A method and device for identifying abnormal search statements, an electronic device, and a medium

Abstract

The present disclosure provides a method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for identifying abnormal search statements, relating to the field of computers, and particularly to the fields of natural language processing and cloud service technologies. The implementation solution is as follows: obtaining one or more search statements corresponding to a target account in a search statement group, where the search statement group is obtained by dividing the captured search statements according to a specific search intent; obtaining the search time corresponding to the search statement from the search statement group; determining the search intent of the search statement in response to determining that the search time of the search statement meets a preset rule; and identifying the search statement as an abnormal search statement in response to determining that the search intent of the search statement does not match the specific search intent.

Description

Technical Field

[0001] The present disclosure relates to the field of computers, and in particular, to the fields of natural language processing and cloud service technologies. Specifically, it relates to a method, apparatus, electronic device, computer-readable storage medium, and computer program product for identifying abnormal search statements. Background Art

[0002] Artificial intelligence is a discipline that studies how to make computers simulate certain thinking processes and intelligent behaviors of humans (such as learning, reasoning, thinking, planning, etc.). It has both hardware-level technologies and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing: Artificial intelligence software technologies mainly include several major directions such as computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, and knowledge graph technology.

[0003] In some data mining-based projects, such as those used to build smart cities, data mining services can be provided to users so that users can perform intention analysis based on the mined data content. Therefore, users generally have relatively high requirements for the quality of the provided data. Low-quality data has relatively low reference value for users, thus affecting users' satisfaction with the project. Summary of the Invention

[0004] The present disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for identifying abnormal search statements.

[0005] According to one aspect of the present disclosure, a method for identifying abnormal search statements is provided, including: obtaining a search statement corresponding to a target account in a search statement group, where the search statement group is obtained by dividing the captured search statements according to a specific search intention; obtaining the search time corresponding to the search statement from the search statement group; in response to determining that the search time of the search statement meets a preset rule, determining the search intention of the search statement; and in response to determining that the search intention of the search statement does not match the specific search intention, identifying the search statement as an abnormal search statement.

[0006] According to another aspect of the present disclosure, there is provided an apparatus for identifying abnormal search statements, including: a first acquisition unit configured to acquire search statements corresponding to a target account in a search statement group, where the search statement group is obtained by dividing captured search statements according to a specific search intention; a second acquisition unit configured to acquire the search time corresponding to the search statements from the search statement group; a determination unit configured to determine the search intention of the search statements in response to determining that the search time of the search statements meets a preset rule; and an identification unit configured to identify the search statements as abnormal search statements in response to determining that the search intention of the search statements does not match the specific search intention.

[0007] According to another aspect of the present disclosure, there is provided an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and when executed by the at least one processor, the instructions enable the at least one processor to execute the method described in the present disclosure.

[0008] According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method described in the present disclosure.

[0009] According to another aspect of the present disclosure, there is provided a computer program product including a computer program which, when executed by a processor, implements the method described in the present disclosure.

[0010] According to one or more embodiments of the present disclosure, it is possible to accurately and efficiently find bad cases in the system from the data level and the intention judgment level, thereby improving the data quality of the search statement group output by the system.

[0011] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings exemplarily illustrate embodiments and constitute a part of the specification, and are used together with the written description of the specification to explain the exemplary embodiments of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. In all the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1 FIG. shows a schematic diagram of an exemplary system in which the various methods described herein can be implemented according to an embodiment of the present disclosure;

[0014] Figure 2 The flowchart of the method for identifying abnormal search statements according to an embodiment of the present disclosure is shown;

[0015] Figure 3 The flowchart of further obtaining the recognition result of abnormal search statements based on the word segmentation result according to an embodiment of the present disclosure is shown;

[0016] Figure 4 The flowchart of identifying abnormal search statements according to an exemplary embodiment of the present disclosure is shown;

[0017] Figure 5 The structural block diagram of the device for identifying abnormal search statements according to an embodiment of the present disclosure is shown; and

[0018] Figure 6 The structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. Detailed implementation manners

[0019] The following describes exemplary embodiments of the present disclosure in conjunction with the accompanying drawings. Various details of the embodiments of the present disclosure are included to assist understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0020] In the present disclosure, unless otherwise specified, the terms "first", "second", etc. are used to describe various elements and are not intended to limit the positional relationship, timing relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, and in certain cases, based on the context description, they may also refer to different instances.

