Transaction platform management system based on cloud service

Abstract

The invention discloses a transaction platform management system based on cloud service, and relates to the technical field of cloud computing, and the system comprises the following components: a user behavior analysis module which collects the browsing track, purchase record and evaluation information of a user through an e-commerce platform, collects the attribute data and sales data of a commodity, and sends the collected data to a server; the user behavior analysis module accurately extracts key features of user activeness and commodity popularity to provide powerful support for personalized recommendation, the intelligent recommendation module generates a personalized recommendation list based on real-time behavior data and interests of the user, and the intelligent customer service module realizes personalized recommendation through natural language processing and machine learning technologies. According to the system, the user problem is quickly and accurately responded, the customer service efficiency and the user satisfaction degree are remarkably improved, the predictive maintenance module identifies potential risks and abnormities in advance through real-time data analysis, an early warning and solution scheme is provided for an administrator, and the system failure rate and the maintenance cost are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing, and in particular to a transaction platform management system based on cloud services. Background Art

[0002] Traditional trading platform management systems have limitations in resource management and scalability. As the business scale expands and the number of users increases, the amount of data and transaction volume that needs to be processed increases dramatically, which puts higher requirements on resource management and scalability. Traditional systems often find it difficult to respond to these changes quickly, resulting in reduced system performance and impaired user experience.

[0003] Secondly, traditional trading platform management systems are insufficient in data processing and analysis capabilities. Against the backdrop of the rapid development of big data and artificial intelligence technologies, they often lack sufficient data processing and analysis capabilities and are unable to effectively support the realization of these functions.

[0004] In addition, traditional trading platform management systems also have certain problems in terms of security and reliability. With the increasing frequency and complexity of network attacks, ensuring the security of user information and transaction data has become an important task of trading platform management systems. They often lack effective security protection measures and are vulnerable to malicious attacks and data leaks.

[0005] Finally, traditional trading platform management systems also have certain difficulties in maintenance and upgrading. With the continuous development and updating of technology, continuous maintenance and upgrading are required to maintain its competitiveness and stability, making the maintenance and upgrading process complicated and difficult.

[0006] Therefore, a trading platform management system based on cloud services is developed to solve the above problems, user experience and security. Summary of the invention

[0007] The purpose of the present invention is to make up for the deficiencies of the existing technology and provide a trading platform management system based on cloud services. It can significantly improve the operational efficiency, user experience and security of the trading platform by integrating advanced user behavior analysis, intelligent recommendation, intelligent customer service and predictive maintenance technology, overcome the deficiencies of traditional trading management systems in resource management, data processing, security and maintenance, and achieve rapid deployment, elastic scaling and efficient operation, while reducing operating costs and improving the availability and security of the system.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a trading platform management system based on cloud services, the system includes the following components:

[0009] User behavior analysis module: collects users' browsing tracks, purchase records and evaluation information through e-commerce platforms, collects product attribute data and sales data, cleans and standardizes the data, extracts key features of user activity, product popularity and evaluation sentiment from the original data, builds personalized user portraits based on users' basic information and behavior data, clusters products using hierarchical clustering algorithms, discovers product categories with similar behaviors and attributes, and uses time series analysis to predict users' future purchase frequency and purchase preferences;

[0010] Intelligent recommendation module: using the data and analysis results provided by the user behavior analysis module, comprehensively considering the user's personal preferences and needs, as well as the influence of other factors, to generate a personalized product recommendation list for the user;

[0011] Intelligent customer service module: automatically understands users' questions and needs through natural language processing, provides accurate answers and solutions, reduces user waiting time, and improves user satisfaction;

[0012] Predictive maintenance module: Use machine learning algorithms to analyze collected data in real time, identify potential risks and anomalies, and provide administrators with early warnings and solutions to ensure stable operation of the system;

[0013] Cloud service support: The entire system is built on a cloud service architecture. Through the powerful computing and storage capabilities of cloud service providers, it can process massive amounts of user data, support high-concurrency access, and provide flexible configuration and deployment options, allowing users to adjust and optimize according to actual needs.

