Information consultation service system and method based on cloud computing platform

Abstract

The invention discloses an information consultation service system and method based on a cloud computing platform, and the method comprises the steps: accessing a user request through a plurality of channels, analyzing multi-modal data through a natural language processing technology, and carrying out the analysis in combination with the historical data of a user; data formats are unified through data cleaning and standardization tools, the data formats are stored in a cloud computing platform, and user requirements are understood through knowledge maps and semantic analysis; providing related consultation contents based on the user behavior data by using deep learning and machine learning recommendation algorithms; a / B testing and performance analysis are carried out regularly, and the system and the model are optimized; safety is guaranteed by adopting TLS and AES encryption and multi-factor authentication, the performance is monitored in real time, the response type design is adapted to different devices, and user feedback is collected to improve the experience; according to the information consultation service system and method based on the cloud computing platform, the accuracy and adaptability of the AI model are improved through reinforcement learning and deep learning technologies, and more accurate consultation services are provided for users.

Description

Technical Field

[0001] The present invention belongs to the field of information consulting services on a cloud computing platform, and in particular relates to an information consulting service system and method based on a cloud computing platform. Background Art

[0002] Information consulting services based on cloud computing platforms use the resources and services provided by cloud computing technology to provide users with information query, consulting, advice, data analysis and other related services. This type of service usually relies on the elastic computing power, storage capacity and powerful data processing capabilities of the cloud computing platform, which can respond to user needs in real time and provide personalized consulting content. The core advantages of cloud computing platforms, such as pay-as-you-go, elastic expansion and high availability, make information consulting services more efficient, convenient and economical. Cloud computing platforms provide computing, storage, network, database and other resources. These resources can be dynamically allocated according to demand and support highly scalable and efficient data processing capabilities. Common cloud computing platforms include Amazon Web Services (AWS), Microsoft Azure, Google Cloud, etc.; information consulting services refer to providing users with information query, decision support, problem solving and other services through the platform. Including but not limited to data query, intelligent recommendation, real-time consultation, analytical reports, etc.; based on the cloud platform, information consulting services can dynamically allocate resources according to user needs. For example, when users query a large amount of data, the cloud platform will automatically expand computing power and storage space; the use of artificial intelligence technology (such as natural language processing, machine learning, big data analysis, etc.) enables information consulting services to provide users with personalized suggestions and accurate answers; cloud platforms usually have high reliability and fault tolerance, ensuring that information consulting services run uninterruptedly and avoiding service downtime or data loss; users do not need to invest in a large amount of hardware facilities in advance, but pay on demand, which reduces the cost of use and can flexibly expand resources as demand grows; users can Users can make consultation requests through web pages, apps and other interfaces, such as querying the answer to a question, requesting professional advice, etc. After receiving the request, the cloud platform will select appropriate computing resources for processing based on the service type. If the request involves data query, the cloud platform will retrieve relevant data from the database or data warehouse. If the request requires intelligent analysis, the cloud platform will use AI models (such as natural language processing and machine learning) to automatically analyze and answer. The cloud platform will feed back the processed results to the user through the interface, which may be an instant answer, an analysis report, or a recommended next step. As more users use the system, the platform will continue to collect data, optimize service quality through machine learning models, and improve accuracy and response speed.

[0003] However, although information consulting services based on cloud computing platforms have achieved remarkable success in many fields and provided users with efficient, flexible and intelligent services, and although cloud platforms can provide intelligent consulting services based on artificial intelligence (AI) and machine learning (ML) technologies, current intelligent consulting services still face problems such as low algorithm accuracy and insufficient personalization. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide an information consulting service system and method based on a cloud computing platform, which improves the accuracy and adaptability of the AI ​​model through reinforcement learning, deep learning and other technologies, uses user behavior data and historical records to optimize the personalized recommendation algorithm, and provides users with more accurate consulting services.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] An information consulting service system based on a cloud computing platform, comprising:

[0007] System architecture module, used for adopting a cloud computing platform with elastic scalability and multi-region deployment, the cloud computing platform is one of AWS, Azure, Google Cloud or a combination thereof, adopting a microservice architecture to split the information consulting service into multiple independent service modules, and adopting containers Docker and Kubernetes for container orchestration;

[0008] Security design module for full-link encryption, multi-factor authentication for all users and administrators, and high availability and disaster recovery through cross-region deployment and data backup;

[0009] Service availability and stability module, based on Kubernetes and cloud-native architecture, the system automatically expands or shrinks according to the load, uses Prometheus and Grafana tools to monitor the health of services, identifies failures and automatically alarms, deploys data centers across multiple regions, and uses load balancers to automatically distribute traffic;

[0010] The intelligent and personalized module uses a natural language processing model to analyze users' consulting questions and generate answers. It uses machine learning to make personalized consulting recommendations based on user behavior data, historical consulting records, and user preferences.

[0011] Data management and governance module, which uses automated data cleaning tools to deduplicate, format, and clean data before storage, integrates multiple data sources, stores raw data in data lakes, uses data warehouses for data analysis and report generation, and uses Apache Kafka and Apache Flink tools to process real-time data streams;

[0012] Network optimization and performance module, which is used to deploy globally distributed content delivery network CDN nodes, accelerate the distribution of data and content, and use the intelligent load balancing service provided by the cloud platform to dynamically optimize traffic distribution;

[0013] Cost optimization and management module, which is used to automatically adjust the allocation of computing and storage resources according to load conditions and forecast analysis. The core module adopts database, message queue, and NLP model open source technologies, and adopts the on-demand billing model of the cloud computing platform. Through multi-cloud strategies, it optimizes resource allocation between different cloud platforms;

[0014] The user experience and interface design module is used to set up a simple and intuitive front-end user interface, using intelligent search functions based on keywords entered by users to automatically generate relevant questions and answers;

[0015] The continuous update and iteration module is used to adopt agile development methods, continuously optimize functions and fix problems through rapid iteration and continuous integration. By analyzing user usage behavior and feedback, potential problems or improvement opportunities in the system are identified and resolved in each version update. At the same time, the underlying framework and model of the system are regularly upgraded.

