Methods, apparatus and equipment for identifying target customer groups based on blockchain

Abstract

This application discloses a method, apparatus, and device for determining a target customer group based on blockchain, relating to the fields of blockchain and fintech. The method includes: receiving transaction requests sent by clients at preset time intervals through a blockchain network and verifying the client's permissions; if the client passes the permission verification, invoking and executing the corresponding smart contract based on the smart contract identifier contained in the transaction request to obtain the target customer group for the target exchange product; after feeding back the target customer group to the client, receiving transaction data sent by the client and performing consensus processing on the transaction data; generating a block based on the consensus-recognized transaction data and verifying the block; if the block passes the verification, adding the block to the blockchain network. This application solves the technical problem of low security and accuracy in existing technologies that use sampling detection methods to determine the target customer group for exchange products.

Description

Technical Field

[0001] This application relates to the fields of blockchain and fintech, and more specifically, to a method, apparatus, and device for determining a target customer group based on blockchain. Background Technology

[0002] Currently, when identifying target customer groups for redeemable products, sampling methods are typically used to understand the significant characteristics of target customers (such as occupation). This approach has low information security, poses a risk of information leakage, and employs a single analytical method, resulting in low accuracy in identifying customer groups.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, and equipment for determining the target customer group based on blockchain, so as to at least solve the technical problem that the existing technology of using sampling detection to determine the target customer group for redeeming products has low security and accuracy.

[0005] To achieve the above objectives, according to one aspect of this application, a method for determining a target customer group based on blockchain is provided, comprising: receiving a transaction request sent by a client at a preset time interval through a blockchain network, and verifying the client's permissions, wherein the transaction request is used to request customer group mining for a target exchange product; if the client passes the permission verification, invoking and executing the corresponding smart contract based on the smart contract identifier contained in the transaction request to obtain the target customer group for the target exchange product, wherein the smart contract is used to implement the process of customer group mining according to an association rule algorithm; after feeding back the target customer group to the client, receiving transaction data sent by the client, and performing consensus processing on the transaction data, wherein the transaction data includes at least the target customer group; generating a block based on the consensus-resolved transaction data, and verifying the block; if the block passes the verification, adding the block to the blockchain network.

[0006] Furthermore, based on the smart contract identifier contained in the transaction request, the corresponding smart contract is invoked and executed to obtain the target customer group for the target exchange product. This includes: obtaining object information for multiple objects and performing data transformation processing on the object information to obtain transformed object data; obtaining the indicator threshold of the association rule algorithm and configuring multiple rule fields of the association rule algorithm based on the indicator threshold; and processing the transformed object data according to the configured association rule algorithm to obtain the target customer group.

[0007] Furthermore, the indicator thresholds include support threshold, confidence threshold, and variable count threshold. The multiple rule fields of the association rule algorithm are configured based on the indicator thresholds, including: configuring the support rule field of the association rule algorithm as the support threshold; configuring the confidence rule field of the association rule algorithm as the confidence threshold; and configuring the rule length field of the association rule algorithm as the variable count threshold.

[0008] Furthermore, the transformed object data is processed according to the configured association rule algorithm to obtain the target customer group, including: using the frequency of occurrence greater than the support threshold as a constraint, data mining is performed on the transformed object data according to the configured association rule algorithm to obtain multiple itemsets; multiple association rules are generated based on the multiple itemsets, and the target association rule is determined from the multiple association rules based on the confidence threshold and the number of variables threshold; the target customer group is determined based on the target association rule.

[0009] Furthermore, consensus processing of the transaction data includes: processing the transaction data according to a preset message format to obtain processed transaction data; sending the processed transaction data to a preset message queue, wherein the preset message queue is used to sort the processed transaction data to obtain sorted transaction data; receiving the sorted transaction data returned by the preset message queue, and using the sorted transaction data as the consensus-reached transaction data.

[0010] Furthermore, before receiving transaction requests sent by clients at preset time intervals through the blockchain network, the method also includes: receiving permission request sent by clients through the blockchain network, registering and enrolling clients based on permission request, and sending the client's identity certificate back to the client.

[0011] Furthermore, after the target customer groups are fed back to the client, the client sorts them based on the improvement rate of each customer group in the target customer group and displays the sorted customer groups.

[0012] To achieve the above objectives, according to another aspect of this application, a device for determining a target customer group based on blockchain is also provided, comprising: a first processing unit, configured to receive transaction requests sent by a client at preset time intervals through a blockchain network and to perform permission verification on the client, wherein the transaction request is used to request customer group mining for a target exchange product; a second processing unit, configured to, if the client passes the permission verification, invoke and execute the corresponding smart contract based on the smart contract identifier contained in the transaction request to obtain the target customer group for the target exchange product, wherein the smart contract is used to implement the process of customer group mining according to an association rule algorithm; a third processing unit, configured to, after feeding back the target customer group to the client, receive transaction data sent by the client and perform consensus processing on the transaction data, wherein the transaction data includes at least the target customer group; and a fourth processing unit, configured to generate a block based on the consensus-resolved transaction data and to verify the block, wherein if the block passes the verification, the block is added to the blockchain network.

