CN116739662A - Abnormality detection method, device and system for electronic coupon information - Google Patents
Abnormality detection method, device and system for electronic coupon information Download PDFInfo
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Abstract
The application discloses an anomaly detection method, device and system for electronic coupon information, wherein the method comprises the following steps: after the electronic coupon information of the user is obtained, searching the coupon characteristic information corresponding to the electronic coupon information in a preset database; assigning a computing weight to feature data contained in the preferential feature information based on transaction time, and computing a preferential value based on the computing weight; if the preferential value is larger than the preset monetary value, determining that the electronic coupon information is abnormal information, and feeding back abnormal alarm information to the user terminal. According to the application, after the electronic coupon information of the user is acquired, the coupon characteristic information corresponding to the electronic coupon information is searched, the corresponding calculation weight is distributed by utilizing the coupon characteristic information, the coupon value is calculated based on the calculation weight, the abnormality of the electronic coupon information is determined according to the comparison result of the coupon value and the cost value, and the alarm information is fed back, so that the abnormal electronic coupon information is screened out.
Description
Technical Field
The present application relates to the field of online data detection technologies, and in particular, to a method, an apparatus, and a system for detecting abnormality of electronic coupon information.
Background
With the continuous development of internet technology, online platforms are rapidly developed, and more shops offer online shops for different users to purchase goods online and conduct online transactions. In order to stimulate online consumption, coupons or discount coupons are generally set on an online platform, and the purchased goods are exempted through the coupons, so that the price of the goods is reduced, and consumers are attracted to purchase the goods.
The general coupons are set by different background management staff, and the priorities of the coupons set by different staff are different, so that part of coupons are overlapped or contradicted when in use, the selling price is lower than the cost, even great loss is brought to the company, and the selling price caused by misoperation is too low to influence the income of the company.
Disclosure of Invention
The application provides an anomaly detection method, device and system for electronic coupon information, wherein the method collects electronic coupon information of a user, searches for coupon characteristic information corresponding to the electronic coupon information, distributes corresponding calculation weights by utilizing the coupon characteristic information, calculates coupon values based on the calculation weights, determines that the electronic coupon information is anomalous according to comparison results of the coupon values and cost values, and feeds back alarm information, so that the anomalous electronic coupon information is screened out.
A first aspect of an embodiment of the present application provides a method for detecting abnormality of electronic coupon information, where the method includes:
after the electronic coupon information of the user is obtained, searching the coupon characteristic information corresponding to the electronic coupon information in a preset database;
assigning a computing weight to feature data contained in the preferential feature information based on transaction time, and computing a preferential value based on the computing weight;
if the preferential value is larger than the preset monetary value, determining that the electronic coupon information is abnormal information, and feeding back abnormal alarm information to the user terminal.
In a possible implementation manner of the first aspect, the assigning a computing weight to feature data included in the preferential feature information based on a transaction time includes:
if the obtained electronic coupon information is one, respectively obtaining a transaction time node of the user and a preferential time node corresponding to the preferential feature information;
extracting feature data from the offer feature information when the transaction time node precedes the offer time node, the feature data comprising: the number of the consumer goods and the total price;
and determining the calculation weight of the characteristic data according to the comparison result of the characteristic data and the corresponding data threshold interval.
In a possible implementation manner of the first aspect, the assigning a computing weight to feature data included in the preferential feature information based on a transaction time includes:
if the number of the acquired electronic coupon information is at least two, respectively acquiring the priority corresponding to each piece of the preferential feature information;
screening at least one target preferential feature information according to the priority, and extracting feature data from the target preferential feature information, wherein the feature data comprises: the type of the consumer goods and the total price of the consumer;
and distributing calculation weights to the characteristic data according to the priority.
In a possible implementation manner of the first aspect, the assigning a calculation weight to the feature data according to the size of the priority includes:
if the target preferential feature information is one, multiplying the numerical value corresponding to the priority by the weight base corresponding to the feature data to obtain a calculation weight;
if the target preferential feature information is two or more, calculating algorithm weights corresponding to the feature data by adopting a variation coefficient method, a CRITIC weight method and an entropy weight method respectively, and averaging to obtain calculation weights.
