CN118505382A - Data processing method and device, electronic equipment and storage medium - Google Patents

Data processing method and device, electronic equipment and storage medium Download PDF

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CN118505382A
CN118505382A CN202410432948.8A CN202410432948A CN118505382A CN 118505382 A CN118505382 A CN 118505382A CN 202410432948 A CN202410432948 A CN 202410432948A CN 118505382 A CN118505382 A CN 118505382A
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transaction
value
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顾河建
潘婧
刘红宝
李晓刚
陈滢
赵金涛
高鹏飞
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China Unionpay Co Ltd
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Abstract

本发明公开了一种数据处理方法、装置、电子设备及存储介质,该方法为:构建以目标业务场景中的交易主体为节点、交易主体间的关联关系为边的图关联结构;基于图关联结构中每个节点所指示的交易主体的业务信息、交易主体间的关联关系和预设异动分析规则,确定每个节点对应的异动值;每个异动值用于指示一个节点所指示交易主体交易的异常波动程度;异常波动程度基于交易主体的自身业务信息以及与其具备关联关系的其他交易主体的业务信息所确定;当第一节点对应的异动值大于预设阈值时,确定第一节点为异动节点,并将第一节点所指示的交易主体确定为异动交易主体。利用图关联结构并融合多种因素确定异动值以确定比较结果,快速、精准地确定异动节点。

The present invention discloses a data processing method, device, electronic device and storage medium. The method comprises: constructing a graph association structure with transaction subjects in a target business scenario as nodes and association relationships between transaction subjects as edges; determining an abnormal value corresponding to each node based on the business information of the transaction subject indicated by each node in the graph association structure, the association relationship between the transaction subjects and a preset abnormal analysis rule; each abnormal value is used to indicate the abnormal fluctuation degree of the transaction of the transaction subject indicated by a node; the abnormal fluctuation degree is determined based on the transaction subject's own business information and the business information of other transaction subjects with which it has an association relationship; when the abnormal value corresponding to the first node is greater than a preset threshold, determining the first node as an abnormal node, and determining the transaction subject indicated by the first node as an abnormal transaction subject. The graph association structure is used to determine the abnormal value by integrating multiple factors to determine the comparison result, and the abnormal node is determined quickly and accurately.

Description

一种数据处理方法、装置、电子设备及存储介质Data processing method, device, electronic device and storage medium

技术领域Technical Field

本发明涉及计算机技术领域,尤其涉及一种数据处理方法、装置、电子设备及存储介质。The present invention relates to the field of computer technology, and in particular to a data processing method, device, electronic equipment and storage medium.

背景技术Background Art

目前,庞大交易网络中,每天都将面临产生海量的交易数据以及由交易主体构成的复杂交易行为关系。因此,当交易数据中出现交易笔数突减等异动表现时,需要确定出可能存在异动表现的交易主体,将该交易主体作为待优化的异动交易主体,从而调整优化对该异动交易主体具体的经营策略,确保交易网络的正常运行。At present, in the huge trading network, there will be a huge amount of trading data and complex trading behavior relationships formed by trading entities every day. Therefore, when there are abnormal performances such as a sudden decrease in the number of transactions in the trading data, it is necessary to identify the trading entity that may have abnormal performance and take it as the abnormal trading entity to be optimized, so as to adjust and optimize the specific business strategy for the abnormal trading entity to ensure the normal operation of the trading network.

因此,如何确定待优化的异动交易主体,成为亟待解决的技术问题。Therefore, how to determine the abnormal transaction subjects to be optimized has become a technical problem that needs to be solved urgently.

发明内容Summary of the invention

本发明实施例中提供了一种数据处理方法、装置、电子设备及存储介质,使得确定待优化的异动交易主体的方法的效率和准确度更高。The embodiments of the present invention provide a data processing method, device, electronic device and storage medium, so that the method for determining the abnormal transaction subject to be optimized is more efficient and accurate.

第一方面,本发明实施例提供一种数据处理方法,所述方法包括:In a first aspect, an embodiment of the present invention provides a data processing method, the method comprising:

构建以目标业务场景中的交易主体为节点、交易主体间的关联关系为边的图关联结构;Construct a graph association structure with transaction entities in the target business scenario as nodes and association relationships between transaction entities as edges;

基于所述图关联结构中每个所述节点所指示的交易主体的业务信息、交易主体间的关联关系和预设异动分析规则,确定每个所述节点对应的异动值;其中,每个所述异动值用于指示一个节点所指示交易主体交易的异常波动程度;所述异常波动程度基于交易主体的自身业务信息以及与其具备关联关系的其他交易主体的业务信息所确定;Based on the business information of the transaction subject indicated by each node in the graph association structure, the association relationship between the transaction subjects and the preset abnormality analysis rules, the abnormality value corresponding to each node is determined; wherein each abnormality value is used to indicate the abnormal fluctuation degree of the transaction of the transaction subject indicated by a node; the abnormal fluctuation degree is determined based on the business information of the transaction subject itself and the business information of other transaction subjects with which it has an association relationship;

当第一节点对应的异动值大于预设阈值时,确定所述第一节点为异动节点,并将所述第一节点所指示的交易主体确定为异动交易主体。When the change value corresponding to the first node is greater than a preset threshold, the first node is determined to be a change node, and the transaction subject indicated by the first node is determined to be a change transaction subject.

在一种可能的实施方式中,基于所述图关联结构中每个所述节点所指示的交易主体的业务信息、交易主体间的关联关系和预设异动分析规则,确定每个所述节点对应的异动值,包括:In a possible implementation, based on the business information of the transaction subject indicated by each node in the graph association structure, the association relationship between the transaction subjects and the preset change analysis rule, determining the change value corresponding to each node includes:

分别基于所述图关联结构中每个所述节点所指示的交易主体的业务信息和所述预设异动分析规则中的第一子规则,确定每个所述节点的波动值;所述第一子规则用于对基于不同量级的业务信息所获得节点的初始波动值进行量级归一化处理;Determine the fluctuation value of each node based on the business information of the transaction subject indicated by each node in the graph association structure and the first sub-rule in the preset abnormality analysis rule; the first sub-rule is used to perform magnitude normalization processing on the initial fluctuation value of the node obtained based on the business information of different magnitudes;

分别基于所述交易主体间的关联关系和所述预设异动分析规则中的第二子规则,确定每个所述节点的业务信息的波动受其他节点影响的影响值;所述第二子规则用于计算下层节点被上层节点的波动所影响的程度,所述下层节点和上层节点基于交易主体间的关联关系所确定;Based on the association relationship between the transaction entities and the second sub-rule in the preset abnormality analysis rule, the influence value of the fluctuation of the business information of each node affected by other nodes is determined; the second sub-rule is used to calculate the degree to which the lower-level node is affected by the fluctuation of the upper-level node, and the lower-level node and the upper-level node are determined based on the association relationship between the transaction entities;

根据每个所述节点的波动值和对应的影响值,确定每个所述节点对应的异动值。According to the fluctuation value and the corresponding impact value of each node, the abnormal change value corresponding to each node is determined.

在一种可能的实施方式中,分别基于所述图关联结构中每个所述节点所指示的交易主体的业务信息和所述预设异动分析规则中的第一子规则,确定每个所述节点的波动值,包括:In a possible implementation, determining the fluctuation value of each node based on the business information of the transaction subject indicated by each node in the graph association structure and the first sub-rule in the preset abnormality analysis rule includes:

分别将每个所述节点所指示交易主体的业务信息输入预设时序归因模型,获得所述预设时序归因模型输出的每个所述节点对应的初始波动值;所述预设时序归因模型基于一个交易主体在预设时间段内的业务信息和预设量级波动评价规则,确定所述交易主体的初始波动值;所述预设量级波动评价规则包括多条映射关系,不同映射关系包括不同量级的业务信息对应的数值范围,以及与所述数值范围对应的初始波动值;The business information of the transaction subject indicated by each node is respectively input into a preset time series attribution model to obtain the initial fluctuation value corresponding to each node output by the preset time series attribution model; the preset time series attribution model determines the initial fluctuation value of a transaction subject based on the business information of the transaction subject within a preset time period and a preset magnitude fluctuation evaluation rule; the preset magnitude fluctuation evaluation rule includes a plurality of mapping relationships, and different mapping relationships include numerical ranges corresponding to business information of different magnitudes, and initial fluctuation values corresponding to the numerical ranges;

对每个所述交易主体对应的初始波动值进行归一化处理,获得每个所述节点的波动值。The initial fluctuation value corresponding to each of the transaction entities is normalized to obtain the fluctuation value of each of the nodes.

在一种可能的实施方式中,所述归一化处理基于以下方式实现:In a possible implementation, the normalization process is implemented based on the following method:

其中,Si表示节点的波动值,ri表示节点对应的初始波动值,K用于表示所述图关联结构中的节点总数量。Among them, Si represents the fluctuation value of the node, ri represents the initial fluctuation value corresponding to the node, and K is used to represent the total number of nodes in the graph association structure.

在一种可能的实施方式中,所述第二子规则,基于以下公式确定:In a possible implementation manner, the second sub-rule is determined based on the following formula:

其中,epij表示节点i相对于节点j的影响值,Δi表示节点i的两个时刻间的业务信息值的变化值,Δj表示节点j的两个时刻间的业务信息值的变化值,eit表示t时刻节点i的业务信息值,eit’表示t’时刻节点i的业务信息值,ejt表示t时刻节点j的业务信息值,ejt’表示t’时刻节点j的业务信息值,节点i和节点j为所述图关联结构中任意节点。Among them, ep ij represents the influence value of node i relative to node j, Δi represents the change value of the business information value of node i between two moments, Δj represents the change value of the business information value of node j between two moments, e it represents the business information value of node i at moment t, e it' represents the business information value of node i at moment t', e jt represents the business information value of node j at moment t, e jt' represents the business information value of node j at moment t', and node i and node j are any nodes in the graph association structure.

在一种可能的实施方式中,根据每个所述节点的波动值和对应的影响值,确定每个所述节点对应的异动值,包括:In a possible implementation, determining the change value corresponding to each node according to the fluctuation value and the corresponding impact value of each node includes:

将与一个所述节点存在关联关系的一个关联节点的影响值,作为一个矩阵因素,构建初始异动迭代矩阵;Taking the influence value of an associated node associated with one of the nodes as a matrix factor, constructing an initial change iteration matrix;

根据所述初始异动迭代矩阵和每个所述节点对应的波动值代入所述预设异动分析规则的第三子规则中,分别确定每个所述节点对应的异动值;所述第三子规则用于结合所有与节点存在关联关系的节点对应的影响值对节点的波动值进行迭代优化。According to the initial change iteration matrix and the fluctuation value corresponding to each node, the third sub-rule of the preset change analysis rule is substituted to determine the change value corresponding to each node respectively; the third sub-rule is used to iteratively optimize the fluctuation value of the node in combination with the influence values corresponding to all nodes that have an associated relationship with the node.

在一种可能的实施方式中,所述第三子规则基于以下公式确定:In a possible implementation manner, the third sub-rule is determined based on the following formula:

Hn←θHn-1+dH n ←θH n-1 +d

其中,Hn表示所有节点的异动值的集合,n用于表征迭代轮数,当n等于1时,H1←θS′+d,S′表示所有节点的波动值的集合,S′={S1,S2,……,SK},epij表示节点i相对于节点j的影响值,节点i和节点j为所述图关联结构中任意节点,Sk表示节点k的波动值,d为非零调节系数,i,j为正整数。Where H n represents the set of fluctuation values of all nodes, n is used to represent the number of iterations, when n is equal to 1, H 1 ←θS′+d, S′ represents the set of fluctuation values of all nodes, S′={S 1 , S 2 , …, S K }, ep ij represents the influence value of node i relative to node j, node i and node j are any nodes in the graph association structure, S k represents the fluctuation value of node k, d is a non-zero adjustment coefficient, and i and j are positive integers.