[0013] Furthermore, the second processing unit includes: a first acquisition subunit, used to acquire object information of multiple objects and perform data transformation processing on the object information to obtain transformed object data; a second acquisition subunit, used to acquire the indicator threshold of the association rule algorithm and configure multiple rule fields of the association rule algorithm according to the indicator threshold; and a first processing subunit, used to process the transformed object data according to the configured association rule algorithm to obtain the target customer group.

[0014] Furthermore, the second acquisition subunit includes: a first configuration module, used to configure the support rule field of the association rule algorithm as a support threshold; a second configuration module, used to configure the confidence rule field of the association rule algorithm as a confidence threshold; and a third configuration module, used to configure the rule length field of the association rule algorithm as a variable number threshold.

[0015] Furthermore, the first processing subunit includes: a processing module, used to perform data mining on the transformed object data according to a configured association rule algorithm, with the occurrence frequency being greater than the support threshold as a constraint, to obtain multiple itemsets; a first determination module, used to generate multiple association rules based on the multiple itemsets, and determine the target association rule from the multiple association rules based on the confidence threshold and the number of variables threshold; and a second determination module, used to determine the target customer group based on the target association rule.

[0016] Furthermore, the third processing unit includes: a second processing subunit, used to process transaction data according to a preset message format to obtain processed transaction data; a third processing subunit, used to send the processed transaction data to a preset message queue, wherein the preset message queue is used to sort the processed transaction data to obtain sorted transaction data; and a fourth processing subunit, used to receive the sorted transaction data returned by the preset message queue and use the sorted transaction data as the consensus-reached transaction data.

[0017] Furthermore, the device also includes: a receiving unit, used to receive a permission request sent by the client through the blockchain network before receiving a transaction request sent by the client based on a preset time interval through the blockchain network, registering and enrolling the client based on the permission request, and feeding back the client's identity certificate to the client.

[0018] Furthermore, after the target customer groups are fed back to the client, the client sorts them based on the improvement rate of each customer group in the target customer group and displays the sorted customer groups.

[0019] To achieve the above objectives, according to another aspect of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the above-described method for determining a blockchain-based target customer group.

[0020] To achieve the above objectives, according to another aspect of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, which is configured to execute the above-described method for determining a blockchain-based target customer group at runtime.

[0021] To achieve the above objectives, according to another aspect of this application, an electronic device is also provided, comprising one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are configured to run the programs, wherein the programs are configured to execute the aforementioned method for determining a blockchain-based target customer group during runtime.

[0022] In this embodiment, a blockchain network is used to receive transaction requests sent by clients at preset time intervals, and the client's permissions are verified. The transaction request requests customer group mining for the target redemption product. If the client passes the permission verification, the corresponding smart contract is invoked and executed based on the smart contract identifier contained in the transaction request to obtain the target customer group for the target redemption product. The smart contract implements the customer group mining process according to an association rule algorithm. After the target customer group is fed back to the client, transaction data sent by the client is received, and consensus processing is performed on the transaction data. The transaction data includes at least the target customer group. Based on the consensus-resolved transaction... The data is generated into blocks, and these blocks are verified. If a block passes verification, it is added to the blockchain network. This method, combined with the blockchain network and association rule algorithms, enables the mining of intentions for target redemption products, thereby identifying the target customer group. This ensures data security and immutability. By introducing association rule algorithms, the intention of customers to use redemption products can be presented in a comprehensive way, which helps to make accurate recommendations for redemption products. This achieves the goal of better mining the intentions for target redemption products, thus improving the technical effect of data security and accuracy. In turn, it solves the technical problem of low security and accuracy in the existing technology that uses sampling detection to determine the target customer group for redemption products. Attached Figure Description

[0023] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 This is a flowchart of an optional blockchain-based method for determining a target customer group according to an embodiment of this application;

[0025] Figure 2 This is a schematic diagram of the frame of an optional customer acquisition device according to an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the network structure of an optional customer acquisition device according to an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of an optional customer segmentation transaction process according to an embodiment of this application;

[0028] Figure 5 This is a schematic diagram of an optional blockchain-based device for determining a target customer group according to an embodiment of this application;

[0029] Figure 6 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0030] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings 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 in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0033] Example 1

[0034] According to an embodiment of this application, an embodiment of a method for determining a target customer group based on blockchain is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] Figure 1This is a flowchart of an optional blockchain-based method for determining a target customer group according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0036] Step S101: Receive transaction requests sent by clients at preset time intervals through the blockchain network and verify the client's permissions. The transaction request is used to request customer group mining for the target exchange product.

