E-commerce transaction behavior risk management method and storage medium

Abstract

The invention relates to the field of risk management of e-commerce transaction behaviors, in particular to an e-commerce transaction behavior risk management method and a storage medium. The method comprises the following steps: collecting transaction data, analyzing the transaction data by using a transaction behavior analysis model based on a deep residual network (ResNet) and an attention mechanism, and identifying an abnormal transaction mode; in combination with a natural language processing (NLP) technology, performing sentiment analysis on user comments, social media and news, and predicting a transaction risk; an analysis result is input into a smart contract based on the block chain, a transaction rule is automatically executed, and transaction transparency and security are improved; a distributed account book technology of a block chain is utilized to ensure that transaction records cannot be tampered, and data security is enhanced; and a multi-mode biological recognition technology is adopted, so that the accuracy and safety of user identity verification are improved.

Description

Technical Field

[0001] The present application relates to the field of risk management of e-commerce transaction behaviors, and in particular to a method and storage medium for risk management of e-commerce transaction behaviors. Background Art

[0002] E-commerce transaction behavior risk management is an important topic in the current e-commerce platform and e-commerce service field. With the rapid development of Internet technology, e-commerce has become an important part of global trade and has greatly promoted the process of economic globalization. The rise of e-commerce trading platforms not only facilitates consumers' shopping, but also provides merchants with a broad market space. However, the accompanying transaction security issues have become increasingly prominent, bringing huge economic losses and reputation risks to e-commerce platforms. Therefore, how to effectively manage and prevent these transaction risks has become a key issue that the e-commerce industry needs to solve. Existing e-commerce transaction behavior risk management methods mainly include traditional statistical analysis, rule engines, and simple machine learning models. Specifically, traditional methods often rely on manually set rules and thresholds to identify abnormal transactions, such as setting transaction amount limits, transaction frequency limits, and other measures to prevent risks. In addition, some e-commerce platforms have also adopted basic machine learning models, such as logistic regression and support vector machines, to predict possible future risks by learning from historical transaction data. Although these methods can mitigate risks to a certain extent, their accuracy and timeliness are still greatly limited when facing complex and changing transaction environments. However, these traditional methods perform poorly when dealing with complex transaction patterns and large-scale data, and it is difficult to detect and respond to new types of fraud in a timely manner. The limitations of traditional methods are particularly evident when facing cross-border transactions, high-frequency transactions, and high-risk transactions. For example, traditional statistical analysis methods cannot effectively capture abnormal behaviors hidden in a large number of normal transactions, while simple rule engines are prone to false positives and false negatives. Therefore, existing risk management methods have obvious deficiencies in accuracy and real-time performance, and are difficult to meet the high requirements of modern e-commerce transactions. Summary of the invention

