendency of all the search requests; a credibility value corresponding to the target web page is determined, wherein the above target web page is a web page containing search content associated with the above target search request, and the above credibility value is used to characterize the degree of credibility of the above target web page in supporting the above target search request; based on the above proportion value and the above credibility value, the object category to which the above target object belongs is determined, thereby achieving the purpose of identifying the target object, thereby realizing the technical effect of judging the target object category based on the search proportion and credibility, and further solving the technical problem in the prior art that the search-based object recognition method uses click-through rate to measure credibility, resulting in deviations in the results.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0020] Figure 1 is a schematic flow chart of the steps of the search-based object recognition method according to the first embodiment of the present disclosure;

[0021] Figure 2 is a schematic diagram of a process for determining a credibility value corresponding to a target web page according to the first embodiment of the present disclosure;

[0022] Figure 3 is a schematic diagram of a process for determining the object category to which a target object belongs according to the first embodiment of the present disclosure;

[0023] Figure 4 is a structural diagram of a search-based object recognition device according to a second embodiment of the present disclosure;

[0024] Figure 5 A schematic block diagram of an example electronic device 800 is shown, which may be used to implement embodiments of the present disclosure. DETAILED DESCRIPTION

[0025] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Example 1

[0028] According to an embodiment of the present disclosure, an embodiment of a search-based object recognition method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] Figure 1 FIG. 1 is a flowchart of the steps of the search-based object recognition method according to the first embodiment of the present disclosure. Figure 1 As shown, the method includes the following steps:

[0030] Step S102, obtaining all search requests associated with the target object;

[0031] Step S104, determining the proportion of the target search request in all the search requests, wherein the target search request is determined by determining the sentiment tendency of all the search requests;

[0032] Step S106: determining a credibility value corresponding to a target webpage, wherein the target webpage is a webpage containing search content associated with the target search request, and the credibility value is used to indicate the credibility of the target webpage in supporting the target search request;

[0033] Step S108: Determine the object category to which the target object belongs based on the proportion value and the credibility value.

[0034] In an embodiment of the present disclosure, all search requests associated with a target object are obtained; the proportion of the target search request in all the above search requests is determined, wherein the above target search request is determined by judging the sentiment tendency of all the search requests; a credibility value corresponding to the target web page is determined, wherein the above target web page is a web page containing search content associated with the above target search request, and the above credibility value is used to characterize the degree of credibility of the above target web page in supporting the above target search request; based on the above proportion value and the above credibility value, the object category to which the above target object belongs is determined.

[0035] It should be noted that the above-mentioned target object is not specifically limited, and can be a person, a group, or an object; all the above-mentioned search requests associated with the target object, that is, all the search requests containing the above-mentioned target object keywords, the keywords can be a person's name, a group's team name, etc.; by analyzing the specific content of all the above-mentioned search requests, the emotional tendency of the user who issued the search request is judged, and the above-mentioned emotional tendency is used to determine the above-mentioned target search request. The above-mentioned target search request can be a negative search request (a negative search request, for example, searching for a person's negative comments) or a positive search request (a positive search request, for example, searching for a person's advantages or positive things); the above-mentioned target web page is a web page containing the search content associated with the above-mentioned target search request, and the above-mentioned credibility value is used to characterize the credibility of the above-mentioned target web page in supporting the above-mentioned target search request.

[0036] Optionally, the object category to which the target object belongs includes a person category and a non-person category. When the target object is a person, the object category may be whether the person is a suspicious person, for example: Zhang San is a liar, Li Si is a wanted fugitive, etc.; when the target object is a non-person, the object category may be whether the person is a suspicious area, for example: a library is a crime scene, etc. The object category to which the target object belongs can be more specifically defined according to actual conditions.

[0037] In an optional embodiment, the obtaining of all search requests associated with the target object includes:

[0038] Step S202: detecting whether the received search request contains description information associated with the target object;

[0039] Step S204: If the received search request contains the description information, the received search request is aggregated based on the description information to obtain all the search requests associated with the target object.

[0040] In the embodiment of the present disclosure, taking the case where the above-mentioned target object is a person as an example, first, all search requests related to the person are mined and collected, and it is detected whether there is descriptive information associated with the above-mentioned target person in all the received search requests, for example: whether the name, code name and other information of the target person appear in the search request; if there is descriptive information associated with the above-mentioned target person, the received search requests are aggregated based on the relevant descriptive information to obtain all the above-mentioned search requests associated with the above-mentioned target person, and the total traffic of the target person can be calculated based on all the above-mentioned search requests associated with the above-mentioned target person.

[0041] In an optional embodiment, the target search request is determined as follows:

[0042] Step S302: Obtain an initial search request from all the above search requests using a preset sample template;

[0043] Step S304: in the process of retracing the initial search requests, determining the sentiment tendency of each of the initial search requests using a sentiment tendency model;

[0044] Step S306 , expanding the sample template based on the target sentiment tendency, and using the expanded sample template to obtain the initial search request again from all the search requests, and obtaining the target search request after multiple iterations.

