Value data processing method and apparatus, electronic device, and computer-readable medium

Abstract

Embodiments of the present disclosure disclose a value data processing method, device, electronic equipment and computer readable medium. A specific embodiment of the method comprises: determining a set of similar value product information and a set of user group information; for each similar value product information, performing a first generation step: determining a first matching information set; performing data screening on a set of similar risk flow data to generate a screened data group; for each user information, determining a second matching information; screening at least one user information to generate a set of extended value flow data sequences; generating an extended data set; generating value risk information; storing a plurality of data or information, and sending information links and link descriptions to a value monitoring terminal. The embodiment can accurately generate value risk information for a target user to avoid value loss to the target user by performing corresponding value operations on the target user.

Description

TECHNICAL FIELD

[0001] Embodiments of the present disclosure relate to the technical field of computer technology, and particularly, to a value data processing method and device, electronic equipment and computer readable medium. BACKGROUND

[0002] At present, the related value products developed by various industries will confirm the value qualification of the user to avoid value loss. For the generation of value risk information of the user using the value product, the commonly used way is: through the value risk analysis of the related experts according to the value flow data sequence of the target user and the related risk data set corresponding to the target value product, to generate the value risk information.

[0003] However, the inventors have found that when the above-mentioned way is used to generate the value risk information, the following technical problems often exist:

[0004] The artificial value risk analysis has the problems of inaccurate value risk analysis and low efficiency, and wastes a large amount of server resources and central processing unit resources. In addition, the value flow data cannot be processed in a confidential manner, which may lead to the leakage of value risk related information.

[0005] The above information disclosed in the background section is only for the purpose of enhancing the understanding of the background of the present inventive concept, and therefore, it can include information that does not form the prior art known to those of ordinary skill in the art in the country. SUMMARY

[0006] The summary section is provided to introduce the concepts briefly in a simplified form, which will be described in detail in the specific embodiments section later. The summary section is not intended to identify key or essential features of the claimed technical solution, nor is it intended to be used to limit the scope of the claimed technical solution.

[0007] Some embodiments of the present disclosure propose a value data processing method and device, electronic equipment and computer readable medium to solve one or more of the technical problems mentioned in the background section.

[0008] In a first aspect, some embodiments of the present disclosure provide a value data processing method, comprising: obtaining a historical value flow data sequence for a target user and a risk flow data set corresponding to a target value product from a target server, wherein the target value product is a value product in which the target user participates in value; determining a similar value product information set corresponding to the target value product and user group information corresponding to the target user; for each similar value product information in the similar value product information set, using a central processing unit to perform a first generation step: determining a first matching information set between a similar risk flow data set corresponding to the similar value product information and the target value product; performing data screening on the similar risk flow data set according to the first matching information set to generate a screened data group; for each user information in the user group information, determining a second matching information between a similar historical value flow data sequence corresponding to the user information and the target user; screening at least one user information corresponding to a second matching information greater than a target value from the user group information, and generating an expanded value flow data sequence set according to at least one similar historical value flow data sequence corresponding to the at least one user information and the historical value flow data sequence; generating an expanded data set according to the obtained screened data group set and the risk flow data set; generating value risk information representing the target user using the target value product according to the expanded value flow data sequence set and the expanded data set; storing the expanded value flow data sequence set, the expanded data set, the similar value product information set, the user information corresponding to the target user, and the user group information in the target data storage end, and sending information links and link descriptions corresponding to the value risk information to a value monitoring terminal in the form of encrypted emails, so that a monitoring user can perform corresponding value operations by decrypting the value risk information through a key.

[0009] In a second aspect, some embodiments of the present disclosure provide a value data processing apparatus, comprising: an acquisition unit configured to acquire, from a target server, a historical value flow data sequence of a target user and a risk flow data set corresponding to a target value product, wherein the target value product is a value product in which the target user participates in value; a first determination unit configured to determine a similar value product information set corresponding to the target value product and a user group information corresponding to the target user; a first execution unit configured to, for each similar value product information in the similar value product information set, execute, by using a central processing unit, a first generation step of determining a first matching information set between a similar risk flow data set corresponding to the similar value product information and the target value product; and performing data screening on the similar risk flow data set according to the first matching information set to generate a screened data group; a second determination unit configured to, for each user information in the user group information, determine a second matching information between a similar historical value flow data sequence corresponding to the user information and the target user; a screening generation unit configured to screen at least one user information corresponding to the second matching information greater than a target value from the user group information, and generate an extended value flow data sequence set according to at least one similar historical value flow data sequence corresponding to the at least one user information and the historical value flow data sequence; a first generation unit configured to generate an extended data set according to the obtained screened data group set and the risk flow data set; a second generation unit configured to generate value risk information representing that the target user uses the target value product according to the extended value flow data sequence set and the extended data set; and a second execution unit configured to store the extended value flow data sequence set, the extended data set, the similar value product information set, the user information corresponding to the target user, and the user group information in the target data storage end correspondingly, and send information link and link description corresponding to the value risk information to a value monitoring terminal in the form of encrypted email, so that a monitoring user performs corresponding value operation by decrypting the value risk through a key.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the first aspect is implemented.

