Blockchain-based targeted payment transaction processing method and apparatus, and electronic device

Abstract

This invention discloses a blockchain-based method, apparatus, and electronic device for processing targeted payment transactions, relating to the field of blockchain technology or other related technical fields. The method includes: receiving a targeted payment transaction request initiated by a user; obtaining an order message based on the request; parsing the order message using a content parsing smart agent to obtain transaction features; performing multi-dimensional compliance verification and abnormal transaction identification based on the transaction features to obtain a verification result; and, if the verification result indicates that the targeted payment transaction has passed verification, invoking a smart contract to execute the targeted fund transfer, generating a transaction result for the targeted payment transaction, and storing the transaction result on the blockchain. This invention solves the technical problem in related technologies where the targeted payment processing method based on a combination of fund locking and rule triggering has a coarse control granularity and contains risk control blind spots.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology or other related technical fields. Specifically, it relates to a blockchain-based method, apparatus, and electronic device for processing targeted payment transactions. Background Technology

[0002] With the deep integration of blockchain technology and financial payment systems, targeted payments based on smart contracts are gradually being applied as a new transaction mechanism in scenarios such as education subsidies, supply chain finance, and subsidy distribution. Smart contracts define the conditions for fund transfers through code, and can be automatically executed once the preset conditions are met, thereby improving the automation and transparency of fund transfers to a certain extent.

[0003] In related technologies, blockchain-based targeted payment solutions typically employ a combination of fund locking and rule triggering. This involves the payer depositing funds into a smart contract and setting constraints such as the payee's address, transaction amount, and time window. When a user initiates a transaction that conforms to these constraints, the smart contract automatically releases the funds to complete the transaction. However, this method of fund management is coarse-grained and lacks in-depth verification of targeted payment transactions, resulting in blind spots in risk control.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a blockchain-based method, apparatus, and electronic device for processing targeted payment transactions, which at least solves the technical problem in related technologies where targeted payment processing methods based on a combination of fund locking and rule triggering have coarse control granularity and risk control blind spots.

[0006] According to one aspect of the present invention, a blockchain-based method for processing targeted payment transactions is provided, comprising: receiving a targeted payment transaction request initiated by a user terminal; obtaining an order message based on the targeted payment transaction request; parsing the order message using a content parsing smart agent to obtain transaction features; performing multi-dimensional compliance verification and abnormal transaction identification based on the transaction features to obtain a verification result; and, if the verification result indicates that the targeted payment transaction has passed verification, invoking a smart contract to execute a targeted fund transfer, generating a transaction result for the targeted payment transaction, and storing the transaction result in the blockchain.

[0007] Furthermore, before receiving the targeted payment transaction request initiated by the user, the process includes: receiving the targeted payment smart contract creation request initiated by the payer, and configuring multi-dimensional locking rules based on the targeted payment smart contract creation request; generating the targeted payment smart contract based on the multi-dimensional locking rules, and locking the targeted funds for their intended use based on the targeted payment smart contract; and generating a lock-up certificate for the targeted funds for their intended use.

[0008] Furthermore, the steps for generating lock-up certificates for targeted funds subject to purpose-based lock-up include: parsing the multi-dimensional lock-up rules in the targeted payment smart contract, extracting primary purpose tags and corresponding secondary sub-purpose tags; constructing structured purpose digital certificates based on the primary purpose tags and secondary sub-purpose tags; mapping the purpose digital certificates to lock-up identifiers using key-value pairs to generate the lock-up certificates, and storing the lock-up certificates in an off-chain database. After storing the lock-up certificates in the off-chain database, a hash calculation is performed on the lock-up certificates, and the calculated hash value is written to the consortium blockchain as a record of evidence.

[0009] Furthermore, the step of parsing the order message using a content parsing agent to obtain transaction features includes: extracting visual features of the product images in the order message using a convolutional neural network; extracting semantic features of the product text in the order message using a natural language processing model; extracting preset fields in the order message using a rule engine; and integrating the visual features, the semantic features, and the preset fields to obtain the transaction features.

[0010] Further, the steps for multi-dimensional compliance verification based on the transaction features include: extracting merchant features from the transaction features, matching the merchant features with the merchant list in the locked-up certificate to obtain merchant matching results, and verifying the qualifications of the transaction merchants based on the merchant matching results; extracting transaction purpose features from the transaction features, matching the transaction purpose features with the primary purpose tags in the purpose digital certificate to obtain purpose matching results, and verifying the purpose of the targeted payment transaction based on the purpose matching results; and extracting product attribute features from the transaction features, matching the product attribute features with the risk quotient in the locked-up certificate. The system matches the product list to obtain risk product matching results, and performs product attribute verification on the targeted payment transaction based on the risk product matching results. It extracts the transaction price feature from the transaction features and matches the transaction price feature with the market transaction price to obtain a transaction price matching result, and performs price verification on the targeted payment transaction based on the transaction price matching result. It also extracts the transaction spatiotemporal features from the transaction features and compares the transaction spatiotemporal features with the transaction spatiotemporal information in the lock-up certificate to obtain a transaction spatiotemporal comparison result, and performs spatiotemporal consistency verification on the targeted payment transaction based on the transaction spatiotemporal comparison result.

[0011] Furthermore, after obtaining the purpose matching result, the method further includes: if the purpose matching result indicates that the transaction purpose feature does not match the primary purpose tag of the purpose digital certificate, inputting the order message into the semantic recognition model, and outputting a first semantic vector through the semantic recognition model; encoding the secondary purpose tag in the purpose digital certificate to obtain a second semantic vector; calculating the similarity value between the first semantic vector and the second semantic vector, and performing purpose verification on the targeted payment transaction based on the similarity value.

[0012] Further, the steps for identifying abnormal transactions based on the transaction characteristics include: obtaining the user identifier of the user terminal and querying the historical transaction record sequence within a preset time window based on the user identifier; traversing the historical transaction record sequence, extracting the transaction amount, transaction timestamp, and payee identifier for each transaction, and constructing a transaction association graph; based on the transaction association graph, detecting whether a dense subgraph structure exists, wherein the dense subgraph structure is used to indicate that the user terminal initiates targeted payment transactions to multiple associated accounts within the preset time window, or that the number of times the user terminal initiates targeted payment transactions to the same account within the preset time window exceeds a preset number threshold; if the dense subgraph structure is detected, determining that the targeted payment transaction is an abnormal transaction and generating an abnormal transaction identification result.

[0013] According to another aspect of the present invention, a blockchain-based targeted payment transaction processing device is also provided, comprising: a receiving unit, configured to receive a targeted payment transaction request initiated by a user terminal and obtain an order message based on the targeted payment transaction request; a parsing unit, configured to parse the order message using a content parsing smart agent to obtain transaction features; a verification unit, configured to perform multi-dimensional compliance verification and abnormal transaction identification based on the transaction features to obtain a verification result; and an execution unit, configured to, if the verification result indicates that the targeted payment transaction has passed verification, invoke a smart contract to execute a targeted fund transfer, generate a transaction result for the targeted payment transaction, and store the transaction result in the blockchain.

[0014] Furthermore, the blockchain-based targeted payment transaction processing device further includes: a first receiving module, used to receive a targeted payment smart contract creation request initiated by the payer, and configure multi-dimensional locking rules based on the targeted payment smart contract creation request; a first generating module, used to generate a targeted payment smart contract based on the multi-dimensional locking rules, and lock the targeted funds for their intended use based on the targeted payment smart contract; and a second generating module, used to generate locking certificates for the targeted funds for their intended use.

