CN112435126B - Account identification method and device, computer equipment and storage medium - Google Patents
Account identification method and device, computer equipment and storage medium Download PDFInfo
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- CN112435126B CN112435126B CN202110100216.5A CN202110100216A CN112435126B CN 112435126 B CN112435126 B CN 112435126B CN 202110100216 A CN202110100216 A CN 202110100216A CN 112435126 B CN112435126 B CN 112435126B
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Abstract
The application relates to an account identification method, an account identification device, computer equipment and a storage medium. The method comprises the following steps: acquiring security data, and determining the abnormal securities on a single day according to the security data; combining candidate exception accounts participating in a plurality of transaction securities into a coincidence group; the candidate abnormal account is a trading account participating in the transaction securities; acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group; determining the single-day coincidence rate of the associated account group according to the coincidence group; determining the coincidence days and the average coincidence rate of the associated account group in the identification time period according to the single-day coincidence rate; identifying a preset number of consecutive transaction days in a time period; inputting the identification time period and the number of coincident days into a time model to obtain an output result; determining the relevance of the associated account group according to the average coincidence rate and the output result; and when the relevance is larger than a preset relevance threshold, determining that the transaction accounts in the relevant account group are abnormal. The method can improve the identification accuracy of the abnormal account.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to an account identification method and apparatus, a computer device, and a storage medium.
Background
With the continuous development of computer technology, the application of information technology in the securities industry is also deepened, some novel security transaction illegal behaviors begin to appear, and the security of the security transaction market is influenced by illegal manipulation of the security transaction. By monitoring the transaction account, the illegally manipulated abnormal transaction account is identified, and the corresponding control is performed on the abnormal transaction account, so that the security of the security transaction can be guaranteed. Conventionally, the abnormal account is usually identified by single account monitoring, that is, each account is individually monitored to identify the abnormal account. However, the single-account monitoring method cannot effectively and accurately identify the abnormal account, so that the identification accuracy of the abnormal account is low.
Disclosure of Invention
In view of the above, it is necessary to provide an account identification method, an account identification apparatus, a computer device, and a storage medium, which can improve the accuracy of identifying an abnormal account.
An account identification method, the method comprising:
acquiring security data, and determining abnormal securities within a single day according to the security data;
combining candidate abnormal accounts participating in a plurality of abnormal securities simultaneously into a superposition group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities;
acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group;
determining the single-day coincidence rate of the associated account group according to the coincidence group;
determining the coincidence days and the average coincidence rate of the associated account group in an identification time period according to the single-day coincidence rate; the identification time period is a preset number of consecutive transaction days;
inputting the identification time period and the coincidence days into a pre-constructed time model to obtain an output result;
determining the association degree between the transaction accounts in the associated account group according to the average coincidence rate and the output result;
and when the relevance is greater than a preset relevance threshold, determining that the transaction account in the relevant account group is abnormal.
In one embodiment, the securities data includes a trading volume and a unit value fluctuation range of securities, and the determining of the transaction securities within a single day according to the securities data includes:
monitoring the transaction quantity and unit value fluctuation range of the certificate in a single day in real time;
and determining the securities of which the transaction quantity is greater than a preset transaction quantity threshold value and/or the unit value fluctuation range is greater than a preset unit value fluctuation range threshold value as the abnormal securities in a single day.
In one embodiment, the obtaining target candidate exception accounts from the candidate exception accounts and combining the target candidate exception accounts into a linked account group includes:
arranging and combining the candidate abnormal accounts to obtain a candidate associated account group;
and randomly grabbing any candidate associated account group, taking the transaction account in the grabbed candidate associated account group as a target candidate abnormal account, and taking the grabbed candidate associated account group as an associated account group.
In one embodiment, the determining the single-day coincidence ratio of the associated-account group according to the coincidence group includes:
determining a total number of the coincident groups;
determining a total number of occurrences of the set of associated accounts in the coincident set;
and taking the ratio of the total times of the associated account group appearing in the coincidence group to the total number of the coincidence group as the single-day coincidence rate of the associated account group.
In one embodiment, the determining the number of coincident days of the associated account group in the identification period according to the single-day coincidence rate includes:
when the single-day coincidence rate is larger than zero, judging that the associated account group is coincided in the identification time period;
and determining the numerical value corresponding to the number of the single-day coincidence rate larger than zero as the coincidence days of the associated account group in the identification time period.
In one embodiment, the step of constructing the temporal model includes:
constructing an initial model based on a Sigmoid function;
adjusting the time parameter of the Sigmoid function to adjust the change rate of the initial model;
and when the change rate of the initial model is equal to a preset change rate, taking the initial model as a time model.