[0012] The above various embodiments of the present disclosure have the following beneficial effects: the value data processing method of some embodiments of the present disclosure can accurately generate value risk information for the target user, so as to avoid the occurrence of value loss for the target user by performing corresponding value operations on the target user. Specifically, the reason why the related value risk information is not accurate is that the value risk analysis is performed artificially, and there are problems of inaccurate and low efficiency of value risk analysis. Based on this, the value data processing method of some embodiments of the present disclosure first acquires a historical value flow data sequence for a target user and a risk flow data set corresponding to a target value product from a target server. The target value product is a value product in which the target user participates in value. Here, the acquired historical value flow data sequence and risk flow data set are used as a data basis for subsequent generation of value risk information. Then, the similar value product information set corresponding to the target value product and the user group information corresponding to the target user are determined. Here, the similar value product information set and the user group information are used to realize data supplement of the historical value flow data sequence and the risk flow data set. Next, for each similar value product information in the similar value product information set, a central processing unit is used to perform a first generation step: first, the central processing unit can accurately and efficiently determine a first matching information set between the similar risk flow data set corresponding to the similar value product information and the target value product. Here, the obtained first matching information set is used for subsequent screening of the similar risk flow data set to obtain a data set matching the target value product as a data basis for subsequent generation of value risk information. Second, according to the first matching information set, the similar risk flow data set is screened to generate a screening data group, and the obtained screening data group is a data set having a data association with the target value product. Next, for each user information in the user group information, a second matching information between the similar historical value flow data sequence corresponding to the user information and the target user is determined, which is used for subsequent determination of similar data sets having a data association with the target user for data set supplement of the historical value flow data sequence. Next, at least one user information corresponding to the second matching information greater than a target value is screened from the user group information, and an extended value flow data sequence set is generated according to at least one similar historical value flow data sequence corresponding to the at least one user information and the historical value flow data sequence, so as to generate a value flow data set having a data similar relationship or a data association relationship with the target user. Furthermore, an extended data set is generated according to the obtained screening data group set and the risk flow data set, so as to generate a data set having a data similar relationship or a data association relationship with the target value product.Then, according to the above-mentioned extended value flow data sequence set and the above-mentioned extended data set, the value risk information of the target user using the target value product can be accurately generated in the case of a large amount of data. According to the value risk information, the corresponding accurate value operation is performed on the target user. Finally, the extended value flow data sequence set, the extended data set, the similar value product information set, the target user corresponding user information and the user group information are stored in the target data storage end, and the information link and link description corresponding to the value risk information are sent to the value monitoring terminal in the form of email encryption, so that the monitoring user can perform the corresponding value operation by decrypting the value risk through the key. Here, the email encryption and key decryption can greatly ensure the security of the value risk related information. Similarly, the storage of each information can facilitate subsequent retrieval and use, greatly saving the use of calling resources and improving the calling efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0013] The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by describing in detail some embodiments thereof with reference to the attached drawings in which:

[0014] Figure 1 is a flowchart of some embodiments of a value data processing method according to the present disclosure;

[0015] Figure 2 is a structural schematic diagram of some embodiments of a value data processing apparatus according to the present disclosure;

[0016] Figure 3 is a structural schematic diagram of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0017] Embodiments of the present disclosure will be described below in more detail with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes, and are not intended to limit the scope of protection of the present disclosure.

[0018] In addition, it should be noted that only parts related to the invention are shown in the drawings for ease of description. The embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0019] It should be noted that the terms “first”, “second”, and the like mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0020] It should be noted that the terms “one”, “multiple” mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that “one or more” should be understood unless otherwise explicitly indicated in the context.

[0021] The names of the messages or information exchanged between the plurality of devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0022] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0023] Reference Figure 1 , shows the flow 100 of some embodiments of the value data processing method according to the present disclosure. The value data processing method comprises the following steps:

[0024] Step 101, obtaining a historical value flow data sequence for a target user and a risk flow data set corresponding to a target value product from a target server.

[0025] In some embodiments, the subject performing the above value data processing method can obtain the historical value flow data sequence for the target user and the risk flow data set corresponding to the target value product through wired connection or wireless connection. Among them, the target value product is the value product in which the target user participates in value. The target user can be a user to be detected for risk. In practice, the target user can be a user to be detected for financial risk. The historical value flow data sequence can be a value flow data sequence during the historical period. In practice, the value flow data sequence can be a product consumption data sequence. The product consumption data can be product input data, and can also be product output data. The target value product can be a risk prediction product. For example, for the field of credit investigation, the target value product can be a bank credit product. The risk flow data set can be a risk data set for each product use user of the target value product.

[0026] Step 102, determining a similar value product information set corresponding to the target value product and user group information corresponding to the target user.

[0027] In some embodiments, the execution subject can determine a similar value product information set corresponding to the target value product and user group information corresponding to the target user. The similar value product information in the similar value product information set can be product information of a value product with product features similar to the product features of the target value product. In practice, the product information can be a product name. The user group information can be group information of a user group with user features similar to the user features of the target user. In practice, the user group information can be group information of a user group. The user group can include a plurality of users with user features similar to the target user.

[0028] In some optional implementations of some embodiments, the determination of the similar value product information set corresponding to the target value product and the user group information corresponding to the target user includes:

[0029] First, the product display image and product display information corresponding to the target value product are obtained, and the full-amount product display image and full-amount product display information corresponding to each full-amount value product information in the full-amount value product information set are obtained. The product display image can be an image showing product-related information of the target value product. The product-related information can include product-related promotional information and product-related feature attribute information. The product display information can be textual information showing the product-related feature attributes. The full-amount product display image can be an image showing product-related information of the full-amount value product information. The full-amount product display information can be textual information showing product-related feature attributes of the full-amount value product information. The image resolution of the product display image and the full-amount product display image is the same.

[0030] Second, for each full-amount value product information in the full-amount value product information set, the following second generation step is performed:

[0031] Sub-step 1: Extract the keyword set included in the full-amount product display image corresponding to the full-amount value product information and the corresponding keyword position information set. The keyword set can be a keyword set in each word set in the full-amount product display image. The keyword position information can be coordinate information of the keyword in the full-amount product display image.

[0032] As an example, first, the execution subject can input the full-amount product display image into an image recognition model to generate a word set and a word position information set corresponding to the word set. The words in the word set and the word position information in the word position information set have a one-to-one correspondence. Then, the keywords corresponding to the full-amount value product information are filtered from the word set to obtain the keyword set. Finally, the keyword position information set corresponding to the keyword set is determined. The image recognition model can be a YOLO v5 model.

[0033] Sub-step 2, determine the word attribute information corresponding to each keyword in the keyword set. Wherein, the word attribute information corresponding to the keyword can be the word attribute name corresponding to the semantic of the keyword.

[0034] Sub-step 3, information association is performed between each keyword in the keyword set and the corresponding word attribute information to generate an associated word and word attribute table.

