A blockchain-based intelligent e-commerce transaction platform management system

Abstract

This invention relates to the field of e-commerce management technology and discloses a blockchain-based intelligent e-commerce transaction platform management system. The system includes a distributed transaction node cluster, a blockchain consensus network, and a smart contract execution engine. The distributed transaction node cluster collects transaction request data submitted by user terminals, containing product information text, buyer identification identifiers, and payment mode selection instructions. The blockchain consensus network connects to the distributed transaction node cluster, verifies the validity of the transaction request data through a consensus algorithm, and writes valid transaction request data into a distributed ledger. The smart contract execution engine calls the data in the distributed ledger, executes a preset trade rule logic chain, and generates a transaction verification result. This system can improve transaction data security, ensure transaction fairness, accelerate transaction verification speed, and achieve automatic execution of trade rules, solving many problems of traditional e-commerce platforms and meeting the needs of secure, fair, and efficient e-commerce transactions.

Description

Technical Field

[0001] This invention relates to the field of e-commerce management technology, specifically to a blockchain-based intelligent e-commerce transaction platform management system. Background Technology

[0002] In the current development of the e-commerce industry, the traditional transaction platform management model has gradually exposed many unavoidable problems. These problems not only affect the user's transaction experience but also hinder the stable operation of the entire e-commerce ecosystem. Traditional e-commerce transaction platforms mostly adopt a centralized architecture, with all transaction data stored centrally on the platform operator's servers. This architecture results in significant limitations in data management. Under the centralized storage model, the security of transaction data is difficult to guarantee. Once the server is attacked by hackers, experiences hardware failure, or suffers human error, it is highly susceptible to the leakage, loss, or alteration of a large amount of user transaction information, causing financial losses and information security risks to users, while also reducing user trust in the platform. Centralized platforms hold absolute dominance in the transaction process, and users lack control over transaction data. The platform controls the modification and query permissions of transaction records, which may lead to the platform arbitrarily adjusting transaction data for its own benefit, undermining the fairness of transactions.

[0003] From the perspective of transaction verification, traditional e-commerce platforms typically rely on third-party institutions or internal platform verification mechanisms, resulting in cumbersome and time-consuming processes. After a user submits a transaction request, the platform needs to go through multiple layers of data verification and identity verification, which not only prolongs the transaction completion time but also increases the risk of human intervention during the verification process, potentially leading to inaccurate verification results. Furthermore, transaction data between different e-commerce platforms is independent, lacking an effective data sharing mechanism. When users transact on different platforms, transaction information cannot be shared, increasing the difficulty of cross-platform transactions and hindering regulatory oversight of the overall e-commerce market.

[0004] Regarding the enforcement of trade rules, traditional e-commerce platforms often use platform terms and conditions for their rules, relying on platform staff for execution and lacking intelligent processing mechanisms such as neural network models and behavioral pattern recognition. When transaction disputes arise, manual mediation and handling based on trade rules are required, which is not only inefficient but also prone to unfair outcomes due to human factors. Furthermore, modifying and updating trade rules involves complex processes, hindering rapid adaptation to market changes and user needs, and impacting the platform's operational flexibility. These problems make traditional e-commerce platforms unable to meet current user demands for secure, fair, and efficient transactions, necessitating a new transaction platform management system to address these issues. Summary of the Invention

[0005] The purpose of this invention is to provide a blockchain-based intelligent e-commerce transaction platform management system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a blockchain-based intelligent e-commerce transaction platform management system, the system comprising:

[0007] A distributed transaction node cluster is used to collect transaction request data submitted by user terminals. The transaction request data includes product information text, buyer identification identifier, and payment mode selection instructions.

[0008] A blockchain consensus network, connected to the distributed transaction node cluster, is used to verify the validity of transaction request data through a consensus algorithm and write valid transaction request data into the distributed ledger.

[0009] The smart contract execution engine calls the data in the distributed ledger, executes the preset trade rule logic chain, and generates transaction verification results.

[0010] Compared with the prior art, the beneficial effects of the present invention are:

[0011] This blockchain-based intelligent e-commerce transaction platform management system effectively avoids the security risks associated with centralized data storage in traditional centralized architectures by collecting transaction request data submitted by user terminals through a distributed transaction node cluster. The distributed node cluster disperses transaction request data across multiple nodes, with each node possessing a complete copy of the transaction data. Even if some nodes experience failures or attacks, the integrity and availability of the overall data remain unaffected, significantly improving transaction data security. Simultaneously, the distributed architecture allows each node to participate in the data collection process, reducing the monopoly of a single node on data collection and ensuring that transaction request data is collected truthfully and accurately, preventing the possibility of data tampering and guaranteeing the fairness of transactions.

[0012] The blockchain consensus network provides a reliable mechanism for verifying the validity of transaction request data. The consensus algorithm allows all nodes in the blockchain consensus network to participate in verifying transaction data. Only when a majority of nodes agree on the validity of the transaction data can it be written into the distributed ledger. This verification method abandons the traditional verification model that relies on third-party institutions or single platforms, reducing human intervention and improving the objectivity and fairness of the verification results. Simultaneously, the consensus verification process is transparent; all nodes can view the verification process and results in real time, and users can also query the verification status through relevant interfaces, enhancing user trust in transaction verification. Furthermore, the consensus verification process is automated, requiring no manual operation, significantly shortening verification time, accelerating transaction completion, and improving transaction efficiency, better meeting users' needs for rapid transaction completion.

[0013] The smart contract execution engine invokes data from the distributed ledger to execute a pre-defined chain of trade rules, combining natural language processing and behavioral pattern recognition to achieve intelligent and automated execution of these rules. Smart contracts write trade rules into the system as code; once preset trigger conditions are met, the corresponding rules are automatically executed without human intervention, avoiding the inefficiencies and human biases inherent in traditional manual execution. In the event of a transaction dispute, the smart contract can automatically handle it according to preset rules, quickly providing transaction verification results, reducing dispute resolution time and improving user experience. Furthermore, the smart contract code is immutable; once deployed to the system, the trade rules cannot be arbitrarily modified, ensuring the stability and consistency of trade rule execution and preventing platforms from unilaterally adjusting rules for their own benefit.