[0016] An information consulting service method based on a cloud computing platform, comprising:

[0017] Access user requests through multiple channels such as the Web, mobile applications, voice assistants, and chatbots, and use natural language processing technology to parse user input, which includes multimodal data such as text, voice, and images. Combined with the user's historical consultation records, click streams, and search behavior data, the machine learning algorithm is used to analyze the user's interests, preferences, and potential needs;

[0018] Obtain user request data from multiple channels. Before the data enters the system, use an automated data cleaning process and data standardization tools to convert data from different channels into a unified format. The data is then stored in a distributed storage system on a cloud computing platform. Based on machine learning and NLP technology, a knowledge graph is constructed to understand the specific needs of users through semantic analysis.

[0019] Through deep learning models, intelligent chatbots are built, and machine learning recommendation algorithms are used to recommend relevant consulting content or solutions to users based on user historical behavior data and interest preferences.

[0020] Through continuous training and optimization of AI models, regular A / B testing is conducted to test the effectiveness of different consulting service strategies, evaluate the impact of each solution on user experience and service efficiency, and conduct in-depth analysis through system logs and performance data to identify potential system bottlenecks or abnormal situations;

[0021] Adopt industry-standard TLS and AES encryption technologies, provide multi-factor authentication in the system's management background and key parts of user accounts, and conduct regular security audits;

[0022] Use cloud platform monitoring tools to track system performance in real time and detect potential performance issues, including excessive latency and service downtime. Use responsive design to ensure that services adapt to different screen sizes and device types. After user consultation, send users a satisfaction survey or feedback channel.

[0023] Preferably, the user request is accessed through multiple channels such as the Web, mobile applications, voice assistants, and chatbots, and the user input is analyzed using natural language processing technology. The input includes multimodal data such as text, voice, and images. The user's historical consultation records, click streams, and search behavior data are combined, and the method of analyzing the user's interests, preferences, and potential needs through a machine learning algorithm is as follows:

[0024] Receive user requests through multiple channels such as the Web, mobile apps, voice assistants, and chatbots. User requests include data in the form of text, voice, and images.

[0025] Use natural language processing technology to parse the data input by users. For text input, use word segmentation and semantic analysis technology. For voice input, use voice recognition technology to convert voice into text. For picture input, use image recognition technology to extract key information from the picture.

[0026] Combine the user's historical consultation records, click streams, and search behavior data information to conduct a comprehensive analysis of the user's behavior. Use machine learning algorithms to model the user's historical data and analyze the user's interests, preferences, and potential needs.

[0027] Based on collaborative filtering, classification algorithms or clustering algorithms, identify users' preferences on certain topics, questions or services, and predict the content that users are interested in.

[0028] As a preferred method, user request data is obtained based on multiple channels. Before the data enters the system, an automated data cleaning process is adopted, and data standardization tools are used to convert data from different channels into a unified format, and the data is stored in a distributed storage system of a cloud computing platform. Based on machine learning and NLP technology, a knowledge graph is constructed, and the method of understanding the specific needs of users through semantic analysis is as follows:

[0029] Access user request data through Web, mobile applications, voice assistants, and chatbot channels. Before the data enters the system, it is pre-processed through an automated data cleaning process. The pre-processing includes removing irrelevant data, processing missing values, removing noise data, and marking abnormal data.

[0030] Use data standardization tools to unify the formats of data with different formats and structures from different channels, and store the cleaned and standardized data in a distributed database through the distributed storage system of the cloud computing platform;

[0031] Based on the stored data, a knowledge graph is constructed through machine learning and natural language processing (NLP) technology. The knowledge graph organizes and integrates the entities, relationships, and attributes in the data in a graph structure.

[0032] Use semantic analysis technology, combined with knowledge graphs and machine learning models, to deeply analyze and understand users' specific needs;

[0033] Through machine learning models and constructed knowledge graphs, the system accurately matches user needs based on the results of semantic analysis.

[0034] As a preferred method, an intelligent chatbot is built through a deep learning model, and a machine learning recommendation algorithm is used to recommend relevant consulting content or solutions to users in combination with user historical behavior data and interest preferences:

[0035] Use deep learning models for natural language understanding to parse text, voice, or other forms of requests input by users. Use sequence-to-sequence models or dialogue strategies based on reinforcement learning to manage and generate dialogues with users. Based on deep generative models, generate natural language responses related to user needs.

[0036] Adopt machine learning collaborative filtering, matrix decomposition, and deep learning recommendation network algorithms, combined with users' historical click records, search records, consultation history data, and interest preferences, to predict the content or solutions that users are interested in;

[0037] By collecting and analyzing the user's historical behavior data, combined with the user's geographic location, age, and occupational tags, a user profile is established;

[0038] Based on deep learning models and machine learning recommendation algorithms, the intelligent chatbot recommends relevant consulting content, question answers or solutions to users. Through multiple rounds of conversations and real-time feedback, the system continuously learns user feedback and optimizes the recommendation algorithm.

[0039] As a preferred method, we continuously train and optimize AI models, conduct regular A / B testing to test the effectiveness of different consulting service strategies, evaluate the impact of each solution on user experience and service efficiency, and conduct in-depth analysis through system logs and performance data to identify potential system bottlenecks or abnormal situations:

[0040] By continuously collecting user interaction data and system operation data, the data feedback mechanism continuously optimizes the AI ​​model, and adopts incremental learning, transfer learning or reinforcement learning methods to regularly retrain the AI ​​model;

[0041] Compare the impact of automatic replies and manual intervention, or different recommendation algorithm consulting service strategies on user experience and service efficiency based on the planned test objectives and assumptions;

[0042] Randomly distribute user traffic to two or more test groups, with each group using a different consulting service strategy or optimization solution. Based on the results of the A / B test, measure the performance of different solutions.

[0043] Evaluate the impact of different strategies on user experience through user satisfaction surveys, click-through rates, conversion rates, and user retention rate indicators. Evaluate key service efficiency indicators including response time, processing time, and automated processing ratio. Evaluate the optimization effect of different strategies on system efficiency by comparing with benchmark data or historical data.

[0044] Through the log framework and tools, detailed system log information is collected, including user request logs, service response logs, and error logs. Through log analysis, common user problems, bottlenecks, and system load conditions are identified. Prometheus and Grafana performance monitoring tools are used to track system health in real time.

