CN116433255B - Method, device, equipment and medium for determining suspicion of bill - Google Patents
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Abstract
The application provides a method, a device, equipment and a medium for determining suspicion of a bill, which relate to the technical field of big data and artificial intelligence, wherein the method comprises the following steps: acquiring industry index values for evaluating n parameters of a store, wherein n is a positive integer; according to the industry index values of the n parameters, n first deviation rates of the n parameters of the shop to be detected relative to the corresponding industry index values respectively, and n second deviation rates of the n parameters of the m reference shops relative to the corresponding industry index values respectively are obtained, wherein the reference shops are the same-industry shops with confirmed bill-refreshing behaviors, and m is a positive integer; performing correlation calculation based on the n first deviation rates and the n second deviation rates of each reference store to obtain m correlation coefficients; and determining the suspicion of the bill of the store to be detected based on the m correlation coefficients. The method can improve the accuracy of determining the suspicion of the bill of the store to be detected.
Description
Technical Field
The application relates to the technical field of big data and artificial intelligence, in particular to a method, a device, equipment and a medium for determining the suspicion of a bill.
Background
At present, an electronic commerce platform is adopted, and a plurality of shops have the behavior of ordering; at present, the main detection means of the e-commerce platform in the industry is to detect abnormal behaviors of a buyer account or abnormal IP addresses, and no detection means aiming at the shops is provided. Even if the account number of the abnormal buyer is blocked, the shopkeeper can continue to ask other accounts for the bill, so that the symptoms and the root causes are not cured.
In the prior art, the means for detecting the bill swiping behavior of each e-commerce platform is easy to avoid. For example, the behavior of a normal buyer account can be simulated by the batch operation of the scripts, and the abnormal behavior, abnormal IP addresses and the like of the buyer account can be avoided by simulating the browsing path of a real buyer by using third party software to simulate the time zone, language, environment and the like of the IP.
That is, detection of the store's billing behavior is currently inaccurate.
Disclosure of Invention
The method, the device, the equipment and the medium for determining the suspicion of the bill can determine the suspicion of the bill of the shop, and realize the accuracy of detecting the single bill of the shop.
In a first aspect, an embodiment of the present application provides a method for determining suspicion of a bill, where the method includes:
acquiring industry index values for evaluating n parameters of a store, wherein n is a positive integer;
according to the industry index values of the n parameters, n first deviation rates of the n parameters of the shop to be detected relative to the corresponding industry index values respectively and n second deviation rates of the n parameters of m reference shop to the corresponding industry index values respectively are obtained, wherein the reference shop is the same shop with confirmed bill swiping behavior, and m is a positive integer;
performing correlation calculation based on the n first deviation rates and the n second deviation rates of each reference store to obtain m correlation coefficients;
and determining the suspicion of the to-be-detected store according to the m correlation coefficients.
In a second aspect, the present application provides a device for determining suspicion of a sheet, the device comprising:
the first acquisition module is used for acquiring industry index values of n parameters for evaluating shops, wherein n is a positive integer;
the second acquisition module is used for acquiring n first deviation rates of the n parameters of the shop to be detected relative to the corresponding industry index values respectively and n second deviation rates of the n parameters of m reference shop to the corresponding industry index values respectively according to the industry index values of the n parameters, wherein the reference shop is the same shop with confirmed bill swiping behavior, and m is a positive integer;
the third acquisition module is used for carrying out correlation calculation based on the n first deviation rates and the n second deviation rates of each reference store so as to obtain m correlation coefficients;
and the determining module is used for determining the suspicion of the list of the shops to be detected based on the m correlation coefficients.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method for determining suspicion of a brush list as in any of the embodiments of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer storage medium, where computer program instructions are stored, where the computer program instructions, when executed by a processor, implement a method for determining a suspicion of a statement as in any one of the embodiments of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product, where instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform a method for determining a suspicion of a sheet according to any one of the embodiments of the first aspect.