[0045] Optionally, a preset sample template is used to obtain an initial search request from all the above search requests. The above sample template can be set or modified according to actual needs. The collection of various search requests obtained through different preset templates is the above initial search request; the above sample template is expanded based on the target emotional tendency. For example: through templates such as "Is xxx a liar?" and "Is xxx reliable?", negative search requests related to "xxx" can be obtained. Similarly, other search requests can be obtained through other templates; the above target search request is obtained after multiple iterations.

[0046] In an optional embodiment, determining the proportion of the target search request in all the search requests includes:

[0047] Step S402, determining the first search traffic value of all search requests and the second search traffic value of the target search request;

[0048] Step S404: Calculate the proportion of the target search request in all the search requests based on the first search traffic value and the second search traffic value.

[0049] In an embodiment of the present disclosure, the first search traffic value of all search requests and the second search traffic value of the target search request are determined, and the proportion of the second search traffic value in the first search traffic value is calculated to obtain the proportion value.

[0050] In an optional embodiment, determining the first search traffic value of all search requests and the second search traffic value of the target search request includes:

[0051] Step S502: Calculate the total traffic value of all the search requests to obtain the first search traffic value;

[0052] Step S504: Calculate the search traffic value corresponding to the target search request to obtain the second search traffic value.

[0053] In the embodiment of the present disclosure, still taking the target object as a person as an example, first calculate the total traffic value of all search requests for "xxx" to obtain the above-mentioned first search traffic value; then calculate the search traffic value corresponding to the above-mentioned target search request, and the above-mentioned target search request can be a specific search request, for example, a search request for "xxx is a liar", and then mine the search request template related to "xxx", for example: after mining "xxx was notified by the police", the relevant templates of "xxx is a liar" are expanded, and the search traffic value of the search request for "xxx was notified by the police" is determined; calculate the search traffic value corresponding to the expanded target search request to obtain the above-mentioned second search traffic value.

[0054] It should be noted that the number of the above-mentioned target search traffic values is not specifically limited and may vary according to the number of relevant templates mined.

[0055] In an optional embodiment, the determining of the credibility value corresponding to the target webpage includes:

[0056] Step S602, obtaining the number of clicks and impressions related to the target web page;

[0057] Step S604, calculating the ratio of the number of clicks to the number of impressions;

[0058] Step S606: Determine the ratio as the reliability value.

[0059] Optional, such as Figure 2 As shown in the diagram, the process of determining the credibility value corresponding to the target web page is as follows: the number of clicks associated with the target web page is the number of times the user clicks to enter the specific content page after searching; the number of impressions associated with the target web page is the number of times the target web page is searched; the ratio of the number of clicks to the number of impressions is calculated; and the ratio is determined as the credibility value.

[0060] For example, the number of clicks and impressions of the page searched by the search request "xxx is a liar" is obtained. However, in reality, "xxx" cannot be a liar. Therefore, the number of clicks on the page searched by the search request "xxx is a liar" is low, and the ratio obtained by calculating the above number of clicks and impressions is low, and the credibility calculated in the end is low.

[0061] In an optional embodiment, determining the object category to which the target object belongs based on the proportion value and the credibility value includes:

[0062] Step S702: comparing a preset credibility threshold with the credibility value to obtain a comparison result;

[0063] Step S704: Filter all web pages based on the comparison result to obtain an evidence list, wherein each evidence in the evidence list includes: the target web page and the credibility value of the target web page;

[0064] Step S706: Determine the object category to which the target object belongs based on the proportion value and the evidence list.

[0065] In the embodiment of the present disclosure, Figure 3 The schematic diagram of the process of determining the object category to which the target object belongs is shown, by presetting a credibility threshold and comparing the above credibility value with the size of the credibility threshold to obtain a comparison result; all web pages are screened according to the above comparison result to obtain an evidence list including the above target web page and the credibility value of the above target web page; and the object category to which the above target object belongs is determined based on the above proportion value and the above evidence list.

[0066] For example, if the credibility of the web page "xxx is a liar" is lower than the preset credibility threshold, then when determining the object category of "xxx", the web page "xxx is a liar" will not affect the determination of the object category of "xxx"; if the credibility of the web page "xxx is a liar" is higher than the preset credibility threshold, then when determining the object category of "xxx", it can be determined that "xxx" is a liar.

[0067] Through the embodiments of the present disclosure, a method is adopted for obtaining all search requests associated with a target object; determining the proportion of the target search request in all the above search requests; determining the credibility value corresponding to the target web page; and determining the object category to which the above target object belongs based on the above proportion value and the above credibility value; a large amount of labor costs are reduced, manual judgment is avoided, and the evidence is all third-party web pages, which ensures the fairness and objectivity of the product; and solves the technical problem that the current judgment of suspicious persons mainly uses image processing technology, which is applied in the field of video surveillance and security, and there is no report that is purely based on text analysis.