[0014] Furthermore, the hierarchical clustering algorithm is used to cluster the commodities, collect the attribute data and sales data of the commodities, convert the commodity information into a vector form, and use the Euclidean distance formula to calculate the distance between the commodities. The Euclidean distance formula is: Where: p and q are vectors of two products, n is the number of features, p i and q i are the i-th eigenvalues ​​of p and q respectively. Each product is regarded as a separate cluster. The distances between all cluster pairs are calculated. The two closest clusters are selected to merge. The merged cluster will inherit all its data points. The merging process is repeated until the distance between clusters exceeds a certain threshold. The characteristics of the products in each cluster are analyzed to understand their similarities and differences. The products are grouped according to the clustering results.

[0015] Furthermore, the ARIMA model is used to analyze and predict the user's future purchase frequency and purchase preference, extract the characteristics of the user's purchase frequency, purchase amount, and purchase time interval from the user's purchase record, divide the data into a training set and a validation set, use the ADF test to check whether the data is stable, if not, perform a difference on the data to make it stable, observe the autocorrelation diagram (ACF) and partial autocorrelation diagram (PACF) of the data to determine the parameters of p and q, and the parameter of d is determined according to the stability of the data after difference, and use historical data to train the ARIMA model. The mathematical expression of the ARIMA model is: φ(B) d (1-B) p X t =θ(B)∈ t , where: X t is the user's purchase frequency, B is the backward shift operator, (B k X t =X t-k ), φ(B) and θ(B) are polynomials about B, representing the autoregressive coefficient and the moving average coefficient, ∈ t is a white noise sequence, and the root mean square error is calculated using the test set to evaluate the performance of the model. The formula for the root mean square error is: Where: y i is the true value, is the predicted value, n is the number of samples. If the model performance is poor, adjust the parameters (p, d, q) of the ARIMA model to optimize the model, and use the trained ARIMA model to predict the user's future purchase frequency.

[0016] Furthermore, the intelligent recommendation module obtains the user's behavior data from the user behavior analysis module, obtains the user's personal information, collects the feature data of the items, extracts the features related to the recommendation from the user's behavior data, extracts the key features from the item information data, converts these features into numerical data, and recommends items similar to the user's interests based on the content-based recommendation model. The model is trained using historical data to optimize the model's parameters to improve the accuracy of the recommendation. A personalized recommendation list is generated based on the user's real-time behavior data and features in combination with the trained recommendation model, and the recommendation results are dynamically adjusted taking into account the user's real-time feedback and behavior changes.

[0017] Furthermore, the content-based recommendation model recommends items similar to the user's interests to the user, analyzes the user's historical behavior and interests, represents the user as a series of feature vectors, represents the key features of the item as a vector, and uses cosine similarity to calculate the similarity between the user feature vector and the item feature vector. The mathematical expression of cosine similarity is: in: is the user feature vector, is the key feature vector of the item, represents the dot product of two vectors, Represent the modulus of the user feature vector and item feature vector respectively. Based on the similarity calculation results, the most similar items to the user's interests are recommended to the user.

[0018] Furthermore, the intelligent customer service module collects questions and demands input by users in text, voice and other forms, performs word segmentation, stop word removal, and part-of-speech tagging for text input, uses speech recognition technology to convert speech into text for voice input, uses naive Bayes to identify the intention of user input, designs dialogue processes, uses state machines to manage dialogue states, builds and maintains knowledge bases, uses machine learning algorithms to train question-and-answer systems, uses user intentions and entities as query conditions, searches for relevant answers and solutions in the knowledge base, generates answers and solutions based on the query results, outputs the generated answers to users in the form of text, voice and other forms, receives user feedback on the answers, and uses user feedback to optimize models and update training data.

[0019] Furthermore, the method of using naive Bayes to identify the intent of user input collects user input data, labels the data with corresponding intent, preprocesses the collected data, converts the preprocessed text into a numerical feature vector through a bag-of-words model, and constructs a naive Bayes classifier. For each intent category C k , calculate its frequency of occurrence in the training data as the prior probability P(C k ), the prior probability formula is: For each feature x i and for each intent category C k , calculate the conditional probability p(x i |C k ), the conditional probability formula is: For a new user input text, first preprocess and extract features to obtain its feature vector, and use the naive Bayes formula to calculate the posterior probability of the text belonging to each intent category. The posterior probability formula is: Where: x is the feature vector of the text, n is the number of features, C j are all possible intent categories, and the intent category with the largest posterior probability is selected as the prediction result.