[0045] Through log analysis and performance data, anomaly detection algorithms are used to identify potential system bottlenecks or abnormal situations. Combined with system monitoring data and log data, performance bottlenecks in the system are identified. By analyzing abnormal logs, performance data, and user behavior, the root causes of system anomalies are deeply explored. Based on the bottleneck analysis results, database queries are performed on the system, load balancing strategies are adjusted, and cache mechanism optimization is added.

[0046] As a preferred method, the industry-standard TLS and AES encryption technologies are used to provide multi-factor authentication in the system's management background and key parts of user accounts, and regular security audits are performed as follows:

[0047] Use the latest version of TLS protocol to encrypt communication data between the client and the server, use SSL / TLS certificates issued by a trusted certificate authority, update certificates regularly, and configure the server with strong encryption algorithms such as RSA, ECDSA, and AEAD, and disable weak encryption suites;

[0048] When storing sensitive information such as user passwords, personal information, and payment information, use AES encryption technology to encrypt data, adopt key management measures, and use hardware security modules or key management services to generate, store, and manage encryption keys;

[0049] In the key parts of the system's management backend and user accounts, knowledge factor, possession factor, and biometric factor authentication are used to strengthen identity authentication;

[0050] Through centralized log management tools, system logs are collected and analyzed to identify abnormal behaviors, including abnormal logins and access to sensitive operations. Automated tools are used to perform regular vulnerability scans to check whether the system has known security vulnerabilities and configuration errors. Penetration tests are also performed regularly to simulate attacker behavior to test the system's protection capabilities.

[0051] As a preferred option, use cloud platform monitoring tools to track system performance in real time and detect potential performance issues, such as excessive latency and service downtime. Use responsive design to ensure that services adapt to different screen sizes and device types. After the user consultation is completed, send a satisfaction survey or feedback channel to the user as follows:

[0052] Use the monitoring tools provided by the cloud platform to monitor and manage system performance in real time, and adopt cloud monitoring services, performance monitoring, latency detection, downtime detection and automatic recovery, log and exception monitoring measures to identify and discover potential performance problems;

[0053] Through CSS3 media queries and flexible layout methods, web pages automatically adjust the display effect according to the user's mobile phone, tablet, and desktop computer devices. The design plan includes:

[0054] Use relative units instead of fixed pixel units to achieve flexible layout;

[0055] Optimize images and media files to load corresponding resources according to device screen size and resolution;

[0056] Adopt a mobile-first design strategy, starting with small-screen devices and gradually expanding functions and layouts to larger screen devices;

[0057] After the user consultation or interaction, send a satisfaction survey or feedback channel to the user to collect user opinions and suggestions through automated feedback requests, satisfaction surveys, real-time feedback channels, NPS surveys, integrated feedback platforms, and incentive implementation methods.

[0058] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, an information consulting service system and method based on a cloud computing platform as described above is implemented.

[0059] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, an information consulting service system and method based on a cloud computing platform is implemented.

[0060] The beneficial effects of the present invention are:

[0061] By accessing user requests through multiple channels, including the Web, mobile applications, and chatbots, the system provides flexible and convenient consultation methods to meet the needs of different users. It supports text, voice, and image input, which improves parsing capabilities and user-friendliness. It uses machine learning to analyze user behavior and preferences, provide personalized content recommendations, and improve user satisfaction. It builds a knowledge graph based on natural language processing, and understands needs more accurately through semantic analysis to improve efficiency. It uses intelligent chatbots to automatically respond to user requests and reduce the burden on manual customer service.

[0062] Conduct A / B testing regularly, optimize service strategies based on user feedback, and improve experience. Clean and standardize data before the system to ensure format consistency and improve data quality. Use cloud computing distributed storage to ensure high data availability and security, and use elastic computing to respond to a large number of user requests. Monitor system performance to detect problems in a timely manner, use encryption technology to ensure user data security, conduct regular security audits to discover vulnerabilities, and enhance system reliability. Obtain user feedback through satisfaction surveys, and use responsive design to ensure that services adapt to different devices, reduce infrastructure costs, and improve operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The present invention is a flowchart of an information consulting service system based on a cloud computing platform. DETAILED DESCRIPTION

[0064] The principles and features of the present invention are described below, and the examples are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described more specifically by way of example in the following paragraphs. According to the following description and claims, the advantages and features of the present invention will become clearer.

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0066] Example

[0067] An information consulting service system based on a cloud computing platform, comprising:

[0068] System architecture module, used for adopting a cloud computing platform with elastic scalability and multi-region deployment, the cloud computing platform is one of AWS, Azure, Google Cloud or a combination thereof, adopting a microservice architecture to split the information consulting service into multiple independent service modules, and adopting containers Docker and Kubernetes for container orchestration;

[0069] Security design module for full-link encryption, multi-factor authentication for all users and administrators, and high availability and disaster recovery through cross-region deployment and data backup;

[0070] Service availability and stability module, based on Kubernetes and cloud-native architecture, the system automatically expands or shrinks according to the load, uses Prometheus and Grafana tools to monitor the health of services, identifies failures and automatically alarms, deploys data centers across multiple regions, and uses load balancers to automatically distribute traffic;

[0071] The intelligent and personalized module uses a natural language processing model to analyze users' consulting questions and generate answers. It uses machine learning to make personalized consulting recommendations based on user behavior data, historical consulting records, and user preferences.

[0072] Data management and governance module, which uses automated data cleaning tools to deduplicate, format, and clean data before storage, integrates multiple data sources, stores raw data in data lakes, uses data warehouses for data analysis and report generation, and uses Apache Kafka and Apache Flink tools to process real-time data streams;

[0073] Network optimization and performance module, which is used to deploy globally distributed content delivery network CDN nodes, accelerate the distribution of data and content, and use the intelligent load balancing service provided by the cloud platform to dynamically optimize traffic distribution;

[0074] Cost optimization and management module, which is used to automatically adjust the allocation of computing and storage resources according to load conditions and forecast analysis. The core module adopts database, message queue, and NLP model open source technologies, and adopts the on-demand billing model of the cloud computing platform. Through multi-cloud strategies, it optimizes resource allocation between different cloud platforms;

[0075] The user experience and interface design module is used to set up a simple and intuitive front-end user interface, using intelligent search functions based on keywords entered by users to automatically generate relevant questions and answers;

[0076] The continuous update and iteration module is used to adopt agile development methods, continuously optimize functions and fix problems through rapid iteration and continuous integration. By analyzing user usage behavior and feedback, potential problems or improvement opportunities in the system are identified and resolved in each version update. At the same time, the underlying framework and model of the system are regularly upgraded.