[0014] The application of distributed ledgers enables transparent and traceable transaction data. Once all valid transaction request data is written to the distributed ledger, the data is permanently stored and immutable. Users and regulatory authorities can query transaction records at any time to understand detailed transaction information. This traceability not only facilitates users in verifying their own transactions but also enables regulatory authorities to supervise the e-commerce market, promptly detect and handle illegal transactions, and maintain market order. Furthermore, data in the distributed ledger can be shared throughout the system, allowing different nodes to access transaction data in real time. This breaks down the data silos between traditional e-commerce platforms, facilitating cross-platform transactions for users and enabling overall data integration and analysis within the e-commerce industry. Combining differentiated neural network models and behavioral pattern recognition algorithms enables intelligent processing of the entire transaction process, while integrating blockchain technology further enhances system efficiency and security, contributing to the standardized development of the e-commerce industry. Attached Figure Description

[0015] Figure 1 This is a sequence diagram of the blockchain-based intelligent e-commerce transaction platform management system described in this invention.

[0016] Figure 2 A flowchart of a blockchain consensus network;

[0017] Figure 3 A flowchart for the multilingual interaction module;

[0018] Figure 4 This is a flowchart of the multimodal payment revenue sharing module. Detailed Implementation

[0019] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0020] Please see Figure 1 This invention provides a blockchain-based intelligent e-commerce transaction platform management system, the system comprising:

[0021] This system leverages the collaborative operation of a distributed transaction node cluster, a blockchain consensus network, and a smart contract execution engine. The distributed transaction node cluster, deployed across multiple data centers globally, receives transaction request data submitted by user terminals via an API gateway. This data includes product information text, a buyer's identity identifier, and a payment mode selection instruction. The product information text is encapsulated in JSON-LD structured format, and the buyer's identity identifier is a digital identity hash value generated using public-key cryptography. The blockchain consensus network employs a multi-layered architecture, connecting to the distributed transaction node cluster. It verifies the validity of the digital signature, data integrity, and identity authenticity of the transaction request data using a practical Byzantine fault-tolerant algorithm. Valid transaction request data that passes verification is encoded into a Merkle tree structure and written to the distributed ledger. The smart contract execution engine invokes the transaction data in the distributed ledger through a cross-chain data interface, loading a pre-defined trade rule logic chain. This logic chain includes rules for goods delivery verification, payment condition judgment, and dispute resolution. Upon execution, it generates a transaction verification result containing a transaction status code and a verification timestamp.

[0022] Example 1: See Figure 2 This system involves the data sharding processing flow of the blockchain consensus network and the contract generation mechanism of the smart contract execution engine. Through a multi-layered data processing and verification architecture, it achieves efficient and reliable e-commerce transaction management. Upon receiving transaction request data transmitted from the distributed transaction node cluster, the data sharding module first parses the product information text. Product category codes are identified using the UN Standard Product and Service Code system; for example, agricultural products correspond to UNSPSC code "50000000", and industrial equipment corresponds to "40000000". The system maintains a classification index mapping table, mapping different ranges of code prefixes to corresponding data storage partitions. When the product category code in the transaction request is identified as "50100000" (belonging to the fruit and vegetable subcategory), the data sharding module automatically classifies the transaction data into the fresh agricultural products data subset. Each data subset contains complete transaction information, including product description, buyer identification, and payment instructions, and is tagged with the corresponding category for subsequent processing.

[0023] Cross-link processing involves nodes directing data subsets to corresponding sub-blockchain channels based on their category labels. The system pre-defines multiple parallel-running sub-blockchain channels, such as agricultural product channels, electronics channels, and clothing channels. Each sub-channel employs an independent network topology and validator node cluster. Routing nodes use a consistent hashing algorithm to calculate the target channel identifier, ensuring that similar transaction data is always routed to the same processing channel. For example, all agricultural product transactions with UNSPSC codes beginning with "50" are mapped to a dedicated agricultural product sub-blockchain, which consists of validator nodes distributed across major agricultural production areas.

[0024] The channel verification module configures differentiated consensus mechanisms for each sub-blockchain channel. The agricultural product trading channel uses a proof-of-stake mechanism, verified by nodes holding specific agricultural tokens; the high-value industrial product channel uses a Byzantine fault-tolerant mechanism, requiring verification by multiple authoritative nodes. During verification, each sub-channel processes its respective category of transaction data in parallel, verifying digital signature validity, transaction amount reasonableness, and product information completeness. Verified transactions are packaged into blocks, and the block header hashes of each sub-chain are periodically synchronized to the main chain for notarization, forming a hierarchical blockchain network structure.

[0025] The rule parsing unit of the smart contract execution engine extracts key trade terms from verified transaction data. Using natural language processing technology, it analyzes price terms, delivery conditions, and quality requirements within the product information text. For example, from the text "CIF Shanghai Port, price including tax USD 10,000, ISO standard packaging," it extracts elements such as the trade term CIF, price amount USD, currency unit USD, delivery location Shanghai Port, and packaging standard ISO. These elements are then transformed into structured data objects for subsequent processing.

[0026] The terms matching unit accesses a standardized contract template library, which stores various contract templates categorized according to the Incoterms. Based on the extracted trade terms features, the system searches the library for the most suitable contract template. For example, when a transaction using CIF terms and involving agricultural products is identified, the system automatically matches the standard contract template from the International Grain and Feed Trade Association. The matching process considers multiple dimensions such as trade method, commodity type, and transaction amount, using a weighted similarity algorithm to determine the most suitable template version.

