Electronic commerce transaction verification system based on block chain

Abstract

The invention relates to the technical field of transaction verification, in particular to an electronic commerce transaction verification system based on a block chain, and the system comprises a transaction identity verification module, a fund flow monitoring module, an order payment state confirmation module, an abnormal transaction comparison module and a risk control strategy adjustment module. According to the invention, identity verification is carried out by using the hash calculation of the timestamp, the device identifier and the user public key and the block chain technology, so that the authorization and override accuracy of the transaction request is ensured, dynamic monitoring and trend analysis are carried out on the fund flow direction, including the measurement of the fund flow path and the evaluation of the fund transfer change rate, and the fund transfer accuracy is improved. The method effectively monitors the stable flow condition of funds, improves the recognition capability of transaction abnormity through integrating multi-source data analysis, enhances the real-time performance and accuracy of risk management, improves the safety and transparency of transaction, enhances the rapid response capability of abnormal behaviors, and improves the safety and reliability of the transaction. And higher-level trust and security assurance are brought to the user and the platform.

Description

Technical Field

[0001] The present invention relates to the field of transaction verification technology, and in particular to an e-commerce transaction verification system based on blockchain. Background Art

[0002] The field of transaction verification technology includes a variety of methods and systems for ensuring the legality, integrity, and security of electronic transactions, including transaction identity authentication, transaction data signatures, transaction traceability, transaction consistency verification, and anti-tampering mechanisms. This field involves cryptographic security protocols, identity authentication mechanisms, distributed ledger management, trusted execution environments, and multi-signature technologies, and combines public key infrastructure PKI, zero-knowledge proof ZKP, hash chains and other technical means to achieve data verification. Transaction verification technology is widely used in financial payments, e-commerce, digital asset transactions, and other transaction scenarios that require high security to ensure the identity validity of both parties to the transaction, the integrity of transaction data, and data non-repudiation.

[0003] Among them, the e-commerce transaction verification system refers to a technical solution used to ensure the authenticity of order, payment, logistics and transaction-related data in online transaction scenarios. The system covers technical links such as order generation and encrypted storage, payment authorization and transaction signature, hash storage of logistics tracking data, transaction data chain verification and smart contract automatic settlement. The system usually performs transaction identity authentication through public and private key encryption mechanism, uses Merkle tree structure and block hash algorithm for data integrity verification, uses smart contracts to execute preset transaction verification rules, and combines multi-party secure computing technology to ensure transaction privacy.

[0004] Traditional systems are deficient in processing large-scale data and real-time transaction monitoring, which can lead to processing delays and data synchronization issues when faced with complex, high-speed transaction scenarios. For example, in financial payment systems, due to the limitations of technical processing capabilities, it is impossible to instantly identify and respond to rapidly changing fraudulent behaviors or abnormal fund flows. Traditional systems rely on existing databases and predefined rules for user identity authentication and transaction data consistency verification, which limits the flexibility and efficiency of the system when encountering new or complex fraud patterns. In actual operations, it can lead to incorrect risk assessments or misjudgment of normal transactions, affecting user experience and the overall security of the system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose an e-commerce transaction verification system based on blockchain.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an e-commerce transaction verification system based on blockchain, the system comprising: The transaction identity authentication module obtains the e-commerce transaction verification request, performs hash calculation on the transaction identity data, calls the user identity hash record stored in the blockchain, determines whether it matches, and obtains the transaction identity matching result; The fund flow monitoring module obtains the payment transaction fund flow direction based on the transaction identity matching result, calculates the fund flow trend, matches the fund flow stability, calculates the fund transfer change rate, and obtains the fund flow stability information; The order payment status confirmation module obtains the payment transaction status based on the fund flow stability information, calculates the payment status deviation, determines the payment status synchronization, and obtains the payment status consistency comparison information; The abnormal transaction comparison module calls the fund flow path record based on the payment status consistency comparison information, compares the transaction account changes and withdrawal behavior in a short period of time, calculates the abnormal fund flow rate, and obtains the abnormal transaction comparison information; The risk control strategy adjustment module calls the order risk data measurement based on the abnormal transaction comparison information, matches the degree of transaction abnormality, evaluates the transaction risk level, determines whether transaction restriction measures need to be implemented, and obtains order transaction verification information.

[0007] The present invention has improvements in that the transaction identity matching result specifically includes identity matching status, transaction authorization status and identity hash comparison result; the fund flow stability information includes fund path stability, fund transfer change rate and historical transaction matching degree; the payment status consistency comparison information includes payment data synchronization status, account confirmation status and payment platform feedback consistency; the abnormal transaction comparison information specifically includes account transaction abnormality rate, device transaction activity and fund withdrawal abnormal distribution; the order transaction verification information includes transaction risk level, restriction execution status and abnormal transaction mark.

[0008] The present invention is improved in that the transaction identity authentication module comprises: The identity information extraction submodule obtains the e-commerce transaction verification request, calls the user public key, device identification, transaction timestamp, order amount, parses and extracts the user identity data, and obtains the transaction identity data set; The identity hash calculation submodule uses hash calculation based on the transaction identity data set to obtain a transaction identity hash value; The identity matching judgment submodule calls the identity hash record stored in the blockchain based on the transaction identity hash value, compares the transaction identity hash value with the identity hash record, and determines whether the two match. If they match, the e-commerce transaction request is authorized; if they do not match, the e-commerce transaction request is rejected to obtain a transaction identity matching result.

