E-commerce online settlement system based on transaction behavior analysis

Abstract

The invention discloses an e-commerce online settlement system based on transaction behavior analysis, which relates to the technical field of online settlement and comprises a commodity preliminary prediction unit, an aggregation feature determination unit, a commodity final prediction unit, a transaction settlement unit and a bogus transaction identification unit. The commodity preliminary prediction unit ensures that the system can preliminarily predict part of commodities required by each user in the next round of shopping, and the prediction accuracy is further improved. The aggregation feature determination unit and the commodity final prediction unit are utilized to determine the aggregation feature representation and the associated feature representation corresponding to each independent sequence set, and the behaviors of browsing and collecting the commodities by the user are combined in the analysis process, so that the subsequent prediction of the commodity selection priority according to the feature representations can be more accurate, and the user experience is improved. The system summarizes the commodity numbers predicted twice, and pushes the commodity information to the user interface according to the commodity numbers, so that the user experience is improved, and meanwhile, the transaction conclusion probability is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of online settlement, and in particular to an e-commerce online settlement system based on transaction behavior analysis. Background Art

[0002] The e-commerce online settlement system based on transaction behavior analysis provides personalized recommendations, risk management and precision marketing functions by deeply analyzing user behavior, helping merchants improve user experience, optimize operations and increase sales. The invention patent with application number 202310890337.3 discloses "an online payment optimization system and method based on an e-commerce platform. The invention can monitor the payment delay parameters in real time and issue an alarm signal when it exceeds the standard delay threshold. When it does not exceed the standard delay threshold, it will be summarized as a data set to be evaluated, so as to predict the payment delay parameters of the predicted payment information, so that the optimization steps can be performed before it exceeds the standard delay threshold, thereby ensuring the smooth operation of the online payment system. In the optimization process, all historical payment information will not be uploaded to the cloud storage, but some historical payment information will be retained in the payment system, which is convenient for users to review historical payment information in a timely manner, further enhancing the user experience."

[0003] The above-mentioned existing technology solves the problem of reduced user experience caused by excessive payment delay parameters. However, when the system is running, since the user's browsing history, collection history and purchase history are not analyzed, the recommended product information cannot attract users to purchase, making the system unable to increase sales for merchants. At the same time, when false transactions occur on the e-commerce platform, the system cannot provide regulators with more accurate data in a short period of time, resulting in the regulators being unable to discover in time and take measures to intervene. Summary of the invention

[0004] The purpose of the present invention is to provide an e-commerce online settlement system based on transaction behavior analysis to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an e-commerce online settlement system based on transaction behavior analysis, comprising a false transaction identification unit; A preliminary commodity prediction unit, wherein the preliminary commodity prediction unit extracts purchase records of all users in a designated e-commerce platform, divides all purchase records according to user numbers and transaction times in the records to obtain multiple groups, constructs multiple independent sequence sets for each group according to the transaction time, counts time feature vectors corresponding to all independent sequence sets in the group, converts them into discrete features using Fourier transform, sets a weight matrix, analyzes frequency feature vectors of each independent sequence set using the weight matrix and discrete features, converts frequency feature vectors of all independent sequence sets into corresponding time feature vectors using inverse Fourier transform, uses an FNN model to analyze feature representations of multiple independent sequence sets corresponding to each user, counts the number of occurrences of each commodity in all independent sequence sets corresponding to the user, and predicts the commodity number corresponding to the independent sequence set of each user in the next round of transactions using the number of occurrences of the commodity and the feature representation of the independent sequence set; An aggregate feature determination unit, wherein the aggregate feature determination unit determines the embedding vectors and interaction label information of the commodities in all independent sequence sets corresponding to each user, combines and analyzes the embedding vectors of the commodities in the independent sequence sets and the interaction label information, thereby determining the interaction time feature vector corresponding to each independent sequence set at different scales, converts the interaction time feature vector into a discrete feature vector, performs a dot multiplication operation on the discrete feature vector through a weight matrix, obtains an optimized interaction frequency feature vector, converts the interaction frequency feature vector into a corresponding interaction time feature vector, and analyzes the aggregate feature representation of multiple independent sequence sets corresponding to each user according to the interaction time feature vectors at different scales; A final product prediction unit, after receiving the product numbers and interaction label information in all independent sequence sets corresponding to each user, analyzes them to obtain a connection matrix corresponding to each user, calculates the connection matrix using a convolutional network, obtains the associated feature representations of multiple independent sequence sets corresponding to each user, analyzes the aggregate feature representation and the associated feature representation of each independent sequence set using a fusion feature analysis algorithm, obtains the fusion feature representation of each independent sequence set corresponding to each user, and predicts the product number corresponding to the independent sequence set of each user in the next round of transactions according to the fusion features of each independent sequence set corresponding to each user; A transaction settlement unit, which extracts all predicted product numbers for each user's next round of transactions, transmits the product information to the user recommendation interface according to the predicted product numbers, and after receiving the settlement request from the user, returns the payment method and process to the user settlement interface, determines the payment completion time, uses it as the transaction time, and generates a purchase record based on the user number, product number, product name and transaction time.

[0006] Preferably, the commodity preliminary prediction unit includes a purchase record division module, a sequence set generation module and a time feature analysis module. The purchase record division module extracts the purchase records of all users in the specified e-commerce platform, the purchase records include user numbers, commodity numbers, commodity names and transaction times, divides all purchase records according to user numbers and transaction times, stores purchase records of the same user number in the same group, sorts the purchase records in the group according to the transaction time, thereby obtaining a plurality of sorted groups. The sequence set generation module determines a plurality of independent sequence sets corresponding to each group according to the transaction time, counts the purchase records with the same transaction time in each group, extracts the commodity numbers in the purchase records with the same transaction time, and stores them in the corresponding independent sequence sets according to the transaction time. The time feature analysis module determines the commodity embedding dimension, the total number of different commodities and the commodity unique hot vector, and then calculates the embedding vector corresponding to the commodity in each independent sequence set according to the embedding dimension, the total number and the unique hot vector, performs a maximum pooling operation on the embedding vector, and obtains the time feature vector of each independent sequence set.

