Abnormal user determination method and device, computer equipment, readable storage medium and program product
By analyzing power transaction data, calculating abnormal coefficients to identify abnormal transaction users, the problem of poor stability in the power market is solved, and effective monitoring and stability improvement of power transactions are achieved.
Patent Information
- Application Number
- CN202411605262.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-12
- Publication Date
- 2025-02-07
AI Technical Summary
The power market is poorly stable, mainly due to the abnormal user's trading behavior affecting market fluctuations.
By obtaining the transaction data of each target transaction within the preset time period, determining the abnormality degree information and transaction volume proportion information, calculating the abnormality coefficient, and then identifying the abnormal transaction user.
It realizes identification and monitoring of abnormal transaction users in power trading, effectively improving the stability of the power market.
Smart Images
Figure CN119398782A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to a method, apparatus, computer device, computer-readable storage medium and computer program product for determining abnormal users. Background Art
[0002] The power market not only involves the production, transmission and consumption of electricity, but also includes various transactions and services related to electricity. With the continuous development of the power market, the number of users in power transactions has also increased, which has led to the problem of poor stability in the power market. Therefore, a method that can solve the problem of poor stability in the power market is urgently needed. Summary of the invention
[0003] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for determining abnormal users that can improve the stability of the electricity market in response to the above technical problems.
[0004] In a first aspect, the present application provides a method for determining an abnormal user, comprising:
[0005] Acquire transaction data of each target transaction within a preset time period, the transaction data including transaction volume information and transaction price information of each transaction user in each target transaction;
[0006] Determine abnormality degree information based on the transaction data, and determine transaction volume ratio information based on the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each transaction user in each target transaction, and the transaction volume ratio is used to characterize the ratio of the transaction volume of each transaction user in each target transaction to the total transaction volume of each transaction user for the target transaction in the preset time period;
[0007] The abnormal coefficient of each transaction user is determined according to the abnormal degree information and the transaction volume ratio information, and the abnormal transaction user for the target transaction is determined according to the abnormal coefficient.
[0008] In one of the embodiments, determining the abnormality degree information based on the transaction data includes: determining the target quantity and price information of each transaction user in each target transaction in the quantity and price space based on the transaction data; determining the benchmark quantity and price information of each target transaction in the quantity and price space based on the transaction data; and determining the abnormality degree information based on the target quantity and price information and the benchmark quantity and price information.
[0009] In one embodiment, the determining the abnormality degree information according to the target quantity and price information and the benchmark quantity and price information includes: determining the abnormality degree information based on a Euclidean distance method, the target quantity and price information, and the benchmark quantity and price information.
[0010] In one of the embodiments, the abnormality coefficient of each transaction user is determined based on the abnormality degree information and the transaction volume proportion information, including: obtaining transaction attribute information of each transaction user, the transaction attribute information is used to characterize the transaction identity of the transaction user in the target transaction; determining the abnormality coefficient based on the transaction attribute information, the abnormality degree information and the transaction volume proportion information.
[0011] In one embodiment, the transaction attribute information includes a first transaction attribute and a second transaction attribute, and the abnormality coefficient is determined according to the transaction attribute information, the abnormality degree information and the transaction volume proportion information, including: when the transaction attribute information of the transaction user is the first transaction attribute, the abnormality coefficient of the transaction user is determined based on a first algorithm, the abnormality degree information and the transaction volume proportion information; when the transaction attribute information of the transaction user is the second transaction attribute, the abnormality coefficient is determined based on a second algorithm, the abnormality degree information and the transaction volume proportion information; wherein the first algorithm includes a first coefficient, the second algorithm includes a second coefficient, a method for determining the value of the first coefficient is different from a method for determining the value of the second coefficient, and the method for determining the value of the first coefficient and the method for determining the value of the second coefficient are related to the transaction identity of the transaction user.
[0012] In one of the embodiments, the determining of the abnormal transaction user for the target transaction according to the abnormal coefficient includes: when the transaction attribute information of the transaction user is the first transaction attribute, sorting the transaction users whose transaction attribute information is the first transaction attribute according to the abnormal coefficient to obtain a first sequence, and determining the first abnormal transaction user according to the first sequence; when the transaction attribute information of the transaction user is the second transaction attribute, sorting the transaction users whose transaction attribute information is the second transaction attribute according to the abnormal coefficient to obtain a second sequence, and determining the second abnormal transaction user according to the second sequence; determining the abnormal transaction user according to the first abnormal transaction user and the second abnormal transaction user.
[0013] In a second aspect, the present application further provides an abnormal user determination device, comprising:
[0014] An acquisition module is used to acquire transaction data of each target transaction within a preset time period, wherein the transaction data includes transaction volume information and transaction price information of each transaction user in each target transaction;
[0015] A first determination module is used to determine abnormality degree information according to the transaction data, and to determine transaction volume ratio information according to the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each transaction user in each target transaction, and the transaction volume ratio is used to characterize the ratio of the transaction volume of each transaction user in each target transaction to the total transaction volume of each transaction user for the target transaction within the preset time period;
[0016] The second determination module is used to determine the abnormal coefficient of each transaction user according to the abnormal degree information and the transaction volume proportion information, and determine the abnormal transaction user for the target transaction according to the abnormal coefficient.