In a possible implementation manner of the first aspect, after the step of calculating a coupon value based on the calculation weight, the method further includes:
if the preferential value is smaller than the preset monetary value, the transaction data of the user is adjusted according to the electronic coupon information;
and generating transaction record information by adopting the adjusted transaction data, encrypting and storing the transaction record information.
In a possible implementation manner of the first aspect, after the step of encrypting and storing the transaction record information, the method further includes:
counting the information quantity of the transaction record information, and calculating a transaction frequency value by utilizing the information quantity;
and if the transaction frequency value is larger than the preset frequency value, adjusting the authority information of the user.
A second aspect of an embodiment of the present application provides an abnormality detection apparatus for electronic coupon information, the apparatus including:
the information searching module is used for searching the preferential feature information corresponding to the electronic coupon information in a preset database after the electronic coupon information of the user is acquired;
the weight distribution module is used for distributing calculation weights to the feature data contained in the preferential feature information based on the transaction time and calculating preferential values based on the calculation weights;
and the abnormality detection module is used for determining the electronic coupon information as abnormality information if the preferential value is larger than a preset monetary value and feeding back abnormality warning information to the user terminal.
A third aspect of an embodiment of the present application provides an abnormality detection system for electronic coupon information, the system including:
the system comprises a background management device, an online platform and a plurality of user terminals, wherein the background management device is suitable for the abnormality detection method of the electronic coupon information;
the background management device is connected with the online platform, and the online platform is respectively connected with the plurality of user terminals.
Compared with the prior art, the … … method and the … … device provided by the embodiment of the application have the beneficial effects that: according to the application, after the electronic coupon information of the user is acquired, the coupon characteristic information corresponding to the electronic coupon information is searched, the corresponding calculation weight is distributed by utilizing the coupon characteristic information, the coupon value is calculated based on the calculation weight, the abnormality of the electronic coupon information is determined according to the comparison result of the coupon value and the cost value, and the alarm information is fed back, so that the abnormal electronic coupon information is screened out.
Drawings
Fig. 1 is a flowchart of a method for detecting abnormality of electronic coupon information according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an abnormality detection device for electronic coupon information according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an abnormality detection system for electronic coupon information according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order to solve the above problems, a method for detecting abnormality of electronic coupon information according to the embodiment of the present application will be described and illustrated in detail by the following specific embodiments.
Referring to fig. 1, a flowchart of a method for detecting abnormality of electronic coupon information according to an embodiment of the present application is shown.
In an embodiment, the method is suitable for a background management device of the online platform, and the background management device can manage data of the online platform in real time and prompt a manager when detecting data abnormality, so as to avoid transaction conflict or transaction abnormality.
In a specific implementation manner, the online platform can be in communication connection with a plurality of user terminals, a user can make online shopping through the user terminals, and related data of shopping, including the number of commodities, unit price, used electronic coupons and the like, are uploaded through the user terminals.
As an example, the method for detecting abnormality of the electronic coupon information may include:
s11, after the electronic coupon information of the user is obtained, the coupon characteristic information corresponding to the electronic coupon information is searched in a preset database.
In an implementation manner, if the user performs online transaction and sends and uses the electronic coupon, the user can execute operation through the user terminal, and the user terminal can upload related shopping data and information corresponding to the electronic coupon to the online platform, and the online platform transmits the related shopping data and information corresponding to the electronic coupon to the background management device.
After receiving the electronic coupon information, the background management device can search corresponding coupon characteristic information from a preset database according to the electronic coupon information.
The preset database may be a database storing data of different stores.
The manager of each store can upload the relevant data of the preferential to the online platform, and the online platform can integrate the relevant data of the preferential into electronic coupon information and send the electronic coupon information to different users so that the users can call the electronic coupon information when shopping in the store. Meanwhile, the online platform can also store preferential related data, and the related data are recalled when the user uses the online platform.