[0007] Preferably, the commodity preliminary prediction unit further comprises a frequency feature analysis module, a feature conversion module and a commodity number prediction module. The frequency feature analysis module statistically groups The time feature vectors corresponding to all independent sequence sets in ,in is the group number, For grouping The number of independent sequence sets contained in , using Fourier transform to convert the time feature vectors of all independent sequence sets Convert to corresponding discrete features ,in Number independent sequence sets and set grouping The two weight matrices and After that, use , and Discrete features of a set of independent sequences Calculate the frequency feature vector of the independent sequence set ,in , represents the activation function, represents the adjustment factor, The characteristic conversion module repeats the operation until the frequency characteristic vectors of each independent sequence set in each group are determined, and then the frequency characteristic vectors of the independent sequence set are analyzed by the sequence characteristic analysis algorithm to obtain the simplified frequency characteristic vectors. The simplified frequency characteristic vectors of all independent sequence sets are converted into corresponding time characteristic vectors by inverse Fourier transform. The product number prediction module transmits the time characteristic vectors of each independent sequence set in each group to the FNN model for analysis. After obtaining the characteristic representations of multiple independent sequence sets corresponding to each user, the number of occurrences of each product in the independent sequence set corresponding to the user is counted, and the number of occurrences of the product and the characteristic representation of the independent sequence set are aggregated and analyzed to obtain the selection priority corresponding to each product, set the number of screening, and predict the product number corresponding to the independent sequence set of each user in the next round of transactions according to the selection priority and the number of screening. The sequence characteristic analysis algorithm is specifically as follows: ; in, Indicates grouping No. The frequency feature vector after simplification of independent sequence sets, Indicates grouping No. The original frequency feature vectors of the independent sequence sets, represents the shrinkage parameter, represents the weight matrix, Indicates the group number. represents the number of the independent sequence set, Indicates the parameters, represents the symbolic function, Represents the maximum value function.

[0008] Preferably, the aggregate feature determination unit includes a record extraction module, a label setting module, an embedded vector analysis module, an interactive feature optimization module and a feature splicing module. The record extraction module extracts browsing records and collection records of all users in the specified e-commerce platform, the browsing records include user numbers, product numbers, product names and browsing time, the collection records include user numbers, product numbers, product names and collection time, the label setting module divides the interaction types into three types, namely collection, browsing and purchase, and determines the interactive label information of the products in all independent sequence sets corresponding to each user according to the browsing records and collection records, wherein the interactive label information includes browsing labels and collection labels, if the transaction time of the product in the independent sequence set is later than the browsing time, the corresponding browsing label is set to 1, otherwise it is set to 0, if the transaction time of the product in the independent sequence set is later than the collection time, the corresponding browsing label is set to 1, otherwise it is set to 0, The embedding vector analysis module combines and analyzes the product embedding vectors and interaction label information in all independent sequence sets corresponding to each user to obtain the interaction embedding vectors of the products in the sequence set, and performs a pooling operation on the interaction embedding vectors of each product to determine the interaction time feature vectors corresponding to each independent sequence set at different scales. The interaction feature optimization module uses Fourier transform to convert the interaction time feature vector of the independent sequence set into a discrete feature vector, and then performs a dot multiplication operation on the discrete feature vector through a weight matrix to obtain an optimized interaction frequency feature vector. The optimized interaction frequency feature vector is converted into a corresponding interaction time feature vector using an inverse Fourier transform. The feature splicing module performs a splicing operation on the interaction time feature vectors at different scales of each independent sequence set to obtain an aggregated time feature vector of the independent sequence set, which is transmitted to the PFFN model for analysis to obtain an aggregated feature representation of multiple independent sequence sets corresponding to each user.

[0009] Preferably, the commodity final prediction unit includes a matrix generation module, an associated feature analysis module, a fusion feature analysis module and a transaction prediction module. After receiving the commodity numbers and commodity interaction label information in all independent sequence sets corresponding to each user, the matrix generation module uses a matrix generation algorithm to analyze the commodity numbers and interaction label information in each independent sequence set to obtain a relationship matrix and an interaction matrix corresponding to each user. The associated feature analysis module performs a splicing operation on the relationship matrix and the interaction matrix corresponding to each user to generate a connection matrix, and uses a convolutional network to analyze the connection matrix to obtain an associated feature representation of multiple independent sequence sets corresponding to each user. The fusion feature analysis module performs a splicing operation on the relationship matrix and the interaction matrix corresponding to each user to generate a connection matrix, and uses a convolutional network to analyze the connection matrix to obtain an associated feature representation of multiple independent sequence sets corresponding to each user. After receiving the aggregate feature representation and associated feature representation of multiple independent sequence sets corresponding to each user, the module calculates the aggregate feature representation and the associated feature representation respectively, and after determining the dynamic weights corresponding to the two, the dynamic weight, aggregate feature representation and associated feature representation of each independent sequence set are analyzed by the fusion feature analysis algorithm to obtain the fusion feature representation of each independent sequence set corresponding to each user. The transaction prediction module analyzes the commodity selection priority corresponding to each user according to the fusion features of each independent sequence set corresponding to each user, and predicts the commodity number corresponding to the independent sequence set of each user in the next round of transactions according to the selection priority and screening number of the commodities. The matrix generation algorithm is specifically as follows: ; ; in, Indicates user The relationship matrix, Indicates The product nodes in the independent sequence set, Indicates The relationship edge between items in the independent sequence set, Indicates user The interaction matrix, Indicates The commodity nodes with interactive label information in the independent sequence set, Indicates The interaction edges between the products in the independent sequence sets, Indicates The set of all commodity nodes in an independent sequence set, Indicates The set of all commodity nodes with interactive label information in an independent sequence set, Indicates the parameters, represents the number of the independent sequence set, Indicates Users, Indicates the user's ID.