[0037] In the above steps, the blockchain network serves as the execution entity (i.e., the mining device) for identifying the target customer group, interacting with the client. For example, the application client, using a software development kit, initiates a transaction proposal (i.e., a transaction request) to the endorsing node of the blockchain network at preset time intervals (e.g., every five minutes). This proposal includes the smart contract identifier to be invoked (e.g., the name of the smart contract), the contract method (the specific implementation logic of the smart contract method), parameter information (the required input fields for calling the corresponding smart contract and the unique identifier of the smart contract), and the client's signature (an identifier that identifies which transaction channel the client is in and a label for verifying identity). After receiving the transaction proposal, the endorsing node verifies the client's permissions, for example, by verifying the signature and confirming whether the submitter has the authority to execute the operation.

[0038] Step S102: If the client passes the permission verification, the corresponding smart contract is invoked and executed based on the smart contract identifier contained in the transaction request to obtain the target customer group of the target redemption product. The smart contract is used to implement the process of customer group mining according to the association rule algorithm.

[0039] Optionally, if the client passes the permission verification, it invokes and executes the corresponding smart contract based on the smart contract identifier contained in the transaction proposal to obtain the target customer group for the target redemption product (e.g., video membership, shopping card, etc.). For example, the endorsing node invokes and executes the smart contract for each transaction proposal, returning the execution result and CA signature of each transaction proposal to the application client.

[0040] Optionally, the smart contract includes processes such as data preprocessing, obtaining threshold values ​​for association rule indicators, association sorting, redundancy elimination, and data transformation and visualization before returning to the client. First, data preprocessing is performed. The influencing variables involved in this solution include occupation, gender, and income. Occupation can include employees of state-owned enterprises, entrepreneurs, employees of private enterprises, and freelancers, with state-owned enterprise employees converted to 1, entrepreneurs to 2, private enterprise employees to 3, and freelancers to 4. Gender includes male and female, with male converted to 1 and female to 2. Income includes low income, middle income, and high income, with low income converted to 1, middle income to 2, and high income to 3.

[0041] Furthermore, the threshold values ​​for association rule indicators are obtained. The association rule indicators involved in this solution include support, confidence, and minimum number of variables. These three parameters are obtained from the parameter information of the transaction proposal, passed in by the client. For example, the support is 0.005, the confidence is 0.8, and the minimum number of variables is 2. Since this solution's dataset has approximately 2500 data points, each case must contain at least 2500 * 0.005 = 13 (rounded) data points. The sample data size meets the requirement of generally greater than or equal to 10 data points, and the support parameter setting is reasonable. Setting the confidence to 0.8 indicates that under certain specific conditions, the probability that a customer has the intention to use the redeemed product or not exceeds 80%. Generally, reaching 80% can preliminarily indicate that this group of customers has a relatively strong willingness to use the redeemed product.

[0042] Further, association sorting is performed. First, field configuration is done. Since this scheme targets customers with the intention to redeem products, Rhs = c(Purchase intention = true), where Rhs is the right side of the rule and Purchase intention is the purchase intention. default = Lhs is set, meaning all itemsets can appear on the left side of the rule (Lhs), i.e., the support, confidence, and minimum number of variables involved in this scheme can all appear in the itemsets. supp (minimum support threshold for association rules) is set to 0.005, conf (minimum confidence threshold for association rules) is set to 0.8, and maxlen (maximum length of association rules) is set to 2. After configuring the parameters of the association rule algorithm, data on variables related to the influencing factors of redeemed products are obtained from the blockchain and substituted into the algorithm to obtain the processing results, as shown in Table 1.

[0043] Table 1

[0044]

[0045]

[0046] Furthermore, redundancy is eliminated. Since some of the association rules generated during the association ranking process provide only a small amount of additional information compared to other rules, redundant rules need to be pruned. All rules are sorted in descending order of lift, and the results are shown in Table 2.

[0047] Table 2

[0048]

[0049] Furthermore, the data is transformed and visualized back to the client, which will show four customer scenarios where the willingness to use the redemption product is strongest: Combination 1 is for entrepreneurs with middle-income earners; Combination 2 is for state-owned enterprise employees who are female; Combination 3 is for entrepreneurs who are female; and Combination 4 is for freelancers who are female. Optionally, based on these results, redemption products can be recommended to customer groups that meet these characteristics.

[0050] Step S103: After feeding back the target customer group to the client, receive the transaction data sent by the client and perform consensus processing on the transaction data, wherein the transaction data includes at least the target customer group.

[0051] Step S104: Generate a block based on the consensus transaction data and verify the block. If the block passes the verification, add the block to the blockchain network.

[0052] Optionally, the endorsing node returns the customer combinations of the four scenarios identified above as the target customer group to the client. The client determines whether the target customer group mined for each transaction proposal is consistent. If they are inconsistent, the processing is stopped. If they are consistent, the client packages all the transaction data related to each transaction proposal into a transaction, signs it, and sends it to the sorting service node of the blockchain network. Therefore, after the target customer group is fed back to the client, the sorting service node of the blockchain network receives the transaction data (i.e., multiple transactions) sent by the client, performs consensus processing on the transaction data, and then packages the consensus-agreed transaction data together to generate a new block, which is sent to the commit node of the blockchain network. The commit node verifies each transaction in the block. If the block passes the verification, the block is added to the blockchain network, and the world state (the latest value of all keys) is modified.