[0035] Sub-step 4, the product display image is divided into image regions to obtain a set of divided display images, wherein the divided display image can be an irregularly shaped image.

[0036] As an example, the execution subject can input the product display image into an image segmentation model to perform image region division to obtain a set of divided display images. The image segmentation model can be a U-net model.

[0037] Sub-step 5, according to the image content weight corresponding to each divided display image in the set of divided display images according to the image template corresponding to the product display image, an image content weight set is obtained. Wherein, the image template can be the initial template corresponding to the product display image. Wherein, the image content weight can represent the importance of the image content in the divided display image. The image content weight can be a value between 0 and 1. Wherein, the image template is marked with the importance of the content semantics of each template module.

[0038] Sub-step 6, according to the image content weight set, determine the position weight corresponding to each keyword position information in the keyword position information set, to obtain a position weight set.

[0039] As is, the execution subject can determine the image content weight corresponding to each keyword position information in the keyword position information set, as the position weight corresponding to the keyword position information, to obtain the position weight set.

[0040] Sub-step 7, add the keyword position information set and the position weight set to the associated word and word attribute table to generate an information association table.

[0041] Third step, determine the product information association table corresponding to the product display image. Wherein, the generation method of the product information association table can refer to the generation method of the information association table.

[0042] Fourth step, determine the table content difference information corresponding to each information association table in the set of information association tables obtained by the product information association table and the difference information is generated. Wherein, the difference information can be a value between 0 and 1, the larger the value, the greater the difference in table content.

[0043] In the fifth step, the full-value product information satisfying the preset information condition is determined as similar-value product information, and a similar-value product information set is obtained. The preset information condition can be that the difference information is less than 30% of the full-value product information.

[0044] Optionally, the determining of the similar-value product information set corresponding to the target value product and the user group information corresponding to the target user further includes the following steps:

[0045] In the first step, the object feature information set corresponding to the target user is determined. The object feature information set can be the feature values of each object feature corresponding to the target user. For example, each object feature can include but is not limited to at least one of the following: object name, object asset, and object work information.

[0046] In the second step, the user feature information set corresponding to each user information in the initial user group information is determined, and a user feature information set is obtained. The initial user group information can be the group information corresponding to the initial user group. The initial user group information can be a group identifier. The initial user group can be a traceable group that is filtered out from the user group having a user feature association relationship with the target user.

[0047] In the third step, the selected feature attribute information set and the clustering purpose information are obtained. The feature attribute information can be an attribute identifier corresponding to the feature attribute. The clustering purpose information can represent the association relationship between the determined user group information and the target user. For example, the clustering purpose information can be consumption level clustering purpose information, and can also be asset proximity clustering purpose information.

[0048] In the fourth step, according to the clustering purpose information, each feature attribute information in the feature attribute information set is given an information weight value to generate a weight information, and a weight information set is obtained. The information size corresponding to the weight information is between -1 and 1. The feature attribute information having a close association with the clustering target information is given a higher weight, and the feature attribute information having a close association with the clustering target information is given a lower weight.

[0049] As an example, first, the execution subject can input the clustering purpose information and each feature attribute information in the feature attribute information set into a convolutional neural network model to generate a weight information and obtain a weight information set.

[0050] In the fifth step, a first feature attribute information subset having an information size greater than 0 corresponding to the weight information is filtered out from the feature attribute information set, and a second feature attribute information subset having an information size less than 0 corresponding to the weight information is filtered out.

[0051] Step 6, according to the first feature attribute information subset and the corresponding first weight information subset, the object feature information group and the user feature information group are taken as the clustering data set, and the user information clustering is performed on the user information set corresponding to the target user and the initial user group information, to generate the first user information cluster set. The user information clustering method can be a K-means algorithm based on weight information. That is, in the process of generating vector distance, the influence of weight information needs to be considered.

[0052] Step 7, according to the second feature attribute information subset and the corresponding second weight information subset, the object feature information group and the user feature information group are taken as the clustering data set, and the user information clustering is performed on the user information set corresponding to the target user and the initial user group information, to generate the second user information cluster set.

[0053] Step 8, using each cluster in the second user information cluster set, the cluster verification is performed on each cluster in the first user information cluster set, to generate the verification result. The verification result includes: the result of the first user information cluster set passing the cluster verification, and the result of the first user information cluster set failing the cluster verification.

[0054] As an example, first, the first user information cluster including the user information corresponding to the target user is selected from the first user information cluster set as the first target user information cluster. Then, the second user information cluster including the user information corresponding to the target user is selected from the second user information cluster set as the second target user information cluster. Next, the user information difference rate between each user information in the first target user information cluster and each user information in the second target user information cluster is determined. Finally, in response to determining that the user information difference rate is greater than or equal to a fixed value, the result of the first user information cluster set failing the cluster verification is generated. In response to determining that the user information difference rate is less than the fixed value, the result of the first user information cluster set passing the cluster verification is generated.

[0055] Step 9, according to the verification result, the user group information corresponding to the target user is determined.

[0056] As an example, in response to determining that the verification result is the result of the first user information cluster set passing the cluster verification, the group information of the user group corresponding to the first user information cluster in which the target user is located in the first user information cluster set is taken as the user group information. In response to determining that the verification result is the result of the first user information cluster set failing the cluster verification, the same user information between the first target user information cluster and the second target user information cluster is removed from the first target user information cluster to obtain the first target user information cluster after removal, as the user group information.

[0057] Step 103, for each similar value product information in the similar value product information set, using a central processing unit, a first generation step is performed:

[0058] Step 1031, determine the first matching information set between the similar risk circulation data set corresponding to the similar value product information and the target value product.

[0059] In some embodiments, the execution subject can determine the first matching information set between the similar risk circulation data set corresponding to the similar value product information and the target value product. Among them, the first matching information in the first matching information set has corresponding similar risk circulation data. That is, there is a one-to-one correspondence between the first matching information in the first matching information set and the similar risk circulation data in the similar risk circulation data set. The first matching information can be a matching score. The first matching information can represent the matching degree between the similar risk circulation data and the target value product. The greater the corresponding numerical value of the first matching information, the higher the corresponding matching degree.