The method, the device, the equipment and the medium for determining the suspicion degree of the bill, provided by the embodiment of the application, comprise the following steps: acquiring industry index values for evaluating n parameters of a store, wherein n is a positive integer; according to the industry index values of the n parameters, n first deviation rates of the n parameters of the shop to be detected relative to the corresponding industry index values respectively and n second deviation rates of the n parameters of m reference shop to the corresponding industry index values respectively are obtained, wherein the reference shop is the same shop with confirmed bill swiping behavior, and m is a positive integer; performing correlation calculation based on the n first deviation rates and the n second deviation rates of each reference store to obtain m correlation coefficients; and determining the suspicion of the to-be-detected store according to the m correlation coefficients. In the above, the correlation coefficient is obtained by performing correlation calculation on the to-be-detected shop and the reference shop of the same industry with the confirmed behavior of the bill, and the suspicion of the bill of the to-be-detected shop is determined by the correlation coefficient, so that the to-be-detected shop is difficult to avoid the suspicion of the bill by avoiding the abnormal behavior of the buyer account and the abnormal IP address, thereby improving the accuracy of determining the suspicion of the bill of the to-be-detected shop.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present application, the drawings that are needed to be used in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.
FIG. 1 is a flow chart of a method for determining suspicion of a bill according to an embodiment of the present application;
FIG. 2 is a schematic diagram of another flow chart of a method for determining suspicion of a bill according to one embodiment of the present application;
FIG. 3 is a schematic flow chart of a method for determining suspicion of a bill according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a device for determining suspicion of a bill according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
In order to solve the problems in the prior art, the embodiment of the application provides a method, a device, equipment, a medium and a product for determining the suspicion of a bill. The following first describes a method for determining suspicion of a bill according to an embodiment of the present application.
Fig. 1 is a flow chart illustrating a method for determining suspicion of a bill according to an embodiment of the present application. As shown in fig. 1, the method specifically may include the following steps:
step 101, acquiring industry index values of n parameters for evaluating shops, wherein n is a positive integer.
The n parameters may include 5 parameters of store score, refund rate, rating rate, good content repetition rate, and medium rating rate.
Illustratively, the values of the n parameters of a plurality of shops in the same industry as the shop to be detected can be collected from the network through a crawler technology; and for any one first parameter of the n parameters, calculating an average value based on the values of the first parameters of each of the plurality of shops to obtain an industry index value of the first parameter.
When a plurality of shops of the same industry as the shop to be detected are collected from the network, the collection may be random, that is, the collected shops may include the same-industry shops with the behavior of the bill and the same-industry shops without the behavior of the bill. Alternatively, the collected store may include only the same business stores that have the action of the swipe. For each first parameter, the collected values of the first parameters of each store are averaged to obtain the industry index value of the first parameter.
The same industry may be understood as belonging to the same type as the commodity sold by the shop to be detected or the service provided by the shop, for example, if the commodity sold by the shop to be detected is clothing, the industry to which the shop to be detected belongs is clothing industry, and the same industry as the shop to be detected refers to clothing industry; if the commodity sold by the to-be-detected shop is food, the industry to which the to-be-detected shop belongs is food industry, and the same industry with the to-be-detected shop is food industry. Further, the method may further be further subdivided, for example, if the commodity sold by the shop to be detected is a sock, the industry to which the shop to be detected belongs is a sock industry in the clothing industry. The division modes of the same industry can be set according to actual conditions, and are not limited herein.
Step 102, obtaining n first deviation rates of the n parameters of the shop to be detected relative to the corresponding industry index values respectively and n second deviation rates of the n parameters of m reference shop to the corresponding industry index values respectively according to the industry index values of the n parameters, wherein the reference shop is the same shop with the confirmed bill behavior, and m is a positive integer.
For example, a first deviation rate corresponding to a first parameter of a store to be detected may be determined according to the following expression:
；
wherein A1 is the value of the first parameter of the shop to be detected, and B is the industry index value of the first parameter.
The second deviation rate corresponding to the first parameter of the reference store may be determined according to the following expression:
；
wherein A2 is a value of a first parameter of a reference store, and B is an industry index value of the first parameter.
