CN112907263A - Abnormal order quantity detection method, device, equipment and storage medium - Google Patents
Abnormal order quantity detection method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of big data processing, and discloses a method, a device, equipment and a storage medium for detecting abnormal orders. The method comprises the steps of obtaining an order log, and analyzing the order log to obtain a target order; reading a buyer identification number in the target order, and obtaining a purchase price value according to the target order; updating the historical value in a preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value; when the actual value score is smaller than the preset score, the fact that the order quantity of the buyer corresponding to the buyer identification number is abnormal is judged, the increase rate of the order with the same buyer identification number is analyzed in real time by stacking the order with the same buyer identification number, the effect of timely finding the abnormal order quantity is achieved, and the problem that the abnormal order quantity cannot be detected is solved.
Description
Technical Field
The invention relates to the technical field of big data processing, in particular to a method, a device, equipment and a storage medium for detecting abnormal order quantity.
Background
With the rapid development of internet technology, online shopping is an indispensable part of people's lives, and more people select and purchase commodities through the internet.
At present, in order backgrounds of most internet merchants, real-time orders and historical orders can be seen through a viewing function, and commodities of people are planned through analyzing purchase records of the people on a merchant platform. But due to the gradual rise of the way of online shopping, malicious competition among business merchants and attack of third parties are brought. The attack mode is usually malicious bill swiping in an internet store of a merchant, and in such a case, the merchant cannot deal with the malicious bill swiping through the existing background management technology. Therefore, how to detect the abnormal order quantity becomes a problem to be solved urgently.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for detecting abnormal order quantity, and aims to solve the technical problem that the prior art cannot detect the abnormal order quantity.
In order to achieve the above object, the present invention provides an abnormal order quantity detection method, including the following steps:
obtaining an order log, and analyzing the order log to obtain a target order;
reading a buyer identification number in the target order, and obtaining a purchase price value according to the target order;
updating the historical value in a preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value;
and when the actual value score is smaller than a preset score, judging that the order quantity of the buyer corresponding to the buyer identification number is abnormal.
Optionally, the step of reading the buyer identification number in the target order and obtaining the order price value according to the target order includes:
reading a buyer identification number in the target order;
acquiring commodity value weight and commodity value from the target order;
and determining a booking price value according to the commodity value and the commodity value weight.
Optionally, before the step of reading the buyer identification number in the target order and obtaining the order price value according to the target order, the method further includes:
reading a transaction result in the target order;
when the transaction result in the target order is incomplete, judging that the target order does not belong to an effective order;
and updating the order price value in the corresponding preset storage table according to the buyer identification number in the target order.
Optionally, the step of updating the order price value in the corresponding preset storage table according to the buyer identification number in the target order includes:
acquiring the buyer identification number from the target order, and matching and corresponding to a preset storage table according to the buyer identification number;
updating the abnormal order growth rate stored in a preset storage table corresponding to the buyer identification number;
and when the abnormal order growth rate is larger than a preset threshold value, updating the order price value in the preset storage table.
Optionally, the step of updating the historical value in the preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value includes:
judging whether the target order growth rate corresponding to the buyer identification number is greater than a first preset threshold value or not;
and when the target order growth rate corresponding to the buyer identification number is larger than a first preset threshold value, deducting the order price value corresponding to the target order from the preset storage table to obtain an actual price value.
Optionally, after the step of determining whether the target order growth rate corresponding to the buyer identification number is greater than a first preset threshold, the method further includes:
and when the target order growth rate corresponding to the buyer identification number is not larger than a first preset threshold value, increasing the order price value corresponding to the target order in the preset storage table to obtain an actual price value.
Optionally, after the step of determining that the order quantity abnormality exists in the buyer corresponding to the buyer identification number when the actual value score is smaller than the preset score, the method further includes:
adding the buyer identification number corresponding to the abnormal order quantity to a system blacklist;
and rejecting the order analysis request corresponding to the buyer identification number in the system blacklist.