在一种可能的实施方式中,确定所述第一节点为异动节点之后,所述方法还包括:In a possible implementation manner, after determining that the first node is a changed node, the method further includes:

基于所述图关联结构,筛选包括第一节点的候选路径,将所述候选路径作为异动路径;Based on the graph association structure, screening candidate paths including the first node, and using the candidate paths as change paths;

对所述异动路径进行分析,获得异动分析结果;所述异动分析结果用于指示与所述异动节点关联的影响节点,并基于所述异动节点所对应的业务信息对所述影响节点对应的交易主体的业务进行优化。The change path is analyzed to obtain a change analysis result; the change analysis result is used to indicate an impact node associated with the change node, and based on the business information corresponding to the change node, the business of the transaction subject corresponding to the impact node is optimized.

第二方面,本发明实施例提供一种数据处理装置,所述装置包括:In a second aspect, an embodiment of the present invention provides a data processing device, the device comprising:

构建单元,用于构建以目标业务场景中的交易主体为节点、交易主体间的关联关系为边的图关联结构;A construction unit, used to construct a graph association structure with transaction subjects in the target business scenario as nodes and association relationships between transaction subjects as edges;

确定单元,用于基于所述图关联结构中每个所述节点所指示的交易主体的业务信息、交易主体间的关联关系和预设异动分析规则,确定每个所述节点对应的异动值;其中,每个所述异动值用于指示一个节点所指示交易主体交易的异常波动程度;所述异常波动程度基于交易主体的自身业务信息以及与其具备关联关系的其他交易主体的业务信息所确定;A determination unit, configured to determine an abnormal change value corresponding to each of the nodes based on the business information of the transaction subject indicated by each of the nodes in the graph association structure, the association relationship between the transaction subjects and the preset abnormal change analysis rules; wherein each of the abnormal change values is used to indicate the abnormal fluctuation degree of the transaction of the transaction subject indicated by a node; the abnormal fluctuation degree is determined based on the transaction subject's own business information and the business information of other transaction subjects with which it has an association relationship;

处理单元,用于当第一节点对应的异动值大于预设阈值时,确定所述第一节点为异动节点,并将所述第一节点所指示的交易主体确定为异动交易主体。The processing unit is used to determine that the first node is a changed node when the change value corresponding to the first node is greater than a preset threshold, and determine the transaction subject indicated by the first node as the changed transaction subject.

在一种可能的实施方式中,所述确定单元,具体用于:In a possible implementation manner, the determining unit is specifically configured to:

分别基于所述图关联结构中每个所述节点所指示的交易主体的业务信息和所述预设异动分析规则中的第一子规则,确定每个所述节点的波动值;所述第一子规则用于对基于不同量级的业务信息所获得节点的初始波动值进行量级归一化处理;Determine the fluctuation value of each node based on the business information of the transaction subject indicated by each node in the graph association structure and the first sub-rule in the preset abnormality analysis rule; the first sub-rule is used to perform magnitude normalization processing on the initial fluctuation value of the node obtained based on the business information of different magnitudes;

分别基于所述交易主体间的关联关系和所述预设异动分析规则中的第二子规则,确定每个所述节点的业务信息的波动受其他节点影响的影响值;所述第二子规则用于计算下层节点被上层节点的波动所影响的程度,所述下层节点和上层节点基于交易主体间的关联关系所确定;Based on the association relationship between the transaction entities and the second sub-rule in the preset abnormality analysis rule, the influence value of the fluctuation of the business information of each node affected by other nodes is determined; the second sub-rule is used to calculate the degree to which the lower-level node is affected by the fluctuation of the upper-level node, and the lower-level node and the upper-level node are determined based on the association relationship between the transaction entities;

根据每个所述节点的波动值和对应的影响值,确定每个所述节点对应的异动值。According to the fluctuation value and the corresponding impact value of each node, the abnormal change value corresponding to each node is determined.

在一种可能的实施方式中,所述确定单元,具体用于:In a possible implementation manner, the determining unit is specifically configured to:

分别将每个所述节点所指示交易主体的业务信息输入预设时序归因模型,获得所述预设时序归因模型输出的每个所述节点对应的初始波动值;所述预设时序归因模型基于一个交易主体在预设时间段内的业务信息和预设量级波动评价规则,确定所述交易主体的初始波动值;所述预设量级波动评价规则包括多条映射关系,不同映射关系包括不同量级的业务信息对应的数值范围,以及与所述数值范围对应的初始波动值;The business information of the transaction subject indicated by each node is respectively input into a preset time series attribution model to obtain the initial fluctuation value corresponding to each node output by the preset time series attribution model; the preset time series attribution model determines the initial fluctuation value of a transaction subject based on the business information of the transaction subject within a preset time period and a preset magnitude fluctuation evaluation rule; the preset magnitude fluctuation evaluation rule includes a plurality of mapping relationships, and different mapping relationships include numerical ranges corresponding to business information of different magnitudes, and initial fluctuation values corresponding to the numerical ranges;

对每个所述交易主体对应的初始波动值进行归一化处理,获得每个所述节点的波动值。The initial fluctuation value corresponding to each of the transaction entities is normalized to obtain the fluctuation value of each of the nodes.

在一种可能的实施方式中,所述归一化处理基于以下方式实现:In a possible implementation, the normalization process is implemented based on the following method:

其中,Si表示节点的波动值,ri表示节点对应的初始波动值,K用于表示所述图关联结构中的节点总数量。Among them, Si represents the fluctuation value of the node, ri represents the initial fluctuation value corresponding to the node, and K is used to represent the total number of nodes in the graph association structure.

在一种可能的实施方式中,所述第二子规则,基于以下公式确定:In a possible implementation manner, the second sub-rule is determined based on the following formula:

其中,epij表示节点i相对于节点j的影响值,Δi表示节点i的两个时刻间的业务信息值的变化值,Δj表示节点j的两个时刻间的业务信息值的变化值,eit表示t时刻节点i的业务信息值,eit’表示t’时刻节点i的业务信息值,ejt表示t时刻节点j的业务信息值,ejt’表示t’时刻节点j的业务信息值,节点i和节点j为所述图关联结构中任意节点。Among them, ep ij represents the influence value of node i relative to node j, Δi represents the change value of the business information value of node i between two moments, Δj represents the change value of the business information value of node j between two moments, e it represents the business information value of node i at moment t, e it' represents the business information value of node i at moment t', e jt represents the business information value of node j at moment t, e jt' represents the business information value of node j at moment t', and node i and node j are any nodes in the graph association structure.

在一种可能的实施方式中,所述确定单元,具体用于:In a possible implementation manner, the determining unit is specifically configured to:

将与一个所述节点存在关联关系的一个关联节点的影响值,作为一个矩阵因素,构建初始异动迭代矩阵;Taking the influence value of an associated node associated with one of the nodes as a matrix factor, constructing an initial change iteration matrix;

根据所述初始异动迭代矩阵和每个所述节点对应的波动值代入所述预设异动分析规则的第三子规则中,分别确定每个所述节点对应的异动值;所述第三子规则用于结合所有与节点存在关联关系的节点对应的影响值对节点的波动值进行迭代优化。According to the initial change iteration matrix and the fluctuation value corresponding to each node, the third sub-rule of the preset change analysis rule is substituted to determine the change value corresponding to each node respectively; the third sub-rule is used to iteratively optimize the fluctuation value of the node in combination with the influence values corresponding to all nodes that have an associated relationship with the node.

在一种可能的实施方式中,所述第三子规则基于以下公式确定:In a possible implementation manner, the third sub-rule is determined based on the following formula:

Hn←θHn-1+dH n ←θH n-1 +d

其中,Hn表示所有节点的异动值的集合,n用于表征迭代轮数,当n等于1时,H1←θS′+d,S′表示所有节点的波动值的集合,S′={S1,S2,……,SK},epij表示节点i相对于节点j的影响值,节点i和节点j为所述图关联结构中任意节点,Sk表示节点k的波动值,d为非零调节系数,i,j为正整数。Where H n represents the set of fluctuation values of all nodes, n is used to represent the number of iterations, when n is equal to 1, H 1 ←θS′+d, S′ represents the set of fluctuation values of all nodes, S′={S 1 , S 2 , …, S K }, ep ij represents the influence value of node i relative to node j, node i and node j are any nodes in the graph association structure, S k represents the fluctuation value of node k, d is a non-zero adjustment coefficient, and i and j are positive integers.

在一种可能的实施方式中,确定所述第一节点为异动节点之后,所述装置还包括优化单元,用于:In a possible implementation manner, after determining that the first node is a changed node, the device further includes an optimization unit, which is configured to:

基于所述图关联结构,筛选包括第一节点的候选路径,将所述候选路径作为异动路径;Based on the graph association structure, screening candidate paths including the first node, and using the candidate paths as change paths;

对所述异动路径进行分析,获得异动分析结果;所述异动分析结果用于指示与所述异动节点关联的影响节点,并基于所述异动节点所对应的业务信息对所述影响节点对应的交易主体的业务进行优化。The change path is analyzed to obtain a change analysis result; the change analysis result is used to indicate an impact node associated with the change node, and based on the business information corresponding to the change node, the business of the transaction subject corresponding to the impact node is optimized.

第三方面,本发明实施例提供了一种电子设备,包括至少一个处理器;以及与所述至少一个处理器通信连接的存储器;其中,所述存储器存储有可被所述至少一个处理器执行的指令,所述指令被所述至少一个处理器执行,以使所述至少一个处理器能够执行本发明第一方面实施例提供的任一方法。In a third aspect, an embodiment of the present invention provides an electronic device, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any method provided in the embodiment of the first aspect of the present invention.

第四方面,本发明实施例提供了一种计算机存储介质,其中,所述计算机可读存储介质存储有计算机程序,所述计算机程序用于使计算机执行本发明第一方面实施例提供的任一方法。In a fourth aspect, an embodiment of the present invention provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is used to enable a computer to execute any method provided by the embodiment of the first aspect of the present invention.

第五方面,本发明实施例提供了一种计算机程序产品,所述计算机程序产品包括:计算机程序代码,当所述计算机程序代码在计算机上运行时,使得计算机执行第一方面实施例提供的任一方法。In a fifth aspect, an embodiment of the present invention provides a computer program product, comprising: a computer program code, when the computer program code is run on a computer, the computer executes any one of the methods provided in the embodiment of the first aspect.

