Resource matching processing method, device, electronic device and storage medium

Abstract

The present disclosure relates to a resource matching processing method, device, electronic device, computer-readable storage medium, and computer program product. The method obtains object identifiers of multiple candidate objects corresponding to network resources, and determines target information of target object identifiers from the object identifiers of multiple candidate objects. When the object identifier of the candidate object contains target information, the candidate object is determined to be a target object matching the network resource, and an association relationship between the target object and the network resource is established. Since the present embodiment automatically reviews multiple candidate objects based on the target information of the target object identifier, and establishes an association relationship between the target object that has passed the review and the network resource, the review efficiency and accuracy of the candidate objects can be improved compared to manual review.

Description

Technical Field

[0001] The present disclosure relates to the field of information processing technology, and in particular to a resource matching processing method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] With the development of Internet technology, various online platforms have sprung up, and people can shop, entertain, etc. through online platforms. For example, you can buy coupons in groups through online platforms, and then go to the store to redeem them. Therefore, when online platforms launch coupons, they need to associate them with the corresponding applicable stores so that users can go to the store to redeem them. Usually, there are dozens to thousands of applicable stores associated with a coupon.

[0003] In the related art, the validity of the applicable stores corresponding to the gift vouchers is generally reviewed manually, and only the stores that have been reviewed to be valid can be associated with the corresponding gift vouchers.

[0004] However, the manual review method is inefficient, and long-term reviews will lead to review fatigue, which will affect the accuracy of the review. Summary of the invention

[0005] The present disclosure provides a resource matching processing method, device, electronic device, computer-readable storage medium, and computer program product to at least solve the problem of low efficiency in manually reviewing the effectiveness of applicable stores corresponding to commodity coupons in the related art. The technical solution of the present disclosure is as follows:

[0006] According to a first aspect of an embodiment of the present disclosure, a resource matching processing method is provided, including:

[0007] Obtaining object identifiers of multiple candidate objects corresponding to the network resource;

[0008] Determine target information of a target object identifier from among the object identifiers of a plurality of candidate objects;

[0009] When the object identifier of the candidate object includes the target information, the candidate object is determined to be a target object matching the network resource, and an association relationship between the target object and the network resource is established.

[0010] In one of the embodiments, the method further includes: when the object identifier of the candidate object contains at least one target sub-information in the target information, obtaining the similarity between the object identifier of the candidate object and the target information; when the similarity is greater than a similarity threshold, determining that the candidate object is a target object that matches the network resource, and establishing an association relationship between the target object and the network resource.

[0011] In one of the embodiments, the method further includes: when the similarity is less than the similarity threshold, determining that the candidate object does not match the network resource; or when the object identifier of the candidate object does not contain the target information, determining that the candidate object does not match the network resource.

[0012] In one of the embodiments, when the target information includes keywords, determining the target information of the target object identifier from the object identifiers of multiple candidate objects includes: obtaining the number of occurrences of each character in the object identifiers of the multiple candidate objects; obtaining the average number of characters corresponding to the object identifiers of the multiple candidate objects, and determining the number of characters of the keyword based on the average number; and based on the number of characters of the keyword, obtaining the corresponding number of characters with the highest number of occurrences as the keywords of the target information.

[0013] In one of the embodiments, obtaining the number of occurrences of each character in the object identifiers of multiple candidate objects includes: performing word-segmentation processing on the object identifiers of multiple candidate objects respectively to obtain the characters corresponding to the object identifiers of the multiple candidate objects respectively; traversing each character and performing statistical counting to obtain the number of occurrences of each character.

[0014] In one of the embodiments, according to the number of characters in the keyword, obtaining a corresponding number of characters with the highest number of occurrences as keywords for the target information includes: sorting the characters from high to low according to the number of occurrences of each character to obtain a sorting result; and extracting from the sorting result a number of characters with a corresponding number of characters in the keyword that are ranked high as keywords for the target information.