In one embodiment, the method further comprises:
and sending the transaction account identified with the abnormality to a transaction platform so as to instruct the transaction platform to monitor the transaction account identified with the abnormality in real time.
An account identification apparatus, the apparatus comprising:
the acquisition module is used for acquiring the stock data and determining the abnormal movement stocks in the single day according to the stock data;
a combination module for combining candidate exception accounts participating in multiple exception securities simultaneously into a coincident group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities; acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group;
the determining module is used for determining the single-day coincidence rate of the associated account group according to the coincidence group; determining the coincidence days and the average coincidence rate of the associated account group in an identification time period according to the single-day coincidence rate; the identification time period is a preset number of consecutive transaction days;
the input module is used for inputting the identification time period and the coincidence days into a pre-constructed time model to obtain an output result;
the determining module is further configured to determine a degree of association between transaction accounts in the associated account group according to the average coincidence rate and the output result;
and the judging module is used for judging that the transaction accounts in the associated account group are abnormal when the association degree is greater than a preset association degree threshold value.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring security data, and determining abnormal securities within a single day according to the security data;
combining candidate abnormal accounts participating in a plurality of abnormal securities simultaneously into a superposition group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities;
acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group;
determining the single-day coincidence rate of the associated account group according to the coincidence group;
determining the coincidence days and the average coincidence rate of the associated account group in an identification time period according to the single-day coincidence rate; the identification time period is a preset number of consecutive transaction days;
inputting the identification time period and the coincidence days into a pre-constructed time model to obtain an output result;
determining the association degree between the transaction accounts in the associated account group according to the average coincidence rate and the output result;
and when the relevance is greater than a preset relevance threshold, determining that the transaction account in the relevant account group is abnormal.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring security data, and determining abnormal securities within a single day according to the security data;
combining candidate abnormal accounts participating in a plurality of abnormal securities simultaneously into a superposition group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities;
acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group;
determining the single-day coincidence rate of the associated account group according to the coincidence group;
determining the coincidence days and the average coincidence rate of the associated account group in an identification time period according to the single-day coincidence rate; the identification time period is a preset number of consecutive transaction days;
inputting the identification time period and the coincidence days into a pre-constructed time model to obtain an output result;
determining the association degree between the transaction accounts in the associated account group according to the average coincidence rate and the output result;
and when the relevance is greater than a preset relevance threshold, determining that the transaction account in the relevant account group is abnormal.
According to the account identification method, the account identification device, the computer equipment and the storage medium, the securities data are obtained, and the abnormal securities within the single day are determined according to the securities data; combining candidate abnormal accounts participating in a plurality of abnormal securities simultaneously into a superposition group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities; acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group; determining the single-day coincidence rate of the associated account group according to the coincidence group; determining the coincidence days and the average coincidence rate of the associated account group in the identification time period according to the single-day coincidence rate; identifying a preset number of consecutive transaction days in a time period; inputting the identification time period and the number of coincident days into a pre-constructed time model to obtain an output result; determining the association degree between transaction accounts in the associated account group according to the average coincidence rate and the output result; and when the relevance is larger than a preset relevance threshold, determining that the transaction accounts in the relevant account group are abnormal. Therefore, by automatically constructing the associated account group and calculating the association degree between the transaction accounts in the associated account group, and further performing abnormal recognition on the transaction accounts according to the association degree between the transaction accounts, compared with the traditional mode of recognizing the abnormal accounts by adopting a single account, the method and the device can adapt to more application scenes, recognize the abnormal accounts more accurately, and further effectively improve the recognition accuracy rate of the abnormal accounts.
Drawings
FIG. 1 is a diagram of an application scenario of an account identification method in one embodiment;
FIG. 2 is a flow diagram illustrating a method for account identification in one embodiment;
FIG. 3 is a diagram that illustrates a degree of association between transaction accounts in a set of associated accounts, according to one embodiment;
FIG. 4 is a schematic illustration of a transaction security distribution in one embodiment;
FIG. 5 is a block diagram showing the structure of an account identification device according to an embodiment;
FIG. 6 is a block diagram showing the structure of an account identifying apparatus according to another embodiment;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The account identification method provided by the application can be applied to the application environment shown in fig. 1. The application environment includes a trading system 102 and a server 104 of a exchange. The transaction system 102 communicates with the server 104 over a network. The server 104 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers. Those skilled in the art will understand that the application environment shown in fig. 1 is only a part of the scenario related to the present application, and does not constitute a limitation to the application environment of the present application.