[0053] Based on the scheme defined in steps S101 to S104 above, it can be understood that in this embodiment, a transaction request sent by a client at a preset time interval is received through a blockchain network, and the client's permissions are verified. The transaction request is used to request customer group mining for the target exchange product. If the client passes the permission verification, the corresponding smart contract is invoked and executed according to the smart contract identifier contained in the transaction request to obtain the target customer group for the target exchange product. The smart contract is used to implement the process of customer group mining according to the association rule algorithm. After the target customer group is fed back to the client, transaction data sent by the client is received, and consensus processing is performed on the transaction data. The transaction data includes at least... The target customer group is identified through a process that generates blocks based on consensus-based transaction data and verifies them. If a block passes verification, it is added to the blockchain network. This approach, combining the blockchain network with association rule algorithms, enables the discovery of the target customer group's intention to redeem products. This ensures data security and immutability. By introducing association rule algorithms, the intention of customers to redeem products can be presented in a comprehensive manner, facilitating accurate recommendations for redeem products. This achieves a better understanding of the target customer group's intention to redeem products, thereby improving data security and accuracy. Furthermore, it solves the technical problem of low security and accuracy associated with existing technologies that use sampling methods to determine the target customer group for redeem products.

[0054] In one optional embodiment, the process of calling and executing the corresponding smart contract based on the smart contract identifier contained in the transaction request to obtain the target customer group for the target exchange product includes: obtaining object information of multiple objects and performing data transformation processing on the object information to obtain transformed object data; obtaining the indicator threshold of the association rule algorithm and configuring multiple rule fields of the association rule algorithm based on the indicator threshold; and processing the transformed object data according to the configured association rule algorithm to obtain the target customer group.

[0055] Optionally, data preprocessing is first performed to obtain object information for multiple objects, such as occupation, gender, and income. This object information is then transformed to obtain transformed object data. For example, state-owned enterprise employees are transformed into 1, entrepreneurs into 2, private enterprise employees into 3, and freelancers into 4; males into 1 and females into 2; low-income into 1, middle-income into 2, and high-income into 3. Then, the threshold values ​​for the association rule algorithm are obtained, and multiple rule fields are configured based on these threshold values. For example, the support, confidence, and minimum number of variables passed in by the client are obtained from the transaction proposal parameters. For instance, the support is set to 0.005, the confidence to 0.8, and the minimum number of variables to 2. Set Rhs = c(Purchase intention = true), where Rhs is the right-hand side of the rule and Purchase intention is the purchase intention; set default = Lhs, meaning all itemsets can appear on the left-hand side of the rule (Lhs), i.e., the support, confidence, and minimum number of variables involved in this scheme can all appear in the itemsets; set supp (minimum support threshold for association rules) to 0.005, conf (minimum confidence threshold for association rules) to 0.8, and maxlen (maximum length of association rules) to 2. Then, process the transformed object data according to the configured association rule algorithm to obtain the target customer group. The specific implementation process can refer to the aforementioned association sorting and redundancy elimination, and will not be elaborated here.

[0056] In one optional embodiment, the indicator thresholds include a support threshold, a confidence threshold, and a variable count threshold. The configuration of multiple rule fields of the association rule algorithm based on the indicator thresholds includes: configuring the support rule field of the association rule algorithm as a support threshold; configuring the confidence rule field of the association rule algorithm as a confidence threshold; and configuring the rule length field of the association rule algorithm as a variable count threshold.

[0057] Optionally, the support rule field of the association rule algorithm can be configured as a support threshold, for example, setting supp to 0.005; the confidence rule field of the association rule algorithm can be configured as a confidence threshold, for example, setting conf to 0.8; and the rule length field of the association rule algorithm can be configured as a variable number threshold, for example, setting maxlen to 2.

[0058] In one optional embodiment, the transformed object data is processed according to a configured association rule algorithm to obtain a target customer group, including: using the occurrence frequency greater than a support threshold as a constraint, performing data mining on the transformed object data according to the configured association rule algorithm to obtain multiple itemsets; generating multiple association rules based on the multiple itemsets, and determining a target association rule from the multiple association rules based on a confidence threshold and a variable number threshold; and determining the target customer group based on the target association rule.