[0015] In one of the embodiments, the method further includes: filtering the characters corresponding to the object identifiers of the plurality of candidate objects according to preset noise characters to obtain the characters after the noise characters are removed.

[0016] According to a second aspect of an embodiment of the present disclosure, a resource matching processing device is provided, including:

[0017] A resource acquisition module is configured to acquire object identifiers of a plurality of candidate objects corresponding to network resources, wherein the candidate objects have real geographical locations;

[0018] A target information determination module is configured to determine target information of a target object identifier from object identifiers of a plurality of candidate objects;

[0019] The resource matching module is configured to determine that the candidate object is a target object matching the network resource when the object identifier of the candidate object contains the target information, and to establish an association relationship between the target object and the network resource.

[0020] In one of the embodiments, the resource matching module is further configured to execute: when the object identifier of the candidate object contains at least one target sub-information in the target information, obtaining the similarity between the object identifier of the candidate object and the target information; when the similarity is greater than a similarity threshold, determining that the candidate object is a target object that matches the network resource, and establishing an association relationship between the target object and the network resource.

[0021] In one of the embodiments, the resource matching module is further configured to execute: when the similarity is less than the similarity threshold, determining that the candidate object does not match the network resource; or when the object identifier of the candidate object does not contain the target information, determining that the candidate object does not match the network resource.

[0022] In one of the embodiments, when the target information includes keywords, the target information determination module includes: an acquisition unit, configured to execute acquisition of the number of occurrences of each character in the object identifiers of multiple candidate objects; a character number determination unit, configured to execute acquisition of the average number of characters corresponding to the object identifiers of multiple candidate objects, and determine the number of characters of the keyword based on the average number; a keyword determination unit, configured to execute acquisition of the corresponding number of characters with the highest number of occurrences based on the number of characters of the keyword as the keyword of the target information.

[0023] In one of the embodiments, the acquisition unit is configured to execute: performing word segmentation processing on the object identifiers of multiple candidate objects respectively to obtain the characters corresponding to the object identifiers of the multiple candidate objects respectively; traversing each character and performing statistical counting to obtain the number of occurrences of each character.

[0024] In one of the embodiments, the keyword determination unit is configured to perform: sorting the characters from high to low according to the number of occurrences of each character to obtain a sorting result; extracting from the sorting result the characters with a number corresponding to the number of characters of the keyword that are ranked high as the keywords of the target information.

[0025] In one of the embodiments, the acquisition unit is further configured to perform: filtering the characters corresponding to the object identifiers of the plurality of candidate objects respectively according to the preset noise characters to obtain the characters after the noise characters are removed.

[0026] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the resource matching processing method as described in the first aspect above.

[0027] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, characterized in that when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the resource matching processing method described in the first aspect above.

[0028] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, which includes instructions, and is characterized in that when the instructions are executed by a processor of an electronic device, the electronic device is enabled to execute the resource matching processing method described in the first aspect above.

[0029] The technical solution provided by the embodiment of the present disclosure brings at least the following beneficial effects: by obtaining the object identifications of multiple candidate objects corresponding to the network resources, and determining the target information of the target object identification from the object identifications of the multiple candidate objects, when the object identification of the candidate object contains the target information, the candidate object is determined to be the target object matching the network resource, and an association relationship between the target object and the network resource is established. Since the present embodiment automatically reviews multiple candidate objects based on the target information of the target object identification, and establishes an association relationship between the target object that has passed the review and the network resource, the review efficiency and accuracy of the candidate objects can be improved compared to manual review.

[0030] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0032] Figure 1 The present invention is a flowchart of a resource matching processing method according to an exemplary embodiment.

[0033] Figure 2 The present invention is a flowchart of a resource matching processing method according to another exemplary embodiment.

[0034] Figure 3 The figure is a flowchart showing a step of determining target information according to an exemplary embodiment.

[0035] Figure 4 is a flowchart of steps for obtaining target information according to another exemplary embodiment.

[0036] Figure 5 It is a block diagram of a resource matching processing device according to an exemplary embodiment.