[0009] The present invention is improved in that the fund flow monitoring module includes: The fund flow extraction submodule obtains the payment transaction fund flow based on the transaction identity matching result, calls the fund inflow account, transfer path and fund splitting situation, extracts the fund flow situation between accounts, sorts out the transaction path, calculates the fund distribution ratio of each path, and obtains the fund flow path data; The capital flow trend calculation submodule is based on the capital flow path data and adopts the formula: ; Calculate and obtain the rate of change of funds and obtain the information on the trend of fund flow; in, Represents the rate of change of funds. Indicates the path In time The amount of funds, Indicates the amount of funds at the previous time point, represents the adjacent time interval, Indicates the total number of transaction paths; The capital flow stability assessment submodule calls the transaction capital path measurement based on the capital flow trend information, matches the capital flow stability, calculates the capital transfer change rate, determines the capital fluctuation range, and obtains the capital flow stability information.

[0010] The present invention is improved in that the order payment status confirmation module includes: The payment information extraction submodule obtains the payment transaction status based on the fund flow stability information, calls the payment platform feedback status, bank flow records, payment account confirmation records, and user payment vouchers, parses the payment information data, and obtains the payment status data set; The payment status deviation calculation submodule is based on the payment status data set and adopts the formula: ; Calculate and obtain the payment status deviation rate; in, represents the payment status deviation rate, Represents the payment platform to return the status value. Represents the bank transaction record status value. Represents the current transaction timestamp, represents the time mean of payment data, Represents the number of payment records; The payment synchronization judgment submodule compares the payment status deviation rate with a preset deviation threshold value based on the payment status, judges the payment status synchronization status, and obtains payment status consistency comparison information.

[0011] The present invention is improved in that the abnormal transaction comparison module includes: The multiple order payment status acquisition submodule acquires the payment status of multiple orders of the same device and IP address based on the payment status consistency comparison information, calls the fund flow path record, extracts the order payment data under the same device and IP address, screens the transaction records with close time intervals, and acquires the payment records of multiple orders; The short-term account change comparison submodule compares the transaction account changes and withdrawal behaviors within a short period of time based on the multiple order payment records, calculates the account balance change rate, extracts the transaction frequency and withdrawal interval data, and obtains the account short-term transaction fluctuation information; The abnormal capital flow rate calculation submodule adopts the formula based on the short-term transaction fluctuation information of the account: ; Calculate and obtain abnormal fund flow ratio and abnormal transaction comparison information; in, Represents the abnormal liquidity ratio of funds. Representation cycle The total number of transactions within Representation cycle Total number of withdrawals within Representation cycle The account balance at the end of the represents the standard deviation of the trading time interval, Indicates the standard deviation of the withdrawal time interval.

[0012] The present invention is improved in that the risk control strategy adjustment module includes: The transaction abnormality matching submodule calls the order risk data measurement based on the abnormal transaction comparison information, matches the transaction abnormality degree, compares the abnormal transaction characteristics with the set transaction abnormality classification standard, extracts the transaction abnormality category, and obtains the transaction abnormality classification information; The transaction risk level calculation submodule adopts the formula based on the transaction abnormality classification information: ; Calculate and obtain the transaction risk value and obtain the transaction risk level information; in, Represents the transaction risk value, Indicates the risk coefficient corresponding to the abnormal transaction category, Indicates the frequency of abnormal transactions, Represents the total transaction volume within the transaction time window, Indicates the fluctuation range of transaction amount. Indicates the base value of transaction amount; The transaction restriction determination submodule determines whether the transaction risk value exceeds the set threshold based on the transaction risk level information. If it exceeds, the transaction is marked as high risk and transaction restriction measures are implemented. If it does not exceed, the transaction proceeds normally and the order transaction verification information is obtained.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, identity verification is performed by using timestamp, device identification, hash calculation of user public key and blockchain technology, thereby ensuring the accuracy of authorization and rejection of transaction requests, dynamically monitoring and trend analysis of fund flows, including measurement of fund flow paths and evaluation of fund transfer change rates, effectively monitoring the stable flow of funds, and accurately evaluating the consistency of payment transaction status to ensure a high degree of consistency between payment behavior and system records. By integrating multi-source data analysis, the ability to identify transaction anomalies is improved, the real-time and accuracy of risk management is enhanced, the security and transparency of transactions are improved, and the ability to respond quickly to abnormal behaviors is enhanced, bringing a higher level of trust and security to users and platforms. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 It is a flow chart of the transaction identity authentication module of the present invention; Figure 3 This is a flow chart of the fund flow monitoring module of the present invention; Figure 4 This is a flow chart of the order payment status confirmation module of the present invention; Figure 5 This is a flow chart of the abnormal transaction comparison module of the present invention; Figure 6 This is a flow chart of the risk control strategy adjustment module of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0017] See also Figure 1 The present invention provides a technical solution: an e-commerce transaction verification system based on blockchain, the system comprising: The transaction identity authentication module obtains the e-commerce transaction verification request, calls the user public key, device identification, transaction timestamp, order amount, performs hash calculation on the transaction identity data, obtains the transaction identity hash value, calls the user identity hash record stored in the blockchain, compares it with the transaction identity hash value, and determines whether it matches. If it matches, the e-commerce transaction request is authorized. If it does not match, the e-commerce transaction request is rejected to obtain the transaction identity matching result; The fund flow monitoring module obtains the payment transaction fund flow based on the transaction identity matching results, calls the fund inflow account, transfer path and fund splitting situation, calculates the fund flow trend, calls the transaction fund path measurement, matches the fund flow stability, calculates the fund transfer change rate, and obtains the fund flow stability information; The order payment status confirmation module obtains the payment transaction status based on the stable information of fund flow, calls the payment platform feedback status, bank flow records, payment account confirmation records and user payment vouchers, calculates the payment status deviation, determines the payment status synchronization, and obtains the payment status consistency comparison information; The abnormal transaction comparison module obtains payment status of multiple orders from the same device and IP address based on payment status consistency comparison information, calls fund flow path records, compares transaction account changes and withdrawal behaviors in a short period of time, calculates abnormal fund flow rate, and obtains abnormal transaction comparison information; The risk control strategy adjustment module is based on abnormal transaction comparison information, calls order risk data measurement, matches the degree of transaction abnormality, evaluates the transaction risk level, determines whether transaction restriction measures need to be implemented, and obtains order transaction verification information.