本发明有益效果如下:The beneficial effects of the present invention are as follows:

在本发明实施例中,可以构建以目标业务场景中的交易主体为节点、交易主体间的关联关系为边的图关联结构;进一步的,基于图关联结构中每个节点所指示的交易主体的业务信息、交易主体间的关联关系和预设异动分析规则,确定每个节点对应的异动值;其中,每个异动值用于指示一个节点所指示交易主体交易的异常波动程度;异常波动程度基于交易主体的自身业务信息以及与其具备关联关系的其他交易主体的业务信息所确定。可见,本发明实施例中从图关联结构的角度出发,确定节点对应的异动值,且异动值的确定同时考虑了交易主体的自身业务信息以及与其具备关联关系的其他交易主体的业务信息这两个方面,提升了对异动表现的归因分析的准确度。这样,当第一节点对应的异动值大于预设阈值时,确定第一节点为异动节点,并将第一节点所指示的交易主体确定为异动交易主体,从而可以准确且高效的确定导致异动事项的异动交易主体。In an embodiment of the present invention, a graph association structure can be constructed with the transaction subject in the target business scenario as a node and the association relationship between the transaction subjects as an edge; further, based on the business information of the transaction subject indicated by each node in the graph association structure, the association relationship between the transaction subjects and the preset abnormal analysis rules, the abnormal value corresponding to each node is determined; wherein each abnormal value is used to indicate the abnormal fluctuation degree of the transaction of the transaction subject indicated by a node; the abnormal fluctuation degree is determined based on the business information of the transaction subject itself and the business information of other transaction subjects with which it has an association relationship. It can be seen that in the embodiment of the present invention, the abnormal value corresponding to the node is determined from the perspective of the graph association structure, and the determination of the abnormal value simultaneously considers the business information of the transaction subject itself and the business information of other transaction subjects with which it has an association relationship, thereby improving the accuracy of the attribution analysis of the abnormal performance. In this way, when the abnormal value corresponding to the first node is greater than the preset threshold, the first node is determined to be an abnormal node, and the transaction subject indicated by the first node is determined to be an abnormal transaction subject, so that the abnormal transaction subject that causes the abnormal event can be accurately and efficiently determined.

本发明的其它特征和优点将在随后的说明书中阐述,并且,部分地从说明书中变得显而易见,或者通过实施本发明而了解。本发明的目的和其他优点可通过在所写的说明书、权利要求书、以及附图中所特别指出的结构来实现和获得。Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

为了更清楚地说明本发明实施例或相关技术中的技术方案,下面将对实施例或相关技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据提供的附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

图1为本发明实施例中的一种应用场景的一个可选的示意图;FIG1 is an optional schematic diagram of an application scenario in an embodiment of the present invention;

图2为本发明实施例中的一种应用场景的一个可选的示意图;FIG2 is an optional schematic diagram of an application scenario in an embodiment of the present invention;

图3为本发明实施例中的一种数据处理方法流程示意图;FIG3 is a schematic flow chart of a data processing method according to an embodiment of the present invention;

图4为本发明实施例中一种图关联结构的示意图;FIG4 is a schematic diagram of a graph association structure according to an embodiment of the present invention;

图5为本发明实施例中的一种图关联结构中节点的波动值的示意图;FIG5 is a schematic diagram of a fluctuation value of a node in a graph association structure according to an embodiment of the present invention;

图6为本发明实施例中的一种图关联结构中节点的影响值的示意图;FIG6 is a schematic diagram of an influence value of a node in a graph association structure according to an embodiment of the present invention;

图7为本发明实施例中的一种计算节点对应的异动值的示意图;FIG7 is a schematic diagram of a calculation node corresponding to an abnormal value in an embodiment of the present invention;

图8为本发明实施例中的一种异动路径的示意图;FIG8 is a schematic diagram of an abnormal path in an embodiment of the present invention;

图9为本发明实施例中的一种数据处理装置的组成结构示意图;FIG9 is a schematic diagram of the structure of a data processing device according to an embodiment of the present invention;

图10为本发明实施例中的一种电子设备的一种结构示意图;FIG10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;

图11为本发明实施例中的一种电子设备的又一种结构示意图。FIG. 11 is another schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

具体实施方式DETAILED DESCRIPTION

为使本发明的目的、技术方案和优点更加清楚明白,下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。在不冲突的情况下,本发明中的实施例及实施例中的特征可以相互任意组合。并且,虽然在流程图中示出了逻辑顺序,但是在某些情况下,可以以不同于此处的顺序执行所示出或描述的步骤。In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the absence of conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other arbitrarily. In addition, although the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in an order different from that here.

本发明的说明书和权利要求书及上述附图中的术语,第一”、“第二”等是用于区别类似的对象,而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换,以便这里描述的本发明的实施例能够在除了这里图示或描述的那些以外的顺序实施。The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

本发明中所用的词语“示例性”的意思为“用作例子、实施例或说明性”。作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。The word “exemplary” is used herein to mean “serving as an example, example, or illustration.” Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

以下结合附图对本发明的示范性实施例做出说明,其中包括本发明实施例的各种细节以助于理解,应当将它们认为仅仅是示范性的。因此,本领域普通技术人员应当认识到,可以对这里描述的实施例做出各种改变和修改,而不会背离本发明公开的范围。同样,为了清楚和简明,以下的描述中省略了对公知功能和结构的描述。需要说明的是,在本发明实施例中,可能提及某些软件、组件、模型等业界已有方案,应当将它们认为是示范性的,其目的仅仅是为了说明本发明技术方案实施中的可行性,但并不意味着申请人已经或者必然用到了该方案。The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, which includes various details of the embodiments of the present invention to facilitate understanding, and they should be considered as merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description. It should be noted that in the embodiments of the present invention, certain software, components, models and other existing solutions in the industry may be mentioned, which should be considered as exemplary, and their purpose is only to illustrate the feasibility of the implementation of the technical solution of the present invention, but it does not mean that the applicant has or will necessarily use the solution.

本发明技术方案中,对数据的采集、传播、使用等,均符合国家相关法律法规要求。In the technical solution of the present invention, the collection, dissemination, and use of data are in compliance with the requirements of relevant national laws and regulations.

目前,如前所述,如何确定待优化的异动交易主体,成为亟待解决的技术问题。At present, as mentioned above, how to determine the abnormal transaction subjects to be optimized has become a technical problem that needs to be solved urgently.

鉴于此,本发明实施例提供一种数据处理方法,通过该方法,可以构建以目标业务场景中的交易主体为节点、交易主体间的关联关系为边的图关联结构;进一步的,基于图关联结构中每个节点所指示的交易主体的业务信息、交易主体间的关联关系和预设异动分析规则,确定每个节点对应的异动值;其中,每个异动值用于指示一个节点所指示交易主体交易的异常波动程度;异常波动程度基于交易主体的自身业务信息以及与其具备关联关系的其他交易主体的业务信息所确定。可见,本发明实施例中从图关联结构的角度出发,确定节点对应的异动值,且异动值的确定同时考虑了交易主体的自身业务信息以及与其具备关联关系的其他交易主体的业务信息这两个方面,提升了对异动表现的归因分析的准确度。这样,当第一节点对应的异动值大于预设阈值时,确定第一节点为异动节点,并将第一节点所指示的交易主体确定为异动交易主体,从而可以准确且高效的确定导致异动事项的异动交易主体。In view of this, the embodiment of the present invention provides a data processing method, through which a graph association structure can be constructed with the transaction subject in the target business scenario as a node and the association relationship between the transaction subjects as an edge; further, based on the business information of the transaction subject indicated by each node in the graph association structure, the association relationship between the transaction subjects and the preset abnormal analysis rules, the abnormal value corresponding to each node is determined; wherein each abnormal value is used to indicate the abnormal fluctuation degree of the transaction of the transaction subject indicated by a node; the abnormal fluctuation degree is determined based on the business information of the transaction subject itself and the business information of other transaction subjects with which it has an association relationship. It can be seen that in the embodiment of the present invention, starting from the perspective of the graph association structure, the abnormal value corresponding to the node is determined, and the determination of the abnormal value simultaneously considers the business information of the transaction subject itself and the business information of other transaction subjects with which it has an association relationship, thereby improving the accuracy of the attribution analysis of the abnormal performance. In this way, when the abnormal value corresponding to the first node is greater than the preset threshold, the first node is determined to be an abnormal node, and the transaction subject indicated by the first node is determined to be an abnormal transaction subject, so that the abnormal transaction subject causing the abnormal event can be accurately and efficiently determined.

在介绍完本发明实施例的设计思想之后,下面对本发明实施例的技术方案能够适用的应用场景做一些简单介绍,需要说明的是,以下介绍的应用场景仅用于说明本发明实施例而非限定。在具体实施过程中,可以根据实际需要灵活地应用本发明实施例提供的技术方案。After introducing the design concept of the embodiment of the present invention, the following briefly introduces the application scenarios to which the technical solution of the embodiment of the present invention can be applied. It should be noted that the application scenarios introduced below are only used to illustrate the embodiment of the present invention and are not limited. In the specific implementation process, the technical solution provided by the embodiment of the present invention can be flexibly applied according to actual needs.

在本发明实施例中,本发明实施例提供的数据处理方法可以应用于任何需要进行异动归因分析的业务场景,例如对A业务的软件系统中各个功能模块进行异动归因分析的业务场景,对网络平台中各个功能单元进行异动归因分析的业务场景等,又例如对金融领域的支付系统中各个主体进行异动归因分析的业务场景、金融领域的借贷系统中各个主体进行异动归因分析的业务场景,制造领域的制造系统中各个主体进行异动归因分析的业务场景、管理领域的企业管理系统中各个主体进行异动归因分析的业务场景等,本发明实施例中对此不做限定。In an embodiment of the present invention, the data processing method provided by the embodiment of the present invention can be applied to any business scenario that requires abnormal attribution analysis, such as a business scenario in which abnormal attribution analysis is performed on each functional module in the software system of business A, a business scenario in which abnormal attribution analysis is performed on each functional unit in a network platform, etc. For example, a business scenario in which abnormal attribution analysis is performed on each subject in a payment system in the financial field, a business scenario in which abnormal attribution analysis is performed on each subject in a lending system in the financial field, a business scenario in which abnormal attribution analysis is performed on each subject in a manufacturing system in the manufacturing field, a business scenario in which abnormal attribution analysis is performed on each subject in an enterprise management system in the management field, etc., and this is not limited in the embodiment of the present invention.

请参阅图1所示,图1为本发明实施例的技术方案能够适用的一种应用场景。在该场景示意图中,包括多个收单机构对应的设备101、多个支付子公司对应的设备102、支付总公司对应的设备103以及用于对支付系统进行异动归因分析的分析系统对应的设备104。需要说明的是,收单机构、支付子公司以及支付总公司都可以称作交易主体。Please refer to Figure 1, which is an application scenario to which the technical solution of the embodiment of the present invention can be applied. In the scenario diagram, there are devices 101 corresponding to multiple acquiring institutions, devices 102 corresponding to multiple payment subsidiaries, devices 103 corresponding to the payment head office, and devices 104 corresponding to the analysis system for performing abnormal attribution analysis on the payment system. It should be noted that acquiring institutions, payment subsidiaries and payment head offices can all be referred to as transaction entities.

其中,每个分析系统对应的设备104均可以包括一个或多个处理器1041、存储器1042以及与设备交互的I/O接口1043等。以及,设备101、设备102设备103以及设备104之间,以及各个设备之间均可以通过一个或者多个网络105进行直接或间接的通信连接。The device 104 corresponding to each analysis system may include one or more processors 1041, a memory 1042, and an I/O interface 1043 for interacting with the device, etc. Furthermore, the devices 101, 102, 103, and 104, as well as the devices themselves, may be directly or indirectly connected to each other through one or more networks 105.

需要说明的是,在本发明实施例中,使用设备101-1、设备101-2、……、设备101-n的对象1、对象2、……、对象n,n为正整数,可以同时发起支付交易,当然,也可以是对象1先发起交易,对象2再发起交易,本发明实施例中对此不做限制,从而分析系统可以接收大量并发的支付交易数据,对支付交易数据进行异动归因分析,即确定支付交易数据中是否存在异动表现。其中,对支付交易数据进行异动归因分析的具体方案可以参见本发明实施例提供的数据处理方法,后文详细描述,这里不在赘述。It should be noted that in the embodiment of the present invention, using device 101-1, device 101-2, ..., device 101-n, object 1, object 2, ..., object n, where n is a positive integer, payment transactions can be initiated at the same time. Of course, object 1 can initiate a transaction first, and object 2 can initiate a transaction later. The embodiment of the present invention does not limit this, so that the analysis system can receive a large amount of concurrent payment transaction data, and perform abnormal attribution analysis on the payment transaction data, that is, determine whether there is abnormal performance in the payment transaction data. Among them, the specific scheme for performing abnormal attribution analysis on payment transaction data can refer to the data processing method provided in the embodiment of the present invention, which is described in detail later and will not be repeated here.