[0021] In the description of various examples in the present disclosure, the terms used are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in the present disclosure covers any one of the listed items and all possible combinations.

[0022] Embodiments of the present disclosure will be described in detail below in conjunction with the accompanying drawings.

[0023] Figure 1 The schematic diagram of an exemplary system 100 in which various methods and devices described herein can be implemented according to an embodiment of the present disclosure is shown. Refer to Figure 1, the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 that couple the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more applications.

[0024] In an embodiment of the present disclosure, the server 120 can run one or more services or software applications that enable a method for automatically identifying an abnormal search statement to be executed.

[0025] In certain embodiments, the server 120 can also provide other services or software applications that can include a non-virtual environment and a virtual environment. In certain embodiments, these services can be provided as web-based services or cloud services, such as provided to users of the client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0026] In Figure 1 In the configuration shown, the server 120 can include one or more components that implement the functions performed by the server 120. These components can include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating the client devices 101, 102, 103, 104, 105, and / or 106 can in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that various different system configurations are possible, which can be different from the system 100. Therefore, Figure 1 is an example of a system for implementing the various methods described herein and is not intended to be limiting.

[0027] Users can use the client devices 101, 102, 103, 104, 105, and / or 106 to obtain the identified abnormal search statements, etc. The client device can provide an interface that enables a user of the client device to interact with the client device. The client device can also output information to the user via this interface. Although Figure 1 only six client devices are depicted, those skilled in the art will be able to understand that the present disclosure can support any number of client devices.

[0028] Client devices 101, 102, 103, 104, 105, and / or 106 can include various types of computing devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors, or other sensing devices, etc. These computing devices can run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (such as GOOGLE Chrome OS); or include various mobile operating systems, such as MICROSOFT WindowsMobile OS, iOS, Windows Phone, Android. Portable handheld devices can include cellular phones, smartphones, tablets, personal digital assistants (PDAs), etc. Wearable devices can include head-mounted displays (such as smart glasses) and other devices. Gaming systems can include various handheld gaming devices, Internet-enabled gaming devices, etc. Client devices are capable of executing various different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and can use various communication protocols.

[0029] Network 110 can be any type of network known to those skilled in the art, which can support data communication using any one of a variety of available protocols (including but not limited to TCP / IP, SNA, IPX, etc.). By way of example only, one or more networks 110 can be a local area network (LAN), an Ethernet-based network, token ring, wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infrared network, a wireless network (such as Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0030] Server 120 can include one or more general-purpose computers, dedicated server computers (such as PC (personal computer) servers, UNIX servers, midrange servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 can include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (such as one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for the server). In various embodiments, server 120 can run one or more services or software applications that provide the functions described below.

[0031] The computing unit in server 120 can run one or more operating systems including any of the above - mentioned operating systems and any commercially available server operating systems. Server 120 can also run any one of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0032] In some embodiments, server 120 can include one or more applications to analyze and combine data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 can also include one or more applications to display data feeds and / or real - time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.

[0033] In some embodiments, server 120 can be a server of a distributed system or a server incorporating a blockchain. Server 120 can also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in the cloud computing service system, which solves the defects of difficult management and weak business scalability existing in traditional physical hosts and virtual private server (VPS) services.

[0034] System 100 can also include one or more databases 130. In certain embodiments, these databases can be used to store data and other information. For example, one or more of databases 130 can be used to store information such as audio files and video files. Databases 130 can reside in various locations. For example, the databases used by server 120 can be local to server 120, or can be remote from server 120 and can communicate with server 120 via a network - based or dedicated connection. Databases 130 can be of different types. In certain embodiments, the databases used by server 120 can be, for example, relational databases. One or more of these databases can store, update, and retrieve data to and from the databases in response to commands.

[0035] In certain embodiments, one or more of databases 130 can also be used by applications to store application data. The databases used by applications can be different types of databases, such as key - value repositories, object repositories, or conventional repositories supported by a file system.

[0036] Figure 1 System 100 can be configured and operated in various ways so as to enable the application of the various methods and apparatuses described according to the present disclosure.

