CN112783934B - Transaction data interval determining method and device, storage medium and computer equipment - Google Patents
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Abstract
The invention discloses a transaction data interval determining method and device, a storage medium and computer equipment, and relates to the technical field of computers. The method comprises the following steps: acquiring transaction data of each article in the preselected article types in a preset transaction period; counting the transaction data according to a preset transaction data step length to generate a frequency sequence, wherein each frequency in the frequency sequence corresponds to one transaction data segment; based on a predefined statistic function, turning point detection is carried out on the frequency sequence, and a preset number of turning points are determined; and determining a plurality of transaction data intervals according to the preset number of turning points. According to the transaction data interval determining method provided by the invention, the frequency sequence showing the distribution characteristics of the transaction data is created, and the turning points in the frequency sequence are detected and mined, so that the change of the crowd transaction preference can be responded in time, and the division of the transaction data interval can be objectively and automatically completed.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a transaction data interval determining method and apparatus, a storage medium, and a computer device.
Background
With the development of the information age and the internet of electronic commerce, people receive explosive information every day. How to identify information closely related to a receiver from a large amount of information is not only a problem faced by the receiver but also an information sender should consider.
The above problems are solved by taking a display scene of e-commerce transaction data as an example by means of certain auxiliary information: the information of what the transaction preference of the consumer for a certain type of commodity is, how much proportion of people can purchase the commodity in which transaction data section, and the like is simple, but the information can intuitively and effectively assist the electric business user in personalized purchase. The transaction data intervals are divided, and the result of counting the consumption proportion of each transaction data interval is the basis of reference of a user of the electronic business during transaction.
At present, the division of transaction data intervals is mainly finished manually, but the manual method has the following defects: 1) Severely relying on subjective decisions, lacking objective criteria; 2) The transaction data distribution characteristics are not fixed and directly integrated into the interval dividing process; 3) Changes in the crowd trade preferences cannot be captured in time.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the invention and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
In view of the above, the present invention provides a transaction data interval determining method and apparatus, a storage medium and a computer device.
Other features and advantages of the invention will be apparent from the following detailed description, or may be learned by the practice of the invention.
According to an aspect of the present invention, there is provided a transaction data interval determining method, including: acquiring transaction data of each article in the preselected article types in a preset transaction period; counting the transaction data according to a preset transaction data step length to generate a frequency sequence, wherein each frequency in the frequency sequence corresponds to one transaction data segment; based on a predefined statistic function, turning point detection is carried out on the frequency sequence, and a preset number of turning points are determined; and determining a plurality of transaction data intervals according to the preset number of turning points.
According to an embodiment of the present invention, performing turning point detection on the frequency sequence based on a predefined statistic function, and determining a preset number of turning points includes: determining the preset number of conditional expressions based on a predefined statistic function C ()Obtain the minimum frequency sequence number t i The method comprises the steps of carrying out a first treatment on the surface of the Wherein m is the number of preset turning points, respectively is the frequency sequence number (t i-1 +1)～t i Corresponding frequency; determining the sequence number t of each frequency i The end point of the corresponding transaction data segment is the turning point.
According to one embodiment of the present invention, the predetermined number of the conditional expressions is determinedObtain the minimum frequency sequence number t i Comprising the following steps: sequentially determining the preset number of the formula +.>Obtain the minimum frequency sequence number t i 。
According to an embodiment of the present invention, the expression of the predefined statistics function C () is as follows:wherein sigma 2 For the frequency sequence number t a ～t b Corresponding to the variance of each frequency.
According to an embodiment of the present invention, before the counting of the transaction data, the method further comprises: and naturally sequencing the transaction data according to the size.
According to an embodiment of the invention, the method further comprises: and removing outlier data in the transaction data after natural sequencing.
According to one embodiment of the present invention, the transaction data step size is preset according to the item transaction data level and the expected transaction data segment number of the preselected item type.
According to another aspect of the present invention, there is provided a transaction data section determining apparatus including: the data acquisition module is used for acquiring transaction data of each article in the preselected article types in a preset transaction period; the sequence generation module is used for counting the transaction data according to a preset transaction data step length to generate a frequency sequence, and each frequency in the frequency sequence corresponds to one transaction data segment; the turning detection module is used for detecting turning points of the frequency sequence based on a predefined statistic function and determining a preset number of turning points; and the interval determining module is used for determining a plurality of transaction data intervals according to the preset number of turning points.
