CN113111949A - Method and device for detecting repeated service - Google Patents
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Abstract
The present disclosure provides a method for detecting duplicate traffic, which can be applied to the financial field or other fields. The method comprises the following steps: acquiring a first target image and a second target image; determining the number of the main parts according to the first target image and the second target image; responding to the number of the main parts, and comparing the main parts by using a preset model to obtain an image comparison confidence coefficient; and if the image comparison confidence coefficient exceeds a second preset threshold, determining that the service to be detected is repeated with the historical service, and manually discriminating. The present disclosure also provides an apparatus, device, storage medium, and program product for detecting duplicate traffic.
Description
Technical Field
The present disclosure relates to the field of image processing, and more particularly, to a method, apparatus, device, medium, and program product for detecting duplicate traffic.
Background
In current financial institution business systems such as banks, a large amount of suspected repeated business exists due to repeated entry of workers or repeated handling of users, and the like, and the suspected repeated business needs to be screened.
The method mainly depends on manual work to identify the image of the handled business, whether the image is repeated with other images in the existing system or not, however, the manual screening efficiency is low, and the accuracy rate is difficult to effectively guarantee.
Disclosure of Invention
In view of the foregoing, the present disclosure provides a method, apparatus, device, medium, and program product for detecting repetitive traffic that improves the screening efficiency and accuracy.
According to a first aspect of the present disclosure, there is provided a method of detecting duplicate traffic, comprising:
acquiring a first target image and a second target image, wherein the first target image is an image corresponding to a to-be-detected service master, and the second target image is an image corresponding to a historical service master;
determining the number of the main parts according to the first target image and the second target image;
responding to the number of the main parts, and comparing the main parts by using a preset model to obtain an image comparison confidence coefficient;
if the number of the main parts does not exceed a first preset threshold value, obtaining an image comparison confidence coefficient;
if the number of the main parts exceeds a first preset threshold value, obtaining a plurality of image comparison confidence coefficients;
and if the image comparison confidence coefficient exceeds a second preset threshold, determining that the service to be detected is repeated with the historical service, and manually discriminating.
According to an embodiment of the present disclosure, the comparing the master part with the preset model to obtain the image comparison confidence includes:
calculating the similarity of the first target image and the second target image;
and if the similarity is smaller than a third preset threshold, correcting the similarity to obtain an image comparison confidence.
According to an embodiment of the present disclosure, the calculating the similarity between the first target image and the second target image includes:
acquiring feature point information of the first target image and the second target image;
determining the number of matching points according to the feature point information and a preset matching algorithm;
and determining the similarity according to the feature point information and the number of the matching points.
According to an embodiment of the present disclosure, the acquiring feature point information of the first target image and the second target image includes:
and extracting the feature points of the first target image and the second target image by using a scale invariant feature transformation algorithm.
According to the embodiment of the disclosure, the preset matching algorithm is a fast nearest neighbor approximation search function library algorithm.
According to an embodiment of the present disclosure, the modifying the similarity to obtain an image comparison confidence includes:
extracting key field information of the first target image and the second target image;
comparing the keyword information;
and correcting the similarity according to the comparison result.
According to an embodiment of the present disclosure, the correcting the similarity according to the comparison result includes:
if the comparison results are the same, adding the first weight value to the similarity to obtain the corrected similarity;
and if the comparison results are different, subtracting a second weight value from the similarity to obtain the corrected similarity.
According to an embodiment of the present disclosure, the comparing the master part with the preset model to obtain the image comparison confidence further includes:
denoising the first target image and the second target image;
and compressing the sizes of the first target image and the second target image to a preset range.
According to an embodiment of the present disclosure, if the number of masters exceeds a first preset threshold, obtaining a plurality of image comparison confidence levels includes:
and comparing the main parts in pairs by using a preset model to respectively obtain an image comparison confidence coefficient.
