Order data processing method and system

Abstract

The invention relates to the technical field of transaction processing, in particular to an order data processing method and system, and the method comprises the following steps: collecting the biological feature information and equipment features of a user in real time based on purchase request information, recording the login location and time of the user, verifying the identity of the user, and generating purchase identity verification information. According to the method, the biological characteristic information and the equipment characteristics of the user are collected in real time, the login location and time records are combined, the prevention and control capability on fraudulent behaviors is enhanced, the secret key is generated by using the time and position information, the data transmission safety is ensured, the data is prevented from being leaked and tampered in the transmission process, and the user experience is improved. The processing speed and the response efficiency are improved by combining dynamic ordering and priority adjustment of order queues, risk orders are identified by using order amount comparison and source IP, the ability of identifying and marking repeated and abnormal orders is enhanced, and the processing speed and the security of data are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of transaction processing, and in particular, to an order data processing method and system. Background Art

[0002] The technical field of transaction processing focuses on efficiently and accurately processing and recording transaction information, including aspects such as transaction entry, verification, data synchronization, and real-time analysis, aiming to ensure data integrity and consistency. Through database management, multithreading, support for the atomicity, consistency, isolation, and durability of transactions, batch transaction data is processed to achieve high availability and strong fault recovery capabilities, support various commercial operations, including banking, online shopping, and ticket sales, and ensure the security and accuracy of transaction data.

[0003] Among them, the order data processing method is used to efficiently manage and process order data, optimizing multiple aspects of lottery sales, including order reception, processing, verification, and data storage. Through an automated control process, the processing speed and accuracy are improved, manual operation errors are reduced, the system's ability to prevent fraud is enhanced, the user experience and operational efficiency are improved, and it helps lottery sales agencies gain insights into sales trends and consumer behavior to formulate effective marketing strategies.

[0004] Traditional order data processing methods are insufficient in processing real-time dynamic data, including security verification and data encryption. They rely on static passwords or simple verification mechanisms and are vulnerable to attacks. They generally lack sufficient flexibility and automation capabilities in order priority adjustment and risk assessment, resulting in processing delays and omission of high-risk transactions. The lack of dynamic adjustment and real-time analysis capabilities limits the performance of the transaction system in high-pressure environments, affecting the efficiency and security of transactions. During peak periods, fixed processing mechanisms are difficult to effectively allocate resources, resulting in slower processing speeds for important orders and affecting customer satisfaction and the market competitiveness of enterprises. Summary of the Invention

[0005] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose an order data processing method and system.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] On the one hand, an order data processing method is provided, including the following steps:

[0008] S1: Based on the purchase request information, collect the user's biometric information and device characteristics in real time, record the user's login location and time, verify the user's identity, and generate purchase identity verification information;

[0009] S2: Receive the purchase authentication information, match keys for multiple orders according to the purchase time and location information, encrypt the order data, and generate an order encryption record;

[0010] S3: Utilize the order encryption record, sort and adjust the priorities of the orders in the queue according to the urgency levels, customer levels, and purchase frequencies of the multiple orders, and generate order queue management parameters;

[0011] S4: Based on the order queue management parameters, compare the order amounts and analyze the source IPs, conduct risk analysis on the multiple orders, and mark the repeatedly submitted orders to generate a risk order identification record;

[0012] S5: According to the risk order identification record, in real time review the consistency between the payment information of the order and the user account information according to the submitted refund request information, verify the legality of the refund application and execute the refund, and generate a refund application processing record;

[0013] S6: Use the refund application processing record, classify the order data according to the order type and processing result, combine time series analysis to identify market change trends, and generate an order data processing result.

[0014] Optionally, the purchase authentication information includes the verified user biometric matching result, device security status, login location and time information, the order encryption record includes an order encryption key data set, encryption algorithm type information, and the security level of the encrypted order data, the order queue management parameters include the priority arrangement of the orders, processing order adjustment indicators, and service priority logic based on customer levels, the risk order identification record includes the risk level of the order, the credibility score of the source IP, and the marked repeated orders, the refund application processing record includes the time stamp of the refund processing, the approval status of the refund operation, and the customer feedback information on the refund result, and the order data processing result includes the classification label of the order data, the optimized storage index, and the market trend information of time series analysis.

[0015] Optionally, based on the purchase request information, the steps of collecting the user's biometric information and device characteristics in real time, recording the user's login location and time, and verifying the user's identity to generate the purchase authentication information are specifically as follows:

[0016] S101: Based on the purchase request information, collect the user's biometric information in real time, including fingerprint data, record the device model and identification information used by the user, and generate biometric and device information;

[0017] S102: Based on the biometric and device information, collect the user's login location and time data in real time, and generate login time and location data;

[0018] S103: Based on the login time and location data, generate purchase authentication information by matching the user's biometric and device data with the verified user profiles in the database.