[0018] The transaction identity matching results include identity matching status, transaction authorization status and identity hash comparison results. The fund flow stability information includes fund path stability, fund transfer change rate and historical transaction matching degree. The payment status consistency comparison information includes payment data synchronization status, account confirmation status and payment platform feedback consistency. The abnormal transaction comparison information includes account transaction abnormality rate, device transaction activity and fund withdrawal abnormal distribution. The order transaction verification information includes transaction risk level, restriction execution status and abnormal transaction mark.

[0019] See also Figure 2 , the transaction authentication module includes: The identity information extraction submodule obtains the e-commerce transaction verification request, calls the user public key, device identification, transaction timestamp, order amount, parses and extracts the user identity data, and obtains the transaction identity data set; After receiving the e-commerce transaction verification request, the identity information extraction submodule parses the request data and extracts the user public key, device identification, transaction timestamp and order amount from it. Each data is obtained in a specific way. For example, the user public key comes from the public key field in the user's digital certificate, the device identification can be based on the device's unique identification code (such as IMEI or MAC address), and the transaction timestamp is recorded by the system The server time when the transaction occurred and stored in the standard UTC format, the order amount is parsed from the payment amount field in the transaction request. During the parsing process, in order to ensure data accuracy, data format verification is required. For example, for the user public key, it should be checked whether it conforms to the RSA or ECC public key format, the device identification should match the expected hexadecimal format, the transaction timestamp should ensure that its time format is synchronized with the server time, and the order amount needs to be parsed as a floating point number and ensure that it is in the legal The transaction identity data set is within a reasonable range, such as between 0.01 and 100000.00. After the data verification is passed, the information is assembled into a transaction identity data set and stored in a temporary storage space. For example, a user uses the IMEI code "358240051111110" to make a transaction, the transaction time is "2025-02-14T12:30:45Z", and the order amount is "99.99". The specific storage content of the transaction identity data set is {user public key: "MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQC...", device identification: "358240051111110", transaction timestamp: "2025-02-14T12:30:45Z", order amount: "99.99"}. After the data extraction is completed, the transaction identity data set is returned as the input for subsequent processing.

[0020] The identity hash calculation submodule uses hash calculation based on the transaction identity data set to obtain the transaction identity hash value; After receiving the transaction identity data set, the identity hash calculation submodule obtains the user public key, device identification, transaction timestamp and order amount in turn, and generates the transaction identity hash value through hash calculation. Specifically, each field is standardized, such as converting the transaction timestamp into an integer number of seconds, formatting the order amount to two decimal places, and then concatenating all fields in a fixed order, such as "user public key + device identification + transaction timestamp + order amount". The concatenated string is used as the input of the hash calculation. During the hash calculation process, the SHA-256 algorithm is used to calculate the data summary. The SHA-256 algorithm is used to calculate the input data. Perform multiple rounds of bit operations, permutations, and merging to ensure that a fixed-length hash value is generated. For example, for the sample data, assuming that the user's public key hash part is "ABC123" (actually longer), the concatenated string is "ABC1233582400511111102025021412304599.99". After performing SHA-256 calculation on the string, the resulting hash value is "5f4dcc3b5aa765d61d8327deb882cf99" (example). This hash value will be stored as the transaction identity hash value and used as input for subsequent identity matching judgments.

[0021] The identity matching judgment submodule calls the identity hash record stored in the blockchain based on the transaction identity hash value, compares the transaction identity hash value with the identity hash record, and determines whether the two match. If they match, the e-commerce transaction request is authorized. If they do not match, the e-commerce transaction request is rejected to obtain the transaction identity matching result; After receiving the transaction identity hash value, the identity matching judgment submodule calls the identity hash record stored in the blockchain, queries the historical record matching the transaction identity hash value, calls the blockchain node interface, retrieves the transaction record corresponding to the hash value, and uses binary search or hash indexing to improve query efficiency. For example, it queries whether "5f4dcc3b5aa765d61d8327deb882cf99" exists in the data structure stored in the blockchain. If it matches, it means that the transaction identity already exists in the system's trusted identity list. The system returns a matching result and authorizes the e-commerce transaction request. If no matching record is found, the system rejects the transaction request and records the reason for the failure. As shown in Table 1, sample data of the transaction identity hash comparison is recorded, where "matching status" of "yes" means the transaction is allowed, and "no" means the transaction is rejected.

[0022] Table 1 Transaction identity hash comparison results: ; As shown in Table 1, the transaction identity hash value "5f4dcc3b5aa765d61d8327deb882cf99" matches the blockchain storage record, and the transaction request is authorized, while the hash value "d41d8cd98f00b204e9800998ecf8427e" does not match, so the transaction is rejected, and the transaction identity matching result is finally output.