[0017] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described in any embodiment of the first aspect when executing the computer program.
[0018] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any embodiment of the first aspect above.
[0019] In a fifth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method described in any embodiment of the first aspect above.
[0020] The above-mentioned abnormal user determination method, device, computer equipment, computer-readable storage medium and computer program product first obtain the transaction data of each target transaction within a preset time period, and the transaction data includes the transaction volume information and transaction price information of each transaction user in each target transaction; then determine the abnormality degree information according to the transaction data, and determine the transaction volume proportion information according to the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each transaction user in each target transaction, and the transaction volume proportion is used to characterize the transaction volume of each transaction user in each target transaction, and the proportional relationship with the total transaction volume of each transaction user for the target transaction within the preset time period; then, determine the abnormal coefficient of each transaction user according to the abnormality degree information and the transaction volume proportion information, and determine the abnormal transaction user for the target transaction according to the abnormal coefficient. The abnormal user determination method provided in the present application identifies the abnormal transaction users in the target transaction through transaction data, and the abnormal transaction users are one of the important factors affecting the stability of the power market. Therefore, by identifying the abnormal transaction users, the monitoring of power transactions can be realized, thereby solving the problem of poor stability of the power market in the prior art, so as to improve the stability of the power market. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.
[0022] Figure 1 A schematic diagram of a flow chart of a method for determining an abnormal user in one embodiment;
[0023] Figure 2 A schematic diagram of a flow chart of a method for determining abnormality degree information based on transaction data in one embodiment;
[0024] Figure 3 It is a flowchart of a method for determining the abnormal coefficient of each transaction user according to the abnormal degree information and the transaction volume proportion information in one embodiment;
[0025] Figure 4 It is a flowchart of a method for determining the abnormality coefficient according to the transaction attribute information, the abnormality degree information and the transaction volume ratio information in one embodiment;
[0026] Figure 5 It is a flowchart of a method for determining an abnormal transaction user for the target transaction according to the abnormal coefficient in one embodiment;
[0027] Figure 6 A flowchart of a method for determining an abnormal user in another embodiment;
[0028] Figure 7 It is a structural block diagram of an abnormal user determination device in one embodiment;
[0029] Figure 8 is an internal structure diagram of a computer device in one embodiment;
[0030] Fig. 9 is an internal structure diagram of a computer device in another embodiment;
[0031] Fig.10 A schematic diagram of transaction data in one embodiment;
[0032] Fig.11 A schematic diagram of a sorting result in an embodiment. DETAILED DESCRIPTION
[0033] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0034] The power market not only involves the production, transmission and consumption of electricity, but also includes various transactions and services related to electricity. With the continuous development of the power market, the number of users in power transactions has also increased, which has led to the problem of poor stability in the power market. Therefore, a method that can solve the problem of poor stability in the power market is urgently needed.
[0035] In view of this, the present application provides a method for determining abnormal users, which monitors power transactions by identifying abnormal trading users in power transactions, so as to solve the problem of poor power market stability in the prior art and improve the stability of the power market.
[0036] The abnormal user determination method provided in the present application can be executed by a computer device, which can be a terminal or a server. The terminal can be, but is not limited to, various personal computers, laptops, tablet computers, and Internet of Things devices. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.
[0037] In an exemplary embodiment, Figure 1 As shown, a method for determining an abnormal user is provided, the method comprising the following steps:
[0038] Step 101: Obtain transaction data of each target transaction within a preset time period.
[0039] The transaction data includes transaction volume information and transaction price information of each transaction user in each target transaction.
[0040] Optionally, the target transaction may be an electricity transaction in the electricity market. The preset time period may be pre-set by a technician according to actual needs. The preset time period may be a settlement period for electricity transactions in the electricity market.
[0041] The transaction user may be an individual, an institution, an enterprise or a company. This application does not specifically limit the type of transaction user.
[0042] The transaction volume information can be used to characterize the amount of electricity traded by the trading user in a certain target transaction, and the transaction price information can be used to characterize the price of electricity traded by the trading user in a certain target transaction.
[0043] In some exemplary embodiments, transaction data of each target transaction within a preset time period may be obtained through a database of the electricity market.
[0044] Step 102: Determine abnormality degree information based on the transaction data, and determine transaction volume ratio information based on the transaction data.
[0045] The abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each transaction user in each target transaction.
[0046] The transaction volume ratio is used to represent the ratio of the transaction volume of each transaction user in each target transaction to the total transaction volume of each transaction user for the target transaction within the preset time period.