[0027] The dynamic assembly unit populates the extracted clause elements into the selected contract template. The template is defined using an XML structure, containing fixed clause areas and variable field areas. The system automatically populates the corresponding fields in the template with buyer identity information, product descriptions, price terms, etc., while retaining the standard clause content in the template. After population, a structured electronic contract conforming to the LegalXML specification is generated, containing complete agreements on rights and obligations and a description of transaction conditions. A digital timestamp and hash value are appended to the generated contract to ensure its immutability and verifiability.

[0028] The entire process leverages the efficient collaboration of various components within the blockchain network to achieve the classification, parallel verification, and smart contract generation of transaction data. Data sharding improves processing efficiency, a differentiated consensus mechanism ensures verification quality, and smart contract generation guarantees the standardization and enforceability of transaction terms. All processing steps are completed within a distributed network environment, with each node maintaining state consistency through a consensus algorithm, and all processing results recorded in an immutable distributed ledger.

[0029] The system maintains a high level of automation throughout the processing. From receiving a transaction request to generating the final contract, all steps are completed automatically through pre-defined business logic. Human intervention is limited to system monitoring and anomaly handling; normal business processes are entirely driven by smart contracts and consensus mechanisms. This design ensures both processing efficiency and, through its distributed architecture, system reliability and transparency. Data transmission at each stage is encrypted, and sensitive information such as price terms and identity identifiers is protected using asymmetric encryption algorithms. Interface calls between processing modules undergo identity authentication and permission verification to prevent unauthorized access and malicious operations. System logs comprehensively record all processing activities for easy auditing and problem tracking.

[0030] The resulting structured electronic contract possesses both legal and technical validity, meeting the legal requirements of commercial transactions while also exhibiting the verifiable characteristics of blockchain technology. The contract text is pushed to relevant parties via a standard data interface and permanently stored in a distributed ledger, serving as the basis and proof for transaction execution. The entire implementation process demonstrates the innovative application of blockchain technology in e-commerce, improving the efficiency and reliability of transaction processing through technological means.

[0031] Example 2: See Figure 3This system involves the semantic processing flow of a multilingual interaction module and a contract language adaptation mechanism. Through intelligent text analysis and multilingual processing technology, the system eliminates language barriers and ensures legal compliance in cross-border e-commerce. The semantic separation unit of the multilingual interaction module uses a bidirectional long short-term memory network based on an attention mechanism to process product information text. When receiving product description data from a distributed transaction node cluster, the system first performs text preprocessing, including word segmentation, part-of-speech tagging, and syntactic analysis. For a mixed text containing technical parameters and marketing content, such as "This smartphone is equipped with the latest generation processor, with a main frequency of up to 3.2GHz, and a 48-megapixel optical image stabilization camera, allowing you to capture wonderful moments anytime, anywhere," the semantic separation unit can accurately identify the technical parameter part "processor main frequency 3.2GHz, 48-megapixel optical image stabilization camera" and the marketing description part "allowing you to capture wonderful moments anytime, anywhere." The separation process is based on deep learning sequence labeling technology, where each word is classified as either a technical term or a marketing phrase, ultimately forming two independent text streams.

[0032] The dual-channel translation unit employs differentiated processing strategies for different text types. Technical parameter descriptions are transmitted to the specialized terminology translation channel, which connects to multiple specialized dictionary databases, including those for electronic engineering and mechanical manufacturing. During the translation process, the accuracy and consistency of technical parameters are strictly maintained; numerical units, specifications, and other elements are preserved in their original meaning. For example, "3.2GHz" maintains the same numerical value and unit representation across different language versions. Marketing descriptions, on the other hand, enter the semantic translation channel, employing a neural machine translation model to prioritize preserving the original text's emotional tone and rhetorical effect. Cultural differences and expression habits are considered during the translation process to ensure the translated text conforms to the reading experience of users of the target language.

[0033] The data fusion unit receives the translated dual-text data stream and reassembles it according to the structural features of the original text. The system records the relative positions and logical connections between technical parameters and marketing content in the original text, maintaining the same narrative order and paragraph structure in the target language version. The reassembled multilingual product information maintains both the accuracy of technical parameters and the persuasiveness of the marketing copy, forming a complete product description that conforms to the expression habits of the target language.

[0034] The contract language adaptation unit determines the output language of the contract based on the buyer's place of registration code in the buyer's identification identifier. The system maintains a mapping database containing ISO 3166-1 standard country codes and their official languages. When the buyer's place of registration code is detected as "FR", the contract output language is automatically set to French; when the place of registration code is "DE", the German version is output. The language determination process takes into account the special cases of multilingual countries; for example, Swiss buyers can choose German, French, or Italian versions depending on their canton.

[0035] The contract language adaptation unit calls upon a database of legal provisions from the target language region for compliance verification. This database integrates commercial laws from various jurisdictions, including relevant provisions of consumer protection laws and e-commerce laws. The system intelligently compares the rights and obligations clauses in the structured electronic contract with local legal requirements, identifying contract clauses that may have compliance risks. For example, for buyers in the European Union, the system verifies whether the return policy complies with the EU's 14-day no-reason return policy; for buyers in California, USA, it checks whether the privacy policy meets the requirements of California's Consumer Privacy Act.

[0036] When potential risks are identified during compliance verification, the system automatically generates revision suggestions. These suggestions, based on specific legal requirements and industry best practices, provide alternative clause options that comply with local laws. These suggestions are pushed to contract administrators via a standard interface, and upon confirmation, trigger a contract restructuring process. The restructured contract version undergoes compliance verification again until all clauses meet the legal requirements of the target region.

[0037] The entire multilingual processing workflow employs an asynchronous parallel processing approach, allowing multiple translation tasks to run simultaneously, thus improving system throughput. Each processing stage is equipped with a quality control mechanism; translation results undergo automatic verification and manual sampling review to ensure output quality. The system supports mutual translation of mainstream business languages, including more than ten languages ​​such as English, Chinese, Spanish, French, and German.

[0038] Intermediate data and final results generated during processing are recorded in a distributed ledger, forming a complete processing log. Any modifications or adjustments to the original text are traceable, ensuring the transparency and auditability of the processing. Multilingual product information and localized contracts are pushed to the buyer's terminal through a standard data interface and presented to the user in an appropriate format.