[0023] See also Figure 3 , the fund flow monitoring module includes: The fund flow extraction submodule obtains the payment transaction fund flow based on the transaction identity matching results, calls the fund inflow account, transfer path and fund splitting situation, extracts the fund flow situation between accounts, sorts out the transaction path, calculates the fund distribution ratio of each path, and obtains the fund flow path data; After receiving the transaction identity matching result, the fund flow extraction submodule calls the fund inflow account, transfer path and fund splitting in sequence to obtain the fund flow between accounts. First, the transaction matching result is parsed to confirm the starting account of the fund flow, and the target receiving account is identified according to the transaction record. The system calls the transaction database to retrieve the fund flow path involved in the current transaction, and obtains the initiating account, receiving account and transaction amount of each transaction. For example, if user A transfers 500 yuan to user B, and user B transfers 300 yuan to user C at the same time, the initial fund flow path parsed by the system is A→B (500 yuan), B→C (300 yuan), In the case of fund splitting, if there is 200 yuan remaining in account B and it is deposited into different accounts D and E, the system further analyzes B→D (100 yuan) and B→E (100 yuan). After completing the extraction of the fund flow path, the system organizes all transaction paths, stores the fund flow in the form shown in Table 2, and calculates the fund distribution ratio of each path. The fund distribution ratio is calculated by the ratio of the transaction amount of the path to the total inflow amount. For example, the total inflow of account B is 500 yuan, and its fund distribution ratio to accounts C, D, and E are 0.6, 0.2, and 0.2 respectively. The calculated fund flow path data is stored in the database for subsequent calculation of fund flow trends.

[0024] Table 2 Data table of fund flow path: ; As shown in Table 2, A transfers 500 yuan to account B. After the funds flow to account B, the funds in account B are further allocated to accounts C, D, and E, with account C receiving 60% of the funds and accounts D and E each receiving 20% ​​of the funds, thus completing the collation of the funds flow path data.

[0025] The capital flow trend calculation submodule is based on the capital flow path data and uses the formula: ; Calculate and obtain the rate of change of funds and obtain the information on the trend of fund flow; in, Represents the rate of change of funds. Indicates the path In time The amount of funds, Indicates the amount of funds at the previous time point, represents the adjacent time interval, Indicates the total number of transaction paths; After receiving the capital flow path data, the capital flow trend calculation submodule calculates the capital change rate and obtains the capital flow trend information. The specific calculation process is as follows: first, the capital flow path data is parsed to determine the amount of capital in each transaction path at different time points, and the corresponding timestamps are recorded. For example, at time T1, the capital amount Q_BC,1 of path B→C is 300 yuan, and at time T2, the capital amount Q_BC,2 of path B→C increases to 450 yuan. The capital change rate of the path is calculated using the formula Perform calculations, assuming T1=10:00, T2=11:00, then , substitute into the calculation Yuan / hour, indicating that the capital flow rate of this path within 1 hour is 150 yuan / hour. If the total number of transaction paths n=3, the system calculates the capital change rate of other paths and sums them up to get the global capital change rate. The capital change rate data records of all paths are shown in Table 3.

[0026] Table 3 Calculation results of fund flow trend: ; As shown in Table 3, the capital change rate of each transaction path is calculated, and the system stores the capital change rate information for use by the subsequent capital flow stability assessment submodule.

[0027] The capital flow stability assessment submodule is based on capital flow trend information, calls transaction capital path measurement, matches capital flow stability, calculates capital transfer change rate, determines capital fluctuation range, and obtains capital flow stability information.

[0028] After receiving the information on the trend of capital flow, the capital flow stability assessment submodule first calculates the transaction capital path measurement and matches the capital flow stability. The specific execution process is as follows: the system calls the historical transaction data to obtain the standard deviation of the capital change rate of each path to measure the stability of capital flow. For example, for path B→C, the capital change rate in the last three hours is 150 yuan / hour, 140 yuan / hour, and 160 yuan / hour respectively, then the standard deviation is calculated. for , if the standard deviation If it is lower than the set threshold, such as 20 yuan / hour, the capital flow of this path is deemed to be relatively stable. The same calculation is performed on all paths and classified within the preset capital fluctuation range. For example, the fluctuation range of 0-20 yuan / hour is a stable range, 20-50 yuan / hour is a slight fluctuation range, and greater than 50 yuan / hour is a violent fluctuation range, ultimately obtaining information on stable capital flow.

[0029] See also Figure 4 , the order payment status confirmation module includes: The payment information extraction submodule obtains the payment transaction status based on the stable information of fund flow, calls the payment platform feedback status, bank flow records, payment account confirmation records, and user payment vouchers, parses the payment information data, and obtains the payment status data set; After receiving the stable funds flow information, the payment information extraction submodule obtains the payment transaction status in sequence, and calls the payment platform feedback status, bank flow records, receiving account confirmation records and user payment vouchers to parse the payment information data. First, the payment platform feedback status is parsed to obtain the payment success, failure or processing status code, and match the unique transaction number of the payment request. For example, the transaction number "20250214001" corresponds to the payment platform return status "success", and then calls the bank flow record to query the payment amount, payment account, receiving account and transaction time according to the transaction number. For example, if the transaction record is in "2 025-02-1412:45:30" successfully transfers 1,000 yuan to the target account, and then stores the corresponding transaction data. Then, the system parses the confirmation record of the receiving account to check whether the target account actually receives the funds. If the confirmation record of the receiving account shows that the arrival time is "2025-02-1412:46:15", the arrival status is recorded as "confirmed", and the user's payment voucher, such as electronic receipt, payment screenshot or transaction SMS, is obtained, and data is extracted from the voucher content to ensure that the payment information is complete, as shown in Table 4. Finally, the payment status data set is obtained for subsequent payment status deviation calculation.