[0047] Optionally, the transaction volume ratio information can be used to measure the strategy allocation of each trading user in different target trading sessions. For example, if trading user A accounts for a large proportion of the trading volume in target A trading, it can be determined that trading user A has shown abnormal behavior in target A trading. Based on this, it can be considered that trading user A may have market manipulation behavior.
[0048] In some exemplary embodiments, after obtaining the transaction data of each target transaction within a preset time period, the transaction data can be input into a pre-trained abnormality degree information determination model to obtain the abnormality degree information of each transaction user output by the abnormal information determination model.
[0049] The abnormality degree information of each transaction user can also be determined based on the preset abnormal behavior determination rules and the transaction data.
[0050] It is also possible to determine the target quantity and price information of each transaction user in each target transaction in the quantity and price space based on the transaction data, and then determine the benchmark quantity and price information of each target transaction in the quantity and price space based on the transaction data, and then determine the abnormality degree information based on the target quantity and price information and the benchmark quantity and price information.
[0051] Furthermore, the abnormality degree information is determined based on the transaction data, and the transaction volume ratio information is also determined based on the transaction data. The transaction data can be input into a pre-trained transaction volume ratio information determination model to obtain the transaction volume ratio information of each transaction user output by the transaction volume ratio information determination model.
[0052] The transaction volume ratio information can also be determined based on the transaction data and the transaction volume ratio algorithm. Specifically, the transaction volume ratio algorithm can be expressed as ,in, is the proportion of trading volume of the trading user in the i-th target transaction, is the trading volume of the trading user in the i-th target transaction, It is the total transaction volume of all target transactions of the trading user within the preset time period.
[0053] Step 103: determine the abnormal coefficient of each transaction user according to the abnormal degree information and the transaction volume ratio information, and determine the abnormal transaction user for the target transaction according to the abnormal coefficient.
[0054] In some exemplary embodiments, the abnormality degree information and the transaction volume ratio information may be input into a pre-trained abnormality coefficient determination model to obtain the abnormality coefficient of each transaction user output by the abnormality coefficient determination model.
[0055] In other exemplary embodiments, it is also possible to first obtain transaction attribute information for characterizing the transaction identity of each transaction user in the target transaction, and then determine the abnormality coefficient based on the transaction attribute information, the abnormality degree information and the transaction volume ratio information.
[0056] As described above, after determining the abnormal coefficient of each transaction user according to the abnormal degree information and the transaction volume proportion information, the abnormal transaction user for the target transaction can also be determined according to the abnormal coefficient.
[0057] In some exemplary embodiments, an abnormal transaction user may be determined from among the transaction users according to a preset abnormal coefficient threshold and the abnormal coefficient of each transaction user. Specifically, a transaction user whose abnormal coefficient is greater than the preset abnormal coefficient threshold may be determined as an abnormal transaction user.
[0058] The above abnormal user determination method first obtains the transaction data of each target transaction within a preset time period, and the transaction data includes the transaction volume information and transaction price information of each transaction user in each target transaction; then determines the abnormality degree information based on the transaction data, and determines the transaction volume proportion information based on the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each transaction user in each target transaction, and the transaction volume proportion is used to characterize the transaction volume of each transaction user in each target transaction, and the proportional relationship with the total transaction volume of each transaction user for the target transaction within the preset time period; then, the abnormal coefficient of each transaction user is determined according to the abnormality degree information and the transaction volume proportion information, and the abnormal transaction user for the target transaction is determined according to the abnormal coefficient. The abnormal user determination method provided in the present application identifies the abnormal transaction users in the target transaction through transaction data, and the abnormal transaction users are one of the important factors affecting the stability of the power market. Therefore, by identifying the abnormal transaction users, the monitoring of power transactions can be realized, thereby solving the problem of poor stability of the power market in the prior art, so as to improve the stability of the power market.
[0059] In an exemplary embodiment, Figure 2As shown, determining the abnormality degree information according to the transaction data includes the following steps:
[0060] Step 201: Determine the target quantity and price information of each transaction user in the quantity and price space in each target transaction according to the transaction data.
[0061] Optionally, the volume-price space may be a space composed of transaction volume and transaction price, wherein the transaction volume may be the horizontal axis and the transaction price may be the vertical axis.
[0062] In some exemplary embodiments, the target quantity and price information of each trading user in each target transaction in the quantity and price space can be determined based on the transaction data and the preset quantity and price information algorithm. Specifically, the target quantity and price information of trading user i in target transaction t is used as an example for illustration. The target quantity and price information is used to represent the target quantity and price, and the target quantity and price can be expressed as ,in, , , is the transaction price of market member i in target transaction t, is the highest transaction price in the target transaction t, is the lowest transaction price in the target transaction t, is the trading volume of market member i in target transaction t, is the maximum transaction volume in the target transaction t, is the minimum transaction price in the target transaction t.
[0063] Step 202: Determine the benchmark quantity and price information of each target transaction in the quantity and price space according to the transaction data.
