A method and device for searching a target object

Abstract

The present invention discloses a method and device for searching a target object, and relates to the field of computer technology. A specific implementation of the method includes: obtaining a configured combination rule, and generating a data source of a set data format according to the combination rule; wherein the combination rule includes multiple rule conditions; parsing the data source according to the data format to map the data source to a corresponding entity class; obtaining the multiple rule conditions from the entity class, accessing a storage device, and querying the target object that meets the multiple rule conditions from the storage device. This implementation generates a multi-dimensional data source based on the configured combination rule, and then uses the data source to search for the target object, which enriches the search dimension, improves the accuracy of the search, and is easy to expand.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a target object search method and device. Background Art

[0002] Whether the crowd selected from the massive amount of Internet data is accurate directly determines the operation effect. In the existing technology, the user behavior is generally labeled, and a single label is used as a selection condition to select the required crowd, and the crowd is used for new customer acquisition, remarketing, statistical analysis, etc. For example, user A often buys cosmetics and clothes, and the label will be defined as beauty and clothing. If the selection condition is clothing, user A will be selected into the crowd.

[0003] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art:

[0004] Existing technologies only support crowd selection based on a single tag. The selection search dimension is single, with poor accuracy and scalability, and is not easy to connect with a third party. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a target object search method and device, which generates a multi-dimensional data source based on configured combination rules, and then uses the data source to search for the target object, thereby enriching the search dimensions, improving the accuracy of the search, and being easy to expand.

[0006] To achieve the above objective, according to one aspect of an embodiment of the present invention, a target object search method is provided.

[0007] A target object search method according to an embodiment of the present invention includes: obtaining a configured combination rule, and generating a data source of a set data format according to the combination rule; wherein the combination rule includes multiple rule conditions; parsing the data source according to the data format to map the data source to a corresponding entity class; obtaining the multiple rule conditions from the entity class, accessing a storage device, and querying the storage device for target objects that meet the multiple rule conditions.

[0008] Optionally, there are multiple combination rules, and one combination rule corresponds to generating one data source; after the step of generating a data source with a set data format according to the combination rule, the method further includes: combining and nesting the multiple data sources according to the set execution information of the multiple combination rules and using the set operator.

[0009] Optionally, the data format is JSON format; parsing the data source according to the data format to map the data source to a corresponding entity class includes: using a JSON parser to parse the data source, mapping the parsing result with a predefined Bean object, and generating an entity class.

[0010] Optionally, the data format is JSON format; parsing the data source according to the data format to map the data source to a corresponding entity class, including: using a JSON parser to parse the combined and nested data source, mapping the parsing results to predefined multiple Bean objects one by one, and generating corresponding multiple entity classes.

[0011] Optionally, the operator is any one or more of intersection, union, and difference.

[0012] Optionally, the method further includes: acquiring a pre-built user portrait according to an object identifier of the target object; and performing statistical analysis on the target object according to the user portrait.

[0013] To achieve the above objective, according to another aspect of an embodiment of the present invention, a target object searching device is provided.

[0014] A target object search device according to an embodiment of the present invention includes: a generation module, which is used to obtain a configured combination rule and generate a data source of a set data format according to the combination rule; wherein the combination rule includes multiple rule conditions; a parsing module, which is used to parse the data source according to the data format to map the data source to a corresponding entity class; and a query module, which is used to obtain the multiple rule conditions from the entity class, access a storage device, and query the storage device for target objects that meet the multiple rule conditions.

[0015] Optionally, there are multiple combination rules, and one combination rule generates one data source; the device also includes: a combination and nesting module, which is used to combine and nest multiple data sources according to the set execution information of the multiple combination rules and using the set operators.

[0016] Optionally, the data format is JSON format; the parsing module is further used to parse the data source using a JSON parser, map the parsing result with a predefined Bean object, and generate an entity class.

[0017] Optionally, the data format is JSON format; the parsing module is further used to use a JSON parser to parse the combined and nested data source, map the parsing results with multiple predefined Bean objects one by one, and generate corresponding multiple entity classes.

[0018] Optionally, the operator is any one or more of intersection, union, and difference.

[0019] Optionally, the device further includes: a statistical analysis module, configured to obtain a pre-built user portrait according to an object identifier of the target object; and perform statistical analysis on the target object according to the user portrait.

[0020] To achieve the above objective, according to another aspect of an embodiment of the present invention, a target object searching device is provided.

[0021] To achieve the above objective, according to another aspect of an embodiment of the present invention, an electronic device is provided.