[0003] The purpose of this application is to overcome the above technical problems and provide an e-commerce transaction behavior risk management method and storage medium An e-commerce transaction behavior risk management method includes the following steps: S1: collect transaction data, including user transaction records, user comments, social media and news information; S2: use a transaction behavior analysis model based on a deep residual network (ResNet) and an attention mechanism to analyze the transaction data and identify abnormal transaction patterns; S3: combine natural language processing (NLP) technology, especially pre-trained models such as BERT or GPT, to perform sentiment analysis on user comments, social media and news to predict transaction risks; S4: input the analysis results into a blockchain-based smart contract to automatically execute transaction rules and improve transaction transparency and security; S5: use the distributed ledger technology of blockchain to ensure the immutability of transaction records and enhance data security; S6: use multimodal biometric technology, combined with facial recognition, fingerprint recognition, iris recognition and other biometric technologies, to improve the accuracy and security of user identity authentication. By adopting the above technical solution, collecting transaction data and using a transaction behavior analysis model based on a deep residual network (ResNet) and an attention mechanism, it is possible to automatically learn and identify complex transaction patterns and improve the recognition accuracy of abnormal transactions. Combined with natural language processing (NLP) technology, especially pre-trained models such as BERT or GPT, sentiment analysis is performed on user comments, social media and news to predict transaction risks, thereby improving the accuracy and timeliness of risk prediction. The analysis results are input into blockchain-based smart contracts to automatically execute transaction rules, thereby improving the transparency and security of transactions. The distributed ledger technology of blockchain is used to ensure the immutability of transaction records and enhance data security. Multimodal biometric technology is adopted, combined with facial recognition, fingerprint recognition, iris recognition and other biometric technologies, to improve the accuracy and security of user identity authentication and effectively reduce the risk of identity fraud. Preferably, the transaction behavior analysis model uses a deep learning model to perform multi-level feature extraction and complex pattern recognition, combined with time series analysis and multi-dimensional data analysis, to significantly improve the recognition accuracy of abnormal transactions; and the model also uses transfer learning technology to extract common features from existing large-scale transaction data sets, further improving the generalization ability and robustness of the model. By adopting the above technical solution, the transaction behavior analysis model uses a deep learning model to perform multi-level feature extraction and complex pattern recognition, combined with time series analysis and multi-dimensional data analysis, to significantly improve the recognition accuracy of abnormal transactions. In addition, the model uses transfer learning technology to extract common features from existing large-scale transaction data sets, further improving the generalization and robustness of the model. This enables the method to more accurately identify abnormal transactions in complex and changing transaction environments, reduce false positives and false negatives, and enhance the risk management capabilities of transaction behaviors.Preferably, the natural language processing technology not only includes sentiment analysis of user comments, social media and news, but also includes entity recognition, relationship extraction and event detection of text, comprehensively predicting changes in market sentiment, thereby early warning of transaction risks; and the technology is also fine-tuned in combination with a domain-specific corpus to improve the accuracy and relevance of the analysis. By adopting the above technical solution, the natural language processing technology can not only perform sentiment analysis on user comments, social media and news, but also perform entity recognition, relationship extraction and event detection on text, comprehensively predict changes in market sentiment, thereby early warning of transaction risks. Fine-tuning in combination with a domain-specific corpus improves the accuracy and relevance of the analysis and significantly enhances the effectiveness of transaction risk management. Preferably, the blockchain-based smart contract uses a programmable smart contract language such as Solidity, combined with formal verification technology to ensure the correctness and security of the contract, and through the automatic audit function of the smart contract, further improves the security and reliability of the contract; and the smart contract also supports dynamic updates and version control to ensure the flexibility and maintainability of the contract. By adopting the above technical solution, the blockchain-based smart contract adopts a programmable smart contract language such as Solidity, combined with formal verification technology, to ensure the correctness and security of the contract, and improve the credibility and execution efficiency of the contract. Through the automatic audit function of the smart contract, the security and reliability of the contract are further enhanced, and the risk of human intervention is reduced. Smart contracts also support dynamic updates and version control to ensure the flexibility and maintainability of the contract, can adapt to changes in business needs, and improve the overall robustness and sustainability of the system. Preferably, the distributed ledger technology adopts a consortium chain or a private chain, combined with zero-knowledge proof technology, to protect the privacy of sensitive transaction information, and further enhances the security and tamper-proof ability of the data through multi-party signature and threshold signature mechanism; and the technology also supports cross-chain interoperability to achieve data exchange and sharing between different blockchain platforms. By adopting the above technical solution, the distributed ledger technology adopts a consortium chain or a private chain, combined with zero-knowledge proof technology, to effectively protect the privacy of sensitive transaction information. Through multi-party signature and threshold signature mechanism, the security and tamper-proof ability of the data are further enhanced. In addition, cross-chain interoperability is supported, data exchange and sharing between different blockchain platforms are achieved, and the flexibility and scalability of the system are improved. Preferably, the multimodal biometric technology uses a deep learning model to perform feature fusion, combined with federated learning technology, to protect the privacy of the user's biometric data, and through a multiple verification mechanism of biometric data, further improves the accuracy and security of user identity verification; and the technology also supports real-time updates and online learning, and continuously optimizes the performance of the biometric model. By adopting the above technical solution, the multimodal biometric technology uses a deep learning model to perform feature fusion, which improves the accuracy and security of user identity verification.Combined with federated learning technology, the privacy of user biometric data is effectively protected and the risk of data leakage is avoided. The reliability of user identity authentication is further enhanced through the multiple verification mechanism of biometric data. In addition, the technology also supports real-time updates and online learning, which can continuously optimize the performance of biometric models and improve the adaptability and robustness of the system. Preferably, the quantum computing technology specifically includes quantum key distribution (QKD) technology and quantum random number generator, which significantly improves the security of the encryption system through the high unpredictability of the quantum random number generator and the high security of quantum key distribution; and the technology is also combined with quantum error correction code to further enhance the reliability and stability of the encryption system. By adopting the above technical solution, the combination of quantum key distribution (QKD) technology and quantum random number generator significantly improves the security of the encryption system. The high unpredictability of the quantum random number generator makes the generated random numbers more difficult to crack, which enhances the security of the encryption system. Quantum key distribution technology ensures that the transmission process of the key is not eavesdropped through the principles of quantum physics, further improving the security of the data. Combined with quantum error correction code technology, the reliability and stability of the encryption system are further enhanced, ensuring that high performance and high security can be maintained in various complex environments. Preferably, the real-time data transmission utilizes the high-speed data transmission capability and edge computing technology of 5G, combined with the real-time data stream processing framework, to reduce data transmission delays and improve the real-time performance and response speed of the system; and the technology also supports multi-source data fusion to achieve comprehensive analysis and processing of data from different sources. By adopting the above technical solution, utilizing the high-speed data transmission capability and edge computing technology of 5G, combined with the real-time data stream processing framework, data transmission delays can be significantly reduced, and the real-time performance and response speed of the system can be improved. This makes transaction monitoring and anomaly detection more timely and effective, and improves the efficiency and accuracy of risk management. At the same time, the technology also supports multi-source data fusion, realizes comprehensive analysis and processing of data from different sources, enhances the comprehensiveness and reliability of data, and further enhances the ability to identify and respond to risks. Preferably, the personalized risk management is based on the user's historical transaction behavior and preferences, adopts user portrait technology, combines the recommendation system algorithm and decision tree model, provides highly personalized risk assessment and management strategies, and significantly optimizes user experience and security; and the technology also supports dynamic adjustment and real-time feedback, and continuously optimizes the effect of risk management strategies. By adopting the above technical solutions, personalized risk management is based on the user's historical trading behavior and preferences, using user profiling technology, combined with recommendation system algorithms and decision tree models, to provide highly personalized risk assessment and management strategies, significantly optimizing user experience and security; and the technology also supports dynamic adjustment and real-time feedback to continuously optimize the effectiveness of risk management strategies.A storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the e-commerce transaction behavior risk management method as described in any one of claims 1 to 9 are implemented. By adopting the above technical solution, the storage medium can effectively store and execute the e-commerce transaction behavior risk management method, accurately identify abnormal transaction patterns and predict transaction risks through deep learning models and natural language processing technology, improve transaction transparency and data security by combining blockchain technology, ensure the accuracy and security of user identity authentication with multimodal biometric technology, enhance the security and reliability of encryption systems with quantum computing technology, realize high-speed real-time data transmission and anomaly detection with 5G and Internet of Things technology, and optimize user experience and security with personalized risk management strategies, thus improving the overall risk management level of e-commerce transactions.