[0010] Preferably, the transaction settlement unit includes a product recommendation module, a price determination module and a product settlement module. After the product recommendation module extracts all predicted product numbers of each user's next round of transactions, it queries the database according to the predicted product numbers, and the obtained product information is transmitted to the user recommendation interface. When the user browses and collects products, a corresponding record is generated and transmitted to the database. After the price determination module receives the settlement request issued by the user, it determines the actual price of the product according to the product number, user number and request time contained in the settlement request, returns it to the user settlement interface and waits for the user confirmation information. If the product settlement module does not receive the confirmation information within the preset time, it is determined that the current user has not purchased the product, and the request reception time is used as the collection time of the current user for the product. A collection record is generated according to the user number, product number, product name and collection time, and stored in the database. If the confirmation information is received within the preset time, the payment method and process are returned to the user settlement interface, the payment completion time is determined, and it is used as the transaction time. A purchase record is generated according to the user number, product number, product name and transaction time, and stored in the database.

[0011] Preferably, the false transaction identification unit includes a comment record screening module, a user node storage module and a node marking module, the comment record screening module extracts the product comment records of each user in the specified e-commerce platform, wherein the product comment record includes the user number, product number, comment content and comment time, determines the total number of corresponding purchase records according to the user number, if the total number is lower than the threshold, then all the product comment records of the user are removed, determines the total number of comment records according to the product number, if the total number is lower than the threshold, then all the comment records corresponding to the product are removed, after the user node storage module determines the product comment records and purchase records that have not been removed, calculates the similarity between users according to the product comment records and purchase records, and calculates the similarity between users according to the user A plurality of user nodes are constructed based on the total number and user numbers, the distance values ​​between the user nodes are determined by the similarity between the users, and all the user nodes are stored in a set. After the node marking module sets the first distance value and the second distance value, an unmarked user node is randomly selected from the set, and it is used as the target node and marked. The distance values ​​between the target node and other nodes are counted. If the distance value is less than the first distance value, the current node is added to the adjacent set of the target node and marked. If the distance value is greater than or equal to the first distance value and less than the second distance value, the current node is added to the adjacent set without marking. If the distance value is greater than the second distance value, the current node is not added to the adjacent set. The operation is repeated until all nodes in the set are marked.

[0012] Preferably, the false transaction identification unit also includes a group scoring module and a falseness display module. The group scoring module counts the center points in each adjacent set and uses it as the initial center point. The K-Mean clustering algorithm is used to classify all nodes in the set according to the initial center point to obtain multiple candidate groups. The transaction evaluation algorithm is used to analyze each candidate group to determine the transaction time interval variance, closeness, commodity co-occurrence times and repeated purchase times corresponding to each group, and each group is scored according to the transaction time interval variance, closeness, commodity co-occurrence times and repeated purchase times. The falseness display module selects the group with the highest score, determines the commodity scoring records and purchase records corresponding to all user nodes in the group, analyzes the transaction falseness of the corresponding commodity according to the commodity scoring records and purchase records, and outputs the user's commodity scoring records, purchase records and commodity transaction falseness through a visual interface.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention divides the purchase record of each user into multiple independent sequence sets through a preliminary commodity prediction unit, so as to combine the number of commodity purchases with the corresponding shopping needs in different transaction times, and ensure that the system can preliminarily predict some of the commodities required by each user in the next round of shopping. In order to further improve the accuracy of the prediction, the multiple independent sequence sets contained in each user are analyzed by using an aggregate feature determination unit and a final commodity prediction unit, and the aggregate feature representation and the associated feature representation corresponding to each independent sequence set are obtained. Since the user's browsing and collection of commodity behaviors are combined in the analysis process, the subsequent prediction of the commodity selection priority according to these feature representations can be more accurate. The system summarizes the commodity numbers predicted twice, and pushes the commodity information to the user interface according to the commodity numbers, so as to improve the user experience and increase the probability of transaction completion. The present invention receives the settlement request sent by the user through the transaction settlement unit, and automatically generates a purchase record when the transaction is completed, which is convenient for predicting the user's next round of shopping needs. At the same time, when the transaction fails, the collection record is automatically generated according to the user information and product information. On the one hand, it is convenient for the user to re-settle, and on the other hand, it also supplements the user behavior data to ensure that the setting of the product selection priority can be scientific and reasonable. The system uses the false transaction identification unit to analyze the user's purchase record and comment record to prevent the generation of false transactions from interfering with the user's selection of products, and quickly identify the user numbers and product numbers involved in these non-compliant transactions, so that the platform regulators can discover them in time and take measures to intervene. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Provides a schematic diagram of the overall system flow for an embodiment of the present invention; Figure 2A block diagram of the internal modules of the commodity preliminary prediction unit provided by an embodiment of the present invention; Figure 3 A block diagram of the internal modules of the aggregate feature determination unit provided in an embodiment of the present invention; Figure 4 A block diagram of the internal modules of the commodity final prediction unit provided by an embodiment of the present invention; Figure 5 A block diagram of the internal modules of a transaction settlement unit provided in an embodiment of the present invention; Figure 6 This is a block diagram of the internal modules of the false transaction identification unit provided in an embodiment of the present invention.