[0037] In data exploration projects such as smart cities, data mining services are provided to users. Generally, search queries (queries) of the general public or a specific group are mined to perform intent analysis based on the data content. Therefore, users have relatively high requirements for the quality of the provided data. If the quality of the provided data is relatively low, the user satisfaction with the project will be greatly reduced. Therefore, quality assurance personnel are required to accurately and efficiently find the data that does not meet the preset requirements (badcase, i.e., bad data) mined in the project, and then provide the found badcase to the R & D personnel, thereby promoting the R & D personnel to continue to repair. Thus, high-quality mined data is provided to users to improve user satisfaction with the data.

[0038] Currently, the more common method for finding badcases is the way of extraction and manual annotation, so as to feedback the badcases to the developers and promote the developers to repair the badcases one by one. However, the method of manual annotation is often relatively inefficient. In the big data scenario, the badcases found through manual annotation are often just the tip of the iceberg, and a large number of unknown badcases still need to invest huge time costs and labor costs to discover. Therefore, the discovery cycle of badcases may be much longer than the project cycle. In the big data scenario, the data is often relatively diverse, and the extracted samples will have certain deviations. Once falling into the survivor bias, the final evaluation result will be very different from the real result. The direct consequence is that the gap between the user experience and the expected experience is huge, seriously affecting the reputation of the project.

[0039] Therefore, according to the embodiments of the present disclosure, a method for identifying abnormal search queries is provided. As Figure 2 shown, the method 200 for identifying abnormal search queries includes: obtaining the search query corresponding to the target account in the search query group, where the search query group is obtained by dividing the captured search queries according to a specific search intent (step 210); obtaining the search time corresponding to the search query from the search query group (step 220); determining the search intent of the search query in response to determining that the search time of the search query meets a preset rule (step 230); and identifying the search query as an abnormal search query in response to determining that the search intent of the search query does not match the specific search intent (step 240).

[0040] According to the embodiments of the present disclosure, it is possible to accurately and efficiently find badcases in the system from the data level and the intent judgment level, thereby improving the data quality of the search query group output by the system.

[0041] In the present disclosure, the target account is the search account of one or more users who search for information based on the search query.

[0042] The most core elements in a data mining project are the search statements and the search parameter information (such as search time, etc.) that accompany the search statements. Data mining analyzes the search statements to extract valuable information from the data. If the basic data quality of the search statements is relatively low, or even invalid data, then the value of the mining is relatively low, and mining this information not only wastes time but also wastes manpower.

[0043] According to an embodiment of the present disclosure, after obtaining the search time corresponding to the search statement, it may further include: determining whether the search time of the search statement meets a preset rule, so as to determine whether the search statement is an abnormal search statement based on the determination result. Exemplarily, the method according to the present disclosure may further include at least one of the following: in response to determining that the search time corresponding to the search statement is empty, identifying the search statement as an abnormal search statement; and in response to determining that the search time corresponding to the search statement does not conform to the preset format, identifying the search statement as an abnormal search statement.

[0044] It can be understood that other suitable ways to determine whether the search time of the search statement meets the preset rule are also possible, because the search time parameters of different search systems may vary.

[0045] According to some embodiments, the method according to the present disclosure may further include: in response to there being at least two search statements, determining the number of search statements within a first predetermined time period based on the search time; in response to determining that the number of search statements is greater than a second threshold, determining that the search times of the search statements within the first predetermined time period do not meet the preset rule; in response to determining that the number of search statements is not greater than the second threshold, determining that the search times of the search statements within the first predetermined time period meet the preset rule; and in response to determining that the search times of the search statements within the first predetermined time period do not meet the preset rule, identifying the search statements within the first predetermined time period as abnormal search statements.

[0046] Exemplarily, when there are at least two search statements corresponding to the target account obtained, the obtained search statements may be sorted according to their search times to determine the search statements within the first predetermined time period and their number. Thus, abnormal search statements are identified based on this number.

[0047] In the above embodiments, it is determined whether there are a large number of repeated searches for the target account within a short period of time. For example, the mined search statements show that the target user searched 10 times within 1 second, which is obviously impossible and unreasonable for ordinary people. For example, this unreasonable data is generated due to a system failure. Therefore, this set of search statements may not be very meaningful for data mining, and the mining value is relatively low. By identifying the number of repeated searches or a large number of searches within this short period of time, the screening of bad cases in the search statements can be achieved at the data layer. Thus, with the continuous iteration of the business, the data mining system can have better reusability, maintainability, and scalability, and can more efficiently complete the maintenance and development of the data mining system, continuously supporting the development of the project business.