[0030] Table 4 Payment status dataset: ; As shown in Table 4, transaction number 20250214001 shows successful payment in the payment platform, bank statements and receiving account confirmation records, and the payment voucher is an electronic receipt, indicating that the transaction is completed. However, the payment status of transaction number 20250214002 is still in processing, and the bank statement does not show the credit. The system stores the payment status data set for subsequent calculations.

[0031] The payment status deviation calculation submodule is based on the payment status data set and uses the formula: ; Calculate and obtain the payment status deviation rate; in, represents the payment status deviation rate, Represents the payment platform to return the status value. Represents the bank transaction record status value. Represents the current transaction timestamp, represents the time mean of payment data, Represents the number of payment records; The payment status deviation calculation submodule calculates the payment status deviation rate based on the payment status data set. The specific calculation process is as follows: the system first parses the payment platform feedback status, bank flow record status, receiving account confirmation record and user payment voucher, and calculates the deviation value between each status. and bank statement status , define the status value mark, such as "success" is marked as 1, "processing" is marked as 0.5, and "failure" or "not credited" is marked as 0. Calculate the status deviation value of each payment record , if the payment platform status of transaction number 20250214001 is "successful" (1), and the bank statement status is "credited" (1), then the status deviation is: ; For transaction number 20250214002, the payment platform status is "processing" (0.5), and the bank statement status is "not credited" (0), then the status deviation is: ; Calculate the average state deviation value of all transaction records, where the total number of payment records is ,Right now ; Calculate the sum of squared deviations of transaction time. Extract transaction timestamps of all payment records , and calculate the time mean . Transaction time of transaction number 20250214001 The transaction time of transaction number 20250214002 is 12:45:30 is 12:46:15, then the mean time The calculation is as follows: ; Calculate the sum of squares of deviations for each trading time: ; Compute the square root of the time deviation term: ; Calculate the payment status deviation rate : ; Final payment status deviation rate After the calculation is completed, the calculation results are stored in the database, as shown in Table 5.

[0032] Table 5 Payment status deviation calculation results: ; As shown in Table 5, the payment status deviation rate of transaction number 20250214001 is 31.82, while the payment status deviation rate of transaction number 20250214002 is 32.07. After the calculation is completed, the system stores the payment status deviation rate data for subsequent payment synchronization judgment. In the payment status deviation rate calculation process, the numerical marking reference values ​​of the payment platform status and the bank flow status are set as 0 (failure), 0.5 (processing), and 1 (success). The setting basis of the marking value is the three common status classifications in the payment process, and its value is related to the payment progress. When the payment platform feedback status is "processing", there is a risk of payment delay, so the marking value is set to 0.5. If the feedback status is "successful" and the bank flow is recorded, the value is set to 1. If the payment fails or is not recorded, the value is set to 0. This marking value is used as the input for the payment status deviation calculation, which directly affects the deviation rate calculation result. At the same time, in the time deviation calculation, the time mean It is used as a benchmark value to measure the degree of deviation of different payment transactions. The benchmark value is calculated as the arithmetic mean of all payment times. During the calculation of the payment status deviation rate, the calculation of the sum of squares of time deviation is used to measure the degree of time dispersion. The larger the deviation value, the greater the difference in payment timeliness. The calculation method of the sum of squares of time deviation adopts the mean square error calculation to ensure accurate time stability analysis.

[0033] The payment synchronization judgment submodule compares the payment status deviation rate with the preset deviation threshold to judge the payment status synchronization and obtain payment status consistency comparison information.

[0034] After receiving the payment status deviation rate, the payment synchronization judgment submodule compares it with the preset deviation threshold to judge the payment status synchronization status. The system first sets the payment status synchronization deviation threshold, such as the threshold is set to 30, and then compares the payment status deviation rate of each transaction. For example, the payment status deviation rate of transaction number 20250214001 is 31.75, which is greater than the threshold 30, and is judged as "out of synchronization". The payment status deviation rate of transaction number 20250214002 is 32.25, which is also greater than the threshold and is judged as "out of synchronization". The system stores the payment status consistency comparison information and outputs the comparison results. The payment status synchronization deviation threshold is set to 30. The value is set based on the payment time synchronization error range of banks and payment platforms. In actual transaction scenarios, there is a certain error in the payment time of different banks and payment platforms, which usually fluctuates in the range of 0-60 seconds. After the sum of squares of the time deviation is calculated, combined with the average deviation level measured by the system, the deviation rate of 30 is determined as the judgment threshold. When the payment status deviation rate exceeds this value, it indicates that there are large differences in the payment platform status, bank transaction records, collection account records and user payment vouchers. The setting of this threshold mainly affects the consistency judgment of the payment status. If the deviation rate is lower than the threshold, the payment status is judged to be consistent, otherwise the payment status is judged to be out of sync, which affects the payment status reconciliation and exception handling process.