[0059] Optionally, with the constraint that the frequency of occurrence exceeds a support threshold, data mining is performed on the transformed object data according to a configured association rule algorithm to obtain multiple itemsets. For example, a widely used association rule algorithm is used to find all itemsets whose frequency of occurrence exceeds a set support threshold. These itemsets may contain features such as customer occupation, gender, and income. For example, if the support threshold is 0.005, then any feature combination that occurs more than 0.5% of the dataset will be identified as the target itemset. Then, multiple association rules are generated based on the multiple itemsets, and the target association rule is determined from the multiple association rules based on the confidence threshold and the number of variables threshold. For example, the association rule is represented in the form A => B, where A is a set of one or more features, and B is another feature or feature set. The algorithm attempts to generate as many rules as possible from each itemset, while calculating the support and confidence of each rule. Based on the confidence and lift thresholds, association rules that meet the conditions are selected, for example, redundant rules are eliminated, to obtain the target association rule. Then, the target customer group is determined based on the target association rules. For example, the rules are ranked according to the lift. The higher the lift, the more valuable the rule is, because it indicates that the feature combination has a greater impact on the target redemption product.

[0060] For example, the data obtained after preprocessing includes customers' occupations (state-owned enterprise employee = 1, entrepreneur = 2, private enterprise employee = 3, freelancer = 4), genders (male = 1, female = 2), and income levels (low income = 1, middle income = 2, high income = 3). Applying the association rule algorithm, we can obtain the rule {occupation = 2, income = 2} => {redemption intention = true}, with support = 0.011, confidence = 1.000, and lift = 3.096. This means that in the dataset, customers with the occupation of entrepreneur and middle income have a confidence level of 1.000 that they are inclined to redeem the product, and the lift of this rule indicates that this customer group's inclination to redeem the product is much higher than that of randomly selected customers.

[0061] It should be noted that by introducing association rule algorithms and setting thresholds for support, confidence, and the minimum number of variables, the customer groups intending to use the redemption products can be presented in a comprehensive way, which helps to make accurate recommendations for redemption products.

[0062] In one optional embodiment, consensus processing of transaction data includes: processing the transaction data according to a preset message format to obtain processed transaction data; sending the processed transaction data to a preset message queue, wherein the preset message queue is used to sort the processed transaction data to obtain sorted transaction data; receiving the sorted transaction data returned by the preset message queue, and using the sorted transaction data as the consensus-reached transaction data.

[0063] Optionally, the transaction data is processed according to a preset message format (e.g., the Kafka message format) to obtain processed transaction data. This processed transaction data is then sent to a preset message queue (e.g., a Kafka message queue). The sorted transaction data returned by the preset message queue is then received, and this sorted transaction data is used as the consensus-based transaction data. Optionally, this solution employs a modified Kafka-based consensus algorithm, with sorting service nodes interfacing with Kafka nodes to utilize Kafka for message consensus. Since data preprocessing, obtaining association rule indicator thresholds, association sorting, and eliminating redundancy in smart contracts involve storing data on-chain, four sets of sorting service nodes are set up for these four steps. Kafka is used for message consensus, and the smart contract's computational processing results are stored on-chain. Specifically, messages sent by clients are endorsed by the sorting service node group, assembled into the Kafka message format, and then sent to the Kafka cluster to complete the unified sorting of transaction information. The smart contract's processing results are then stored on-chain.

[0064] It should be noted that the improved Kafka-based consensus algorithm enhances device performance and processing efficiency, ensuring that data is not repeatedly executed.

[0065] In one optional embodiment, before receiving a transaction request sent by a client at a preset time interval through the blockchain network, the system receives a permission request sent by the client through the blockchain network, registers and enrolls the client based on the permission request, and sends the client's identity certificate back to the client.

[0066] Optionally, the blockchain network first receives permission request requests from clients, registers and enrolls clients based on these requests, and returns the client's identity certificate to the client. For example, the application client uses a software development kit to call a member service node of the blockchain network to register and enroll, obtaining an identity certificate that allows the use of the mining device, i.e., obtaining permission to use the device. Optionally, this solution implements an identity authentication mechanism through a module component that manages and verifies the identities of network members.

[0067] It should be noted that registering and recording the client based on permission request provides a foundation for subsequent permission verification of the client.

[0068] In one alternative embodiment, after the target customer group is fed back to the client, the client sorts the customers based on the improvement rate of each customer group in the target customer group and displays the sorted customer groups.

[0069] For example, all rules are sorted in descending order of their boost level. The client will show the four scenarios in which customers have the strongest willingness to use the redemption product: Combination 1 is an entrepreneur with a middle-income status; Combination 2 is a state-owned enterprise employee with a female gender; Combination 3 is an entrepreneur with a female gender; and Combination 4 is a freelancer with a female gender.

[0070] It should be noted that by displaying the sorted customer groups, a comprehensive view of the customer groups intending to use the redeemed products is achieved, which helps to make accurate recommendations for redeemed products.

[0071] In one alternative embodiment, a blockchain network (i.e., a mining device) is implemented using a framework that provides a modular distributed ledger solution and employs an asymmetric encryption algorithm. Figure 2 This is a schematic diagram of the frame of an optional customer acquisition device according to an embodiment of this application, such as... Figure 2 As shown, a distributed ledger platform is used as the underlying network environment. At the business layer, the relevant APIs of the distributed ledger platform are used to call the chaincode (i.e., smart contracts). A web application example program is implemented using a Linux operating system, an HTTP server, and a MySQL database. The front end uses Vue.js to implement visualization and receive input commands.