[0064] In some exemplary embodiments, for each target transaction in each target transaction, all transaction price information and all transaction volume information in the target transaction can be obtained according to the transaction data, and all transaction price information can be averaged to obtain the benchmark transaction price information. The benchmark transaction price information can be expressed as , all the transaction volume information is averaged to obtain the benchmark transaction volume information, which can be expressed as .
[0065] Furthermore, the benchmark transaction price information and the benchmark transaction volume information can be used to determine the benchmark volume and price information of the target transaction in the volume and price space. The benchmark volume and price information can be expressed as .
[0066] Step 203: Determine the abnormality degree information according to the target quantity and price information and the reference quantity and price information.
[0067] In some exemplary embodiments, the target quantity price information and the reference quantity price information may be input into a pre-trained abnormality degree information determination model to obtain the abnormality degree information output by the abnormality degree information determination model.
[0068] In an optional embodiment of the present application, the target quantity and price information may be compared with the reference quantity and price information to determine the abnormality degree information.
[0069] Specifically, in an optional embodiment of the present application, determining the abnormality degree information according to the target quantity price information and the benchmark quantity price information includes: determining the abnormality degree information based on the Euclidean distance method, the target quantity price information and the benchmark quantity price information.
[0070] In some exemplary embodiments, the Euclidean distance method can be expressed as .
[0071] In an exemplary embodiment, Figure 3 As shown, the abnormal coefficient of each transaction user is determined according to the abnormal degree information and the transaction volume proportion information, including the following steps:
[0072] Step 301: Obtain transaction attribute information of each transaction user.
[0073] The transaction attribute information is used to characterize the transaction identity of the transaction user in the target transaction.
[0074] Optionally, the transaction identity may include a buyer and a seller, that is, a buyer and a seller.
[0075] In some exemplary embodiments, the transaction attribute information of each transaction user in each target transaction can be determined based on the transaction data. Specifically, the transaction identity of each transaction user in each target transaction can be determined based on the transaction operation of each transaction user in each target transaction.
[0076] Step 302: determine the abnormality coefficient according to the transaction attribute information, the abnormality degree information and the transaction volume ratio information.
[0077] In some exemplary embodiments, the transaction attribute information, the abnormality degree information and the transaction volume ratio information may be input into a pre-trained abnormality coefficient determination model to obtain the abnormality coefficient output by the abnormality coefficient determination model.
[0078] In an exemplary embodiment, Figure 4 As shown, the transaction attribute information includes a first transaction attribute and a second transaction attribute, and determining the abnormality coefficient according to the transaction attribute information, the abnormality degree information and the transaction volume proportion information includes the following steps:
[0079] Step 401: When the transaction attribute information of the transaction user is the first transaction attribute, determine the abnormality coefficient of the transaction user based on the first algorithm, the abnormality degree information and the transaction volume proportion information.
[0080] Step 402: When the transaction attribute information of the transaction user is the second transaction attribute, determine the abnormality coefficient based on the second algorithm, the abnormality degree information and the transaction volume ratio information.
[0081] Among them, the first algorithm includes a first coefficient, and the second algorithm includes a second coefficient. The method for determining the value of the first coefficient is different from the method for determining the value of the second coefficient. The method for determining the value of the first coefficient and the method for determining the value of the second coefficient are related to the transaction identity of the transaction user.
[0082] Optionally, the first transaction attribute may be used to characterize that the transaction user is a seller in the target transaction, and the second transaction attribute may be used to characterize that the transaction user is a buyer in the target transaction.
[0083] In some exemplary embodiments, the first algorithm can be expressed as ,in, is the first coefficient, and the method for determining the value of the first coefficient is as follows: hour , hour The second algorithm can be expressed as ,in, is the second coefficient, and the method for determining the value of the second coefficient is as follows: hour , hour .
[0084] In the above embodiment, the values of the first coefficient and the second coefficient are used to characterize whether the seller and the seller have the behavior of manipulating the market. Specifically, if the buyer user trades when the transaction price is high, it can be considered that the seller user does not have the behavior of manipulating the market. Therefore, when the abnormal coefficient is included, its value is set to 0. Conversely, if the seller user trades when the transaction price is lower than the average transaction price, it can also be considered that the seller user does not have the behavior of manipulating the market.
[0085] In an exemplary embodiment, Figure 5 As shown, determining the abnormal transaction user for the target transaction according to the abnormal coefficient includes the following steps:
[0086] Step 501: When the transaction attribute information of the transaction user is the first transaction attribute, the transaction users whose transaction attribute information is the first transaction attribute are sorted according to the abnormality coefficient to obtain a first sequence, and a first abnormal transaction user is determined according to the first sequence;
[0087] Step 502: When the transaction attribute information of the transaction user is the second transaction attribute, the transaction users whose transaction attribute information is the second transaction attribute are sorted according to the abnormality coefficient to obtain a second sequence, and a second abnormal transaction user is determined according to the second sequence;
[0088] Step 503: Determine the abnormal transaction user according to the first abnormal transaction user and the second abnormal transaction user.