[0037] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0038] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0039] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0040] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0041] In an exemplary embodiment, if Figure 1 As shown, a resource matching processing method is provided. This embodiment takes the method applied to a server as an example, wherein the server can be implemented as an independent server or a server cluster composed of multiple servers. In this embodiment, the method includes the following steps:

[0042] In step S110, object identifiers of a plurality of candidate objects corresponding to the network resource are obtained.

[0043] Among them, network resources refer to various information resources that can be used with the help of the network environment, such as vouchers, commodity vouchers, discount vouchers, red envelopes, etc. issued by the network platform. Usually, network resources need to be verified with the corresponding objects. Objects refer to individuals or organizations engaged in commercial activities (production and operation of related items), such as shops that provide goods and services. Candidate objects refer to all shops that are preliminarily determined to be able to verify a certain network resource. Candidate objects have real geographical locations and corresponding object identifiers. Object identifiers are marks used to mark and identify different stores, for example, they can be store names, business names, etc. The real geographical location refers to the location information of the candidate object in the real world, for example, the latitude and longitude information of the candidate object determined with the earth as the reference system and the latitude and longitude as the measurement standard; or, it can also be the regional location information of the candidate object determined based on the administrative region, for example, No. Y, X Street (Road), CC District (County), AA Province, BB City, CC District (County).

[0044] Since network resources are intended for a wide range of user groups, there are dozens to thousands of candidate objects corresponding to a network resource. In order to avoid the problem of misassociating objects that do not meet the verification qualifications with network resources, resulting in poor user experience, this embodiment uses a server to review the validity of the initially determined candidate objects corresponding to the network resources to improve the efficiency of the review, and then associates the candidate objects that are reviewed as valid with the network resources, thereby improving the user experience.

[0045] Specifically, when performing validity audit, the server first needs to obtain object identifiers of a plurality of candidate objects that are preliminarily determined corresponding to the network resources.

[0046] In step S120, target information of a target object identifier is determined from the object identifiers of a plurality of candidate objects.

[0047] The target information of the target object identification is a representative identification term determined based on the object identifications of multiple candidate objects. Specifically, the server can determine the target information of the target object identification by statistically analyzing the object identifications of multiple candidate objects. For example, it can be a character or word that appears more frequently in the object identifications of multiple candidate objects. Then, based on the object identifications of multiple candidate objects, it is determined whether the object identification of each candidate object contains the target information of the target object identification.

[0048] In step S130, when the object identifier of the candidate object includes target information, the candidate object is determined to be a target object matching the network resource, and an association relationship between the target object and the network resource is established.

[0049] The target object refers to a candidate object that is verified as valid by the server. Specifically, after the server determines the target information of the target object identifier through the above steps, it can then determine whether the candidate object is valid by judging whether the object identifier of each candidate object contains the target information. For example, when the object identifier of a candidate object contains the target information, it can be determined that the candidate object is valid, that is, the candidate object is determined to be a target object that matches the network resource, and then the association relationship between the target object and the network resource is established, so that the network resource can be written off at the target object.

[0050] In the resource matching processing method, by obtaining the object identifiers of multiple candidate objects corresponding to the network resource, and determining the target information of the target object identifier from the object identifiers of the multiple candidate objects, when the object identifier of the candidate object contains the target information, the candidate object is determined to be the target object matching the network resource, and an association relationship between the target object and the network resource is established. Since the present embodiment automatically reviews multiple candidate objects based on the target information of the target object identifier, and establishes an association relationship between the target objects that have passed the review and the network resource, the review efficiency and accuracy of the candidate objects can be improved compared to manual review.

[0051] In an exemplary embodiment, the method may further include: when the object identifier of the candidate object does not contain the target information, determining that the candidate object does not match the network resource. Since the target information is a representative identification term determined based on the object identifiers of multiple candidate objects, when the server determines that the object identifier of a candidate object does not contain the target information, it can be determined that the candidate object is invalid, that is, it is determined that the candidate object does not match the network resource, that is, the candidate object is not qualified to write off the corresponding network resource. This avoids users from writing off network resources with candidate objects, thereby improving user experience.