The server 104 obtains the securities data from the exchange's trading system 102 and determines the errant securities within the order date based on the securities data. The server 104 combines the candidate abnormal accounts participating in a plurality of abnormal securities simultaneously into a superposition group; the candidate exception account is used to represent a trading account that participates in the transaction security. Server 104 obtains target candidate exception accounts from the candidate exception accounts and combines the target candidate exception accounts into a set of associated accounts. The server 104 determines the single-day coincidence rate of the associated account group according to the coincidence group, and determines the coincidence days and the average coincidence rate of the associated account group in the identification time period according to the single-day coincidence rate; the time period is identified as a preset number of consecutive transaction days. The server 104 inputs the identification time period and the number of coincident days into a pre-constructed time model to obtain an output result. And the server 104 determines the association degree between the transaction accounts in the associated account group according to the average coincidence rate and the output result. When the degree of association is greater than the preset degree of association threshold, the server 104 determines that the transaction account in the associated account group is abnormal.
In one embodiment, as shown in fig. 2, an account identification method is provided, which is described by taking the method as an example applied to the server 104 in fig. 1, and includes the following steps:
s202, obtaining the security data, and determining the abnormal securities within the single day according to the security data.
The securities data is the trading data of securities, and the abnormal securities are the securities with abnormal fluctuation.
In particular, the server may obtain securities data from a trading system of the exchange, which may characterize the fluctuating state of the securities, so that the server may determine the errant securities within an order date from the securities data.
S204, combining the candidate abnormal accounts participating in a plurality of abnormal securities into a superposition group; the candidate exception account is used to represent a trading account that participates in the transaction security.
Specifically, the candidate abnormal account is used to represent a trading account participating in the transaction security, and it can be understood that the server may determine the trading account participating in the transaction security as the candidate abnormal account, and the server may also directly obtain the preset candidate abnormal account. The server can monitor the condition that the candidate abnormal account participates in the abnormal securities and combine the candidate abnormal accounts participating in a plurality of abnormal securities into a superposition group.
S206, acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into an associated account group.
Specifically, the server may randomly acquire target candidate exceptional accounts from the candidate exceptional accounts and combine the target candidate exceptional accounts into a related account group.
And S208, determining the single-day coincidence rate of the associated account group according to the coincidence group.
And the single-day coincidence rate is the probability of coincidence of the associated account groups in the coincidence group in a single day.
Specifically, the server may determine the single-day coincidence of the associated-account group based on the total number of times the associated-account group appears in the coincident group and the total number of coincident groups.
S210, determining the coincidence days and the average coincidence rate of the associated account group in the identification time period according to the single-day coincidence rate; the time period is identified as a preset number of consecutive transaction days.
Specifically, the identification time period is a preset number of consecutive transaction days, and it can be understood that the server may determine the preset number of consecutive transaction days as the identification time period, and the server may also directly obtain the identification time period. The server can determine the coincidence days of the associated account group in the identification time period according to the single-day coincidence rate, and determine the average coincidence rate of the associated account group in the identification time period according to the coincidence days.
In one embodiment, the server may determine 22 consecutive transaction days as the identification period, and if the number of coincident days of the associated account group within 22 days is 3 days, the single-day coincidence rates of the 3 coincident days are summed and then divided by 3 to obtain the average coincidence rate of the associated account group within 22 days.
And S212, inputting the identification time period and the number of the coincident days into a pre-constructed time model to obtain an output result.
Specifically, the server may obtain a pre-constructed time model, and input the identification time period and the number of overlapping days into the pre-constructed time model for calculation to obtain an output result.
And S214, determining the association degree between the transaction accounts in the associated account group according to the average coincidence rate and the output result.
Wherein the degree of association is the degree of association between the transaction accounts in the associated account group.
Specifically, the server may directly determine the association degree between the transaction accounts in the associated account group according to the average coincidence rate and the output result. In one embodiment, the server may use the product of the average coincidence rate and the output result as the degree of association between the transaction accounts in the associated account group.
In one embodiment, as shown in FIG. 3, the degree of association between transaction accounts in the associated account group may be graphically displayed. For example, in FIG. 3, a value 90 may be used to indicate the degree of association between A, B, C and D, the four customers' transaction accounts.
S216, when the degree of association is larger than a preset degree of association threshold, determining that the transaction account in the associated account group is abnormal.
Specifically, the server may compare the association degree with a preset association degree threshold, and when the association degree is greater than the preset association degree threshold, the server may determine that the transaction account in the associated account group is abnormal. When the degree of association is less than or equal to a preset degree of association threshold, the server may determine that the transaction accounts in the associated account group are normal.