[0077] The system is based on cloud computing platforms (such as AWS, Azure, and Google Cloud), and takes advantage of its elastic scaling and multi-regional deployment to dynamically adapt to different loads and demands. The information consulting service is divided into multiple independent service modules, each of which can be independently developed, deployed, expanded, and maintained, increasing the flexibility and maintainability of the system. Containers (such as Docker) provide a high degree of portability and isolation, while Kubernetes is responsible for the automated deployment, expansion, and management of containers to ensure efficient operation of the system. The system can automatically expand or shrink resources according to traffic fluctuations to avoid resource waste or overload. Through multi-regional deployment, it is guaranteed that when a system fails in one area, other areas can take over to ensure the continuous availability of services. Encryption technology (such as TLS, SSL, etc.) is used to protect the security of data during transmission to prevent data leakage or tampering. Users and administrators need to go through multiple authentication steps (such as passwords, SMS verification codes, fingerprint recognition, etc.) when logging into the system to improve the security of the system. Through data backup across multiple geographic regions, data can be ensured to be available in the event of a catastrophic event. Recovery; encryption and multi-factor authentication during data transmission effectively prevent malicious attacks and illegal access, and cross-regional data backup ensures that the system can recover quickly in the event of a disaster, minimizing the risk of data loss and service interruption; the system can automatically expand or reduce resources according to the load situation to ensure the stability of services under high concurrency. Prometheus and Grafana tools are used to monitor the health status of the system in real time, detect problems in a timely manner and handle them through automatic alarm mechanisms. The system is deployed across multiple data centers and uses load balancers to dynamically allocate traffic to improve the system's fault tolerance and stability. Load balancing and automatic expansion ensure that the system remains stable under high load or traffic fluctuations, avoiding downtime or excessive delays. Through automatic alarms and cross-regional backups, faults can be quickly identified and restored, reducing business interruption time; NLP technology is used to parse user consulting questions and generate accurate answers to provide intelligent customer support. Combined with user historical behavior data and preference information, machine learning algorithms are used to recommend relevant consulting content to improve user experience.

[0078] An information consulting service method based on a cloud computing platform, comprising:

[0079] Access user requests through multiple channels such as the Web, mobile applications, voice assistants, and chatbots, and use natural language processing technology to parse user input, which includes multimodal data such as text, voice, and images. Combined with the user's historical consultation records, click streams, and search behavior data, the machine learning algorithm is used to analyze the user's interests, preferences, and potential needs;

[0080] Obtain user request data from multiple channels. Before the data enters the system, use an automated data cleaning process and data standardization tools to convert data from different channels into a unified format. The data is then stored in a distributed storage system on a cloud computing platform. Based on machine learning and NLP technology, a knowledge graph is constructed to understand the specific needs of users through semantic analysis.

[0081] Through deep learning models, intelligent chatbots are built, and machine learning recommendation algorithms are used to recommend relevant consulting content or solutions to users based on user historical behavior data and interest preferences.

[0082] Through continuous training and optimization of AI models, regular A / B testing is conducted to test the effectiveness of different consulting service strategies, evaluate the impact of each solution on user experience and service efficiency, and conduct in-depth analysis through system logs and performance data to identify potential system bottlenecks or abnormal situations;

[0083] Adopt industry-standard TLS and AES encryption technologies, provide multi-factor authentication in the system's management background and key parts of user accounts, and conduct regular security audits;

[0084] Use cloud platform monitoring tools to track system performance in real time and detect potential performance issues, including excessive latency and service downtime. Use responsive design to ensure that services adapt to different screen sizes and device types. After user consultation, send users a satisfaction survey or feedback channel.

[0085] Access user requests through multiple channels such as the Web, mobile applications, voice assistants, and chatbots. Support multimodal data such as text, voice, and images. Use natural language processing technology (NLP) to parse user input, understand user needs, and extract keywords through semantic analysis to generate responses. Support multiple input methods such as text, voice, and images. Users can choose the most convenient way to consult and improve the interactive experience. Through NLP technology, the system can accurately understand the diverse content of user input and provide services that are closer to user needs. Combined with user historical consultation records, click streams, search behaviors and other data, the system uses machine learning algorithms to analyze user interests, preferences, and potential needs. Based on user interests and historical behaviors, the system can intelligently recommend relevant consulting content or solutions to improve service personalization. By analyzing user historical behaviors, the system can accurately predict and recommend content or solutions that users are interested in, enhance user stickiness and satisfaction, customize consulting content according to user needs, reduce user information search time, and improve user service efficiency. Before data enters the system, use an automated data cleaning process to remove invalid data, remove duplicates, and format. Use data standardization tools to convert data from different channels into a standard format to ensure data consistency and completeness. The integrity of the data is stored in the distributed storage system of the cloud computing platform to ensure the high availability and scalability of the data. Through cleaning and standardization, the quality of the input data is guaranteed, data redundancy and errors are reduced, and the accuracy of subsequent data analysis is improved. Through the distributed storage of the cloud platform, the system is ensured to be able to handle large-scale data and support efficient access and expansion of data. The knowledge graph is constructed using machine learning and NLP technology, and the specific needs of users are understood through semantic analysis. The knowledge graph enhances the system's ability to understand user problems and provide more accurate responses by representing entities, relationships, events and other information. Through semantic analysis, the system can more accurately grasp the specific problems and backgrounds of users and avoid misunderstandings based on traditional keyword matching. The construction of the knowledge graph enables the system to have higher knowledge processing capabilities and can respond to complex consulting requests. The intelligent chatbot is constructed using a deep learning model, which can handle more complex conversations and generate more natural responses. Combined with the user's historical behavior and interest preferences, the machine learning recommendation algorithm provides users with more personalized consulting content and solutions. The deep learning model enables the chatbot to communicate with users more naturally and smoothly, improving the user experience. The recommendation algorithm based on historical behavior and preferences can improve user satisfaction and consulting efficiency.Through continuous training and optimization of artificial intelligence models, regular A / B testing is conducted to compare the effects of different consulting service strategies. By analyzing the results of A / B testing, the impact of different solutions on user experience and service efficiency is evaluated, and system performance is continuously improved. Combined with system logs and performance data, system bottlenecks or abnormal situations are identified and targeted optimization is performed. A / B testing and continuous training enable the system to be continuously optimized based on feedback and data to improve service quality. Through log and performance analysis, potential problems can be discovered and resolved in real time to ensure that the system always maintains optimal performance; TLS and AES encryption technologies are used to ensure the security of data transmission and storage in the system to prevent data leakage or tampering. Multi-factor authentication is used to enhance security in key parts of the system management background and user accounts. Regular security audits are conducted to ensure that the system complies with industry standards and best practices. Encryption technology and multi-factor authentication are used to ensure the security of user data and sensitive information. Regular security audits ensure that the system always meets security standards , timely discover and repair potential security vulnerabilities; use cloud platform monitoring tools to track system performance in real time, monitor key indicators such as latency, response time and system load, and take prompt measures to solve potential performance problems (such as excessive latency, service downtime, etc.) when the system detects potential performance problems. Responsive design ensures that services can adapt to different screen sizes and device types to improve user experience. The system can promptly discover performance bottlenecks and failures, respond quickly, and avoid affecting user experience. Responsive design allows users to have a good experience whether using PC, mobile phone or tablet devices. After the user consultation, the system collects user feedback through satisfaction surveys or feedback channels to understand the user's evaluation of the service. Based on user feedback, the service process, recommendation strategy and system functions are continuously optimized and adjusted. Through real-time feedback, the user's pain points and needs can be quickly identified, targeted improvements can be made, and service quality can be continuously improved. User feedback can directly affect system improvements and enhance user participation and loyalty. ;