The relevant data of the offers may include transaction time, amount of consumption, type of goods purchased, priority of consumption, amount of exemption, minimum amount of consumption, amount of consumption of the user, limited number of purchases, minimum number of consumption, minimum amount of consumption, priority of use of the coupons, etc.
The relevant data of each coupon may be coupon feature information.
In actual operation, the corresponding online store can be determined according to the electronic coupon information, and then the relevant coupon data uploaded by the administrator of the online store is searched in the database, so that the coupon characteristic information is obtained.
And S12, distributing calculation weights to the feature data contained in the preferential feature information based on the transaction time, and calculating preferential values based on the calculation weights.
In one embodiment, after the preferential feature information is obtained, weights may be assigned to various feature data included in the preferential feature information based on the transaction time of the user making the purchase.
Because the transaction time of the user shopping may be in the preferential time interval corresponding to the preferential feature information or not, if the electronic coupon cannot be used in the preferential time interval, the corresponding preferential feature information also fails, and the weight can be assigned to various feature data contained in the preferential feature information as 0. If a fixed value or a specific value can be allocated correspondingly in the preferential time interval as the calculation weight, for example, 1, 3, 5 can be allocated to each feature data included in the preferential feature information as the calculation weight thereof.
Alternatively, if the time difference between the transaction time of the user shopping and the deadline time of the preferential time interval is calculated in the preferential time interval, the time difference is converted according to a certain proportion, and the converted value is used as the calculation weight.
For example, the preferential time interval is 6 months 1-6 months 30, the transaction time of the user shopping is 6 months 5, the time difference between the transaction time of the user shopping and the deadline time of the preferential time interval is 25, and the preset ratio is 0.1. The value obtained after conversion was 25×0.1=2.5. 2.5 may be used as the calculation weight of each feature data.
In an alternative embodiment, the user may use only one coupon, with one corresponding electronic coupon information. In order to accurately weight distribution for a single electronic coupon information, step S12 may include the following sub-steps, as an example:
s21, if the obtained electronic coupon information is one, respectively obtaining a transaction time node of the user and a preferential time node corresponding to the preferential feature information.
S22, when the transaction time node is before the preferential time node, extracting feature data from the preferential feature information, wherein the feature data comprises the following components: the number of goods consumed and the total price consumed.
S23, determining the calculation weight of the characteristic data according to the comparison result of the characteristic data and the corresponding data threshold interval.
If the electronic coupon information is one, whether the electronic coupon information is effective or not is judged first, and the electronic coupon information can be determined through the using period of the electronic coupon information.
Specifically, the transaction time node of the user and the preferential time node corresponding to the preferential feature information can be obtained respectively.
Wherein, the transaction time node of the user is the time node of the user executing the shopping operation; the preferential time node corresponding to the preferential feature information is a use absolute time node of the electronic coupon information.
For example, if the time of the preferential activity is 6 months 1-6 months 30, the preferential time node corresponding to the preferential feature information is 23:59:59 of 6 months 30 days.
Correspondingly, if the transaction time node of the user is 23:12:45 of 3 days of 6 months. The transaction time node indicates that the electronic coupon information is valid before the preferential time node, and can extract characteristic data from the preferential characteristic information, wherein the characteristic data comprises: the number of goods consumed and the total price consumed.
Specifically, the consumer good quantity is the quantity of goods purchased by the user, for example, xx facial cleanser 3 bottles. The total cost is the total amount of the purchased goods. For example, 12xx members.
Next, it may be determined whether the number of consumer goods is within a corresponding number threshold interval, and correspondingly, whether the total cost of consumption is within a corresponding total cost threshold interval.
The data threshold interval may be determined from preferential related data, for example, the quantity threshold interval corresponds to the lowest consumption quantity to the limited purchase quantity. For example, 10 to 20 pieces, 30 to 50 pieces, etc.
If the characteristic data is in the corresponding data threshold interval, a specific calculation weight can be allocated; if the feature data is smaller than the minimum value of the corresponding data threshold interval, a second specific calculation weight may be assigned, and if the feature data is larger than the corresponding data threshold interval, a third specific calculation weight may be assigned.