[0015] Furthermore, the second generation module includes: a first extraction submodule, used to parse the multi-dimensional lock-up rules in the targeted payment smart contract and extract the primary use tag and the corresponding secondary sub-use tag; a first construction submodule, used to construct a structured use digital certificate based on the primary use tag and the secondary sub-use tag; and a first generation submodule, used to perform key-value pair mapping between the use digital certificate and the lock-up identifier to generate the lock-up certificate, and store the lock-up certificate in an off-chain database. After storing the lock-up certificate in the off-chain database, a hash calculation is performed on the lock-up certificate, and the calculated hash value is written into the consortium blockchain as a record of evidence storage.

[0016] Furthermore, the parsing unit includes: a first extraction module, used to extract visual features of product images in the order message using a convolutional neural network; a second extraction module, used to extract semantic features of product text in the order message using a natural language processing model; a third extraction module, used to extract preset fields in the order message using a rule engine; and a first acquisition module, used to integrate the visual features, the semantic features, and the preset fields to obtain the transaction features.

[0017] Further, the verification unit includes: a first matching module, used to extract merchant features from the transaction features, match the merchant features with the merchant list in the lock-up certificate to obtain a merchant matching result, and perform qualification verification on the transaction merchant based on the merchant matching result; a second matching module, used to extract transaction purpose features from the transaction features, match the transaction purpose features with the primary purpose tag in the purpose digital certificate to obtain a purpose matching result, and perform purpose verification on the targeted payment transaction based on the purpose matching result; and a third matching module, used to extract commodity attribute features from the transaction features, match the commodity attribute features with the risk commodity list in the lock-up certificate. The system performs matching to obtain risk product matching results, and verifies the product attributes of the targeted payment transaction based on the risk product matching results; the fourth matching module is used to extract the transaction price feature from the transaction features, match the transaction price feature with the market transaction price to obtain the transaction price matching result, and verify the price of the targeted payment transaction based on the transaction price matching result; the first comparison module is used to extract the transaction spatiotemporal feature from the transaction features, compare the transaction spatiotemporal feature with the transaction spatiotemporal information in the lock-up certificate to obtain the transaction spatiotemporal comparison result, and verify the spatiotemporal consistency of the targeted payment transaction based on the transaction spatiotemporal comparison result.

[0018] Furthermore, the blockchain-based targeted payment transaction processing device further includes: a first identification module, used to input the order message into a semantic recognition model and output a first semantic vector through the semantic recognition model when the purpose matching result indicates that the transaction purpose feature does not match the primary purpose tag of the purpose digital certificate; a first encoding module, used to encode the secondary purpose tag in the purpose digital certificate to obtain a second semantic vector; and a first verification module, used to calculate the similarity value between the first semantic vector and the second semantic vector, and perform purpose verification on the targeted payment transaction based on the similarity value.

[0019] Furthermore, the verification unit further includes: a first query module, used to obtain the user identifier of the user terminal and query the historical transaction record sequence within a preset time window based on the user identifier; a first traversal module, used to traverse the historical transaction record sequence, extract the transaction amount, transaction timestamp, and payee identifier of each transaction, and construct a transaction association graph; a first detection module, used to detect whether a dense subgraph structure exists based on the transaction association graph, wherein the dense subgraph structure is used to indicate that the user terminal initiates targeted payment transactions to multiple associated accounts within the preset time window, or that the number of times the user terminal initiates targeted payment transactions to the same account within the preset time window is greater than a preset number threshold; and a first determination module, used to determine that the targeted payment transaction is an abnormal transaction when the dense subgraph structure is detected, and generate an abnormal transaction identification result.

[0020] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute any of the above-described blockchain-based targeted payment transaction processing methods.

[0021] According to another aspect of the present invention, an electronic device is also provided, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any of the above-described blockchain-based targeted payment transaction processing methods.

[0022] According to another aspect of the present invention, a computer program product is also provided, the computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements any of the above-described blockchain-based targeted payment transaction processing methods.

[0023] In this application, the following steps are taken: receiving a targeted payment transaction request initiated by a user client; obtaining an order message based on the targeted payment transaction request; parsing the order message through a content parsing smart agent to obtain transaction features; performing multi-dimensional compliance verification and abnormal transaction identification based on the transaction features to obtain verification results; if the verification results indicate that the targeted payment transaction has passed verification, calling a smart contract to execute the targeted fund transfer, generating a transaction result for the targeted payment transaction, and storing the transaction result on the blockchain.

[0024] In this application, a content parsing intelligent agent is used to parse order messages to obtain transaction characteristics. Based on these characteristics, multi-dimensional compliance verification and abnormal transaction identification are performed. This enables refined control over targeted payment transactions from basic rules to business semantics and behavioral patterns, reducing risk control blind spots. After verification, a smart contract is invoked to execute fund transfer and record it on the blockchain, ensuring the automation, compliance, and traceability of fund flow and improving the security of targeted payment transactions. This solves the technical problem in related technologies where the targeted payment processing method based on a combination of fund locking and rule triggering has a coarse control granularity and risk control blind spots. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0026] Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing a blockchain-based targeted payment transaction processing method is shown.

[0027] Figure 2 This is a flowchart of an optional blockchain-based targeted payment transaction processing method according to an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of an optional blockchain-based targeted payment transaction processing flow according to an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of an optional multi-dimensional compliance verification process according to an embodiment of the present invention;

[0030] Figure 5 This is a schematic diagram of an optional blockchain-based targeted payment transaction processing device according to an embodiment of the present invention;

[0031] Figure 6 This is a hardware structure block diagram of an electronic device (or mobile device) that performs a blockchain-based targeted payment transaction processing method according to an embodiment of the present invention. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention 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 the invention 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 a 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.

[0034] It should be noted that the blockchain-based targeted payment transaction processing method and apparatus in this application can be used in the field of blockchain technology to process targeted payment transactions based on multi-dimensional verification, and can also be used in any field other than blockchain technology to process targeted payment transactions based on multi-dimensional verification. This application does not limit the application field of the blockchain-based targeted payment transaction processing method and apparatus.

[0035] 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.

[0036] The following embodiments of the present invention can be applied to various blockchain-based targeted payment transaction processing systems / applications / devices. The present invention uses a content parsing intelligent agent to parse order messages to obtain transaction characteristics, and performs multi-dimensional compliance verification and abnormal transaction identification based on these characteristics. This achieves refined control over targeted payment transactions from basic rules to business semantics and behavioral patterns, reducing risk control blind spots. After verification, a smart contract is invoked to execute fund transfer and record it on the blockchain, ensuring the automation, compliance, and traceability of fund flows, and improving the security of targeted payment transactions.

[0037] The present invention will now be described in detail with reference to various embodiments.

[0038] Example 1

[0039] According to an embodiment of the present invention, an embodiment of a blockchain-based targeted payment transaction processing method 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.

[0040] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a blockchain-based targeted payment transaction processing method is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0041] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0042] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the blockchain-based targeted payment transaction processing method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned blockchain-based targeted payment transaction processing method. The memory 104 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 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 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.

[0043] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0044] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0045] Under the aforementioned operating environment, this application provides the following: Figure 2 The method for processing targeted payment transactions based on blockchain is shown, and the main body implementing this method is a targeted payment transaction processing system based on blockchain.