According to still another aspect of the present invention, there is provided a computer apparatus comprising: the system comprises a memory, a processor and executable instructions stored in the memory and executable in the processor, wherein the processor implements any one of the methods when executing the executable instructions.
According to yet another aspect of the present invention, there is provided a computer readable storage medium having stored thereon computer executable instructions which when executed by a processor implement any of the methods described above.
According to the transaction data interval determining method provided by the invention, the frequency sequence showing the distribution characteristics of the transaction data is created, and the turning points in the frequency sequence are detected and mined, so that the change of the crowd transaction preference can be responded in time, and the division of the transaction data interval can be objectively and automatically completed.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 is a flow chart illustrating a transaction data interval determination method according to an exemplary embodiment.
Fig. 2 is a flow chart illustrating another transaction data interval determination method according to an exemplary embodiment.
Fig. 3 is a flow chart illustrating yet another transaction data interval determination method according to an exemplary embodiment.
Fig. 4 is a block diagram illustrating a transaction data interval determination device, according to an example embodiment.
Fig. 5 is a schematic diagram of a computer device according to an exemplary embodiment.
Fig. 6 is a distribution histogram of a frequency sequence generated from transaction data, according to an example embodiment.
Fig. 7 is a diagram illustrating the results of a transaction data interval according to an exemplary embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, apparatus, steps, etc. In other instances, well-known structures, methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
As described above, the conventional method for dividing transaction data sections has many drawbacks such as lack of objectivity. Accordingly, the present invention provides a new transaction data interval determination method, which will be described in detail below with reference to various embodiments of the present invention.
Fig. 1 is a flow chart illustrating a transaction data interval determination method according to an exemplary embodiment. The transaction data interval determining method shown in fig. 1 may be applied to a scenario of dividing the price interval of the e-commerce transaction object, i.e. the transaction data described below may refer to the object price data, but the invention is not limited thereto.
Referring to fig. 1, the transaction data interval determination method 10 includes:
in step S102, transaction data for each item in the preselected item categories within the preset transaction period is obtained.
In step S104, according to the preset transaction data step length, the transaction data is counted to generate a frequency sequence.
Wherein each frequency in the sequence of frequencies corresponds to a transaction data segment.
In some embodiments, the transaction data step size is preset based on the item transaction data level and the expected transaction data segment number for the preselected item category. In specific practice, setting the transaction data step size may follow, for example, the following principles: 1) The higher the item transaction data level, the correspondingly larger the transaction data step size; 2) The expected transaction data segments may be on the order of hundreds, preferably in the range 500-800; 3) The transaction data step size should not be too large, and may be, for example, 0.2, 0.5, 1, 2, 5, etc. It should be noted that the present invention is not limited to the above-mentioned setting principles and numerical ranges.
In some embodiments, prior to step S104, the method 10 further comprises: naturally ordering the transaction data according to the size so as to facilitate statistics of the transaction data entering a corresponding data range, and further sequentially combining corresponding frequencies to generate a frequency sequence; further, in some embodiments, the method 10 further comprises: the outlier data in the transaction data after natural ordering is removed, for example, the largest 1% and the smallest 1% of the transaction data after natural ordering can be removed as outlier data (extreme value) according to the actual dividing requirement.
In step S106, turning point detection is performed on the frequency sequence based on a predefined statistic function, and a preset number of turning points are determined.
On the basis of the frequency sequence generated in the step S104, turning points are detected for all the frequencies, and the detection result is used as the basis for dividing the transaction data interval. The range between two turning points is an interval, each of which may represent, for example, a price of an item potentially meeting a certain consumption expectation.
In step S108, a plurality of transaction data intervals are determined according to a preset number of turning points.
According to the transaction data interval determining method provided by the embodiment of the invention, the frequency sequence showing the distribution characteristics of the transaction data is created, and the turning points in the frequency sequence are detected and mined, so that the change of the crowd transaction preference can be responded in time, and the division of the transaction data interval can be objectively and automatically completed.