In addition, in order to achieve the above object, the present invention further provides an abnormal order quantity detection apparatus, including:
the reading module is used for acquiring an order log and analyzing the order log to obtain a target order;
the analysis module is used for reading a buyer identification number in the target order and obtaining a price value of the order according to the target order;
the updating module is used for updating the historical value in the preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value;
and the judging module is used for judging that the order quantity abnormality exists in the buyer corresponding to the buyer identification number when the actual value score is smaller than the preset score.
In addition, in order to achieve the above object, the present invention further provides an abnormal order quantity detection apparatus, including: a memory, a processor and an abnormal order quantity detection program stored on the memory and executable on the processor, the abnormal order quantity detection program being configured to implement the steps of the abnormal order quantity detection method as described above.
In order to achieve the above object, the present invention further provides a storage medium, wherein the storage medium stores thereon an abnormal order quantity detection program, and the abnormal order quantity detection program, when executed by a processor, implements the steps of the abnormal order quantity detection method as described above.
The method comprises the steps of obtaining an order log, and analyzing the order log to obtain a target order; reading a buyer identification number in the target order, and obtaining a purchase price value according to the target order; updating the historical value in a preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value; when the actual value score is smaller than the preset score, the fact that the order quantity of the buyer corresponding to the buyer identification number is abnormal is judged, the increase rate of the order with the same buyer identification number is analyzed in real time by stacking the order with the same buyer identification number, the effect of timely finding the abnormal order quantity is achieved, and the problem that the abnormal order quantity cannot be detected is solved.
Drawings
Fig. 1 is a schematic structural diagram of an abnormal order quantity detection apparatus in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for detecting an abnormal order quantity according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a method for detecting an abnormal order quantity according to a second embodiment of the present invention;
fig. 4 is a block diagram of a first embodiment of an abnormal order quantity detection apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an abnormal order quantity detection device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the abnormal order quantity detection apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in FIG. 1 does not constitute a limitation of the anomalous order quantity detection device and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a data storage module, a network communication module, a user interface module, and an abnormal order quantity detection program.
In the abnormal order quantity detection apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the abnormal order quantity detection apparatus of the present invention may be disposed in the abnormal order quantity detection apparatus, and the abnormal order quantity detection apparatus calls the abnormal order quantity detection program stored in the memory 1005 through the processor 1001 and executes the abnormal order quantity detection method provided by the embodiment of the present invention.
An embodiment of the present invention provides a method for detecting an abnormal order quantity, and referring to fig. 2, fig. 2 is a flowchart illustrating a first embodiment of the method for detecting an abnormal order quantity according to the present invention.
In this embodiment, the method for detecting the real-time abnormal order quantity includes the following steps.
Step S10: and obtaining an order log, and analyzing the order log to obtain a target order.
It should be noted that the execution main body of the method of the present embodiment may be an abnormal order quantity detection device or other devices having the same function, and the present embodiment and the following embodiments are described by taking the abnormal order quantity detection device as an example.
It is understood that the method of the present embodiment works based on a stream data processing engine, and the data generated by the merchant in operation is stream data, which is defined as a set of sequential, large-volume, fast, continuous data sequences, and in general, the stream of data can be regarded as a dynamic data set that grows infinitely with time. Therefore, the embodiment needs to process the streaming data by means of the streaming data processing platform in order to solve the problem of abnormal order quantity detection. The implementation is processed with Apache Flink, which is an open source stream processing framework developed by the Apache software foundation, and the core of which is a distributed stream data stream engine written in Java and Scala. Flink executes arbitrary stream data programs in a data parallel and pipelined manner, and Flink's pipelined runtime system can execute batch and stream processing programs. In addition, the runtime of Flink itself supports the execution of iterative algorithms.
It should be noted that the order log is order data generated by a merchant during operation when a customer purchases a commodity on the internet, and the order log includes a user identification number, a user address, commodity information, a time node, a payment method, a payment result, and an actual payment amount.