[0086] The method of accessing user requests through multiple channels such as the Web, mobile applications, voice assistants, and chatbots, and using natural language processing technology to parse user input, which includes multimodal data such as text, voice, and images, combined with the user's historical consultation records, click streams, and search behavior data, and using machine learning algorithms to analyze the user's interests, preferences, and potential needs is as follows:

[0087] Receive user requests through multiple channels such as the Web, mobile apps, voice assistants, and chatbots. User requests include data in the form of text, voice, and images.

[0088] Use natural language processing technology to parse the data input by users. For text input, use word segmentation and semantic analysis technology. For voice input, use voice recognition technology to convert voice into text. For picture input, use image recognition technology to extract key information from the picture.

[0089] Combine the user's historical consultation records, click streams, and search behavior data information to conduct a comprehensive analysis of the user's behavior. Use machine learning algorithms to model the user's historical data and analyze the user's interests, preferences, and potential needs.

[0090] Based on collaborative filtering, classification algorithms or clustering algorithms, identify users' preferences on certain topics, questions or services, and predict the content that users are interested in.

[0091] Use word segmentation and semantic analysis technology to understand user intentions; use voice recognition technology to convert voice into text to improve the accuracy of voice interaction; use image recognition technology to extract key information from images to further understand user needs; combine historical consultation records, click streams, search behaviors and other data to analyze user interests and behavior patterns, use machine learning algorithms to predict users' potential needs and interests through analysis and modeling, and improve the accuracy of predictions; based on collaborative filtering, classification algorithms or clustering algorithms, identify users' preferences for specific topics or services, and predict their content of interest based on their historical behavior to provide personalized recommendation services; support multiple input methods such as text, voice, and pictures to improve user experience and interaction flexibility; combine historical data with real-time input to accurately identify user needs and enhance service personalization; through intelligent recommendations and behavior analysis, users can quickly obtain relevant information or services, reduce search time, and improve satisfaction; based on machine learning and algorithm optimization, continuously improve the accuracy of recommendations and the intelligence level of the system.

[0092] Based on multiple channels to obtain user request data, before the data enters the system, an automated data cleaning process is adopted, and data standardization tools are used to convert data from different channels into a unified format, and then stored in the distributed storage system of the cloud computing platform. Based on machine learning and NLP technology, a knowledge graph is constructed, and the method of understanding the specific needs of users through semantic analysis is as follows:

[0093] Access user request data through Web, mobile applications, voice assistants, and chatbot channels. Before the data enters the system, it is pre-processed through an automated data cleaning process. The pre-processing includes removing irrelevant data, processing missing values, removing noise data, and marking abnormal data.

[0094] Use data standardization tools to unify the formats of data with different formats and structures from different channels, and store the cleaned and standardized data in a distributed database through the distributed storage system of the cloud computing platform;

[0095] Based on the stored data, a knowledge graph is constructed through machine learning and natural language processing (NLP) technology. The knowledge graph organizes and integrates the entities, relationships, and attributes in the data in a graph structure.

[0096] Use semantic analysis technology, combined with knowledge graphs and machine learning models, to deeply analyze and understand users' specific needs;

[0097] Through machine learning models and constructed knowledge graphs, the system accurately matches user needs based on the results of semantic analysis.

[0098] Data cleaning and standardization ensure the quality of input data and avoid problems caused by junk data and inconsistent formats. The automated preprocessing process reduces manual intervention and improves the processing efficiency of the system. The distributed storage system of the cloud computing platform can store massive amounts of data and provide high availability and scalability, supporting concurrent processing of large-scale user requests. Distributed storage ensures efficient access to data in different geographical locations and improves the performance and fault tolerance of the system. Based on NLP and semantic analysis, the system can understand the user's true intentions, especially can process multimodal inputs such as text, voice and pictures, improving the accuracy of the user's interactive experience. The introduction of knowledge graphs not only helps to organize and store information, but also enhances semantic understanding and improves response to complex needs. Capabilities; by building a knowledge graph and combining it with a machine learning model, the system can provide accurate services based on the user's historical behavior, real-time input, and interest preferences. The knowledge graph can achieve more personalized recommendations and customized services, such as intelligent recommendations, automatic question answering, etc.; the machine learning model can be continuously updated and trained with the data of user interactions, and gradually optimize the recommendation accuracy and demand matching capabilities. The system can gradually improve the user experience and response quality through continuous learning and optimization; by supporting multiple channels (Web, mobile applications, voice assistants, chatbots, etc.), the system can receive and process user requests across platforms, and achieve seamless connection and service across all channels. Whether it is text input, voice request or image recognition, it can be processed uniformly and provide a consistent user experience.