[0046] Figure 2 This is a flowchart of an optional blockchain-based targeted payment transaction processing method according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0047] In one optional embodiment, before receiving a targeted payment transaction request initiated by the user, the method further includes: receiving a targeted payment smart contract creation request initiated by the payer, and configuring multi-dimensional locking rules based on the targeted payment smart contract creation request; generating a targeted payment smart contract based on the multi-dimensional locking rules, and locking the targeted funds for their intended use based on the targeted payment smart contract; and generating a lock-up certificate for the targeted funds for their intended use.

[0048] Furthermore, to construct a secure and controllable fund transfer environment, the system first executes a contract initialization and fund locking process. This process begins with receiving a request from the payer to create a targeted payment smart contract through the terminal interface, establishing the conditions for fund usage and the foundation of technical constraints. Subsequently, the system guides the payer to configure multi-dimensional locking rules based on business needs. These rules, serving as the core control basis, cover multiple dimensions such as the payee list, fund usage type, amount limit, and time window, ensuring that funds can only flow under preset strict conditions. After completing the rule configuration, the system automatically generates a targeted payment smart contract based on a preset code template. A targeted payment smart contract is a programmable contract configured and activated by the payer to lock in constraints such as the payee, purpose, amount, and time of funds, enabling targeted fund transfer and automatic execution. This contract is then deployed to the blockchain network to take effect, thereby giving it the attribute of immutable automatic execution.

[0049] Subsequently, the system performs a purpose-locking operation on the targeted funds based on the effective smart contract. This involves freezing and isolating the specified amount of funds from the payer's account to prevent the funds from being misappropriated for other unauthorized purposes, achieving dual control over funds at both the physical and logical levels. Finally, the system generates a unique lock-up certificate for the targeted funds that have completed the purpose-locking. This certificate serves as a digital mapping of the fund's status, recording the lock-up status, associated contract information, and details of the purpose constraints, providing a reliable data anchor for subsequent transaction verification and write-off.

[0050] By pre-configuring multi-dimensional lock-up rules and generating smart contracts, fund usage constraints are implemented, regulating fund flows from the source. By locking up targeted funds for specific purposes and generating lock-up certificates, the funds are kept in a closed state before authorization, effectively preventing fund misuse. At the same time, the generation of lock-up certificates provides a basis for subsequent transaction verification, providing basic data support for precise risk control based on transaction characteristics, thereby significantly improving the security and compliance of targeted payment business.

[0051] In one optional embodiment, the step of generating a lock-up certificate for targeted funds subject to purpose-based lock-up includes: parsing the multi-dimensional lock-up rules in the targeted payment smart contract, extracting the primary purpose tag and the corresponding secondary sub-purpose tag; constructing a structured purpose digital certificate based on the primary purpose tag and the secondary sub-purpose tag; mapping the purpose digital certificate to the lock-up identifier using key-value pairs to generate a lock-up certificate, and storing the lock-up certificate in an off-chain database. After storing the lock-up certificate in the off-chain database, a hash calculation is performed on the lock-up certificate, and the calculated hash value is written to the consortium blockchain as a record of evidence.

[0052] Furthermore, the process of generating lock-up certificates for targeted funds with locked-up purposes aims to establish a structured mapping relationship for fund usage, supporting subsequent refined compliance verification. Specifically, the process first analyzes the multi-dimensional lock-up rules configured in the targeted payment smart contract, extracting primary-level usage tags that define the macro-flow of funds and secondary-level sub-use tags that refine the specific usage scenarios of funds. The primary-level usage tags define the broad category of fund usage, such as education funding; the secondary-level sub-use tags further specify the allowed types of goods or services, such as tuition payment and textbook purchase. This hierarchical tag structure provides the data foundation for subsequent two-layer progressive verification.

[0053] Subsequently, based on the extracted primary purpose tags and corresponding secondary sub-purpose tags, the system constructs a structured digital certificate of purpose. This digital certificate of purpose adopts a nested data format, containing a hierarchical relationship of purpose types, ensuring clear boundaries of fund usage and machine parsing capability. Simultaneously, the system maps the generated digital certificate of purpose to a lock-up identifier, representing the unique identity of the locked-up funds, and constructs a lock-up certificate by combining constraints such as the list of merchants allowed to trade under that purpose (corresponding to the aforementioned payee list) and product keywords.

[0054] Subsequently, to balance query efficiency and data security, the system stores the locked-up certificates in an off-chain database, leveraging the database's efficient read / write capabilities to support high-frequency transaction verification requests. After storage, the system performs hash calculations on key information within the locked-up certificates and writes the resulting hash values ​​into the consortium blockchain as evidence. Utilizing the immutability of blockchain, the integrity and authenticity of the digital certificates are ensured, allowing any tampering to be immediately detected by comparing the on-chain hash values. This provides a robust technical guarantee for the traceability of fund usage and regulatory auditing.

[0055] Step S201: Receive the targeted payment transaction request initiated by the user terminal, and obtain the order message based on the targeted payment transaction request.

[0056] In step S201 above, the targeted payment risk control process is triggered based on the targeted payment transaction request initiated by the user terminal, and the basic data source for subsequent compliance verification is obtained. The user terminal refers to the terminal device or application that initiates the payment operation, such as a financial APP or mobile terminal. The targeted payment transaction request refers to the fund payment instruction initiated by the user and bound by a specific smart contract. The order message refers to the structured data message that records transaction details, including information such as the payee, amount, time, and product description, and is the main basis for the system to perform semantic analysis and rule matching.

[0057] Specifically, the user first initiates a targeted payment transaction request through the terminal interaction module. This request signifies the formal establishment of the intention to transfer funds. The system then responds to this request, capturing and extracting the original order message accompanying the transaction from the payment channel or business system. The order message is a standard data carrier recording the core elements of the transaction, covering key information such as transaction time, transaction amount, payee information, merchant identification, and specific descriptions of goods or services. By acquiring this order message, the system provides complete input data for subsequent content analysis, ensuring accurate reconstruction of the actual business scenario in which the transaction occurred, thus laying the data foundation for in-depth risk control verification based on transaction characteristics.

[0058] Step S202: The order message is parsed by the content parsing agent to obtain transaction characteristics.

[0059] In step S202 above, the content parsing agent refers to an intelligent data processing module that integrates a natural language processing model, a convolutional neural network, and a rule engine. It is used to extract key information from complex order messages. The content parsing agent parses the order messages to obtain transaction features. Transaction features refer to the structured data extracted after parsing, such as merchant tags, product categories, and product prices, which are the direct basis for compliance verification.

[0060] In one optional embodiment, the step of parsing the order message using a content parsing agent to obtain transaction features includes: extracting visual features of product images in the order message using a convolutional neural network; extracting semantic features of product text in the order message using a natural language processing model; extracting preset fields in the order message using a rule engine; and integrating visual features, semantic features, and preset fields to obtain transaction features.

[0061] Furthermore, the process of parsing order messages using a content parsing intelligent agent to obtain transaction features aims to construct a multi-dimensional, in-depth view of transaction data, thereby providing comprehensive and accurate data support for subsequent risk control verification. The parsing process first utilizes a convolutional neural network to process the product images contained in the order message. By recognizing objects, text, and scenes in the image, visual features that characterize the physical attributes of the product are extracted. For example, identifying the clothing style or electronic product appearance shown in the image helps determine the true category of the product.

[0062] Meanwhile, the system utilizes natural language processing models to conduct in-depth analysis of the product description text involved in the order messages, extracting the semantic features behind the text through semantic understanding technology. In addition, the system also uses a rule engine to extract preset fields from the order messages. These fields are usually structured basic information, such as merchant identification, order amount, transaction time, and geographical location, ensuring the accuracy and consistency of the basic data.