[0039] This system effectively addresses language barriers and legal differences in cross-border e-commerce through intelligent multilingual processing and legal adaptation. Buyers can access product information in their native language and obtain transaction contracts that comply with local laws, enhancing their cross-border shopping experience. Simultaneously, it helps sellers avoid compliance risks caused by legal differences, promoting the healthy development of cross-border e-commerce. The entire implementation process demonstrates the deep integration of blockchain technology and multilingual processing technology, providing technical support for global e-commerce.

[0040] Example 3: See Figure 4Upon receiving a payment mode selection instruction, the payment channel binding unit of the multimodal payment revenue sharing module initiates the payment protocol interface activation process. The system supports multiple payment modes, including instant settlement of fiat currency payments, cryptocurrency payments, and escrow transactions for specific scenarios. When a user selects the "cryptocurrency payment" mode, the payment channel binding unit parses the mode code in the instruction and calls the corresponding digital wallet interface protocol. The system maintains a payment protocol registry, recording the technical parameters and access specifications of each payment channel. The activation process includes establishing a secure communication link, exchanging digital certificates, negotiating encryption parameters, and ultimately establishing an end-to-end payment processing channel.

[0041] The asset locking unit executes the asset freezing operation of the buyer's digital wallet through a blockchain consensus network. Once the payment channel is established, the system generates an asset locking instruction, which includes elements such as the transaction number, asset type, and frozen amount. This instruction is sent to the blockchain network where the buyer's digital wallet resides via a smart contract. For cryptocurrency payments, the locking operation creates a time-locked multi-signature transaction on the corresponding blockchain; for fiat currency payments, a pre-authorization of funds is initiated through a bank API interface. The frozen amount is calculated based on the total transaction amount, including the price of the goods, transaction fees, and any possible exchange rate differences. All freezing operations are recorded in a distributed ledger, forming an immutable audit trail.

[0042] The channel status monitoring unit tracks the operational status of each payment protocol interface in real time. The system deploys multiple monitoring nodes that periodically send probe requests to each payment channel, collecting performance metrics such as response time, success rate, and processing capacity. Monitoring data includes network layer transmission latency, application layer processing latency, and business layer confirmation time. These metrics are stored in time-series format for subsequent channel quality assessment and decision analysis. The monitoring frequency is dynamically adjusted based on the importance and historical performance of the payment channel, with critical channels employing a higher-frequency monitoring strategy.

[0043] The dynamic switching decision unit calculates the availability weight of payment channels based on monitoring data. The weight calculation considers multiple dimensions and uses the following evaluation formula:

[0044]

[0045] in: Indicates channel availability weight. Represents network round-trip latency (milliseconds). Indicates the transaction confirmation time (in seconds). This indicates the interface availability rate (percentage). Coefficient , , The weights are respectively assigned to the three dimensions of network performance, processing efficiency, and availability, and satisfy the following conditions: These coefficients are configured based on the type of payment channel and business needs; for example, for cryptocurrency channels with high real-time requirements, The value is relatively high; for fiat currency channels with high stability requirements, The value is relatively high.

[0046] The real-time routing optimization unit monitors the weight changes of each channel. When it detects that the availability weight of the main payment channel is lower than a preset threshold, it initiates a channel switching process. The switching decision is based on weight comparison and business rules, and the system maintains a priority list of alternative channels. The switching process employs a seamless transition mechanism: first, a payment session is initialized on the new channel; then, the transaction status is synchronized; and finally, the connection to the original channel is closed. The entire switching process ensures the integrity and consistency of transaction data, avoiding payment interruptions or duplicate payments.

[0047] The cross-chain settlement coordination unit handles asset transfer operations involving multiple blockchains. When a payment requires cross-chain execution, the system employs a hash time-locking protocol to ensure atomicity. This protocol requires participants to complete their operations within a specified time; otherwise, all operations are automatically rolled back. The coordination unit monitors the operational status on each chain and triggers the final asset transfer operation when all preconditions are met. The cross-chain settlement status is synchronized to all relevant blockchains, maintaining the consistency of the distributed ledger.

[0048] The payment mode parsing unit of the payment channel binder performs in-depth analysis of the detailed characteristics of payment instructions. The system identifies the hierarchical payment type identifiers in the payment mode selection instructions, which use a standardized encoding format. The instant settlement mode code "IMD" indicates a transaction requiring real-time settlement, the escrow transaction mode code "ESC" indicates a transaction requiring third-party escrow, and the installment payment mode code "INS" indicates a transaction requiring multiple payments. The parsing process includes syntax analysis and semantic verification to ensure the integrity and correctness of the instructions.

[0049] The multi-parameter mapper converts payment type identifiers into specific business parameters. The system invokes a pre-defined payment rule chain, which includes fund processing rules, risk control rules, and fee calculation rules. The mapping results include the fund freeze ratio, settlement trigger conditions, and fee calculation methods. For example, in a secured transaction model, the fund freeze ratio is 100% of the transaction amount, the settlement trigger condition is the buyer's confirmation of receipt, and the fee is calculated as a specific percentage of the transaction amount. These parameters are dynamically configured according to business strategies and risk management requirements.

[0050] The fund flow control instruction generator generates specific operation instructions based on the mapping results. For payment methods requiring frozen funds, it generates asset freeze instructions and sends them to the corresponding blockchain or payment system. The instructions include detailed freeze parameters, including the frozen amount, unfreezing conditions, and validity period. Simultaneously, settlement trigger conditions are linked to the smart contract execution engine as the basis for subsequent automatic settlement. All fund flow control operations are recorded in the distributed ledger, forming a complete audit trail of fund flows.