According to an embodiment of the present disclosure, further comprising:
and if the main part has abnormal conditions, manually screening, wherein the abnormal conditions comprise that the main part has a folded angle and/or is inverted.
According to an embodiment of the present disclosure, the third preset threshold, the first weight value, and the second weight value are obtained by preset model self-learning.
A second aspect of the present disclosure provides an apparatus for detecting a duplicate service, including:
the acquisition module is used for acquiring a first target image and a second target image, wherein the first target image is an image corresponding to a to-be-detected service master, and the second target image is an image corresponding to a historical service master;
the first determining module is used for determining the number of the masters according to the first target image and the second target image;
the comparison module is used for responding to the number of the main parts and comparing the main parts by using a preset model to obtain an image comparison confidence coefficient; if the number of the main parts does not exceed a first preset threshold value, obtaining an image comparison confidence coefficient; if the number of the main parts exceeds a first preset threshold value, obtaining a plurality of image comparison confidence coefficients; and
and the second determining module is used for determining that the service to be detected is repeated with the historical service and manually discriminating if the image comparison confidence coefficient exceeds a second preset threshold.
A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of the first aspect.
A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method of the first aspect described above.
A fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect described above.
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The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds with reference to the accompanying drawings, in which:
fig. 1 schematically shows a flow chart of a method of detecting duplicate traffic according to an embodiment of the present disclosure;
fig. 2 schematically shows a flow chart of a method of detecting duplicate traffic according to another embodiment of the present disclosure;
fig. 3 schematically shows a block diagram of an apparatus for detecting duplicate traffic according to an embodiment of the present disclosure; and
fig. 4 schematically shows a block diagram of an electronic device adapted to implement a method of detecting repeat traffic according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The embodiment of the present disclosure provides a method for detecting a duplicate service, including:
acquiring a first target image and a second target image, wherein the first target image is an image corresponding to a to-be-detected service master, and the second target image is an image corresponding to a historical service master; determining the number of the main parts according to the first target image and the second target image; responding to the number of the main parts, and comparing the main parts by using a preset model to obtain an image comparison confidence coefficient; if the number of the main parts does not exceed a first preset threshold value, obtaining an image comparison confidence coefficient; if the number of the main parts exceeds a first preset threshold value, obtaining a plurality of image comparison confidence coefficients; and if the image comparison confidence coefficient exceeds a second preset threshold, determining that the service to be detected is repeated with the historical service, and manually discriminating.
The application scene of the method is a financial institution business entry scene, and when a user transacts business, a large amount of suspected repeated business exists due to repeated entry of staff or repeated transaction of the user. In one example, according to the input key field, whether a historical service with consistent elements exists is searched in a historical library, and the suspected repeated service is judged to be a repeated service by manually screening the image of the handled service. Because the identification of the repeated business completely depends on the professional ability of the working personnel, the identification accuracy is not high, and the identification efficiency is low.
Based on the above problems, the present disclosure provides a method for detecting duplicate services, which obtains service master certificates of suspected duplicate services, and compares the master certificates to obtain image comparison confidence levels, thereby further reducing the number of suspected duplicate services and improving the identification efficiency.
The method of detecting repetitive traffic of the disclosed embodiment will be described in detail below with reference to fig. 1 to 2.
Fig. 1 schematically shows a flow chart of a method of detecting duplicate traffic according to an embodiment of the present disclosure.
As shown in fig. 1, the embodiment includes operations S110 to S140, an execution subject of the embodiment of the disclosure may be a background system or a server, or may be an apparatus or a device that executes the method of the disclosure, and in the embodiment, a background system is explained as the execution subject.
In operation S110, a first target image and a second target image are obtained, where the first target image is an image corresponding to a service master to be detected, and the second target image is an image corresponding to a historical service master.
According to the embodiment of the disclosure, for suspected repeated services retrieved according to the keywords, a first target image and a second target image are firstly obtained, wherein the first target image is a certificate corresponding to a service master of the service to be detected, and the second target image is a certificate corresponding to a service master of the historical service.