[0022] An electronic device according to an embodiment of the present invention comprises: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement a target object search method according to an embodiment of the present invention.

[0023] To achieve the above objective, according to another aspect of the embodiments of the present invention, a computer readable medium is provided.

[0024] A computer-readable medium according to an embodiment of the present invention stores a computer program, and when the program is executed by a processor, a target object search method according to an embodiment of the present invention is implemented.

[0025] An embodiment of the above invention has the following advantages or beneficial effects: based on the configured combination rules, a multi-dimensional data source is generated, and then the data source is used to search for the target object, which enriches the search dimension, improves the accuracy of the search, and is easy to expand; the data source is combined and nested using operators to achieve the free combination of multiple data sources, and third-party data can be accessed, and the data source is subsequently used to search for the target object, which further enriches the search dimension and improves the accuracy of the search; the data source uses the JSON format, which is easy to expand and identify; by converting the JSON data source into an entity class, data flow can be conveniently and simply realized; the intersection, union, and difference methods are used to make it possible to arbitrarily nest and combine data sources, and it can also be expanded on the data source; statistical analysis based on the searched target object can better help enterprises understand industry trends, conduct precision marketing, and attract new users.

[0026] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0028] Figure 1 is a schematic diagram of the main steps of a target object search method according to an embodiment of the present invention;

[0029] Figure 2 is a schematic diagram of the main flow of a target object search method according to another embodiment of the present invention;

[0030] Figure 3 is a schematic diagram of the main flow of a target object search method according to yet another embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram of the main process of selecting a crowd using a target object search method according to another embodiment of the present invention;

[0032] Figure 5 is a schematic diagram of main modules of a target object search device according to an embodiment of the present invention;

[0033] Figure 6 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0034] Figure 7 It is a schematic diagram of the structure of a computer device suitable for implementing the electronic device of the embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0036] Figure 1 FIG. 1 is a schematic diagram of the main steps of a target object search method according to an embodiment of the present invention. Figure 1 As shown, the target object search method of the embodiment of the present invention mainly includes the following steps:

[0037] Step S101: Obtain the configured combination rule, and generate a data source of a set data format according to the combination rule. The user customizes multiple rule conditions according to business needs, and these rule conditions constitute the combination rule. Among them, the rule condition is the condition required for searching the target object, such as browsing an item, purchasing an item, and being between 20 and 40 years old.

[0038] After obtaining the combination rules configured by the user, the combination rules are used to generate data sources according to the set data format. Data formats include JSON (JavaScript Object Notation) format, XML (Extensible Markup Language) format, etc. Among them, JSON format is a lightweight data exchange format, and XML is an extensible markup language.

[0039] Step S102: Parse the data source according to the data format to map the data source to a corresponding entity class. Parse the data source using a parsing method that matches the data format to determine which rule conditions the data source contains to generate an entity class for describing the data source. An entity class is a class that defines properties and methods. This step generates a recognizable entity class by parsing the data source, and the entity class corresponds one-to-one to the combination rule.

[0040] Step S103: Obtain the multiple rule conditions from the entity class, access the storage device, and query the target objects that meet the multiple rule conditions from the storage device. Obtain multiple rule conditions from the entity class, then initiate access to the storage device, use these rule conditions as query conditions, and query the target objects that meet all the rule conditions from the storage device. According to business needs, the target object here can be a target population, a target enterprise, a target item, etc. The storage device stores information related to the target object, which can be a database in the embodiment.

[0041] Figure 2 FIG. 1 is a schematic diagram of the main flow of a target object search method according to another embodiment of the present invention. Figure 2 As shown, the target object search method of the embodiment of the present invention is executed by a target object search device, supports a single data source to generate a target object, and mainly includes the following steps:

[0042] Step S201: Obtain the user-defined combination rule and generate a JSON data source based on the combination rule. In the embodiment, the user can customize multiple rule conditions in the rule configuration interface, and these rule conditions constitute the combination rule. After obtaining the user-defined combination rule from the rule configuration interface, the combination rule is used to generate a data source in JSON format, and this data source is a JSON file. One customized combination rule generates one data source.

[0043] Assume that the user can customize 5 rule conditions in the rule configuration interface: from January 2, 2020 to March 2, 2020, no price limit, no frequency limit, and browsing a certain brand. Combining these rule conditions together in JSON format will form a separate data source.

[0044] In an optional embodiment, a user may upload a user-defined data source package, which may include one or more data sources. Accordingly, the target object search device may directly obtain the user-defined data source package and perform subsequent processing.