[0052] In an exemplary embodiment, if Figure 2 As shown, the resource matching processing method may further include the following steps:

[0053] In step S210, when the object identifier of the candidate object includes at least one target sub-information in the target information, the similarity between the object identifier of the candidate object and the target information is obtained.

[0054] The target sub-information is part of the target information. Specifically, the target sub-information may be one or more characters in the target information. For example, if the target information includes 5 characters in total, each character may be used as a target sub-information. If the object identifier of a candidate object includes at least one of the 5 characters and less than 5, then the object identifier of the candidate object includes at least one target sub-information in the target information.

[0055] Similarity is used to characterize the similarity between the object identifier of the candidate object and the target information. Specifically, the distance between the object identifier and the target information can be calculated based on the text features of the two. If the distance is small, the similarity is large, and if the distance is large, the similarity is small. The distance can be calculated using methods such as cosine algorithm, Euclidean distance, and Manhattan distance.

[0056] Specifically, when the object identifier of the candidate object contains at least one target sub-information in the target information, the server further obtains the similarity between the object identifier of the candidate object and the target information, and determines whether the candidate object is valid based on subsequent steps.

[0057] In step S220, when the similarity is greater than the similarity threshold, the candidate object is determined to be a target object that matches the network resource, and an association relationship between the target object and the network resource is established.

[0058] The similarity threshold is a pre-set basis for measuring whether the candidate object matches the network resource, and has a specific value range.

[0059] Specifically, when the server determines that the similarity between the object identifier of the candidate object and the target information is greater than the similarity threshold, the candidate object can be determined to be valid, that is, the candidate object is determined to be the target object that matches the network resource, and then the association relationship between the target object and the network resource is established, so that the network resource can be written off at the target object. For example, if the similarity threshold is 70%, then when the similarity between the object identifier of the candidate object and the target information is greater than 70%, the candidate object is determined to be the target object that matches the network resource.

[0060] In step S230, when the similarity is less than the similarity threshold, it is determined that the candidate object does not match the network resource.

[0061] Specifically, when the server determines that the similarity between the object identifier of the candidate object and the target information is less than the similarity threshold, it can be determined that the candidate object is invalid, that is, it is determined that the candidate object does not match the network resource, that is, the candidate object does not have the qualifications to write off the corresponding network resource. For example, if the similarity threshold is 70%, then when the similarity between the object identifier of the candidate object and the target information is less than 70%, it is determined that the candidate object does not match the network resource. This avoids users from writing off network resources with candidate objects to improve user experience.

[0062] In the above embodiment, when the object identifier of the candidate object contains part of the target information in the target information, the similarity between the object identifier of the candidate object and the target information is obtained. When the similarity is greater than the similarity threshold, the candidate object is determined to be a target object that matches the network resource, and an association relationship between the target object and the network resource is established; and when the similarity is less than the similarity threshold, it is determined that the candidate object does not match the network resource. In this way, the validity of the candidate object can be reviewed by the machine to improve the efficiency and accuracy of the review.

[0063] In an exemplary embodiment, taking the target information including keywords as an example, Figure 3 As shown, in step S120, the target information of the target object identifier is determined from the object identifiers of the multiple candidate objects, which may specifically include the following steps:

[0064] In step S310, the number of occurrences of each character in the object identifiers of the plurality of candidate objects is obtained.

[0065] Since the target information is a representative identification term determined based on the object identifications of the multiple candidate objects, in this embodiment, the keywords of the target information are determined based on the characters that appear more frequently in the object identifications of the multiple candidate objects.

[0066] Specifically, the server obtains the number of occurrences of each character by counting the number of occurrences of each character in the object identifiers of the plurality of candidate objects.