[0099] The method of building an intelligent chatbot through a deep learning model, using a machine learning recommendation algorithm, and combining user historical behavior data and interest preferences to recommend relevant consulting content or solutions to users is as follows:

[0100] Use deep learning models for natural language understanding to parse text, voice, or other forms of requests input by users. Use sequence-to-sequence models or dialogue strategies based on reinforcement learning to manage and generate dialogues with users. Based on deep generative models, generate natural language responses related to user needs.

[0101] Adopt machine learning collaborative filtering, matrix decomposition, and deep learning recommendation network algorithms, combined with users' historical click records, search records, consultation history data, and interest preferences, to predict the content or solutions that users are interested in;

[0102] By collecting and analyzing the user's historical behavior data, combined with the user's geographic location, age, and occupational tags, a user profile is established;

[0103] Based on deep learning models and machine learning recommendation algorithms, the intelligent chatbot recommends relevant consulting content, question answers or solutions to users. Through multiple rounds of conversations and real-time feedback, the system continuously learns user feedback and optimizes the recommendation algorithm.

[0104] Through deep learning models and recommendation algorithms, the system can deeply analyze users' interests and behaviors and provide each user with personalized consulting content and solutions. Accurate recommendations can improve user satisfaction, enhance user stickiness, and enable users to quickly find the information or services they need, reducing information overload. Deep generative models and natural language understanding technology enable chatbots to have more natural and smooth conversations, avoiding the rigidity of traditional rule-based conversations. Users can get responses that are more in line with their needs, improving user experience. Through multiple rounds of conversations, chatbots can dynamically adjust the content of conversations based on context, making communication smoother. The real-time feedback mechanism enables the system to dynamically adjust the recommendation strategy based on user behavior data. For example, if a recommended content is ignored multiple times, the system can learn this preference and avoid recommending similar content again. As user behavior data changes, the system can With continuous accumulation, the recommendation algorithm can be continuously optimized, making the recommendation results more accurate and gradually adapting to changes in user needs; the system supports multiple input methods such as text and voice, and can flexibly respond according to different input forms, which improves the user's interactive experience. Users can interact with the robot through multiple channels such as chat and voice assistant, which enhances the usability of the system; by continuously providing personalized and valuable recommendations and solutions, the intelligent chatbot can effectively improve user participation and retention rate. The system can continuously attract users to participate in new conversations and consultations, forming a virtuous circle and enhancing users' dependence on the system; the system can be expanded as demand changes, whether it is adding new users, accessing new data sources, or supporting more channels, it can be flexibly adjusted within the original framework, and the accuracy and service quality of the system can be continuously improved through continuous training and optimization of deep learning models.

[0105] By continuously training and optimizing AI models, conducting regular A / B tests to test the effectiveness of different consulting service strategies, evaluating the impact of each solution on user experience and service efficiency, and conducting in-depth analysis through system logs and performance data, the methods to identify potential system bottlenecks or abnormal situations are as follows:

[0106] By continuously collecting user interaction data and system operation data, the data feedback mechanism continuously optimizes the AI ​​model, and adopts incremental learning, transfer learning or reinforcement learning methods to regularly retrain the AI ​​model;

[0107] Compare the impact of automatic replies and manual intervention, or different recommendation algorithm consulting service strategies on user experience and service efficiency based on the planned test objectives and assumptions;

[0108] Randomly distribute user traffic to two or more test groups, with each group using a different consulting service strategy or optimization solution. Based on the results of the A / B test, measure the performance of different solutions.

[0109] Evaluate the impact of different strategies on user experience through user satisfaction surveys, click-through rates, conversion rates, and user retention rate indicators. Evaluate key service efficiency indicators including response time, processing time, and automated processing ratio. Evaluate the optimization effect of different strategies on system efficiency by comparing with benchmark data or historical data.

[0110] Through the log framework and tools, detailed system log information is collected, including user request logs, service response logs, and error logs. Through log analysis, common user problems, bottlenecks, and system load conditions are identified. Prometheus and Grafana performance monitoring tools are used to track system health in real time.

[0111] Through log analysis and performance data, anomaly detection algorithms are used to identify potential system bottlenecks or abnormal situations. Combined with system monitoring data and log data, performance bottlenecks in the system are identified. By analyzing abnormal logs, performance data, and user behavior, the root causes of system anomalies are deeply explored. Based on the bottleneck analysis results, database queries are performed on the system, load balancing strategies are adjusted, and cache mechanism optimization is added.

[0112] The method of using industry-standard TLS and AES encryption technologies, providing multi-factor authentication in the system's management background and key parts of user accounts, and conducting regular security audits is as follows:

[0113] Use the latest version of TLS protocol to encrypt communication data between the client and the server, use SSL / TLS certificates issued by a trusted certificate authority, update certificates regularly, and configure the server with strong encryption algorithms such as RSA, ECDSA, and AEAD, and disable weak encryption suites;

[0114] When storing sensitive information such as user passwords, personal information, and payment information, use AES encryption technology to encrypt data, adopt key management measures, and use hardware security modules or key management services to generate, store, and manage encryption keys;

[0115] In the key parts of the system's management backend and user accounts, knowledge factor, possession factor, and biometric factor authentication are used to strengthen identity authentication;

[0116] Through centralized log management tools, system logs are collected and analyzed to identify abnormal behaviors, including abnormal logins and access to sensitive operations. Automated tools are used to perform regular vulnerability scans to check whether the system has known security vulnerabilities and configuration errors. Penetration tests are also performed regularly to simulate attacker behavior to test the system's protection capabilities.