[0045] Step S202: Generate a single Bean entity class corresponding to the JSON data source by parsing the JSON data source. Use the JSON parser to parse the data source, map the parsing result with the predefined Bean object, and generate a single Bean entity class. Among them, Bean is a reusable Java component that can be called by Java applications such as Applet, Servlet, SP, etc., and can also be used visually by Java development tools. Bean contains properties, methods, events, etc.

[0046] Assume the JSON data source is:

[0047]

[0048] The Bean entity class obtained by parsing is: {age=25, sex='male'}. This step can make subsequent data operations convenient and simple, and identifiable.

[0049] Step S203: Obtain all rule conditions through the Bean entity class, and query the target objects that meet all rule conditions from the database table. In this embodiment, the database table stores relevant information of the objects to be screened, such as object identification, age, gender, etc. Still taking the above JSON data source as an example, after step S203, all objects with an age of 25 and a gender of male can be screened from the database table.

[0050] Figure 3 FIG. 1 is a schematic diagram of the main flow of a target object search method according to another embodiment of the present invention. Figure 3 As shown, the target object search method of the embodiment of the present invention supports multiple data sources to generate target objects, and mainly includes the following steps:

[0051] Step S301: Obtain multiple combination rules and execution information defined by the user, and generate multiple data sources in JSON format based on the multiple combination rules and execution information. The user defines multiple combination rules and configures the execution information of these combination rules. After obtaining all the combination rules and execution information, first generate the corresponding data source for each combination rule in JSON format, and then combine and nest the data sources using matching operators according to the execution information to generate multiple data sources in JSON format. In the embodiment, the execution information describes the execution order of the combination rules and the specific operations of the execution, and the operators include any one or more of intersection, union, and difference.

[0052] For example, the user defines three combination rules, namely combination rule 1, combination rule 2 and combination rule 3. The execution information of these three combination rules is: after intersection of combination rule 1 and combination rule 2, union with combination rule 3. Then first generate corresponding data sources for combination rule 1, combination rule 2 and combination rule 3, namely data source 1, data source 2 and data source 3; then connect data source 1 and data source 2 using the intersection operator, and then connect the connection result and data source 3 using the union operator to generate a multi-data source. This step freely combines the data sources corresponding to multiple custom combination rules into a multi-data source.

[0053] Step S302: Generate multiple Bean entity classes corresponding to the multiple data sources by parsing the multiple data sources in JSON format. Use the JSON parser to parse the multiple data sources, map the parsing results to the predefined multiple Bean objects one by one, and generate multiple corresponding Bean entity classes. During the parsing process, the parameters of the JSON correspond to the parameters of the entity one by one, so by parsing the multiple data sources, they can be mapped to multiple entity classes.

[0054] Step S303: Obtain all rule conditions through the Bean entity class, and query the target objects that meet all rule conditions from the database table. In the embodiment, an executable expression is first generated according to the Bean entity class, and then the expression is used to query the target objects that meet the expression from the database table, and the intersection, union, and difference operations are performed on these target objects in sequence according to the execution information to generate the final target object.

[0055] For example, the target objects corresponding to data source 1, data source 2 and data source 3 are target object 1, target object 2 and target object 3 respectively. After intersecting target object 1 and target object 2 according to the object identifier, and then merging them with target object 3, the final target object can be obtained.

[0056] In a preferred embodiment, the user can add a new data source to the data source generated by step S101, step S201 and step S301 (hereinafter referred to as the original data source, which can be a single data source or multiple data sources) by customizing the data source. The data source can be composed of one rule condition or multiple rule conditions. The customized data source will participate in the combined nesting as one of the data sources. The specific implementation of adding a new data source is: defining the rule conditions of a single data source, generating a data source based on the rule conditions, and automatically embedding the generated data source into the original data source using an operator.

[0057] In the above embodiment, when adding a new data source, the user only needs to provide the rule conditions, which greatly simplifies the new data source. At the same time, the search for the target object is no longer performed in a single way, and the free combination of multiple data sources is realized to the greatest extent. Moreover, the combination rules of each data source are independent, and the data sources can be connected to each other, and can also receive customized groups (i.e. third-party groups) or customized labels and other dimensions to enrich the concept of groups. This makes the sources of the data sources in this embodiment compatible to the greatest extent.