[0089] In some exemplary embodiments, the transaction users can be classified according to their transaction attribute information, that is, the transaction users can be divided into seller users and buyer users. For seller users, they can be sorted according to their abnormal coefficients to obtain a seller user sequence, that is, a first sequence. For buyer users, they can also be sorted according to their abnormal coefficients to obtain a buyer user sequence, that is, a second sequence.
[0090] Furthermore, the seller user whose serial number in the first sequence is before the preset serial number can be determined as the first abnormal transaction user, and the buyer user whose serial number in the second sequence is before the preset serial number can be determined as the second abnormal transaction user, and then the first abnormal transaction user and the second abnormal transaction user can be determined as the abnormal transaction user. Among them, the preset serial number can be pre-set by the technical staff according to actual needs, and the sorting can be from large to small, then the user before the preset serial number is determined as the abnormal transaction user, on the contrary, if the sorting is from small to large, then the user after the preset serial number is determined as the abnormal transaction user.
[0091] In an exemplary embodiment, Figure 6 As shown, another abnormal user determination method is provided, comprising the following steps:
[0092] Step 601: Acquire transaction data of each target transaction within a preset time period, the transaction data including transaction volume information and transaction price information of each transaction user in each target transaction; determine the target quantity and price information of each transaction user in each target transaction in the quantity and price space according to the transaction data; determine the benchmark quantity and price information of each target transaction in the quantity and price space according to the transaction data; determine the abnormality degree information based on the Euclidean distance method, the target quantity and price information and the benchmark quantity and price information.
[0093] Step 602: determine the transaction volume ratio information based on the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each transaction user in each target transaction, and the transaction volume ratio is used to characterize the transaction volume of each transaction user in each target transaction, and the ratio of the total transaction volume of each transaction user for the target transaction within the preset time period.
[0094] Step 603: Acquire transaction attribute information of each transaction user, where the transaction attribute information is used to characterize the transaction identity of the transaction user in the target transaction; the transaction attribute information includes a first transaction attribute and a second transaction attribute.
[0095] Step 604: When the transaction attribute information of the transaction user is the first transaction attribute, the abnormality coefficient of the transaction user is determined based on the first algorithm, the abnormality degree information and the transaction volume proportion information; when the transaction attribute information of the transaction user is the second transaction attribute, the abnormality coefficient is determined based on the second algorithm, the abnormality degree information and the transaction volume proportion information; wherein the first algorithm includes a first coefficient, the second algorithm includes a second coefficient, a method for determining the value of the first coefficient is different from a method for determining the value of the second coefficient, and the method for determining the value of the first coefficient and the method for determining the value of the second coefficient are related to the transaction identity of the transaction user.
[0096] Step 605: When the transaction attribute information of the transaction user is the first transaction attribute, the transaction users whose transaction attribute information is the first transaction attribute are sorted according to the abnormal coefficient to obtain a first sequence, and a first abnormal transaction user is determined according to the first sequence; when the transaction attribute information of the transaction user is the second transaction attribute, the transaction users whose transaction attribute information is the second transaction attribute are sorted according to the abnormal coefficient to obtain a second sequence, and a second abnormal transaction user is determined according to the second sequence; the abnormal transaction user is determined according to the first abnormal transaction user and the second abnormal transaction user.
[0097] It should be noted that the inventor of the present application conducted relevant tests using the abnormal user identification method provided in the present application. Specifically, in the relevant tests, the transaction data of 14 target transactions in a certain month (within a preset time period) were first obtained. The transaction data can be Fig.10 As shown, the method provided by this application is used to determine the abnormal coefficient of each transaction user and sort them. The sorting results can be shown as follows Fig.11As shown. Take user 53 among multiple abnormal users as an example. User 5 traded a total of 7040 MWh of electricity in the target transaction for the whole month, with 2300 MWh of electricity traded in the 10th transaction and 4000 MWh of electricity traded in the 13th transaction, accounting for 32% and 57% respectively. In these two target transactions, the transaction price of user 53 was lower than the benchmark transaction price. It can be determined that user 53 is an abnormal transaction user, and the market behavior of this user is worthy of being monitored by the regulatory agency. In the ranking results of this application, user 53 is ranked first.
[0098] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.
[0099] Based on the same inventive concept, the embodiment of the present application also provides an abnormal user determination device for implementing the abnormal user determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more abnormal user determination device embodiments provided below can refer to the limitations of the abnormal user determination method above, and will not be repeated here.
[0100] In an exemplary embodiment, Figure 7 As shown, an abnormal user determination device 700 is provided, comprising: an acquisition module 701, a first determination module 702 and a second determination module 703, wherein:
[0101] The acquisition module 701 is used to acquire the transaction data of each target transaction within a preset time period, and the transaction data includes the transaction volume information and transaction price information of each transaction user in each target transaction;
[0102] The first determination module 702 is used to determine abnormality degree information according to the transaction data, and to determine transaction volume ratio information according to the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each transaction user in each target transaction, and the transaction volume ratio is used to characterize the ratio of the transaction volume of each transaction user in each target transaction to the total transaction volume of each transaction user for the target transaction within the preset time period;
[0103] The second determination module 703 is used to determine the abnormal coefficient of each transaction user according to the abnormal degree information and the transaction volume proportion information, and determine the abnormal transaction user for the target transaction according to the abnormal coefficient.