[0068] Example 2

[0069] According to an embodiment of the present disclosure, there is also provided an embodiment of a device for implementing the above-mentioned search-based object recognition method. Figure 4 is a schematic structural diagram of a search-based object recognition device according to a second embodiment of the present disclosure. Figure 4 As shown, the search-based object recognition device includes: an acquisition module 40, a first determination module 42, a second determination module 44 and a third determination module 46, wherein:

[0070] An acquisition module 40 is configured to acquire all search requests associated with a target object;

[0071] A first determining module 42 is configured to determine a proportion of a target search request in all the search requests, wherein the target search request is determined by determining the sentiment tendency of all the search requests;

[0072] A second determining module 44 is configured to determine a credibility value corresponding to a target webpage, wherein the target webpage is a webpage containing search content associated with the target search request, and the credibility value is used to indicate the credibility of the target webpage in supporting the target search request;

[0073] The third determining module 46 is configured to determine the object category to which the target object belongs based on the proportion value and the credibility value.

[0074] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0075] It should be noted that the acquisition module 40, the first determination module 42, the second determination module 44, and the third determination module 46 correspond to steps S102 to S108 in Example 1. The examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in Example 1. It should be noted that the modules, as part of the device, can be run in a computer terminal.

[0076] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in Example 1 and will not be repeated here.

[0077] The above-mentioned search-based object recognition device can also include a processor and a memory. The above-mentioned acquisition module 40, first determination module 42, second determination module 44 and third determination module 46 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0078] The processor includes a core, which retrieves the corresponding program unit from memory. There can be one or more cores. Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0079] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0080] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0081] Figure 5 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0082] like Figure 5As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the device 800 can also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0083] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0084] The computing unit 801 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the method for obtaining all search requests associated with the target object. For example, in some embodiments, the method for obtaining all search requests associated with the target object can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the method for obtaining all search requests associated with the target object described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured in any other appropriate manner (eg, by means of firmware) to execute a method to obtain all search requests associated with a target object.

[0085] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0086] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0087] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0088] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0089] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0090] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0091] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0092] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A search-based object recognition method, comprising: Get all search requests associated with the target object; Determining a proportion of a target search request in all the search requests, wherein the target search request is determined by determining the sentiment tendency of all the search requests; Determining a credibility value corresponding to a target webpage, wherein the target webpage is a webpage containing search content associated with the target search request, and the credibility value is used to represent the credibility of the target webpage in supporting the target search request; Determining the object category to which the target object belongs based on the proportion value and the credibility value; Among them, the target search request is determined in the following manner: an initial search request is obtained from all the search requests using a preset sample template; in the process of retracing the initial search request, an emotional tendency model is used to determine the emotional tendency of each of the initial search requests; the sample template is expanded based on the target emotional tendency, and the initial search request is obtained again from all the search requests using the expanded sample template, and the target search request is obtained after multiple iterations.

2. The method according to claim 1, wherein The obtaining of all search requests associated with the target object includes: Detecting whether the received search request contains description information associated with the target object; If the description information exists in the received search request, the received search request is aggregated based on the description information to obtain all the search requests associated with the target object.

3. The method according to claim 1, wherein Determining the proportion of the target search request in all the search requests includes: Determining a first search traffic value of all search requests and a second search traffic value of the target search request; The proportion of the target search request in all the search requests is calculated based on the first search traffic value and the second search traffic value.

4. The method according to claim 3, wherein: The determining of the first search traffic value of all search requests and the second search traffic value of the target search request includes: Calculating the total traffic value of all search requests to obtain the first search traffic value; Calculate the search traffic value corresponding to the target search request to obtain the second search traffic value.

5. The method according to claim 1, wherein Determining the credibility value corresponding to the target webpage includes: Obtain the number of clicks and impressions related to the target web page; Calculating the ratio of the number of clicks to the number of impressions; The ratio is determined as the confidence value.

6. The method according to claim 1, wherein The determining, based on the proportion value and the credibility value, the object category to which the target object belongs includes: Comparing a preset credibility threshold with the credibility value to obtain a comparison result; Screening all web pages according to the comparison result to obtain an evidence list, wherein each evidence in the evidence list includes: the target web page and the credibility value of the target web page; Based on the proportion value and the evidence list, determine the object category to which the target object belongs.

7. A search-based object recognition device comprising: An acquisition module, used to acquire all search requests associated with a target object; A first determining module is configured to determine a proportion of a target search request in all search requests, wherein the target search request is determined by determining the sentiment tendency of all search requests; a second determining module, configured to determine a credibility value corresponding to a target webpage, wherein the target webpage is a webpage containing search content associated with the target search request, and the credibility value is used to represent the credibility of the target webpage in supporting the target search request; A third determination module for determining the object category to which the target object belongs based on the proportion value and the credibility value; Among them, the first determination module is also used to: use a preset sample template to obtain an initial search request from all the search requests; in the process of retracing the initial search request, use the sentiment tendency model to determine the sentiment tendency of each of the initial search requests; expand the sample template based on the target sentiment tendency, and use the expanded sample template to obtain the initial search request again from all the search requests, and obtain the target search request after multiple iterations.

8. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the search-based object recognition method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the search-based object recognition method according to any one of claims 1-6.

10. A computer program product comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the search-based object recognition method according to any one of claims 1 to 6.

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