And 103, performing correlation calculation based on the n first deviation rates and the n second deviation rates of each reference store, and obtaining m correlation coefficients.
And carrying out correlation calculation on the n first deviation rates and the n second deviation rates of one reference store, so as to obtain a correlation coefficient, and carrying out correlation calculation on the n second deviation rates of each reference store and the n first deviation rates, so as to obtain m correlation coefficients.
And 104, determining the suspicion of the list of the shops to be detected based on the m correlation coefficients.
Specifically, an average value of the m correlation coefficients may be calculated, and the average value may be used as the suspicion of the list of shops to be detected. The closer the suspicion is to 1, the greater the probability that the store to be detected has a billing action.
In this embodiment, industry index values for evaluating n parameters of a store are obtained, where n is a positive integer; according to the industry index values of the n parameters, n first deviation rates of the n parameters of the shop to be detected relative to the corresponding industry index values respectively and n second deviation rates of the n parameters of m reference shop to the corresponding industry index values respectively are obtained, wherein the reference shop is the same shop with confirmed bill swiping behavior, and m is a positive integer; performing correlation calculation based on the n first deviation rates and the n second deviation rates of each reference store to obtain m correlation coefficients; and determining the suspicion of the to-be-detected store according to the m correlation coefficients. In the above, the correlation coefficient is obtained by performing correlation calculation on the to-be-detected shop and the reference shop of the same industry with the confirmed behavior of the bill, and the suspicion of the bill of the to-be-detected shop is determined by the correlation coefficient, so that the to-be-detected shop is difficult to avoid the suspicion of the bill by avoiding the abnormal behavior of the buyer account and the abnormal IP address, thereby improving the accuracy of determining the suspicion of the bill of the to-be-detected shop.
Further, it may be determined whether the store to be detected has a list of prescriptions based on the suspicion of the list of prescriptions, for example, if the suspicion of the swipe is greater than a preset threshold (the preset threshold is less than 1, for example, the preset threshold is 0.6), determining that the swipe exists in the shop to be detected.
Fig. 2 is a flowchart illustrating a method for determining suspicion of a bill according to an embodiment of the present application. As shown in fig. 2, the method specifically may include the following steps:
step 201, acquiring industry index values for evaluating n parameters of a store, wherein n is a positive integer.
The n parameters may include 5 parameters of store score, refund rate, rating rate, good content repetition rate, and medium rating rate.
Illustratively, the values of the n parameters of a plurality of shops in the same industry as the shop to be detected can be collected from the network through a crawler technology; and for any one first parameter of the n parameters, calculating an average value based on the values of the first parameters of each of the plurality of shops to obtain an industry index value of the first parameter.
When a plurality of shops of the same industry as the shop to be detected are collected from the network, the collection may be random, that is, the collected shops may include the same-industry shops with the behavior of the bill and the same-industry shops without the behavior of the bill. Alternatively, the collected store may include only the same business stores that have the action of the swipe. For each first parameter, the collected values of the first parameters of each store are averaged to obtain the industry index value of the first parameter.
Step 202, obtaining n first deviation rates of the n parameters of the store to be detected relative to the corresponding industry index values respectively and n second deviation rates of the n parameters of m reference stores relative to the corresponding industry index values respectively according to the industry index values of the n parameters, wherein the reference stores are the same industry stores with confirmed bill swiping behaviors, and m is a positive integer.
The implementation manner of step 202 is the same as that of step 102, and the details of step 102 are specifically referred to herein and will not be described in detail.
Step 203, performing correlation calculation on the first vectors of the shops to be detected and m second vectors of the reference shops, so as to obtain m correlation coefficients, wherein the first vectors are determined according to the n first deviation rates, and the second vectors of the reference shops are determined according to the n second deviation rates of the reference shops.
Step 203 is one implementation of step 103. In this step, a first vector is determined from n first deviation rates, for example, the n first deviation rates are taken as elements of the first vector, the first vector includes 5 elements, and values of the 5 elements are respectively a store score, a refund rate, an evaluation rate, a good content repetition rate, and a medium score ratio of a store to be detected.