[0020] Furthermore, the predictive maintenance module collects real-time and historical sensor readings, operation logs, and maintenance records related to the operation process, preprocesses and standardizes the collected data, performs feature engineering on the data, extracts meaningful features, selects logistic regression to identify potential risks and anomalies, integrates the trained model into the predictive maintenance system, collects new data in real time, and uses the model for prediction. The model predicts potential risks and anomalies and sends an alert to the administrator. Based on the type and severity of the alert, the administrator is provided with solutions and suggestions, and the new data and alert results are fed back to the system for updating the model and adjusting the prediction strategy.

[0021] Furthermore, the selection of logistic regression to identify potential risks and anomalies assumes that the existence of risk and anomaly is a binomial distributed random variable, whose value is only 0 no risk and 1 risk. The model calculates the linear combination of input features and applies the sigmoid function to estimate the probability that the target variable takes the value of 1. The linear combination expression is: z = θ0 + θ1x1 + θ2x2 + ... + θ n x n =θ T X, where: θ is the parameter vector of the model, X is the sensor data, and the sigmoid function maps the result of the linear combination z to the (0, 1) interval, indicating the probability of predicting risk. The sigmoid function expression is: According to the output value of the sigmoid function To determine whether the system has potential risks and anomalies, the output value formula of the sigmoid function is: when No risk, There are risks.

[0022] Furthermore, the cloud service supports the use of the infrastructure resources of the cloud service provider to quickly deploy the computing, storage and network resources required for the trading platform management system, automatically adjust the resource scale according to the system load and business needs, realize elastic scaling and pay-as-you-go, use the backup and recovery functions of the cloud service to regularly back up system data and applications, and be able to quickly restore them when needed, improve the system's availability and fault tolerance by configuring load balancing and fault tolerance strategies, and the system can still operate normally in the event of a single point of failure, and use the security protection measures provided by the cloud service to protect the system from malicious attacks and data leakage risks.

[0023] Compared with the existing technology, the trading platform management system based on cloud service has the following beneficial effects:

[0024] 1. The present invention provides strong support for personalized recommendations through the user behavior analysis module by accurately extracting key features of user activity and commodity popularity. The intelligent recommendation module generates a personalized recommendation list based on the user's real-time behavior data and interests. The intelligent customer service module responds to user questions quickly and accurately through natural language processing and machine learning technology, significantly improving customer service efficiency and user satisfaction. The predictive maintenance module identifies potential risks and anomalies in advance through real-time data analysis, provides early warnings and solutions to administrators, and effectively reduces system failure rates and maintenance costs.

[0025] 2. The present invention supports rapid deployment and elastic scaling through cloud services, and can automatically adjust the scale of resources according to business needs, ensuring that the system remains efficient under high load, while realizing pay-as-you-go payment and reducing operating costs. The backup and recovery functions of cloud services ensure the security and reliability of system data and applications, and can be quickly restored even in the event of an unexpected situation, ensuring business continuity. The load balancing and fault-tolerant strategies provided by cloud services further improve the availability and fault-tolerance of the system, effectively reducing the risk of single point failures. The security protection measures provided by cloud services can effectively resist the risks of malicious attacks and data leakage, and ensure the security of user information and transaction data.

[0026] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 This is a flow chart of a trading platform management system based on cloud services. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Embodiment 1:

[0031] This embodiment provides a trading platform management system based on cloud services. The system significantly improves the operational efficiency, user experience and security of the trading platform by integrating modules such as user behavior analysis, intelligent recommendation, intelligent customer service and predictive maintenance. The advantages of rapid deployment, elastic scaling, data security and load balancing brought by cloud services enable the system to flexibly respond to business changes, ensure the security and reliability of data, and reduce operating costs. Through intelligent analysis and recommendation technology, it improves user satisfaction and purchase conversion rate, injecting new vitality into the development of the trading platform.