It is understood that the target order is a single order that is captured by the abnormal order quantity detection apparatus in an order log that is composed of all target orders. Since the technical effect is achieved by the streaming data processing engine in the present embodiment, the target order is obtained by simultaneously capturing a plurality of target orders in the order log for parsing.
In a specific implementation, the order log is obtained by reading the order log in the streaming data through a FLINK streaming data processing engine.
In a specific implementation, the analyzing of the order log to obtain the target order is to capture a single order from the order log for analysis, the capture rule is implemented by being formulated on a FLINK stream data processing engine, and in order to achieve the technical effect to be implemented in this embodiment, the capture rule is formulated according to a time sequence rule in the stream data.
Step S20: and reading the buyer identification number in the target order, and obtaining a purchase price value according to the target order.
It can be understood that the buyer identification number is allocated by the merchant through the device address of the buyer logged in the internet during the operation process, and the same device address corresponds to the unique buyer identification number in the background of the merchant.
It should be noted that the order price value is the value that the order represents, and is often determined by correlating key factors within the order, typically the order price, the product profit, and the actual amount paid. For example: a customer purchases goods on the online shopping platform, and the details in the generated target order are as follows: commodity name a, total commodity price: 2000 yuan, actual profit: 200 yuan, payment result: if successful, then the target order value score is 200, as calculated by the system. However, the value of the target order cannot directly determine the value, for example: when the actual profit of the order generated by the customer B for purchasing the goods reaches 300 yuan, but the payment result fails, the corresponding value point of the order is 0 point.
In specific implementation, reading the buyer identification number in the target order is to search information corresponding to the buyer identification number in the target order, and when the corresponding buyer identification number is detected to be unreadable characters or invalid length, feeding the result back to the merchant background to clear the order.
In a specific implementation, deriving the order price value according to the target order is implemented by calling a preset formula inside the system, for example: the formula for calculating the value in the merchant background is that the total profit of the target order is multiplied by the order state value, the order state value is 1 when the order is valid, and the order state is-1 when the order is invalid.
Further, in order to calculate the bid value more accurately, the step S20 further includes: reading a buyer identification number in the target order; acquiring commodity value weight and commodity value from the target order; and determining a booking price value according to the commodity value and the commodity value weight. For example: the weight of the commodity A existing in the merchant system is 50, the price is 200 yuan, the profit generated by selling the commodity is 150 yuan, and if the user B purchases a commodity A and completes the payment, the corresponding value in the corresponding target order is 37.5. The specific calculation is the profit generated by the good divided by the total unit price multiplied by the corresponding weight of the good.
As can be appreciated, the product weight is the ratio of the product profit to the profit of all the products. The value score is established through the commodity weight, so that the abnormal order of the key commodity of the merchant can be effectively detected.
Step S30: and updating the historical value in the preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value.
It should be noted that the preset storage table is a table with a storage calculation function, and occupies a certain space of the memory. The preset storage table has the function of storing information data in the target orders, the orders with different buyer identification numbers are stored according to different preset storage tables, and the orders with the same buyer identification number are stored in the same preset storage table. The system administrator may set the validity time and when the target order has survived in the memory table for a specified period of time, the system may leave the historical score to delete orders that have exceeded the length of time that survived.
It will be appreciated that the historical value is divided into the value that the corresponding preset memory table had prior to adding the current order.
It should be noted that the actual value is divided into the total value of the preset storage table, for example: if the storage table a1 is preset, the historical value is divided into 200 points, and an entry with the order value of 500 points is added, the actual value of a1 is divided into 700 points.
In specific implementation, the historical value in the preset storage table corresponding to the buyer identification number is updated according to the order price value, and the historical value is added. For example: and if the current order value is divided into-100 points and the historical value score of the target preset storage table is 50 points, adding the scores in the target preset storage table to obtain the order price value of-50 points to achieve the updating effect.