For example, for reference store 1, the second vectors corresponding to reference store 1 may be formed by combining n second deviation rates of reference store 1 into the second vectors corresponding to reference store 1: the n second deviation rates with reference to the store 1 are taken as elements of a second vector, the second vector includes 5 elements, and the values of the 5 elements are respectively the store score, refund rate, evaluation rate, good content repetition rate and medium score ratio of the store to be detected.
In the first vector and the second vector, elements at the same position are values corresponding to the same parameter. For example, if the element at the first position in the first vector is the store score of the store to be detected, the element at the first position in the second vector is the store score of the reference store.
After obtaining the first vector and m second vectors, respectively carrying out pearson correlation coefficient calculation on the first vector and the m second vectors to obtain m correlation coefficients, namely m pearson correlation coefficients. The pearson correlation coefficient is a measure of the degree of correlation between two variables. It is a value between 1 and-1, where 1 represents a complete positive correlation of the two variables, 0 represents a non-linear correlation of the two variables, and-1 represents a complete negative correlation of the two variables.
And 204, determining the suspicion of the list of the shops to be detected based on the m correlation coefficients.
Specifically, an average value of the m correlation coefficients may be calculated, and the average value may be used as the suspicion of the list of shops to be detected.
The method for determining the suspicion of a bill according to the present application is illustrated below.
Fig. 3 is a flow chart of a method for determining suspicion of a bill according to an embodiment of the present application, and specifically, refer to fig. 3, and details thereof are not described herein. For example, 6 virtual shops can be created, 1 of which is set as the current shop, namely, the shop to be detected, 5 of which is set as the reference shop, and the parameters of the brush list are respectively set; taking table 1 as an example, the suspicion of the current store's statement is determined.
TABLE 1
According to the calculation of the pearson correlation coefficient, the correlation coefficientA quotient of the covariance and the standard deviation between the deviation rate formed according to the parameters of the current store and the deviation rate formed according to the parameters of the reference store:
；
x represents a first vector, Y represents a second vector,is the covariance of X and Y, +.>Is the standard deviation of X and is,is the standard deviation of Y.
The calculations can yield the results (rounded) as shown in table 2:
TABLE 2
Taking the average value of the correlation coefficients= (0.9+0.85+0.59+0.99+0.97)/5=0.86, the probability of the current store bill is 0.86, and the probability of the bill-presence behavior is high.
According to the method for determining the suspicion of the form, the characteristics of the existing form-refreshing shops are collected, the form-refreshing shops are defined as the reference shops, and the correlation degree of the characteristics selected by the current shops and the reference shops is compared in batches, so that the shop is difficult to avoid the suspicion of the form-refreshing, and the accuracy of detecting the suspicion of the form-refreshing shops is improved.
Fig. 4 is a schematic structural diagram of a device for determining suspicion of a bill according to an embodiment of the present application, and for convenience of explanation, only a portion related to the embodiment of the present application is shown.
Referring to fig. 4, the sheet suspicion determination apparatus 400 may include:
a first obtaining module 401, configured to obtain industry index values for evaluating n parameters of a store, where n is a positive integer;
a second obtaining module 402, configured to obtain n first deviation rates of the n parameters of the store to be detected with respect to the corresponding industry index values, and n second deviation rates of the n parameters of m reference stores with respect to the corresponding industry index values, where the reference stores are the same industry stores having confirmed the behavior of the bill, and m is a positive integer;
a third obtaining module 403, configured to perform correlation calculation based on the n first deviation rates and the n second deviation rates of each reference store, and obtain m correlation coefficients;
and the determining module 404 is configured to determine the suspicion of the to-be-detected store according to the m correlation coefficients.
Optionally, the third obtaining module 403 includes an obtaining sub-module, configured to perform correlation calculation on a first vector of the to-be-detected shop and m second vectors of the reference shop, to obtain m correlation coefficients, where the first vector is determined according to the n first deviation rates, and the second vector of the reference shop is determined according to the n second deviation rates of the reference shop.