[0015] In the figure: 1. Product preliminary prediction unit; 101. Purchase record division module; 102. Sequence set generation module; 103. Time feature analysis module; 104. Frequency feature analysis module; 105. Feature conversion module; 106. Product number prediction module; 2. Aggregate feature determination unit; 201. Record extraction module; 202. Label setting module; 203. Embedded vector analysis module; 204. Interactive feature optimization module; 205. Feature splicing module; 3. Product final prediction unit; 301. Matrix generation module; 302. Correlation feature analysis module; 303. Fusion feature analysis module; 304. Transaction prediction module; 4. Transaction settlement unit; 401. Product recommendation module; 402. Price determination module; 403. Product settlement module; 5. False transaction identification unit; 501. Comment record screening module; 502. User node storage module; 503. Node marking module; 504. Group scoring module; 505. False degree display module. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1-Figure 6 ,The present invention provides a technical solution: an e-commerce online settlement system based on transaction behavior analysis, comprising a false transaction identification unit 5; The preliminary commodity prediction unit 1 extracts the purchase records of all users in the designated e-commerce platform, divides all the purchase records according to the user number and transaction time in the record, obtains multiple groups, constructs multiple independent sequence sets for each group according to the transaction time, counts the time feature vectors corresponding to all the independent sequence sets in the group, converts them into discrete features by Fourier transform, sets the weight matrix, analyzes the frequency feature vector of each independent sequence set by using the weight matrix and the discrete features, converts the frequency feature vectors of all the independent sequence sets into the corresponding time feature vector by inverse Fourier transform, uses the FNN model to analyze the feature representation of multiple independent sequence sets corresponding to each user, counts the number of occurrences of each commodity in all the independent sequence sets corresponding to the user, and predicts the commodity number corresponding to the independent sequence set of each user in the next round of transactions by using the number of occurrences of the commodity and the feature representation of the independent sequence set; Aggregate feature determination unit 2, aggregate feature determination unit 2 determines the embedding vectors and interaction label information of the commodities in all independent sequence sets corresponding to each user, combines and analyzes the embedding vectors of the commodities in the independent sequence sets and the interaction label information, thereby determining the interaction time feature vector corresponding to each independent sequence set at different scales, converts the interaction time feature vector into a discrete feature vector, performs a dot multiplication operation on the discrete feature vector through a weight matrix, obtains an optimized interaction frequency feature vector, converts the interaction frequency feature vector into a corresponding interaction time feature vector, and analyzes the aggregate feature representation of multiple independent sequence sets corresponding to each user according to the interaction time feature vectors at different scales; The final product prediction unit 3 receives the product numbers and interaction label information in all independent sequence sets corresponding to each user, analyzes them to obtain the connection matrix corresponding to each user, calculates the connection matrix using a convolutional network, obtains the associated feature representations of multiple independent sequence sets corresponding to each user, analyzes the aggregate feature representation and the associated feature representation of each independent sequence set using a fusion feature analysis algorithm, obtains the fusion feature representation of each independent sequence set corresponding to each user, and predicts the product number corresponding to the independent sequence set of each user in the next round of transactions based on the fusion features of each independent sequence set corresponding to each user; Transaction settlement unit 4, after extracting all predicted product numbers of each user's next round of transactions, transmits the product information to the user recommendation interface according to the predicted product numbers, and after receiving the settlement request from the user, returns the payment method and process to the user settlement interface, determines the payment completion time, uses it as the transaction time, and generates a purchase record based on the user number, product number, product name and transaction time.