[0060] As an example, first, the execution subject can encode each similar risk circulation data to generate encoded vector information, obtaining an encoded vector information set. Then, splice each encoded vector information in the encoded vector information set to generate a spliced vector. Next, encode the product attribute information set corresponding to the target value product to generate an attribute encoded vector. Finally, determine the vector cosine distance between the attribute encoded vector and the spliced vector as the first matching information.

[0061] Here, using a central processing unit to perform the first generation step can realize the advance arrangement of computing resources and improve the processing efficiency of the generation step.

[0062] Step 1032, according to the first matching information set, data filtering is performed on the similar risk circulation data set to generate a filtered data group.

[0063] In some embodiments, the execution subject can perform data filtering on the similar risk circulation data set according to the first matching information set to generate a filtered data group.

[0064] As an example, the execution subject can filter the similar risk circulation data corresponding to the first matching information greater than a preset value from the similar risk circulation data set to obtain the filtered data group. For example, the preset value can be 0.7.

[0065] Step 104, for each user information in the user group information, determine the second matching information between the similar historical value circulation data sequence corresponding to the user information and the target user.

[0066] In some embodiments, the execution subject can determine, for each user information in the user group information, second matching information between the user information and the similar historical value flow data sequence corresponding to the target user. Among them, the second matching information in the second matching information set has corresponding similar risk flow data. That is, there is a one-to-one correspondence between the first matching information in the first matching information set and the similar risk flow data in the similar risk flow data set. The first matching information can be a matching score. The first matching information can represent the matching degree between the similar risk flow data and the target value product. The greater the first matching information corresponding value, the higher the corresponding matching degree.

[0067] As an example, first, the execution subject can encode each similar historical value flow data in the similar historical value flow data sequence to generate a value flow encoding vector, obtaining a value flow encoding vector sequence. Then, each value flow encoding vector in the value flow encoding vector sequence is fused to generate a fusion vector. Next, the user attribute feature information set corresponding to the target user is converted into a vector to generate an attribute fusion feature vector. Finally, the vector cosine distance between the fusion vector and the attribute fusion feature vector is determined as the second matching information.

[0068] Step 105, filtering at least one user information corresponding to the second matching information greater than the target value from the user group information, and generating an expanded value flow data sequence set according to at least one similar historical value flow data sequence corresponding to the at least one user information and the historical value flow data sequence.

[0069] In some embodiments, the execution subject can filter at least one user information corresponding to the second matching information greater than the target value from the user group information, and generate an expanded value flow data sequence set according to at least one similar historical value flow data sequence corresponding to the at least one user information and the historical value flow data sequence. Among them, the target value can be a pre-set value.

[0070] Step 106, generating an expanded data set according to the obtained filtered data group set and the risk flow data set.

[0071] In some embodiments, the execution subject can generate an expanded data set according to the obtained filtered data group set and the risk flow data set. In practice, the expanded data set includes each filtered data group in the filtered data group set and the risk flow data set.

[0072] As an example, the execution subject can combine the filtered data group set and the risk flow data set to generate a combined data set as the expanded data set.

[0073] Step 107, generating value risk information representing the target user using the target value product according to the extended value flow data sequence set and the extended data set.

[0074] In some embodiments, the execution subject can generate value risk information representing the target user using the target value product according to the extended value flow data sequence set and the extended data set. The value risk information can be the value risk that the target user using the target value product may bring.

[0075] In some optional implementations of some embodiments, the generation of value risk information representing the target user using the target value product according to the extended value flow data sequence set and the extended data set can include the following steps:

[0076] First, obtain a first value risk feature attribute group based on a product angle and a second value risk feature attribute group based on a user angle. The first value risk feature attribute in the first value risk feature attribute group based on the product angle can be a feature attribute closely related to the value risk information from the product angle. The first value risk feature attribute in the first value risk feature attribute group based on the user angle can be a feature attribute closely related to the value risk information from the user angle.

[0077] Second, extract a first value risk feature attribute information group set for the first value risk feature attribute group from the extended value flow data sequence set, and extract a second value risk feature attribute information group set for the second value risk feature attribute group from the extended value flow data sequence set.

[0078] Third, extract a third value risk feature attribute information group set for the first value risk feature attribute group from the extended data set, and extract a fourth value risk feature attribute information group set for the second value risk feature attribute group from the extended data set.

[0079] Fourth, generate first candidate value risk information according to the first value risk feature attribute information group set and the third value risk feature attribute information group set.

[0080] As an example, the execution subject can input the first value risk feature attribute information group set and the third value risk feature attribute information group set into a convolutional neural network model connected in parallel + a multi-layer convolutional neural network model connected in series to generate the first candidate value risk information.

[0081] In the fifth step, the second candidate value risk information is generated according to the second set of value risk characteristic attribute information and the fourth set of value risk characteristic attribute information.

[0082] As an example, the second set of value risk characteristic attribute information and the fourth set of value risk characteristic attribute information can be input into a convolutional neural network model connected in parallel + a multi-layer convolutional neural network model connected in series to generate the second candidate value risk information.

[0083] In the sixth step, the information level corresponding to the first candidate value risk information and the information level corresponding to the second candidate value risk information are determined as the first information level and the second information level, respectively.

[0084] In the seventh step, the value risk information is generated according to the first information level, the second information level, a first threshold for the first candidate value risk information, and a second threshold for the second candidate value risk information.

[0085] In some optional implementations of some embodiments, the generation of the value risk information representing the use of the target value product by the target user according to the extended value flow data sequence set and the extended data set can include the following steps:

[0086] In the first step, for each extended value flow data sequence in the extended value flow data sequence set, the following third generation step is performed:

[0087] Substep 1: Extract a first value flow characteristic information set corresponding to the extended value flow data sequence. The first value flow characteristic information set is an information set corresponding to the user value flow characteristic set. The first value flow characteristic information set can be a set of feature contents of the extended value flow data sequence under the user value flow characteristic set.