[0019] Optionally, the steps of receiving the purchase authentication information, matching keys for multiple orders according to the purchase time and location information, encrypting the order data, and generating an order encryption record are specifically as follows:

[0020] S201: Receive the purchase authentication information, match keys for multiple purchase orders according to the user's purchase time and location information, and generate a key matching record;

[0021] S202: Based on the key matching record, encrypt multiple order data according to the key to generate an order data encryption result;

[0022] S203: Based on the order data encryption result, optimize the integrity and non - readability of the data during transmission by checking the security of the encrypted data in simulated network transmission, and generate an order encryption record.

[0023] Optionally, the steps of using the order encryption record, sorting and adjusting the priorities of the orders in the queue according to the urgency levels, customer levels, and purchase frequencies of multiple orders, and generating order queue management parameters are specifically as follows:

[0024] S301: According to the order encryption record, analyze the urgency levels of multiple orders according to the order submission time, and combine the customer level information and purchase frequency data to generate order priority data;

[0025] S302: Based on the order priority data, generate an order priority score table by analyzing the processing urgency and customer value of the orders;

[0026] S303: According to the order priority score table, optimize the processing process and efficiency of the order queue by adjusting the positions of the orders in the processing queue, and generate order queue management parameters.

[0027] Optionally, the steps of performing risk analysis on multiple orders based on the order queue management parameters, comparing the order amounts and analyzing the source IPs, and marking the repeatedly submitted orders to generate a risk order identification record are specifically as follows:

[0028] S401: Receive the order queue management parameters, analyze and identify the payment amounts and source IP addresses of multiple orders, calculate the risk levels of the orders, and generate a risk analysis result;

[0029] S402: Based on the risk analysis results, according to the risk levels of multiple orders, match verification processes for multiple risky orders to generate verification process matching information;

[0030] S403: Use the verification process matching information to record risky orders and real-time update the risk status in the order database, identify duplicate order records, and generate risky order identification records.

[0031] Optionally, the specific formula for calculating the risk level of an order is:

[0032]

[0033] Wherein, represents the absolute value of the difference between the payment amount of the order and the average payment amount, and is used to evaluate the abnormality degree of the amount. represents the reciprocal of the matching degree between the source IP and the user's common IP, and is used to measure the suspicious degree of the IP source. represents the ratio of the occurrence frequency of the order to the user's average purchase frequency, and is used to identify frequent trading behaviors. 、 、 respectively represent the weight coefficients of payment amount difference, IP matching degree, and trading frequency, and are used to adjust the influence of multiple factors in the risk score.

[0034] Optionally, according to the risky order identification record, according to the submitted refund request information, the steps of real-time reviewing the consistency of the order payment information and the user account information, verifying the legality of the refund application and executing the refund, and generating a refund application processing record are specifically as follows:

[0035] S501: Based on the risky order identification record, according to the submitted refund request information, real-time collect and review the order payment information and the user account information in the refund request, verify the consistency of the payment and refund application identities, and generate refund verification data;

[0036] S502: Based on the refund verification data, real-time review the legality of multiple refund requests, evaluate the effectiveness of the refund requests by considering the relevance to risky orders, and generate refund review results;

[0037] S503: Use the refund review results to perform a refund operation and record the time, amount, and receiving account of the operation, and generate a refund application processing record.

[0038] Optionally, using the refund application processing record, according to the type and processing result of the order, classify the order data, combine time series analysis to identify market change trends, and the steps of generating order data processing results are specifically as follows:

[0039] S601: Based on the refund application processing record, classify the order data by analyzing the order type and processing result, mark the processing status and type, and generate an order classification marking result;

[0040] S602: Based on the order classification marking result, adjust the data storage structure configuration, optimize the data retrieval efficiency, adjust the storage parameters to match various types of query requirements, and generate a storage optimization configuration;

[0041] S603: Utilize the storage optimization configuration to perform time series analysis on the archived data, evaluate the market change trend of the order data, and generate an order data processing result.