[0018] The commodity preliminary prediction unit 1 includes a purchase record division module 101, a sequence set generation module 102 and a time feature analysis module 103. The purchase record division module 101 extracts the purchase records of all users in the specified e-commerce platform. The purchase records include user numbers, commodity numbers, commodity names and transaction times. All purchase records are divided according to user numbers and transaction times, and purchase records of the same user number are stored in the same group. The purchase records in the group are sorted according to the transaction time, so as to obtain multiple sorted groups. The sequence set generation module 102 determines multiple independent sequence sets corresponding to each group according to the transaction time, counts the purchase records with the same transaction time in each group, extracts the commodity numbers in the purchase records with the same transaction time, and stores them in the corresponding independent sequence sets according to the transaction time. The time feature analysis module 103 determines the commodity embedding dimension, the total number of different commodities and the commodity unique hot vector, and then calculates the embedding vector corresponding to the commodity in each independent sequence set according to the embedding dimension, the total number and the unique hot vector, performs a maximum pooling operation on the embedding vector, and obtains the time feature vector of each independent sequence set. The commodity preliminary prediction unit 1 further includes a frequency feature analysis module 104, a feature conversion module 105 and a commodity number prediction module 106. The frequency feature analysis module 104 performs statistical grouping. The time feature vectors corresponding to all independent sequence sets in ,in is the group number, For grouping The number of independent sequence sets contained in , using Fourier transform to convert the time feature vectors of all independent sequence sets Convert to corresponding discrete features ,in Number independent sequence sets and set grouping The two weight matrices and After that, use , and Discrete features of a set of independent sequences Calculate the frequency feature vector of the independent sequence set ,in , represents the activation function, represents the adjustment factor, The characteristic conversion module 105 repeats the operation until the frequency characteristic vector of each independent sequence set in each group is determined, and then the frequency characteristic vector of the independent sequence set is analyzed by using the sequence characteristic analysis algorithm to obtain a simplified frequency characteristic vector, and the simplified frequency characteristic vectors of all independent sequence sets are converted into corresponding time characteristic vectors by inverse Fourier transform. The product number prediction module 106 transmits the time characteristic vector of each independent sequence set in each group to the FNN model for analysis, and after obtaining the characteristic representation of multiple independent sequence sets corresponding to each user, the number of occurrences of each product in the independent sequence set corresponding to the user is counted, and the number of occurrences of the product and the characteristic representation of the independent sequence set are aggregated and analyzed to obtain the selection priority corresponding to each product, and the number of screening is set. According to the selection priority and the number of screening of the product, the product number corresponding to the independent sequence set of each user in the next round of transactions is predicted. The sequence characteristic analysis algorithm is specifically as follows: ; in, Indicates grouping No. The frequency feature vector after simplification of independent sequence sets, Indicates grouping No. The original frequency feature vectors of the independent sequence sets, represents the shrinkage parameter, represents the weight matrix, Indicates the group number. represents the number of the independent sequence set, Indicates the parameters, represents the symbolic function, represents the maximum value function; The aggregate feature determination unit 2 includes a record extraction module 201, a label setting module 202, an embedded vector analysis module 203, an interactive feature optimization module 204 and a feature splicing module 205. The record extraction module 201 extracts the browsing records and collection records of all users in the specified e-commerce platform. The browsing records include user numbers, product numbers, product names and browsing time. The collection records include user numbers, product numbers, product names and collection time. The label setting module 202 divides the interaction types into three types, namely collection, browsing and purchase. The interactive label information of the products in all independent sequence sets corresponding to each user is determined according to the browsing records and collection records, wherein the interactive label information includes browsing labels and collection labels. If the transaction time of the product in the independent sequence set is later than the browsing time, the corresponding browsing label is set to 1, otherwise it is set to 0. If the transaction time of the product in the independent sequence set is later than the collection time, the corresponding browsing label is set to 1, otherwise it is set to 0. The embedding vector analysis module 203 combines and analyzes the product embedding vectors and interaction label information in all independent sequence sets corresponding to each user to obtain the interaction embedding vectors of the products in the sequence set, and performs a pooling operation on the interaction embedding vectors of each product to determine the interaction time feature vectors corresponding to each independent sequence set at different scales. The interaction feature optimization module 204 uses Fourier transform to convert the interaction time feature vector of the independent sequence set into a discrete feature vector, and then performs a point multiplication operation on the discrete feature vector through a weight matrix to obtain an optimized interaction frequency feature vector. The optimized interaction frequency feature vector is converted into a corresponding interaction time feature vector using an inverse Fourier transform. The feature splicing module 205 splices the interaction time feature vectors at different scales of each independent sequence set to obtain an aggregated time feature vector of the independent sequence set, and transmits it to the PFFN model for analysis to obtain an aggregated feature representation of multiple independent sequence sets corresponding to each user. The commodity final prediction unit 3 includes a matrix generation module 301, an associated feature analysis module 302, a fusion feature analysis module 303 and a transaction prediction module 304. After receiving the commodity numbers and commodity interaction label information in all independent sequence sets corresponding to each user, the matrix generation module 301 uses a matrix generation algorithm to analyze the commodity numbers and interaction label information in each independent sequence set to obtain a relationship matrix and an interaction matrix corresponding to each user. The associated feature analysis module 302 performs a splicing operation on the relationship matrix and the interaction matrix corresponding to each user to generate a connection matrix. The connection matrix is ​​analyzed using a convolutional network to obtain the associated feature representation of multiple independent sequence sets corresponding to each user. The fusion feature After receiving the aggregated feature representations and associated feature representations of multiple independent sequence sets corresponding to each user, the analysis module 303 calculates the aggregated feature representation and the associated feature representation respectively, and after determining the dynamic weights corresponding to the two, analyzes the dynamic weights, aggregated feature representations and associated feature representations of each independent sequence set through a fusion feature analysis algorithm to obtain the fusion feature representations of each independent sequence set corresponding to each user. The transaction prediction module 304 analyzes the commodity selection priority corresponding to each user according to the fusion features of each independent sequence set corresponding to each user, and predicts the commodity number corresponding to the independent sequence set of each user in the next round of transactions according to the commodity selection priority and the number of screenings. The matrix generation algorithm is specifically as follows: ; ; in, Indicates user The relationship matrix, Indicates The product nodes in the independent sequence set, Indicates The relationship edge between items in the independent sequence set, Indicates user The interaction matrix, Indicates The commodity nodes with interactive label information in the independent sequence set, Indicates The interaction edges between the products in the independent sequence sets, Indicates The set of all commodity nodes in an independent sequence set, Indicates The set of all commodity nodes with interactive label information in an independent sequence set, Indicates the parameters, represents the number of the independent sequence set, Indicates Users, Indicates the user's ID; The specific fusion feature analysis algorithm is as follows: ; in, Indicates user No. The fusion feature representation of independent sequence sets is represents the training matrix, represents the transposed training matrix, represents the parameter matrix, Indicates user No. The aggregate feature representation of a set of independent sequences, Indicates user No. The associated feature representation of a set of independent sequences, Indicates the user's ID. represents the number of the independent sequence set, Indicates Users; The transaction settlement unit 4 includes a product recommendation module 401, a price determination module 402 and a product settlement module 403. After the product recommendation module 401 extracts all predicted product numbers of each user's next round of transactions, it searches the database according to the predicted product numbers, and transmits the obtained product information to the user recommendation interface. When the user browses and collects products, a corresponding record is generated and transmitted to the database. After the price determination module 402 receives the settlement request issued by the user, it determines the actual price of the product according to the product number, user number and request time contained in the settlement request, returns it to the user settlement interface and waits for the user's confirmation information. If the product settlement module 403 does not receive the confirmation information within the preset time, it determines that the current user has not purchased the product, and uses the request reception time as the current user's collection time for the product. A collection record is generated according to the user number, product number, product name and collection time, and stored in the database. If the confirmation information is received within the preset time, the payment method and process are returned to the user settlement interface, the payment completion time is determined, and it is used as the transaction time. A purchase record is generated according to the user number, product number, product name and transaction time, and stored in the database; The fake transaction identification unit 5 includes a comment record screening module 501, a user node storage module 502 and a node marking module 503. The comment record screening module 501 extracts the product comment records of each user in the specified e-commerce platform, wherein the product comment record includes the user number, product number, comment content and comment time, determines the total number of corresponding purchase records according to the user number, and if the total number is lower than the threshold, all the product comment records of the user are removed, determines the total number of comment records according to the product number, and if the total number is lower than the threshold, all the comment records corresponding to the product are removed, and after the user node storage module 502 determines the product comment records and purchase records that have not been removed, it calculates the similarity between users according to the product comment records and purchase records, and then calculates the similarity between users according to the product comment records and purchase records. Construct multiple user nodes according to the total number of users and user numbers, determine the distance value between user nodes according to the similarity between users, store all user nodes in a set, and after the node marking module 503 sets the first distance value and the second distance value, arbitrarily select an unmarked user node from the set, use it as the target node and mark it, count the distance value between the target node and other nodes, if the distance value is less than the first distance value, add the current node to the adjacent set of the target node and mark it, if the distance value is greater than or equal to the first distance value and less than the second distance value, add the current node to the adjacent set without marking it, if the distance value is greater than the second distance value, do not add the current node to the adjacent set, and repeat the operation until all nodes in the set are marked; The fake transaction identification unit 5 also includes a group scoring module 504 and a false degree display module 505. After the group scoring module 504 counts the center point in each adjacent set, it takes it as the initial center point, and uses the K-Mean clustering algorithm to classify all nodes in the set according to the initial center point to obtain multiple candidate groups. The transaction evaluation algorithm is used to analyze each candidate group to determine the transaction time interval variance, closeness, commodity co-occurrence number and repeated purchase number corresponding to each group, and each group is scored according to the transaction time interval variance, closeness, commodity co-occurrence number and repeated purchase number. The false degree display module 505 selects the group with the highest score, determines the commodity scoring records and purchase records corresponding to all user nodes in the group, and analyzes the transaction false degree of the corresponding commodity according to the commodity scoring records and purchase records, and outputs the user's commodity scoring records, purchase records and commodity transaction false degree through a visual interface. The transaction evaluation algorithm is specifically as follows: ; in, Indicates The maximum number of co-occurrences of products in a group, Indicates Average number of repeat purchases in a group, Indicates the transaction interval time corresponding to the user. Represents the average value of the transaction interval time, Indicates the number of user nodes in the group, Indicates Total number of comment records for groups, Indicates The Cartesian product of users and items in a group, Indicates The number of co-occurrences of products, Indicates The number of repeated purchases of products, and Indicates a sequence number.