[0088] Substep 2: Input the first value flow characteristic information set into a product type information classification model to generate product type information. The product type information classification model can be a neural network model for generating product type information. The product type information can be a type identifier corresponding to the product type. In practice, the product type information classification model can be a multi-layer convolutional layer.

[0089] Substep 3: Obtain a first product characteristic information set corresponding to the product type information.

[0090] In the fourth generation step, for each extended data in the extended data set, the following fourth generation step is performed:

[0091] The first sub-step is to extract a second product feature information set corresponding to the extended data. The second product feature information set is an information set corresponding to the product value flow conversion feature set.

[0092] The second sub-step is to determine a feature information similarity between the first product feature information set and the second product feature information set as a first feature similarity. The feature information similarity can represent the information similarity between two product feature information sets. The feature information similarity can be a value between 0 and 1. The higher the value, the more similar the two product feature information sets.

[0093] As an example, the first feature similarity can be determined by the cosine distance between the first product feature information set and the second product feature information set.

[0094] The third sub-step is to determine a first data similarity between the extended value flow conversion data sequence and the historical value flow conversion data sequence. The first data similarity can represent the data similarity between the extended value flow conversion data sequence and the historical value flow conversion data sequence.

[0095] The fourth sub-step is to determine a second data similarity corresponding to the extended data.

[0096] The fifth sub-step is to generate a first initial risk similarity according to the first feature similarity, the first data similarity, and the second data similarity.

[0097] As an example, the first initial risk similarity can be generated by adding the first feature similarity, the first data similarity, and the second data similarity.

[0098] As another example, the first initial risk similarity can be generated by weighted sum processing of the first feature similarity, the first data similarity, and the second data similarity.

[0099] The second step is to generate a first initial risk information according to the obtained first initial risk similarity set.

[0100] As an example, the first initial risk information can be generated by adding each first initial risk similarity in the first initial risk similarity set.

[0101] The third step is to generate the value risk information according to the first initial risk information.

[0102] Optionally, generating the value risk information according to the first initial risk information can include the following steps:

[0103] The first step, for each of the above expansion data in the expansion data set, the following fifth generation step is executed:

[0104] Substep 1, determine the second product feature information set corresponding to the above expansion data as the target second product feature information set.

[0105] Substep 2, input the above target second product feature information set into the user type information classification model to generate user type information. Wherein, the user type information classification model can be a neural network model for generating user type information. The user type information can be a type identifier corresponding to the user type. In practice, the user type information classification model can be a multi-layer convolutional layer in series.

[0106] Substep 3, obtain the first user feature information set corresponding to the above user type information.

[0107] Substep 4, for each of the above expansion value flow data sequence set, the following sixth generation step is executed:

[0108] First substep, determine the user feature information set corresponding to the above expansion value flow data sequence as the second user feature information set.

[0109] Second substep, determine the feature information similarity between the above first user feature information set and the above second user feature information set as the second feature similarity.

[0110] Third substep, determine the third data similarity corresponding to the above expansion data.

[0111] Fourth substep, determine the fourth data similarity corresponding to the above expansion value flow data sequence.

[0112] Fifth substep, according to the above second feature similarity, the above third data similarity and the above fourth data similarity, generate the second initial risk similarity. The specific implementation can refer to the generation of the first initial risk similarity.

[0113] The second step, according to the obtained second initial risk similarity set, generates the second initial risk information. The specific implementation can refer to the generation of the first initial risk information.

[0114] The third step, according to the above second initial risk information and the above first initial risk information, generates the above value risk information.

[0115] As an example, the above execution subject can perform weighted processing on the second initial risk information and the first initial risk information to generate the value risk information.

[0116] Optionally, the generating the value risk information according to the second initial risk information and the first initial risk information can include the following steps:

[0117] In a first step, value risk tendency information is determined, wherein the value risk tendency information includes information representing a tendency of the user to take risks and information representing a tendency of the product to take risks.

[0118] In a second step, the value risk information is generated according to the value risk tendency information, the second initial risk information, and the first initial risk information.

[0119] As an example, the execution subject can perform a weighting process on the value risk tendency information, the second initial risk information, and the first initial risk information to generate the value risk information.

[0120] Optionally, as one of the points of the invention, another technical problem of "inaccurate generation of value risk information" is solved. Based on this, the present disclosure can generate first initial risk information and second initial risk information from the perspective of user types and product types, thereby accurately and comprehensively generating value risk information.

[0121] In step 108, the extended value flow data sequence set, the extended data set, the similar value product information set, the target user corresponding user information, and the user group information are stored in the target data storage end, and the information link corresponding to the value risk information and the link description are sent to the value monitoring terminal in the form of email encryption, so that the monitoring user can perform corresponding value operations by decrypting the value risk through the key.

[0122] In some embodiments, the execution subject can store the extended value flow data sequence set, the extended data set, the similar value product information set, the target user corresponding user information, and the user group information in the target data storage end, and send the information link corresponding to the value risk information and the link description to the value monitoring terminal in the form of email encryption, so that the monitoring user can perform corresponding value operations by decrypting the value risk through the key.

[0123] In some optional implementations of some embodiments, after step 108, the steps further include:

[0124] In a first step, the value risk execution result corresponding to the target user is determined. The value risk execution result can be the result after the value operation is performed on the target user. For example, the value operation can be an interest rate increase operation, and the corresponding value risk execution result can be the interest rate after the target user increases the interest rate.

[0125] Secondly, the value risk execution result, the value risk information corresponding to the target user, the extended value flow data sequence set and the extended data set are stored in each first product deployment terminal. In practice, each product deployment terminal stores product data under each product. The product deployment terminals can be divided into multiple levels, and the first product deployment terminal can be a product deployment terminal with a city size greater than a preset size. For example, the first product deployment terminal can be a product deployment terminal deployed at the provincial level. The remaining product terminals can be product terminals distributed in small cities.

[0126] Thirdly, in response to receiving value risk complaint information initiated by a target user at a target terminal, it is determined whether the target terminal is a first product deployment terminal. The value risk complaint information can be a request for re-complaint of value risk. For example, the value risk complaint information can be information for complaint of interest rate reduction.