[0051] The entire payment processing flow adopts an event-driven architecture, with components communicating asynchronously via message queues. Changes in payment status trigger corresponding event handling logic, ensuring the system can respond promptly to the needs of various payment scenarios. An exception handling mechanism monitors for failures and timeouts during the payment process, automatically initiating retry or compensation procedures to guarantee eventual payment consistency. The system tracks the entire lifecycle of payment processing in real time through multi-layered monitoring and logging. Monitoring data includes performance metrics, error statistics, and business metrics, used for system optimization and troubleshooting. Log information records the timestamp, execution result, and relevant context for each payment operation in detail, supporting post-event auditing and dispute resolution. Through intelligent payment channel management and dynamic optimization, the system provides flexible and reliable payment solutions for e-commerce transactions. It supports parallel processing of multiple payment modes and can intelligently select the optimal payment path based on real-time network conditions, ensuring the efficiency and reliability of the payment process. The cross-chain settlement mechanism solves the asset transfer problem in multi-blockchain environments, achieving true interoperability of the value internet. The entire implementation process demonstrates the innovative application of blockchain technology in the payment field, enhancing the payment experience of cross-border e-commerce through technological means.

[0052] Example 4: The voucher element extraction unit of the settlement voucher synthesis engine separates key data elements from the payment success event. When the multimodal payment accounting module detects a payment success status, the system captures the event and parses the transaction information contained therein. The extraction process uses a pattern matching algorithm to identify three core elements from the event data stream: transaction amount, timestamp, and digital signature. The transaction amount field contains a numerical value and currency unit, such as "1000.00 USD"; the timestamp follows an international standard format, recording the precise time of payment completion; the digital signature is generated using an asymmetric encryption algorithm to verify the authenticity of the payment event. These elements are extracted separately and then transferred to different processing pipelines.

[0053] The multi-source data integration unit receives the extracted transaction elements and performs standardization processing. The transaction amount needs to be converted to the target fiat currency, a process achieved through a real-time exchange rate conversion interface. The system connects to multiple foreign exchange data sources and obtains the market exchange rate at the time of payment based on the payment timestamp. For example, when the payment amount is 1000 euros and the target fiat currency is US dollars, the system queries the EUR / USD exchange rate at that point in time and performs the conversion. The conversion result is rounded to two decimal places and labeled with the source and target currency codes. The timestamp data is decomposed into date and time components, which are then formatted separately. The integration unit associates the converted transaction amount with the formatted timestamp using a data binding algorithm, generating a data packet with temporal correlation. The binding process uses hash chaining technology, where each data element contains the hash value of the previous element, forming an indivisible data unit.

[0054] The blockchain notarization unit writes the processed data packet into an immutable notarization block. The notarization block uses a specialized data structure, consisting of a block header and a data body. The block header records the notarization time, the hash of the previous block, and the Merkle root value; the data body stores the bound transaction data packet and digital signature. The writing process is verified through a consensus mechanism, requiring confirmation from multiple notarization nodes. After notarization is complete, a unique notarization hash is generated, which can be used as an index key for credential lookup and verification.

[0055] The payment mode parsing unit of the payment channel binder processes payment mode selection instructions in detail. This unit uses syntax analysis technology to parse the instruction text and identify the hierarchical payment type identifiers within it. The system defines three types of payment mode codes: Instant Settlement mode code "IMD" indicates real-time payment, Escrow Transaction mode code "ESC" indicates escrow payment, and Installment Payment mode code "INS" indicates multi-period payment. During the parsing process, the legality and completeness of the codes are verified to ensure the correctness of subsequent processing.

[0056] The multi-parameter mapper converts payment type identifiers into specific business parameters. The system maintains a payment rule configuration table, recording the processing parameters corresponding to different payment modes. This table includes fields such as payment mode code, fund freeze ratio, settlement trigger conditions, and transaction fee calculation rules. The mapping process queries this table based on the parsed payment mode code to obtain the corresponding business parameter values ​​(see Table 1).

[0057] Table 1: Payment Rule Configuration Table

[0058] Payment mode code Funds Freezing Ratio Settlement Triggering Conditions Fee Calculation Rules IMD 100% Payment takes effect immediately Calculated at 0.5% of the amount ESC 100% Buyer confirms receipt Calculated at 1% of the amount INS 30% in the first installment Installment due date Calculated at 0.8% of the amount

[0059] The fund flow control instruction generator generates executable fund operation instructions based on the mapping results. For payment methods requiring fund freezing, an asset freeze instruction is generated and sent to the blockchain network. The instruction includes detailed parameters such as the frozen account, the frozen amount, and the unfreezing conditions. Simultaneously, the settlement trigger conditions are converted into executable code for a smart contract and deployed to the blockchain network for automatic settlement operations. All instruction generation processes are logged, including instruction content, generation time, and execution status.

[0060] The entire settlement voucher processing workflow adopts a pipeline architecture, with data exchanged between processing units via message queues. After completing a specific function, each processing unit passes the result to the next unit, while simultaneously saving intermediate states for error recovery. The system employs multiple checkpoints; if a processing step fails, it can restart from the nearest checkpoint, ensuring data integrity and processing continuity. Voucher data is protected by multi-layered encryption, with sensitive information such as transaction amounts and account identifiers encrypted using asymmetric encryption algorithms. The access control system is based on role-based access control, ensuring that users at different levels can only access data within their corresponding permission scope. All data access operations are logged, including access time, visitor, and access content.

[0061] This system provides complete payment proof and audit trails for e-commerce transactions through an automated settlement voucher generation and storage mechanism. Structured data processing ensures the accuracy and consistency of voucher information, while blockchain storage ensures the immutability and verifiability of the vouchers. Multimodal payment support meets the payment needs of different scenarios, and parameterized configuration provides flexible rule adjustment capabilities. The entire implementation process demonstrates the deep application of blockchain technology in the field of transaction settlement, improving the reliability and transparency of e-commerce transactions through technological means.

[0062] All data records and operation logs generated during the processing are permanently stored in the distributed ledger, forming a complete transaction history. This data can be used for subsequent accounting reconciliation, dispute resolution, and audit investigations. The system provides a standard data query interface, supporting the retrieval of settlement voucher information by various criteria such as transaction number, time range, and payment mode. The query results contain complete voucher data and evidence storage information, which can be verified and used by relevant parties.