[0042] On the other hand, an order data processing system is provided. The order data processing system is used to execute the above-mentioned order data processing method. The system includes:

[0043] The authentication and data encryption module collects the user's biometric characteristics and device information based on the purchase request, records the login location and time, verifies the user's identity, and matches encryption keys for multiple orders and performs data encryption to generate an encryption processing record;

[0044] The queue real-time adjustment module receives the encryption processing record, analyzes the urgency, customer level, and purchase frequency of multiple orders in real time, dynamically sorts and adjusts the priority of the processing queue, and generates a processing queue adjustment record;

[0045] The risk monitoring and recording module performs order risk assessment based on the processing queue adjustment record, by comparing the order amount in real time and analyzing the source IP, identifies and records risk orders and duplicate submission orders, and generates an order risk assessment record;

[0046] The refund application processing module utilizes the order risk assessment record, verifies the legitimacy of the request and executes the refund operation by comparing the consistency of the payment information and user account information of the order in the refund request, records the refund activity, and generates a refund operation record;

[0047] The data classification and storage module classifies the order data based on the refund operation record according to the order type and result, optimizes the storage structure and retrieval efficiency, and combines time series analysis to identify the market change trend, and generates an order data processing result.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0049] In the present invention, by collecting the user's biometric information and device characteristics in real time, combining with the records of the login location and time, the prevention and control ability against fraud behaviors is enhanced. A key is generated using the time and location information to ensure the security of data transmission, preventing the data from being leaked and tampered with during the transmission process. By combining the dynamic sorting and priority adjustment of the order queue, the processing speed and response efficiency are improved. Risk orders are identified by comparing the order amounts and the source IPs, enhancing the ability to identify and mark duplicate and abnormal orders, and strengthening the processing speed and security of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a schematic diagram of the working process of the present invention;

[0051] Figure 2 is a detailed flowchart of S1 of the present invention;

[0052] Figure 3 is a detailed flowchart of S2 of the present invention;

[0053] Figure 4 is a detailed flowchart of S3 of the present invention;

[0054] Figure 5 is a detailed flowchart of S4 of the present invention;

[0055] Figure 6 is a detailed flowchart of S5 of the present invention;

[0056] Figure 7 is a detailed flowchart of S6 of the present invention;

[0057] Figure 8 is the system flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0059] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the 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 thus should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0060] Please refer to Figure 1 , the present invention provides an order data processing method, including the following steps:

[0061] S1: Based on the purchase request information, real-time collect the user's biometric information and device characteristics, record the user's login location and time, verify the user's identity, and generate purchase authentication information;

[0062] S2: Receive the purchase authentication information, match keys for multiple orders according to the purchase time and location information, encrypt the order data, and generate order encryption records;

[0063] S3: Utilize the order encryption records, sort and adjust the priorities of the orders in the queue according to the urgency, customer level, and purchase frequency of multiple orders, and generate order queue management parameters;

[0064] S4: Based on the order queue management parameters, compare the order amounts and analyze the source IPs, conduct risk analysis on multiple orders, and mark the repeatedly submitted orders to generate risk order identification records;

[0065] S5: According to the risk order identification records, in accordance with the submitted refund request information, real-time review the consistency of the order payment information and the user account information, verify the legality of the refund application and execute the refund, and generate refund application processing records;

[0066] S6: Use the refund application processing records, classify the order data according to the order type and processing results, combine time series analysis to identify market change trends, and generate order data processing results.

[0067] The purchase authentication information includes the verified user biometric matching results, device security status, login location and time information. The order encryption records include the order encryption key data set, encryption algorithm type information, and encrypted order data security level. The order queue management parameters include the order priority arrangement, processing order adjustment indicators, and service priority logic based on the customer level. The risk order identification records include the order risk level, credibility score of the source IP, and marked repeated orders. The refund application processing records include the time stamp of the refund processing, the approval status of the refund operation, and the customer feedback information on the refund result. The order data processing results include the classification labels of the order data, the optimized storage index, and the market trend information of the time series analysis.

[0068] Please refer to Figure 2 , based on the purchase request information, the specific steps of real-time collecting the user's biometric information and device characteristics, recording the user's login location and time, and verifying the user's identity to generate purchase authentication information are as follows:

[0069] S101: Based on the purchase request information, collect the user's biometric information in real time, including fingerprint data, record the device model and identification information used by the user, and generate biometric and device information;

[0070] In sub-step S101, based on the purchase request information, scan the user's fingerprint through a biometric device, including a fingerprint scanner, and adopt high-resolution imaging technology to ensure that the obtained biometric information has high accuracy and unique non-replicability, ensuring system security. Obtain the model and unique identification information of the user's device through a network request, and use device recognition algorithms, including device fingerprint technology, to extract the hardware and software configuration data of the device. The target data includes the operating system version and hardware serial number, forming a complete device information file. The collected biometric and device information is encrypted and stored in the central database as the basis for identity verification, generating biometric and device information records.