In the account identification method, by acquiring the security data, the transaction security within the single day is determined according to the security data; combining candidate abnormal accounts participating in a plurality of abnormal securities simultaneously into a superposition group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities; acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group; determining the single-day coincidence rate of the associated account group according to the coincidence group; determining the coincidence days and the average coincidence rate of the associated account group in the identification time period according to the single-day coincidence rate; identifying a preset number of consecutive transaction days in a time period; inputting the identification time period and the number of coincident days into a pre-constructed time model to obtain an output result; determining the association degree between transaction accounts in the associated account group according to the average coincidence rate and the output result; and when the relevance is larger than a preset relevance threshold, determining that the transaction accounts in the relevant account group are abnormal. Therefore, by automatically constructing the associated account group and calculating the association degree between the transaction accounts in the associated account group, and further performing abnormal recognition on the transaction accounts according to the association degree between the transaction accounts, compared with the traditional mode of recognizing the abnormal accounts by adopting a single account, the method and the device can adapt to more application scenes, recognize the abnormal accounts more accurately, and further effectively improve the recognition accuracy rate of the abnormal accounts.
In one embodiment, the security data includes the trading volume and unit value fluctuation range of the security, and the step of determining the abnormal security within a single day according to the security data in step S202 specifically includes: monitoring the transaction quantity and unit value fluctuation range of the certificate in a single day in real time; and determining the securities with the transaction quantity larger than a preset transaction quantity threshold value and/or the unit value fluctuation range larger than a preset unit value fluctuation range threshold value as the abnormal securities within a single day.
In particular, the security data may include a trading volume of the security and a unit value fluctuation range, wherein the unit value fluctuation range may be understood as a security price fluctuation range. The server can monitor the transaction quantity and unit value fluctuation range of the certificate in a single day in real time. The server can compare the transaction quantity with a preset transaction quantity threshold value, and compare the fluctuation range of the unit numerical value with a preset unit numerical value fluctuation range threshold value. The server can determine the securities with the transaction quantity larger than the preset transaction quantity threshold value and/or the unit value fluctuation range larger than the preset unit value fluctuation range threshold value as the abnormal securities in a single day.
In the embodiment, the transaction quantity and the unit value fluctuation range of the securities within a single day are monitored in real time to judge whether the securities are abnormal securities within the single day, so that the identification accuracy of the abnormal securities is improved.
In an embodiment, in step S206, that is, the step of obtaining target candidate exception accounts from the candidate exception accounts and combining the target candidate exception accounts into an associated account group specifically includes: arranging and combining the candidate abnormal accounts to obtain a candidate associated account group; and randomly grabbing any candidate associated account group, taking the transaction account in the grabbed candidate associated account group as a target candidate abnormal account, and taking the grabbed candidate associated account group as an associated account group.
Specifically, the server may perform permutation and combination processing on the candidate abnormal accounts to obtain a candidate associated account group. The server can randomly grab any candidate associated account group, the candidate associated account group comprises transaction accounts, and the server can take the transaction accounts in the grabbed candidate associated account group as target candidate abnormal accounts. And the server can directly take the captured candidate associated account group as the associated account group.
In the above embodiment, the candidate abnormal accounts are arranged and combined, and the candidate associated account group captured randomly is used as the associated account group, so that the calculation accuracy of the single-day coincidence rate of the associated account group can be improved.
In an embodiment, the step S208 of determining the single-day coincidence rate of the associated-account group according to the coincidence group specifically includes: determining a total number of coincident groups; determining a total number of occurrences of the associated account group in the coincident group; and taking the ratio of the total times of the associated account groups appearing in the coincidence group to the total number of the coincidence group as the single-day coincidence rate of the associated account groups.
In particular, the server can determine a total number of occurrences of the associated-account group in the coincident group, as well as a total number of coincident groups. The server may use a ratio of the total number of times the associated-account group appears in the coincident group to the total number of the coincident groups as a single-day coincidence rate of the associated-account group.
In one embodiment, the single-day coincidence rate of the associated-account group may be calculated by the following formula: total number of occurrences of the associated account group in the coincident group/total number of coincident groups.
In one embodiment, as shown in fig. 4, the anomaly accounts BCD appear in the transaction securities 1 and 2, the anomaly accounts EF appear in the transaction securities 1 and 3, and the anomaly accounts DG appear in the transaction securities 2 and 3, respectively, then the coincidence group includes the following three types: BCD (1-2), EF (1-3) and DG (1-3). In the abnormal account ABCDEFGHJK, two or more abnormal accounts are randomly captured to be combined into an associated account group, for example, if the captured associated account group is BCD, the BCD appears 1 time in the coincidence group in the current day, and if the total number of the coincidence groups is 3, the single-day coincidence rate of the associated account group BCD is 1/3.