[0063] Finally, the system integrates the visual features, semantic features, and preset fields obtained through different technical paths to form a unified set of transaction features, realizing the fusion of multi-source heterogeneous data. At the same time, it can enhance the robustness of the data through feature complementarity. For example, when the text description is ambiguous, visual features can provide supplementary judgment basis, while when the confidence of image recognition is low, semantic features and structured fields can be used as verification basis, thereby generating high-quality transaction features and laying a solid data foundation for subsequent multi-dimensional compliance verification and abnormal transaction identification.

[0064] The above steps overcome the limitations of a single data source or a single parsing technology, and significantly improve the accuracy and completeness of transaction feature extraction by integrating visual, semantic and structured data.

[0065] Step S203: Perform multi-dimensional compliance verification and abnormal transaction identification based on transaction characteristics to obtain verification results.

[0066] In step S203 above, the steps of multi-dimensional compliance verification and abnormal transaction identification based on transaction characteristics aim to comprehensively evaluate the legality and security of targeted payment transactions through a multi-layered and three-dimensional risk control mechanism, thereby generating the final transaction verification result. The aforementioned multi-dimensional compliance verification refers to a comprehensive inspection process covering multiple dimensions such as merchant qualifications, industry classification, product keywords, price reasonableness, and spatiotemporal consistency, aiming to ensure that transactions comply with preset smart contract rules. Abnormal transaction identification refers to using time window aggregation algorithms and graph analysis technology to identify abnormal transaction behavior patterns that circumvent risk control, such as split transactions, high-frequency transactions, and concentrated related transactions, thereby obtaining the verification result.

[0067] In one optional embodiment, the steps of performing multi-dimensional compliance verification based on transaction features include: extracting merchant features from the transaction features, matching the merchant features with the merchant list in the lock-up certificate to obtain a merchant matching result, and verifying the qualifications of the transaction merchants based on the merchant matching result; extracting transaction purpose features from the transaction features, matching the transaction purpose features with the primary purpose tag in the purpose digital certificate to obtain a purpose matching result, and verifying the purpose of the targeted payment transaction based on the purpose matching result; extracting commodity attribute features from the transaction features, matching the commodity attribute features with the risk commodity list in the lock-up certificate to obtain a risk commodity matching result, and verifying the commodity attribute of the targeted payment transaction based on the risk commodity matching result; extracting transaction price features from the transaction features, matching the transaction price features with the market transaction price to obtain a transaction price matching result, and verifying the price of the targeted payment transaction based on the transaction price matching result; extracting transaction spatiotemporal features from the transaction features, comparing the transaction spatiotemporal features with the transaction spatiotemporal information in the lock-up certificate to obtain a transaction spatiotemporal comparison result, and verifying the spatiotemporal consistency of the targeted payment transaction based on the transaction spatiotemporal comparison result.

[0068] Furthermore, multi-dimensional compliance verification ensures that every targeted payment transaction strictly complies with the constraints preset by the smart contract through a progressive logical judgment, thereby generating reliable verification results. Specifically, the system first extracts merchant characteristics from the transaction features, including merchant industry classification and merchant identifiers, and accurately matches them with the pre-configured merchant list in the locked-up tokens to obtain merchant matching results. Based on these matching results, the system further verifies the qualifications of the transaction merchants to check whether they have a legal business scope and the corresponding industry licenses. If the transaction merchant is not on the list or the qualifications have expired, it is directly determined to be non-compliant.

[0069] Subsequently, the system extracts the transaction purpose features from the transaction characteristics, which mainly cover the merchant's main business category and the business scenario tags associated with the order. It then performs hierarchical matching with the primary purpose tags defined in the purpose digital voucher to obtain the purpose matching results. Based on these results, the system verifies the purpose of the targeted payment transaction to ensure that the general flow of funds is consistent with the purpose type stipulated in the contract, and to prevent funds from flowing into unrelated industries.

[0070] The system deeply extracts product attribute features from transaction characteristics, parses specific product names and key descriptive words from order details, and matches them with a pre-set list of risky products in the lock-up certificate to determine whether the traded product falls into the range of risky products that are not allowed to be traded. Based on the matching results with the risky product list, the system performs product attribute verification on targeted payment transactions, aiming to intercept any behavior that attempts to circumvent usage restrictions by disguising product categories.

[0071] Based on this, the system extracts transaction price features from transaction characteristics, including unit price, total price and quantity information of goods, and matches them with the market transaction price database or historical guidance price maintained in the system to obtain transaction price matching results. Based on the results, the system performs price verification on targeted payment transactions, identifies and intercepts abnormal pricing that is significantly higher or lower than the reasonable market range, in order to prevent the risk of obtaining funds through inflated or fraudulent transactions.

[0072] Finally, the system extracts the spatiotemporal features of the transaction from the transaction characteristics, including the specific time point of the transaction, geographical coordinates, and network access IP, and compares them with the spatiotemporal information configured in the lock-up certificate to obtain the transaction spatiotemporal comparison result. Based on this result, the system performs spatiotemporal consistency verification on the targeted payment transaction to verify the authenticity and logical rationality of the transaction scenario and prevent false location or abnormal transactions from different locations. Only when the targeted payment transaction meets all compliance verifications is it determined that the targeted payment transaction has passed the multi-dimensional compliance verification.

[0073] The above steps extract transaction features across five dimensions: merchant, purpose, product, price, and time and space, and then match and verify them against the corresponding rules in the lock-up certificate. This achieves comprehensive and refined control over targeted payment transactions. It constructs a multi-layered risk control defense line, from macro-industry classification to micro-product prices, effectively identifying and intercepting various risky transactions such as merchant violations, mismatched purposes, prohibited products, abnormal prices, and time-space logic errors. This multi-dimensional cross-validation mechanism significantly improves the accuracy and robustness of risk control verification, ensuring that funds only circulate in real transaction scenarios that strictly meet preset conditions. This minimizes the risk of fund misappropriation and anomalies, guaranteeing the security and compliance of targeted payments.

[0074] In an optional embodiment, after obtaining the purpose matching result, the method further includes: if the purpose matching result indicates that the transaction purpose feature does not match the primary purpose label of the purpose digital certificate, inputting the order message into the semantic recognition model, and outputting a first semantic vector through the semantic recognition model; encoding the secondary purpose label in the purpose digital certificate to obtain a second semantic vector; calculating the similarity value between the first semantic vector and the second semantic vector, and performing purpose verification on the targeted payment transaction based on the similarity value.

[0075] Furthermore, when the system obtains a usage matching result indicating that the transaction usage feature fails to match the primary usage label of the usage digital certificate, it initiates a deep semantic verification process to address the fuzzy semantic matching problem that traditional rule engines struggle to handle. First, the original order message is input into a pre-trained semantic recognition model. This model utilizes deep learning algorithms to deeply understand and extract features from the unstructured text descriptions in the order, thereby outputting a first semantic vector that represents the actual semantic meaning of the order. Simultaneously, the system vectorizes the predefined secondary sub-use labels in the usage digital certificate, transforming them into a second semantic vector in a high-dimensional space. This converts discrete labels into machine-computable continuous numerical representations, preserving the semantic association information between labels.