[0019] Working principle: The present invention obtains a plurality of sorted groups through the purchase record division module 101 in the commodity preliminary prediction unit 1, the sequence set generation module 102 determines a plurality of independent sequence sets corresponding to each group, obtains a time feature vector of each independent sequence set through the time feature analysis module 103, calculates a frequency feature vector of the independent sequence set through the frequency feature analysis module 104, converts the simplified frequency feature vector into a corresponding time feature vector through the feature conversion module 105, predicts the commodity number corresponding to the independent sequence set of each user in the next round of transactions through the commodity number prediction module 106, and the record extraction module 20 in the aggregation feature determination unit 20 1 extracts the browsing records and collection records of all users in the specified e-commerce platform, the label setting module 202 determines the interactive label information of the products in all independent sequence sets corresponding to each user, and uses the embedded vector analysis module 203 to obtain the interactive time feature vector of each independent sequence set at different scales. The interactive feature optimization module 204 converts the optimized interactive frequency feature vector into the interactive time feature vector, and uses the feature splicing module 205 to obtain the aggregated feature representation of multiple independent sequence sets corresponding to each user. The matrix generation module 301 in the product final prediction unit 3 outputs the relationship matrix and interaction matrix of each user, and the correlation feature analysis module 302 obtains the interaction matrix of each user. The associated feature representations of multiple independent sequence sets corresponding to each user are obtained by the fusion feature analysis module 303, which analyzes the aggregate feature representation and the associated feature representation of each independent sequence set to obtain the fusion feature representation of each independent sequence set corresponding to each user. The transaction prediction module 304 is used to predict the product number corresponding to the independent sequence set of each user in the next round of transactions. The product recommendation module 401 in the transaction settlement unit 4 queries the database according to the predicted product number, and the obtained product information is transmitted to the user recommendation interface. The price determination module 402 determines the actual price of the product and returns it to the user settlement interface. The product settlement module 403 records the payment completion. Time, the comment record screening module 501 in the false transaction identification unit 5 extracts the product comment record of each user in the specified e-commerce platform, the user node storage module 502 constructs multiple user nodes according to the total number of users and user numbers, and determines the distance value between user nodes by the similarity between users. The node marking module 503 constructs a corresponding adjacent set for each target node, and the group scoring module 504 scores each group according to the transaction time interval variance, closeness, number of co-occurrences of goods and number of repeated purchases. The falseness display module 505 outputs the user's product rating record, purchase record and transaction falseness of the product through a visual interface.