In operation S120, a master number is determined according to the first target image and the second target image.
In one example, the number of the business masters of a business is related to the business type, and may be one or more pieces, and the number of the business masters may be determined according to the first target image and the second target image obtained in operation S110, and the number of the masters may be one image voucher or may be multiple image vouchers.
In operation S130, in response to the number of masters, the masters are compared using a preset model to obtain an image comparison confidence.
According to the embodiment of the disclosure, if the number of the masters does not exceed a first preset threshold, that is, the number of the masters is 1, comparing the master certificate image of the service to be detected with the master certificate image of the historical service by using a preset model to obtain an image comparison confidence; if the number of the main parts exceeds a first preset threshold value, namely the number of the main parts exceeds 1, comparing every two main part certificate images of the service to be detected and the main part certificate images of the historical service by using a preset model to respectively obtain an image comparison confidence; specifically, the preset model processes the first target image and the second target image, and compares the first target image and the second target image to obtain an image comparison confidence, where the number of the first target images may be 1 or more, and thus the number of the image comparison confidence may be 1 or more.
In operation S140, if the image comparison confidence exceeds a second preset threshold, it is determined that the service to be detected is repeated with the historical service, and manual screening is performed.
According to the embodiment of the present disclosure, if the comparison confidence of at least one image obtained in operation S130 exceeds a second preset threshold, that is, a group of images corresponding to the comparison confidence of the image is similar, it is determined that the service to be detected is repeated with the historical service, and manual screening is performed to further confirm that the service to be detected is repeated with the historical service, where the comparison confidence of the image is a decimal number from 0 to 1, and the value range of the second preset threshold may be 0.5 to 0.7, preferably, in the embodiment of the present disclosure, the second preset threshold is 0.5, when all the comparison confidence of the images are less than 0.5, it is determined that the first target image is different from the second target image, and it is determined that the service to be detected is not repeated with the historical service, and when the comparison confidence of at least one image is greater than 0.5, it is determined that the first target image is similar to the second target image, and it is determined that the service, and further discrimination is carried out manually.
According to the method provided by the embodiment of the disclosure, the number of the masters is determined according to the first target image and the second target image by acquiring the first target image and the second target image; responding to the number of the main parts, and comparing the main parts by using a preset model to obtain an image comparison confidence coefficient; and comparing the two target images to obtain an image comparison confidence coefficient, and further determining whether the service to be detected is similar to the historical service according to the influence comparison confidence coefficient, so that the screening number of suspected repeated services is reduced, and the identification efficiency and accuracy are improved.
Fig. 2 schematically shows a flow chart of a method of detecting duplicate traffic according to another embodiment of the present disclosure.
As shown in fig. 2, the embodiment of the present disclosure includes operations S210 to S260.
In operation S210, a first target image and a second target image are obtained, where the first target image is an image corresponding to a service to be detected, and the second target image is an image corresponding to a historical service.
The operation S210 is the same as the operation S110 shown in fig. 1, and is not described herein again.
In operation S220, a master number is determined according to the first target image and the second target image.
According to the embodiment of the disclosure, if the main part has an abnormal condition, manual screening is performed, wherein the abnormal condition comprises that the main part has a folded angle and/or is inverted.
In one example, the number of the business masters of a business is related to the business type, and may be one or more pieces, and the number of the business masters may be determined according to the first target image and the second target image obtained in operation S110, and the number of the masters may be one image voucher or may be multiple image vouchers. Abnormal conditions such as angle folding and inversion may exist in the process of scanning and uploading the service main part to the background system, and at this time, the target image cannot be further compared, and manual screening is required.
In operation S230, a similarity of the first target image and the second target image is calculated.
According to the embodiment of the present disclosure, before calculating the similarity between the first target image and the second target image, the first target image and the second target image need to be preprocessed, where the preprocessing includes: denoising the first target image and the second target image; and compressing the sizes of the first target image and the second target image to a preset range.