In the above embodiment, the ratio of the total number of times of occurrence of the associated account group in the coincidence group to the total number of the coincidence group is used as the single-day coincidence rate of the associated account group, so that the calculation accuracy of the single-day coincidence rate is further improved.
In an embodiment, the step S210, that is, the step of determining the number of coincidence days of the associated account group in the recognition time period according to the single-day coincidence rate specifically includes: when the single-day coincidence rate is larger than zero, judging that the associated account group is coincided in the identification time period; and determining the numerical value corresponding to the number of the single-day coincidence rate larger than zero as the coincidence days of the associated account group in the identification time period.
Specifically, the server may compare the single-day coincidence rate of the associated account group in the recognition time period with zero, and when the single-day coincidence rate is greater than zero, the server may determine that the associated account group is coincident in the recognition time period. The server can count the number of the single-day coincidence rate larger than zero, and the numerical value corresponding to the number of the single-day coincidence rate larger than zero is determined as the coincidence days of the associated account group in the identification time period.
In the embodiment, the number of coincidence days of the associated account group in the identification time period is determined by the numerical value corresponding to the number of the single-day coincidence rate larger than zero, so that the calculation efficiency of the coincidence days is improved.
In one embodiment, the step of constructing the temporal model comprises: constructing an initial model based on a Sigmoid function; adjusting the time parameter of the Sigmoid function to adjust the change rate of the initial model; and when the change rate of the initial model is equal to the preset change rate, taking the initial model as a time model.
The Sigmoid function is an S-type function, and is also called an S-type growth curve. Due to its simple increment and simple increment of anti-function, Sigmoid function is often used as the activation function of neural network, mapping variables between 0 and 1.
Specifically, the server may construct an initial model based on the Sigmoid function and adjust the change rate of the initial model by adjusting the time parameter of the Sigmoid function. The server may compare the change rate of the initial model with a preset change rate, and when the change rate of the initial model is equal to the preset change rate, the server may use the initial model as a time model.
In one embodiment, the Sigmoid function is represented as follows: s (t) = 1/(1 + e)-t) Wherein s (t) represents the output result of the Sigmoid function, and t represents time. The server can take the Sigmoid function as an activation function of the time model, the corresponding time model can be obtained by adjusting and deforming the Sigmoid function, and the abnormal account identification accuracy can be improved by adjusting and deforming the time model. For example, if the identification time period is 22 days, and the number of coincidence days of the associated account group in 22 days is 3 days, the model parameter of the time model may take a value of 3, and the time model may output a corresponding output result.
In the embodiment, the change rate of the initial model is adjusted by adjusting the time parameter of the Sigmoid function, so that the change rate of the constructed time model is consistent with the change rate of the association degree between the transaction accounts in the actual scene, and the identification accuracy of the abnormal accounts is further improved.
In one embodiment, the account identification method specifically further includes: and sending the transaction account identified with the abnormality to a transaction platform so as to instruct the transaction platform to monitor the transaction account identified with the abnormality in real time.
Specifically, the server may send the transaction account identified with the abnormality to the transaction platform, and the transaction platform may receive the abnormal transaction account sent by the server and monitor the abnormal transaction account in real time to further determine whether there is a relevant illegal operation.
In one embodiment, the server may also send transaction accounts which have no identified abnormality but have a relatively high association degree in the associated account group to the trading platform, so as to instruct the trading platform to monitor more transaction accounts, thereby better ensuring the security of the stock exchange.
In the embodiment, the transaction account with the identified abnormality is monitored in real time through the transaction platform, so that the fairness and the safety of the stock exchange are improved.
It should be understood that although the various steps of fig. 2 are shown in order, the steps are not necessarily performed in order. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 5, there is provided an account identification apparatus 500, including: an obtaining module 501, a combining module 502, a determining module 503, an inputting module 504 and a judging module 505, wherein:
the obtaining module 501 is configured to obtain security data, and determine an abnormal security within a single day according to the security data.
A combining module 502 for combining candidate exception accounts participating in multiple exception securities simultaneously into a coincident group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities; and acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group.
A determining module 503, configured to determine a single-day coincidence rate of the associated account group according to the coincidence group; determining the coincidence days and the average coincidence rate of the associated account group in the identification time period according to the single-day coincidence rate; the time period is identified as a preset number of consecutive transaction days.