For example, the data threshold interval corresponding to the number of consumer goods is 10 to 20. The consumption number of users is 8, the consumption commodity number is smaller than 10, and the calculation weight of the consumption commodity number can be allocated to be 1; the consumption number of users is 15, the consumption commodity number is more than 10 and less than 20, and the calculation weight of the consumption commodity number can be distributed to be 5; if the number of consumed goods is 26, the number of consumed goods is more than 20, and the calculation weight of the number of consumed goods can be distributed to be 10.
The specific numerical value can be adjusted according to actual requirements.
And comparing the characteristic data with the corresponding data threshold interval, and carrying out weight distribution according to the actual situation so as to calculate the preferential value of the shopping of the user by using the corresponding calculation weight.
In an alternative embodiment, the user may use two or more coupons with two or more corresponding electronic coupon information. If there are two or more coupons, they may or may not be used in a superimposed manner. To avoid re-use of non-stackable coupons, wherein step S12 may comprise the following sub-steps, as an example:
s31, if the number of the obtained electronic coupon information is at least two, respectively obtaining the priority corresponding to each piece of the preferential feature information.
The priority can be the use priority of the electronic coupons set by the manager of the store, the coupons with the same priority can be overlapped and used, and the coupons with different priorities cannot be overlapped and used, so that repeated overlapping and use of the coupons with different priorities can be avoided, transaction conflict is caused, sales price is smaller than cost, and loss of the store is increased.
S32, screening at least one target preferential feature information according to the priority, and extracting feature data from the target preferential feature information, wherein the feature data comprises: the type of goods consumed and the total price consumed.
In an embodiment, the preferential feature information with the highest priority can be screened according to the priority, so as to obtain the target preferential feature information. At least one target preference feature information may be obtained due to preference feature information that may have the same priority.
For example, the number of electronic coupon information is four, and correspondingly, the priorities of the four electronic coupon information are A, B, A, C. And correspondingly, screening the target preferential feature information with the highest priority, thereby screening the first preferential feature information and the third preferential feature information as target preferential feature information.
It should be noted that, different priorities correspond to one priority value, and the higher the priority is, the larger the priority value is. For example, the priority is a, and the priority value is 10; the priority is B, and the priority value is 8; the priority is C and the priority value is 6.
Next, feature data thereof, which may include the type of the consumer goods and the total price of the consumer, may be extracted from the target offer feature information.
Wherein the consumer goods type is the type of goods purchased by the user. The total consumption price may be a transaction total for the user to purchase the item.
The manager of the store may set different commodity types in advance. Because a unified store may sell a variety of different products, different types may be set for different products, while different types may be set with a base value for the type, which may represent the importance or sales of the products at the store. The higher the importance of the commodity in the store, the larger the corresponding base value, and vice versa. Similarly, if the sales of the commodity in the store are higher, the corresponding base value is larger, and conversely, the corresponding base value is smaller.
The basic value can be preset by a manager of a store and stored in a database, and when calculation is needed, the basic value is directly called from the database to calculate.
Similarly, the base corresponding to the total consumption price is obtained by conversion according to a certain proportion.
And S33, distributing calculation weights to the characteristic data according to the priority.
After determining the priority, the computing weight can be allocated to the feature data according to the magnitude of the priority value corresponding to the priority.
Since the target preference information may be one or more, in order to accurately assign the calculation weight according to the number of different target preference information, in an alternative embodiment, step S33 may include the following sub-steps:
and S331, if the target preferential feature information is one, multiplying the numerical value corresponding to the priority by the weight base corresponding to the feature data to obtain the calculated weight.
Specifically, if the target preferential feature information is one, the numerical value corresponding to the priority and the weight base corresponding to the feature data can be directly multiplied to obtain the calculated weight.
For example, the target preference feature information is one, its priority is B, and the priority value is 8, correspondingly. The consumer goods type is facial cleanser, the corresponding base value is 0.8, and 0.8 can be used as the weight base corresponding to the consumer goods type, so that the calculated weight of the consumer goods type is 8 x 0.8=6.4.