[0020] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0021] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An e-commerce online settlement system based on transaction behavior analysis, comprising a false transaction identification unit (5), characterized in that: A commodity preliminary prediction unit (1), wherein the commodity preliminary prediction unit (1) extracts purchase records of all users in a designated e-commerce platform, divides all purchase records according to user numbers and transaction times in the records to obtain multiple groups, constructs multiple independent sequence sets for each group according to the transaction time, counts time feature vectors corresponding to all independent sequence sets in the group, converts them into discrete features using Fourier transform, sets a weight matrix, analyzes frequency feature vectors of each independent sequence set using the weight matrix and discrete features, converts the frequency feature vectors of all independent sequence sets into corresponding time feature vectors using inverse Fourier transform, analyzes feature representations of multiple independent sequence sets corresponding to each user using an FNN model, counts the number of occurrences of each commodity in all independent sequence sets corresponding to the user, and predicts the commodity number corresponding to the independent sequence set of each user in the next round of transactions using the number of occurrences of the commodity and the feature representation of the independent sequence set; An aggregate feature determination unit (2), wherein the aggregate feature determination unit (2) determines the embedding vectors and interaction label information of the products in all independent sequence sets corresponding to each user, combines and analyzes the embedding vectors and interaction label information of the products in the independent sequence sets, thereby determining the interaction time feature vectors corresponding to each independent sequence set at different scales, converts the interaction time feature vectors into discrete feature vectors, performs a dot multiplication operation on the discrete feature vectors through a weight matrix, obtains an optimized interaction frequency feature vector, converts the interaction frequency feature vectors into corresponding interaction time feature vectors, and analyzes the aggregate feature representations of multiple independent sequence sets corresponding to each user based on the interaction time feature vectors at different scales; A final product prediction unit (3), wherein the final product prediction unit (3) receives the product numbers and interaction label information in all independent sequence sets corresponding to each user, analyzes them to obtain a connection matrix corresponding to each user, calculates the connection matrix using a convolutional network, obtains the associated feature representations of multiple independent sequence sets corresponding to each user, analyzes the aggregate feature representation and the associated feature representation of each independent sequence set using a fusion feature analysis algorithm, obtains the fusion feature representation of each independent sequence set corresponding to each user, and predicts the product number corresponding to the independent sequence set of each user in the next round of transactions based on the fusion features of each independent sequence set corresponding to each user; A transaction settlement unit (4), wherein the transaction settlement unit (4) extracts all predicted product numbers for the next round of transactions of each user, transmits product information to the user recommendation interface according to the predicted product numbers, receives a settlement request from the user, returns the payment method and process to the user settlement interface, determines the payment completion time, uses it as the transaction time, and generates a purchase record based on the user number, product number, product name and transaction time.

2. The e-commerce online settlement system based on transaction behavior analysis according to claim 1, characterized in that: The commodity preliminary prediction unit (1) comprises a purchase record division module (101), a sequence set generation module (102) and a time feature analysis module (103). The purchase record division module (101) extracts purchase records of all users in a specified e-commerce platform, wherein the purchase records include a user number, a commodity number, a commodity name and a transaction time, divides all purchase records according to the user number and the transaction time, stores purchase records of the same user number in the same group, and sorts the purchase records in the group according to the transaction time, thereby obtaining a plurality of sorted groups. The sequence set generation module (102) determines a plurality of independent sequence sets corresponding to each group according to the transaction time, counts purchase records with the same transaction time in each group, extracts commodity numbers in purchase records with the same transaction time, and stores them in the corresponding independent sequence sets according to the transaction time. The time feature analysis module (103) determines the commodity embedding dimension, the total number of different commodities and the commodity unique hot vector, and then calculates the embedding vector corresponding to the commodity in each independent sequence set according to the embedding dimension, the total number and the unique hot vector, performs a maximum pooling operation on the embedding vector, and obtains the time feature vector of each independent sequence set.

3. The e-commerce online settlement system based on transaction behavior analysis according to claim 2 is characterized by: The commodity preliminary prediction unit (1) further comprises a frequency feature analysis module (104), a feature conversion module (105) and a commodity number prediction module (106). The frequency feature analysis module (104) performs statistical grouping. The time feature vectors corresponding to all independent sequence sets in ,in is the group number, For grouping The number of independent sequence sets contained in , using Fourier transform to convert the time feature vectors of all independent sequence sets Convert to corresponding discrete features ,in Number independent sequence sets and set groups The two weight matrices and After that, use , and Discrete features of a set of independent sequences Calculate the frequency feature vector of the independent sequence set ,in , represents the activation function, represents the adjustment factor, The feature conversion module (105) repeats the operation until the frequency feature vectors of each independent sequence set in each group are determined, and then uses a sequence feature analysis algorithm to analyze the frequency feature vectors of the independent sequence set to obtain a simplified frequency feature vector. The simplified frequency feature vectors of all independent sequence sets are converted into corresponding time feature vectors by inverse Fourier transform. The product number prediction module (106) transmits the time feature vectors of each independent sequence set in each group to the FNN model for analysis. After obtaining the feature representations of multiple independent sequence sets corresponding to each user, the number of occurrences of each product in the independent sequence set corresponding to the user is counted, and the number of occurrences of the product and the feature representation of the independent sequence set are aggregated and analyzed to obtain the selection priority corresponding to each product, set the number of screening, and predict the product number corresponding to the independent sequence set of each user in the next round of transactions according to the selection priority and the number of screening.

4. The e-commerce online settlement system based on transaction behavior analysis according to claim 1, characterized in that: The aggregate feature determination unit (2) comprises a record extraction module (201), a label setting module (202), an embedded vector analysis module (203), an interactive feature optimization module (204) and a feature splicing module (205). The record extraction module (201) extracts browsing records and collection records of all users in a specified e-commerce platform. The browsing records include a user number, a product number, a product name and a browsing time. The collection records include a user number, a product number, a product name and a collection time. The label setting module (202) divides the interaction types into three types, namely collection, browsing and purchase. The interactive label information of the products in all independent sequence sets corresponding to each user is determined based on the browsing records and the collection records. The interactive label information includes a browsing label and a collection label. If the transaction time of the product in the independent sequence set is later than the browsing time, the corresponding browsing label is set to 1, otherwise it is set to 0. If the transaction time of the product in the independent sequence set is later than the collection time, the corresponding browsing label is set to 1. Otherwise, it is set to 0. The embedding vector analysis module (203) combines and analyzes the product embedding vectors and interaction label information in all independent sequence sets corresponding to each user to obtain the interaction embedding vectors of the products in the sequence set, and performs a pooling operation on the interaction embedding vectors of each product to determine the interaction time feature vectors corresponding to each independent sequence set at different scales. The interaction feature optimization module (204) uses Fourier transform to convert the interaction time feature vector of the independent sequence set into a discrete feature vector, and then performs a dot multiplication operation on the discrete feature vector through a weight matrix to obtain an optimized interaction frequency feature vector. The optimized interaction frequency feature vector is converted into a corresponding interaction time feature vector using an inverse Fourier transform. The feature splicing module (205) performs a splicing operation on the interaction time feature vectors at different scales of each independent sequence set to obtain an aggregated time feature vector of the independent sequence set, which is transmitted to the PFFN model for analysis to obtain an aggregated feature representation of multiple independent sequence sets corresponding to each user.