[0117] Use cloud platform monitoring tools to track system performance in real time and detect potential performance issues, including high latency and service downtime. Use responsive design to ensure that services adapt to different screen sizes and device types. After the user consultation, send a satisfaction survey or feedback channel to the user as follows:

[0118] Use the monitoring tools provided by the cloud platform to monitor and manage system performance in real time, and adopt cloud monitoring services, performance monitoring, latency detection, downtime detection and automatic recovery, log and exception monitoring measures to identify and discover potential performance problems;

[0119] Through CSS3 media queries and flexible layout methods, web pages automatically adjust the display effect according to the user's mobile phone, tablet, and desktop computer devices. The design plan includes:

[0120] Use relative units instead of fixed pixel units to achieve flexible layout;

[0121] Optimize images and media files to load corresponding resources according to device screen size and resolution;

[0122] Adopt a mobile-first design strategy, starting with small-screen devices and gradually expanding functions and layouts to larger screen devices;

[0123] After the user consultation or interaction, send a satisfaction survey or feedback channel to the user to collect user opinions and suggestions through automated feedback requests, satisfaction surveys, real-time feedback channels, NPS surveys, integrated feedback platforms, and incentive implementation methods.

[0124] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an information consulting service system and method based on a cloud computing platform as described above is implemented.

[0125] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the information consulting service system and method based on the cloud computing platform as described above are implemented.

[0126] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0127] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0128] The above embodiments of the present invention are not intended to limit the protection scope of the present invention, and the implementation modes of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, should fall within the protection scope of the present invention.

Claims

1. An information consulting service system based on a cloud computing platform, characterized in that: Included are: System architecture module, used for adopting a cloud computing platform with elastic scalability and multi-region deployment, the cloud computing platform is one of AWS, Azure, Google Cloud or a combination thereof, adopting a microservice architecture to split the information consulting service into multiple independent service modules, and adopting containers Docker and Kubernetes for container orchestration; Security design module for full-link encryption, multi-factor authentication for all users and administrators, and high availability and disaster recovery through cross-region deployment and data backup; Service availability and stability module, based on Kubernetes and cloud-native architecture, the system automatically expands or shrinks according to the load, uses Prometheus and Grafana tools to monitor the health of services, identifies failures and automatically alarms, deploys data centers across multiple regions, and uses load balancers to automatically distribute traffic; The intelligent and personalized module uses a natural language processing model to analyze users' consulting questions and generate answers. It uses machine learning to make personalized consulting recommendations based on user behavior data, historical consulting records, and user preferences. Data management and governance module, which uses automated data cleaning tools to deduplicate, format, and clean data before storage, integrates multiple data sources, stores raw data in data lakes, uses data warehouses for data analysis and report generation, and uses Apache Kafka and Apache Flink tools to process real-time data streams; Network optimization and performance module, which is used to deploy globally distributed content delivery network CDN nodes, accelerate the distribution of data and content, and use the intelligent load balancing service provided by the cloud platform to dynamically optimize traffic distribution; Cost optimization and management module, which is used to automatically adjust the allocation of computing and storage resources according to load conditions and forecast analysis. The core module adopts database, message queue, and NLP model open source technologies, and adopts the on-demand billing model of the cloud computing platform. Through multi-cloud strategies, it optimizes resource allocation between different cloud platforms; The user experience and interface design module is used to set up a simple and intuitive front-end user interface, using intelligent search functions based on keywords entered by users to automatically generate relevant questions and answers; The continuous update and iteration module is used to adopt agile development methods, continuously optimize functions and fix problems through rapid iteration and continuous integration. By analyzing user usage behavior and feedback, potential problems or improvement opportunities in the system are identified and resolved in each version update. At the same time, the underlying framework and model of the system are regularly upgraded.

2. An information consulting service method based on a cloud computing platform, characterized in that: Included are: Access user requests through multiple channels such as the Web, mobile applications, voice assistants, and chatbots, and use natural language processing technology to parse user input, which includes multimodal data such as text, voice, and images. Combined with the user's historical consultation records, click streams, and search behavior data, the machine learning algorithm is used to analyze the user's interests, preferences, and potential needs; Obtain user request data from multiple channels. Before the data enters the system, use an automated data cleaning process and data standardization tools to convert data from different channels into a unified format. The data is then stored in a distributed storage system on a cloud computing platform. Based on machine learning and NLP technology, a knowledge graph is constructed to understand the specific needs of users through semantic analysis. Through deep learning models, intelligent chatbots are built, and machine learning recommendation algorithms are used to recommend relevant consulting content or solutions to users based on user historical behavior data and interest preferences. Through continuous training and optimization of AI models, regular A / B testing is conducted to test the effectiveness of different consulting service strategies, evaluate the impact of each solution on user experience and service efficiency, and conduct in-depth analysis through system logs and performance data to identify potential system bottlenecks or abnormal situations; Adopt industry-standard TLS and AES encryption technologies, provide multi-factor authentication in the system's management background and key parts of user accounts, and conduct regular security audits; Use cloud platform monitoring tools to track system performance in real time and detect potential performance issues, including excessive latency and service downtime. Use responsive design to ensure that services adapt to different screen sizes and device types. After user consultation, send users a satisfaction survey or feedback channel.

3. The information consulting service method based on the cloud computing platform according to claim 2 is characterized in that: The method of accessing user requests through multiple channels such as the Web, mobile applications, voice assistants, and chatbots, and using natural language processing technology to parse user input, which includes multimodal data such as text, voice, and images, combined with the user's historical consultation records, click streams, and search behavior data, and using machine learning algorithms to analyze the user's interests, preferences, and potential needs is as follows: Receive user requests through multiple channels such as the Web, mobile apps, voice assistants, and chatbots. User requests include data in the form of text, voice, and images. Use natural language processing technology to parse the data input by users. For text input, use word segmentation and semantic analysis technology. For voice input, use voice recognition technology to convert voice into text. For picture input, use image recognition technology to extract key information from the picture. Combine the user's historical consultation records, click streams, and search behavior data information to conduct a comprehensive analysis of the user's behavior. Use machine learning algorithms to model the user's historical data and analyze the user's interests, preferences, and potential needs. Based on collaborative filtering, classification algorithms or clustering algorithms, identify users' preferences on certain topics, questions or services, and predict the content that users are interested in.

