A venue SaaS management software based on a multi-tenant architecture

Abstract

The present invention relates to a venue SaaS management software based on a multi-tenant architecture, aiming to provide personalized venue management, resource reservation and scheduling, data analysis and optimization suggestions, and customer service. The software realizes data isolation and resource sharing through a multi-tenant environment module, while ensuring the independent operations of different venue managers. The personalized venue management module allows managers to customize the interface and functions according to their needs. The resource reservation and scheduling module optimizes resource allocation by integrating sensors and intelligent algorithms. The data analysis and optimization suggestions module provides in-depth business insights by using data integration and machine learning models. The customer service module integrates multi-modal input and automated feedback processing to enhance the user experience. The multi-tenant adapter module and the continuous integration / continuous deployment (CI / CD) process unit further ensure the flexibility and efficient operation of the system. The software system of the present invention improves the intelligent level of venue management and user satisfaction through its innovative architecture and functions.

Description

Technical Field

[0001] The present invention relates to the technical field of venue management software, and in particular to a venue SaaS (Software as a Service) management software based on a multi-tenant architecture. Background Art

[0002] With the rapid development of information technology, the SaaS model has become an important form of software services. In the field of venue management, traditional SaaS solutions often adopt a single-tenant architecture, resulting in insufficient personalized services, limited resource scheduling and data analysis capabilities. In addition, the customer service system usually lacks pertinence and intelligence, and cannot meet the specific needs of different venue managers. Summary of the Invention

[0003] To solve the above problems, the present invention provides a venue SaaS management software based on a multi-tenant architecture, which can realize personalized venue management, resource reservation and scheduling, data analysis and optimization suggestions, and the integration of a customer service system.

[0004] To achieve the above object, the present application adopts the following technical solutions:

[0005] A venue SaaS management software based on a multi-tenant architecture, comprising:

[0006] A multi-tenant environment module that uses a distributed database management system to achieve data isolation and resource pool sharing, and supports multiple venue managers to operate through independent instances without interference;

[0007] A personalized venue management module that integrates machine learning algorithms, adopts K-means clustering to analyze the user interface customization process, a dynamic layout engine adjusts the layout of interface elements in real time according to the device size Sscreen and user preferences, and an intelligent user behavior tracking component predicts the user behavior pattern through a Markov model and recommends common function areas;

[0008] A resource reservation and scheduling module that integrates advanced sensors, data collection tools, and intelligent scheduling algorithm components;

[0009] A data analysis and optimization suggestion module that integrates data integration components, multi-data analysis algorithm components, and machine learning model components;

[0010] A customer service module that supports multi-modal input, user navigation, and emergency response components;

[0011] A multi-tenant adapter module that realizes dynamic configuration adjustment, resource allocation optimization, and customized service components.

[0012] The personalized venue management module further includes: an interactive setup wizard unit that uses a decision tree algorithm to guide managers to select customized interface options through logical branches; a template library unit that stores preset interface templates obtained by analyzing venue types and user preferences; an intelligent matching algorithm unit that uses the cosine similarity calculation method to match the similarity between user input and preset templates and automatically selects or creates customized templates.

[0013] The resource reservation and scheduling module further includes: a real-time monitoring unit that uses time series analysis methods such as the exponential smoothing state space model to predict resource usage status; a reservation management unit that uses a weight-based resource allocation algorithm to consider reservation priorities and user historical behaviors for resource scheduling; a machine learning algorithm unit that integrates a support vector machine (SVM) classifier to classify user reservation behaviors and optimize resource allocation.

[0014] The data analysis and optimization recommendation module further includes: a data integration unit that uses ETL tools to extract and transform data from structured and unstructured data sources; a multi-data analysis algorithm unit that integrates principal component analysis (PCA) and singular value decomposition (SVD) algorithms for data dimensionality reduction and feature extraction; a machine learning model component that uses convolutional neural networks (CNNs) in deep learning for image data pattern recognition and long short-term memory networks (LSTMs) for time series prediction.