[0063] Example 5: The behavior pattern analysis unit of the risk control feedback module continuously collects user terminal operation sequence data throughout the entire transaction process. When a user begins browsing a product page, the system records interactive features such as page dwell time, scrolling speed, and click hotspot distribution; during the order filling stage, it captures behavioral trajectories such as form filling order, number of modifications, and input speed; during the payment stage, it monitors operational details such as verification code response time and payment password input interval. The data collection frequency is set to 100 milliseconds per instance, forming a high-precision time-series operation log. All behavioral data is appended with timestamps and session identifiers and transmitted to the analysis engine through an encrypted channel.

[0064] The anomaly identification unit uses a dynamic time warping algorithm to process the collected operation sequences. The system maintains a historical safe operation sequence database, containing verified normal transaction behavior patterns. The comparison process first standardizes the operation sequence to be tested, eliminating biases caused by device differences and network latency. The algorithm calculates the pattern difference degree between the current operation sequence and historical safe sequences in a multi-dimensional space. The main evaluation dimensions include: the distribution characteristics of operation time intervals, such as whether the time difference between consecutive operations conforms to common human reaction speeds; fluctuations in input accuracy, such as whether the error correction frequency exceeds a reasonable range; and abnormal interruptions in interaction coherence, such as unexpected pauses in critical steps. Each dimension is assigned a specific weight coefficient, and the overall pattern difference degree index is calculated comprehensively.

[0065] The intelligent circuit breaker unit receives the pattern difference calculation results in real time. The system's preset risk threshold is 0.85, a value derived from statistical analysis of historical risk events. When the detected pattern difference exceeds the risk threshold, the circuit breaker mechanism immediately initiates a multi-level protection process: first, it sends a termination command to the settlement certificate synthesis engine, suspending the payment pipeline of the current transaction; simultaneously, it invokes the blockchain smart contract to execute an asset rollback operation, releasing the frozen funds; finally, it generates a detailed security audit log, recording the anomaly characteristics, trigger time, and handling measures. The circuit breaker process is completed within 300 milliseconds, minimizing the possibility of risk spread.

[0066] Risk detection results are updated in real time to the risk identification partition of the distributed ledger. Each risk event generates an independent risk record block, containing a feature vector of the abnormal operation sequence, a pattern difference value, and a response measure code. These blocks are verified through a consensus mechanism and appended to the end of the blockchain, forming an immutable risk event chain. During subsequent transaction processing, the system will refer to the data in this risk chain to implement enhanced verification measures for high-risk users or devices.

[0067] The behavior pattern analysis unit employs a progressive learning mechanism to update the secure operation sequence database. The system periodically incorporates validated secure transaction operation sequences into a reference dataset and identifies new behavior pattern categories through clustering algorithms. The database update process utilizes version control, retaining historical data snapshots for retrospective analysis. Anomaly detection model parameters are also dynamically adjusted based on the latest risk characteristics to maintain the timeliness of the identification capabilities.

[0068] The entire risk control process employs a distributed computing architecture, with multiple analysis nodes processing transaction data from different users in parallel. A load balancing mechanism allocates computing tasks based on real-time traffic, ensuring fast response times under high concurrency. Encrypted message queues facilitate communication between processing units, ensuring the secure transmission of behavioral data.

[0069] The system employs a multi-level risk response strategy, including immediate circuit breakers and: triggering a two-factor authentication process when the pattern difference is within the warning range of 0.7 to 0.85, requiring users to confirm their operations through biometrics or dynamic passwords; and implementing transaction limits or mandatory cooling-off periods for accounts exhibiting repeated abnormal characteristics. The response strategy is dynamically selected based on risk type and severity, forming a tiered protection system. The risk control feedback module is deeply integrated with the blockchain consensus network. All risk decisions are executed after consensus is reached across multiple verification nodes, avoiding single-point-of-failure errors. Risk event records are organized using a Merkle tree structure, and state proofs are periodically generated and synchronized to the main chain. This design ensures both the decentralized nature of risk control and the global consistency of risk data.

[0070] By employing sophisticated behavioral pattern analysis and intelligent risk response, a secure protection system for e-commerce transactions is constructed. Real-time monitoring capabilities can promptly detect abnormal operations, circuit breakers effectively prevent risky transactions from completing, and blockchain-based evidence storage provides verifiable traceability for risk events. The entire implementation process embodies a proactive defense philosophy, enhancing system security while ensuring transaction efficiency.

[0071] Example 6: Based on the collaborative operation of a distributed transaction node cluster, a blockchain consensus network, and a smart contract execution engine, the distributed transaction node cluster receives transaction request data submitted by user terminals through an API gateway. After verification by the blockchain consensus network using a practical Byzantine fault-tolerant algorithm, valid transaction data is encoded into a Merkle tree structure and written into the distributed ledger. The smart contract execution engine calls the distributed ledger data, loads the preset trade rule logic chain, and generates a structured electronic contract. At this time, the electronic contract signing management unit starts operating. This unit connects to the smart contract execution engine and deeply participates in the compliance and on-chain rights confirmation process of the electronic contract.

[0072] The contract attachment binding unit of the electronic contract signing and management unit intervenes first, processing the structured electronic contract generated by the smart contract and linking it to the hash-based notarization of the product quality inspection report and logistics documents. The product quality inspection report, issued by a third-party authoritative testing institution, includes testing items, result judgments, testing equipment numbers, and testing personnel qualification information. The system performs a SHA-256 hash operation on the report, obtains a unique hash value, and writes it to the distributed ledger via the blockchain consensus network to complete the notarization. The logistics documents, generated by the cooperating logistics company, cover information such as the shipper, consignee, tracking number, cargo weight, and packaging specifications. They are also hash-based and stored on the blockchain. The contract attachment binding unit retrieves the hash values ​​of the two notarized documents by calling the notarization query interface of the distributed ledger, and maps them to the unique identifier of the structured electronic contract. This creates an inseparable logical whole between the electronic contract and the quality inspection and logistics-related documents, and this association is synchronously recorded in the contract association partition of the distributed ledger.