[0004] In summary, this application includes at least one of the following beneficial technical effects: 1. By utilizing a transaction behavior analysis model based on a deep residual network (ResNet) and an attention mechanism, as well as natural language processing (NLP) technology, especially pre-trained models such as BERT or GPT, to conduct a comprehensive analysis of transaction data, the recognition accuracy of abnormal transactions and the prediction ability of transaction risks can be significantly improved, thereby effectively preventing and managing fraud in e-commerce transactions. 2. The application of blockchain-based smart contracts and distributed ledger technology ensures the immutability of transaction records and the security of data, improves the transparency and security of transactions, reduces fraud risks, and enhances users' trust in the platform. 3. The use of multimodal biometric technology, combined with facial recognition, fingerprint recognition, iris recognition and other biometric technologies, greatly improves the accuracy and security of user identity authentication, effectively prevents identity fraud and illegal access, and ensures the security of users' accounts. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 This is one of the flow charts of a method for managing risk of e-commerce transaction behavior in an embodiment of the present application.

[0006] Figure 2 This is the second flowchart of an e-commerce transaction behavior risk management method in an embodiment of the present application. DETAILED DESCRIPTION

[0007] The following will be combined with the attached Figure 1-Figure 2, a clear and complete description of the technical solutions in the embodiments of the present invention is given. The described embodiments are only possible technical implementations of the present invention, and not all implementations are possible. Those skilled in the art can fully combine the embodiments of the present invention to obtain other embodiments without creative work, and these embodiments are also within the scope of protection of the present invention. The inventors of the present application have found that the existing e-commerce transaction behavior risk management methods are insufficient in innovation and practicality in special fields, special applications or special circumstances. For this reason, the present application mainly adopts the following transaction behavior analysis model based on deep residual network (ResNet) and attention mechanism, combined with natural language processing (NLP) technology, blockchain technology, multimodal biometric technology, etc., to achieve the effects of improving the recognition accuracy of abnormal transactions, predicting transaction risks, improving transaction transparency and security, and improving the accuracy and security of user identity authentication. The following is a further detailed description of the present application. Example 1: With reference to Figure 1 and Figure 2 The present application provides an e-commerce transaction behavior risk management method, comprising the following steps: S1: Collect transaction data, including user transaction records, user comments, social media and news information; S2: Use transaction behavior analysis models based on deep residual network (ResNet) and attention mechanism to analyze transaction data and identify abnormal transaction patterns; S3: Combine natural language processing (NLP) technology, especially pre-trained models such as BERT or GPT, to perform sentiment analysis on user comments, social media and news to predict transaction risks; S4: Input the analysis results into blockchain-based smart contracts to automatically execute transaction rules and improve transaction transparency and security; S5: Use blockchain's distributed ledger technology to ensure that transaction records cannot be tampered with and enhance data security; S6: Use multimodal biometric technology, combined with facial recognition, fingerprint recognition, iris recognition and other biometric technologies to improve the accuracy and security of user identity verification. Specifically, the following instructions are given for the above steps.