[0104] In one embodiment, the first determination module 702 is specifically used to determine the target quantity and price information of each transaction user in each target transaction in the quantity and price space according to the transaction data; determine the benchmark quantity and price information of each target transaction in the quantity and price space according to the transaction data; determine the abnormality degree information according to the target quantity and price information and the benchmark quantity and price information
[0105] In one embodiment, the first determination module 702 is specifically configured to determine the abnormality degree information based on the Euclidean distance method, the target quantity and price information, and the reference quantity and price information.
[0106] In one embodiment, the second determination module 703 is specifically used to obtain transaction attribute information of each transaction user, where the transaction attribute information is used to characterize the transaction identity of the transaction user in the target transaction; and determine the abnormality coefficient according to the transaction attribute information, the abnormality degree information and the transaction volume proportion information.
[0107] In one embodiment, the transaction attribute information includes a first transaction attribute and a second transaction attribute. The second determination module 703 is specifically used to determine the abnormal coefficient of the transaction user based on the first algorithm, the abnormal degree information and the transaction volume proportion information when the transaction attribute information of the transaction user is the first transaction attribute; and to determine the abnormal coefficient based on the second algorithm, the abnormal degree information and the transaction volume proportion information when the transaction attribute information of the transaction user is the second transaction attribute; wherein the first algorithm includes a first coefficient, the second algorithm includes a second coefficient, a method for determining the value of the first coefficient is different from a method for determining the value of the second coefficient, and the method for determining the value of the first coefficient and the method for determining the value of the second coefficient are related to the transaction identity of the transaction user.
[0108] In one embodiment, the second determination module 703 is specifically used to, when the transaction attribute information of the transaction user is the first transaction attribute, sort the transaction users whose transaction attribute information is the first transaction attribute according to the abnormality coefficient to obtain a first sequence, and determine the first abnormal transaction user according to the first sequence; when the transaction attribute information of the transaction user is the second transaction attribute, sort the transaction users whose transaction attribute information is the second transaction attribute according to the abnormality coefficient to obtain a second sequence, and determine the second abnormal transaction user according to the second sequence; determine the abnormal transaction user based on the first abnormal transaction user and the second abnormal transaction user.
[0109] Each module in the abnormal user determination device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.
[0110] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an abnormal user determination method is implemented.
[0111] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig. 9 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, an abnormal user determination method is implemented.
[0112] Those skilled in the art will understand that Figure 8 and Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0113] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:
[0114] Acquire transaction data of each target transaction within a preset time period, the transaction data including transaction volume information and transaction price information of each transaction user in each target transaction;
[0115] Determine abnormality degree information based on the transaction data, and determine transaction volume ratio information based on the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each transaction user in each target transaction, and the transaction volume ratio is used to characterize the ratio of the transaction volume of each transaction user in each target transaction to the total transaction volume of each transaction user for the target transaction in the preset time period;
[0116] The abnormal coefficient of each transaction user is determined according to the abnormal degree information and the transaction volume ratio information, and the abnormal transaction user for the target transaction is determined according to the abnormal coefficient.
[0117] In one embodiment, when the processor executes the computer program, the following steps are also implemented: determining the target quantity and price information of each transaction user in each target transaction in the quantity and price space according to the transaction data; determining the benchmark quantity and price information of each target transaction in the quantity and price space according to the transaction data; and determining the abnormality degree information according to the target quantity and price information and the benchmark quantity and price information.
[0118] In one embodiment, when the processor executes the computer program, the following steps are further implemented: determining the abnormality degree information based on the Euclidean distance method, the target quantity and price information, and the reference quantity and price information.
[0119] In one embodiment, when the processor executes the computer program, the following steps are also implemented: obtaining transaction attribute information of each transaction user, where the transaction attribute information is used to characterize the transaction identity of the transaction user in the target transaction; and determining the abnormality coefficient based on the transaction attribute information, the abnormality degree information and the transaction volume proportion information.
[0120] In one embodiment, the processor further implements the following steps when executing the computer program: when the transaction attribute information of the transaction user is the first transaction attribute, determining the abnormal coefficient of the transaction user based on the first algorithm, the abnormal degree information and the transaction volume proportion information; when the transaction attribute information of the transaction user is the second transaction attribute, determining the abnormal coefficient based on the second algorithm, the abnormal degree information and the transaction volume proportion information; wherein, the first algorithm includes a first coefficient, the second algorithm includes a second coefficient, a method for determining the value of the first coefficient is different from a method for determining the value of the second coefficient, and the method for determining the value of the first coefficient and the method for determining the value of the second coefficient are related to the transaction identity of the transaction user.