In one example, a non-local average algorithm is used for denoising a target image, in order to ensure the accuracy of comparison confidence, the size ranges of a first target image and a second target image need to be controlled and adjusted to be not more than 40%, if the difference between the two target images is too large, the comparison result is affected, and a larger image needs to be compressed until the size ranges of the two images are not more than 40%.
After the target image is preprocessed, the similarity of the first target image and the second target image is calculated, and the method mainly comprises the following steps:
the method comprises the first step of obtaining characteristic point information of a first target image and a second target image.
According to the embodiment of the disclosure, a Scale-invariant feature transform (SIFT) Scale Invariant Feature Transform (SIFT) algorithm is adopted to extract feature points of the first target image and the second target image. SIFT is an algorithm of computer vision to detect and describe the local features in the image, find the extreme points in the spatial scale, and extract the invariant of position, scale and rotation. Feature point extraction is carried out on the picture through an SIFT feature extractor, and feature point information of edges and corners of different layers in the picture can be extracted.
And secondly, determining the number of the matching points according to the feature point information and a preset matching algorithm.
According to the embodiment of the disclosure, the preset matching algorithm is a fast nearest neighbor approximation search function library algorithm. And matching the feature point information of the two images obtained in the first step by using a FLANN (fast approximation Neighbor Search library) fast Nearest Neighbor approximation Search function library matching algorithm to determine the number of matching points of the two images. The technical principles and operation steps of SIFT and FLANN algorithms may be referred to any prior art and will not be described herein.
And step three, determining similarity according to the feature point information and the number of the matching points.
According to the embodiment of the disclosure, the similarity is equal to the number of matching points/the number of sample feature points, wherein the similarity represents the similarity degree of two pictures, the higher the similarity is, the more similar the two pictures are, the similarity range is a decimal between 0 and 1, the similarity represents that the two pictures are completely different when the similarity is 0, and the similarity represents that the two pictures are completely same when the similarity is 1.
In operation S240, if the similarity is smaller than the third preset threshold, the similarity is modified to obtain an image comparison confidence.
After operation S230, since the similarity in operation S230 needs to be corrected in order to ensure the accuracy of picture matching.
According to the embodiment of the present disclosure, if the similarity of operation S230 is less than the third preset threshold, extracting key field information of the first target image and the second target image by an OCR (Optical Character Recognition) technique; comparing the keyword information; correcting the similarity according to the comparison result, specifically, if the comparison result is the same, adding the first weight value to the similarity to obtain the corrected similarity; if the comparison result is different, the similarity is subtracted by the second weight value to obtain the corrected similarity.
In one example, the current service type may be determined through the target image, different service types preset different key field information identification ranges, all text information of the target image does not need to be identified, only specific key fields need to be identified according to the service types, the key field information identified by the two images is compared, if the comparison results are completely the same, it is determined that the service types of the two images may be the same service, and the similarity is corrected by adding a first weight value on the basis of the similarity obtained in operation S230 to obtain an image comparison confidence; if the comparison results are different, the service types of the two images are determined to be different services, and the similarity is corrected by subtracting the second weight value on the basis of the similarity obtained in operation S230 to obtain an image comparison confidence.
It should be noted that the third preset threshold, the first weight value and the second weight value are obtained by self-learning of the preset model, specifically, before the preset model is used, the model is trained, the third preset threshold, the first weight value and the second weight value are continuously adjusted, the comparison accuracy of the preset model is compared, and the third preset threshold, the first weight value and the second weight value are determined according to the comparison result, so that the three parameters can be periodically updated within the preset time.
In operation S250, if the image comparison confidence exceeds a second preset threshold, it is determined that the service to be detected is repeated with the historical service, and manual screening is performed.