[0072] Figure 3 This is a schematic diagram of the network structure of an optional customer acquisition device according to an embodiment of this application, such as... Figure 3 As shown, the interface is implemented using a web browser. It is primarily used to set support, confidence, and minimum variable thresholds, trigger relevant smart contracts, and ultimately return the visualization results of the product redemption intention mining. Optionally, the data structure for storing data in the ledger is shown in Table 3.

[0073] Table 3

[0074] English names of table fields Chinese names of table fields lhs Left side of the rule rhs The right side of the rule support Support of association rules confidence Confidence of association rules lift Lifting of association rules

[0075] Figure 4 This is a schematic diagram of an optional customer segmentation transaction process according to an embodiment of this application, such as... Figure 4As shown, the application client uses a software development kit (SDK) to call the member service, register, and obtain an identity certificate allowing the use of the mining device. The application client then initiates a transaction proposal to the endorsing node of the blockchain network using the SSD. Upon receiving the proposal, the endorsing node verifies the client's permissions, such as verifying the signature and confirming the submitter's authorization. If the client passes the permission verification, it invokes and executes the corresponding smart contract based on the smart contract identifier contained in the transaction proposal, thus identifying the target customer group for the redeemed product (e.g., video membership, shopping card). For example, the endorsing node invokes and executes the smart contract for each transaction proposal, returning the execution result and CA signature to the application client. The client determines whether the target customer group mined by each transaction proposal is consistent. If they are, the client packages all relevant transaction data for each proposal into a single transaction, signs it, and sends it to the sorting service node of the blockchain network. The sorting service node performs consensus processing on the transaction data, then packages the consensus-agreed transaction data together to generate a new block, which is sent to the commit node of the blockchain network. The commit node verifies each transaction in the block; if the block passes verification, it is added to the blockchain network.

[0076] In this embodiment, a blockchain network is used to receive transaction requests sent by clients at preset time intervals, and the client's permissions are verified. The transaction request requests customer group mining for the target redemption product. If the client passes the permission verification, the corresponding smart contract is invoked and executed based on the smart contract identifier contained in the transaction request to obtain the target customer group for the target redemption product. The smart contract implements the customer group mining process according to an association rule algorithm. After the target customer group is fed back to the client, transaction data sent by the client is received, and consensus processing is performed on the transaction data. The transaction data includes at least the target customer group. Based on the consensus-resolved transaction... The data is generated into blocks, and these blocks are verified. If a block passes verification, it is added to the blockchain network. This method, combined with the blockchain network and association rule algorithms, enables the mining of intentions for target redemption products, thereby identifying the target customer group. This ensures data security and immutability. By introducing association rule algorithms, the intention of customers to use redemption products can be presented in a comprehensive way, which helps to make accurate recommendations for redemption products. This achieves the goal of better mining the intentions for target redemption products, thus improving the technical effect of data security and accuracy. In turn, it solves the technical problem of low security and accuracy in the existing technology that uses sampling detection to determine the target customer group for redemption products.

[0077] Example 2

[0078] According to an embodiment of this application, an embodiment of a device for determining a target customer group based on blockchain is provided, wherein... Figure 5 This is a schematic diagram of an optional blockchain-based device for determining a target customer group according to an embodiment of this application, such as... Figure 5 As shown, the device includes: a first processing unit 501, a second processing unit 502, a third processing unit 503, and a fourth processing unit 504.

[0079] The first processing unit 501 is used to receive transaction requests sent by clients at preset time intervals through the blockchain network and to verify the client's permissions. The transaction request is used to request customer group mining for the target exchange product.

[0080] The second processing unit 502 is used to call and execute the corresponding smart contract based on the smart contract identifier contained in the transaction request if the client passes the permission verification, so as to obtain the target customer group of the target redemption product. The smart contract is used to implement the process of customer group mining according to the association rule algorithm.

[0081] The third processing unit 503 is used to receive transaction data sent by the client after feeding back the target customer group to the client, and to perform consensus processing on the transaction data, wherein the transaction data includes at least the target customer group.

[0082] The fourth processing unit 504 is used to generate blocks based on the consensus transaction data and to verify the blocks. If the block passes the verification, it is added to the blockchain network.