[0071] S102: Based on the biometric and device information, collect the user's login location and time data in real time, and generate login time and location data;

[0072] In sub-step S102, the system collects the user's login location and time data based on the generated biometric and device information, and uses the Global Positioning System and Network Time Protocol server to obtain the target information in real time. The location data provides geographical coordinates through GPS, and the time data ensures consistency with Coordinated Universal Time by synchronizing to the NTP server, ensuring the high accuracy and reliability of the time and location information. Format the collected location and time data, such as converting the time format to the unified UTC standard, and standardizing the location data to the latitude and longitude format for subsequent processing and analysis. The target data is encrypted and uploaded to the central server, forming login time and location data records that can be used for identity verification.

[0073] S103: Based on the login time and location data, generate purchase authentication information by matching the user's biometric and device data with the verified user profiles in the database;

[0074] In the above content, by comparing and verifying the user's login location and time data and biometric information, according to the formula , calculate the purchase authentication score,

[0075] where represents the location matching degree, represents the time matching degree, represents the biometric matching degree, , , are the weights of the corresponding parameters respectively, For the purchase authentication score,

[0076] Detailed formula explanation and formula calculation derivation process:

[0077] Assume that the location is completely matched, , the time error is within 5 minutes, , the biometric matching degree , the weight assignment is , , , reflecting the importance of location verification in the security policy,

[0078] Substitute the parameters into the formula for calculation:

[0079]

[0080]

[0081]

[0082] The result of 0.96 indicates that the user's authentication score is very high. The result is used to verify the user's identity and perform subsequent security operations.

[0083] Please refer to Figure 3 , to receive the purchase authentication information, and according to the purchase time and location information, match keys for multiple orders, and encrypt the order data. The specific steps for generating the order encryption record are as follows:

[0084] S201: Receive the purchase authentication information, and according to the user's purchase time and location information, match keys for multiple purchase orders to generate key matching records;

[0085] In the sub-step of S201, receive the pre-processed purchase authentication information, including the user's biometric and device identification information, combine the purchase time and location information, adopt the dynamic key distribution technology, dynamically generate a unique key according to the timestamp and geographical location data of each order, use the key generation algorithm based on the hash function to ensure the randomness and security of each key, adjust the complexity of the key according to the security level of the user identity and the amount of the order, adapt to orders with different security requirements, enhance the security protection ability of the transaction, and generate a key matching record for each purchase order. The target record lists the key information, time and location data of each order, and the corresponding user verification information.

[0086] S202: Based on the key matching record, encrypt multiple order data according to the key to generate the encrypted result of the order data;

[0087] In sub-step S202, the system uses the key matching records generated in the previous step to encrypt the data of each order. The system encrypts the order information, including the order amount, number selection information, and user data, using an encryption standard algorithm to ensure the confidentiality and integrity of the target information during storage and transmission. During the encryption process, a multi-layer verification mechanism is implemented, including key validity checks and order data integrity verification, to prevent data from being tampered with or the key from being leaked during encryption. After encryption, the data encryption results of each order are generated, and the encrypted data content and the key used are recorded to provide reliable protection for subsequent data security transmission and storage.

[0088] S203: Based on the order data encryption results, by testing the security of the encrypted data during simulated network transmission, optimize the integrity and non-interpretability of the data during transmission, and generate order encryption records;

[0089] In sub-step S203, the system performs security tests based on the encrypted order data results, including simulating network attack and data leakage scenarios, testing the security and anti-attack capabilities of the encrypted data in various network environments, using network transmission security testing tools Wireshark and Nmap for packet capture and protocol analysis to confirm the effectiveness of data encryption, and using data integrity verification algorithms MD5 and SHA-256 to verify the integrity of the transmitted encrypted data to ensure that the data has not been modified or damaged during transmission. Through strict security tests and optimization measures for the target, order encryption records are generated, and the records include the security status of the encrypted data, any abnormal situations during transmission, and corresponding measures to ensure the security of the data and the reliability of transmission.

[0090] Please refer to Figure 4 , for the steps of using the order encryption records to sort and adjust the priorities of the orders in the queue according to the urgency, customer level, and purchase frequency of multiple orders to generate order queue management parameters, specifically:

[0091] S301: According to the order encryption records, analyze the urgency of multiple orders based on the order submission time, and combine the customer level information and purchase frequency data to generate order priority data;

[0092] In sub-step S301, the system receives the encrypted order records. The target records include the encryption status and key information of the orders. Analyze the submission time of each order to evaluate the urgency, use time series analysis of the time analysis model to determine the time sensitivity of the orders, and combine the customer level information and purchase frequency data extracted from the database. The target data is updated and managed in real time through the customer relationship management system. Using the comprehensive scoring model, considering the three dimensions of time sensitivity, customer level, and purchase frequency, calculate the comprehensive priority index for each order to ensure that high-value and high-urgency orders are processed first, and generate order priority data including the priority index of each order.