[0032] The user behavior analysis module collects users' browsing tracks, purchase records and evaluation information through the e-commerce platform, collects commodity attribute data (such as price, brand, category) and sales data (such as sales volume, sales amount), cleans duplicate values, missing values ​​and outliers in the data, and standardizes the data to ensure that the scales between different features are consistent for subsequent analysis. The commodity information is converted into a vector form, where each element represents a feature (such as price, sales volume, evaluation score). The distance between commodities is calculated using the Euclidean distance formula, which is: Treat each product as a separate cluster, calculate the distance between all cluster pairs, select the two closest clusters to merge, and the merged cluster will inherit all its data points. Repeat the merging process until the distance between clusters exceeds a preset threshold. Analyze the characteristics of the products in each cluster, understand their similarities and differences, and group the products according to the clustering results to facilitate subsequent recommendations and marketing strategy formulation. Extract the key features of purchase frequency, purchase amount, and purchase time interval from user purchase records, divide the data into training set and validation set, use ADF test to check whether the data is stable, if not, perform differential processing, observe the autocorrelation diagram (ACF) and partial autocorrelation diagram (PACF) of the data to determine the p, d, and q parameters of the ARIMA model. The mathematical expression of the ARIMA model is: φ(B) d (1-B) p X t =θ(B)∈ t , use the training set data to train the ARIMA model, and use the test set to calculate the root mean square error to evaluate the performance of the model. The formula for the root mean square error is: If the performance is not good, adjust the parameters (p, d, q) of the ARIMA model for optimization, and use the trained ARIMA model to predict the user's future purchase frequency.

[0033] The intelligent recommendation module obtains user behavior data from the user behavior analysis module, such as browsing, clicking, purchasing, and rating, obtains and collects user personal information, such as age, gender, and geographic location, and collects feature data of items, such as description, label, classification, and price. It extracts recommendation-related features from user behavior data, such as the type of item, price range, and brand preferred by the user, and extracts key features from item information data, such as the category of the item, keywords in the description, price, and rating. The extracted features are converted into numerical data for easy calculation, and cosine similarity is used to calculate the similarity between the user feature vector and the item feature vector. The mathematical expression of cosine similarity is: Sort items by similarity, select items most similar to the user for recommendation, use historical data to train the recommendation model, optimize the model parameters, evaluate the performance of the model through cross-validation, and adjust the model parameters to improve the accuracy of the recommendation. Based on the user's real-time behavior data and characteristics, combined with the trained recommendation model, generate a personalized recommendation list, consider the user's real-time feedback and behavior changes, and dynamically adjust the recommendation results.

[0034] The intelligent customer service module collects questions and needs entered by users in text, voice and other forms, performs intent annotation on the collected data, removes stop words, punctuation marks, and special characters in the text, and performs part-of-speech tagging on the results after word segmentation, which helps with subsequent feature extraction. The preprocessed text is converted into a bag-of-words model representation, and naive Bayes is used to identify the intent of user input. The frequency of each intent category in the training data is counted as the prior probability of the intent category. The prior probability formula is: For each word (feature) and each intent category, calculate the conditional probability of the word appearing in a given intent category. The conditional probability formula is: Use the labeled training data, as well as the calculated prior probability and conditional probability, to train the naive Bayes classifier. For a new user input text, after preprocessing and feature extraction, use the naive Bayes formula to calculate the posterior probability that the text belongs to each intent category. The posterior probability formula is: Select the intent category with the largest posterior probability as the prediction result, design the dialogue process based on the identified user intent, and use the state machine to manage the dialogue state. Use the key information extracted by user intent and named entity recognition technology as query conditions, find relevant answers and solutions in the knowledge base, generate answers and solutions based on the query results, receive user feedback on the answers, and use user feedback to optimize the model and update the training data to improve the performance and accuracy of the system.