[0048] According to some embodiments, a specific search intent can be determined based on one or more keywords. Therefore, determining the search intent of the search statement may include: performing word segmentation on the search statement respectively to obtain the phrases corresponding to the search statement; and determining the search intent of the search statement based on the phrases. And, in response to determining that there is a search statement in the at least one search statement whose search intent does not match the specific search intent, identifying the unmatched search statement as an abnormal search statement may include: in response to determining that the phrase does not match the keywords in the specific search intent and the preset combination mode of the keywords, identifying the search statement as an abnormal search statement.

[0049] Generally, in a data mining project, judging whether a certain search statement has a certain intent based on keyword matching is the first step in data mining. Therefore, its specific search intent includes many keywords for determining the search intent of a certain search statement. Exemplarily, a word segmentation tool based on natural language processing can be used to perform word segmentation on the corresponding search statement to obtain the word segmentation result. If the word segmentation result hits a certain keyword or a combination of several keywords, for example, the corresponding search statement can be tagged with the label corresponding to the keyword, and then it is further determined whether it meets the specific search intent through a series of rule matches.

[0050] Exemplarily, for the search statement of "XX**", the system determines that this search statement matches the "X*" intent, so it is classified into the search statement group that meets the "X*" search intent. After obtaining this search statement from the search statements, first, the search parameters including the search time can be judged, and after it meets the preset parameter rules, the search statement is segmented. However, through word segmentation, it is found that the word segmentation result is "XX, **", and the intent determined by the system is obviously wrong, so this search statement can be marked as a bad case.

[0051] According to some embodiments, such as Figure 3As shown, the method according to the present disclosure may further include: obtaining the number of occurrences of each word in the mismatched phrase within a second predetermined time period (step 310); sorting the words in the mismatched phrase based on the number of occurrences (step 320); and determining a predetermined number of words with the highest number of occurrences to use the words as the recognition result of the abnormal search statement (step 330).

[0052] In the above embodiment, by performing word frequency statistics on these mismatched word segmentation results, high-frequency words that cause bad cases can be found. That is, by counting the high-frequency words that cause bad cases, as the breakthrough point for finding bad cases and finding patterns, targeted intervention can be performed on this type of search statement, and its efficiency is much greater than finding and solving the corresponding bad cases one by one. Thus, not only the efficiency of finding bad cases is improved, but also the accuracy of system data mining is further improved.

[0053] According to some embodiments, wherein the specific search intention may further include an emotional attribute, and the search intention of the search statement includes an emotional attribute. Therefore, emotional analysis can be performed on the search statement to determine the emotional attribute corresponding to each search statement. And in response to determining that the obtained emotional attribute does not match the emotional attribute in the specific search intention, the search statement is identified as an abnormal search statement.

[0054] Exemplarily, the emotional attribute of the corresponding search statement can be identified by an algorithm or model based on natural language processing (such as the NLPC toolkit).

[0055] Specifically, when determining the intention according to the search query, if this intention has an emotional color, then when mining bad cases, we first determine the emotional color of the search query. If the emotional color determined by the query is inconsistent with the emotional color of the intention determined in the project through the query, then it is very likely to be a bad case.

[0056] Exemplarily, for a news-related search statement "Man in xx area **", when performing emotional analysis on this search statement, its emotional attribute is non-negative emotion. However, the data mining system determines that the intention of this user is "**", and "**" is a negative emotion word. Through the comparison result, it can be found that when the system mines the intention of the search statement, it does not notice that this search statement is a news item. Thus, when the emotional attribute of the search statement obtained through emotional analysis does not match the specific search intention in the system intention mining, this search statement is identified as an abnormal search statement.

[0057] In some embodiments, sentiment analysis can be performed on the search statements obtained from the search statement group through a pre-trained model. Exemplarily, corresponding sample data can be selected according to project requirements to train the model based on the selected sample data to obtain a model adapted to the project requirements.