It should be clearly understood that the present invention describes how to make and use specific examples, but the principles of the present invention are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
Fig. 2 is a flow chart illustrating another transaction data interval determination method according to an exemplary embodiment. The difference from the method 10 shown in fig. 1 is that the method shown in fig. 2 further provides a specific method for detecting the turning point of the frequency sequence, i.e. further provides an embodiment of step S106 described above. Likewise, the transaction data interval determining method shown in fig. 2 may be applied to a scenario of dividing the price interval of the e-commerce transaction object, for example.
Referring to fig. 2, step S106 includes:
in step S202, a predetermined number of conditional expressions are determined based on a predefined statistics function C ()Obtain the minimum frequency sequence number t i 。
Wherein m is the number of preset turning points, respectively is the frequency sequence number (t i-1 +1)～t i Corresponding frequency.
In step S104, assuming that the expected transaction data segment is n and the preset number of turning points is m, the generated frequency sequence may be expressed as y 1:n ＝(y 1 ,y 2 ,…,y n-1 ,y n ) The m turning points can also be expressed as the sequence form T to be determined 1:m ＝(T 1 ,T 2 ,…,T m-1 ,T m ). Wherein, each turning point corresponds to one frequency and its frequency sequence number in the frequency sequence.
From a statistical perspective, the turning point detection problem can be converted into a hypothesis testing problem, namely: in the frequency sequence, taking the 'mean value and variance of subsequences before and after the non-turning point are not changed significantly' as a primary assumption, and calculating whether the test statistic exceeds a critical value according to a certain confidence coefficient by constructing a statistic, thereby determining to accept or reject the primary assumption. For the case where the required frequency sequence is divided into sections by only one turning point T, the check condition may be, for example, the following formula (1):
C(y 1:t )+C(y (t+1):n )+B<C(y 1:n ) (1)
wherein, C () is the statistic function of the structure, T is the frequency sequence number corresponding to the turning point T; b is a punishment term for preventing overfitting, and is positively correlated with the complexity of the model, namely, the more turning points are, the larger the B value is. In the present invention, the number of turning points is preset as required, so that B can be regarded as a constant term.
In some embodiments, the statistics function C () may be defined as the following equation (2):
wherein sigma 2 For the frequency sequence number t a ～t b Corresponding to the variance of each frequency. In the embodiment of the present invention, the statistic function C () may be defined as any other possible form, which is not limited by the present invention.
Based on the principle of hypothesis test, t established in the above formula (1) can be used for subsequent determination of turning points, and the smaller the left value of "<", the more acceptable t is as a basis for determining turning points. Therefore, detecting the turning point is essentially selecting the frequency bin number that makes the left equation as small as possible.
As described above, for the multiple inflection points (T 1 ～T m ) Under the detection condition, the two statistics corresponding to one turning point in the above formula (1) can be generalized to the (m+1) statistics corresponding to m turning points, so that the hypothesis testing problem of the above formula (1) is converted into the minimization problem of the following formula (3).
Wherein, the mathematical meaning of the beta f (m) term is the same as that of B in the formula (1), beta can be understood as a punishment factor, and the intensity of the influence of the number of turning points on the whole formula is regulated; f (m) is a monotonically increasing function of m, and may be equal to m, for example, or any other form of function that increases as m increases.
Likewise, in the present invention, the number of turning points is preset as required, so βf (m) can also be regarded as a constant term, that is, the hypothesis testing problem of the above formula (1) can be finally converted into the minimization problem of the following formula (4):
in step S204, each frequency number t is determined i The end point of the corresponding transaction data segment is the turning point.
As mentioned above, each turning point corresponds to a frequency and its frequency sequence number, and each frequency corresponds to a transaction data segment. Therefore, each turning point corresponds to one transaction data segment, and the turning point detection is to select m from n transaction data segments (or frequencies) as a basis for determining the transaction data segment. In step S204, the frequency numbers t may be determined uniformly i The left end point of the corresponding transaction data segment is a turning point T i Or uniformly determining the sequence number t of each frequency i The right end point of the corresponding transaction data segment is a turning point T i The invention is not limited in this regard.
Fig. 3 is a flow chart illustrating yet another transaction data interval determination method according to an exemplary embodiment. The method shown in fig. 3 further provides an optimization method for quickly determining a plurality of turning points, which is different from the method shown in fig. 2, i.e. an embodiment of the above-mentioned step S202 is further provided. Likewise, the transaction data interval determining method shown in fig. 3 may be applied to a scenario of dividing the price interval of the e-commerce transaction object, for example.