In one example, the image comparison confidence obtained in operation S240 is compared with a second preset threshold, and if the image comparison confidence is smaller than the second preset threshold, it is determined that the first target image is different from the second target image, and it is further determined that the current service to be detected is not repeated with the historical service; if the image comparison confidence is greater than a second preset threshold, determining that the first target image is similar to the second target image, further determining that the current service to be detected is repeated with the historical service, and continuing manual screening to complete detection of the repeated service.
According to the embodiment of the disclosure, the number of masters is determined according to a first target image and a second target image by acquiring the first target image and the second target image; responding to the number of the main parts, comparing the main parts by using a preset model to obtain the similarity of two target images, identifying key field information of the target images according to an OCR technology, and correcting the similarity according to the key field to obtain an image comparison confidence; and further determining whether the service to be detected is similar to the historical service or not according to the influence comparison confidence, so that the screening number of suspected repeated services is reduced, and the identification efficiency and accuracy are improved.
Based on the method for detecting the repeated service, the disclosure also provides a device for detecting the repeated service. The apparatus will be described in detail below with reference to fig. 3.
Fig. 3 schematically shows a block diagram of an apparatus for detecting duplicate traffic according to an embodiment of the present disclosure.
As shown in fig. 3, the apparatus 300 for detecting duplicate traffic of this embodiment includes an obtaining module 310, a first determining module 320, a comparing module 330, and a second determining module 340.
The obtaining module 310 is configured to obtain a first target image and a second target image, where the first target image is an image corresponding to a service master to be detected, and the second target image is an image corresponding to a historical service master. In an embodiment, the obtaining module 310 may be configured to perform the operation S110 described above, which is not described herein again.
The first determining module 320 is configured to determine the number of masters according to the first target image and the second target image. In an embodiment, the first determining module 320 may be configured to perform the operation S120 described above, and is not described herein again.
The comparison module 330 is configured to respond to the number of the masters, and compare the masters with a preset model to obtain an image comparison confidence; if the number of the main parts does not exceed a first preset threshold value, obtaining an image comparison confidence coefficient; and if the number of the main parts exceeds a first preset threshold value, obtaining a plurality of image comparison confidence coefficients. In an embodiment, the comparing module 330 may be configured to perform the operation S130 described above, which is not described herein again.
The second determining module 340 is configured to determine that the service to be detected is repeated with the historical service and perform manual screening if the image comparison confidence exceeds a second preset threshold.
According to the embodiment of the present disclosure, any plurality of the obtaining module 310, the first determining module 320, the comparing module 330, and the second determining module 340 may be combined and implemented in one module, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 310, the first determining module 320, the comparing module 330, and the second determining module 340 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or an appropriate combination of any several of them. Alternatively, at least one of the obtaining module 310, the first determining module 320, the comparing module 330 and the second determining module 340 may be at least partially implemented as a computer program module, which when executed may perform a corresponding function.
Fig. 4 schematically shows a block diagram of an electronic device adapted to implement a method of detecting repeat traffic according to an embodiment of the present disclosure.
As shown in fig. 4, an electronic device 400 according to an embodiment of the present disclosure includes a processor 401 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. Processor 401 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 401 may also include onboard memory for caching purposes. Processor 401 may include a single processing unit or multiple processing units for performing the different actions of the method flows in accordance with embodiments of the present disclosure.
In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are stored. The processor 401, ROM 402 and RAM 403 are connected to each other by a bus 404. The processor 401 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 402 and/or the RAM 403. Note that the programs may also be stored in one or more memories other than the ROM 402 and RAM 403. The processor 401 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the present disclosure, electronic device 400 may also include an input/output (I/O) interface 405, input/output (I/O) interface 405 also being connected to bus 404. Electronic device 400 may also include one or more of the following components connected to I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include ROM 402 and/or RAM 403 and/or one or more memories other than ROM 402 and RAM 403 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the item recommendation method provided by the embodiment of the disclosure.