[0058] Figure 4 FIG. shows a flowchart for identifying abnormal search statements according to an embodiment of the present disclosure. As Figure 4 shown, in step 401, a search statement is obtained, that is, one or more search statements corresponding to accounts are randomly obtained from the classified search statement group, and the search statement group is classified according to a specific search intention. In step 402, it is determined whether the obtained search statement is a bad case at the data layer, that is, whether the search time of the search statement meets a preset rule (such as a large number of searches or repeated searches in a short time, etc.); further, the data layer determination can also check the content of the search statement itself. For example, whether the search content exists / does not exist, whether the search content is too long / too short, whether the encoding format of the search content is correct, and so on. In step 403, operations such as storage and aggregation can be performed on the bad case at the data layer to intervene in the identified bad case at the data layer in step 408. If it is determined in step 402 that it is not a bad case at the data layer, it will be further determined in step 404 whether the specific intention is a sentiment intention. If so, in step 405, the sentiment attribute of the corresponding search statement is determined and obtained. For example, the sentiment attribute can be positive or negative, etc. In step 406, it is determined whether the sentiment attribute obtained in step 405 is consistent with the specific intention. If not, in step 407, operations such as storage and aggregation can be performed on the bad case of the sentiment intention to intervene in the identified bad case at the data layer in step 408. If it is determined in step 404 that the specific intention is not a sentiment intention, the corresponding search statement is tokenized in step 409, for example, based on an algorithm or model of natural language processing, etc. In step 410, it is determined whether the tokenization result matches the keywords corresponding to the specific intention. If not, in step 411, operations such as storage and aggregation can be performed on the bad case of the non-sentiment intention. In step 412, the word frequency of the tokenization result of the bad case of the non-sentiment intention can be counted to determine high-frequency words in step 413. Thus, the bad case identified based on the high-frequency words can be intervened in step 408.

[0059] According to the method of the embodiment of the present disclosure, instead of manually searching for bad cases in a large amount of records, it is from the data layer, sentiment intention determination, and non-sentiment intention Figure 3Mining bad cases existing in the system from multiple aspects can accurately and efficiently find the bad cases existing in the system, thereby improving the data quality output by the system and enhancing the project delivery quality and project reputation.

[0060] According to an embodiment of the present disclosure, as Figure 5 shown, there is also provided a device 500 for identifying abnormal search statements, including: a first acquisition unit 510 configured to acquire the search statements corresponding to the target accounts in the search statement group, where the search statement group is obtained by dividing the captured search statements according to a specific search intention; a second acquisition unit 520 configured to acquire the search time corresponding to the search statements from the search statement group; a determination unit 530 configured to determine the search intention of the search statements in response to determining that the search time of the search statements meets a preset rule; and an identification unit 540 configured to identify the search statements as abnormal search statements in response to determining that the search intention of the search statements does not match the specific search intention.

[0061] Here, the operations of the above units 510-540 of the device 500 for identifying abnormal search statements are respectively similar to the operations of steps 210-240 described above, and will not be elaborated here.

[0062] In the technical solution of the present disclosure, the collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved are all in compliance with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0063] According to an embodiment of the present disclosure, there is also provided an electronic device, a readable storage medium, and a computer program product.

[0064] Referring to Figure 6 , the structural block diagram of an electronic device 600 that can be used as the server or client of the present disclosure will now be described. It is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, 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 processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described herein and / or claimed.

[0065] As Figure 6As shown, the electronic device 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0066] Multiple components in the electronic device 600 are connected to the I / O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 can be any type of device capable of inputting information into the electronic device 600. The input unit 606 can receive input numerical or character information and generate key signal inputs related to user settings and / or function controls of the electronic device, and can include, but is not limited to, a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and / or a remote control. The output unit 607 can be any type of device capable of presenting information and can include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 608 can include, but is not limited to, magnetic disks and optical discs. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and can include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a BluetoothTM device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0067] The computing unit 601 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 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 running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 executes the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method 200 described above may be executed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute method 200 in any other suitable manner (e.g., by means of firmware).

[0068] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-a-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated 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 the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0069] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0070] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. 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, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0071] To provide for 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, 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, speech, or tactile input).

[0072] 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 the user can interact with an implementation 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.

[0073] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is generated by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

[0074] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in this disclosure can be executed 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, and this is not limited herein.

[0075] Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above methods, systems, and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only defined by the authorized claims and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, the steps can be executed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. Importantly, with the evolution of technology, many of the elements described herein can be replaced by equivalent elements that emerge after the present disclosure.