[0076] Subsequently, the system calculates the similarity value between the first and second semantic vectors, quantifying the closeness of the order description and the intended use tag in the semantic space using algorithms such as cosine similarity. Based on the calculated similarity value, the system performs a final purpose verification on the targeted payment transaction: if the similarity value is higher than a preset confidence threshold, the transaction purpose is determined to be consistent with the agreed purpose at the semantic level, and the verification is passed and the transaction is approved; if the similarity value is lower than the preset threshold, a potential purpose deviation is determined, and the system will automatically suspend the transaction and trigger a manual review process, with a risk control specialist making the final decision. This mechanism effectively compensates for the shortcomings of keyword or category tag matching. For example, it can identify transactions such as educational materials that, although not explicitly listed, semantically fall within the scope of education funding, while also intercepting transactions that appear compliant but deviate semantically.

[0077] By using similarity calculation and threshold comparison, the system achieves refined and intelligent verification of the purpose of transactions, significantly improving the risk control system's adaptability and recognition accuracy in complex transaction scenarios, and ensuring the rigor and flexibility of fund usage control.

[0078] In one optional embodiment, the steps for identifying abnormal transactions based on transaction features include: obtaining a user identifier from the user terminal and querying a sequence of historical transaction records within a preset time window based on the user identifier; traversing the sequence of historical transaction records, extracting the transaction amount, transaction timestamp, and payee identifier for each transaction, and constructing a transaction association graph; based on the transaction association graph, detecting whether a dense subgraph structure exists, wherein the dense subgraph structure is used to indicate that the user terminal initiates targeted payment transactions to multiple associated accounts within the preset time window, or that the number of times the user terminal initiates targeted payment transactions to the same account within the preset time window exceeds a preset number threshold; if a dense subgraph structure is detected, determining that the targeted payment transaction is an abnormal transaction and generating an abnormal transaction identification result.

[0079] Furthermore, the step of identifying abnormal transactions based on transaction characteristics aims to use graph theory analysis techniques to delve into the relationships behind user transaction behavior, in order to accurately identify split transactions or concentrated related transactions that attempt to circumvent risk control restrictions. Specifically, the user identifier of the user initiating the current transaction is first obtained, and this identifier is used as a search key to query the historical transaction record sequence within a preset time window of the system, thereby constructing a complete transaction view of the user in recent times.

[0080] Subsequently, the system traverses the extracted historical transaction record sequence, parsing and extracting key elements from each record, including the transaction amount, timestamp, and payee identifier. Based on these elements, a transaction association graph is constructed. This graph uses users and payees as nodes and transaction behaviors as edges, visually displaying the topological structure of fund flows. The system performs structured analysis based on the constructed transaction association graph, focusing on detecting the presence of dense subgraph structures. Dense subgraph structures refer to regions in the graph where connections between nodes are highly concentrated. Such structures typically indicate abnormal fund flow patterns. For example, a user initiates targeted payment transactions to multiple related accounts within a preset time window, exhibiting characteristics of dispersed fund transfers; or a user initiates targeted payment transactions to the same account more times within a preset time window than a preset threshold, exhibiting characteristics of high-frequency concentrated payments. When the system detects a dense subgraph structure, it determines that the current targeted payment transaction is suspected of circumventing limits or being a fraudulent transaction, classifying it as an abnormal transaction. Based on this, an abnormal transaction identification result is generated and fed back to the risk control module to trigger corresponding interception or warning measures.

[0081] Step S204: If the verification result indicates that the targeted payment transaction has passed the verification, the smart contract is invoked to execute the targeted fund transfer, and a transaction result is generated for the targeted payment transaction and stored in the blockchain.

[0082] In step S204 above, when the verification results from multi-dimensional compliance verification and abnormal transaction identification indicate that the targeted payment transaction has passed verification, the system enters the fund transfer and notarization stage. First, the smart contract deployed on the blockchain network is invoked. This smart contract has pre-set four-dimensional lock-up rules and usage verification logic. The system automatically executes the targeted fund transfer operation according to the code instructions of the smart contract, transferring the locked funds from the payer's account to the compliant payee's account, and simultaneously completing the quota verification to ensure that the fund flow complies with the preset usage constraints.

[0083] Subsequently, the system generates a complete transaction result for this targeted payment transaction. This result includes not only basic information such as the transaction amount, time, and payee, but also details of compliance judgments during the verification process, usage matching records, and abnormal behavior detection conclusions, forming a comprehensive transaction voucher. To ensure the immutability and traceability of the data, the system stores the generated transaction result and its hash digest in a blockchain distributed ledger. This leverages the blockchain's consensus mechanism and encryption technology to ensure that once the transaction result is recorded on the chain, it cannot be unilaterally modified or deleted, thus providing reliable data support for subsequent fund tracing, auditing, and dispute resolution.

[0084] In another optional embodiment, the step of calling the smart contract to execute a targeted fund transfer includes: generating a payment execution instruction based on the verification result, the payment execution instruction including the payee identifier and the transfer amount; sending the payment execution instruction to the smart contract execution module to trigger the fund transfer function in the smart contract; the smart contract verifies the signature legality of the payment execution instruction, and if the verification is successful, the fund transfer operation from the locked account to the payee account is executed.

[0085] In another optional embodiment, the steps for generating a transaction result for a targeted payment transaction include: obtaining the execution status, transaction timestamp, and transaction hash value of the fund transfer operation; encapsulating the execution status, transaction timestamp, and transaction hash value into a transaction result object in a specific format; and serializing the transaction result object to obtain serialized transaction data.

[0086] Finally, the transaction results are stored on the blockchain, which includes: performing hash calculations on the serialized transaction data to generate a transaction digest hash value; constructing block header data containing the transaction digest hash value, block height, and timestamp; broadcasting the block header data to consensus nodes in the blockchain network, and writing it into the distributed ledger after consensus confirmation.

[0087] Through the above steps, a targeted payment transaction request initiated by the user is received. The order message is obtained based on the targeted payment transaction request. The order message is parsed by the content parsing smart agent to obtain transaction characteristics. Multi-dimensional compliance verification and abnormal transaction identification are performed based on the transaction characteristics to obtain the verification result. If the verification result indicates that the targeted payment transaction has passed the verification, the smart contract is called to execute the targeted fund transfer and generate a transaction result for the targeted payment transaction. The transaction result is then stored in the blockchain.

[0088] In this embodiment, a content parsing intelligent agent is used to parse order messages to obtain transaction characteristics. Based on these characteristics, multi-dimensional compliance verification and abnormal transaction identification are performed. This achieves refined control over targeted payment transactions from basic rules to business semantics and behavioral patterns, reducing risk control blind spots. After verification, a smart contract is invoked to execute fund transfer and record it on the blockchain, ensuring the automation, compliance, and traceability of fund flow. This improves the security of targeted payment transactions and solves the technical problem in related technologies where the targeted payment processing method based on a combination of fund locking and rule triggering has a coarse control granularity and risk control blind spots.

[0089] The following describes in detail another optional implementation method.

[0090] The blockchain-based targeted payment transaction processing system executes the aforementioned blockchain-based targeted payment transaction processing method to process targeted payment transactions. This blockchain-based targeted payment transaction processing system includes:

[0091] The smart contract configuration module is used to create and edit targeted payment smart contracts and configure multi-dimensional lock-up rules, including the payee, purpose, amount, and time.

[0092] The fund locking and quota management module uses smart contracts to lock up funds for their intended use and freeze quotas, managing quota write-offs and restores. A purpose credential binding mechanism is introduced to generate a unique digital credential for locked funds, which is then matched and verified in real-time with merchant type, product tags, and other information in the transaction order (i.e., order message).

[0093] The transaction information collection module is used to collect data such as payee information, merchant information, order details, transaction amount, time, and location in payment transactions.