As described above, if m is selected directly from n transaction data segments, there areOne possibility is to use a single-piece plastic. For hundreds of transaction data segments, the cost of such a brute force search is very large. Therefore, the embodiment of the invention provides an optimization method for determining a plurality of turning points.
Referring to fig. 3, step S202 includes:
in step S2022, a predetermined number of conditional expressions are sequentially determined by a recurrence algorithmObtain the minimum frequency sequence number t i 。
The above-mentioned order is supportedF (5) may be further derived as follows (5):
according to equation (5) above, one of the n frequencies may be determined, and then the other frequencies may be sequentially determined by recursively starting from the highest transaction data segment to the lowest transaction data segment of the sequence of frequencies, where the frequency is the smallest value obtained by equation (4). The optimization algorithm described above reduces the time complexity to O (nlgn).
According to some embodiments, the transaction data interval determining method provided by the invention changes one-time determination of a plurality of turning points into sequential and one-by-one determination through a recursive algorithm, so that the cost of violent search can be effectively reduced, the preset number of turning points can be rapidly determined, and the efficiency of determining the transaction data interval is further improved.
FIG. 6 is a distribution histogram of a frequency sequence generated from transaction data, wherein the horizontal axis is transaction data in units of elements, according to an example embodiment; the vertical axis is the frequency value. Assuming that 3 turning points are required, the section division result of the transaction data shown in fig. 6 is shown in fig. 7 according to the method provided in the above embodiment.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as a computer program executed by a CPU. When executed by a CPU, performs the functions defined by the above-described method provided by the present invention. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic disk or an optical disk, etc.
Furthermore, it should be noted that the above-described figures are merely illustrative of the processes involved in the method according to the exemplary embodiment of the present invention, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.
The following are examples of the apparatus of the present invention that may be used to perform the method embodiments of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.
Fig. 4 is a block diagram illustrating a transaction data interval determination device, according to an example embodiment.
Referring to fig. 4, the transaction data section determining device 40 includes: a data acquisition module 402, a sequence generation module 408, a turn detection module 410, and an interval determination module 412.
The data acquisition module 402 is configured to acquire transaction data of each item in the preselected item types in a preset transaction period.
The sequence generating module 408 is configured to count the transaction data according to a preset transaction data step size, and generate a frequency sequence.
Wherein each frequency in the sequence of frequencies corresponds to a transaction data segment.
The turning detection module 410 is configured to perform turning point detection on the frequency sequence based on a predefined statistic function, and determine a preset number of turning points.
In some embodiments, the turn detection module 410 may further include: a first determination unit and a second determination unit.
The first determining unit is used for determining a preset number of conditional expressions based on a predefined statistic function C ()Obtain the minimum frequency sequence number t i . Wherein m is the number of preset turning points, respectively is the frequency sequence number (t i-1 +1)～t i Corresponding frequency.
In some embodiments, the first determining unit may further include: a recursion determination subunit for sequentially determining a preset number of messenger formulas through recursion algorithmObtain the minimum frequency sequence number t i 。
The second determining unit is used for determining each frequency sequence number t i The end point of the corresponding transaction data segment is the turning point.
The interval determining module 412 is configured to determine a plurality of transaction data intervals according to a preset number of turning points.
In some embodiments, the transaction data interval determination device 40 may further include: the natural ordering module 404 and the extremum processing module 406.
Wherein the natural ordering module 404 is configured to naturally order the transaction data by size before the statistics of the transaction data are performed by the sequence generating module 408.
The extremum processing module 406 is configured to remove outliers from the naturally ordered transaction data.
In some embodiments, the natural ordering module 404 and the extremum processing module 406 can be the same module, i.e., the module is configured to naturally order the transaction data by size and remove outliers in the naturally ordered transaction data before the statistics of the transaction data are performed by the sequence generating module 408.
According to the transaction data interval determining device provided by the embodiment of the invention, the frequency sequence showing the distribution characteristics of the transaction data is created, and the turning points in the frequency sequence are detected and mined, so that the change of the crowd transaction preference can be responded in time, and the division of the transaction data interval can be objectively and automatically completed.
Further, according to some embodiments, the transaction data interval determining device provided by the invention changes one-time determination of a plurality of turning points into sequential and one-by-one determination through a recursive algorithm, so that the cost of violent search can be effectively reduced, the preset number of turning points can be rapidly determined, and the efficiency of determining the transaction data interval can be further improved.