Optionally, the acquiring sub-module includes:
an obtaining unit, configured to take the n first deviation rates as elements of a first vector, and take the n second deviation rates of each reference store as elements of a second vector of the corresponding reference store;
and the calculating unit is used for respectively carrying out pearson correlation coefficient calculation on the first vector of the storefront to be detected and the second vectors of the m reference storefronts to obtain m correlation coefficients.
Optionally, the first obtaining module 401 includes:
the collecting sub-module is used for collecting the numerical values of the n parameters of a plurality of shops in the same industry as the shop to be detected from the network through a crawler technology;
and the computing sub-module is used for averaging the value of any one first parameter of the n parameters based on the first parameter of each store in the plurality of stores to obtain an industry index value of the first parameter.
Optionally, the determining module 404 is specifically configured to calculate an average value of the m correlation coefficients, and use the average value as the suspicion of the to-be-detected store.
Optionally, the n parameters include a store score, a refund rate, an evaluation rate, a good content repetition rate, and a medium score ratio.
The apparatus 300 for determining suspicion of a bill in the embodiment of the present application can implement each process implemented by the foregoing method embodiment and achieve the same technical effects, and for avoiding repetition, a description is omitted herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Fig. 5 shows a schematic hardware structure of an electronic device according to an embodiment of the present application.
The device may include a processor 501 and a memory 502 in which program instructions are stored.
The steps of any of the various method embodiments described above are implemented when the processor 501 executes a program.
By way of example, a program may be partitioned into one or more modules/units that are stored in the memory 502 and executed by the processor 501 to accomplish the present application. One or more of the modules/units may be a series of program instruction segments capable of performing specific functions to describe the execution of the program in the device.
In particular, the processor 501 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present application.
Memory 502 may include mass storage for data or instructions. By way of example, and not limitation, memory 502 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. Memory 502 may include removable or non-removable (or fixed) media, where appropriate. Memory 502 may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory 502 is a non-volatile solid state memory.
The memory may include Read Only Memory (ROM), random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to methods in accordance with aspects of the present disclosure.
The processor 501 implements any one of the methods of the above embodiments by reading and executing program instructions stored in the memory 502.
In one example, the electronic device may also include a communication interface 503 and a bus 510. The processor 501, the memory 502, and the communication interface 503 are connected to each other via a bus 510 and perform communication with each other.
The communication interface 503 is mainly used to implement communication between each module, apparatus, unit and/or device in the embodiments of the present application.
Bus 510 includes hardware, software, or both that couple the components of the online data flow billing device to each other. By way of example, and not limitation, the buses may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a micro channel architecture (MCa) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus, or a combination of two or more of the above. Bus 510 may include one or more buses, where appropriate. Although embodiments of the application have been described and illustrated with respect to a particular bus, the application contemplates any suitable bus or interconnect.
In addition, in combination with the method in the above embodiment, the embodiment of the present application may be implemented by providing a storage medium. The storage medium has program instructions stored thereon; the program instructions, when executed by a processor, implement any of the methods of the embodiments described above.
The embodiment of the application further provides a chip, the chip comprises a processor and a communication interface, the communication interface is coupled with the processor, the processor is used for running programs or instructions, the processes of the embodiment of the method can be realized, the same technical effects can be achieved, and the repetition is avoided, and the description is omitted here.
It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, chip systems, or system-on-chip chips, etc.
Embodiments of the present application provide a computer program product stored in a storage medium, where the program product is executed by at least one processor to implement the respective processes of the above method embodiments, and achieve the same technical effects, and for avoiding repetition, a detailed description is omitted herein.
It should be understood that the application is not limited to the particular arrangements and instrumentality described above and shown in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer grids such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this disclosure describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, or may be performed in a different order from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present application, and they should be included in the scope of the present application.