[0083] In this embodiment, the first processing unit 501 receives transaction requests sent by the client at preset time intervals through the blockchain network and verifies the client's permissions. The transaction request is used to request customer group mining for the target exchange product. If the client passes the permission verification, the second processing unit 502 calls and executes the corresponding smart contract based on the smart contract identifier contained in the transaction request to obtain the target customer group for the target exchange product. The smart contract is used to implement the process of customer group mining according to the association rule algorithm. After feeding back the target customer group to the client, the third processing unit 503 receives transaction data sent by the client and performs consensus processing on the transaction data. The transaction data includes at least the target customer group. The fourth processing unit 504 generates a block based on the consensus-recognized transaction data and verifies the block. If the block passes the verification, the block is added to the blockchain network. By combining blockchain networks and association rule algorithms, the intention to redeem products is mined, and the target customer group is obtained, ensuring data security and immutability. By introducing association rule algorithms, the customer group intending to use redeem products can be presented in a three-dimensional way, which helps to make accurate recommendations for redeem products. This achieves the goal of better mining the intention to redeem products, thereby improving the technical effect of data security and accuracy. It also solves the technical problem of low security and accuracy in the existing technology that uses sampling detection method to determine the target customer group of redeem products.

[0084] It should be noted that the first processing unit 501, the second processing unit 502, the third processing unit 503, and the fourth processing unit 504 mentioned above correspond to steps S101 to S104 in the above embodiments. The four units and the corresponding steps implement the same examples and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0085] Furthermore, the second processing unit includes: a first acquisition subunit, used to acquire object information of multiple objects and perform data transformation processing on the object information to obtain transformed object data; a second acquisition subunit, used to acquire the indicator threshold of the association rule algorithm and configure multiple rule fields of the association rule algorithm according to the indicator threshold; and a first processing subunit, used to process the transformed object data according to the configured association rule algorithm to obtain the target customer group.

[0086] Furthermore, the second acquisition subunit includes: a first configuration module, used to configure the support rule field of the association rule algorithm as a support threshold; a second configuration module, used to configure the confidence rule field of the association rule algorithm as a confidence threshold; and a third configuration module, used to configure the rule length field of the association rule algorithm as a variable number threshold.

[0087] Furthermore, the first processing subunit includes: a processing module, used to perform data mining on the transformed object data according to a configured association rule algorithm, with the occurrence frequency being greater than the support threshold as a constraint, to obtain multiple itemsets; a first determination module, used to generate multiple association rules based on the multiple itemsets, and determine the target association rule from the multiple association rules based on the confidence threshold and the number of variables threshold; and a second determination module, used to determine the target customer group based on the target association rule.

[0088] Furthermore, the third processing unit includes: a second processing subunit, used to process transaction data according to a preset message format to obtain processed transaction data; a third processing subunit, used to send the processed transaction data to a preset message queue, wherein the preset message queue is used to sort the processed transaction data to obtain sorted transaction data; and a fourth processing subunit, used to receive the sorted transaction data returned by the preset message queue and use the sorted transaction data as the consensus-reached transaction data.

[0089] Furthermore, the device also includes: a receiving unit, used to receive a permission request sent by the client through the blockchain network before receiving a transaction request sent by the client based on a preset time interval through the blockchain network, registering and enrolling the client based on the permission request, and feeding back the client's identity certificate to the client.

[0090] Furthermore, after the target customer groups are fed back to the client, the client sorts them based on the improvement rate of each customer group in the target customer group and displays the sorted customer groups.

[0091] Example 3

[0092] Embodiments of this application may provide an electronic device. Figure 6 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 6 As shown, the electronic device may include: one or more ( Figure 6 (Only one is shown) processor 602, memory 604, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0093] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0094] The processor can access information and applications stored in memory via a transmission device to execute the following steps: receiving transaction requests sent by clients at preset time intervals through the blockchain network and verifying the client's permissions, wherein the transaction request is used to request customer group mining for the target exchange product; if the client passes the permission verification, the processor calls and executes the corresponding smart contract based on the smart contract identifier contained in the transaction request to obtain the target customer group for the target exchange product, wherein the smart contract is used to implement the process of customer group mining according to the association rule algorithm; after feeding back the target customer group to the client, the processor receives transaction data sent by the client and performs consensus processing on the transaction data, wherein the transaction data includes at least the target customer group; generating a block based on the consensus-based transaction data and verifying the block; if the block passes the verification, the block is added to the blockchain network.

[0095] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: obtain object information of multiple objects, and perform data transformation processing on the object information to obtain transformed object data; obtain the indicator threshold of the association rule algorithm, and configure multiple rule fields of the association rule algorithm according to the indicator threshold; process the transformed object data according to the configured association rule algorithm to obtain the target customer group.

[0096] The processor can also access information and applications stored in the memory via a transmission device to perform the following steps: configuring the support rule field of the association rule algorithm as the support threshold; configuring the confidence rule field of the association rule algorithm as the confidence threshold; and configuring the rule length field of the association rule algorithm as the number of variables threshold.

[0097] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: using the occurrence frequency being greater than the support threshold as a constraint, perform data mining on the transformed object data according to the configured association rule algorithm to obtain multiple itemsets; generate multiple association rules based on the multiple itemsets, and determine the target association rule from the multiple association rules based on the confidence threshold and the number of variables threshold; determine the target customer group based on the target association rule.