It should be noted that the block diagrams shown in the above figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
Fig. 5 is a schematic diagram of a computer device according to an exemplary embodiment. It should be noted that the computer device shown in fig. 5 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present invention.
As shown in fig. 5, the computer device 800 includes a Central Processing Unit (CPU) 801, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, mouse, etc.; an output portion 807 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 808 including a hard disk or the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. The drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as needed so that a computer program read out therefrom is mounted into the storage section 808 as needed.
In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 809, and/or installed from the removable media 811. The above-described functions defined in the apparatus of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 801.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, 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. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts 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 invention. 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.
The units involved in the embodiments of the present invention may be implemented in software or in hardware. The described units may also be provided in a processor, for example, described as: a processor includes a transmitting unit, an acquiring unit, a determining unit, and a first processing unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the transmitting unit may also be described as "a unit that transmits a picture acquisition request to a connected server".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include:
acquiring transaction data of each article in the preselected article types in a preset transaction period; counting transaction data according to a preset transaction data step length, and generating a frequency sequence, wherein each frequency in the frequency sequence corresponds to one transaction data segment; based on a predefined statistic function, turning point detection is carried out on the frequency sequence, and a preset number of turning points are determined; and determining a plurality of transaction data intervals according to the preset number of turning points.
The exemplary embodiments of the present invention have been particularly shown and described above. It is to be understood that this invention is not limited to the precise arrangements, instrumentalities and instrumentalities described herein; on the contrary, the invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (9)
1. A transaction data interval determining method, comprising:
acquiring transaction data of each article in the preselected article types in a preset transaction period;
counting the transaction data according to a preset transaction data step length to generate a frequency sequence, wherein each frequency in the frequency sequence corresponds to one transaction data segment;
based on a predefined statistic function, turning point detection is carried out on the frequency sequence, and a preset number of turning points are determined; and
determining a plurality of transaction data intervals according to the preset number of turning points;
wherein, based on a predefined statistic function, performing turning point detection on the frequency sequence, and determining a preset number of turning points includes:
based on a predefined statistics functionDetermining said preset number of the formula +.>Frequency sequence number of minimum value>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is the number of the preset turning points,
，/>frequency sequence numbers->Corresponding frequency; and
determining the sequence number of each frequencyThe end point of the corresponding transaction data segment is the turning point.
2. The method of claim 1, wherein the predetermined number of patterns is determinedFrequency sequence number of minimum value>Comprising the following steps: sequentially determining the preset number of the formula +.>Frequency sequence number of minimum value>。
3. The method of claim 1, wherein the predefined statistics functionThe expression of (2) is as follows:
；
wherein,for frequency sequence number->Corresponding to the variance of each frequency.
4. The method of claim 1, wherein prior to the accounting of the transaction data, the method further comprises: and naturally sequencing the transaction data according to the size.
5. The method as recited in claim 4, further comprising: and removing outlier data in the transaction data after natural sequencing.
6. The method of any one of claims 1-5, wherein the transaction data step size is preset based on the item transaction data level and the expected number of transaction data segments for the preselected item category.
7. A transaction data interval determining device, comprising:
the data acquisition module is used for acquiring transaction data of each article in the preselected article types in a preset transaction period;
the sequence generation module is used for counting the transaction data according to a preset transaction data step length to generate a frequency sequence, and each frequency in the frequency sequence corresponds to one transaction data segment;
the turning detection module is used for detecting turning points of the frequency sequence based on a predefined statistic function and determining a preset number of turning points; and
the interval determining module is used for determining a plurality of transaction data intervals according to the preset number of turning points;
the turning detection module is further used for: based on a predefined statistics functionDetermining the preset number of the lead-in typeFrequency sequence number of minimum value>The method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is the number of the preset turning points,，/>respectively is the frequency sequence numberCorresponding frequency; determining the sequence number of each frequency +.>The end point of the corresponding transaction data segment is the turning point.
8. A computer device, comprising: memory, a processor and executable instructions stored in the memory and executable in the processor, wherein the processor implements the method of any of claims 1-6 when executing the executable instructions.
9. A computer readable storage medium having stored thereon computer executable instructions which when executed by a processor implement the method of any of claims 1-6.
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