5. The e-commerce online settlement system based on transaction behavior analysis according to claim 1, characterized in that: The commodity final prediction unit (3) comprises a matrix generation module (301), an associated feature analysis module (302), a fusion feature analysis module (303) and a transaction prediction module (304). After receiving the commodity numbers and commodity interaction tag information in all independent sequence sets corresponding to each user, the matrix generation module (301) uses a matrix generation algorithm to analyze the commodity numbers and interaction tag information in each independent sequence set to obtain a relationship matrix and an interaction matrix corresponding to each user. The associated feature analysis module (302) performs a splicing operation on the relationship matrix and the interaction matrix corresponding to each user to generate a connection matrix. The connection matrix is ​​analyzed using a convolutional network to obtain a plurality of independent sequence sets corresponding to each user. The fusion feature analysis module (303) receives the aggregate feature representation and the associated feature representation of the multiple independent sequence sets corresponding to each user, and then respectively calculates the aggregate feature representation and the associated feature representation, and after determining the dynamic weights corresponding to the two, analyzes the dynamic weight, the aggregate feature representation and the associated feature representation of each independent sequence set through the fusion feature analysis algorithm to obtain the fusion feature representation of each independent sequence set corresponding to each user. The transaction prediction module (304) analyzes the commodity selection priority corresponding to each user according to the fusion features of each independent sequence set corresponding to each user, and predicts the commodity number corresponding to the independent sequence set of each user in the next round of transactions according to the commodity selection priority and the screening number.

6. The e-commerce online settlement system based on transaction behavior analysis according to claim 1, characterized in that: The transaction settlement unit (4) includes a product recommendation module (401), a price determination module (402) and a product settlement module (403). After the product recommendation module (401) extracts all predicted product numbers of each user's next round of transactions, it searches the database according to the predicted product numbers, and transmits the obtained product information to the user recommendation interface. When the user browses and collects products, a corresponding record is generated and transmitted to the database. After the price determination module (402) receives a settlement request from the user, it determines the actual price of the product according to the product number, user number and request time contained in the settlement request, and sends it to the database. Return to the user settlement interface and wait for user confirmation information. If the product settlement module (403) does not receive confirmation information within the preset time, it is determined that the current user has not purchased the product, and the request reception time is used as the current user's collection time for the product. A collection record is generated according to the user number, product number, product name and collection time, and stored in the database. If a confirmation message is received within the preset time, the payment method and process are returned to the user settlement interface, the payment completion time is determined, and it is used as the transaction time. A purchase record is generated according to the user number, product number, product name and transaction time, and stored in the database.

7. The e-commerce online settlement system based on transaction behavior analysis according to claim 1, characterized in that: The false transaction identification unit (5) comprises a comment record screening module (501), a user node storage module (502) and a node marking module (503). The comment record screening module (501) extracts the product comment record of each user in the specified e-commerce platform, wherein the product comment record includes the user number, the product number, the comment content and the comment time, determines the total number of corresponding purchase records according to the user number, and if the total number is lower than a threshold, all the product comment records of the user are removed, determines the total number of comment records according to the product number, and if the total number is lower than the threshold, all the comment records corresponding to the product are removed, and after the user node storage module (502) determines the product comment records and purchase records that have not been removed, calculates the user's purchase history according to the product comment records and purchase records. The similarity between the two nodes is determined, and multiple user nodes are constructed according to the total number of users and the user numbers. The distance values ​​between the user nodes are determined according to the similarity between the users, and all the user nodes are stored in a set. After the node marking module (503) sets the first distance value and the second distance value, an unmarked user node is randomly selected from the set, and the node is used as a target node and marked. The distance values ​​between the target node and other nodes are counted. If the distance value is less than the first distance value, the current node is added to the adjacent set of the target node and marked. If the distance value is greater than or equal to the first distance value and less than the second distance value, the current node is added to the adjacent set without marking. If the distance value is greater than the second distance value, the current node is not added to the adjacent set. The operation is repeated until all nodes in the set are marked.

8. The e-commerce online settlement system based on transaction behavior analysis according to claim 7, characterized in that: The false transaction identification unit (5) further comprises a group scoring module (504) and a false degree display module (505). The group scoring module (504) counts the center points in each adjacent set and uses the center points as the initial center points. The K-Mean clustering algorithm is used to classify all nodes in the set according to the initial center points to obtain multiple candidate groups. The transaction evaluation algorithm is used to analyze each candidate group to determine the transaction time interval variance, closeness, commodity co-occurrence times and repeated purchase times corresponding to each group. Each group is scored according to the transaction time interval variance, closeness, commodity co-occurrence times and repeated purchase times. The false degree display module (505) selects the group with the highest score, determines the commodity scoring records and purchase records corresponding to all user nodes in the group, analyzes the transaction false degree of the corresponding commodity according to the commodity scoring records and purchase records, and outputs the user's commodity scoring records, purchase records and commodity transaction false degree through a visual interface.

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