Claims

1. A method for identifying abnormal search statements, comprising: Obtaining the search statements corresponding to a target account in a search statement group, where the search statement group is obtained by dividing the captured search statements according to a specific search intent, and the specific search intent includes an emotional attribute; Obtaining the search time corresponding to the search statement from the search statement group; In response to the search statements being at least two, determining the number of search statements within a first predetermined time period based on the search time; In response to determining that the number of search statements is not greater than a second threshold, determining that the search times of the search statements within the first predetermined time period satisfy a preset rule; In response to determining that the search times of the search statements satisfy the preset rule and the specific search intent is not an emotional attribute, determining the search intent of the search statements, including: performing word segmentation on the search statements respectively to obtain the word groups corresponding to the search statements; and In response to determining that the search intent of the search statements does not match the specific search intent, identifying the search statements as abnormal search statements, including: in response to determining that the word groups do not match the keywords in the specific search intent and the preset combination mode of the keywords, identifying the search statements as abnormal search statements.

2. The method according to claim 1, wherein, The search intent of the search statements includes an emotional attribute.

3. The method according to claim 1, further comprising: In response to determining that the number of search statements is greater than the second threshold, determining that the search times of the search statements within the first predetermined time period do not satisfy the preset rule; And In response to determining that the search times of the search statements within the first predetermined time period do not satisfy the preset rule, identifying the search statements within the first predetermined time period as abnormal search statements.

4. The method according to claim 1 or 3, further comprising at least one of the following items: In response to determining that the search time corresponding to the search statement is empty, identifying the search statement as an abnormal search statement; and In response to determining that the search time corresponding to the search statement does not satisfy a preset format, identifying the search statement as an abnormal search statement.

5. The method according to claim 1, further comprising: Obtaining the number of occurrences of each word in the unmatched word groups within a second predetermined time period; Sorting the words in the unmatched word groups based on the number of occurrences; And Determining a predetermined number of words with the highest number of occurrences to use the words as the identification result of the abnormal search statements.

6. An apparatus for identifying abnormal search statements, comprising: A first acquisition unit configured to obtain the search statements corresponding to a target account in a search statement group, where the search statement group is obtained by dividing the captured search statements according to a specific search intent, and the specific search intent includes an emotional attribute; A second acquisition unit configured to obtain the search time corresponding to the search statement from the search statement group; A unit for, in response to the search statements being at least two, determining the number of search statements within a first predetermined time period based on the search time; A unit for determining that the number of search statements is not greater than a second threshold and that the search time of the search statements within a first predetermined time period satisfies a preset rule; A determination unit configured to determine the search intent of the search statement in response to determining that the search time of the search statement satisfies a preset rule and that the specific search intent is not an emotional attribute, including: a unit for performing word segmentation on the search statement respectively to obtain the phrases corresponding to the search statement; and An identification unit configured to identify the search statement as an abnormal search statement in response to determining that the search intent of the search statement does not match the specific search intent, including: a unit for identifying the search statement as an abnormal search statement in response to determining that the phrase does not match the keywords in the specific search intent and the preset combination mode of the keywords.

7. The apparatus according to claim 6, wherein, The search intent of the search statement includes an emotional attribute.

8. The apparatus according to claim 6, further comprising: A unit for determining that the number of search statements is greater than a second threshold and that the search time of the search statements within a first predetermined time period does not satisfy a preset rule; And A unit for identifying the search statements within the first predetermined time period as abnormal search statements in response to determining that the search time of the search statements within the first predetermined time period does not satisfy a preset rule.

9. The apparatus according to claim 6 or 8, further comprising at least one of the following items: A unit for identifying the search statement as an abnormal search statement in response to determining that the search time corresponding to the search statement is empty; and A unit for identifying the search statement as an abnormal search statement in response to determining that the search time corresponding to the search statement does not satisfy a preset format.

10. The apparatus according to claim 6, further comprising: A unit for obtaining the number of occurrences of each word in the unmatched phrase within a second predetermined time period; A unit for sorting the words in the unmatched phrase based on the number of occurrences; And A unit for determining a predetermined number of words with the highest number of occurrences to use the words as the identification result of the abnormal search statement.

11. An electronic device, 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 to enable the at least one processor to execute the method according to any one of claims 1-5.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause a computer to execute the method according to any one of claims 1-5.

13. A computer program product comprising a computer program, wherein, The computer program implements the method according to any one of claims 1-5 when executed by a processor.

Images