[0067] In step S320, the average number of characters corresponding to the object identifiers of the plurality of candidate objects is obtained, and the number of characters of the keyword is determined according to the average number.

[0068] The average is the average number of characters corresponding to the object identifiers of multiple candidate objects. For example, if the object identifier of candidate object A includes 8 characters, the object identifier of candidate object B includes 9 characters, and the object identifier of candidate object C includes 10 characters, then the average number of characters corresponding to the corresponding object identifiers is 9 (i.e. (8+9+10) / 3).

[0069] The number of characters of a keyword is used to characterize the length of the keyword. In this embodiment, the number of characters of the keyword is determined according to the average number of characters corresponding to the object identifier. Specifically, half of the average number can be determined as the number of characters of the keyword. Alternatively, any number less than the average number can be determined as the number of characters of the keyword.

[0070] In step S330, based on the number of characters in the keyword, the corresponding number of characters with the highest number of occurrences are obtained as the keywords of the target information.

[0071] Specifically, by the number of occurrences of each character in the above-mentioned statistical object identifier, all characters can be sorted according to the magnitude of the number of occurrences of each character. Furthermore, according to the number of characters of the determined keyword, the corresponding number of characters with the highest number of occurrences are obtained from the sorting as the keyword of the target information. For example, if the number of characters of the keyword is 4, the top 4 characters with the highest number of occurrences are obtained from the sorting as the keyword of the target information.

[0072] In the above embodiment, by obtaining the number of occurrences of each character in the object identifiers of multiple candidate objects, and obtaining the average value of the characters corresponding to the object identifiers of multiple candidate objects, the number of characters of the keyword is determined according to the average value. Furthermore, according to the number of characters of the keyword, the corresponding number of characters with the highest number of occurrences are obtained as the keyword of the target information, so that the keyword is more representative and can improve the accuracy of the review of candidate objects.

[0073] In an exemplary embodiment, in step S310, obtaining the number of occurrences of each character in the object identifiers of multiple candidate objects may specifically further include: performing character splitting on the object identifiers of multiple candidate objects respectively to obtain the characters corresponding to the object identifiers of multiple candidate objects respectively, traversing each character and performing statistical counting to obtain the number of occurrences of each character.

[0074] Specifically, the server performs character splitting on the object identifiers of multiple candidate objects respectively, so as to obtain the characters corresponding to the object identifiers of each candidate object respectively, and then traverses each character for statistical counting to obtain the number of occurrences of each character.

[0075] In an exemplary embodiment, in order to further improve the representativeness of the keyword, after obtaining the characters corresponding to the object identifiers of multiple candidate objects respectively, these characters may also be filtered based on preset noise characters to remove the characters affected by burrs. Among them, the preset noise characters may be characters that are preset without specific meaning or little significance, such as "的", "了", "店", etc. Then, statistical counting is performed on the filtered characters to obtain the number of occurrences of each character. Thus, the extracted keyword is more representative.

[0076] In an exemplary embodiment, as Figure 4 shown, in step S330, obtaining the corresponding number of characters with the highest number of occurrences as the keyword of the target information specifically includes:

[0077] In step S332, the characters are sorted from high to low according to the number of occurrences of each character to obtain a sorting result.

[0078] The sorting result is the result of sorting the characters according to the number of occurrences of the characters. Specifically, the server sorts the characters in descending order according to the number of occurrences of each character, that is, the characters with high occurrences are ranked at the front, and the characters with low occurrences are ranked at the back, thereby obtaining the sorting result.

[0079] In step S334, characters whose number corresponds to the number of characters of the keyword and whose order is high are extracted from the order result as keywords of the target information.

[0080] For example, if it is determined that the number of characters of the keyword is 4, the first 4 characters ranked high are extracted from the sorting result as the keyword for the target object identifier.

[0081] In this embodiment, the characters are sorted from high to low according to the number of occurrences of each character to obtain a sorting result, and then the characters with the same number of characters as the keyword that are ranked first are extracted from the sorting result as the keywords of the target information. The keyword of the target information is determined by a relatively simple sorting method, thereby improving the efficiency of determining the target information and thus improving the efficiency of review.