[0035] The predictive maintenance module collects relevant data such as real-time and historical sensor readings, operation logs, and maintenance records from various devices and systems, cleans the collected raw data, removes duplicate, erroneous, and irrelevant data, and standardizes the data to ensure that features of different dimensions and units have the same weight in the model. It extracts meaningful features related to the equipment's operating status, performance degradation, and failure occurrence. It assumes that the existence of risk and anomaly is a binomial distributed random variable with only 0 (no risk) and 1 (risk). A logistic regression model is used to calculate the linear combination of the input features. The linear combination expression is: z = θ0 + θ1x1 + θ2x2 + … + θ n x n =θ T X, and apply the sigmoid function to estimate the probability that the target variable takes the value of 1. The sigmoid function expression is: Integrate the trained logistic regression model into the predictive maintenance system, collect new sensor data and other relevant information in real time, input the real-time data into the logistic regression model, and the model calculates the probability of risk and anomaly for each data point and outputs the probability. The output value formula of the sigmoid function is: when No risk and There are risks. When the model predicts potential risks and anomalies, an early warning is sent to the administrator. Based on the type and severity of the warning, the administrator is provided with solutions and suggestions, and new data and warning results are fed back into the system to update the model and adjust the prediction strategy.

[0036] Cloud service support: select a reliable cloud service provider and evaluate its service scope, performance, and cost factors according to business needs. According to the business needs and functions of the trading platform management system, plan the overall architecture of the system, including front-end applications, back-end services, databases, and cache components. Use the cloud service provider's virtual machines to deploy the computing resources of the trading platform management system. Use the cloud service provider's object storage service to store system data and applications. Configure virtual private networks (VPCs) and subnets to ensure network isolation and security between internal components of the system. Configure automatic scaling policies based on the system load and business needs. Bill according to the actual amount of computing, storage, and network resources used to avoid resource waste and unnecessary cost expenditures. Use the backup and recovery functions of the cloud service provider to regularly back up system data and applications, and quickly restore data and applications when data is lost or the system fails. Configure a load balancer to distribute user requests to multiple back-end servers to achieve load balancing and failover. Configure network security groups and firewall rules to limit access to system resources. Use the automated operation and maintenance tools and services provided by the cloud service provider to simplify system operation and maintenance work, such as automated deployment, configuration management, and log collection.

[0037] Embodiment 2:

[0038] This embodiment provides a transaction platform management system based on cloud services, with particular emphasis on the intelligent recommendation module:

[0039] The user behavior analysis module obtains the user's browsing history, purchase history, and click behavior, obtains the user's registration information and historical purchase preferences, collects product attribute data, sales data, and user reviews, and cleans, deduplicates, and standardizes the collected data to ensure data quality.

[0040] Extract recommendation-related features from user behavior data, such as the categories of products browsed by users, purchase frequency, and evaluation tendencies; extract key features from item information data, such as product description, price, brand, and evaluation; and convert the extracted features into numerical data to facilitate subsequent model processing.

[0041] A user feature vector is constructed based on the product category, price range, and evaluation tendency purchased by the user. An item feature vector is constructed using the product text description, price, and brand. The cosine similarity algorithm is used to calculate the similarity between the user feature vector and the item feature vector. Cosine similarity evaluates the similarity of two vectors by calculating the cosine value of the angle between them. The closer the value is to 1, the higher the similarity.

[0042] Based on the similarity calculation results, we recommend items that are most similar to the user's interests, and dynamically adjust the recommendation results based on the user's real-time behavior data and characteristics.

[0043] Use historical data to train content-based recommendation models, optimize model parameters, improve recommendation accuracy, collect real-time feedback and behavior changes from users, and continuously optimize and update the model to adapt to changes in user interests.

[0044] Embodiment three:

[0045] Based on the first and second embodiments, the specific process steps of the system are as follows:

[0046] 1. Select a cloud service provider and deploy infrastructure, and configure system security policies;

[0047] 2. Collect user behavior data and product data, clean and standardize the data, extract key features, build personalized user portraits, use clustering algorithms to cluster products, and use ARIMA models to predict user purchasing behavior;

[0048] 3. Extract the features of users and items, train the recommendation model and generate a recommendation list, and optimize the recommendation results based on user feedback;

[0049] 4. Collect user questions and process them, identify user intent and generate answers, and use user feedback to optimize the customer service system;

[0050] 5. Collect equipment and system data, train models to predict potential risks, send warnings and provide solutions;

[0051] 6. Monitor system performance, adjust resource scale according to demand, back up data regularly and ensure system security.