[0008] 1. Collecting transaction data: This step involves collecting user transaction records, user comments, social media, and news information. Specifically, transaction data such as user purchase records, browsing records, and search records can be obtained through the log system of the e-commerce platform. For example, suppose user A has purchased 10 items, browsed 50 product pages, and searched for keywords 20 times in the past month. At the same time, crawler technology can also be used to capture external information such as user comments on social media and reports on news websites to enrich data sources. For example, suppose user A posted 10 comments on Weibo about recently purchased items, including 3 positive reviews and 7 negative reviews. 2. Transaction behavior analysis model: The transaction data is analyzed using a transaction behavior analysis model based on a deep residual network (ResNet) and an attention mechanism to identify abnormal transaction patterns. Specifically, the deep residual network (ResNet) is used to extract high-level features from transaction data, while the attention mechanism helps the model focus on important transaction features. For example, if a user's transaction frequency suddenly increases, or the transaction amount is much higher than usual, the model will give a higher weight, making it easier to identify abnormal transactions. For example, suppose user A has made 20 transactions in the past 10 days, with a total amount of 100,000 yuan, while the average transaction amount in the previous month was only 1,000 yuan. The model will consider this to be an abnormal transaction pattern. Deep Residual Network (ResNet): ResNet is a deep convolutional neural network that solves the gradient vanishing problem by introducing residual blocks, allowing the network to be deeper and thus extract richer features. In this embodiment, ResNet can extract multi-level time series features and multi-dimensional spatial features from transaction data to improve the recognition accuracy of the model. For example, ResNet can extract features such as the user's monthly transaction frequency, daily transaction amount distribution, and transaction time distribution. Specifically, the loss function of ResNet can be expressed as: \[ L = \frac{1}{N} \sum_{i=1}^{N} (y_i \hat{y}_i)^2 \] where \( y_i \) is the true label, \(\hat{y}_i\) is the model prediction value, and \( N \) is the number of samples. Attention mechanism: The attention mechanism allows the model to dynamically focus on certain key parts when processing data, thereby improving the efficiency and accuracy of the model. In this embodiment, the attention mechanism can help the model pay more attention to behavioral features that may lead to abnormal transactions when analyzing user behavior. For example, the attention mechanism can focus on behavioral features such as frequent transactions within a short period of time and sudden increases in transaction amounts.Specifically, the weight calculation formula of the attention mechanism can be expressed as: \[ \alpha_i = \frac{\exp(e_i)}{\sum_{j=1}^{N} \exp(e_j)} \] where \( e_i \) is the importance score of the \( i \)th feature and \(\alpha_i\) is the corresponding weight. 3. Natural Language Processing (NLP) Technology: Combine natural language processing (NLP) technology, especially pre-trained models such as BERT or GPT, to perform sentiment analysis on user comments, social media and news to predict transaction risks. Specifically, pre-trained models such as BERT and GPT have learned a large number of language rules and can be fine-tuned based on a small amount of labeled data to better understand and analyze user emotions and intentions. For example, suppose user A posted 10 comments on Weibo, 3 of which were positive and 7 were negative. The model can analyze that user A is not satisfied with the recently purchased goods and may have quality problems or fraud, which will be marked as a risk signal. Sentiment analysis: Through sentiment analysis, we can determine the emotional state of users, such as whether they are satisfied or dissatisfied with a product or service. For example, by performing sentiment analysis on 10 comments from user A, the model can determine that user A is less satisfied with a product, which may be due to product quality problems or poor after-sales service. Specifically, the calculation formula for sentiment analysis can be expressed as: \[ s = \text{softmax}(W^T x + b) \] where \( x \) is the input comment vector, \( W \) and \( b \) are model parameters, and \( s\) is the probability distribution of sentiment classification. Entity recognition, relationship extraction, and event detection: In addition to sentiment analysis, NLP technology can also perform entity recognition, relationship extraction, and event detection to help the system understand the market situation more comprehensively. For example, entity recognition can identify the number of times a brand or product is mentioned, relationship extraction can understand the relationship between these brands or products, and event detection can detect major changes in the market, such as new product launches and company bankruptcies. For example, suppose that brand X mentioned by user A recently released new product Y. The model can confirm the relationship between brand X and product Y through entity recognition and relationship extraction, and identify this major change through event detection. Specifically, the calculation formula for entity recognition can be expressed as: \[ p = \sigma(W^T x + b) \] where \( p \) is the probability of entity recognition and \(\sigma\) is the activation function. 4. Blockchain-based smart contracts: Input the analysis results into blockchain-based smart contracts to automatically execute transaction rules and improve transaction transparency and security.Specifically, smart contracts can pre-set a series of transaction rules and automatically perform corresponding operations when the conditions are met, such as freezing suspicious accounts and sending warning notifications. In this way, the strict implementation of transaction rules can be ensured and the uncertainty of human intervention can be reduced. For example, if the transaction behavior of user A is identified as abnormal by the model, the smart contract can automatically freeze user A's account and send a warning notification to the administrator of the e-commerce platform. Smart contract language: Smart contracts can be written in programmable smart contract languages ​​such as Solidity, combined with formal verification technology to ensure the correctness and security of the contract. Formal verification technology can prove the correctness of the contract code through mathematical methods to reduce vulnerabilities and errors. For example, if the smart contract contains a function to freeze an account, formal verification can ensure that this function can be executed correctly in all cases. Specifically, the formal verification method can be expressed as: \[ V(P) = T \] where \( P \) is the contract code, \( V \) is the verification function, and \( T \) indicates successful verification. Automatic audit function of smart contracts: Smart contracts also have automatic audit functions, which can regularly check the status and execution of contracts to ensure the continuous security and reliability of contracts. For example, smart contracts can automatically check the transaction records of all users every day to ensure that there are no abnormal transaction behaviors. Specifically, the process of automatic audit can be expressed as: \[ A(S) = R \] Where \( S \) is the contract state, \( A \) is the audit function, and \( R \) is the audit result. 