[0015] The customer service module further includes: an automated customer feedback processing unit that uses sentiment analysis algorithms to identify the sentiment tendency in text based on dictionaries and machine learning models; a customer behavior pattern analysis unit that uses association rule mining techniques such as the Apriori algorithm to discover customer behavior patterns; a service process automatic adjustment mechanism unit that uses reinforcement learning algorithms to dynamically optimize service processes based on real-time feedback.

[0016] The multi-tenant architecture design includes: a data isolation measures unit that ensures the isolation of different tenant data through attribute-based access control methods; a cross-venue data analysis unit that uses data fusion techniques to combine data from different venues for comprehensive analysis and decision support; a role-based access control unit that implements fine-grained permission management and uses role engineering methods to define permissions and roles.

[0017] The data security and privacy protection measures include: a data encryption technology unit that uses the AES-256 algorithm to encrypt sensitive data statically and encrypts data transmission through the TLS1.3 protocol; a secure communication protocol unit that implements two-way authentication and data integrity verification and enhances communication security through digital certificates and elliptic curve cryptography algorithms (ECCs); a regularly executed security audit and vulnerability scanning unit that uses automated tools to detect system vulnerabilities and generate audit reports.

[0018] The user feedback integration and service iteration functions include: a comprehensive user feedback channel unit that analyzes user feedback through natural language processing technology and extracts user opinions and requirements using sentiment analysis and topic modeling; a user feedback comprehensive analysis unit that classifies and analyzes trends in feedback content using text clustering techniques such as the LDA topic model; and a service iteration engine unit that combines KPI evaluation results and user feedback to optimize service process configuration using genetic algorithms.

[0019] The multi-tenant adapter module includes: a dynamic configuration adjustment unit that stores and manages configuration parameters for different venues using a configuration management database; and a resource allocation optimization unit that dynamically adjusts resource allocation using resource demand prediction algorithms and load balancing techniques.

[0020] The management software also includes a continuous integration / continuous deployment (CI / CD) process unit that ensures code changes are quickly and securely deployed to the production environment, and implements automated testing and deployment using containerization technologies such as Docker and automated deployment tools such as Jenkins.

[0021] Through the above technical solutions, the venue SaaS management software with a multi-tenant architecture of the present application realizes personalized venue management in a multi-tenant environment, allowing each venue manager to customize the management interface and functions according to their own needs. Develop a multi-tenant venue resource reservation and scheduling system to optimize resource allocation and improve resource utilization efficiency. Design a multi-tenant venue data analysis and optimization recommendation system to provide customized data analysis reports and optimization recommendations. Build a multi-tenant venue customer service system that automatically adjusts the service process based on customer feedback and preferences. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To make the embodiments, technical solutions, and technical advantages of the present application clearer, the following drawings provide non-limiting views of the present application:

[0023] Figure 1 Shows the system architecture diagram of the multi-tenant venue SaaS management software.

[0024] Figure 2 Shows the user interface customization flowchart for personalized venue management.

[0025] Figure 3 Shows the workflow diagram of the resource reservation and scheduling system.

[0026] Figure 4 Shows the data processing flowchart of the data analysis and optimization recommendation system.

[0027] Figure 5 Shows the service flowchart of the customer service system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] To elaborate on the embodiments, technical solutions, and technical advantages of this application in detail, the following will provide a detailed and complete description in conjunction with the accompanying drawings. It should be clear that the described embodiments only represent some application scenarios of this application, rather than all possibilities. According to the embodiments of the present disclosure, those skilled in the art can obviously obtain other possible embodiments without creative labor, and these embodiments also fall within the protection scope of this application.