[0073] After the attachments are bound, the multi-party signing coordination unit initiates the signing process, pushing electronic signature requests to both the buyer's and seller's terminals via the blockchain consensus network. The request data packet contains an encrypted preview link to the structured electronic contract, preset signature location coordinates, and the validity period of the signature operation. After verifying their identities using their respective digital identity certificates, the buyer and seller electronically sign at the designated locations. During the signing process, the system automatically adds a timestamp accurate to milliseconds to the signature action. Once both parties have completed their signing operations, the multi-party signing coordination unit extracts the digital signature fingerprints generated from the two electronic signatures, packages them together with the hash value of the electronic contract and the signature timestamp into a signing data packet, verifies it using the consensus algorithm of the blockchain consensus network, and writes it into the signature record partition of the distributed ledger, achieving full traceability of the signing process.

[0074] The contract status tracking unit relies on smart contracts to automatically update the status of electronic contracts throughout their entire lifecycle. When the multi-party signing coordination unit confirms that both parties have completed their signatures and written them into the ledger, the smart contract detects a "signing completed" event and automatically marks the contract status as "effective." The status change record, along with the trigger timestamp, is synchronized to the distributed ledger. After the seller uploads the shipping document through the logistics system, the logistics system pushes the document hash value to the blockchain consensus network. After verification, the smart contract triggers a status update to "performing." When the buyer submits a receipt confirmation instruction, or the smart contract detects that the agreed goods acceptance period has expired and no objection feedback has been received, the contract status automatically switches to "terminated." All status change data is synchronized to the distributed ledger of all network nodes through the consensus mechanism. Any node can query the complete status change trajectory using the contract identifier.

[0075] In the payment process, the peer-to-peer currency settlement payment unit of the multimodal payment revenue sharing module works in tandem with the cross-chain atomic swap engine. When the payment mode selection instruction submitted by the user terminal includes a decentralized payment identifier, the payment channel binding unit receives the instruction, and the peer-to-peer currency settlement payment unit immediately activates the corresponding decentralized wallet protocol interface. During the interface activation process, the system establishes an end-to-end encrypted communication link with the user's decentralized wallet, completes two-way authentication by exchanging public keys, and simultaneously verifies the validity of the wallet address and the amount of the target asset held. After successful verification, a stable payment data transmission channel is established.

[0076] The cross-chain atomic swap engine initiates the cross-chain asset transfer process after the payment channel is established, employing a hash time-locking protocol to ensure transaction atomicity. The engine first generates a hash value corresponding to a random hash preimage, using it as the core locking condition, and sends it to both the buyer's and seller's wallet blockchains. After the buyer confirms payment, they lock the agreed-upon amount of digital assets based on this hash value, with a preset unlocking time threshold included in the lock transaction. Once the seller detects the buyer's asset lock record in the blockchain consensus network, they lock their own wallet permissions to receive the assets using the same hash value. The cross-chain atomic swap engine monitors the locking status of both blockchains in real time. When it confirms that both parties have completed the lock and the unlocking time has not expired, it automatically executes the asset transfer instruction, transferring the buyer's locked digital assets to the seller's wallet and simultaneously unlocking the seller's wallet permissions. If the engine does not detect that both parties have completed the lock within the unlocking time, it triggers a rollback logic, unlocking both the buyer's asset lock and the seller's permission lock. All operations are fully recorded in the distributed ledger.

[0077] After the cross-chain atomic swap engine confirms a successful asset transfer, the settlement proof generator captures the "payment completed" event and extracts key information from the event, such as the wallet addresses of both parties, the type and quantity of transferred assets, and the transaction hash value, to generate standardized settlement proof data. This data is pushed to the settlement certificate synthesis engine via an interface, triggering the certificate synthesis process. The settlement certificate synthesis engine completes data integration and on-chain notarization according to preset logic, linking it to the signature record of the electronic contract.

[0078] The due diligence credit intelligent analysis unit connects to the risk control feedback module to continuously conduct user credit assessments and updates. The cross-chain credit data collector aggregates users' historical transaction behavior data from multiple sub-blockchain channels, such as the agricultural products sub-chain and the electronic products sub-chain, through cross-chain routing nodes. The collected content includes transaction initiation time, payment completion time, operation records during contract performance, handling results of historical transaction disputes, and abnormal operation sequences previously recorded by the risk control feedback module. During the collection process, this unit extracts structured data from each sub-chain through standardized data interfaces, uniformly converts data of different formats, and forms a credit dataset containing the user's complete transaction history.

[0079] The credit scoring model executor invokes a pre-defined smart contract to perform a compliance quantification assessment of the aggregated user operation sequences and transaction data. The smart contract verifies user behavior one by one according to built-in rules: checking for overdue payment records and their duration, analyzing whether any breaches of contract occurred during contract performance, and calculating the proportion of abnormal operation sequences in all transactions. Each verification dimension corresponds to a specific quantitative indicator. The smart contract calculates a comprehensive assessment result through weighted summation, transforming it into a dynamic credit rating code. This rating code is associated with the user's buyer identity identifier and written into the credit information partition of the distributed ledger. When a user generates new transaction behavior, the cross-chain credit data collector automatically updates the credit dataset, and the credit scoring model executor restarts the assessment process, generating a new credit rating and synchronously updating it to the distributed ledger, thus achieving dynamic adjustment of the credit rating.

[0080] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

Claims

1. A blockchain-based intelligent e-commerce transaction platform management system, characterized in that, include: A distributed transaction node cluster is used to collect transaction request data submitted by user terminals. The transaction request data includes product information text, buyer identification identifier, and payment mode selection instructions. A blockchain consensus network, connected to the distributed transaction node cluster, is used to verify the validity of transaction request data through a consensus algorithm and write valid transaction request data into the distributed ledger. The smart contract execution engine calls the data in the distributed ledger, executes the preset trade rule logic chain, and generates transaction verification results.