[0082] In an exemplary embodiment, the resource matching processing method is further described below, specifically including:

[0083] Step 1: Obtain object identifiers of multiple candidate objects corresponding to network resources.

[0084] Step 2: perform word segmentation processing on the object identifiers of the multiple candidate objects respectively, obtain the characters corresponding to the object identifiers of the multiple candidate objects respectively, and remove the characters affected by burrs to obtain the target characters.

[0085] Step 3: traverse each target character and perform statistical counting to obtain the number of occurrences of each target character.

[0086] Step 4: sort the target characters from high to low according to the number of occurrences of each target character to obtain a sorting result.

[0087] Step 5: Obtain the average number of characters corresponding to the object identifiers of multiple candidate objects, and determine half of the average number as the number of characters in the keyword.

[0088] Step six, extracting the target characters whose number corresponds to the number of characters of the keyword and which are ranked at the top from the sorting results as the keywords of the target information.

[0089] Step seven, traverse the object identification of each candidate object, and when the object identification of the candidate object contains target information, determine that the candidate object is a target object that matches the network resource, and establish an association relationship between the target object and the network resource.

[0090] When the object identifier of the candidate object contains at least one target sub-information in the target information, the similarity between the object identifier of the candidate object and the target information is obtained. When the similarity is greater than a similarity threshold, the candidate object is determined to be a target object that matches the network resource, and an association relationship between the target object and the network resource is established.

[0091] When the similarity is less than the similarity threshold, it is determined that the candidate object does not match the network resource; and when the object identifier of the candidate object does not contain target information, it is determined that the candidate object does not match the network resource.

[0092] In the above embodiment, the entire process is completed by the server, so compared with manual review, the review efficiency and accuracy of candidate objects can be improved.

[0093] It should be understood that although Figure 1-Figure 4 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-Figure 4 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0094] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can refer to each other, and each embodiment focuses on the differences from other embodiments. For related points, please refer to the description of other method embodiments.

[0095] Figure 5 is a block diagram of a resource matching processing device according to an exemplary embodiment. Figure 5 The device includes a resource acquisition module 502, a target information determination module 504 and a resource matching module 506.

[0096] The resource acquisition module 502 is configured to execute acquisition of object identifiers of a plurality of candidate objects corresponding to network resources, wherein the candidate objects have real geographical locations;

[0097] A target information determination module 504 is configured to determine target information of a target object identifier from object identifiers of a plurality of candidate objects;

[0098] The resource matching module 506 is configured to determine that the candidate object is a target object matching the network resource when the object identifier of the candidate object contains the target information, and to establish an association relationship between the target object and the network resource.

[0099] In an exemplary embodiment, the resource matching module is further configured to perform: when the object identifier of the candidate object contains at least one target sub-information in the target information, obtaining the similarity between the object identifier of the candidate object and the target information; when the similarity is greater than a similarity threshold, determining that the candidate object is a target object that matches the network resource, and establishing an association relationship between the target object and the network resource.

[0100] In an exemplary embodiment, the resource matching module is further configured to execute: when the similarity is less than the similarity threshold, determining that the candidate object does not match the network resource; or when the object identifier of the candidate object does not contain the target information, determining that the candidate object does not match the network resource.

[0101] In an exemplary embodiment, when the target information includes keywords, the target information determination module includes: an acquisition unit, configured to execute acquisition of the number of occurrences of each character in the object identifiers of multiple candidate objects; a character number determination unit, configured to execute acquisition of the average number of characters corresponding to the object identifiers of multiple candidate objects, and determine the number of characters of the keyword based on the average number; and a keyword determination unit, configured to execute acquisition of the corresponding number of characters with the highest number of occurrences as the keyword of the target information based on the number of characters of the keyword.