[0127] Fourthly, in response to a determination that it is not, the first product deployment terminal corresponding to the target terminal is determined as a target first product deployment terminal.

[0128] Fifthly, a user level corresponding to the target user is determined. The user level can represent the importance of the target user. The higher the corresponding user level, the more efficient the selected information transmission method.

[0129] Sixthly, an information transmission method corresponding to the user level is determined as a target information transmission method.

[0130] Seventhly, the value risk execution result stored in the target first product deployment terminal, the value risk information corresponding to the target user, the extended value flow data sequence set and the extended data set are sent to the target terminal by using the target information transmission method, so that the target terminal audits the value risk complaint information.

[0131] The above various embodiments of the present disclosure have the following beneficial effects: the value data processing method of some embodiments of the present disclosure can accurately generate value risk information for the target user, so as to avoid the occurrence of value loss for the target user by performing corresponding value operations on the target user. Specifically, the reason why the related value risk information is not accurate is that the value risk analysis is performed artificially, and there are problems of inaccurate and low efficiency of value risk analysis. Based on this, the value data processing method of some embodiments of the present disclosure first acquires a historical value flow data sequence for a target user and a risk flow data set corresponding to a target value product from a target server. The target value product is a value product in which the target user participates in value. Here, the acquired historical value flow data sequence and risk flow data set are used as a data basis for subsequent generation of value risk information. Then, the similar value product information set corresponding to the target value product and the user group information corresponding to the target user are determined. Here, the similar value product information set and the user group information are used to realize data supplement of the historical value flow data sequence and the risk flow data set. Next, for each similar value product information in the similar value product information set, a central processing unit is used to perform a first generation step: first, the central processing unit can accurately and efficiently determine a first matching information set between the similar risk flow data set corresponding to the similar value product information and the target value product. Here, the obtained first matching information set is used for subsequent screening of the similar risk flow data set to obtain a data set matching the target value product as a data basis for subsequent generation of value risk information. Second, according to the first matching information set, the similar risk flow data set is screened to generate a screening data group, and the obtained screening data group is a data set having a data association with the target value product. Next, for each user information in the user group information, a second matching information between the similar historical value flow data sequence corresponding to the user information and the target user is determined, which is used for subsequent determination of similar data sets having a data association with the target user for data set supplement of the historical value flow data sequence. Next, at least one user information corresponding to the second matching information greater than a target value is screened from the user group information, and an extended value flow data sequence set is generated according to at least one similar historical value flow data sequence corresponding to the at least one user information and the historical value flow data sequence, so as to generate a value flow data set having a data similar relationship or a data association relationship with the target user. Furthermore, an extended data set is generated according to the obtained screening data group set and the risk flow data set, so as to generate a data set having a data similar relationship or a data association relationship with the target value product.Then, according to the above-mentioned extended value flow data sequence set and the above-mentioned extended data set, the value risk information representing the use of the target value product by the target user can be accurately generated in the case of a large amount of data. According to the value risk information, the corresponding accurate value operation is performed on the target user. Finally, the extended value flow data sequence set, the extended data set, the similar value product information set, the target user corresponding user information and the user group information are stored in the target data storage end, and the information link and link description corresponding to the value risk information are sent to the value monitoring terminal in the form of email encryption, so that the monitoring user can perform the corresponding value operation by decrypting the value risk through the key.

[0132] Further referring to Figure 2 , as an implementation of the method shown in each of the above figures, the present disclosure provides some embodiments of a value data processing device, which corresponds to the method embodiments shown in Figure 1 , and the value data processing device can be specifically applied to various electronic devices.

[0133] As Figure 2As shown, a value data processing apparatus 200 includes an acquisition unit 201, a first determination unit 202, a first execution unit 203, a second determination unit 204, a screening generation unit 205, a first generation unit 206, a second generation unit 207, and a second execution unit 208. The acquisition unit 201 is configured to acquire, from a target server, a historical value flow data sequence for a target user and a risk flow data set corresponding to a target value product, wherein the target value product is a value product in which the target user participates in value; the first determination unit 202 is configured to determine a similar value product information set corresponding to the target value product and user group information corresponding to the target user; the first execution unit 203 is configured to, for each similar value product information in the similar value product information set, use a central processing unit to perform a first generation step of determining a first matching information set between a similar risk flow data set corresponding to the similar value product information and the target value product; and performing data screening on the similar risk flow data set according to the first matching information set to generate a screened data group; the second determination unit 204 is configured to, for each user information in the user group information, determine a second matching information between a similar historical value flow data sequence corresponding to the user information and the target user; the screening generation unit 205 is configured to screen at least one user information corresponding to the second matching information greater than a target value from the user group information, and generate an expanded value flow data sequence set according to at least one similar historical value flow data sequence corresponding to the at least one user information and the historical value flow data sequence; the first generation unit 206 is configured to generate an expanded data set according to the obtained screened data group set and the risk flow data set; the second generation unit 207 is configured to generate value risk information representing use of the target value product by the target user according to the expanded value flow data sequence set and the expanded data set; and the second execution unit 208 is configured to store the expanded value flow data sequence set, the expanded data set, the similar value product information set, user information corresponding to the target user, and the user group information in the target data storage end, and send information linked with the value risk information and link instructions in the form of encrypted email to a value monitoring terminal, so that a monitoring user can perform corresponding value operations by decrypting the value risk information through a key.

[0134] It can be understood that the units described in the value data processing apparatus 200 correspond to the respective steps of the method described above. Figure 1 The operations, features, and advantages described above for the method also apply to the value data processing apparatus 200 and the units included therein, and will not be repeated here.

[0135] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device (e.g., an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0136] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0137] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0138] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0139] Note that the computer-readable medium or media used to provide the computer program sequence to the computer system can be embedded in a computer program product, which comprises all the respective features, which are provided with the computer program sequence, and which are enumerated above. It is understood that the computer-readable medium or media described herein are included in the computer program product, or are a component of the computer program product. In some embodiments of the disclosure, the computer-readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer-readable storage medium can be any tangible medium that contains, or stores a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the disclosure, a computer-readable signal medium can include a computer-readable storage medium in baseband or propagated as a carrier wave in a propagated data signal, which contains a computer-readable program code. Such a propagated signal can take a wide variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium that is not a computer-readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0140] In some embodiments, the client, server, or both can communicate using any current known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current known or future developed networks.