Similarly, the weight base corresponding to the total consumption price can be obtained by conversion according to the proportion of 1:100. For example, the total consumption value is 1345 yuan, and the corresponding weight base is 1345×0.01=13.45. The weight base corresponding to the total consumption price is 13.45×8=107.6 different from the value corresponding to the priority.
And S332, if the target preferential feature information is two or more, calculating algorithm weights corresponding to the feature data by adopting a variation coefficient method, a CRITIC weight method and an entropy weight method respectively, and averaging to obtain calculation weights.
If the target preferential feature information is two or more, the two target preferential feature information can be overlapped for use, and in order to synthesize the two target preferential feature information, the weights corresponding to the feature data can be calculated by a variation coefficient method, a CRITIC weight method and an entropy weight method respectively to obtain algorithm weights. And then, calculating the average value of the algorithm weights of the same characteristic data to obtain the corresponding calculation weight of the characteristic data.
For example, the feature data is a consumption total price, three algorithm weights can be obtained for the consumption total price after three algorithms are utilized, and then the sum of the three algorithm weights is calculated and averaged to obtain the calculation weight corresponding to the consumption total price.
Similarly, the characteristic data is a consumer commodity type, three algorithm weights can be obtained for the consumer commodity type after three algorithms are utilized, and then the sum of the three algorithm weights is calculated and then averaged to obtain the calculation weight corresponding to the consumer commodity type.
After the calculation weight corresponding to the feature data is calculated in the above manner, the privilege value can be calculated by using the calculation weight.
Specifically, the way of calculating the coupon value may be as follows:
Y=AX-X。
wherein Y is a preferential value; a is the sum of the calculated weights, and taking the above description as an example, assuming that there are two calculated weights, a is the sum of the two calculated weights. The cost price of the X commodity may be a value preset by the user.
And S13, if the preferential value is larger than the preset monetary value, determining the electronic coupon information as abnormal information, and feeding back abnormal alarm information to the user terminal.
The preset monetary value is the lowest profit value set by a store manager or the lowest cost price of the commodity, and the value can be 0 or can be adjusted according to the actual needs of the store. If the preferential value is larger than the preset monetary value, selling the commodity after the preferential, and possibly losing the commodity in shops. In order to ensure the interests of the store, the electronic coupon information can be determined to be abnormal information, and meanwhile, abnormal alarm information can be fed back to the user terminal and the intelligent terminal of the store manager.
The users who purchase the commodity and the manager of the store are respectively prompted.
In order to record the transaction behavior of the user, which may not result in a shop deficit from the user purchase, in an embodiment, the method may further include:
s14, if the preferential value is smaller than the preset monetary value, the transaction data of the user are adjusted according to the electronic coupon information.
S15, generating transaction record information by adopting the adjusted transaction data, encrypting and storing the transaction record information.
If the preferential value is smaller than the preset monetary value, the user purchases goods and the store cannot lose the goods, the transaction data of the user can be adjusted according to the electronic coupon information, the user is subjected to expense deduction, the transaction data of the user is notified and recorded, and the transaction is completed.
To facilitate recording of the user's transaction, corresponding transaction record information may be generated that may record data related to the merchandise purchased by the user, coupon information used, and store transaction is related data.
In order to ensure the information security of the user, transaction record information can be encrypted and stored, so that the store can conveniently call or analyze the transaction record information.
After completing the transaction, the user may consume multiple times, and in order to enhance the user's consumption experience, in an embodiment, the method may further include:
s15, counting the information quantity of the transaction record information, and calculating a transaction frequency value by using the information quantity.
S16, if the transaction frequency value is larger than a preset frequency value, adjusting authority information of the user.
Specifically, the number of transaction record information of the user in the preset time period can be counted to obtain the information number. For example, it may be the amount of transaction record information in a month or a quarter.
And then calculating the transaction frequency of the user by adopting the information quantity and the preset duration. If the transaction frequency is greater than the preset frequency value, the user is informed to purchase goods in the store for many times, and in order to improve the experience of the user, the authority information of the user in the store can be adjusted, for example, the VIP (virtual important person) grade of the user is adjusted, so that the user has better consumption experience.