5. Distributed ledger technology: Use the distributed ledger technology of blockchain to ensure the immutability of transaction records and enhance data security. Specifically, distributed ledger technology can ensure that each transaction is recorded by multiple nodes, and any attempt to tamper with the transaction record will be immediately discovered and blocked. For example, suppose that a transaction record of user A is recorded synchronously by multiple nodes, and any attempt by any node to tamper with this record will be discovered and rejected by other nodes. Consortium chain or private chain: You can choose consortium chain or private chain according to specific needs. Consortium chain is suitable for scenarios where multiple trust institutions participate together, while private chain is suitable for use within a single enterprise. Both chains can be combined with zero-knowledge proof technology to protect the privacy of sensitive transaction information. For example, suppose an e-commerce platform cooperates with other financial institutions and uses consortium chain to record transaction data to ensure data transparency and security. Specifically, the consensus algorithm of the alliance chain can be expressed as: \[ C(B) = T \] where \( B \) is the block, \( C \) is the consensus algorithm, and \( T \) indicates that the consensus is successful. Multi-party signature and threshold signature mechanism: Through the multi-party signature and threshold signature mechanism, it can be ensured that an operation can only be performed when multiple authorized parties reach a consensus, further enhancing the security and tamper-proof ability of the data.For example, suppose user A's account is frozen, and multiple administrators need to sign the unfreezing request to unfreeze the account. Specifically, the formula for multi-party signature can be expressed as: \[ S = \prod_{i=1}^{n} S_i \] where \( S_i \) is the signature of the \( i \)th administrator and \( n \) is the number of administrators. 6. Multimodal biometric technology: Combining multiple biometric technologies such as facial recognition, fingerprint recognition, and iris recognition to improve the accuracy and security of user identity authentication. Specifically, multimodal biometric technology can comprehensively utilize multiple biometric features to form a more powerful identity authentication mechanism. For example, suppose user A needs to verify his identity through facial recognition, fingerprint recognition, and iris recognition when logging into an e-commerce platform to ensure the authenticity of the user's identity. Feature fusion using deep learning models: Through deep learning models, different types of biometric data can be fused to form a comprehensive identity authentication model. This can improve the accuracy and robustness of verification and reduce misjudgments and missed judgments. For example, suppose that the facial features, fingerprint features, and iris features of user A are extracted through a deep learning model, and finally form a comprehensive identity authentication model. Specifically, the calculation formula for feature fusion can be expressed as: \[ F = f(x_1, x_2, \ldots, x_n)\] where \( x_i \) is the \( i \)th biometric feature, \( f \) is the fusion function, and \( F \) is the comprehensive feature. Federated learning technology: Federated learning technology can jointly train the model without leaking the user's original data to protect the privacy of the user's biometric data. For example, suppose that multiple e-commerce platforms jointly train a facial recognition model. Through federated learning technology, each platform can share model parameters without leaking the user's specific facial data. Specifically, the model update formula of federated learning can be expressed as: \[ w_{t+1} = w_t + \eta \sum_{i=1}^{n} \Delta w_i \] where \( w_t \) is the current model parameter, \( \eta \) is the learning rate, and \( \Delta w_i\) is the parameter update of the \( i \)th platform. The implementation principle of this embodiment is: by combining deep learning, natural language processing, blockchain and multimodal biometric technology, this method can achieve all-round risk management of e-commerce transaction behavior. The deep residual network and attention mechanism improve the recognition accuracy of abnormal transactions, natural language processing technology enhances the ability to predict market sentiment, blockchain technology ensures the transparency and security of transactions, and multimodal biometric technology improves the accuracy of user identity authentication.On the whole, this method not only improves the accuracy and real-time performance of risk management, but also enhances the security and reliability of the system, providing strong support for e-commerce platforms. Example 2: The difference between this embodiment and the above embodiment is that the application of quantum computing technology is added, as follows: 1. Quantum computing technology: On the original basis, quantum key distribution (QKD) technology and quantum random number generator are added to further improve the security of the encryption system. Quantum key distribution (QKD) technology: Quantum key distribution uses the principles of quantum mechanics to ensure the absolute security of the key. Specifically, QKD technology can establish an absolutely secure key channel between two communicating parties, and even if there is an eavesdropper in the middle, the key information cannot be obtained. In this way, it can be ensured that the transaction data will not be stolen or tampered with during the transmission process. For example, suppose that a transaction is conducted between user A and e-commerce platform B, and both parties generate a set of keys through QKD technology to ensure that the transaction data is completely confidential during the transmission process. Specifically, the key generation formula of QKD can be expressed as: \[ K =H(Q) \] Where \( Q \) is the quantum state, \( H \) is the hash function, and \( K \) is the key. Quantum random number generator: Quantum random number generator uses the principles of quantum physics to generate true random numbers. Compared with traditional pseudo-random number generators, the random numbers generated by quantum random number generators are more unpredictable and can significantly improve the security of encryption systems. For example, assuming that a new random number is generated as part of the key for each transaction, the random number generated by the quantum random number generator can ensure that the key for each transaction is unique. Specifically, the formula for quantum random number generation can be expressed as: \[ r = \text{quantum\_random\_generator}() \]Where, \( r \) is a random number. Quantum error correction code: Combined with quantum error correction code, the reliability and stability of the encryption system are further enhanced. Specifically, quantum error correction code can correct errors that may occur during transmission and ensure the integrity of data. In this way, the anti-interference ability and fault tolerance of the system can be further improved. For example, assuming that a bit flip error occurs during the transmission of transaction data, the quantum error correction code can automatically correct these errors to ensure the integrity and accuracy of the data. Specifically, the correction formula of the quantum error correction code can be expressed as: \[ D(C(E)) = M \] where \( M \) is the original message, \( E\) is the error, \( C \) is the encoding function, and \( D \) is the decoding function. The implementation principle of this embodiment is: by introducing quantum computing technology, this method not only improves the security of the encryption system, but also enhances the reliability and stability of the system.Quantum key distribution technology ensures the absolute security of the key, the random numbers generated by the quantum random number generator have higher unpredictability, and the quantum error correction code further enhances the system's anti-interference and fault tolerance capabilities. Overall, this method not only improves the accuracy and real-time performance of risk management, but also enhances the security and reliability of the system, providing more powerful technical support for the e-commerce platform. Example 3: The difference between this embodiment and the above embodiment is that the application of 5G and Internet