[0052] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A cloud-based trading platform management system, characterized in that: The system includes: User behavior analysis module: collects users' browsing tracks, purchase records and evaluation information through e-commerce platforms, collects product attribute data and sales data, cleans and standardizes the data, extracts key features of user activity, product popularity and evaluation sentiment from the original data, builds personalized user portraits based on users' basic information and behavior data, clusters products using hierarchical clustering algorithms, discovers product categories with similar behaviors and attributes, and uses time series analysis to predict users' future purchase frequency and purchase preferences; Intelligent recommendation module: using the data and analysis results provided by the user behavior analysis module, comprehensively considering the user's personal preferences and needs, as well as the influence of other factors, to generate a personalized product recommendation list for the user; Intelligent customer service module: automatically understands users' questions and needs through natural language processing, provides accurate answers and solutions, reduces user waiting time, and improves user satisfaction; Predictive maintenance module: Use machine learning algorithms to analyze collected data in real time, identify potential risks and anomalies, and provide administrators with early warnings and solutions to ensure stable operation of the system; Cloud service support: The entire system is built on a cloud service architecture. Through the powerful computing and storage capabilities of cloud service providers, it can process massive amounts of user data, support high-concurrency access, and provide flexible configuration and deployment options, allowing users to adjust and optimize according to actual needs.

2. The cloud service-based transaction platform management system according to claim 1, characterized in that: The hierarchical clustering algorithm is used to cluster the commodities, collect the attribute data and sales data of the commodities, convert the commodity information into a vector form, and use the Euclidean distance formula to calculate the distance between the commodities. The Euclidean distance formula is: Where: p and q are vectors of two products, n is the number of features, p i and q i are the i-th eigenvalues ​​of p and q respectively. Each product is regarded as a separate cluster. The distances between all cluster pairs are calculated. The two closest clusters are selected to merge. The merged cluster will inherit all its data points. The merging process is repeated until the distance between clusters exceeds a certain threshold. The characteristics of the products in each cluster are analyzed to understand their similarities and differences. The products are grouped according to the clustering results.

3. The cloud service-based transaction platform management system according to claim 1, characterized in that: The ARIMA model is used to analyze and predict the user's future purchase frequency and purchase preference, and the characteristics of the user's purchase frequency, purchase amount, and purchase time interval are extracted from the user's purchase record. The data is divided into a training set and a validation set. The ADF test is used to check whether the data is stable. If it is not stable, the data is differentiated to make the data stable. The autocorrelation diagram (ACF) and partial autocorrelation diagram (PACF) of the data are observed to determine the parameters of p and q. The parameter of d is determined according to the stability of the data after differentiation. The ARIMA model is trained using historical data. The mathematical expression of the ARIMA model is: φ(B) d (1-B) p X t =θ(B)∈ t , where: X t is the user's purchase frequency, B is the backward shift operator, (B k X t =X t-k ), φ(B) and θ(B) are polynomials about B, representing the autoregressive coefficient and the moving average coefficient, ∈ t is a white noise sequence, and the root mean square error is calculated using the test set to evaluate the performance of the model. The formula for the root mean square error is: Where: y i is the true value, is the predicted value, n is the number of samples. If the model performance is poor, adjust the parameters (p, d, q) of the ARIMA model to optimize the model, and use the trained ARIMA model to predict the user's future purchase frequency.

4. The cloud service-based transaction platform management system according to claim 1, characterized in that: The intelligent recommendation module obtains the user's behavior data from the user behavior analysis module, obtains the user's personal information, collects the feature data of the items, extracts the features related to the recommendation from the user's behavior data, extracts the key features from the item information data, converts these features into numerical data, and recommends items similar to the user's interests based on the content-based recommendation model. The model is trained using historical data to optimize the model's parameters and improve the accuracy of the recommendation. A personalized recommendation list is generated based on the user's real-time behavior data and features in combination with the trained recommendation model, and the recommendation results are dynamically adjusted taking into account the user's real-time feedback and behavior changes.