[0093] S302: Based on the order priority data, by analyzing the processing urgency and customer value of the orders, conduct a priority scoring for the orders to generate an order priority scoring table;

[0094] In the above content, by analyzing the processing urgency and customer value of the orders, use the formula to calculate the priority score of the order,

[0095] In the formula, represents the quantitative score of the order urgency, represents the quantitative score of the customer value, and are the weights of the corresponding parameters respectively, is the priority score of the order,

[0096] Detailed explanation of the formula and the derivation process of the formula calculation:

[0097] Suppose the target order has a high urgency, , and the customer is a high-value customer, , the weight , ,

[0098] Substitute the parameters into the formula for calculation:

[0099]

[0100]

[0101]

[0102] The result 0.84 indicates that the order has a high score in the priority scoring and should be processed first in the processing queue. The score helps the management system allocate resources to ensure that high-value and urgent orders are quickly responded to and processed.

[0103] S303: According to the order priority scoring table, optimize the order queue processing flow and efficiency by adjusting the position of orders in the processing queue, and generate order queue management parameters;

[0104] In sub-step S303, utilize the generated order priority scoring table to perform dynamic adjustment of the order queue. Adopt a queue management algorithm to dynamically adjust the position of orders in the processing queue according to the priority scores of orders, give priority to processing high-score orders, and implement queue optimization measures such as time-sharing processing strategies and load balancing technologies to ensure efficient processing of a large number of orders even during peak periods. Improve the efficiency and response speed of the order processing flow through the target measures, reduce waiting time and processing delays, generate optimized order queue management parameters, and the target parameters update the queue status and adjustment strategies in real time to ensure that the system can maintain optimal performance under various operating conditions.

[0105] Please refer to Figure 5 , based on the order queue management parameters, compare the order amounts and analyze the source IPs, conduct risk analysis on multiple orders, and mark the repeatedly submitted orders to generate the risk order identification record. The specific steps are as follows:

[0106] S401: Receive the order queue management parameters, analyze and identify the payment amounts and source IP addresses of multiple orders, calculate the risk levels of the orders, and generate risk analysis results;

[0107] The specific formula for calculating the risk level of an order is:

[0108]

[0109] Among them, represents the absolute value of the difference between the payment amount of the order and the average payment amount, which is used to evaluate the abnormality degree of the amount, represents the reciprocal of the matching degree between the source IP and the user's common IP, which is used to measure the suspicious degree of the IP source, represents the ratio of the occurrence frequency of the order to the user's average purchase frequency, which is used to identify frequent trading behaviors, 、 、 respectively represent the weight coefficients of payment amount difference, IP matching degree, and trading frequency, which are used to adjust the influence of multiple factors in the risk scoring.

[0110] Formula:

[0111]

[0112] Detailed explanation of the formula and the formula calculation derivation process:

[0113] The formula is used to calculate the comprehensive risk score of an order, and the result is used to determine the priority of order processing and the degree of further review required;

[0114] Parameter meanings and set values:

[0115] is the payment amount deviation. The parameter reflects the abnormality degree of the order amount. Suppose the average order amount of the user is 200 yuan, and the target order amount is 500 yuan, yuan;

[0116] is the matching degree of the source IP. When the IP does not match the user's common IP, it is 1, and when it matches, it is 0.1. Suppose the IP does not match, ;

[0117] is the order frequency deviation. Suppose the average number of purchases per month of the user is 2 times, and the number of purchases this month is 5 times, ;

[0118] , , are the weight coefficients of the payment amount difference, IP matching degree, and transaction frequency respectively. Suppose

[0119] , , ;

[0120] Substitute the parameters into the formula for calculation:

[0121]

[0122]

[0123]

[0124] The result 44.14 indicates that the comprehensive risk score of the order is above medium, indicating that the order has a medium risk and needs to be reviewed. The score is used to quickly identify and process possible risky orders, optimizing the security of transactions and customer satisfaction.

[0125] S402: Based on the risk analysis results, according to the risk levels of multiple orders, match the verification process for multiple risky orders to generate verification process matching information;

[0126] In sub-step S402, using the risk analysis results, for orders with different risk levels, apply a dynamic verification process matching algorithm, automatically select the verification process according to the risk score of the order. High-risk orders will trigger more stringent verification measures, such as two-factor authentication and manual review, while low-risk orders will pass basic automated checks, optimizing the use efficiency of resources, ensuring that risk orders receive sufficient attention without affecting the overall processing speed, generating verification process matching information. The target information records the risk level of each order and the corresponding processing process, providing a solid foundation for ensuring transaction security.