of Things (IoT) technology is added, as follows: 1. Real-time data transmission: Real-time transaction monitoring and anomaly detection are achieved by utilizing 5G's high-speed data transmission capability and edge computing technology. Specifically, 5G technology can provide low-latency, high-bandwidth data transmission, allowing the system to obtain transaction data in real time, and perform rapid analysis and response. For example, assuming that user A conducts a large transaction on an e-commerce platform, 5G technology can ensure that the transaction data is transmitted to the backend server within a few milliseconds for real-time analysis. Edge computing technology: Edge computing technology can distribute computing tasks to edge devices close to the data source, reduce data transmission delays, and improve the real-time performance of the system. Specifically, edge computing nodes deployed near the e-commerce platform server can be used to perform preliminary data processing and analysis, and then the results can be aggregated to the central server for further in-depth analysis. For example, assume that the edge computing node can process the transaction data of user A in real time, extract key features, and then send these features to the central server for further analysis. Specifically, the processing formula of edge computing can be expressed as: \[X'= f(X) \]Where \( X \) is the original data, \( f \) is the processing function, and \( X'\) is the processed data. 2. IoT device monitoring: Collect user behavior data through IoT devices, combine transaction data for comprehensive analysis, and identify potential risks. Specifically, smart devices (such as smart speakers, smart cameras, etc.) deployed in the user's home can be used to collect the user's daily behavior data, combine transaction data for comprehensive analysis, and identify potential abnormal behaviors. For example, assume that user A has installed a smart camera at home. The camera can record user A's daily activities. Combined with transaction data, it can be identified whether user A has abnormal behavior. Device fingerprint technology: Through device fingerprint technology, each IoT device can be uniquely identified, and combined with behavioral analysis models, abnormal device behavior can be identified. Specifically, a unique device fingerprint can be generated through the device's hardware features, software features, network features and other information. Once a device's behavior is found to be abnormal, measures can be taken immediately to prevent the potential risk from expanding. For example, if the device fingerprint of user A's smart camera does not match the normal device fingerprint, the system can immediately issue an alarm to remind user A to check whether the device has been maliciously invaded.Specifically, the generation formula of the device fingerprint can be expressed as: \[ F = h(D) \] Wherein, \( D \) is the device feature, \( h \) is the hash function, and \( F\) is the device fingerprint. The implementation principle of this embodiment is: by combining 5G and Internet of Things technology, this method realizes real-time monitoring and anomaly detection of e-commerce transaction behavior. 5G technology provides low-latency, high-bandwidth data transmission capabilities, and edge computing technology reduces data transmission delays and improves the real-time performance of the system. Internet of Things devices can collect users' daily behavior data, combine transaction data for comprehensive analysis, and identify potential abnormal behaviors. Overall, this method not only improves the accuracy and real-time performance of risk management, but also enhances the security and reliability of the system, providing more comprehensive technical support for e-commerce platforms. Example 4: The difference between this embodiment and the above embodiment is that the application of personalized risk management is added, as follows: 1. User behavior analysis: Based on the user's historical transaction behavior and preferences, user portrait technology is used, combined with the recommendation system algorithm and decision tree model, to provide highly personalized risk assessment and management strategies. Specifically, user portraits can be constructed through the user's historical transaction records, search records, browsing records and other data to understand the user's consumption habits and preferences. For example, suppose user A purchased 100 items in the past year, of which 60% were electronic products, 30% were household goods, and 10% were clothing. The system can generate a user portrait of user A based on this. User portrait technology: User portrait technology can generate multi-dimensional features of users, such as age, gender, occupation, interests and hobbies, through big data analysis. Through these features, the user's risk level can be more accurately identified and personalized risk management suggestions can be provided. For example, suppose user A is a 30-year-old male engineer who likes electronic products and household goods. The system can generate a user portrait of user A based on this and provide corresponding risk management suggestions. Specifically, the generation formula of user portraits can be expressed as: \[ U= g(T, S, B) \]Where \( T \) is transaction data, \( S \) is search data, \( B \) is browsing data, \( g \) is the generation function, and \( U \) is the user portrait. Recommendation system algorithm: The recommendation system algorithm can recommend suitable products and services based on the user's consumption habits and preferences. At the same time, it can also predict future risk points based on the user's transaction behavior and provide corresponding risk management strategies. For example, if user A often buys electronic products, the system can recommend some popular electronic products and remind user A to pay attention to the return policy and after-sales service of electronic products.Specifically, the calculation formula of the recommendation system can be expressed as: \[ R = \text{recommendation\_algorithm}(U, I) \] Where \( U \) is the user portrait, \( I \) is the item database, and \( R \) is the recommendation list. Decision tree model: The decision tree model can generate a decision tree by analyzing user behavior data, helping the system to more intuitively understand the user's decision path, thereby providing more accurate risk management suggestions. For example, assuming that user A usually chooses products with moderate prices and good reviews when purchasing electronic products, the system can generate a corresponding decision tree to help user A make a more informed purchase decision. Specifically, the generation formula of the decision tree can be expressed as: \[ D = \text{decision\_tree\_algorithm}(U, T) \] Where \(U \) is the user portrait, \( T \) is the transaction data, and \( D \) is the decision tree. 2. Dynamic adjustment and real-time feedback: Support dynamic adjustment and real-time feedback to continuously optimize the effect of risk management strategies. Specifically, the risk management strategy can be adjusted dynamically by real-time monitoring of the user's transaction behavior to ensure the real-time and effectiveness of risk management. Dynamic adjustment: The system can dynamically adjust the parameters of the risk assessment model according to the user's latest transaction behavior to ensure the accuracy and timeliness of the model. For example, assuming that user A has recently frequently purchased large-value goods, the system can adjust the parameters of the risk assessment model to improve the monitoring of large-value transactions. Specifically, the formula for dynamic adjustment can be expressed as: \[ \theta_{t+1} = \theta_t+ \eta (\nabla J(\theta_t)) \]Where, \( \theta_t \) is the current parameter, \( \eta \) is the learning rate, \( \nabla J(\theta_t) \) is the gradient, and \( \theta_{t+1} \) is the updated parameter. Real-time feedback: The system can provide real-time feedback on the results of risk management to help users understand their own risk status