[0121] In one embodiment, the processor further implements the following steps when executing the computer program: when the transaction attribute information of the transaction user is the first transaction attribute, sorting the transaction users whose transaction attribute information is the first transaction attribute according to the abnormal coefficient to obtain a first sequence, and determining the first abnormal transaction user according to the first sequence; when the transaction attribute information of the transaction user is the second transaction attribute, sorting the transaction users whose transaction attribute information is the second transaction attribute according to the abnormal coefficient to obtain a second sequence, and determining the second abnormal transaction user according to the second sequence; determining the abnormal transaction user based on the first abnormal transaction user and the second abnormal transaction user.
[0122] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
[0123] Acquire transaction data of each target transaction within a preset time period, the transaction data including transaction volume information and transaction price information of each transaction user in each target transaction;
[0124] Determine abnormality degree information based on the transaction data, and determine transaction volume ratio information based on the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each transaction user in each target transaction, and the transaction volume ratio is used to characterize the ratio of the transaction volume of each transaction user in each target transaction to the total transaction volume of each transaction user for the target transaction in the preset time period;
[0125] The abnormal coefficient of each transaction user is determined according to the abnormal degree information and the transaction volume ratio information, and the abnormal transaction user for the target transaction is determined according to the abnormal coefficient.
[0126] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the target quantity and price information of each transaction user in each target transaction in the quantity and price space based on the transaction data; determining the benchmark quantity and price information of each target transaction in the quantity and price space based on the transaction data; determining the abnormality degree information based on the target quantity and price information and the benchmark quantity and price information.
[0127] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: determining the abnormality degree information based on the Euclidean distance method, the target quantity and price information, and the reference quantity and price information.
[0128] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining transaction attribute information of each transaction user, where the transaction attribute information is used to characterize the transaction identity of the transaction user in the target transaction; determining the abnormality coefficient based on the transaction attribute information, the abnormality degree information and the transaction volume proportion information.
[0129] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the transaction attribute information of the transaction user is the first transaction attribute, the abnormal coefficient of the transaction user is determined based on the first algorithm, the abnormal degree information and the transaction volume proportion information; when the transaction attribute information of the transaction user is the second transaction attribute, the abnormal coefficient is determined based on the second algorithm, the abnormal degree information and the transaction volume proportion information; wherein, the first algorithm includes a first coefficient, the second algorithm includes a second coefficient, the method for determining the value of the first coefficient is different from the method for determining the value of the second coefficient, and the method for determining the value of the first coefficient and the method for determining the value of the second coefficient are related to the transaction identity of the transaction user.
[0130] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the transaction attribute information of the transaction user is the first transaction attribute, the transaction users whose transaction attribute information is the first transaction attribute are sorted according to the abnormal coefficient to obtain a first sequence, and a first abnormal transaction user is determined according to the first sequence; when the transaction attribute information of the transaction user is the second transaction attribute, the transaction users whose transaction attribute information is the second transaction attribute are sorted according to the abnormal coefficient to obtain a second sequence, and a second abnormal transaction user is determined according to the second sequence; the abnormal transaction user is determined based on the first abnormal transaction user and the second abnormal transaction user.
[0131] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:
[0132] Acquire transaction data of each target transaction within a preset time period, the transaction data including transaction volume information and transaction price information of each transaction user in each target transaction;
[0133] Determine abnormality degree information based on the transaction data, and determine transaction volume ratio information based on the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each transaction user in each target transaction, and the transaction volume ratio is used to characterize the ratio of the transaction volume of each transaction user in each target transaction to the total transaction volume of each transaction user for the target transaction in the preset time period;
[0134] The abnormal coefficient of each transaction user is determined according to the abnormal degree information and the transaction volume ratio information, and the abnormal transaction user for the target transaction is determined according to the abnormal coefficient.
[0135] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: determining the target quantity and price information of each transaction user in each target transaction in the quantity and price space based on the transaction data; determining the benchmark quantity and price information of each target transaction in the quantity and price space based on the transaction data; determining the abnormality degree information based on the target quantity and price information and the benchmark quantity and price information.
[0136] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented: determining the abnormality degree information based on the Euclidean distance method, the target quantity and price information, and the reference quantity and price information.
[0137] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: obtaining transaction attribute information of each transaction user, where the transaction attribute information is used to characterize the transaction identity of the transaction user in the target transaction; determining the abnormality coefficient based on the transaction attribute information, the abnormality degree information and the transaction volume proportion information.
[0138] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the transaction attribute information of the transaction user is the first transaction attribute, the abnormal coefficient of the transaction user is determined based on the first algorithm, the abnormal degree information and the transaction volume proportion information; when the transaction attribute information of the transaction user is the second transaction attribute, the abnormal coefficient is determined based on the second algorithm, the abnormal degree information and the transaction volume proportion information; wherein, the first algorithm includes a first coefficient, the second algorithm includes a second coefficient, the method for determining the value of the first coefficient is different from the method for determining the value of the second coefficient, and the method for determining the value of the first coefficient and the method for determining the value of the second coefficient are related to the transaction identity of the transaction user.