Claims (7)
1. A method for determining suspicion of a sheet, the method comprising:
acquiring industry index values for evaluating n parameters of a store, wherein n is a positive integer;
according to the industry index values of the n parameters, n first deviation rates of the n parameters of the shop to be detected relative to the corresponding industry index values respectively and n second deviation rates of the n parameters of m reference shop to the corresponding industry index values respectively are obtained, wherein the reference shop is the same shop with confirmed bill swiping behavior, and m is a positive integer;
performing correlation calculation based on the n first deviation rates and the n second deviation rates of each reference store to obtain m correlation coefficients;
determining the suspicion of the list of shops to be detected based on the m correlation coefficients;
performing correlation calculation based on the n first deviation rates and the n second deviation rates of each reference store, to obtain m correlation coefficients, including:
respectively carrying out correlation calculation on the first vector of the storefront to be detected and the second vectors of m reference storefront to obtain m correlation coefficients, wherein the first vector is determined according to the n first deviation rates, and the second vector of the reference storefront is determined according to the n second deviation rates of the reference storefront;
performing correlation calculation on the first vector of the to-be-detected store and the second vectors of m reference stores respectively to obtain m correlation coefficients, wherein the method comprises the following steps:
taking the n first deviation rates as elements of a first vector, and taking the n second deviation rates of each reference store as elements of a corresponding second vector of the reference store;
respectively carrying out pearson correlation coefficient calculation on the first vector of the storefront to be detected and the second vectors of m reference storefronts to obtain m correlation coefficients;
based on the m correlation coefficients, determining the suspicion of the list of shops to be detected includes:
and calculating an average value of the m correlation coefficients, and taking the average value as the suspicion of the list of the shops to be detected.
2. The method of claim 1, wherein the acquiring industry index values for evaluating n parameters of a store comprises:
collecting values of the n parameters of a plurality of shops in the same industry as the shop to be detected from a network through a crawler technology;
and for any one first parameter of the n parameters, calculating an average value based on the values of the first parameters of each of the plurality of shops to obtain an industry index value of the first parameter.
3. The method of any one of claims 1-2, wherein the n parameters include a store score, a refund rate, an evaluation rate, a good content repetition rate, and a medium score ratio.
4. A sheet suspicion determination device, the device comprising:
the first acquisition module is used for acquiring industry index values of n parameters for evaluating shops, wherein n is a positive integer;
the second acquisition module is used for acquiring n first deviation rates of the n parameters of the shop to be detected relative to the corresponding industry index values respectively and n second deviation rates of the n parameters of m reference shop to the corresponding industry index values respectively according to the industry index values of the n parameters, wherein the reference shop is the same shop with confirmed bill swiping behavior, and m is a positive integer;
the third acquisition module is used for carrying out correlation calculation based on the n first deviation rates and the n second deviation rates of each reference store so as to obtain m correlation coefficients;
the determining module is used for determining the suspicion of the list of the shops to be detected based on the m correlation coefficients;
the third acquisition module comprises an acquisition sub-module, wherein the acquisition sub-module is used for respectively carrying out correlation calculation on the first vector of the storefront to be detected and the second vectors of m reference storefront to obtain m correlation coefficients, the first vector is determined according to the n first deviation rates, and the second vector of the reference storefront is determined according to the n second deviation rates of the reference storefront;
an acquisition sub-module comprising:
an obtaining unit, configured to take the n first deviation rates as elements of a first vector, and take the n second deviation rates of each reference store as elements of a second vector of the corresponding reference store;
the computing unit is used for computing pearson correlation coefficients of the first vector of the storefront to be detected and the second vectors of the m reference storefront respectively to obtain m correlation coefficients;
the determining module is specifically configured to calculate an average value of the m correlation coefficients, and use the average value as the suspicion of the list of shops to be detected.
5. An electronic device, the device comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements the method for determining suspicion of a sheet as claimed in any one of claims 1 to 3.
6. A computer readable storage medium having stored thereon computer program instructions which when executed by a processor implement the method of determining a suspicion of a swipe as claimed in any of claims 1 to 3.
7. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the method of determining a suspicion of a sheet as claimed in any one of claims 1-3.
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