The input module 504 is configured to input the identification time period and the number of overlapping days into a pre-constructed time model to obtain an output result.
The determining module 503 is further configured to determine a degree of association between the transaction accounts in the associated account group according to the average coincidence rate and the output result.
And the determining module 505 is configured to determine that the transaction account in the associated account group is abnormal when the association degree is greater than a preset association degree threshold.
In one embodiment, the security data includes the transaction quantity and unit value fluctuation range of the security, and the obtaining module 501 is further configured to monitor the transaction quantity and unit value fluctuation range of the security within a single day in real time; and determining the securities with the transaction quantity larger than a preset transaction quantity threshold value and/or the unit value fluctuation range larger than a preset unit value fluctuation range threshold value as the abnormal securities within a single day.
In one embodiment, the combining module 502 is further configured to perform permutation and combination processing on the candidate abnormal accounts to obtain a candidate associated account group; and randomly grabbing any candidate associated account group, taking the transaction account in the grabbed candidate associated account group as a target candidate abnormal account, and taking the grabbed candidate associated account group as an associated account group.
In one embodiment, the determination module 503 is further configured to determine a total number of coincident groups; determining a total number of occurrences of the associated account group in the coincident group; and taking the ratio of the total times of the associated account groups appearing in the coincidence group to the total number of the coincidence group as the single-day coincidence rate of the associated account groups.
In one embodiment, the determining module 503 is further configured to determine that the associated account groups are overlapped within the identification time period when the single-day overlapping rate is greater than zero; and determining the numerical value corresponding to the number of the single-day coincidence rate larger than zero as the coincidence days of the associated account group in the identification time period.
Referring to fig. 6, in one embodiment, the account identification apparatus 500 further includes: a building module 506 and a sending module 507, wherein:
a constructing module 506, configured to construct an initial model based on a Sigmoid function; adjusting the time parameter of the Sigmoid function to adjust the change rate of the initial model; and when the change rate of the initial model is equal to the preset change rate, taking the initial model as a time model.
The sending module 507 is configured to send the transaction account with the identified abnormality to the transaction platform, so as to instruct the transaction platform to perform real-time monitoring on the transaction account with the identified abnormality.
The account identification device acquires the security data and determines the abnormal security within the single day according to the security data; combining candidate abnormal accounts participating in a plurality of abnormal securities simultaneously into a superposition group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities; acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group; determining the single-day coincidence rate of the associated account group according to the coincidence group; determining the coincidence days and the average coincidence rate of the associated account group in the identification time period according to the single-day coincidence rate; identifying a preset number of consecutive transaction days in a time period; inputting the identification time period and the number of coincident days into a pre-constructed time model to obtain an output result; determining the association degree between transaction accounts in the associated account group according to the average coincidence rate and the output result; and when the relevance is larger than a preset relevance threshold, determining that the transaction accounts in the relevant account group are abnormal. Therefore, by automatically constructing the associated account group and calculating the association degree between the transaction accounts in the associated account group, and further performing abnormal recognition on the transaction accounts according to the association degree between the transaction accounts, compared with the traditional mode of recognizing the abnormal accounts by adopting a single account, the method and the device can adapt to more application scenes, recognize the abnormal accounts more accurately, and further effectively improve the recognition accuracy rate of the abnormal accounts.