[0127] S403: Using the verification process matching information, record risk orders and update the risk status in the order database in real time, identify duplicate order records, and generate risk order identification records;

[0128] In sub-step S403, use the verification process matching information to strengthen the monitoring and management of risk orders. Real-time update the order risk status in the order database through an integrated order management system. This process uses real-time data synchronization technology to ensure the immediacy and accuracy of order status updates. Combine automatic identification and recording of duplicate orders, use a unique order identification algorithm, and identify duplicate orders by analyzing multiple dimensions such as the order timestamp, user ID, and payment details. The generated risk order identification records list all marked risk orders and duplicate orders, providing data support for risk management and review.

[0129] Please refer to Figure 6 , according to the risk order identification records, according to the submitted refund request information, in real-time review the consistency of the order payment information and the user account information, verify the legality of the refund application and execute the refund. The specific steps for generating the refund application processing record are as follows:

[0130] S501: Based on the risk order identification records, according to the submitted refund request information, in real-time collect and review the order payment information and the user account information in the refund request, verify the consistency of the payment and refund application identities, and generate refund verification data;

[0131] In sub-step S501, based on the identified risk order records, receive the refund request information submitted by the user, including real-time collection and verification of the order payment information and user account information for each request. Use information matching technologies such as pattern matching and data verification algorithms to check whether the account name, transaction number, and payment time in the payment information are consistent with the records in the user account database, ensuring the legality and accuracy of the source of the refund request. Authenticate the request information to ensure that the refund applicant is the real initiator of the payment behavior. Utilize authentication technologies, including digital signature and biometric verification technologies, to ensure user identity and reduce refund fraud. Through the target checking and verification steps, generate refund verification data, which contains all necessary verification results and user identity information.

[0132] S502: Based on the refund verification data, review the legality of multiple refund requests in real time. By considering the relevance to risk orders, evaluate the validity of the refund requests and generate refund review results.

[0133] In sub-step S502, the system conducts a real-time review of the legality of refund requests based on the generated refund verification data. Adopt a refund evaluation model and consider the relevance of the refund requests to known risk orders, such as whether there are duplicate refund requests, the time of the requests, and the time relationship with reported risk events. Evaluate the risk level and legality of each refund request. Use decision tree algorithms to classify and determine the refund requests that should be approved and those that need to be investigated and verified, ensuring that only legal and reasonable refund requests are processed, reducing the risk of fraud and the possibility of incorrect refunds. Generate refund review results. The target results record the review situation and decision basis for each refund request, providing a solid review foundation for subsequent refund operations.

[0134] S503: Use the refund review results to perform refund operations and record the time, amount, and receiving account of the operations, generating a refund application processing record.

[0135] In sub-step S503, perform actual refund operations using the audited refund review results. Process each legal refund request according to the refund review results, including setting the refund amount, selecting the refund method, and entering the receiving account information. Adopt automated refund processing technologies, including electronic payment systems and automatic account management software, to ensure the speed and accuracy of refund operations. At the same time, record the information of each refund, including the time, amount, and receiving account of the operation. The refund operation data is recorded in the financial management database for auditing and financial analysis. Through the target systematic operation, generate a complete refund application processing record. The target record provides a clear basis for financial tracking and user service optimization for the platform.

[0136] Please refer to Figure 7, using the refund application processing records, classify the order data according to the order type and processing result, and combine time series analysis to identify market change trends. The specific steps for generating the order data processing result are as follows:

[0137] S601: Based on the refund application processing records, classify the order data by analyzing the order type and processing result, mark the processing status and type, and generate the order classification marking result;

[0138] In sub-step S601, the system uses the refund application processing records. The target records reflect the processing situations and results of each refund order. The system classifies the orders through the data classification algorithm decision tree classifier. The classification basis includes the order type, processing result, order timestamp, and amount size, which helps the system manage order data and provides a structured input for data processing and analysis. During the classification process, mark the processing status and type of each order to quickly identify and respond to the requirements and problems of multiple types of orders, and generate the order classification marking result. The target result lays a foundation for data storage and analysis.

[0139] S602: Based on the order classification marking result, adjust the data storage structure configuration, optimize the data retrieval efficiency, adjust the storage parameters to match various types of query requirements, and generate the storage optimization configuration;

[0140] In sub-step S602, the system adjusts the data storage structure based on the order classification marking result generated by S601, and uses data warehouse technology to adjust and optimize the data storage configuration, including data partitioning, index optimization, and data compression technology, to improve the data retrieval efficiency and the utilization efficiency of storage space, and achieve support for various types of query requirements, including time range query, amount range query, and customer behavior analysis. Dynamically adjust the storage parameters such as partition keys and index strategies to match the target requirements, and use the functions of the database management system to automate the performance tuning and query optimization suggestions at the data level to ensure the optimal performance of data operations. Through the application of the target technology, generate the storage optimization configuration, which improves the speed and accuracy of data processing and query.