5. The cloud service-based transaction platform management system according to claim 4, characterized in that: The content-based recommendation model recommends items similar to the user's interests to the user, analyzes the user's historical behavior and interests, represents the user as a series of feature vectors, and also represents the key features of the item as a vector. The cosine similarity is used to calculate the similarity between the user feature vector and the item feature vector. The mathematical expression of cosine similarity is: in: is the user feature vector, is the key feature vector of the item, represents the dot product of two vectors, Represent the modulus of the user feature vector and item feature vector respectively. Based on the similarity calculation results, the most similar items to the user's interests are recommended to the user.

6. The cloud service-based transaction platform management system according to claim 1, characterized in that: The intelligent customer service module collects questions and needs input by users in text, voice and other forms. For text input, it performs word segmentation, removes stop words, and performs part-of-speech tagging. For voice input, it uses speech recognition technology to convert speech into text, uses naive Bayes to identify the intention of user input, designs dialogue processes, uses state machines to manage dialogue states, builds and maintains knowledge bases, uses machine learning algorithms to train question-and-answer systems, uses user intentions and entities as query conditions, searches for relevant answers and solutions in the knowledge base, generates answers and solutions based on the query results, outputs the generated answers to users in text, voice and other forms, receives user feedback on the answers, and uses user feedback to optimize models and update training data.

7. The cloud service-based transaction platform management system according to claim 6, characterized in that: The method of using naive Bayes to identify the intention of user input collects user input data, labels the data with corresponding intentions, preprocesses the collected data, converts the preprocessed text into a numerical feature vector through a bag-of-words model, and constructs a naive Bayes classifier. For each intention category C k , calculate its frequency of occurrence in the training data as the prior probability P(C k ), the prior probability formula is: For each feature x i and for each intent category C k , calculate the conditional probability p(x i |C k ), the conditional probability formula is: For a new user input text, first preprocess and extract features to obtain its feature vector, and use the naive Bayes formula to calculate the posterior probability of the text belonging to each intent category. The posterior probability formula is: Where: x is the feature vector of the text, n is the number of features, C j are all possible intent categories, and the intent category with the largest posterior probability is selected as the prediction result.

8. The cloud service-based transaction platform management system according to claim 1, characterized in that: The predictive maintenance module collects real-time and historical sensor readings, operation logs, and maintenance records related to the operation process, preprocesses and standardizes the collected data, performs feature engineering on the data, extracts meaningful features, selects logistic regression to identify potential risks and anomalies, integrates the trained model into the predictive maintenance system, collects new data in real time, and uses the model for prediction. The model predicts potential risks and anomalies and sends an early warning to the administrator. Based on the type and severity of the early warning, the administrator is provided with solutions and suggestions, and the new data and early warning results are fed back to the system for updating the model and adjusting the prediction strategy.

9. The cloud service-based transaction platform management system according to claim 1, characterized in that: The logistic regression is selected to identify potential risks and anomalies. The logistic regression model assumes that the existence of risk and anomaly is a binomial distributed random variable, whose value is only 0 no risk and 1 risk. The model calculates the linear combination of input features and applies the sigmoid function to estimate the probability that the target variable takes the value of 1. The linear combination expression is: z = θ0 + θ1x1 + θ2x2 + ... + θ n x n =θ T X, where: θ is the parameter vector of the model, X is the sensor data, and the sigmoid function maps the result of the linear combination z to the (0, 1) interval, indicating the probability of predicting risk. The sigmoid function expression is: According to the output value of the sigmoid function To determine whether the system has potential risks and anomalies, the output value formula of the sigmoid function is: when No risk, There are risks.

10. The cloud service-based transaction platform management system according to claim 9, characterized in that: The cloud service supports the use of the cloud service provider's infrastructure resources to quickly deploy the computing, storage and network resources required for the trading platform management system. It automatically adjusts the resource scale according to the system load and business needs to achieve elastic scaling and pay-as-you-go. It uses the backup and recovery functions of the cloud service to regularly back up system data and applications and quickly restore them when needed. It improves the system's availability and fault tolerance by configuring load balancing and fault tolerance strategies. The system can still operate normally in the event of a single point of failure. It uses the security protection measures provided by the cloud service to protect the system from malicious attacks and data leakage risks.

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