Further, in order to obtain the actual value more accurately, the step S30 further includes: cutting off whether the target order growth rate corresponding to the buyer identification number is greater than a first preset threshold value or not; when the target order growth rate corresponding to the buyer identification number is larger than a first preset threshold value, deducting a booking price value corresponding to the target order from the preset storage table to obtain an actual price value; and when the target order growth rate corresponding to the buyer identification number is not larger than a first preset threshold value, increasing the order price value corresponding to the target order in the preset storage table to obtain an actual price value.
The growth rate (growth rate) is also called the growth rate, and is a result of subtracting 1 from the ratio of the observed value in the report phase to the observed value in the basal phase in the time series, and is expressed by%. Because of the different basal periods of comparison, the growth rate can be divided into ring ratio growth rate and fixed basal growth rate. In this embodiment, the order growth rate is based on the ratio of the growth value of the order placed by the buyer identification number name to the growth value of all the buyer orders in the merchant order library. If the buyer identification number places an abrupt order increase, the order increase rate corresponding to the buyer identification number is increased.
In a specific implementation, when the target order growth rate corresponding to the buyer identification number is greater than a first preset threshold, deducting the order price value corresponding to the target order from the preset storage table to obtain the actual value score, where the actual value score is obtained when the buyer identification number is determined to grow faster, and the order value score corresponding to the buyer identification number is correspondingly changed into a negative number at this time. And when the target order growth rate corresponding to the buyer identification number is not larger than a first preset threshold value, increasing the order price value corresponding to the target order in the preset storage table to obtain an actual price value. By the mode, the abnormal order quantity can be accurately detected by means of the real-time processing capacity of the flow data processing engine.
Step S40: and when the actual value score is smaller than a preset score, judging that the order quantity of the buyer corresponding to the buyer identification number is abnormal.
It should be noted that the preset score is set by the system according to the total order of the merchant and the order generation speed.
In a specific implementation, for a real-time order for which the system determines that the order is abnormal, the value score of the order is defined as a negative number, and for the storage table corresponding to the buyer identification number, the system determines that the order quantity of the buyer corresponding to the buyer identification number is abnormal when the order with the value score reaching the negative number exceeds a certain quantity. For example: the buyer A generates a large number of unfinished payment orders in the merchant platform all the time, the actual value of the buyer identification number corresponding to the buyer A is reduced all the time, and when the actual value reaches the preset value, the system judges that the order under the name of the buyer A has abnormal behaviors.
Further, in order to reduce the resource waste of the execution subject in the abnormal order detection process, the step S40 further includes: adding the buyer identification number corresponding to the abnormal order quantity to a system blacklist; and rejecting the order analysis request corresponding to the buyer identification number in the system blacklist.
It should be noted that, when the buyer identification number is added into the system blacklist, information is fed back to the stream data processing engine according to the buyer identification number, and when the subsequent stream data processing engine processes the order log and identifies the target order with the buyer identification number in the blacklist, the target order is directly added into the exception library without reading other information.
In the embodiment, a target order is obtained by obtaining an order log and analyzing the order log; reading a buyer identification number in the target order, and obtaining a purchase price value according to the target order; updating the historical value in a preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value; when the actual value score is smaller than the preset score, the fact that the order quantity of the buyer corresponding to the buyer identification number is abnormal is judged, the increase rate of the order with the same buyer identification number is analyzed in real time by stacking the order with the same buyer identification number, the effect of timely finding the abnormal order quantity is achieved, and the problem that the abnormal order quantity cannot be detected is solved.
Referring to fig. 3, fig. 3 is a flowchart illustrating a method for detecting an abnormal order quantity according to a second embodiment of the present invention.
Based on the first embodiment described above, in the present embodiment, the step S20 includes:
s201: and reading a transaction result in the target order.
It is understood that reading the transaction result in the target order refers to looking at whether the transaction is finally completed in the target order, looking at the transaction result field.