[0098] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: process the transaction data according to the preset message format to obtain the processed transaction data; send the processed transaction data to the preset message queue, wherein the preset message queue is used to sort the processed transaction data to obtain the sorted transaction data; receive the sorted transaction data returned by the preset message queue, and use the sorted transaction data as the consensus transaction data.

[0099] The processor can also access information and applications stored in the memory via the transmission device to perform the following steps: before receiving transaction requests sent by clients at preset time intervals through the blockchain network, receiving permission request requests sent by clients through the blockchain network, registering and enrolling clients based on permission request requests, and sending the client's identity certificate back to the client.

[0100] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: after the target customer group is fed back to the client, the client sorts the customers based on the improvement degree corresponding to each customer group in the target customer group and displays the sorted customer groups.

[0101] Those skilled in the art will understand that Figure 6 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones (such as Android phones, iOS phones, etc.), tablets, PDAs, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 6 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 6 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 6 The different configurations shown.

[0102] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0103] Example 4

[0104] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the blockchain-based method for determining the target customer group provided in Embodiment 1.

[0105] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0106] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing steps of a method for determining a target customer group based on blockchain.

[0107] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0108] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0110] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0113] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining a target customer group based on blockchain, characterized in that, include: The system receives transaction requests from clients at preset time intervals through a blockchain network and verifies the client's permissions. The transaction requests are used to request customer group mining for the target redemption product. If the client passes the permission verification, the corresponding smart contract is invoked and executed based on the smart contract identifier contained in the transaction request to obtain the target customer group for the target exchange product. Specifically, this includes: obtaining object information of multiple objects and performing data transformation processing on the object information to obtain transformed object data; obtaining the indicator threshold of the association rule algorithm and configuring multiple rule fields of the association rule algorithm based on the indicator threshold, with the occurrence frequency being greater than the support threshold as a constraint condition; performing data mining on the transformed object data based on the configured association rule algorithm to obtain multiple itemsets; generating multiple association rules based on the multiple itemsets; determining the target association rule from the multiple association rules based on the confidence threshold and the number of variables threshold; and determining the target customer group based on the target association rule. The indicator thresholds include a support threshold, a confidence threshold, and a variable count threshold. The configuration of multiple rule fields of the association rule algorithm based on these indicator thresholds includes: configuring the support rule field of the association rule algorithm as the support threshold; configuring the confidence rule field of the association rule algorithm as the confidence threshold; and configuring the rule length field of the association rule algorithm as the variable count threshold. The smart contract is used to implement the process of customer group mining according to the association rule algorithm; After feeding back the target customer group to the client, the system receives transaction data sent by the client and performs consensus processing on the transaction data, wherein the transaction data includes at least the target customer group. A block is generated based on the consensus-reached transaction data, and the block is verified. If the block passes the verification, the block is added to the blockchain network. Consensus processing of the transaction data includes: The transaction data is processed according to a preset message format to obtain processed transaction data; The processed transaction data is sent to a preset message queue, wherein the preset message queue is used to sort the processed transaction data to obtain sorted transaction data. The sorted transaction data returned by the preset message queue is received, and the sorted transaction data is used as the consensus-reached transaction data.

2. The method according to claim 1, characterized in that, Before receiving transaction requests sent by clients at preset time intervals via the blockchain network, the method further includes: The blockchain network receives permission request requests sent by the client, registers and enrolls the client based on the permission request, and sends the client's identity certificate back to the client.

3. The method according to claim 1, characterized in that, After the target customer group is fed back to the client, the client sorts the customers based on the improvement rate of each customer group in the target customer group and displays the sorted customer groups.

4. A device for determining a target customer group based on blockchain, used to execute the method for determining a target customer group based on blockchain as described in claim 1, characterized in that, include: The first processing unit is used to receive transaction requests sent by clients at preset time intervals through the blockchain network and to verify the permissions of the clients, wherein the transaction requests are used to request customer group mining for the target exchange product; The second processing unit is used to, if the client passes the permission verification, call and execute the corresponding smart contract based on the smart contract identifier contained in the transaction request to obtain the target customer group of the target redemption product, wherein the smart contract is used to implement the process of customer group mining according to the association rule algorithm; The third processing unit is configured to receive transaction data sent by the client after the target customer group is fed back to the client, and to perform consensus processing on the transaction data, wherein the transaction data includes at least the target customer group; The fourth processing unit is used to generate blocks based on the consensus transaction data and to verify the blocks. If the blocks pass the verification, the blocks are added to the blockchain network.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute, when running, the method for determining a target customer group based on blockchain as described in any one of claims 1 to 3.

6. An electronic device, characterized in that, The electronic device includes one or more processors; A memory for storing one or more programs, which, when executed by one or more processors, enable the processors to run the programs, wherein the programs are configured to execute the blockchain-based method for determining a target customer group as described in any one of claims 1 to 3.

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