又例如,请参阅图2所示,图2为本发明实施例的技术方案能够适用的另一种应用场景,在该场景示意图中,包括多个售卖机构对应的设备201、多个制造子公司对应的设备202、制造总公司对应的设备203以及用于对售卖系统进行异动归因分析的分析系统对应的设备104。需要说明的是,售卖机构、制造子公司以及制造总公司都可以称作交易主体。For another example, please refer to FIG. 2, which is another application scenario to which the technical solution of the embodiment of the present invention can be applied. In the scenario diagram, there are devices 201 corresponding to multiple sales organizations, devices 202 corresponding to multiple manufacturing subsidiaries, devices 203 corresponding to the manufacturing head office, and devices 104 corresponding to the analysis system for performing attribution analysis on the sales system. It should be noted that the sales organizations, manufacturing subsidiaries, and the manufacturing head office can all be referred to as transaction entities.

其中,每个分析系统对应的设备104均可以包括一个或多个处理器1041、存储器1042以及与设备交互的I/O接口1043等。以及,设备201、设备202设备203以及设备104之间,以及各个设备之间均可以通过一个或者多个网络105进行直接或间接的通信连接。The device 104 corresponding to each analysis system may include one or more processors 1041, a memory 1042, and an I/O interface 1043 for interacting with the device, etc. Furthermore, the devices 201, 202, 203, and 104, as well as the devices themselves, may be directly or indirectly connected to each other through one or more networks 105.

需要说明的是,在本发明实施例中,使用设备201-1、设备201-2、……、设备201-n的售卖机构1、售卖机构2、……、售卖机构n,n为正整数,可以同时发起售卖交易,当然,也可以是售卖机构1先发起售卖交易,售卖机构2再发起售卖交易,本发明实施例中对此不做限制,从而分析系统可以接收大量并发的售卖交易数据,对售卖交易数据进行异动归因分析,即确定售卖交易数据中是否存在异动表现。其中,对售卖交易数据进行异动归因分析的具体方案可以参见本发明实施例提供的数据处理方法,后文详细描述,这里不在赘述。It should be noted that in the embodiment of the present invention, the sales agency 1, sales agency 2, ..., sales agency n, where n is a positive integer, using devices 201-1, 201-2, ..., and 201-n can initiate sales transactions at the same time. Of course, sales agency 1 can initiate sales transactions first, and then sales agency 2 can initiate sales transactions. There is no restriction on this in the embodiment of the present invention, so that the analysis system can receive a large amount of concurrent sales transaction data and perform abnormal attribution analysis on the sales transaction data, that is, determine whether there is abnormal performance in the sales transaction data. Among them, the specific scheme for performing abnormal attribution analysis on sales transaction data can refer to the data processing method provided in the embodiment of the present invention, which is described in detail later and will not be repeated here.

其中,图1、图2中的各个设备可以为手机、平板电脑(PAD)、个人计算机(Personalcomputer,PC)、智能电视、智能手表、智能音箱、智能车载设备以及可穿戴设备等,但并不局限于此,这些设备可以具有登录并使用学习网站的功能。Among them, the devices in Figures 1 and 2 can be mobile phones, tablet computers (PADs), personal computers (PCs), smart TVs, smart watches, smart speakers, smart car devices, and wearable devices, but are not limited to these. These devices can have the function of logging in to and using the learning website.

以及,图1、图2中设备也可以是独立的物理服务器,也可以是多个物理服务器构成的服务器集群或者分布式系统,还可以是以提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、中间件服务、域名服务、安全服务、内容分发网络(Content DeliveryNetwork,CDN)、以及大数据和人工智能平台等基础云计算服务的云服务器,但并不局限于此。In addition, the device in Figures 1 and 2 can also be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), as well as big data and artificial intelligence platforms, but is not limited to these.

该网络105可以是有线网络,也可以是无线网络,例如无线网络可以是移动蜂窝网络,或者可以是无线保真(Wireless-Fidelity,WIFI)网络,当然还可以是其他可能的网络,本发明实施例对此不做限制。The network 105 may be a wired network or a wireless network. For example, the wireless network may be a mobile cellular network or a Wireless-Fidelity (WIFI) network. Of course, it may also be other possible networks, which are not limited in the embodiments of the present invention.

当然,本发明实施例提供的方法并不限用于图1和图2所示的应用场景中,还可以用于其他可能的应用场景,本发明实施例并不进行限制。Of course, the method provided in the embodiment of the present invention is not limited to the application scenarios shown in Figures 1 and 2, and can also be used in other possible application scenarios, which are not limited by the embodiment of the present invention.

为进一步说明本发明实施例提供的技术方案,下面结合附图以及具体实施方式对此进行详细的说明。虽然本发明实施例提供了如下述实施例或附图所示的方法操作步骤,但基于常规或者无需创造性的劳动在所述方法中可以包括更多或者更少的操作步骤。在逻辑上不存在必要因果关系的步骤中,这些步骤的执行顺序不限于本发明实施例提供的执行顺序。所述方法在实际的处理过程中或者装置执行时,可按照实施例或者附图所示的方法顺序执行或者并行执行。To further illustrate the technical solution provided by the embodiment of the present invention, the following is a detailed description of this in conjunction with the accompanying drawings and specific implementation methods. Although the embodiment of the present invention provides the method operation steps shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on routine or no creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiment of the present invention. The method may be executed in the order of the method shown in the embodiment or drawings or in parallel during the actual processing process or when the device is executed.

请参见图3,图3为本发明实施例中的一种数据处理方法流程示意图,其中,数据处理方法可以由前述图1、2中的设备104执行,且设备104上部署有异动归因分析的分析系统。Please refer to Figure 3, which is a flow chart of a data processing method in an embodiment of the present invention, wherein the data processing method can be executed by the device 104 in the aforementioned Figures 1 and 2, and an analysis system for abnormal attribution analysis is deployed on the device 104.

步骤301:构建以目标业务场景中的交易主体为节点、交易主体间的关联关系为边的图关联结构。Step 301: Construct a graph association structure with transaction entities in the target business scenario as nodes and association relationships between transaction entities as edges.

在本发明实施例中,电子设备可以先确定目标业务场景,后文中以目标业务场景为金融领域的支付系统的业务场景为例进行说明。其中,金融领域的支付系统的业务场景包括支付总公司、多个支付子公司以及多个收单机构,支付总公司、支付子公司以及收单机构可以理解为产生交易数据的主体,为了便于描述,后文中将该主体称作交易主体。然后,可以基于业务信息确定支付总公司、多个支付子公司以及多个收单机构之间的关联关系,例如,业务信息为商户数,则可以确定支付总公司和支付子公司存在商户数的关联关系,支付子公司和收单机构间存在商户数的关联关系。又例如,业务信息为交易笔数,则可以确定支付总公司和支付子公司存在交易笔数的关联关系,支付子公司和收单机构间存在交易笔数的关联关系。In an embodiment of the present invention, the electronic device may first determine the target business scenario, and the target business scenario is a business scenario of a payment system in the financial field as an example for explanation in the following text. Among them, the business scenario of the payment system in the financial field includes a payment company, multiple payment subsidiaries, and multiple acquiring institutions. The payment company, the payment subsidiary, and the acquiring institution can be understood as the subject that generates transaction data. For the sake of convenience of description, the subject is referred to as the transaction subject in the following text. Then, the association relationship between the payment company, the multiple payment subsidiaries, and the multiple acquiring institutions can be determined based on the business information. For example, if the business information is the number of merchants, it can be determined that there is an association relationship between the payment company and the payment subsidiary in terms of the number of merchants, and there is an association relationship between the payment subsidiary and the acquiring institution in terms of the number of merchants. For another example, if the business information is the number of transactions, it can be determined that there is an association relationship between the payment company and the payment subsidiary in terms of the number of transactions, and there is an association relationship between the payment subsidiary and the acquiring institution in terms of the number of transactions.

进一步的,可以构建以目标业务场景中的交易主体为节点、交易主体间的关联关系为边的图关联结构。Furthermore, a graph association structure can be constructed with the transaction subjects in the target business scenario as nodes and the association relationships between the transaction subjects as edges.

例如,请参见图4,图4为本发明实施例提供的一种图关联结构的示意图。其中,图4中的四边形图案用于表征交易主体为收单机构的节点、五边形图案用于表征交易主体为支付子公司的节点、六边形图案用于表征交易主体为支付总公司的节点。For example, please refer to Figure 4, which is a schematic diagram of a graph association structure provided by an embodiment of the present invention. In Figure 4, the quadrilateral pattern is used to represent the node of the acquiring institution as the transaction subject, the pentagonal pattern is used to represent the node of the payment subsidiary as the transaction subject, and the hexagonal pattern is used to represent the node of the payment head office as the transaction subject.

可选的,图关联结构还可以以数学的形式化表达,具体如下:Optionally, the graph association structure can also be expressed in a mathematical form, as follows:

G={V,ε}G={V,ε}

其中,V表示图关联结构包括的节点集合,ε表示图关联结构的边集合。Among them, V represents the node set included in the graph association structure, and ε represents the edge set of the graph association structure.

步骤302:基于图关联结构中每个节点所指示的交易主体的业务信息、交易主体间的关联关系和预设异动分析规则,确定每个节点对应的异动值;其中,每个异动值用于指示一个节点所指示交易主体交易的异常波动程度;异常波动程度基于交易主体的自身业务信息以及与其具备关联关系的其他交易主体的业务信息所确定。Step 302: Based on the business information of the transaction subject indicated by each node in the graph association structure, the association relationship between the transaction subjects and the preset abnormality analysis rules, determine the abnormality value corresponding to each node; wherein each abnormality value is used to indicate the degree of abnormal fluctuation of the transaction of the transaction subject indicated by a node; the abnormal fluctuation degree is determined based on the transaction subject's own business information and the business information of other transaction subjects with which it has an association relationship.

在本发明实施例中,电子设备可以采用但不限于以下步骤每个节点对应的异动值:In the embodiment of the present invention, the electronic device may adopt but is not limited to the following steps to obtain the change value corresponding to each node:

步骤A:分别基于图关联结构中每个节点所指示的交易主体的业务信息和预设异动分析规则中的第一子规则,确定每个节点的波动值;第一子规则用于对基于不同量级的业务信息所获得节点的初始波动值进行量级归一化处理。Step A: Determine the fluctuation value of each node based on the business information of the transaction subject indicated by each node in the graph association structure and the first sub-rule in the preset abnormal change analysis rule; the first sub-rule is used to normalize the initial fluctuation value of the node obtained based on business information of different magnitudes.

在本发明实施例中,电子设备可以分别将每个节点所指示交易主体的业务信息输入预设时序归因模型,获得预设时序归因模型输出的每个节点对应的初始波动值;其中,预设时序归因模型基于一个交易主体在预设时间段内的业务信息和预设量级波动评价规则,确定交易主体的初始波动值。其中,预设量级波动评价规则包括多条映射关系,不同映射关系包括不同量级的业务信息对应的数值范围,以及与数值范围对应的初始波动值。In an embodiment of the present invention, the electronic device can input the business information of the transaction subject indicated by each node into a preset time series attribution model to obtain the initial fluctuation value corresponding to each node output by the preset time series attribution model; wherein the preset time series attribution model determines the initial fluctuation value of the transaction subject based on the business information of a transaction subject within a preset time period and a preset magnitude fluctuation evaluation rule. wherein the preset magnitude fluctuation evaluation rule includes multiple mapping relationships, and different mapping relationships include numerical ranges corresponding to business information of different magnitudes, and initial fluctuation values corresponding to the numerical ranges.