[0035] See also Figure 5 , the abnormal transaction comparison module includes: The multiple order payment status acquisition submodule obtains the payment status of multiple orders of the same device and IP address based on the payment status consistency comparison information, calls the fund flow path record, extracts the order payment data under the same device and IP address, screens the transaction records with close time intervals, and obtains the payment records of multiple orders; After receiving the payment status consistency comparison information, the multiple order payment status acquisition submodule obtains the payment status of multiple orders of the same device and IP address, and calls the fund flow path record. The system first parses the historical payment transaction data, and filters all transaction records under the same device and the same IP address according to the device's unique identifier (such as IMEI code, MAC address) and IP address. For example, the device IMEI code "358240051111110" corresponds to the IP address "192.168.1.10". The system filters out all orders completed under the device and IP address, and then calls the fund flow path record to parse the payment amount, payment time and fund flow direction of all related transactions. For example, a user made a payment at 12:00 on February 14, 2025. Three orders are submitted at 12:00, 12:02 and 12:05 respectively, and the amount of each order is 200 yuan, 300 yuan and 500 yuan respectively. The payment path of the transaction is recorded and sorted by time. The time interval between adjacent orders is calculated. The system filters out transaction records with close time intervals and sets the screening threshold of transaction time interval to 300 seconds. The threshold is obtained based on the historical data statistics of the payment system and ranges from 100 to 600 seconds. It is mainly affected by factors such as the confirmation time of the payment system and the bank flow processing time. If a transaction interval is less than the threshold, it is determined to be a continuous transaction in a short period of time. For example, orders with a time interval of less than 5 minutes are determined to be continuous transactions in a short period of time, and multiple order payment records are stored, as shown in Table 6, to obtain multiple order payment records.

[0036] Table 6 Multiple order payment records: ; As shown in Table 6, the transaction time interval of the device IMEI code "358240051111110" under the IP address "192.168.1.10" is relatively short. The time intervals between the three orders are 120 seconds and 180 seconds respectively. The system stores the payment records of these multiple orders for subsequent comparison of short-term account changes.

[0037] The short-term account change comparison submodule compares the changes in trading accounts and withdrawal behaviors in a short period of time based on multiple order payment records, calculates the rate of change of account balances, extracts transaction frequency and withdrawal interval data, and obtains information on short-term account transaction fluctuations; After receiving multiple order payment records, the short-term account change comparison submodule performs a comparison of trading account changes and withdrawal behaviors in a short period of time, calculates the rate of change of account balances, and extracts transaction frequency and withdrawal interval data. The specific execution process is as follows: The system first parses the flow of funds in the trading account and calculates the changes in account balances. For example, if the initial balance of an account is 10,000 yuan, and after completing transactions at 12:00, 12:02, and 12:05 on February 14, 2025, the account balance changes to 9,800 yuan, 9,500 yuan, and 9,000 yuan, then the rate of change of the account balance after each transaction is calculated. The rate of change of the balance is calculated according to the change in the amount within the time interval. For example, the balance change rate is calculated as: ; Substitute into the calculation, the change rate of the balance of the first transaction is Yuan / second, the balance change rate of the second transaction is Yuan / second, store the calculated account balance change rate, and extract withdrawal records. For example, if an account withdraws 4,000 yuan at 12:10, calculate the withdrawal interval, which is the difference between the last transaction time and the withdrawal time, that is, Minutes, store the withdrawal interval data, and obtain the account's short-term transaction fluctuation information. To ensure the rationality of the withdrawal interval, the system sets the withdrawal interval's benchmark value to 300 seconds. The benchmark value is set based on the average withdrawal processing time, and its value range is generally between 240-600 seconds. If the withdrawal interval is less than 300 seconds, the account is marked as having high-frequency transactions and withdrawals in a short period of time.

[0038] The abnormal capital flow rate calculation submodule is based on the account short-term transaction fluctuation information and uses the formula: ; Calculate and obtain abnormal fund flow ratio and abnormal transaction comparison information; in, Represents the abnormal liquidity ratio of funds. Representation cycle The total number of transactions within Representation cycle Total number of withdrawals within Representation cycle The account balance at the end of the represents the standard deviation of the trading time interval, Indicates the standard deviation of the withdrawal time interval.

[0039] After receiving the account short-term transaction fluctuation information, the abnormal capital flow rate calculation submodule calculates the abnormal capital flow rate. First, the total number of transactions in the period is counted. and total number of withdrawals , if an account has 3 transactions and 1 withdrawal from 12:00 to 12:10, then , , the system calculates the account balance at the end of the period , if the account balance at the end of the period is 5,000 yuan, then , and then calculate the standard deviation of the trading time interval and the standard deviation of the withdrawal interval , the transaction time interval data is 120 seconds and 180 seconds, the withdrawal time interval data is 300 seconds, and the standard deviation is calculated as follows: ; (Since there is only one withdrawal, the standard deviation is 0); Substitute into the formula , then the calculation is as follows: ; The calculation results are stored, as shown in Table 7. Finally, the abnormal transaction comparison information is obtained, and the threshold of the abnormal capital flow ratio is set to 0.005. The threshold is set based on the normal fluctuation range of the trading account and the value range is 0.002-0.008. If the amount is higher than the threshold, it is determined that the account may have abnormal fund flow behavior.

[0040] Table 7 Calculation results of abnormal fund flow rate: ; As shown in Table 7, the abnormal liquidity ratio of user A is 0.0022. If the value is lower than the threshold of 0.005, the account is not judged as an abnormal fund flow account, and the system stores the calculation results for subsequent transaction risk analysis.