In this embodiment, the embodiment of the present application provides a method for detecting abnormality of electronic coupon information, which has the following beneficial effects: according to the application, after the electronic coupon information of the user is acquired, the coupon characteristic information corresponding to the electronic coupon information is searched, the corresponding calculation weight is distributed by utilizing the coupon characteristic information, the coupon value is calculated based on the calculation weight, the abnormality of the electronic coupon information is determined according to the comparison result of the coupon value and the cost value, and the alarm information is fed back, so that the abnormal electronic coupon information is screened out.
The embodiment of the application also provides an abnormality detection device for the electronic coupon information, and referring to fig. 2, a schematic structural diagram of the abnormality detection device for the electronic coupon information provided by the embodiment of the application is shown.
As an example, the abnormality detection device of the electronic coupon information may include:
the search information module 201 is configured to search, after acquiring electronic coupon information of a user, coupon feature information corresponding to the electronic coupon information in a preset database;
a weight distribution module 202, configured to distribute a computation weight to feature data included in the preferential feature information based on a transaction time, and compute a preferential value based on the computation weight;
and the abnormality detection module 203 is configured to determine that the electronic coupon information is abnormality information if the privilege value is greater than a preset monetary value, and feed back abnormality warning information to the user terminal.
Optionally, the weight allocation module is further configured to:
if the obtained electronic coupon information is one, respectively obtaining a transaction time node of the user and a preferential time node corresponding to the preferential feature information;
extracting feature data from the offer feature information when the transaction time node precedes the offer time node, the feature data comprising: the number of the consumer goods and the total price;
and determining the calculation weight of the characteristic data according to the comparison result of the characteristic data and the corresponding data threshold interval.
Optionally, the weight allocation module is further configured to:
if the number of the acquired electronic coupon information is at least two, respectively acquiring the priority corresponding to each piece of the preferential feature information;
screening at least one target preferential feature information according to the priority, and extracting feature data from the target preferential feature information, wherein the feature data comprises: the type of the consumer goods and the total price of the consumer;
and distributing calculation weights to the characteristic data according to the priority.
Optionally, the weight allocation module is further configured to:
if the target preferential feature information is one, multiplying the numerical value corresponding to the priority by the weight base corresponding to the feature data to obtain a calculation weight;
if the target preferential feature information is two or more, calculating algorithm weights corresponding to the feature data by adopting a variation coefficient method, a CRITIC weight method and an entropy weight method respectively, and averaging to obtain calculation weights.
Optionally, the apparatus further comprises:
the adjustment data module is used for adjusting the transaction data of the user according to the electronic coupon information if the preferential value is smaller than the preset monetary value;
and the encryption storage module is used for generating transaction record information by adopting the adjusted transaction data, encrypting and storing the transaction record information.
Optionally, the apparatus further comprises:
the statistical information quantity module is used for counting the information quantity of the transaction record information and calculating a transaction frequency value by utilizing the information quantity;
and the permission adjustment module is used for adjusting the permission information of the user if the transaction frequency value is larger than the preset frequency value.
The embodiment of the application also provides an abnormality detection system for the electronic coupon information, and referring to fig. 3, a schematic structural diagram of the abnormality detection system for the electronic coupon information provided by the embodiment of the application is shown.
As an example, the abnormality detection system of the electronic coupon information may include:
the electronic coupon information anomaly detection method comprises a background management device, an online platform and a plurality of user terminals, wherein the background management device is suitable for the electronic coupon information anomaly detection method according to the embodiment;
the background management device is connected with the online platform, and the online platform is respectively connected with the plurality of user terminals.
Specifically, the background management device can be used for managing data of an online platform by a manager, the online platform is used for managing store data, and the user terminal is used for a user to mobilize and shop on the online platform.
It will be clearly understood by those skilled in the art that, for convenience and brevity, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Further, an embodiment of the present application further provides an electronic device, including: the electronic coupon information anomaly detection method is characterized by comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor is used for realizing the electronic coupon information anomaly detection method according to the embodiment.