[0058] Figure 4 FIG. 1 is a schematic diagram of the main process of selecting a crowd using a target object search method according to another embodiment of the present invention. Figure 4 As shown, the crowd selection process of the embodiment of the present invention mainly includes the following steps:

[0059] (1) Obtain multiple combination rules defined by the user and generate a corresponding data source for each combination rule. After obtaining the combination rules defined by the user, generate a corresponding data source for each combination rule in JSON format. Multiple data sources exist in parallel.

[0060] (2) Multiple data sources are combined through operators to obtain a multi-data source. Multiple data sources can be combined with different operators (intersection, union, difference). This embodiment can nest the combination of multiple data sources, and can also combine them on top of the combination. The combination of different rule conditions is realized to the greatest extent, so that the population package mined is realized and concretized to the greatest extent. The following is an example JSON definition:

[0061]

[0062]

[0063] The JSON definition in the above example contains three layers of nesting, and is formed by four nested data sources, namely "dateSource1", "dateSource2", "dateSource3" and "dateSource4". After dateSource3 and dateSource4 are diffed, they are intersected with dateSource2, and finally they are unioned with dateSource1. The above method realizes the arbitrary nesting combination of data sources, and can also be expanded on the data source.

[0064] (3) Parse the multiple data sources into corresponding Bean entity classes, and obtain the crowd package from the database table through the Bean entity class. The specific implementation of this step is shown in step S302 and step S303, which will not be repeated here.

[0065] (4) Perform a profile analysis on the users in the crowd package. The generated crowd package includes the user ID of each user. The corresponding user profile is obtained based on the user ID. The crowd package can be statistically analyzed based on the user profile. For example, there are 1,000 users in the crowd package. Based on the user profile, it can be counted that there are 300 male users and 700 female users. The user profile is pre-constructed based on the user's basic information (such as age, gender, city, etc.), user behavior (such as purchase, browsing, sharing, etc.), etc.

[0066] It is understandable that the crowd package of this embodiment is a separate individual. In addition to the portrait analysis of the crowd package, further RFM analysis, purchase analysis, etc. can also be performed. Among them, RFM is the initials of the three indicators of Rencency (most recent consumption), Frequency (consumption frequency) and Monetary (consumption amount).

[0067] Based on the target object search method of this embodiment, to select a crowd, the user only needs to customize the combination rules of a data source, which is simple and easy to expand. In addition, this embodiment generates multiple data sources by abstracting different dimension (label) combinations, and then uses operators to connect the data sources to generate the final crowd package. The data source can be freely and infinitely expanded, has good flexibility, and is easy to combine and use. In the future, it can better help advertisers to implement relatively complex and sophisticated directional logic and accurately select the required crowd.

[0068] Figure 5 FIG. 1 is a schematic diagram of the main modules of the target object search device according to an embodiment of the present invention. Figure 5 As shown, the target object search device 500 of the embodiment of the present invention mainly includes:

[0069] The generation module 501 is used to obtain the configured combination rules and generate a data source in a set data format according to the combination rules. The user customizes multiple rule conditions according to business needs, and these rule conditions constitute the combination rules. Among them, the rule conditions are the conditions required to search for the target object, such as browsing a certain item, purchasing a certain item, and being between 20 and 40 years old. After obtaining the combination rules configured by the user, the combination rules are used to generate a data source according to the set data format. The data format is, for example, JSON format, XML format, etc.

[0070] The parsing module 502 is used to parse the data source according to the data format to map the data source to a corresponding entity class. The data source is parsed using a parsing method that matches the data format to determine which rule conditions the data source contains to generate an entity class for describing the data source. An entity class is a class that defines attributes and methods. This module generates a recognizable entity class by parsing the data source, and the entity class corresponds to the combination rule one by one.

[0071] The query module 503 is used to obtain the multiple rule conditions from the entity class, access the storage device, and query the target objects that meet the multiple rule conditions from the storage device. Multiple rule conditions are obtained from the entity class, and then access is initiated to the storage device, and these rule conditions are used as query conditions to query the target objects that meet all the rule conditions from the storage device. According to business needs, the target object here can be a target population, a target enterprise, a target item, etc. The storage device stores information related to the target object, which can be a database in the embodiment.

[0072] In addition, the target object search device 500 of the embodiment of the present invention may also include: a combined nesting module and a statistical analysis module ( Figure 5 The combination and nesting module is used to combine and nest the multiple data sources according to the execution information of the multiple combination rules, using the set operator. The statistical analysis module is used to obtain a pre-built user portrait according to the object identifier of the target object; and perform statistical analysis on the target object according to the user portrait.