In a specific implementation, reading the transaction result in the target order refers to finding a transaction result field in the target order and viewing the content of the field. If the transaction result field is successful, the transaction is successful, and if the transaction result field is unpaid, the transaction is failed.
S202: and when the transaction result in the target order is incomplete, judging that the target order does not belong to an effective order.
It should be noted that when the transaction result in the target order is failure, it means that the user does not pay in time for some reason in the target order, but the creation of the target order may still affect the resource utilization of the merchant, so the target order does not belong to a valid target order.
S203: and updating the order price value in the corresponding preset storage table according to the buyer identification number in the target order.
It can be understood that updating the order price value in the corresponding preset storage table according to the buyer identification number in the target order means that the buyer identification number is obtained from the target order, the abnormal order growth rate stored in the preset storage table corresponding to the buyer identification number is updated according to the fact that the buyer identification number matches with the corresponding preset storage table, and the order price value in the preset storage table is updated when the abnormal order growth rate is greater than a preset threshold value.
The embodiment reads the transaction result in the target order; when the transaction result in the target order is incomplete, judging that the target order does not belong to an effective order; and updating the order price value in the corresponding preset storage table according to the buyer identification number in the target order. According to the method and the device, the invalid orders are eliminated by judging in the key fields, and compared with the traditional method and the device for detecting the total order quantity data, the method and the device can more accurately realize abnormal order quantity detection, so that the order quantity detection is more accurate.
In addition, an embodiment of the present invention further provides a storage medium, where an abnormal order quantity detection shared program is stored on the storage medium, and when the abnormal order quantity detection program is executed by a processor, the steps of the abnormal order quantity detection method described above are implemented.
Referring to fig. 4, fig. 4 is a block diagram illustrating a first embodiment of an abnormal order quantity detection apparatus according to the present invention.
As shown in fig. 4, the abnormal order quantity detection apparatus according to the embodiment of the present invention includes:
the reading module 301 obtains an order log, and analyzes the order log to obtain a target order;
the analysis module 302 is used for reading a buyer identification number in the target order and obtaining a price value of the order according to the target order;
the updating module 303 is configured to update the historical value in the preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value;
the determining module 304 determines that the order quantity of the buyer corresponding to the buyer identification number is abnormal when the actual value score is smaller than a preset score.
In the embodiment, a target order is obtained by obtaining an order log and analyzing the order log; reading a buyer identification number in the target order, and obtaining a purchase price value according to the target order; updating the historical value in a preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value; when the actual value score is smaller than the preset score, the fact that the order quantity of the buyer corresponding to the buyer identification number is abnormal is judged, the increase rate of the order with the same buyer identification number is analyzed in real time by stacking the order with the same buyer identification number, the effect of timely finding the abnormal order quantity is achieved, and the problem that the abnormal order quantity cannot be detected is solved.
In an embodiment, the parsing module 302 is further configured to read a buyer identification number in the target order; acquiring commodity value weight and commodity value from the target order; and determining a booking price value according to the commodity value and the commodity value weight.
In an embodiment, the parsing module 302 is further configured to read a transaction result in the target order; when the transaction result in the target order is incomplete, judging that the target order does not belong to an effective order; and updating the order price value in the corresponding preset storage table according to the buyer identification number in the target order.
In an embodiment, the parsing module 302 is further configured to obtain the buyer identification number from the target order, and match a corresponding preset storage table according to the buyer identification number; updating the abnormal order growth rate stored in a preset storage table corresponding to the buyer identification number; and when the abnormal order growth rate is larger than a preset threshold value, updating the order price value in the preset storage table.
In an embodiment, the updating module 303 is further configured to determine whether a target order growth rate corresponding to the buyer identification number is greater than a first preset threshold; and when the target order growth rate corresponding to the buyer identification number is larger than a first preset threshold value, deducting the order price value corresponding to the target order from the preset storage table to obtain an actual price value.