[0102] In an exemplary embodiment, the acquisition unit is configured to execute: performing word segmentation processing on the object identifiers of multiple candidate objects respectively to obtain the characters corresponding to the object identifiers of the multiple candidate objects respectively; traversing each character and performing statistical counting to obtain the number of occurrences of each character.

[0103] In an exemplary embodiment, the keyword determination unit is configured to perform: sorting the characters from high to low according to the number of occurrences of each character to obtain a sorting result; extracting from the sorting result the top-ranked characters corresponding to the number of characters of the keyword as keywords of the target information.

[0104] In an exemplary embodiment, the acquisition unit is further configured to perform: filtering the characters corresponding to the object identifiers of the plurality of candidate objects respectively according to preset noise characters to obtain the characters after the noise characters are removed.

[0105] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0106] Figure 6 is a block diagram of an electronic device S00 for resource matching processing according to an exemplary embodiment. For example, the electronic device S00 may be a server. Figure 6 The electronic device S00 includes a processing component S20, which further includes one or more processors, and a memory resource represented by a memory S22 for storing instructions executable by the processing component S20, such as an application. The application stored in the memory S22 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component S20 is configured to execute the instructions to perform the above method.

[0107] The electronic device S00 may further include: a power supply component S24 configured to perform power management of the electronic device S00, a wired or wireless network interface S26 configured to connect the electronic device S00 to a network, and an input / output (I / O) interface S28. The electronic device S00 may operate based on an operating system stored in the memory S22, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD or the like.

[0108] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory S22 including instructions, and the above instructions can be executed by a processor of the electronic device S00 to complete the above method. The storage medium can be a computer-readable storage medium, for example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0109] In an exemplary embodiment, a computer program product is further provided. The computer program product includes instructions. The instructions can be executed by a processor of the electronic device S00 to complete the above method.

[0110] It should be noted that the above-mentioned devices, electronic devices, computer-readable storage media, computer program products, etc. may also include other implementation methods according to the description of the method embodiments. The specific implementation methods can refer to the description of the relevant method embodiments, which will not be described one by one here.

[0111] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0112] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A resource matching processing method, characterized in that: The method comprises: Obtaining object identifiers of a plurality of candidate objects corresponding to network resources; the network resources include at least one of vouchers, commodity vouchers, discount coupons and red envelopes; the candidate objects are preliminarily determined objects for writing off the network resources; the objects include shops engaged in commercial activities; the object identifiers are marks for marking different shops; Determine target information of a target object identifier from among the object identifiers of a plurality of candidate objects; When the object identifier of the candidate object contains the target information, determining the candidate object as a target object matching the network resource, and establishing an association relationship between the target object and the network resource; In the case where the target information includes a keyword, the step of determining the target information of the target object identifier from the object identifiers of the plurality of candidate objects includes: Obtain the number of occurrences of each character in the object identifiers of multiple candidate objects; obtain the average number of characters corresponding to the object identifiers of multiple candidate objects, and determine the number of characters of the keyword based on the average number; based on the number of characters of the keyword, obtain the corresponding number of characters with the highest number of occurrences as the keyword of the target information.

2. The resource matching processing method according to claim 1, characterized in that: The method further comprises: When the object identifier of the candidate object contains at least one target sub-information in the target information, obtaining the similarity between the object identifier of the candidate object and the target information; When the similarity is greater than a similarity threshold, the candidate object is determined to be a target object that matches the network resource, and an association relationship between the target object and the network resource is established.

3. The resource matching processing method according to claim 2, characterized in that: The method further comprises: When the similarity is less than the similarity threshold, it is determined that the candidate object does not match the network resource; or, When the object identifier of the candidate object does not include the target information, it is determined that the candidate object does not match the network resource.

4. The resource matching processing method according to claim 1, characterized in that: Determining the number of characters of the keyword according to the average number includes: Determine half of the average as the number of characters in the keyword; or, Any number smaller than the average number is determined as the number of characters of the keyword.