For the specific definition of the account identification device, reference may be made to the above definition of the account identification method, which is not described herein again. The modules in the account identification device can be implemented in whole or in part by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be the server 104 in fig. 1 described above, and its internal structure diagram may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The database of the computer device is for storing account identification data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an account identification method.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring security data, and determining abnormal securities within a single day according to the security data;
combining candidate abnormal accounts participating in a plurality of abnormal securities simultaneously into a superposition group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities;
acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group;
determining the single-day coincidence rate of the associated account group according to the coincidence group;
determining the coincidence days and the average coincidence rate of the associated account group in the identification time period according to the single-day coincidence rate; identifying a preset number of consecutive transaction days in a time period;
inputting the identification time period and the number of coincident days into a pre-constructed time model to obtain an output result;
determining the association degree between transaction accounts in the associated account group according to the average coincidence rate and the output result;
and when the relevance is larger than a preset relevance threshold, determining that the transaction accounts in the relevant account group are abnormal.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
monitoring the transaction quantity and unit value fluctuation range of the certificate in a single day in real time;
and determining the securities with the transaction quantity larger than a preset transaction quantity threshold value and/or the unit value fluctuation range larger than a preset unit value fluctuation range threshold value as the abnormal securities within a single day.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
arranging and combining the candidate abnormal accounts to obtain a candidate associated account group;
and randomly grabbing any candidate associated account group, taking the transaction account in the grabbed candidate associated account group as a target candidate abnormal account, and taking the grabbed candidate associated account group as an associated account group.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
determining a total number of coincident groups;
determining a total number of occurrences of the associated account group in the coincident group;
and taking the ratio of the total times of the associated account groups appearing in the coincidence group to the total number of the coincidence group as the single-day coincidence rate of the associated account groups.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
when the single-day coincidence rate is larger than zero, judging that the associated account group is coincided in the identification time period;
and determining the numerical value corresponding to the number of the single-day coincidence rate larger than zero as the coincidence days of the associated account group in the identification time period.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
constructing an initial model based on a Sigmoid function;
adjusting the time parameter of the Sigmoid function to adjust the change rate of the initial model;
and when the change rate of the initial model is equal to the preset change rate, taking the initial model as a time model.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and sending the transaction account identified with the abnormality to a transaction platform so as to instruct the transaction platform to monitor the transaction account identified with the abnormality in real time.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring security data, and determining abnormal securities within a single day according to the security data;
combining candidate abnormal accounts participating in a plurality of abnormal securities simultaneously into a superposition group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities;
acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group;
determining the single-day coincidence rate of the associated account group according to the coincidence group;
determining the coincidence days and the average coincidence rate of the associated account group in the identification time period according to the single-day coincidence rate; identifying a preset number of consecutive transaction days in a time period;
inputting the identification time period and the number of coincident days into a pre-constructed time model to obtain an output result;
determining the association degree between transaction accounts in the associated account group according to the average coincidence rate and the output result;
and when the relevance is larger than a preset relevance threshold, determining that the transaction accounts in the relevant account group are abnormal.
In one embodiment, the computer program when executed by the processor further performs the steps of:
monitoring the transaction quantity and unit value fluctuation range of the certificate in a single day in real time;
and determining the securities with the transaction quantity larger than a preset transaction quantity threshold value and/or the unit value fluctuation range larger than a preset unit value fluctuation range threshold value as the abnormal securities within a single day.
In one embodiment, the computer program when executed by the processor further performs the steps of:
arranging and combining the candidate abnormal accounts to obtain a candidate associated account group;
and randomly grabbing any candidate associated account group, taking the transaction account in the grabbed candidate associated account group as a target candidate abnormal account, and taking the grabbed candidate associated account group as an associated account group.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining a total number of coincident groups;
determining a total number of occurrences of the associated account group in the coincident group;
and taking the ratio of the total times of the associated account groups appearing in the coincidence group to the total number of the coincidence group as the single-day coincidence rate of the associated account groups.
In one embodiment, the computer program when executed by the processor further performs the steps of:
when the single-day coincidence rate is larger than zero, judging that the associated account group is coincided in the identification time period;
and determining the numerical value corresponding to the number of the single-day coincidence rate larger than zero as the coincidence days of the associated account group in the identification time period.
In one embodiment, the computer program when executed by the processor further performs the steps of:
constructing an initial model based on a Sigmoid function;
adjusting the time parameter of the Sigmoid function to adjust the change rate of the initial model;
and when the change rate of the initial model is equal to the preset change rate, taking the initial model as a time model.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and sending the transaction account identified with the abnormality to a transaction platform so as to instruct the transaction platform to monitor the transaction account identified with the abnormality in real time.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An account identification method, the method comprising:
acquiring security data, and determining abnormal securities within a single day according to the security data;
combining candidate abnormal accounts participating in a plurality of abnormal securities simultaneously into a superposition group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities;
acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group;
determining the single-day coincidence rate of the associated account group according to the coincidence group;
determining the coincidence days and the average coincidence rate of the associated account group in an identification time period according to the single-day coincidence rate; the identification time period is a preset number of consecutive transaction days;
inputting the identification time period and the coincidence days into a pre-constructed time model to obtain an output result; the time model construction step comprises: constructing an initial model based on a Sigmoid function; adjusting the time parameter of the Sigmoid function to adjust the change rate of the initial model; when the change rate of the initial model is equal to a preset change rate, taking the initial model as a time model; the output result is a numerical value of a preset multiple between 0 and 1;
determining the association degree between the transaction accounts in the associated account group according to the average coincidence rate and the output result;
and when the relevance is greater than a preset relevance threshold, determining that the transaction account in the relevant account group is abnormal.