In an embodiment, the determining module 304 is further configured to add the buyer identification number corresponding to the abnormal order quantity to a system blacklist; and rejecting the order analysis request corresponding to the buyer identification number in the system blacklist.
Other embodiments or specific implementation manners of the abnormal order quantity detection apparatus of the present invention may refer to the above method embodiments, and are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., a rom/ram, a magnetic disk, an optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An abnormal order quantity detection method is characterized by comprising the following steps:
obtaining an order log, and analyzing the order log to obtain a target order;
reading a buyer identification number in the target order, and obtaining a purchase price value according to the target order;
updating the historical value in a preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value;
and when the actual value score is smaller than a preset score, judging that the order quantity of the buyer corresponding to the buyer identification number is abnormal.
2. The method of claim 1, wherein said step of reading a buyer identification number in said target order and deriving a price value for the order based on said target order comprises:
reading a buyer identification number in the target order;
acquiring commodity value weight and commodity value from the target order;
and determining a booking price value according to the commodity value and the commodity value weight.
3. The method of claim 1, wherein prior to the step of reading the buyer identification number in the target order and deriving a price value for the order based on the target order, further comprising:
reading a transaction result in the target order;
when the transaction result in the target order is incomplete, judging that the target order does not belong to an effective order;
and updating the order price value in the corresponding preset storage table according to the buyer identification number in the target order.
4. The method of claim 3, wherein the step of updating the order price value in the corresponding pre-determined stored table based on the buyer identification number in the target order comprises:
acquiring the buyer identification number from the target order, and matching and corresponding to a preset storage table according to the buyer identification number;
updating the abnormal order growth rate stored in a preset storage table corresponding to the buyer identification number;
and when the abnormal order growth rate is larger than a preset threshold value, updating the order price value in the preset storage table.
5. The method of claim 1, wherein the step of updating the historical value in the preset storage table corresponding to the buyer identification number according to the order price value to obtain the actual price value comprises:
judging whether the target order growth rate corresponding to the buyer identification number is greater than a first preset threshold value or not;
and when the target order growth rate corresponding to the buyer identification number is larger than a first preset threshold value, deducting the order price value corresponding to the target order from the preset storage table to obtain an actual price value.
6. The method of claim 5, wherein after the step of determining whether the target order growth rate corresponding to the buyer identification number is greater than a first preset threshold, the method further comprises:
and when the target order growth rate corresponding to the buyer identification number is not larger than a first preset threshold value, increasing the order price value corresponding to the target order in the preset storage table to obtain an actual price value.
7. The method of claim 1, wherein after the step of determining that the order quantity abnormality exists for the buyer corresponding to the buyer identification number when the actual value score is smaller than the preset score, the method further comprises:
adding the buyer identification number corresponding to the abnormal order quantity to a system blacklist;
and rejecting the order analysis request corresponding to the buyer identification number in the system blacklist.
8. An abnormal order quantity detection device, characterized in that the abnormal order quantity detection device comprises:
the reading module is used for acquiring an order log and analyzing the order log to obtain a target order;
the analysis module is used for reading a buyer identification number in the target order and obtaining a price value of the order according to the target order;
the updating module is used for updating the historical value in the preset storage table corresponding to the buyer identification number according to the order price value to obtain an actual price value;
and the judging module is used for judging that the order quantity abnormality exists in the buyer corresponding to the buyer identification number when the actual value score is smaller than the preset score.
9. An abnormal order quantity detection apparatus, characterized in that the apparatus comprises: a memory, a processor and an abnormal order quantity detection program stored on the memory and executable on the processor, the abnormal order quantity detection program being configured to implement the steps of the abnormal order quantity detection method according to any one of claims 1 to 7.
10. A storage medium having stored thereon an abnormal order quantity detection program which, when executed by a processor, implements the steps of the abnormal order quantity detection method according to any one of claims 1 to 7.
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