The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 401. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal on a network medium, downloaded and installed through the communication section 409, and/or installed from the removable medium 411. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program, when executed by the processor 401, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It should be noted that the method and apparatus provided by the embodiments of the present disclosure may be used in the financial field, and may also be used in any field other than the financial field.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (15)
1. A method of detecting duplicate traffic, comprising:
acquiring a first target image and a second target image, wherein the first target image is an image corresponding to a to-be-detected service master, and the second target image is an image corresponding to a historical service master;
determining the number of the main parts according to the first target image and the second target image;
responding to the number of the main parts, and comparing the main parts by using a preset model to obtain an image comparison confidence coefficient; if the number of the main parts does not exceed a first preset threshold value, obtaining an image comparison confidence coefficient; if the number of the main parts exceeds a first preset threshold value, obtaining a plurality of image comparison confidence coefficients;
and if the image comparison confidence coefficient exceeds a second preset threshold, determining that the service to be detected is repeated with the historical service, and manually discriminating.
2. The method of claim 1, wherein the comparing the master with the predetermined model to obtain the image comparison confidence level comprises:
calculating the similarity of the first target image and the second target image;
and if the similarity is smaller than a third preset threshold, correcting the similarity to obtain an image comparison confidence.
3. The method of claim 2, wherein calculating the similarity between the first target image and the second target image comprises:
acquiring feature point information of the first target image and the second target image;
determining the number of matching points according to the feature point information and a preset matching algorithm;
and determining the similarity according to the feature point information and the number of the matching points.
4. The method according to claim 3, wherein the acquiring feature point information of the first target image and the second target image comprises:
and extracting the feature points of the first target image and the second target image by using a scale invariant feature transformation algorithm.
5. The method of claim 3, wherein the predetermined matching algorithm is a fast nearest neighbor approximation search function library algorithm.
6. The method of claim 1, wherein the modifying the similarity to obtain an image comparison confidence level comprises:
extracting key field information of the first target image and the second target image;
comparing the keyword information;
and correcting the similarity according to the comparison result.
7. The method of claim 6, wherein the modifying the similarity according to the comparison comprises:
if the comparison results are the same, adding the first weight value to the similarity to obtain the corrected similarity;
and if the comparison results are different, subtracting a second weight value from the similarity to obtain the corrected similarity.
8. The method of claim 2, wherein the comparing the master with the predetermined model to obtain the image comparison confidence level further comprises:
denoising the first target image and the second target image;
and compressing the sizes of the first target image and the second target image to a preset range.
9. The method of claim 1, wherein obtaining a plurality of image comparison confidences if the number of masters exceeds a first predetermined threshold comprises:
and comparing the main parts in pairs by using a preset model to respectively obtain an image comparison confidence coefficient.
10. The method of claim 1, further comprising:
and if the main part has abnormal conditions, manually screening, wherein the abnormal conditions comprise that the main part has a folded angle and/or is inverted.
11. The method according to any one of claims 1 to 10, wherein the third predetermined threshold, the first weight value and the second weight value are obtained by self-learning of a predetermined model.
12. An apparatus for detecting duplicate traffic, comprising:
the acquisition module is used for acquiring a first target image and a second target image, wherein the first target image is an image corresponding to a to-be-detected service master, and the second target image is an image corresponding to a historical service master;
the first determining module is used for determining the number of the masters according to the first target image and the second target image;
the comparison module is used for responding to the number of the main parts and comparing the main parts by using a preset model to obtain an image comparison confidence coefficient; if the number of the main parts does not exceed a first preset threshold value, obtaining an image comparison confidence coefficient; if the number of the main parts exceeds a first preset threshold value, obtaining a plurality of image comparison confidence coefficients; and
and the second determining module is used for determining that the service to be detected is repeated with the historical service and manually discriminating if the image comparison confidence coefficient exceeds a second preset threshold.
13. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-11.
14. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 11.
15. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 11.
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