5. The resource matching processing method according to claim 1, characterized in that: The step of obtaining the number of occurrences of each character in the object identifiers of the plurality of candidate objects includes: Performing character segmentation processing on the object identifiers of the multiple candidate objects respectively to obtain characters corresponding to the object identifiers of the multiple candidate objects respectively; Traverse each character and perform statistical counting to get the number of occurrences of each character.

6. The resource matching processing method according to claim 1, characterized in that: The step of obtaining, according to the number of characters of the keyword, a corresponding number of characters with the highest number of occurrences as the keyword of the target information includes: Sort the characters according to the number of occurrences of each character from high to low to obtain a sorting result; From the sorting results, characters whose number corresponds to the number of characters of the keyword and whose order is high are extracted as keywords of the target information.

7. The resource matching processing method according to claim 5, characterized in that: The method further comprises: The characters corresponding to the object identifiers of the plurality of candidate objects are respectively filtered according to the preset noise characters to obtain the characters after the noise characters are removed.

8. A resource matching processing device, characterized in that: include: The resource acquisition module is configured to execute the acquisition of object identifiers of a plurality of candidate objects corresponding to network resources; the network resources include at least one of vouchers, commodity coupons, discount coupons and red envelopes; the candidate objects are objects preliminarily determined for writing off the network resources; the objects include shops engaged in commercial activities, and the object identifiers are marks for marking different shops; A target information determination module is configured to determine target information of a target object identifier from object identifiers of a plurality of candidate objects; a resource matching module configured to determine, when the object identifier of the candidate object contains the target information, that the candidate object is a target object that matches the network resource, and to establish an association relationship between the target object and the network resource; In the case where the target information includes keywords, the target information determination module includes: An acquiring unit is configured to acquire the number of occurrences of each character in the object identifiers of the plurality of candidate objects; a character number determination unit configured to obtain an average number of characters corresponding to object identifiers of a plurality of candidate objects, and determine the number of characters of the keyword according to the average number; The keyword determination unit is configured to execute, according to the number of characters of the keyword, obtaining a corresponding number of characters with the highest number of occurrences as keywords of the target information.

9. The device according to claim 8, characterized in that The resource matching module is further configured to perform: When the object identifier of the candidate object contains at least one target sub-information in the target information, obtaining the similarity between the object identifier of the candidate object and the target information; When the similarity is greater than a similarity threshold, the candidate object is determined to be a target object that matches the network resource, and an association relationship between the target object and the network resource is established.

10. The device according to claim 9, characterized in that The resource matching module is further configured to perform: When the similarity is less than the similarity threshold, it is determined that the candidate object does not match the network resource; or, When the object identifier of the candidate object does not include the target information, it is determined that the candidate object does not match the network resource.

11. The device according to claim 8, characterized in that The character number determination unit is further configured to execute: Determine half of the average as the number of characters in the keyword; or, Any number smaller than the average number is determined as the number of characters of the keyword.

12. The device according to claim 8, characterized in that The acquisition unit is configured to perform: Performing character segmentation processing on the object identifiers of the multiple candidate objects respectively to obtain characters corresponding to the object identifiers of the multiple candidate objects respectively; Traverse each character and perform statistical counting to get the number of occurrences of each character.

13. The device according to claim 8, characterized in that The keyword determination unit is configured to perform: Sort the characters according to the number of occurrences of each character from high to low to obtain a sorting result; From the sorting results, characters whose number corresponds to the number of characters of the keyword and whose order is high are extracted as keywords of the target information.

14. The device according to claim 12, characterized in that The acquisition unit is further configured to execute: The characters corresponding to the object identifiers of the plurality of candidate objects are respectively filtered according to the preset noise characters to obtain the characters after the noise characters are removed.

15. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the resource matching processing method according to any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the resource matching processing method as claimed in any one of claims 1 to 7.

17. A computer program product, comprising instructions, characterized in that: When the instruction is executed by a processor of an electronic device, the electronic device is enabled to execute the resource matching processing method as described in any one of claims 1 to 7.

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