[0073] From the above description, it can be seen that based on the configured combination rules, a multi-dimensional data source is generated, and then the data source is used to search for the target object, which enriches the search dimension, improves the accuracy of the search, and is easy to expand.

[0074] Figure 6 An exemplary system architecture 600 to which the target object search method or target object search device according to the embodiment of the present invention can be applied is shown.

[0075] like Figure 6As shown, system architecture 600 may include terminal devices 601, 602, 603, network 604 and server 605. Network 604 is used to provide a medium for communication links between terminal devices 601, 602, 603 and server 605. Network 604 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0076] The user can use the terminal devices 601, 602, 603 to interact with the server 605 through the network 604 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 601, 602, 603, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0077] The terminal devices 601 , 602 , and 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers, etc.

[0078] The server 605 may be a server that provides various services, such as a background management server for processing using the combination rules sent by the terminal devices 601, 602, and 603. The background management server may generate data sources, parse data sources, query storage devices, and feed back processing results (such as the queried target object) to the terminal device.

[0079] It should be noted that the target object search method provided in the embodiment of the present application is generally executed by the server 605 , and accordingly, the target object search device is generally set in the server 605 .

[0080] It should be understood that Figure 6 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0081] According to an embodiment of the present invention, the present invention also provides an electronic device and a computer-readable medium.

[0082] The electronic device of the present invention comprises: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement a target object search method of an embodiment of the present invention.

[0083] The computer-readable medium of the present invention stores a computer program thereon, and when the program is executed by a processor, a target object search method according to an embodiment of the present invention is implemented.

[0084] Reference below Figure 7, which shows a schematic diagram of the structure of a computer system 700 suitable for implementing an electronic device of an embodiment of the present invention. Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0085] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the computer system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0086] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage section 708 as needed.

[0087] In particular, according to the embodiments disclosed in the present invention, the process described in the main step diagram above can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the main step diagram. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, the above-mentioned functions defined in the system of the present invention are executed.

[0088] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, 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 above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0089] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0090] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor includes a generation module, a parsing module and a query module. The names of these modules do not constitute a limitation on the modules themselves in some cases. For example, the generation module may also be described as "a module for obtaining a combination rule of a configuration and generating a data source of a set data format according to the combination rule".

[0091] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: obtaining a configured combination rule, and generating a data source of a set data format according to the combination rule; wherein the combination rule includes multiple rule conditions; parsing the data source according to the data format to map the data source to a corresponding entity class; obtaining the multiple rule conditions from the entity class, accessing a storage device, and querying the target object that meets the multiple rule conditions from the storage device.

[0092] According to the technical solution of the embodiment of the present invention, a multi-dimensional data source is generated based on the configured combination rules, and then the data source is used to search for the target object, which enriches the search dimensions, improves the accuracy of the search, and is easy to expand.

[0093] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0094] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A target object search method, characterized in that: include: Acquire a configured combination rule, and generate a data source of a set data format according to the combination rule; wherein the combination rule includes a plurality of rule conditions; There are multiple combination rules, one combination rule generates one data source correspondingly, and according to the set execution information of the multiple combination rules, the multiple data sources are combined and nested using the set operators; Parsing the data source according to the data format to map the data source to a corresponding entity class, including: using a JSON parser to parse the combined and nested data source, mapping the parsing result to a plurality of predefined Bean objects one by one, and generating a plurality of corresponding entity classes; wherein the data format is a JSON format; The multiple rule conditions are obtained from the entity class, a storage device is accessed, and a target object that meets the multiple rule conditions is searched from the storage device.

2. The method according to claim 1, characterized in that The operator is any one or more of intersection, union, and difference.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: Obtaining a pre-built user portrait according to the object identifier of the target object; According to the user portrait, statistical analysis is performed on the target object.

4. A target object search device, characterized in that: include: A generating module, used to obtain a configured combination rule, and generate a data source of a set data format according to the combination rule; wherein the combination rule includes a plurality of rule conditions; A combination and nesting module, used to combine and nest the multiple data sources according to the execution information of the multiple combination rules, using the set operators; wherein there are multiple combination rules, and one combination rule generates one data source correspondingly; A parsing module, used to parse the data source according to the data format to map the data source to a corresponding entity class, including: using a JSON parser to parse the combined and nested data source, mapping the parsing results to a plurality of predefined Bean objects one by one, and generating a plurality of corresponding entity classes; wherein the data format is a JSON format; The query module is used to obtain the multiple rule conditions from the entity class, access the storage device, and query the target objects that meet the multiple rule conditions from the storage device.

5. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 3.

6. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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