[0141] The computer readable medium can be included in the electronic device, or can exist separately from the electronic device. The computer readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: obtain, from a target server, a historical value flow data sequence for a target user and a risk flow data set corresponding to a target value product, wherein the target value product is a value product in which the target user participates in value; determine a similar value product information set corresponding to the target value product and user group information corresponding to the target user; for each similar value product information in the similar value product information set, use a central processing unit to perform a first generation step: determine a first matching information set between a similar risk flow data set corresponding to the similar value product information and the target value product; perform data screening on the similar risk flow data set according to the first matching information set to generate a screened data group; for each user information in the user group information, determine a second matching information between a similar historical value flow data sequence corresponding to the user information and the target user; screen at least one user information corresponding to a second matching information greater than a target value from the user group information, and generate an expanded value flow data sequence set according to at least one similar historical value flow data sequence corresponding to the at least one user information and the historical value flow data sequence; generate an expanded data set according to the obtained screened data group set and the risk flow data set; generate value risk information representing use of the target value product by the target user according to the expanded value flow data sequence set and the expanded data set; store the expanded value flow data sequence set, the expanded data set, the similar value product information set, user information corresponding to the target user, and the user group information in the target data storage end, and send information links and link descriptions corresponding to the value risk information to a value monitoring terminal in an encrypted email form, so that a monitoring user can perform corresponding value operations by decrypting the value risk information using a key.

[0142] Computer program code for carrying out operations of some embodiments of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0143] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a procedure, or a part of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in some cases, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and / or flow diagrams and combinations of blocks in the block diagrams and / or flow diagrams can be implemented by a dedicated hardware-based system that carries out specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0144] The units described in some embodiments of the present disclosure can be implemented by software, or can be implemented by hardware. The described units can also be arranged in a processor, for example, can be described as: a processor includes an acquisition unit, a first determination unit, a first execution unit, a second determination unit, a screening generation unit, a first generation unit, a second generation unit, and a second execution unit. Among them, the names of these units do not constitute a limitation to the units themselves in some cases, for example, the acquisition unit can also be described as: a unit for acquiring the historical value flow data sequence of the target user and the risk flow data set corresponding to the target value product from the target server.

[0145] The functionality described herein above can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program- specific Integrated Circuits (ASICs), Program- specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0146] The above description is merely exemplary of the disclosure and the application made use of the principles of the technology. It is to be understood that the application scope of the embodiments of the disclosure is not limited to the specific combinations of technical features described above, and should also cover other technical solutions formed by any combination of the above technical features or equivalent features thereof without departing from the inventive concept. For example, the technical solutions formed by replacing the above features with technical features having similar functions disclosed in the embodiments of the disclosure (but not limited to) with each other.

Claims

1. A value data processing method, comprising: obtaining a historical value flow data sequence for a target user and a risk flow data set corresponding to a target value product from a target server, wherein the target value product is a value product in which the target user participates in value participation, the historical value flow data sequence is a value flow data sequence during a historical period, the value flow data sequence is a product consumption data sequence, and the risk flow data set is a risk data set for each product use user of the target value product; determining a similar value product information set corresponding to the target value product and user group information corresponding to the target user, the determination of the similar value product information set corresponding to the target value product and the user group information corresponding to the target user comprising: determining an object feature information group corresponding to the target user; determining a user feature information group corresponding to each user information in initial user group information to obtain a user feature information group set; obtaining a selected feature attribute information set and clustering purpose information; performing information weight assignment on each feature attribute information in the feature attribute information set according to the clustering purpose information to generate weight information, to obtain a weight information set, wherein the information size corresponding to the weight information is between -1 and 1; screening a first feature attribute information subset corresponding to weight information with an information size greater than 0 and a second feature attribute information subset corresponding to weight information with an information size less than 0 from the feature attribute information set; using the object feature information group and the user feature information group set as clustering data set, performing user information clustering on a user information set corresponding to the target user and the initial user group information according to the first feature attribute information subset and a first weight information subset corresponding thereto to generate a first user information cluster set; using the object feature information group and the user feature information group set as clustering data set, performing user information clustering on the user information set corresponding to the target user and the initial user group information according to the second feature attribute information subset and a second weight information subset corresponding thereto to generate a second user information cluster set; using each cluster in the second user information cluster set to perform cluster verification on each cluster in the first user information cluster set to generate a verification result; and determining the user group information corresponding to the target user according to the verification result; for each similar value product information in the similar value product information set, using a central processing unit to perform a first generation step: determining a first matching information set between a similar risk flow data set corresponding to the similar value product information and the target value product; performing data screening on the similar risk flow data set according to the first matching information set to generate a screened data group; for each user information in the user group information, determining a second matching information between a similar historical value flow data sequence corresponding to the user information and the target user. screening at least one user information corresponding to second matching information greater than a target value from the user group information, and generating an extended value flow data sequence set according to at least one similar historical value flow data sequence corresponding to the at least one user information and the historical value flow data sequence; generating an extended data set according to the obtained screening data set and the risk flow data set; generating value risk information representing the target user using the target value product according to the extended value flow data sequence set and the extended data set; storing the extended value flow data sequence set, the extended data set, the similar value product information set, the target user corresponding user information and the user group information in the target data storage end, and sending the information link and link description corresponding to the value risk information to the value monitoring terminal in the form of encrypted email, so that the monitoring user can perform corresponding value operation by decrypting the value risk through the key.