Further, an embodiment of the present application also provides a computer-readable storage medium storing a computer-executable program for causing a computer to execute the abnormality detection method of electronic coupon information as described in the above embodiment.
While the foregoing is directed to the preferred embodiments of the present application, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the application, such changes and modifications are also intended to be within the scope of the application.
Claims (10)
1. An anomaly detection method for electronic coupon information, the method comprising:
after the electronic coupon information of the user is obtained, searching the coupon characteristic information corresponding to the electronic coupon information in a preset database;
assigning a computing weight to feature data contained in the preferential feature information based on transaction time, and computing a preferential value based on the computing weight;
if the preferential value is larger than the preset monetary value, determining that the electronic coupon information is abnormal information, and feeding back abnormal alarm information to the user terminal.
2. The abnormality detection method for electronic coupon information according to claim 1, wherein said assigning a calculation weight to feature data included in said coupon feature information based on a transaction time includes:
if the obtained electronic coupon information is one, respectively obtaining a transaction time node of the user and a preferential time node corresponding to the preferential feature information;
extracting feature data from the offer feature information when the transaction time node precedes the offer time node, the feature data comprising: the number of the consumer goods and the total price;
and determining the calculation weight of the characteristic data according to the comparison result of the characteristic data and the corresponding data threshold interval.
3. The abnormality detection method for electronic coupon information according to claim 1, wherein said assigning a calculation weight to feature data included in said coupon feature information based on a transaction time includes:
if the number of the acquired electronic coupon information is at least two, respectively acquiring the priority corresponding to each piece of the preferential feature information;
screening at least one target preferential feature information according to the priority, and extracting feature data from the target preferential feature information, wherein the feature data comprises: the type of the consumer goods and the total price of the consumer;
and distributing calculation weights to the characteristic data according to the priority.
4. The abnormality detection method for electronic coupon information according to claim 3, wherein said assigning a calculation weight to said feature data according to the magnitude of said priority comprises:
if the target preferential feature information is one, multiplying the numerical value corresponding to the priority by the weight base corresponding to the feature data to obtain a calculation weight;
if the target preferential feature information is two or more, calculating algorithm weights corresponding to the feature data by adopting a variation coefficient method, a CRITIC weight method and an entropy weight method respectively, and averaging to obtain calculation weights.
5. The abnormality detection method of electronic coupon information according to claim 1, characterized in that, after said step of calculating a coupon value based on said calculation weight, the method further comprises:
if the preferential value is smaller than the preset monetary value, the transaction data of the user is adjusted according to the electronic coupon information;
and generating transaction record information by adopting the adjusted transaction data, encrypting and storing the transaction record information.
6. The abnormality detection method of electronic coupon information according to claim 5, characterized in that, after the step of encrypting and storing the transaction record information, the method further comprises:
counting the information quantity of the transaction record information, and calculating a transaction frequency value by utilizing the information quantity;
and if the transaction frequency value is larger than the preset frequency value, adjusting the authority information of the user.
7. An abnormality detection apparatus for electronic coupon information, the apparatus comprising:
the information searching module is used for searching the preferential feature information corresponding to the electronic coupon information in a preset database after the electronic coupon information of the user is acquired;
the weight distribution module is used for distributing calculation weights to the feature data contained in the preferential feature information based on the transaction time and calculating preferential values based on the calculation weights;
and the abnormality detection module is used for determining the electronic coupon information as abnormality information if the preferential value is larger than a preset monetary value and feeding back abnormality warning information to the user terminal.
8. An anomaly detection system for electronic coupon information, the system comprising: a background management device, an online platform and a plurality of user terminals, wherein the background management device is suitable for the abnormality detection method of the electronic coupon information according to any one of claims 1 to 6;
the background management device is connected with the online platform, and the online platform is respectively connected with the plurality of user terminals.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for anomaly detection of electronic coupon information according to any one of claims 1 to 6 when the computer program is executed by the processor.
10. A computer-readable storage medium storing a computer-executable program for causing a computer to execute the abnormality detection method of electronic coupon information according to any one of claims 1 to 6.
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