[0041] See also Figure 6 , the risk control strategy adjustment module includes: The transaction abnormality matching submodule is based on the abnormal transaction comparison information, calls the order risk data measurement, matches the transaction abnormality degree, compares the abnormal transaction characteristics with the set transaction abnormality classification standards, extracts the transaction abnormality category, and obtains the transaction abnormality classification information; After receiving the abnormal transaction comparison information, the transaction abnormality matching submodule calls the order risk data measurement and matches the transaction abnormality degree. The system first extracts the abnormal transaction flow ratio in the abnormal transaction comparison information, and compares it with the historical abnormal transaction data to calculate the abnormality degree of the current transaction. For example, if the abnormal fund flow ratio of an account is 0.0022, the historical transaction data is analyzed to obtain the abnormal fund flow ratio range of normal transactions. If the normal transaction ratio distribution range is between 0.0001 and 0.001, the current transaction abnormality degree is high, and the system will The system sets the risk level to low (0.0001-0.001), medium (0.001-0.002), and high (above 0.002), and classifies the current transaction as high risk. The system then compares the abnormal transaction features, such as frequent small transactions, short-term withdrawals, frequent changes in IP addresses, etc., and matches them according to the abnormal transaction classification standards. If the current transaction contains the features of frequent small transactions, it identifies the transaction as belonging to the "high-frequency small transactions" abnormal category, and stores the abnormal transaction classification information, as shown in Table 8, and finally obtains the abnormal transaction classification information.

[0042] Table 8 Transaction abnormality classification information table: ; The classification threshold is set based on the account transaction behavior pattern and capital flow standard calculation. The distribution range of the capital flow ratio is extracted from the transaction history data, and its mean and standard deviation are calculated. For example, in a sample of 10,000 normal transactions, the mean of the abnormal capital flow ratio is 0.00055 and the standard deviation is 0.00045. The mean + 2 times the standard deviation (0.00145) is used as the upper limit of medium risk, and the mean + 3 times the standard deviation (0.002) is used as the starting value of high risk. The risk level is set accordingly. This setting method ensures that the threshold is calculated based on real transaction behavior data.

[0043] The transaction risk level calculation submodule is based on the transaction abnormality classification information and uses the formula: ; Calculate and obtain the transaction risk value and obtain the transaction risk level information; in, Represents the transaction risk value, Indicates the risk coefficient corresponding to the abnormal transaction category, Indicates the frequency of abnormal transactions, Represents the total transaction volume within the transaction time window, Indicates the fluctuation range of transaction amount. Indicates the base value of transaction amount; After receiving the transaction abnormality classification information, the transaction risk level calculation submodule calculates the transaction risk value and obtains the transaction risk level information. First, the system analyzes the risk coefficient corresponding to the transaction abnormality category. The risk factor is calculated from the actual loss rate caused by the history of each type of transaction. For example, the risk factor of high-frequency trading risk is set to 3.0. This value comes from the proportion of capital losses caused by this type of transaction. For example, in the past 10,000 transactions, the loss amount caused by high-frequency trading accounted for 3%. The system takes this loss proportion as the risk factor. , get abnormal transaction frequency , that is, the number of abnormal transactions per unit time. For example, if an account has 3 abnormal transactions within 1 hour, then , and then get the total transaction volume within the transaction time window , if the account has 10 transactions in 1 hour, then , calculate the transaction amount fluctuation range Transaction amount benchmark value The transaction amount fluctuation range is set to the maximum fluctuation range of the past transaction amount. For example, if the highest transaction amount in the past 10 transactions is 1,000 yuan and the lowest transaction amount is 500 yuan, then the fluctuation range is , reference value Set as the historical transaction average. For example, if the average amount of the past 10 transactions is 700 yuan, then , substitute the parameters into the formula: ; Calculate the transaction risk value , as shown in Table 9, the transaction risk level information is finally obtained.

[0044] Table 9 Transaction risk level calculation results: ; The above calculation parameters are derived from the statistical analysis of historical transaction data and are set based on the actual impact range of abnormal transactions. Corresponding historical loss rate, transaction amount benchmark value Derived from historical average, fluctuation range It is calculated based on the highest and lowest transaction amounts.

[0045] The transaction restriction determination submodule determines whether the transaction risk value exceeds the set threshold based on the transaction risk level information. If it exceeds, the transaction is marked as high risk and transaction restriction measures are implemented. If it does not exceed, the transaction proceeds normally and the order transaction verification information is obtained.

[0046] After receiving the transaction risk level information, the transaction restriction determination submodule determines whether the transaction risk value exceeds the set threshold, and implements corresponding measures to set the transaction risk threshold. The threshold is set through the historical risk control data in the transaction system and is calculated based on the risk distribution of past abnormal transactions. For example, the low-risk transaction threshold is set to 100, the medium-risk transaction threshold is 150, and the high-risk transaction threshold is 200. This setting is based on the risk value distribution of historical transactions. For example, the average risk value of the past 10,000 transactions is 120, and the standard deviation is 40. The thresholds are set to be 1 times, 1.5 times, and 2 times the standard deviation respectively. , and then compare the transaction risk values. For example, if the transaction risk value of transaction number 20250214001 is 200.9, which is higher than the set threshold of 200, the system will mark the transaction as high risk and implement transaction restriction measures, including delayed processing, manual review, temporary account freezing, etc. The system records the transaction restriction status and stores the order transaction verification information.

[0047] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The blockchain-based e-commerce transaction verification system is characterized by: The system comprises: The transaction identity authentication module obtains the e-commerce transaction verification request, performs hash calculation on the transaction identity data, calls the user identity hash record stored in the blockchain, determines whether it matches, and obtains the transaction identity matching result; The fund flow monitoring module obtains the payment transaction fund flow direction based on the transaction identity matching result, calculates the fund flow trend, matches the fund flow stability, calculates the fund transfer change rate, and obtains the fund flow stability information; The order payment status confirmation module obtains the payment transaction status based on the fund flow stability information, calculates the payment status deviation, determines the payment status synchronization, and obtains the payment status consistency comparison information; The abnormal transaction comparison module calls the fund flow path record based on the payment status consistency comparison information, compares the transaction account changes and withdrawal behavior in a short period of time, calculates the abnormal fund flow rate, and obtains the abnormal transaction comparison information; The risk control strategy adjustment module calls the order risk data measurement based on the abnormal transaction comparison information, matches the degree of transaction abnormality, evaluates the transaction risk level, determines whether transaction restriction measures need to be implemented, and obtains order transaction verification information.