2. The method of claim 1, wherein, The determination of the similar value product information set corresponding to the target value product and the user group information corresponding to the target user includes: obtaining the product display graph and product display information corresponding to the target value product, and obtaining the full-amount product display graph and full-amount product display information corresponding to each full-amount value product information in the full-amount value product information set; for each full-amount value product information in the full-amount value product information set, the following second generation step is performed: extracting the keyword set included in the full-amount product display graph corresponding to the full-amount value product information and the corresponding keyword position information set; determining the word attribute information corresponding to each keyword in the keyword set; information association is performed on each keyword in the keyword set and the corresponding word attribute information to generate an associated word and word attribute table; performing graph region division on the product display graph to obtain a divided display graph set, wherein the divided display graph can be an irregularly shaped image; determining the image content weight corresponding to each divided display graph in the divided display graph set according to the graph template corresponding to the product display graph to obtain an image content weight set; determining the position weight corresponding to each keyword position information in the keyword position information set according to the image content weight set to obtain a position weight set; adding the keyword position information set and the position weight set to the associated word and word attribute table to generate an information association table; determining the product information association table corresponding to the product display graph; determining the table content difference information corresponding to each information association table in the obtained information association table set and the product information association table to generate difference information; determining the full-amount value product information corresponding to the difference information satisfying the preset information condition in the full-amount value product information set as the similar value product information to obtain a similar value product information set.

3. The method of claim 2, wherein, The generation of value risk information representing the target user using the target value product according to the extended value flow data sequence set and the extended data set includes: obtain a first value risk feature attribute group based on a product angle and a second value risk feature attribute group based on a user angle; extract a first value risk feature attribute information group set for the first value risk feature attribute group from the extended value flow data sequence set and a second value risk feature attribute information group set for the second value risk feature attribute group from the extended value flow data sequence set; extract a third value risk feature attribute information group set for the first value risk feature attribute group from the extended data set and a fourth value risk feature attribute information group set for the second value risk feature attribute group from the extended data set; generate first candidate value risk information according to the first value risk feature attribute information group set and the third value risk feature attribute information group set; generate second candidate value risk information according to the second value risk feature attribute information group set and the fourth value risk feature attribute information group set; determine an information level corresponding to the first candidate value risk information and an information level corresponding to the second candidate value risk information as a first information level and a second information level, respectively; generate the value risk information according to the first information level, the second information level, a first threshold value for the first candidate value risk information, and a second threshold value for the second candidate value risk information.

4. The method of claim 1, wherein, The method further comprises: determining a value risk execution result corresponding to the target user; distributing the value risk execution result, value risk information corresponding to the target user, the extended value flow data sequence set, and the extended data set in each first product deployment terminal; in response to receiving value risk complaint information initiated by a target user at a target terminal, determining whether the target terminal is a first product deployment terminal; in response to determining that it is not, determining a first product deployment terminal corresponding to the target terminal as a target first product deployment terminal; determining a user level corresponding to the target user; determining an information transmission mode corresponding to the user level as a target information transmission mode; sending the value risk execution result stored by the target first product deployment terminal, the value risk information corresponding to the target user, the extended value flow data sequence set, and the extended data set to the target terminal using the target information transmission mode, so that the target terminal audits the value risk complaint information.

5. A value data processing apparatus, comprising: an obtaining unit configured to obtain, from a target server, a historical value flow data sequence for a target user and a risk flow data set corresponding to a target value product, wherein the target value product is a value product in which the target user participates in value participation, the historical value flow data sequence is a value flow data sequence during a historical period, the value flow data sequence is a product consumption data sequence, and the risk flow data set is a risk data set for each product use user of the target value product; The first determining unit is configured to determine the similar value product information set corresponding to the target value product and the user group information corresponding to the target user, and the determination of the similar value product information set corresponding to the target value product and the user group information corresponding to the target user comprises: determining the object feature information group corresponding to the target user; determining the user feature information group corresponding to each user information in the initial user group information to obtain a user feature information group set; obtaining the selected feature attribute information set and the clustering purpose information; performing information weight assignment on each feature attribute information in the feature attribute information set according to the clustering purpose information to generate weight information and obtain a weight information set, wherein the information size corresponding to the weight information is between-1 and 1; screening a first feature attribute information subset with an information size greater than 0 corresponding to the weight information and a second feature attribute information subset with an information size less than 0 corresponding to the weight information from the feature attribute information set; taking the object feature information group and the user feature information group set as a clustering data set, performing user information clustering on the user information set corresponding to the target user and the initial user group information according to the first feature attribute information subset and the corresponding first weight information subset to generate a first user information cluster set; taking the object feature information group and the user feature information group set as a clustering data set, performing user information clustering on the user information set corresponding to the target user and the initial user group information according to the second feature attribute information subset and the corresponding second weight information subset to generate a second user information cluster set; performing cluster verification on each cluster in the first user information cluster set by using each cluster in the second user information cluster set to generate a verification result; and determining the user group information corresponding to the target user according to the verification result. The first execution unit is configured to, for each similar value product information in the similar value product information set, execute, by using a central processing unit, a first generation step of: determining a first matching information set between a similar risk flow data set corresponding to the similar value product information and the target value product; and performing data screening on the similar risk flow data set according to the first matching information set to generate a screened data group. The second determining unit is configured to, for each user information in the user group information, determine a second matching information between a similar historical value flow data sequence corresponding to the user information and the target user. The screening generation unit is configured to screen at least one user information corresponding to the second matching information greater than a target value from the user group information, and generate an expanded value flow data sequence set according to at least one similar historical value flow data sequence corresponding to the at least one user information and the historical value flow data sequence. The first generation unit is configured to generate an expanded data set according to the obtained screened data group set and the risk flow data set. The second generation unit is configured to generate value risk information representing the target user using the target value product according to the extended value flow data sequence set and the extended data set; The second execution unit is configured to store the extended value flow data sequence set, the extended data set, the similar value product information set, the target user corresponding user information and the user group information in the target data storage end, and send the information link and link description corresponding to the value risk information to the value monitoring terminal in the form of mail encryption, so that the monitoring user can perform corresponding value operation by decrypting the value risk through the key.

6. An electronic device, comprising: one or more processors; storage having stored thereon one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-4.

7. A computer readable medium having stored thereon a computer program, wherein, The program is executed by the processor to implement the method of any one of claims 1-4. The program is executed by the processor to implement the method of any one of claims 1-4.

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