and take timely measures to avoid potential risks. For example, if the system finds that the transaction behavior of user A is abnormal, it can immediately send a risk warning to user A to remind user A to pay attention to transaction security. Specifically, the process of real-time feedback can be expressed as: \[ F(T) = R \] where \( T \) is the transaction data, \( F \) is the feedback function, and \( R \) is the feedback result. The implementation principle of this embodiment is: by combining user profiling technology, recommendation system algorithm and decision tree model, this method realizes highly personalized risk management.User portrait technology can generate multi-dimensional features of users, the recommendation system algorithm can provide personalized services based on the user's consumption habits and preferences, and the decision tree model can generate intuitive decision paths to help the system identify risks more accurately. Overall, this method not only improves the accuracy and real-time performance of risk management, but also enhances user experience and security, providing more intimate service support for e-commerce platforms. Example 5: The difference between this embodiment and the above embodiment is that the application of cross-border e-commerce risk management is added, as follows: 1. Compliance with international regulations: For cross-border e-commerce transactions, a transaction risk management solution that complies with international regulations is developed to reduce legal risks. Specifically, the relevant regulations can be automatically identified and matched through the intelligent matching technology of regulations, combined with natural language processing. For example, assuming that user A purchases an electronic product made in the United States on a cross-border e-commerce platform, the system can automatically identify and match relevant international trade regulations to ensure that the transaction behavior complies with the requirements of laws and regulations. Intelligent matching technology of regulations: Through natural language processing technology, relevant international regulations can be automatically identified and matched to ensure that the transaction behavior complies with the requirements of laws and regulations. Specifically, key information can be extracted from a large number of regulatory documents through technologies such as keyword extraction and syntactic analysis to generate a regulatory matching report. For example, suppose the system extracts keywords such as "tariffs" and "import licenses" from international trade regulations, generates a regulatory matching report, and ensures that user A's trading behavior complies with relevant regulations. Specifically, the formula for regulatory matching can be expressed as: \[ M = \text{matching\_algorithm}(D, R) \] Where \( D \) is the regulatory document, \( R \) is the transaction data, and \( M \) is the matching report. Natural language processing: Natural language processing technology can automatically parse regulatory documents, extract key terms and regulations, and help the system understand and apply regulations more accurately. For example, suppose the system parses international trade regulations through natural language processing technology, extracts key terms, and generates a regulatory parsing report to ensure that user A's trading behavior complies with relevant regulations. Specifically, the formula for natural language processing can be expressed as: \[ P = \text{nlp\_algorithm}(D) \] Where \( D\) is the regulatory document, and \( P \) is the parsing result. 2. Multi-currency transaction processing: Design a risk control system that supports multi-currency transactions to handle the risks brought by exchange rate fluctuations. Specifically, the exchange rate prediction model can be combined with real-time exchange rate data to optimize transaction pricing strategies. For example, if user A buys an electronic product made in the United States on a cross-border e-commerce platform, the system can predict future exchange rate trends and optimize transaction pricing strategies to ensure that user A has the lowest transaction cost. Exchange rate prediction model: Through historical exchange rate data, the exchange rate prediction model can be trained to predict future exchange rate trends. Specifically, time series analysis, machine learning and other technologies can be used to generate exchange rate prediction reports.For example, assuming that the system has trained an exchange rate prediction model through historical exchange rate data and predicts that the exchange rate of the US dollar against the RMB will rise in the next week, the system can suggest that user A pay the goods as soon as possible to reduce the exchange rate risk. Specifically, the formula for exchange rate prediction can be expressed as: \[ F = \text{forecasting\_model}(H) \] Where \( H \) is the historical exchange rate data and \( F \) is the prediction result. Real-time exchange rate data: Through the real-time exchange rate data interface, the latest exchange rate information can be obtained to ensure the accuracy of transaction pricing. Specifically, the foreign exchange rates of major banks can be obtained in real time through the API interface, and the optimal transaction pricing strategy can be generated in combination with the exchange rate prediction model. For example, assuming that the system obtains the latest exchange rate of the US dollar against the RMB through the API interface, and generates the optimal transaction pricing strategy in combination with the exchange rate prediction model to ensure that the transaction cost of user A is the lowest. Specifically, the formula for obtaining real-time exchange rate data can be expressed as: \[ R = \text{api\_request}(URL) \] Where \( URL \) is the API interface address and \( R \) is the real-time exchange rate data. The implementation principle of this embodiment is: by combining international regulatory compliance and multi-currency transaction processing, this method realizes effective risk management of cross-border e-commerce transactions. Intelligent matching technology of regulations can ensure that transaction behaviors comply with the requirements of international regulations, and natural language processing technology can automatically parse regulatory documents and extract key terms. The exchange rate prediction model can predict future exchange rate trends, and real-time exchange rate data can ensure the accuracy of transaction pricing. On the whole, this method not only improves the accuracy and real-time nature of risk management, but also reduces legal risks and exchange rate risks, and provides more comprehensive support for cross-border e-commerce transactions. The above are all preferred embodiments of this application, and the scope of protection of this application is not limited thereto. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for managing the risk of e-commerce transaction behavior, characterized in that: The following steps are involved: S1: Collect transaction data, including user transaction records, user comments, social media and news information; S2: Analyze transaction data and identify abnormal transaction patterns using a transaction behavior analysis model based on a deep residual network (ResNet) and an attention mechanism; S3: Combine natural language processing (NLP) technology, especially pre-trained models such as BERT or GPT, to perform sentiment analysis on user comments, social media, and news to predict transaction risks; S4: Input the analysis results into the blockchain-based smart contract to automatically execute transaction rules and improve transaction transparency and security; S5: Utilize the distributed ledger technology of blockchain to ensure the immutability of transaction records and enhance data security; S6: Adopt multimodal biometric technology, combining facial recognition, fingerprint recognition, iris recognition and other biometric technologies to improve the accuracy and security of user identity authentication.