[0141] S603: Use the storage optimization configuration to perform time series analysis on the archived data, evaluate the market change trends of the order data, and generate the order data processing result;

[0142] In sub-step S603, using the storage optimization configuration, perform time series analysis on the archived order data. Adopt the autoregressive integrated moving average model and the time series model of seasonal decomposition to evaluate the market change trend of order data in different time periods, identify the sales peak, the periodic changes in consumer purchase behavior, and potential market opportunities. The target analysis is based on the statistical characteristics of historical data and the prediction of future trends, making market analysis and decision support more scientific and accurate, and realizing the function of automated report generation. The target report shows the analysis results and market predictions, providing an important basis for the formulation of marketing strategies and resource allocation. Through the target fine analysis and optimization, generate the order data processing results, providing strong data support and market insights for the sales organization.

[0143] Please refer to Figure 8 , an order data processing system. The order data processing system is used to execute the above-mentioned order data processing method. The system includes:

[0144] The authentication and data encryption module collects the biometric characteristics and device information of the user based on the purchase request, records the login location and time, verifies the user identity, and matches the encryption key for multiple orders and performs data encryption to generate the encryption processing record;

[0145] The queue real-time adjustment module receives the encryption processing record, analyzes the urgency, customer level, and purchase frequency of multiple orders in real time, dynamically sorts and adjusts the priority of the processing queue, and generates the processing queue adjustment record;

[0146] The risk monitoring and recording module performs order risk assessment based on the processing queue adjustment record, identifies and records risk orders and duplicate submission orders by comparing the order amount and analyzing the source IP in real time, and generates the order risk assessment record;

[0147] The refund application processing module uses the order risk assessment record to verify the legitimacy of the request and execute the refund operation by comparing the payment information of the order in the refund request with the user account information, records the refund activity, and generates the refund operation record;

[0148] The data classification and storage module classifies the order data according to the type and result of the order based on the refund operation record, optimizes the storage structure and retrieval efficiency, and identifies the market change trend by combining time series analysis to generate the order data processing results.

[0149] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the relevant art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for processing order data, characterized in that: The following steps are involved: Based on the purchase request information, the user's biometric information and device characteristics are collected in real time, and the user's login location and time are recorded to verify the user's identity and generate purchase identity verification information; Receiving the purchase identity verification information, matching keys for multiple orders according to the purchase time and location information, encrypting the order data, and generating an encrypted order record; Using the encrypted order records, the orders in the queue are sorted and prioritized according to the urgency, customer level and purchase frequency of the multiple orders, and order queue management parameters are generated; Based on the order queue management parameters, the order amounts are compared and the source IPs are analyzed, risk analysis is performed on multiple orders, and repeated orders are marked to generate risk order identification records; Based on the risk order identification record and the refund request information submitted, the consistency between the payment information of the order and the user account information is reviewed in real time, the legitimacy of the refund application is verified, the refund is executed, and a refund application processing record is generated; Using the refund application processing record, the order data is classified according to the order type and processing result, and the market change trend is identified in combination with time series analysis to generate order data processing results.

2. The order data processing method according to claim 1, characterized in that: The purchase identity authentication information includes the verified user biometric matching result, device security status, login location and time information; the order encryption record includes the order encryption key data set, encryption algorithm type information, and the encrypted order data security level; the order queue management parameters include order priority arrangement, processing order adjustment indicators, and customer-level based service priority logic; the risk order identification record includes the order risk level, source IP credibility score, and marked duplicate orders; the refund application processing record includes the timestamp of the refund processing, the approval status of the refund operation, and customer feedback information on the refund result; the order data processing result includes the classification label of the order data, the optimized storage index, and the market trend information of the time series analysis.

3. The order data processing method according to claim 1, characterized in that: Based on the purchase request information, the user's biometric information and device characteristics are collected in real time, and the user's login location and time are recorded to verify the user's identity. The specific steps for generating purchase identity verification information are as follows: Based on the purchase request information, collect the user's biometric information in real time, including fingerprint data, record the device model and identification information used by the user, and generate biometric and device information; Based on the biometrics and device information, real-time collection of user login location and time data to generate login time and location data; Based on the login time and location data, purchase authentication information is generated by matching the user's biometrics and device data with a verified user profile in a database.

4. The order data processing method according to claim 1, characterized in that: The steps of receiving the purchase identity verification information, matching keys for multiple orders according to the purchase time and location information, encrypting the order data, and generating order encryption records are specifically as follows: Receiving the purchase identity verification information, matching keys for multiple purchase orders according to the user's purchase time and location information, and generating a key matching record; Based on the key matching record, multiple order data are encrypted according to the key to generate an order data encryption result; Based on the order data encryption result, by verifying the security of the encrypted data in simulated network transmission, optimizing the integrity and unintelligibility of the data during transmission, an order encryption record is generated.