例如,假设节点为收单机构A,且业务信息为收单机构A在2021-2023这2年内的按月统计的交易笔数,则向预设时序归因模型输入的是收单机构A在2021-2023这2年内的按月统计的交易笔数为100,从而可以基于波动评价规则,确定该交易笔数对应的数值范围为100-200,且数值范围为100-200对应的初始波动值为120%,则确定输出该节点在交易笔数量级的初始波动值为120%。For example, assuming that the node is acquiring institution A, and the business information is the number of transactions counted monthly by acquiring institution A in the two years 2021-2023, the input to the preset time series attribution model is that the number of transactions counted monthly by acquiring institution A in the two years 2021-2023 is 100, so that based on the fluctuation evaluation rule, it can be determined that the numerical range corresponding to the number of transactions is 100-200, and the initial fluctuation value corresponding to the numerical range of 100-200 is 120%, then the output initial fluctuation value of the node in the order of transactions is determined to be 120%.

此外,由于不同业务值的尺度不同,例如:交易笔数的数量级别为上亿,商户数的数量级别为千万,即两个业务值的量级存在巨大差异,因此,可以对每个交易主体对应的初始波动值进行归一化处理,从而可以统一评估量级。也就是说,可以对每个交易主体对应的初始波动值进行归一化处理,获得每个节点的波动值。波动值用于指示节点所指示的交易主体的业务信息的值(后文中为了描述方便,称作业务信息值)出现波动的情况。In addition, due to the different scales of different business values, for example, the number of transactions is at the level of hundreds of millions, while the number of merchants is at the level of tens of millions, that is, there is a huge difference in the magnitude of the two business values. Therefore, the initial fluctuation value corresponding to each transaction subject can be normalized, so that the magnitude can be uniformly evaluated. In other words, the initial fluctuation value corresponding to each transaction subject can be normalized to obtain the fluctuation value of each node. The fluctuation value is used to indicate the value of the business information of the transaction subject indicated by the node (hereinafter referred to as the business information value for the convenience of description) fluctuates.

可选的,归一化处理基于以下方式实现:Optionally, normalization is performed based on the following method:

其中,Si表示节点的波动值,ri表示节点对应的初始波动值,K用于表示图关联结构中的节点总数量。Among them, Si represents the fluctuation value of the node, ri represents the initial fluctuation value corresponding to the node, and K is used to represent the total number of nodes in the graph association structure.

例如,请参见图5,图5为本发明实施例提供的一种图关联结构中节点的波动值的示意图。其中,图5中的四边形图案用于表征交易主体为收单机构的节点、五边形图案用于表征交易主体为支付子公司的节点、六边形图案用于表征交易主体为支付总公司的节点。其中,四边形图案所表征的交易主体为收单机构的节点的波动值为0.1,0.1=120%/1200%,其中,120%是交易主体为收单机构的业务信息的同比增幅值,1200%是除交易主体为总公司的节点外的所有节点指示的交易主体的业务信息的同比增幅值总和。For example, please refer to Figure 5, which is a schematic diagram of the fluctuation value of a node in a graph association structure provided by an embodiment of the present invention. Among them, the quadrilateral pattern in Figure 5 is used to represent the node whose transaction subject is the acquiring institution, the pentagonal pattern is used to represent the node whose transaction subject is the payment subsidiary, and the hexagonal pattern is used to represent the node whose transaction subject is the payment head office. Among them, the fluctuation value of the node whose transaction subject is the acquiring institution represented by the quadrilateral pattern is 0.1, 0.1 = 120% / 1200%, wherein 120% is the year-on-year increase value of the business information of the transaction subject being the acquiring institution, and 1200% is the sum of the year-on-year increase values of the business information of the transaction subject indicated by all nodes except the node whose transaction subject is the head office.

步骤B:分别基于交易主体间的关联关系和预设异动分析规则中的第二子规则,确定每个节点的业务信息的波动受其他节点影响的影响值;第二子规则用于计算下层节点被上层节点的波动所影响的程度,下层节点和上层节点基于交易主体间的关联关系所确定;Step B: Based on the association relationship between the transaction subjects and the second sub-rule in the preset abnormality analysis rule, determine the impact value of the fluctuation of the business information of each node affected by other nodes; the second sub-rule is used to calculate the degree to which the lower-level node is affected by the fluctuation of the upper-level node, and the lower-level node and the upper-level node are determined based on the association relationship between the transaction subjects;

在本发明实施例中,第二子规则,基于以下公式确定:In the embodiment of the present invention, the second sub-rule is determined based on the following formula:

其中,epij表示节点i相对于节点j的影响值,Δi表示节点i的两个时刻间的业务信息值的变化值,Δj表示节点j的两个时刻间的业务信息值的变化值,eit表示t时刻节点i的业务信息值,eit’表示t’时刻节点i的业务信息值,ejt表示t时刻节点j的业务信息值,ejt’表示t’时刻节点j的业务信息值,节点i和节点j为图关联结构中任意节点。Among them, ep ij represents the influence value of node i relative to node j, Δi represents the change value of the business information value of node i between two time points, Δj represents the change value of the business information value of node j between two time points, e it represents the business information value of node i at time t, e it' represents the business information value of node i at time t', e jt represents the business information value of node j at time t, e jt' represents the business information value of node j at time t', and node i and node j are arbitrary nodes in the graph association structure.

在本发明实施例中,当确定第二子规则后,可以基于交易主体间的关联关系和前述的第二子规则,确定每个节点的业务信息的波动受其他节点影响的影响值。In the embodiment of the present invention, after the second sub-rule is determined, the impact value of the fluctuation of the business information of each node affected by other nodes can be determined based on the association relationship between the transaction entities and the aforementioned second sub-rule.

例如,请参见图6,图6为本发明实施例提供的一种图关联结构中节点的影响值的示意图。其中,图6中的四边形图案用于表征交易主体为收单机构的节点、五边形图案用于表征交易主体为支付子公司的节点、六边形图案用于表征交易主体为支付总公司的节点。其中,五边形图案所表征的交易主体为子支付公司的节点的影响值为0.2,0.2=3000万/1.5亿,其中,3000万是交易主体为支付子公司的交易笔数变化值,1.5亿是交易主体为总公司的交易笔数变化值。For example, please refer to Figure 6, which is a schematic diagram of the influence value of a node in a graph association structure provided by an embodiment of the present invention. Among them, the quadrilateral pattern in Figure 6 is used to represent the node whose transaction subject is the acquiring institution, the pentagonal pattern is used to represent the node whose transaction subject is the payment subsidiary, and the hexagonal pattern is used to represent the node whose transaction subject is the payment head office. Among them, the influence value of the node whose transaction subject is the subsidiary payment company represented by the pentagonal pattern is 0.2, 0.2 = 30 million/150 million, among which 30 million is the change value of the number of transactions whose transaction subject is the payment subsidiary, and 150 million is the change value of the number of transactions whose transaction subject is the head office.

步骤C:根据每个节点的波动值和对应的影响值,确定每个节点对应的异动值。Step C: Determine the corresponding abnormal value of each node according to the fluctuation value of each node and the corresponding impact value.

在本发明实施例中,电子设备可以将与一个所述节点存在关联关系的一个关联节点的影响值,作为构建初始异动迭代矩阵。例如,初始异动迭代矩阵可以表示为:In the embodiment of the present invention, the electronic device may use the influence value of an associated node that has an associated relationship with the node as the basis for constructing the initial change iteration matrix. For example, the initial change iteration matrix may be expressed as:

其中,θ表示初始异动迭代矩阵,θi,j为θ中的一个矩阵因素,epij表示节点i相对于节点j的影响值,节点i和节点j为图关联结构中任意节点,即i,j∈K。其中,当节点i和节点j不存在关联关系时,Among them, θ represents the initial altered iteration matrix, θ i,j is a matrix factor in θ, ep ij represents the influence value of node i relative to node j. Node i and node j are any nodes in the graph association structure, that is, i, j∈K. Among them, when there is no association relationship between node i and node j,

进一步的,电子设备可以根据初始异动迭代矩阵和每个节点对应的波动值代入预设异动分析规则的第三子规则中,分别确定每个节点对应的异动值;第三子规则用于结合所有与节点存在关联关系的节点对应的影响值对节点的波动值进行迭代优化。Furthermore, the electronic device can substitute the initial abnormality iteration matrix and the fluctuation value corresponding to each node into the third sub-rule of the preset abnormality analysis rule to determine the abnormality value corresponding to each node respectively; the third sub-rule is used to iteratively optimize the fluctuation value of the node in combination with the influence values corresponding to all nodes that have an associated relationship with the node.

可选的,第三子规则基于以下公式确定:Optionally, the third sub-rule is determined based on the following formula:

H1←θS′+dH 1 ←θS′+d

H2←θH1+dH 2 ←θH 1 +d

......

Hn←θHn-1+dH n ←θH n-1 +d

其中,H表示迭代过程的中间变量,Hn表示第n轮迭代结果,n的大小根据Hn和Hn-1间的欧式距离大小决定,一般当Hn和Hn-1间的欧式距离<0.0001即可停止迭代。θ表示初始异动迭代矩阵,S′表示所有节点的波动值的集合,S′={S1,S2,……,SK},d为非零调节系数,i,j为正整数。Among them, H represents the intermediate variable of the iteration process, Hn represents the result of the nth round of iteration, and the size of n is determined by the Euclidean distance between Hn and Hn -1 . Generally, the iteration can be stopped when the Euclidean distance between Hn and Hn -1 is less than 0.0001. θ represents the initial abnormal iteration matrix, S′ represents the set of fluctuation values of all nodes, S′={S 1 , S 2 , …, S K }, d is a non-zero adjustment coefficient, and i and j are positive integers.

最终节点的异动值分布为Hn其中节点1的异动值为即向量Hn的第一个元素。The final node abnormal value distribution is H n is The change value of node 1 is That is, the first element of the vector H n .

可选的,电子设备还可以对所有的节点对应的异动值从大到小进行排序,获得排序后的所有的节点的异动值。例如,请参见图7,图7为本发明实施例提供的一种计算节点对应的异动值的示意图。其中,图7中的四边形图案用于表征交易主体为收单机构的节点、五边形图案用于表征交易主体为支付子公司的节点、六边形图案用于表征交易主体为支付总公司的节点。Optionally, the electronic device can also sort the abnormal values corresponding to all nodes from large to small to obtain the abnormal values of all nodes after sorting. For example, please refer to Figure 7, which is a schematic diagram of a calculation of abnormal values corresponding to nodes provided by an embodiment of the present invention. Among them, the quadrilateral pattern in Figure 7 is used to represent the node whose transaction subject is the acquiring institution, the pentagonal pattern is used to represent the node whose transaction subject is the payment subsidiary, and the hexagonal pattern is used to represent the node whose transaction subject is the payment head office.

步骤303:当第一节点对应的异动值大于预设阈值时,确定第一节点为异动节点,并将第一节点所指示的交易主体确定为异动交易主体。Step 303: When the change value corresponding to the first node is greater than a preset threshold, the first node is determined to be a change node, and the transaction subject indicated by the first node is determined to be a change transaction subject.