[0094] The multi-dimensional contract verification module is used to verify in parallel whether transactions comply with smart contract rules, including the list of payees, usage matching, amount limits, time windows, etc.

[0095] The split transaction identification module is used to identify abnormal transaction behaviors such as split transactions, high-frequency transactions, and concentrated related transactions through time window aggregation algorithms, construct transaction association graphs, and detect abnormally dense subgraphs to identify abnormal transactions.

[0096] The smart contract execution module is used to automatically execute deductions, transfers, and credit limit verification after successful verification; if the verification fails, the transaction is intercepted and the reason for the exception is returned.

[0097] The on-chain evidence storage and supervision module is used to hash key information such as transaction summaries and contract execution results onto the chain, providing regulatory query and fund traceability interfaces. In addition to transaction summaries, it also includes the hashing of a multi-level hash tree composed of purpose certificates and intermediate results of anomaly identification.

[0098] The anomaly warning and handling module is used to issue real-time warnings for illegal transactions and abnormal behaviors, and supports manual review and anomaly handling.

[0099] The terminal interaction module provides users, payers, and regulators with operation access points, including functions such as payment initiation, contract configuration, and query traceability.

[0100] Figure 3 This is a schematic diagram of an optional blockchain-based targeted payment transaction processing flow according to an embodiment of the present invention, such as... Figure 3 As shown, the blockchain-based targeted payment transaction processing flow specifically includes:

[0101] Step 1, Begin;

[0102] Step 2: Configure multi-dimensional lock-up rules;

[0103] The payer initiates a request to create a targeted payment contract through the terminal interaction module. The smart contract configuration module receives the request and guides the payer to configure the lock-up rules, specifically designing the list of payees, usage type, amount limit, and time window.

[0104] Step 3: Create a targeted payment smart contract;

[0105] Once the rules are confirmed, a smart contract is generated and takes effect, and synchronized to the fund lock-up and quota management module.

[0106] Step four: Lock up funds;

[0107] The fund locking and quota management module, according to the contract rules, locks up the target funds for their intended use and freezes their quota, generating a lock-up certificate. When funds are locked, a digital certificate for the intended use is generated. This certificate is generated by nesting primary use tags and secondary sub-use tags and is stored as key-value pairs in an off-chain database. 5. After the lock-up is completed, a contract activation notification is sent to the payer through the terminal interaction module.

[0108] Step 5: Receive and respond to the targeted payment transaction request initiated by the user;

[0109] Users initiate targeted payment transaction requests through the terminal interaction module. The transaction information collection module automatically collects transaction data such as payee information, merchant information, order details, transaction amount, time, and location based on the order message.

[0110] Step Six: Analyzing Transaction Characteristics;

[0111] Step 7: Multi-dimensional compliance verification;

[0112] The multi-dimensional contract verification module performs multi-dimensional compliance verification in parallel. Figure 4This is a schematic diagram of an optional multi-dimensional compliance verification process according to an embodiment of the present invention, such as... Figure 4 As shown, the multi-dimensional compliance verification includes three dimensions: basic information verification, contract rule verification, and abnormal transaction identification. A comprehensive judgment is made based on the verification results of the three dimensions.

[0113] Step 8: Determine if the verification passes. If the verification fails, issue a warning and block the process. If the verification passes, proceed to step 9.

[0114] Step nine, anomaly detection in transactions;

[0115] Step 10: Determine if the transaction is abnormal. If so, issue a warning and block the transaction. If not, the smart contract will automatically execute the fund transfer.

[0116] Step 11: Generate transaction vouchers and store them on the blockchain;

[0117] Step 12, End.

[0118] This invention uses a content parsing smart agent to parse order messages to obtain transaction characteristics, and performs multi-dimensional compliance verification and abnormal transaction identification based on these characteristics. This enables refined control over targeted payment transactions from basic rules to business semantics and behavioral patterns, reducing blind spots in risk control. After verification, a smart contract is invoked to execute fund transfer and record it on the blockchain, ensuring the automation, compliance, and traceability of fund flow and improving the security of targeted payment transactions.

[0119] The following is a detailed description with reference to another embodiment.

[0120] Example 2

[0121] The blockchain-based targeted payment transaction processing device provided in this embodiment includes multiple implementation units, each of which corresponds to a specific implementation step in Embodiment 1 above. The specific implementation method and beneficial effects can be referred to the aforementioned method embodiment, and will not be repeated here.

[0122] Figure 5 This is a schematic diagram of an optional blockchain-based targeted payment transaction processing device according to an embodiment of the present invention, such as... Figure 5 As shown, the blockchain-based targeted payment transaction processing device may include: a receiving unit 51, a parsing unit 52, a verification unit 53, and an execution unit 54, wherein...

[0123] The receiving unit 51 is used to receive a targeted payment transaction request initiated by the user terminal and obtain the order message based on the targeted payment transaction request;

[0124] Parsing unit 52 is used to parse the order message through the content parsing agent to obtain transaction features;

[0125] Verification unit 53 is used to perform multi-dimensional compliance verification and abnormal transaction identification based on transaction characteristics, and obtain verification results.

[0126] The execution unit 54 is used to call the smart contract to execute the targeted fund transfer when the verification result indicates that the targeted payment transaction has passed the verification, and to generate a transaction result for the targeted payment transaction and store the transaction result on the blockchain.

[0127] The aforementioned blockchain-based targeted payment transaction processing device receives targeted payment transaction requests initiated by the user terminal through receiving unit 51 and obtains order messages based on the targeted payment transaction requests; parsing unit 52 parses the order messages through a content parsing smart agent to obtain transaction characteristics; verification unit 53 performs multi-dimensional compliance verification and abnormal transaction identification based on the transaction characteristics to obtain verification results; and execution unit 54, if the verification result indicates that the targeted payment transaction has passed verification, calls a smart contract to execute the targeted fund transfer, generates a transaction result for the targeted payment transaction, and stores the transaction result on the blockchain.

[0128] In this embodiment, a content parsing intelligent agent is used to parse order messages to obtain transaction characteristics. Based on these characteristics, multi-dimensional compliance verification and abnormal transaction identification are performed. This achieves refined control over targeted payment transactions from basic rules to business semantics and behavioral patterns, reducing risk control blind spots. After verification, a smart contract is invoked to execute fund transfer and record it on the blockchain, ensuring the automation, compliance, and traceability of fund flow. This improves the security of targeted payment transactions and solves the technical problem in related technologies where the targeted payment processing method based on a combination of fund locking and rule triggering has a coarse control granularity and risk control blind spots.

[0129] Furthermore, the blockchain-based targeted payment transaction processing device also includes: a first receiving module, used to receive a targeted payment smart contract creation request initiated by the payer, and configure multi-dimensional lock-up rules based on the targeted payment smart contract creation request; a first generating module, used to generate a targeted payment smart contract based on the multi-dimensional lock-up rules, and lock up the targeted funds for their intended use based on the targeted payment smart contract; and a second generating module, used to generate lock-up certificates for the targeted funds that have been locked up for their intended use.

[0130] Furthermore, the second generation module includes: a first extraction submodule, used to parse the multi-dimensional lock-up rules in the targeted payment smart contract and extract the primary use tag and the corresponding secondary sub-use tag; a first construction submodule, used to construct a structured use digital certificate based on the primary use tag and the secondary sub-use tag; and a first generation submodule, used to perform key-value pair mapping between the use digital certificate and the lock-up identifier to generate a lock-up certificate and store the lock-up certificate in an off-chain database. After storing the lock-up certificate in the off-chain database, a hash calculation is performed on the lock-up certificate, and the calculated hash value is written to the consortium blockchain as a record of evidence storage.