2. The blockchain-based intelligent e-commerce transaction platform management system according to claim 1, characterized in that, The blockchain consensus network includes: The data sharding module is used to divide the transaction request data into multiple data subsets based on the product category code in the transaction request data; Cross-link nodes are used to map multiple subsets of data to corresponding sub-blockchain channels; The channel verification module verifies the data subset within each sub-blockchain channel in parallel through a channel-specific consensus mechanism.

3. The blockchain-based intelligent e-commerce transaction platform management system according to claim 2, characterized in that, The smart contract execution engine includes: The rule parsing unit is used to extract key trade terms elements from the transaction verification results; The terms matching unit calls up a pre-set standardized contract template library and matches the target contract template based on key trade terms elements; The dynamic assembly unit populates key trade terms elements into specified fields of the target contract template, generating a structured electronic contract.

4. The blockchain-based intelligent e-commerce transaction platform management system according to claim 3, characterized in that, Also includes: A multilingual interaction module, connecting the distributed transaction node cluster, includes: Semantic separation unit, used to separate technical parameter description text and marketing description text from product information text; The dual-channel translation unit uses a differentiated neural network model to process the technical parameter description text and the marketing description text separately, generating a dual text data stream in the target language; The data fusion unit reassembles the dual-text data streams into multilingual product information according to the original text structure.

5. The blockchain-based intelligent e-commerce transaction platform management system according to claim 4, characterized in that, The multilingual interaction module also includes: The contract language matching unit receives the structured electronic contract and matches the contract output language according to the registration location code in the buyer's identity identifier; The contract language adaptation unit calls upon the legal clause database of the target language region to verify the compliance of structured electronic contracts in the target language.

6. The blockchain-based intelligent e-commerce transaction platform management system according to claim 1, characterized in that, It also includes a multimodal payment revenue sharing module, which includes: The payment channel binding unit activates the corresponding payment protocol interface according to the payment mode selection instruction. The asset locking unit freezes a specified asset class in the buyer's digital wallet through the blockchain consensus network; The channel status monitoring unit collects the response latency parameters of each payment protocol interface in real time.

7. The blockchain-based intelligent e-commerce transaction platform management system according to claim 6, characterized in that, The multimodal payment and revenue sharing module also includes: Dynamically switch decision units and calculate payment channel availability weights based on response delay parameters; The real-time routing optimization unit switches to the backup payment protocol interface when the availability weight is below the threshold. The cross-chain settlement coordination unit synchronously updates the asset status in the distributed ledger through atomic swap protocols.

8. The blockchain-based intelligent e-commerce transaction platform management system according to claim 7, characterized in that, Also includes: The settlement voucher synthesis engine, connected to the multimodal payment and revenue sharing module, includes: The voucher element extraction unit separates the transaction amount, timestamp, and digital signature from a successful payment event; The multi-source data integration unit converts transaction amounts into target fiat currency values ​​and binds them to timestamps. The blockchain evidence storage unit writes the bound data packet and digital signature together into an immutable evidence storage block.

9. The blockchain-based intelligent e-commerce transaction platform management system according to claim 6, characterized in that, The payment channel binder includes: The payment mode parsing unit is used to identify the tiered payment type identifier according to the payment mode selection instruction. The tiered payment type identifier includes the instant settlement mode code, the escrow transaction mode code, and the installment payment mode code. A multi-level parameter mapper calls a preset payment rule chain to convert the tiered payment type identifier into the corresponding fund freeze ratio parameter, settlement trigger condition parameter, and handling fee calculation rule parameter; The fund flow control instruction generator generates an asset freeze instruction for the buyer's digital wallet based on the fund freeze ratio parameter, and associates the settlement trigger condition parameter with the smart contract execution engine.

10. The blockchain-based intelligent e-commerce transaction platform management system according to claim 8, characterized in that, It also includes a risk control feedback module, which contains: The behavior pattern analysis unit collects the user terminal's operation sequence throughout the entire transaction process in real time. Anomaly feature identification unit compares the pattern difference between the operation sequence under test and historical safe operation sequences; The intelligent circuit breaker unit terminates the settlement document synthesis process when the pattern difference exceeds the risk threshold.

11. The blockchain-based intelligent e-commerce transaction platform management system according to claim 1, characterized in that, Also includes: The electronic contract signing and management unit is connected to the smart contract execution engine; The electronic contract signing management unit, connected to the smart contract execution engine, includes: The contract attachment binding unit is used for hash storage of the product quality inspection report and logistics documents associated with the structured electronic contract; The multi-party signing coordination unit pushes electronic signature requests to both the buyer and seller terminals through the blockchain consensus network, and writes the digital signature fingerprint into the distributed ledger after the signature is completed. The contract status tracking unit automatically updates the contract's effective, performance, and termination status to the blockchain ledger based on smart contracts.

12. The blockchain-based intelligent e-commerce transaction platform management system according to claim 1, characterized in that, The multimodal payment and revenue sharing module also includes: The peer-to-peer currency settlement payment unit is used to activate the decentralized wallet protocol interface in the payment channel binding unit; The cross-chain atomic swap engine enables direct on-chain transfer of digital assets between buyers and sellers through a hash time-locking protocol; The settlement proof generator triggers the settlement certificate synthesis engine to generate a storage block when a peer-to-peer payment success event occurs.

13. The blockchain-based intelligent e-commerce transaction platform management system according to claim 1, characterized in that, Also includes: The due diligence credit intelligent analysis unit, connected to the risk control feedback module, includes: A cross-chain credit data collector aggregates users' historical transaction and behavior patterns from multiple sub-blockchain channels; The credit scoring model executor performs compliance quantification assessments on user operation sequences based on smart contracts and outputs dynamic credit ratings to the distributed ledger.

Images