在本发明实施例中,当获得所有节点对应的异动值之后,可以根据历史经验确定预设阈值,该预设阈值例如为0.5。还可以基于获得的所有节点对应的异动值的排序信息,将排序前4个的中间值作为预设阈值,当然,还可以是基于其他方式确定预设阈值,本发明实施例中对此不做限定。In the embodiment of the present invention, after obtaining the change values corresponding to all nodes, a preset threshold value may be determined based on historical experience, and the preset threshold value may be, for example, 0.5. The middle value of the first four values in the sorting may also be used as the preset threshold value based on the sorting information of the change values corresponding to all nodes obtained. Of course, the preset threshold value may also be determined based on other methods, which are not limited in the embodiment of the present invention.

在本发明实施例中,当电子设备确定预设阈值之后,可以筛选出所有节点对应的异动值大于预设阈值的第一节点,并将第一节点作为异动节点。也就是说,第一节点可以是一个节点,也可以是一群节点。In the embodiment of the present invention, after the electronic device determines the preset threshold, it can filter out the first node whose change value corresponding to all nodes is greater than the preset threshold, and use the first node as the change node. That is, the first node can be a node or a group of nodes.

在本发明实施例中,当确定异动节点之后,还可以将异动节点放回到构建的图关联结构进行还原,从而可以得到清晰的关联追溯路径,以提升交易主体归因分析的便利性。In an embodiment of the present invention, after the changed nodes are determined, the changed nodes can be put back into the constructed graph association structure for restoration, so that a clear association tracing path can be obtained to improve the convenience of attribution analysis of transaction entities.

在本发明实施例中,电子设备可以基于图关联结构,筛选包括第一节点的候选路径,将候选路径作为异动路径;对异动路径进行分析,获得异动分析结果;异动分析结果用于指示与异动节点关联的影响节点,并基于异动节点所对应的业务信息对影响节点对应的交易主体的业务进行优化。In an embodiment of the present invention, the electronic device can screen candidate paths including the first node based on the graph association structure, and use the candidate paths as abnormal paths; analyze the abnormal paths to obtain abnormal analysis results; the abnormal analysis results are used to indicate the influencing nodes associated with the abnormal nodes, and optimize the business of the transaction subject corresponding to the influencing nodes based on the business information corresponding to the abnormal nodes.

例如,参见图8所示,图8为本发明实施例提供的一种异动路径的示意图。其中,图8中的四边形图案用于表征交易主体为收单机构的节点、五边形图案用于表征交易主体为支付子公司的节点、六边形图案用于表征交易主体为支付总公司的节点。图8中被完全填充的四边形图案所表征的节点为第一节点,且该节点与被完全填充的五边形图案所表征的节点,以及被完全填充的六边形图案所表征的节点组成一条异动路径。For example, see FIG8 , which is a schematic diagram of an abnormal path provided by an embodiment of the present invention. The quadrilateral pattern in FIG8 is used to represent a node whose transaction subject is an acquiring institution, the pentagonal pattern is used to represent a node whose transaction subject is a payment subsidiary, and the hexagonal pattern is used to represent a node whose transaction subject is a payment head office. The node represented by the fully filled quadrilateral pattern in FIG8 is the first node, and the node, the node represented by the fully filled pentagonal pattern, and the node represented by the fully filled hexagonal pattern form an abnormal path.

在本发明实施例中,借助了图关联结构的优势进一步还原了归因分析的关联追溯路径,即异动路径,提升了交易主体进行异动归因分析的便利性,进而提升对实际业务的策略优化效果。In the embodiment of the present invention, the advantages of the graph association structure are utilized to further restore the association tracing path of the attribution analysis, that is, the change path, thereby improving the convenience of the transaction subject in performing the change attribution analysis, and further improving the strategy optimization effect on the actual business.

基于相同的发明构思,本发明实施例还提供一种数据处理装置。如图9所示,其为数据处理装置900的结构示意图,可以包括:Based on the same inventive concept, an embodiment of the present invention further provides a data processing device. As shown in FIG9 , it is a schematic diagram of the structure of a data processing device 900, which may include:

构建单元901,用于构建以目标业务场景中的交易主体为节点、交易主体间的关联关系为边的图关联结构;A construction unit 901 is used to construct a graph association structure with transaction subjects in a target business scenario as nodes and association relationships between transaction subjects as edges;

确定单元902,用于基于所述图关联结构中每个所述节点所指示的交易主体的业务信息、交易主体间的关联关系和预设异动分析规则,确定每个所述节点对应的异动值;其中,每个所述异动值用于指示一个节点所指示交易主体交易的异常波动程度;所述异常波动程度基于交易主体的自身业务信息以及与其具备关联关系的其他交易主体的业务信息所确定;The determination unit 902 is used to determine the abnormal value corresponding to each node based on the business information of the transaction subject indicated by each node in the graph association structure, the association relationship between the transaction subjects and the preset abnormal analysis rules; wherein each abnormal value is used to indicate the abnormal fluctuation degree of the transaction of the transaction subject indicated by a node; the abnormal fluctuation degree is determined based on the business information of the transaction subject itself and the business information of other transaction subjects with which it has an association relationship;

处理单元903,用于当第一节点对应的异动值大于预设阈值时,确定所述第一节点为异动节点,并将所述第一节点所指示的交易主体确定为异动交易主体。The processing unit 903 is used to determine that the first node is a changed node when the change value corresponding to the first node is greater than a preset threshold, and determine the transaction subject indicated by the first node as the changed transaction subject.

在一种可能的实施方式中,所述确定单元902,具体用于:In a possible implementation manner, the determining unit 902 is specifically configured to:

分别基于所述图关联结构中每个所述节点所指示的交易主体的业务信息和所述预设异动分析规则中的第一子规则,确定每个所述节点的波动值;所述第一子规则用于对基于不同量级的业务信息所获得节点的初始波动值进行量级归一化处理;Determine the fluctuation value of each node based on the business information of the transaction subject indicated by each node in the graph association structure and the first sub-rule in the preset abnormality analysis rule; the first sub-rule is used to perform magnitude normalization processing on the initial fluctuation value of the node obtained based on the business information of different magnitudes;

分别基于所述交易主体间的关联关系和所述预设异动分析规则中的第二子规则,确定每个所述节点的业务信息的波动受其他节点影响的影响值;所述第二子规则用于计算下层节点被上层节点的波动所影响的程度,所述下层节点和上层节点基于交易主体间的关联关系所确定;Based on the association relationship between the transaction entities and the second sub-rule in the preset abnormality analysis rule, the influence value of the fluctuation of the business information of each node affected by other nodes is determined; the second sub-rule is used to calculate the degree to which the lower-level node is affected by the fluctuation of the upper-level node, and the lower-level node and the upper-level node are determined based on the association relationship between the transaction entities;

根据每个所述节点的波动值和对应的影响值,确定每个所述节点对应的异动值。According to the fluctuation value and the corresponding impact value of each node, the abnormal change value corresponding to each node is determined.

在一种可能的实施方式中,所述确定单元902,具体用于:In a possible implementation manner, the determining unit 902 is specifically configured to:

分别将每个所述节点所指示交易主体的业务信息输入预设时序归因模型,获得所述预设时序归因模型输出的每个所述节点对应的初始波动值;所述预设时序归因模型基于一个交易主体在预设时间段内的业务信息和预设量级波动评价规则,确定所述交易主体的初始波动值;所述预设量级波动评价规则包括多条映射关系,不同映射关系包括不同量级业务信息对应的数值范围,以及与所述数值范围对应的初始波动值;The business information of the transaction subject indicated by each node is respectively input into a preset time series attribution model to obtain the initial fluctuation value corresponding to each node output by the preset time series attribution model; the preset time series attribution model determines the initial fluctuation value of a transaction subject based on the business information of the transaction subject within a preset time period and a preset magnitude fluctuation evaluation rule; the preset magnitude fluctuation evaluation rule includes a plurality of mapping relationships, and different mapping relationships include numerical ranges corresponding to business information of different magnitudes, and initial fluctuation values corresponding to the numerical ranges;

对每个所述交易主体对应的初始波动值进行归一化处理,获得每个所述节点的波动值。The initial fluctuation value corresponding to each of the transaction entities is normalized to obtain the fluctuation value of each of the nodes.

在一种可能的实施方式中,所述归一化处理基于以下方式实现:In a possible implementation, the normalization process is implemented based on the following method:

其中,Si表示节点的波动值,ri表示节点对应的初始波动值,K用于表示所述图关联结构中的节点总数量。Among them, Si represents the fluctuation value of the node, ri represents the initial fluctuation value corresponding to the node, and K is used to represent the total number of nodes in the graph association structure.

在一种可能的实施方式中,所述第二子规则,基于以下公式确定:In a possible implementation manner, the second sub-rule is determined based on the following formula:

其中,epij表示节点i相对于节点j的影响值,Δi表示节点i的两个时刻间的业务信息值的变化值,Δj表示节点j的两个时刻间的业务信息值的变化值,eit表示t时刻节点i的业务信息值,eit’表示t’时刻节点i的业务信息值,ejt表示t时刻节点j的业务信息值,ejt’表示t’时刻节点j的业务信息值,节点i和节点j为所述图关联结构中任意节点。Among them, ep ij represents the influence value of node i relative to node j, Δi represents the change value of the business information value of node i between two moments, Δj represents the change value of the business information value of node j between two moments, e it represents the business information value of node i at time t, e it' represents the business information value of node i at time t', e jt represents the business information value of node j at time t, e jt' represents the business information value of node j at time t', and node i and node j are any nodes in the graph association structure.

在一种可能的实施方式中,所述确定单元902,具体用于:In a possible implementation manner, the determining unit 902 is specifically configured to:

将与一个所述节点存在关联关系的一个关联节点的影响值,作为一个矩阵因素,构建初始异动迭代矩阵;Taking the influence value of an associated node associated with one of the nodes as a matrix factor, constructing an initial change iteration matrix;

根据所述初始异动迭代矩阵和每个所述节点对应的波动值代入所述预设异动分析规则的第三子规则中,分别确定每个所述节点对应的异动值;所述第三子规则用于结合所有与节点存在关联关系的节点对应的影响值对节点的波动值进行迭代优化。According to the initial change iteration matrix and the fluctuation value corresponding to each node, the third sub-rule of the preset change analysis rule is substituted to determine the change value corresponding to each node respectively; the third sub-rule is used to iteratively optimize the fluctuation value of the node in combination with the influence values corresponding to all nodes that have an associated relationship with the node.

在一种可能的实施方式中,所述第三子规则基于以下公式确定:In a possible implementation manner, the third sub-rule is determined based on the following formula:

Hn←θHn-1+dH n ←θH n-1 +d

其中,Hn表示所有节点的异动值的集合,n用于表征迭代轮数,当n等于1时,H1←θS′+d,S′表示所有节点的波动值的集合,S′={S1,S2,……,SK},θ表示初始异动迭代矩阵,θi,j为θ中的矩阵因素,epij表示节点i相对于节点j的影响值,节点i和节点j为所述图关联结构中任意节点,Sk表示节点k的波动值,d为非零调节系数,i,j为正整数。Where H n represents the set of fluctuation values of all nodes, n is used to represent the number of iterations, when n is equal to 1, H 1 ←θS′+d, S′ represents the set of fluctuation values of all nodes, S′={S 1 , S 2 , …, S K }, θ represents the initial fluctuation iteration matrix, θ i,j are the matrix factors in θ, ep ij represents the influence value of node i relative to node j, node i and node j are any nodes in the graph association structure, S k represents the fluctuation value of node k, d is a non-zero adjustment coefficient, and i and j are positive integers.