[0131] Furthermore, the parsing unit includes: a first extraction module, used to extract visual features of product images in the order message using a convolutional neural network; a second extraction module, used to extract semantic features of product text in the order message using a natural language processing model; a third extraction module, used to extract preset fields in the order message using a rule engine; and a first acquisition module, used to integrate visual features, semantic features, and preset fields to obtain transaction features.

[0132] Furthermore, the verification unit includes: a first matching module, used to extract merchant features from transaction features, match the merchant features with the merchant list in the lock-up certificate to obtain merchant matching results, and perform qualification verification on the transaction merchants based on the merchant matching results; a second matching module, used to extract transaction purpose features from transaction features, match the transaction purpose features with the primary purpose tags in the purpose digital certificate to obtain purpose matching results, and perform purpose verification on the targeted payment transaction based on the purpose matching results; and a third matching module, used to extract commodity attribute features from transaction features, match the commodity attribute features with the risk commodity list in the lock-up certificate. The system performs matching to obtain risk product matching results, and verifies the product attributes of targeted payment transactions based on these results. The fourth matching module extracts transaction price features from transaction characteristics, matches these features with market transaction prices to obtain transaction price matching results, and verifies the prices of targeted payment transactions based on these results. The first comparison module extracts transaction spatiotemporal features from transaction characteristics, compares these features with the transaction spatiotemporal information in the lock-up certificate to obtain transaction spatiotemporal comparison results, and verifies the spatiotemporal consistency of targeted payment transactions based on these results.

[0133] Furthermore, the blockchain-based targeted payment transaction processing device also includes: a first identification module, used to input the order message into a semantic recognition model and output a first semantic vector when the purpose matching result indicates that the transaction purpose feature does not match the primary purpose tag of the purpose digital certificate; a first encoding module, used to encode the secondary purpose tag in the purpose digital certificate to obtain a second semantic vector; and a first verification module, used to calculate the similarity value between the first semantic vector and the second semantic vector, and to verify the purpose of the targeted payment transaction based on the similarity value.

[0134] Furthermore, the verification unit also includes: a first query module, used to obtain the user identifier of the user terminal and query the historical transaction record sequence within a preset time window based on the user identifier; a first traversal module, used to traverse the historical transaction record sequence, extract the transaction amount, transaction timestamp, and payee identifier of each transaction, and construct a transaction association graph; a first detection module, used to detect whether a dense subgraph structure exists based on the transaction association graph, wherein the dense subgraph structure is used to indicate that the user terminal initiates targeted payment transactions to multiple associated accounts within a preset time window, or that the number of times the user terminal initiates targeted payment transactions to the same account within a preset time window exceeds a preset number threshold; and a first determination module, used to determine that the targeted payment transaction is an abnormal transaction when a dense subgraph structure is detected, and generate an abnormal transaction identification result.

[0135] It should be noted that the receiving unit 51, parsing unit 52, verification unit 53, and execution unit 54 mentioned above correspond to steps S201 to S204 in Embodiment 1. The instances and application scenarios implemented by the above units and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules or units can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0136] The invention will now be described in conjunction with another alternative embodiment.

[0137] Example 3

[0138] The present invention can also provide an electronic device. Figure 6 This is a hardware structure block diagram of an electronic device (or mobile device) that performs a blockchain-based targeted payment transaction processing method according to an embodiment of the present invention, such as... 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.

[0139] 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.

[0140] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: receiving a targeted payment transaction request initiated by the user terminal and obtaining an order message based on the targeted payment transaction request; parsing the order message through a content parsing smart agent to obtain transaction characteristics; performing multi-dimensional compliance verification and abnormal transaction identification based on the transaction characteristics to obtain verification results; if the verification results indicate that the targeted payment transaction has passed verification, calling a smart contract to execute the targeted fund transfer, generating a transaction result for the targeted payment transaction, and storing the transaction result on the blockchain.

[0141] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: receiving a request to create a targeted payment smart contract initiated by the payer, and configuring multi-dimensional locking rules based on the request; generating a targeted payment smart contract based on the multi-dimensional locking rules, and locking the targeted funds for their intended use based on the targeted payment smart contract; generating a lock-up certificate for the targeted funds that have been locked for their intended use.

[0142] The processor can access the information and application stored in the memory via the transmission device to execute the following steps: parse the multi-dimensional lock-up rules in the targeted payment smart contract, extract the primary use tag and the corresponding secondary sub-use tag; construct a structured use digital certificate based on the primary use tag and the secondary sub-use tag; map the use digital certificate to the lock-up identifier using key-value pairs to generate a lock-up certificate, and store the lock-up certificate in an off-chain database. After storing the lock-up certificate in the off-chain database, a hash calculation is performed on the lock-up certificate, and the calculated hash value is written to the consortium blockchain as a record of evidence.

[0143] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: extracting visual features of product images from order messages using a convolutional neural network; extracting semantic features of product text from order messages using a natural language processing model; extracting preset fields from order messages using a rule engine; and integrating visual features, semantic features, and preset fields to obtain transaction features.

[0144] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: extracting merchant features from transaction features, matching the merchant features with the merchant list in the locked-up certificate to obtain merchant matching results, and verifying the qualifications of the transaction merchants based on the merchant matching results; extracting transaction purpose features from transaction features, matching the transaction purpose features with the primary purpose tag in the purpose digital certificate to obtain purpose matching results, and verifying the purpose of the targeted payment transaction based on the purpose matching results; extracting commodity attribute features from transaction features, matching the commodity attribute features with the risk commodity list in the locked-up certificate to obtain risk commodity matching results, and verifying the commodity attributes of the targeted payment transaction based on the risk commodity matching results; extracting transaction price features from transaction features, matching the transaction price features with the market transaction price to obtain transaction price matching results, and verifying the price of the targeted payment transaction based on the transaction price matching results; extracting transaction spatiotemporal features from transaction features, comparing the transaction spatiotemporal features with the transaction spatiotemporal information in the locked-up certificate to obtain transaction spatiotemporal comparison results, and verifying the spatiotemporal consistency of the targeted payment transaction based on the transaction spatiotemporal comparison results.

[0145] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: if the purpose matching result indicates that the transaction purpose feature and the primary purpose tag of the purpose digital certificate are not matched successfully, the order message is input into the semantic recognition model, and the semantic recognition model outputs the first semantic vector; the secondary purpose tag in the purpose digital certificate is encoded to obtain the second semantic vector; the similarity value between the first semantic vector and the second semantic vector is calculated, and the purpose verification of the targeted payment transaction is performed based on the similarity value.

[0146] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: obtain the user's identifier from the user terminal and query the historical transaction record sequence within a preset time window based on the user identifier; traverse the historical transaction record sequence, extract the transaction amount, transaction timestamp, and payee identifier for each transaction, and construct a transaction association graph; based on the transaction association graph, detect whether a dense subgraph structure exists, wherein the dense subgraph structure is used to indicate that the user terminal initiates targeted payment transactions to multiple associated accounts within the preset time window, or that the number of times the user terminal initiates targeted payment transactions to the same account within the preset time window exceeds a preset threshold; if a dense subgraph structure is detected, determine that the targeted payment transaction is an abnormal transaction and generate an abnormal transaction identification result.