4. The information consulting service method based on the cloud computing platform according to claim 3 is characterized in that: Based on multiple channels to obtain user request data, before the data enters the system, an automated data cleaning process is adopted, and data standardization tools are used to convert data from different channels into a unified format, and then stored in the distributed storage system of the cloud computing platform. Based on machine learning and NLP technology, a knowledge graph is constructed, and the method of understanding the specific needs of users through semantic analysis is as follows: Access user request data through Web, mobile applications, voice assistants, and chatbot channels. Before the data enters the system, it is pre-processed through an automated data cleaning process. The pre-processing includes removing irrelevant data, processing missing values, removing noise data, and marking abnormal data. Use data standardization tools to unify the formats of data with different formats and structures from different channels, and store the cleaned and standardized data in a distributed database through the distributed storage system of the cloud computing platform; Based on the stored data, a knowledge graph is constructed through machine learning and natural language processing (NLP) technology. The knowledge graph organizes and integrates the entities, relationships, and attributes in the data in a graph structure. Use semantic analysis technology, combined with knowledge graphs and machine learning models, to deeply analyze and understand users' specific needs; Through machine learning models and constructed knowledge graphs, the system accurately matches user needs based on the results of semantic analysis.

5. The information consulting service method based on the cloud computing platform according to claim 4 is characterized in that: The method of building an intelligent chatbot through a deep learning model, using a machine learning recommendation algorithm, and combining user historical behavior data and interest preferences to recommend relevant consulting content or solutions to users is as follows: Use deep learning models for natural language understanding to parse text, voice, or other forms of requests input by users. Use sequence-to-sequence models or dialogue strategies based on reinforcement learning to manage and generate dialogues with users. Based on deep generative models, generate natural language responses related to user needs. Adopt machine learning collaborative filtering, matrix decomposition, and deep learning recommendation network algorithms, combined with users' historical click records, search records, consultation history data, and interest preferences, to predict the content or solutions that users are interested in; By collecting and analyzing the user's historical behavior data, combined with the user's geographic location, age, and occupational tags, a user profile is established; Based on deep learning models and machine learning recommendation algorithms, the intelligent chatbot recommends relevant consulting content, question answers or solutions to users. Through multiple rounds of conversations and real-time feedback, the system continuously learns user feedback and optimizes the recommendation algorithm.

6. The information consulting service method based on the cloud computing platform according to claim 5 is characterized in that: By continuously training and optimizing AI models, conducting regular A / B tests to test the effectiveness of different consulting service strategies, evaluating the impact of each solution on user experience and service efficiency, and conducting in-depth analysis through system logs and performance data, the methods to identify potential system bottlenecks or abnormal situations are as follows: By continuously collecting user interaction data and system operation data, the data feedback mechanism continuously optimizes the AI ​​model, and adopts incremental learning, transfer learning or reinforcement learning methods to regularly retrain the AI ​​model; Compare the impact of automatic replies and manual intervention, or different recommendation algorithm consulting service strategies on user experience and service efficiency based on the planned test objectives and assumptions; Randomly distribute user traffic to two or more test groups, with each group using a different consulting service strategy or optimization solution. Based on the results of the A / B test, measure the performance of different solutions. Evaluate the impact of different strategies on user experience through user satisfaction surveys, click-through rates, conversion rates, and user retention rate indicators. Evaluate key service efficiency indicators including response time, processing time, and automated processing ratio. Evaluate the optimization effect of different strategies on system efficiency by comparing with benchmark data or historical data. Through the log framework and tools, detailed system log information is collected, including user request logs, service response logs, and error logs. Through log analysis, common user problems, bottlenecks, and system load conditions are identified. Prometheus and Grafana performance monitoring tools are used to track system health in real time. Through log analysis and performance data, anomaly detection algorithms are used to identify potential system bottlenecks or abnormal situations. Combined with system monitoring data and log data, performance bottlenecks in the system are identified. By analyzing abnormal logs, performance data, and user behavior, the root causes of system anomalies are deeply explored. Based on the bottleneck analysis results, database queries are performed on the system, load balancing strategies are adjusted, and cache mechanism optimization is added.

7. The information consulting service method based on the cloud computing platform according to claim 6 is characterized in that: The method of using industry-standard TLS and AES encryption technologies, providing multi-factor authentication in the system's management background and key parts of user accounts, and conducting regular security audits is as follows: Use the latest version of TLS protocol to encrypt communication data between the client and the server, use SSL / TLS certificates issued by a trusted certificate authority, update certificates regularly, and configure the server with strong encryption algorithms such as RSA, ECDSA, and AEAD, and disable weak encryption suites; When storing sensitive information such as user passwords, personal information, and payment information, use AES encryption technology to encrypt data, adopt key management measures, and use hardware security modules or key management services to generate, store, and manage encryption keys; In the key parts of the system's management backend and user accounts, knowledge factor, possession factor, and biometric factor authentication are used to strengthen identity authentication; Through centralized log management tools, system logs are collected and analyzed to identify abnormal behaviors, including abnormal logins and access to sensitive operations. Automated tools are used to perform regular vulnerability scans to check whether the system has known security vulnerabilities and configuration errors. Penetration tests are also performed regularly to simulate attacker behavior to test the system's protection capabilities.

8. The information consulting service method based on the cloud computing platform according to claim 7 is characterized in that: Use cloud platform monitoring tools to track system performance in real time and detect potential performance issues, including high latency and service downtime. Use responsive design to ensure that services adapt to different screen sizes and device types. After the user consultation, send a satisfaction survey or feedback channel to the user as follows: Use the monitoring tools provided by the cloud platform to monitor and manage system performance in real time, and adopt cloud monitoring services, performance monitoring, latency detection, downtime detection and automatic recovery, log and exception monitoring measures to identify and discover potential performance problems; Through CSS3 media queries and flexible layout methods, web pages automatically adjust the display effect according to the user's mobile phone, tablet, and desktop computer devices. The design plan includes: Use relative units instead of fixed pixel units to achieve flexible layout; Optimize images and media files to load corresponding resources according to device screen size and resolution; Adopt a mobile-first design strategy, starting with small-screen devices and gradually expanding functions and layouts to larger screen devices; After the user consultation or interaction, send a satisfaction survey or feedback channel to the user to collect user opinions and suggestions through automated feedback requests, satisfaction surveys, real-time feedback channels, NPS surveys, integrated feedback platforms, and incentive implementation methods.

9. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements an information consulting service method based on a cloud computing platform as described in any one of claims 2 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, an information consulting service method based on a cloud computing platform as described in any one of claims 2 to 8 is implemented.

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