[0139] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: when the transaction attribute information of the transaction user is the first transaction attribute, the transaction users whose transaction attribute information is the first transaction attribute are sorted according to the abnormal coefficient to obtain a first sequence, and a first abnormal transaction user is determined according to the first sequence; when the transaction attribute information of the transaction user is the second transaction attribute, the transaction users whose transaction attribute information is the second transaction attribute are sorted according to the abnormal coefficient to obtain a second sequence, and a second abnormal transaction user is determined according to the second sequence; the abnormal transaction user is determined based on the first abnormal transaction user and the second abnormal transaction user.
[0140] It should be noted that the transaction user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0141] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.
[0142] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0143] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.
Claims
1. A method for determining abnormal users, characterized in that: The method comprises: Acquire transaction data of each target transaction within a preset time period, wherein the transaction data includes transaction volume information and transaction price information of each transaction user in each target transaction; Determine abnormality degree information according to the transaction data, and determine transaction volume ratio information according to the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each of the transaction users in each of the target transactions, and the transaction volume ratio is used to characterize the ratio of the transaction volume of each of the transaction users in each of the target transactions to the total transaction volume of each of the transaction users for the target transactions within the preset time period; The abnormal coefficient of each transaction user is determined according to the abnormal degree information and the transaction volume proportion information, and the abnormal transaction user for the target transaction is determined according to the abnormal coefficient.
2. The method according to claim 1, characterized in that The determining of abnormality degree information according to the transaction data includes: Determining target quantity and price information of each of the transaction users in each of the target transactions in the quantity and price space according to the transaction data; Determine the benchmark quantity and price information of each target transaction in the quantity and price space according to the transaction data; The abnormality degree information is determined according to the target quantity and price information and the reference quantity and price information.
3. The method according to claim 2, characterized in that The determining the abnormality degree information according to the target quantity and price information and the reference quantity and price information includes: The abnormality degree information is determined based on the Euclidean distance method, the target quantity price information, and the reference quantity price information.
4. The method according to claim 1, characterized in that The determining the abnormality coefficient of each transaction user according to the abnormality degree information and the transaction volume proportion information includes: Acquiring transaction attribute information of each of the transaction users, wherein the transaction attribute information is used to characterize the transaction identity of the transaction user in the target transaction; The abnormality coefficient is determined according to the transaction attribute information, the abnormality degree information and the transaction volume ratio information.
5. The method according to claim 4, characterized in that The transaction attribute information includes a first transaction attribute and a second transaction attribute, and determining the abnormality coefficient according to the transaction attribute information, the abnormality degree information and the transaction volume proportion information includes: When the transaction attribute information of the transaction user is the first transaction attribute, determining the abnormality coefficient of the transaction user based on the first algorithm, the abnormality degree information and the transaction volume proportion information; When the transaction attribute information of the transaction user is the second transaction attribute, determining the abnormality coefficient based on a second algorithm, the abnormality degree information and the transaction volume proportion information; Among them, the first algorithm includes a first coefficient, the second algorithm includes a second coefficient, the method for determining the value of the first coefficient is different from the method for determining the value of the second coefficient, and the method for determining the value of the first coefficient and the method for determining the value of the second coefficient are related to the transaction identity of the transaction user.
6. The method according to claim 5, characterized in that The determining, according to the abnormal coefficient, an abnormal transaction user for the target transaction comprises: In a case where the transaction attribute information of the transaction user is the first transaction attribute, the transaction users whose transaction attribute information is the first transaction attribute are sorted according to the abnormality coefficient to obtain a first sequence, and a first abnormal transaction user is determined according to the first sequence; In a case where the transaction attribute information of the transaction user is the second transaction attribute, the transaction users whose transaction attribute information is the second transaction attribute are sorted according to the abnormality coefficient to obtain a second sequence, and a second abnormal transaction user is determined according to the second sequence; The abnormal transaction user is determined according to the first abnormal transaction user and the second abnormal transaction user.
7. An abnormal user identification device, characterized in that: The device comprises: An acquisition module, used to acquire transaction data of each target transaction within a preset time period, wherein the transaction data includes transaction volume information and transaction price information of each transaction user in each target transaction; A first determination module is used to determine abnormality degree information according to the transaction data, and to determine transaction volume ratio information according to the transaction data; the abnormality degree information is used to characterize the abnormality degree of the transaction behavior of each of the transaction users in each of the target transactions, and the transaction volume ratio is used to characterize the ratio of the transaction volume of each of the transaction users in each of the target transactions to the total transaction volume of each of the transaction users for the target transactions within the preset time period; The second determination module is used to determine the abnormal coefficient of each transaction user according to the abnormal degree information and the transaction volume proportion information, and determine the abnormal transaction user for the target transaction according to the abnormal coefficient.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.