在一种可能的实施方式中,确定所述第一节点为异动节点之后,所述装置还包括优化单元,用于:In a possible implementation manner, after determining that the first node is a changed node, the device further includes an optimization unit, which is configured to:

基于所述图关联结构,筛选包括第一节点的候选路径,将所述候选路径作为异动路径;Based on the graph association structure, screening candidate paths including the first node, and using the candidate paths as change paths;

对所述异动路径进行分析,获得异动分析结果;所述异动分析结果用于指示与所述异动节点关联的影响节点,并基于所述异动节点所对应的业务信息对所述影响节点对应的交易主体的业务进行优化。The change path is analyzed to obtain a change analysis result; the change analysis result is used to indicate an impact node associated with the change node, and based on the business information corresponding to the change node, the business of the transaction subject corresponding to the impact node is optimized.

为了描述的方便,以上各部分按照功能划分为各模块(或单元)分别描述。当然,在实施本发明时可以把各模块(或单元)的功能在同一个或多个软件或硬件中实现。For the convenience of description, the above parts are divided into modules (or units) according to their functions and described separately. Of course, when implementing the present invention, the functions of each module (or unit) can be implemented in the same or multiple software or hardware.

在介绍了本发明示例性实施方式的数据处理方法和装置之后,接下来,介绍根据本发明的另一示例性实施方式的电子设备。After introducing the data processing method and apparatus according to the exemplary embodiment of the present invention, next, an electronic device according to another exemplary embodiment of the present invention is introduced.

所属技术领域的技术人员能够理解,本发明的各个方面可以实现为系统、方法或程序产品。因此,本发明的各个方面可以具体实现为以下形式,即:完全的硬件实施方式、完全的软件实施方式(包括固件、微代码等),或硬件和软件方面结合的实施方式,这里可以统称为“电路”、“模块”或“系统”。It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

关于上述实施例中的装置,其中各个模块的具体执行方式已经在有关该方法的实施例中进行了详细描述,此处将不做详细阐述说明。Regarding the device in the above embodiment, the specific execution mode of each module therein has been described in detail in the embodiment of the method, and will not be elaborated here.

与本发明上述方法实施例基于同一发明构思,本发明实施例中还提供了一种电子设备,该电子设备解决问题的原理与上述实施例的方法相似,因此该电子设备的实施可以参见上述方法的实施,重复之处不再赘述。Based on the same inventive concept as the above-mentioned method embodiment of the present invention, an electronic device is also provided in the embodiment of the present invention. The principle of solving the problem by the electronic device is similar to the method of the above-mentioned embodiment. Therefore, the implementation of the electronic device can refer to the implementation of the above-mentioned method, and the repeated parts will not be repeated.

参阅图10所示,图10是根据一示例性实施例示出的一种电子设备1000的框图,本发明实施例中的电子设备包括至少一个处理器1001,以及与至少一个处理器1001连接的存储器1002,本发明实施例中不限定处理器1001与存储器1002之间的具体连接介质,图10中是以处理器1001和存储器1002之间通过总线连接为例,总线在图10中以粗线表示,其它部件之间的连接方式,仅是进行示意性说明,并不引以为限。总线可以分为地址总线、数据总线、控制总线等,为便于表示,图10中仅用一条粗线表示,但并不表示仅有一根总线或一种类型的总线。Referring to FIG. 10 , FIG. 10 is a block diagram of an electronic device 1000 according to an exemplary embodiment. The electronic device in the embodiment of the present invention includes at least one processor 1001 and a memory 1002 connected to the at least one processor 1001. The specific connection medium between the processor 1001 and the memory 1002 is not limited in the embodiment of the present invention. FIG. 10 takes the connection between the processor 1001 and the memory 1002 through a bus as an example. The bus is represented by a thick line in FIG. 10 . The connection mode between other components is only for schematic illustration and is not limited. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, FIG. 10 is represented by only one thick line, but it does not mean that there is only one bus or one type of bus.

在本发明实施例中,存储器1002存储有可被至少一个处理器1001执行的指令,至少一个处理器1001通过执行存储器1002存储的指令,可以执行前述的数据处理方法中所包括的步骤。In the embodiment of the present invention, the memory 1002 stores instructions that can be executed by at least one processor 1001. The at least one processor 1001 can execute the steps included in the aforementioned data processing method by executing the instructions stored in the memory 1002.

其中,处理器1001是电子设备的控制中心,可以利用各种接口和线路连接整个故障检测设备的各个部分,通过运行或执行存储在存储器1002内的指令以及调用存储在存储器1002内的数据,电子设备的各种功能和处理数据,从而对电子设备进行整体监控。可选的,处理器1001可包括一个或多个处理单元,处理器1001可集成应用处理器和调制解调处理器,其中,处理器1001主要处理操作系统、用户界面和应用程序等,调制解调处理器主要处理无线通信。可以理解的是,上述调制解调处理器也可以不集成到处理器1001中。在一些实施例中,处理器1001和存储器1002可以在同一芯片上实现,在一些实施例中,它们也可以在独立的芯片上分别实现。Among them, the processor 1001 is the control center of the electronic device, and can use various interfaces and lines to connect various parts of the entire fault detection device, and monitor the electronic device as a whole by running or executing instructions stored in the memory 1002 and calling data stored in the memory 1002, various functions of the electronic device and processing data. Optionally, the processor 1001 may include one or more processing units, and the processor 1001 may integrate an application processor and a modem processor, wherein the processor 1001 mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 1001. In some embodiments, the processor 1001 and the memory 1002 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on independent chips.

处理器1001可以是通用处理器,例如中央处理器(CPU)、数字信号处理器、专用集成电路、现场可编程门阵列或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件,可以实现或者执行本发明实施例中公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者任何常规的处理器等。结合本发明实施例所公开的方法的步骤可以直接体现为硬件处理器执行完成,或者用处理器中的硬件及软件模块组合执行完成。The processor 1001 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the embodiments of the present invention may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

存储器1002作为一种非易失性计算机可读存储介质,可用于存储非易失性软件程序、非易失性计算机可执行程序以及模块。存储器1002可以包括至少一种类型的存储介质,例如可以包括闪存、硬盘、多媒体卡、卡型存储器、随机访问存储器(Random AccessMemory,RAM)、静态随机访问存储器(Static Random Access Memory,SRAM)、可编程只读存储器(Programmable Read Only Memory,PROM)、只读存储器(Read Only Memory,ROM)、带电可擦除可编程只读存储器(Electrically Erasable Programmable Read-Only Memory,EEPROM)、磁性存储器、磁盘、光盘等等。存储器1002是能够用于携带或存储具有指令或数据结构形式的期望的程序代码并能够由计算机存取的任何其他介质,但不限于此。本发明实施例中的存储器1002还可以是电路或者其它任意能够实现存储功能的装置,用于存储程序指令和/或数据。The memory 1002 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 1002 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 1002 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 1002 in the embodiment of the present invention can also be a circuit or any other device that can realize a storage function, for storing program instructions and/or data.

基于同一发明构思,本发明实施例还提供了一种电子设备的又一示意图,参阅图11所示,该电子设备104包括显示单元1140、处理器1180以及存储器1120,其中,显示单元1140包括显示面板1141,用于显示由用户输入的信息或提供给用户的信息以及电子设备104的各种对象选择界面等,在本发明实施例中主要用于显示电子设备104中已安装的分析系统的相关操作界面、快捷窗口等。可选的,可以采用LCD(Liquid Crystal Display,液晶显示器)或OLED(Organic Light-Emitting Diode,有机发光二极管)等形式来配置显示面板1141。Based on the same inventive concept, the embodiment of the present invention also provides another schematic diagram of an electronic device, as shown in FIG11 , the electronic device 104 includes a display unit 1140, a processor 1180 and a memory 1120, wherein the display unit 1140 includes a display panel 1141, which is used to display information input by a user or information provided to a user and various object selection interfaces of the electronic device 104, etc., and in the embodiment of the present invention, it is mainly used to display the relevant operation interface, shortcut window, etc. of the analysis system installed in the electronic device 104. Optionally, the display panel 1141 can be configured in the form of LCD (Liquid Crystal Display) or OLED (Organic Light-Emitting Diode).

处理器1180用于读取计算机程序,然后执行计算机程序定义的方法,例如处理器1180读取分析系统的应用程序,从而在该电子设备104上运行分析系统,在显示单元1140上显示分析系统的相关操作界面。处理器1180可以包括一个或多个通用处理器,还可包括一个或多个DSP(Digital Signal Processor,数字信号处理器),用于执行相关操作,以实现本发明实施例所提供的技术方案。The processor 1180 is used to read the computer program and then execute the method defined by the computer program. For example, the processor 1180 reads the application program of the analysis system, thereby running the analysis system on the electronic device 104 and displaying the relevant operation interface of the analysis system on the display unit 1140. The processor 1180 may include one or more general-purpose processors and may also include one or more DSPs (Digital Signal Processors) to perform relevant operations to implement the technical solutions provided in the embodiments of the present invention.

存储器1120一般包括内存和外存,内存可以为随机存储器(RAM),只读存储器(ROM),以及高速缓存(CACHE)等。外存可以为硬盘、光盘、USB盘、软盘或磁带机等。存储器1120用于存储计算机程序和其他数据,该计算机程序包括各个软件对应的应用程序等,其他数据可包括操作系统或应用程序被运行后产生的数据,该数据包括系统数据(例如操作系统的配置参数)和用户数据。本发明实施例中程序指令存储在存储器1120中,处理器1180执行存储其中1120中的程序指令,实现前文论述的数据处理方法的功能。The memory 1120 generally includes internal memory and external memory. The internal memory may be a random access memory (RAM), a read-only memory (ROM), and a cache (CACHE), etc. The external memory may be a hard disk, an optical disk, a USB disk, a floppy disk, or a tape drive, etc. The memory 1120 is used to store computer programs and other data. The computer program includes applications corresponding to various software, etc. Other data may include data generated after the operating system or application is run, and the data includes system data (such as configuration parameters of the operating system) and user data. In the embodiment of the present invention, program instructions are stored in the memory 1120, and the processor 1180 executes the program instructions stored in 1120 to implement the functions of the data processing method discussed above.

此外,电子设备104还可以包括显示单元1140,用于接收输入的数字信息、字符信息或接触式触摸操作/非接触式手势,以及产生与电子设备104的用户设置以及功能控制有关的信号输入等。具体地,本发明实施例中,该显示单元1140可以包括显示面板1141。显示面板1141例如触摸屏,可收集用户在其上或附近的触摸操作(比如目标对象使用手指、触笔等任何适合的物体或附件在显示面板1141上或在显示面板1141的操作),并根据预先设定的程式驱动相应的连接装置。可选的,显示面板1141可包括触摸检测装置和触摸控制器两个部分。其中,触摸检测装置用户的触摸方位,并检测触摸操作带来的信号,将信号传送给触摸控制器;触摸控制器从触摸检测装置上接收触摸信息,并将它转换成触点坐标,再送给处理器1180,并能接收处理器1180发来的命令并加以执行。在本发明实施例中,若用户对关联子程序进行选中操作,则在显示面板1141中的触摸检测装置检测到触摸操作,则将检测到的触摸操作对应的信号发送的触摸控制器,触摸控制器将信号转换成触点坐标发送给处理器1180,处理器1180根据接收到的触点坐标确定用户选中的目标业务场景,并控制显示面板1141显示目标业务场景中的交易主体。In addition, the electronic device 104 may also include a display unit 1140 for receiving input digital information, character information or contact touch operation/contactless gesture, and generating signal input related to user settings and function control of the electronic device 104. Specifically, in an embodiment of the present invention, the display unit 1140 may include a display panel 1141. The display panel 1141 is, for example, a touch screen, which can collect the user's touch operation on or near it (such as the target object using any suitable object or accessory such as a finger, stylus, etc. on the display panel 1141 or on the display panel 1141), and drive the corresponding connection device according to a pre-set program. Optionally, the display panel 1141 may include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the touch position of the user, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 1180, and can receive and execute the command sent by the processor 1180. In an embodiment of the present invention, if a user selects an associated subroutine, the touch detection device in the display panel 1141 detects the touch operation, and sends a signal corresponding to the detected touch operation to the touch controller. The touch controller converts the signal into touch point coordinates and sends them to the processor 1180. The processor 1180 determines the target business scenario selected by the user based on the received touch point coordinates, and controls the display panel 1141 to display the transaction subjects in the target business scenario.