2. The e-commerce transaction behavior risk management method according to claim 1, characterized in that: The transaction behavior analysis model uses a deep learning model to perform multi-level feature extraction and complex pattern recognition, combined with time series analysis and multi-dimensional data analysis, to significantly improve the recognition accuracy of abnormal transactions; in addition, the model also uses transfer learning technology to extract common features from existing large-scale transaction data sets, further improving the generalization ability and robustness of the model.

3. The e-commerce transaction behavior risk management method according to claim 1, characterized in that: The natural language processing technology not only includes sentiment analysis of user comments, social media and news, but also entity recognition, relationship extraction and event detection of text, comprehensively predicting changes in market sentiment and thus providing early warning of trading risks; and the technology is also fine-tuned in combination with domain-specific corpora to improve the accuracy and relevance of the analysis.

4. The e-commerce transaction behavior risk management method according to claim 1, characterized in that: The blockchain-based smart contract uses a programmable smart contract language such as Solidity, combined with formal verification technology to ensure the correctness and security of the contract, and further improves the security and reliability of the contract through the automatic audit function of the smart contract; in addition, the smart contract also supports dynamic updates and version control to ensure the flexibility and maintainability of the contract.

5. The e-commerce transaction behavior risk management method according to claim 1, characterized in that: The distributed ledger technology adopts a consortium chain or a private chain, combined with zero-knowledge proof technology, to protect the privacy of sensitive transaction information, and further enhances the security and tamper-proof capabilities of the data through multi-party signature and threshold signature mechanisms; in addition, the technology also supports cross-chain interoperability to achieve data exchange and sharing between different blockchain platforms.

6. The e-commerce transaction behavior risk management method according to claim 1, characterized in that: The multimodal biometric technology uses a deep learning model to perform feature fusion, combined with federated learning technology, to protect the privacy of user biometric data, and through a multiple verification mechanism for biometric data, to further improve the accuracy and security of user identity authentication; moreover, the technology also supports real-time updates and online learning, and continuously optimizes the performance of the biometric model.

7. The e-commerce transaction behavior risk management method according to claim 1, characterized in that: The quantum computing technology specifically includes quantum key distribution (QKD) technology and quantum random number generator, which significantly improves the security of the encryption system through the high unpredictability of the quantum random number generator and the high security of quantum key distribution; and the technology is also combined with quantum error correction codes to further enhance the reliability and stability of the encryption system.

8. The e-commerce transaction behavior risk management method according to claim 1, characterized in that: The real-time data transmission utilizes the high-speed data transmission capability and edge computing technology of 5G, combined with a real-time data stream processing framework, to reduce data transmission delays and improve the real-time performance and response speed of the system; moreover, the technology also supports multi-source data fusion to achieve comprehensive analysis and processing of data from different sources.

9. The e-commerce transaction behavior risk management method according to claim 1, characterized in that: The personalized risk management is based on the user's historical transaction behavior and preferences, adopts user profiling technology, combines recommendation system algorithm and decision tree model, provides highly personalized risk assessment and management strategies, and significantly optimizes user experience and security; moreover, the technology also supports dynamic adjustment and real-time feedback to continuously optimize the effectiveness of risk management strategies.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the e-commerce transaction behavior risk management method according to any one of claims 1 to 9 are implemented.

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