5. The order data processing method according to claim 1, characterized in that: By using the encrypted order records, the orders in the queue are sorted and the priorities are adjusted according to the urgency, customer level and purchase frequency of multiple orders. The steps of generating order queue management parameters are specifically as follows: Analyzing the urgency of multiple orders based on the order encryption records and the order submission time, and generating order priority data in combination with customer level information and purchase frequency data; Based on the order priority data, by analyzing the processing urgency and customer value of the orders, the orders are prioritized and an order priority score table is generated; According to the order priority scoring table, the position of the order in the processing queue is adjusted to optimize the processing flow and efficiency of the order queue and generate order queue management parameters.

6. The order data processing method according to claim 1, characterized in that: Based on the order queue management parameters, the order amounts are compared and the source IPs are analyzed, risk analysis is performed on multiple orders, and repeated orders are marked. The specific steps for generating risk order identification records are as follows: Receiving the order queue management parameters, analyzing and identifying the payment amounts and source IP addresses of multiple orders, calculating the risk levels of the orders, and generating risk analysis results; Based on the risk analysis result, according to the risk levels of the multiple orders, matching the multiple risk orders with the verification process, and generating verification process matching information; The verification process is used to match information, record risky orders and update the risk status in the order database in real time, identify duplicate order records, and generate risky order identification records.

7. The order data processing method according to claim 6, characterized in that: The specific formula for calculating the risk level of an order is: ； in, The absolute value of the difference between the payment amount of the order and the average payment amount, which is used to evaluate the abnormality of the amount. Represents the inverse of the matching degree between the source IP and the user's commonly used IP, which is used to measure the suspiciousness of the IP source. The ratio of the order frequency to the average purchase frequency of the user, used to identify frequent trading behaviors. , , They represent the weight coefficients of payment amount difference, IP matching degree, and transaction frequency, respectively, and are used to adjust the influence of multiple factors in risk scoring.

8. The order data processing method according to claim 1, characterized in that: According to the risk order identification record, according to the submitted refund request information, the consistency of the order payment information and the user account information is reviewed in real time, the legitimacy of the refund application is verified and the refund is executed. The specific steps of generating the refund application processing record are: Based on the risk order identification record, according to the refund request information submitted, the order payment information and user account information in the refund request are collected and reviewed in real time, the consistency of the payment and refund application identities is verified, and refund verification data is generated; Based on the refund verification data, the legitimacy of multiple refund requests is reviewed in real time, and the validity of the refund requests is evaluated by considering the relevance to the risk orders, thereby generating a refund review result; Using the refund review results, perform a refund operation and record the time, amount and receiving account of the operation, and generate a refund application processing record.

9. The order data processing method according to claim 1, characterized in that: Using the refund application processing record, classifying the order data according to the order type and processing result, identifying the market change trend in combination with time series analysis, and generating the order data processing result are specifically as follows: Based on the refund application processing record, by analyzing the type and processing result of the order, classifying the order data, marking the processing status and type, and generating an order classification marking result; Based on the order classification and marking results, adjust the data storage structure configuration, optimize data retrieval efficiency, adjust storage parameters to match various types of query requirements, and generate storage optimization configuration; By utilizing the storage optimization configuration, time series analysis is performed on the archived data, the market trend of the order data is evaluated, and order data processing results are generated.

10. An order data processing system, characterized in that: According to the order data processing method according to any one of claims 1 to 9, the system comprises: The identity authentication and data encryption module collects the user's biometrics and device information based on the purchase request, records the login location and time, verifies the user's identity, matches encryption keys and performs data encryption for multiple orders, and generates encryption processing records; The queue real-time adjustment module receives the encrypted processing record, analyzes the urgency, customer level and purchase frequency of multiple orders in real time, dynamically sorts and adjusts the priority of the processing queue, and generates a processing queue adjustment record; The risk monitoring and recording module adjusts the records based on the processing queue, performs order risk assessment by comparing order amounts and analyzing source IPs in real time, identifies and records risky orders and duplicate submitted orders, and generates order risk assessment records; The refund application processing module uses the order risk assessment record to verify the legitimacy of the request and execute the refund operation by comparing the payment information of the order in the refund request with the consistency of the user account information, records the refund activity, and generates a refund operation record; The data classification and storage module classifies the order data according to the type and result of the order based on the refund operation record, optimizes the storage structure and retrieval efficiency, identifies the market change trend in combination with time series analysis, and generates order data processing results.

Images