2. The method of claim 1, wherein said security data includes a trading volume and unit value fluctuation range for a security, said determining a transaction security within a single day from said security data comprising:
monitoring the transaction quantity and unit value fluctuation range of the certificate in a single day in real time;
and determining the securities of which the transaction quantity is greater than a preset transaction quantity threshold value and/or the unit value fluctuation range is greater than a preset unit value fluctuation range threshold value as the abnormal securities in a single day.
3. The method of claim 1, wherein obtaining target candidate exception accounts from the candidate exception accounts and grouping the target candidate exception accounts into a correlated-account group comprises:
arranging and combining the candidate abnormal accounts to obtain a candidate associated account group;
and randomly grabbing any candidate associated account group, taking the transaction account in the grabbed candidate associated account group as a target candidate abnormal account, and taking the grabbed candidate associated account group as an associated account group.
4. The method of claim 1, wherein determining the single-day coincidence ratio for the associated-account group from the coincidence group comprises:
determining a total number of the coincident groups;
determining a total number of occurrences of the set of associated accounts in the coincident set;
and taking the ratio of the total times of the associated account group appearing in the coincidence group to the total number of the coincidence group as the single-day coincidence rate of the associated account group.
5. The method of claim 1, wherein determining the number of coincident days of the linked account group within an identified time period according to the single-day coincidence ratio comprises:
when the single-day coincidence rate is larger than zero, judging that the associated account group is coincided in the identification time period;
and determining the numerical value corresponding to the number of the single-day coincidence rate larger than zero as the coincidence days of the associated account group in the identification time period.
6. The method according to any one of claims 1 to 5, further comprising:
and sending the transaction account identified with the abnormality to a transaction platform so as to instruct the transaction platform to monitor the transaction account identified with the abnormality in real time.
7. An account identification apparatus, the apparatus comprising:
the acquisition module is used for acquiring the stock data and determining the abnormal movement stocks in the single day according to the stock data;
a combination module for combining candidate exception accounts participating in multiple exception securities simultaneously into a coincident group; the candidate abnormal account is used for representing a trading account participating in the abnormal securities; acquiring target candidate abnormal accounts from the candidate abnormal accounts, and combining the target candidate abnormal accounts into a related account group;
the determining module is used for determining the single-day coincidence rate of the associated account group according to the coincidence group; determining the coincidence days and the average coincidence rate of the associated account group in an identification time period according to the single-day coincidence rate; the identification time period is a preset number of consecutive transaction days;
the input module is used for inputting the identification time period and the coincidence days into a pre-constructed time model to obtain an output result; the time model construction step comprises: constructing an initial model based on a Sigmoid function; adjusting the time parameter of the Sigmoid function to adjust the change rate of the initial model; when the change rate of the initial model is equal to a preset change rate, taking the initial model as a time model; the output result is a numerical value of a preset multiple between 0 and 1;
the determining module is further configured to determine a degree of association between transaction accounts in the associated account group according to the average coincidence rate and the output result;
and the judging module is used for judging that the transaction accounts in the associated account group are abnormal when the association degree is greater than a preset association degree threshold value.
8. The apparatus according to claim 7, wherein the security data comprises a transaction amount and a unit value fluctuation range of the security, and the acquiring module is further configured to monitor the transaction amount and the unit value fluctuation range of the security in real time within a single day; and determining the securities of which the transaction quantity is greater than a preset transaction quantity threshold value and/or the unit value fluctuation range is greater than a preset unit value fluctuation range threshold value as the abnormal securities in a single day.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 6 are implemented by the processor when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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| CN113344062B (en) * | 2021-05-31 | 2024-03-22 | 北京达佳互联信息技术有限公司 | Abnormal account identification method, device, server and storage medium |
| CN113515575A (en) * | 2021-06-16 | 2021-10-19 | 北京格灵深瞳信息技术股份有限公司 | Associated data processing method and device, electronic equipment and storage medium |
| CN113436020B (en) * | 2021-08-26 | 2021-12-10 | 深圳华锐金融技术股份有限公司 | Illegal account identification method and device, computer equipment and storage medium |
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Address after: Room 2301, building 5, Shenzhen new generation industrial park, 136 Zhongkang Road, Meidu community, Meilin street, Futian District, Shenzhen City, Guangdong Province Patentee after: Shenzhen Huarui Distributed Technology Co.,Ltd. Address before: Room 2301, building 5, Shenzhen new generation industrial park, 136 Zhongkang Road, Meidu community, Meilin street, Futian District, Shenzhen City, Guangdong Province Patentee before: SHENZHEN ARCHFORCE FINANCIAL TECHNOLOGY Co.,Ltd. |