其中,显示面板1141可以采用电阻式、电容式、红外线以及表面声波等多种类型实现。除了显示单元1140,电子设备104还可以包括输入单元1130,输入单元1130可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆等中的一种或多种。图11中是以输入单元1130包括图像输入设备1131和其它输入设备1132为例。Among them, the display panel 1141 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the display unit 1140, the electronic device 104 can also include an input unit 1130, and the input unit 1130 can include but is not limited to one or more of a physical keyboard, a function key (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick. FIG. 11 takes the input unit 1130 including an image input device 1131 and other input devices 1132 as an example.

除以上之外,电子设备104还可以包括用于给其他模块供电的电源1190、音频电路1160、近场通信模块1170和RF电路1110。电子设备104还可以包括一个或多个传感器1150,例如加速度传感器、光传感器、压力传感器等。音频电路1160具体包括扬声器1161和麦克风1162等,例如用户可以使用语音控制,电子设备104可以通过麦克风1162采集用户的声音,可以用户的声音进行控制,并在需要提示用户业务信息中存在异动表现时,通过扬声器1161播放对应的提示音。In addition to the above, the electronic device 104 may also include a power supply 1190 for supplying power to other modules, an audio circuit 1160, a near field communication module 1170, and an RF circuit 1110. The electronic device 104 may also include one or more sensors 1150, such as an accelerometer, a light sensor, a pressure sensor, etc. The audio circuit 1160 specifically includes a speaker 1161 and a microphone 1162, etc. For example, the user can use voice control, the electronic device 104 can collect the user's voice through the microphone 1162, can be controlled by the user's voice, and when it is necessary to remind the user that there is an abnormal performance in the business information, the corresponding prompt tone is played through the speaker 1161.

在示例性实施例中,还提供了一种包括操作的存储介质,例如包括操作的存储器1002,上述操作可由电子设备1000的处理器1001执行以完成上述方法。可选地,存储介质可以是非临时性计算机可读存储介质,例如,所述非临时性计算机可读存储介质可以是ROM、随机存取存储器(RAM)、CD-ROM、磁带、软盘和光数据存储设备等。In an exemplary embodiment, a storage medium including operations is also provided, such as a memory 1002 including operations, and the operations can be executed by a processor 1001 of an electronic device 1000 to complete the above method. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

与上述方法实施例基于同一发明构思,本发明提供的数据处理方法的各个方面还可以实现为一种程序产品的形式,其包括程序代码,当程序产品在电子设备上运行时,程序代码用于使电子设备执行本说明书上述描述的根据本发明各种示例性实施方式的数据处理方法中的步骤,例如,电子设备可以执行如图3所示的步骤。Based on the same inventive concept as the above-mentioned method embodiments, various aspects of the data processing method provided by the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on an electronic device, the program code is used to enable the electronic device to execute the steps of the data processing method according to various exemplary embodiments of the present invention described above in this specification. For example, the electronic device can execute the steps shown in Figure 3.

程序产品可以采用一个或多个可读介质的任意组合。可读介质可以是可读信号介质或者可读存储介质。可读存储介质例如可以是但不限于电、磁、光、电磁、红外线、或半导体的系统、装置或器件,或者任意以上的组合。可读存储介质的更具体的例子(非穷举的列表)包括:具有一个或多个导线的电连接、便携式盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

本发明的实施方式的程序产品可以采用便携式紧凑盘只读存储器(CD-ROM)并包括程序代码,并可以在服务器上运行。然而,本发明的程序产品不限于此,在本文件中,可读存储介质可以是任何包含或存储程序的有形介质,该程序可以被命令执行系统、装置或者器件使用或者与其结合使用。The program product of the embodiment of the present invention can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a server. However, the program product of the present invention is not limited to this. In this document, a readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with a command execution system, device or device.

可读信号介质可以包括在基带中或者作为载波一部分传播的数据信号,其中承载了可读程序代码。这种传播的数据信号可以采用多种形式,包括但不限于电磁信号、光信号或上述的任意合适的组合。可读信号介质还可以是可读存储介质以外的任何可读介质,该可读介质可以发送、传播或者传输用于由命令执行系统、装置或者器件使用或者与其结合使用的程序。The readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, wherein the readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with a command execution system, apparatus, or device.

可读介质上包含的程序代码可以用任何适当的介质传输,包括但不限于无线、有线、光缆、RF等等,或者上述的任意合适的组合。The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

尽管已描述了本发明的优选实施例,但本领域内的技术人员一旦得知了基本创造性概念,则可对这些实施例做出另外的变更和修改。所以,所附权利要求意欲解释为包括优选实施例以及落入本发明范围的所有变更和修改。Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

显然,本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样,倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内,则本发明也意图包含这些改动和变型在内。Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims (12)

1. A method of data processing, the method comprising:
Constructing a graph association structure taking a transaction main body in a target service scene as a node and an association relationship between the transaction main bodies as an edge;
Determining a transaction value corresponding to each node based on service information of transaction subjects indicated by each node in the graph association structure, association relations among the transaction subjects and a preset transaction analysis rule; wherein, each abnormal value is used for indicating the abnormal fluctuation degree of the transaction main body indicated by one node; the abnormal fluctuation degree is determined based on the self business information of the transaction main body and the business information of other transaction main bodies with association relation with the self business information;
when the transaction main body indicated by the first node is determined to be the transaction main body, the transaction main body indicated by the first node is determined to be the transaction main body.
2. The method of claim 1, wherein determining the transaction value corresponding to each node based on the transaction information of the transaction subjects indicated by each node in the graph association structure, the association relationship between the transaction subjects, and a preset transaction analysis rule comprises:
Determining a fluctuation value of each node based on the business information of the transaction main body indicated by each node in the graph association structure and a first sub-rule in the preset transaction analysis rule; the first sub-rule is used for carrying out order normalization processing on initial fluctuation values of nodes obtained based on service information of different orders;
determining an influence value of fluctuation of business information of each node affected by other nodes based on the association relation between the transaction main bodies and a second sub-rule in the preset transaction analysis rule respectively; the second sub-rule is used for calculating the degree of influence of fluctuation of the lower node by the upper node, and the lower node and the upper node are determined based on the association relation between transaction subjects;
And determining the corresponding fluctuation value of each node according to the fluctuation value and the corresponding influence value of each node.
3. The method of claim 2, wherein determining the fluctuation value of each of the nodes based on the traffic information of the transaction body indicated by each of the nodes in the graph association structure and the first sub-rule of the preset transaction analysis rule, respectively, comprises:
Respectively inputting the business information of the transaction main body indicated by each node into a preset time sequence attribution model to obtain an initial fluctuation value corresponding to each node output by the preset time sequence attribution model; the preset time sequence attribution model determines an initial fluctuation value of a transaction main body based on service information of the transaction main body in a preset time period and a preset magnitude fluctuation evaluation rule; the preset magnitude fluctuation evaluation rule comprises a plurality of mapping relations, wherein different mapping relations comprise numerical value ranges corresponding to different magnitude business information and initial fluctuation values corresponding to the numerical value ranges;
and carrying out normalization processing on the initial fluctuation value corresponding to each transaction main body to obtain the fluctuation value of each node.
4. A method as claimed in claim 3, wherein the normalization is effected on the basis of:
Wherein S i represents a fluctuation value of the nodes, r i represents an initial fluctuation value corresponding to the nodes, and K is used for representing the total number of nodes in the graph association structure.
5. The method of claim 2, wherein the second sub-rule is determined based on the following formula:
Wherein ep ij represents the influence value of node i relative to node j, Δi represents the change value of the traffic information value between two times of node i, Δj represents the change value of the traffic information value between two times of node j, e it represents the traffic information value of node i at time t, e it' represents the traffic information value of node i at time t ', e jt represents the traffic information value of node j at time t, e jt' represents the traffic information value of node j at time t', and nodes i and j are any nodes in the graph association structure.
6. The method of any of claims 2-5, wherein determining a corresponding transaction value for each of the nodes based on the fluctuation value and the corresponding impact value for each of the nodes comprises:
Constructing an initial abnormal iteration matrix by taking an influence value of an associated node with an associated relation with one node as a matrix factor;
Substituting the initial abnormal iteration matrix and the fluctuation value corresponding to each node into a third sub-rule of the preset abnormal analysis rule, and respectively determining the abnormal value corresponding to each node; and the third sub-rule is used for iteratively optimizing the fluctuation value of the node by combining all the influence values corresponding to the nodes with the association relation with the node.
7. The method of claim 6, wherein the third sub-rule is determined based on the following formula:
Hn←θHn-1+d
Where H n represents a set of fluctuation values of all nodes, n is used to characterize the iteration round, when n is equal to 1, H 1 ++s ' +d, S ' represents a set of fluctuation values of all nodes, S ' = { S 1,S2,……,SK }, θ represents the initial fluctuation iteration matrix, θ i,j is the matrix factor in θ, Ep ij represents the influence value of the node i relative to the node j, the node i and the node j are any nodes in the graph association structure, S k represents the fluctuation value of the node k, d is a non-zero adjustment coefficient, and i and j are positive integers.
8. The method of claim 1, wherein after determining that the first node is a transaction node, the method further comprises:
Screening candidate paths comprising a first node based on the graph association structure, and taking the candidate paths as transaction paths;
Analyzing the abnormal path to obtain an abnormal analysis result; the transaction analysis result is used for indicating an influence node associated with the transaction node, and optimizing the business of the transaction main body corresponding to the influence node based on the business information corresponding to the transaction node.
9. A data processing apparatus, the apparatus comprising:
the construction unit is used for constructing a graph association structure which takes a transaction main body in a target service scene as a node and an association relationship between the transaction main bodies as an edge;
The determining unit is used for determining a transaction value corresponding to each node based on the business information of the transaction subjects indicated by each node in the graph association structure, the association relation among the transaction subjects and a preset transaction analysis rule; wherein, each abnormal value is used for indicating the abnormal fluctuation degree of the transaction main body indicated by one node; the abnormal fluctuation degree is determined based on the self business information of the transaction main body and the business information of other transaction main bodies with association relation with the self business information;
and the processing unit is used for determining the first node as a transaction main body indicated by the first node as a transaction main body when the transaction value corresponding to the first node is larger than a preset threshold value.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1-8 when the computer program is executed.
11. A computer readable storage medium, characterized in that it comprises a program code for causing an electronic device to perform the steps of the method according to any of claims 1-8, when the program product is run on said electronic device.
12. A computer program product, the computer program product comprising: computer program code which, when run on a computer, causes the computer to perform the method of any of the preceding claims 1-8.
CN202410432948.8A 2024-04-10 2024-04-10 Data processing method and device, electronic equipment and storage medium Pending CN118505382A (en)

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CN118505382A true CN118505382A (en) 2024-08-16

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