2. The blockchain-based e-commerce transaction verification system according to claim 1, characterized in that: The transaction identity matching result specifically includes the identity matching status, transaction authorization status and identity hash comparison result; the fund flow stability information includes the fund path stability, fund transfer change rate and historical transaction matching degree; the payment status consistency comparison information includes the payment data synchronization status, account confirmation status and payment platform feedback consistency; the abnormal transaction comparison information specifically includes the account transaction abnormality rate, device transaction activity and fund withdrawal abnormal distribution; the order transaction verification information includes the transaction risk level, restriction execution status and abnormal transaction mark.

3. The blockchain-based e-commerce transaction verification system according to claim 1, characterized in that: The transaction identity verification module comprises: The identity information extraction submodule obtains the e-commerce transaction verification request, calls the user public key, device identification, transaction timestamp, order amount, parses and extracts the user identity data, and obtains the transaction identity data set; The identity hash calculation submodule uses hash calculation based on the transaction identity data set to obtain a transaction identity hash value; The identity matching judgment submodule calls the identity hash record stored in the blockchain based on the transaction identity hash value, compares the transaction identity hash value with the identity hash record, and determines whether the two match. If they match, the e-commerce transaction request is authorized; if they do not match, the e-commerce transaction request is rejected to obtain a transaction identity matching result.

4. The blockchain-based e-commerce transaction verification system according to claim 1, characterized in that: The fund flow monitoring module includes: The fund flow extraction submodule obtains the payment transaction fund flow based on the transaction identity matching result, calls the fund inflow account, transfer path and fund splitting situation, extracts the fund flow situation between accounts, sorts out the transaction path, calculates the fund distribution ratio of each path, and obtains the fund flow path data; The capital flow trend calculation submodule is based on the capital flow path data and adopts the formula: ； Calculate and obtain the rate of change of funds and obtain the information on the trend of fund flow; in, Represents the rate of change of funds. Indicates the path In time The amount of funds, Indicates the amount of funds at the previous time point, represents the adjacent time interval, Indicates the total number of transaction paths; The capital flow stability assessment submodule calls the transaction capital path measurement based on the capital flow trend information, matches the capital flow stability, calculates the capital transfer change rate, determines the capital fluctuation range, and obtains the capital flow stability information.

5. The blockchain-based e-commerce transaction verification system according to claim 1, characterized in that: The order payment status confirmation module includes: The payment information extraction submodule obtains the payment transaction status based on the fund flow stability information, calls the payment platform feedback status, bank flow records, payment account confirmation records, and user payment vouchers, parses the payment information data, and obtains the payment status data set; The payment status deviation calculation submodule is based on the payment status data set and adopts the formula: ； Calculate and obtain the payment status deviation rate; in, represents the payment status deviation rate, Represents the payment platform to return the status value. Represents the bank transaction record status value. Represents the current transaction timestamp, represents the time mean of payment data, Represents the number of payment records; The payment synchronization judgment submodule compares the payment status deviation rate with a preset deviation threshold value based on the payment status, judges the payment status synchronization status, and obtains payment status consistency comparison information.

6. The blockchain-based e-commerce transaction verification system according to claim 1, characterized in that: The abnormal transaction comparison module includes: The multiple order payment status acquisition submodule acquires the payment status of multiple orders of the same device and IP address based on the payment status consistency comparison information, calls the fund flow path record, extracts the order payment data under the same device and IP address, screens the transaction records with close time intervals, and acquires the payment records of multiple orders; The short-term account change comparison submodule compares the transaction account changes and withdrawal behaviors within a short period of time based on the multiple order payment records, calculates the account balance change rate, extracts the transaction frequency and withdrawal interval data, and obtains the account short-term transaction fluctuation information; The abnormal capital flow rate calculation submodule adopts the formula based on the short-term transaction fluctuation information of the account: ； Calculate and obtain abnormal fund flow ratio and abnormal transaction comparison information; in, Represents the abnormal liquidity ratio of funds. Representation cycle The total number of transactions within Representation cycle Total number of withdrawals within Representation cycle The account balance at the end of the represents the standard deviation of the trading time interval, Indicates the standard deviation of the withdrawal time interval.

7. The blockchain-based e-commerce transaction verification system according to claim 1, characterized in that: The risk control strategy adjustment module includes: The transaction abnormality matching submodule calls the order risk data measurement based on the abnormal transaction comparison information, matches the transaction abnormality degree, compares the abnormal transaction characteristics with the set transaction abnormality classification standard, extracts the transaction abnormality category, and obtains the transaction abnormality classification information; The transaction risk level calculation submodule adopts the formula based on the transaction abnormality classification information: ； Calculate and obtain the transaction risk value and obtain the transaction risk level information; in, Represents the transaction risk value, Indicates the risk coefficient corresponding to the abnormal transaction category, Indicates the frequency of abnormal transactions, Represents the total transaction volume within the trading time window, Indicates the fluctuation range of transaction amount. Indicates the base value of transaction amount; The transaction restriction determination submodule determines whether the transaction risk value exceeds the set threshold based on the transaction risk level information. If it exceeds, the transaction is marked as high risk and transaction restriction measures are implemented. If it does not exceed, the transaction proceeds normally and the order transaction verification information is obtained.

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