[0147] This invention provides a blockchain-based targeted payment transaction processing solution. By using a content parsing intelligent agent to analyze order messages and obtain transaction characteristics, multi-dimensional compliance verification and abnormal transaction identification are performed based on these characteristics. This achieves refined control over targeted payment transactions from basic rules to business semantics and behavioral patterns, reducing risk control blind spots. After successful verification, a smart contract is invoked to execute fund transfer and record it on the blockchain, ensuring the automation, compliance, and traceability of fund flows, improving the security of targeted payment transactions. This solves the technical problem in related technologies where targeted payment processing methods based on a combination of fund locking and rule triggering have coarse control granularity and risk control blind spots.

[0148] Those skilled in the art will understand that Figure 6 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, 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.

[0149] 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.

[0150] The invention will now be described in conjunction with another alternative embodiment.

[0151] Example 4

[0152] This invention also provides a computer-readable storage medium. Optionally, in this invention, the computer-readable storage medium can be used to store the program code executed by the blockchain-based targeted payment transaction processing method provided in Embodiment 1.

[0153] Optionally, in this embodiment of the invention, 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.

[0154] This invention also provides a computer program product, which, when executed on a data processing device, is suitable for performing the steps of a blockchain-based targeted payment transaction processing method: receiving a targeted payment transaction request initiated by a user terminal, obtaining an order message based on the targeted payment transaction request; parsing the order message through a content parsing smart agent to obtain transaction features; performing multi-dimensional compliance verification and abnormal transaction identification based on the transaction features to obtain verification results; and, if the verification results indicate that the targeted payment transaction has passed verification, calling a smart contract to execute the targeted fund transfer, generating a transaction result for the targeted payment transaction, and storing the transaction result on the blockchain.

[0155] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0156] In the above embodiments of the present invention, 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.

[0157] 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 can be a logical functional division, and in actual implementation, there may be other division methods. For instance, 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.

[0158] The units described as separate components may or may not be physically separate. 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0159] Furthermore, the functional units in the various embodiments of the present invention 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.

[0160] 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 the present invention, 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 the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0161] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A blockchain-based method for processing targeted payment transactions, characterized in that, include: Receive a targeted payment transaction request initiated by the user terminal, and obtain an order message based on the targeted payment transaction request; The order message is parsed by a content parsing agent to obtain transaction characteristics; Based on the aforementioned transaction characteristics, multi-dimensional compliance verification and abnormal transaction identification are performed to obtain verification results. If the verification result indicates that the targeted payment transaction has passed the verification, the smart contract is invoked to execute the targeted fund transfer, and a transaction result is generated for the targeted payment transaction and stored in the blockchain.

2. The method according to claim 1, characterized in that, Before receiving a targeted payment transaction request initiated by the user, the process also includes: Receive a request to create a targeted payment smart contract initiated by the payer, and configure multi-dimensional lock-up rules based on the request to create the targeted payment smart contract; A targeted payment smart contract is generated based on the multi-dimensional locking rules, and the targeted funds are locked for their intended use based on the targeted payment smart contract. Lock-up certificates are generated for targeted funds that are locked up for a specific purpose.

3. The method according to claim 2, characterized in that, The steps for generating lock-up certificates for targeted funds used for locked-up purposes include: Analyze the multi-dimensional lock-up rules in the targeted payment smart contract and extract the primary purpose tag and the corresponding secondary sub-purpose tag; Construct a structured digital credential based on the primary purpose label and the secondary sub-purpose label; The digital certificate for the stated purpose is mapped to a key-value pair with the lock-up identifier to generate the lock-up certificate, and the lock-up certificate is stored in an off-chain database. After the lock-up certificate is stored in the off-chain database, a hash calculation is performed on the lock-up certificate, and the calculated hash value is written into the consortium blockchain as a record of evidence.

4. The method according to claim 1, characterized in that, The steps of parsing the order message using a content parsing agent to obtain transaction features include: Visual features of product images in the order message are extracted using a convolutional neural network; Semantic features of the product text in the order message are extracted using a natural language processing model; Use a rule engine to extract preset fields from the order message; The transaction features are obtained by integrating the visual features, the semantic features, and the preset fields.

5. The method according to claim 3, characterized in that, The steps for multi-dimensional compliance verification based on the aforementioned transaction characteristics include: Merchant features are extracted from the transaction features, and the merchant features are matched with the merchant list in the locked-up certificate to obtain the merchant matching result. The merchant's qualifications are verified based on the merchant matching result. Extract the transaction purpose feature from the transaction features, match the transaction purpose feature with the primary purpose tag in the purpose digital certificate to obtain the purpose matching result, and perform purpose verification on the targeted payment transaction based on the purpose matching result; Extract the commodity attribute features from the transaction features, match the commodity attribute features with the list of risky commodities in the lock-up certificate to obtain the risky commodity matching result, and perform commodity attribute verification on the targeted payment transaction based on the risky commodity matching result; Extract the transaction price feature from the transaction features, match the transaction price feature with the market transaction price to obtain the transaction price matching result, and perform price verification on the targeted payment transaction based on the transaction price matching result; Extract the transaction spatiotemporal features from the transaction features, compare the transaction spatiotemporal features with the transaction spatiotemporal information in the lock-up certificate to obtain the transaction spatiotemporal comparison result, and perform spatiotemporal consistency verification on the targeted payment transaction based on the transaction spatiotemporal comparison result.

6. The method according to claim 5, characterized in that, After obtaining the usage matching results, the following is also included: If the purpose matching result indicates that the transaction purpose feature does not match the primary purpose tag of the purpose digital certificate, the order message is input into the semantic recognition model, and the semantic recognition model outputs the first semantic vector. Encode the secondary purpose tag in the digital certificate for the stated purpose to obtain a second semantic vector; Calculate the similarity value between the first semantic vector and the second semantic vector, and verify the purpose of the targeted payment transaction based on the similarity value.

7. The method according to claim 1, characterized in that, The steps for identifying abnormal transactions based on the aforementioned transaction characteristics include: Obtain the user identifier from the user terminal, and query the historical transaction record sequence within a preset time window based on the user identifier; Traverse the historical transaction record sequence, extract the transaction amount, transaction timestamp, and payee identifier for each transaction, and construct a transaction association graph; Based on the transaction association graph, the presence of a dense subgraph structure is detected. The dense subgraph structure is used to indicate that the user initiates targeted payment transactions to multiple associated accounts within a preset time window, or that the number of times the user initiates targeted payment transactions to the same account within a preset time window is greater than a preset number threshold. If the dense subgraph structure is detected, the targeted payment transaction is determined to be an abnormal transaction, and an abnormal transaction identification result is generated.

8. A blockchain-based targeted payment transaction processing device, characterized in that, include: The receiving unit is used to receive a targeted payment transaction request initiated by the user terminal and obtain an order message based on the targeted payment transaction request; The parsing unit is used to parse the order message through a content parsing agent to obtain transaction features; The verification unit is used to perform multi-dimensional compliance verification and abnormal transaction identification based on the transaction characteristics, and obtain the verification results. An execution unit is configured to, when the verification result indicates that the targeted payment transaction has passed verification, invoke a smart contract to execute the targeted fund transfer, generate a transaction result for the targeted payment transaction, and store the transaction result in the blockchain.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the blockchain-based targeted payment transaction processing method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the blockchain-based targeted payment transaction processing method according to any one of claims 1 to 7.

11. A computer program product, characterized in that, The computer program product includes a computer program, wherein when the computer program is executed by a processor, it implements the blockchain-based targeted payment transaction processing method according to any one of claims 1 to 7.

Images