[0029] I. Personalized Venue Management

[0030] The system designs a user-friendly interface for receiving personalized setting requests from venue managers. This interface allows managers to input their preferences, such as interface themes, layouts, functional modules, etc. The system designs an interactive personalized setting wizard to guide managers to express their specific requirements for the venue management interface through a series of questions and store them in the database for subsequent use. This includes but is not limited to:

[0031] · Interface style: Provide multiple interface themes, such as sports event mode, dynamic effects, to conform to the atmosphere of the sports venue;

[0032] · Display of functional modules: Particularly emphasize modules such as game arrangement, venue reservation, and audience service, as well as quick access to emergency evacuation and security monitoring;

[0033] · Access privilege levels: Set different levels of access privileges for venue staff, event organizers, and security management personnel.

[0034] This system uses clustering algorithms such as K-means to analyze user operation behaviors, such as click frequency and dwell time. Based on the behavior data, the system dynamically adjusts the interface layout to ensure that frequently used functional areas are more accessible. The algorithm formula is as follows:

[0035] L new = f(C clicks , T duration , S screen )

[0036] = ω1·log(C clicks + 1) + ω2·T [[ID=3S]] duration ·S screen

[0037] Where C clicks is the number of clicks on the interface element within a specific time period, T duration is the average dwell time of the user in the same functional area, and S screen is the screen size of the device used by the manager. Among them, ω1 and ω2 are weight parameters, which are adjusted according to the results of user behavior analysis to optimize the layout of interface elements.

[0038] The system maintains a template library that contains a variety of preset management interface templates. Each template is designed to meet the needs of different venue types or managers. Based on the personalized setting requests collected by the system forms, the system selects the most suitable template from the template library through an intelligent matching algorithm or creates a new customized template based on user input.

[0039] The customized management interface is deployed to the operation platform of the venue manager through a dynamic layout engine. This engine can: dynamically adjust the interface layout according to the manager's device type (PC, tablet, mobile phone, etc.) and screen size; achieve real-time interface preview, allowing the manager to see the actual effect of the interface before applying changes; integrate with other systems of the venue through APIs to ensure data consistency and real-time.

[0040] The system implements an intelligent user behavior tracking system that uses machine learning algorithms to analyze the usage patterns of managers. This system can: identify frequently used functions and less frequently used function blocks; automatically adjust the positions of interface elements according to the usage frequency, highlighting the frequently used functions; learn the working hours and activity types of managers, predict and pre-load relevant resources and data in advance.

[0041] II. Resource Reservation and Scheduling

[0042] The resource reservation and scheduling system integrates intelligent resource scheduling algorithms to form an efficient and dynamic resource management solution. This system not only realizes the real-time monitoring and reservation management of venue resources, but also optimizes resource allocation through advanced data analysis and machine learning technologies, improving operational efficiency and user satisfaction.

[0043] The system realizes the real-time monitoring of the status of venue resources through advanced integrated sensors and data collection tools. These sensors are distributed in various key areas and can collect the following information:

[0044] - Occupancy: Real-time tracking of the usage status of the venue, including the current activity type and the estimated end time.

[0045] - Equipment status: Monitoring the working status of key equipment to prevent technical failures.

[0046] - Environmental parameters: Measuring and recording environmental factors such as temperature, humidity, and lighting to ensure a comfortable experience for guests.

[0047] The frequency of data collection is dynamically adjusted according to the resource characteristics and requirements. For example, for temperature and humidity sensitive equipment that requires fine control, the data collection frequency will be higher, while for general venues, the collection frequency may be reduced to optimize system performance.

[0048] The collected data is processed through a data cleaning module to ensure data quality. This module identifies and corrects incorrect and inconsistent data, removes outliers, fills in missing values, and standardizes data from different sources into a unified format, providing a clean and accurate dataset for subsequent data analysis and machine learning model training.

[0049] The intelligent scheduling algorithm is the core of the system. It uses the collected historical data and real-time data, applies machine learning algorithms such as decision trees, random forests, and neural networks to predict resource usage trends and user demands. The algorithm takes into account seasonal variations, special events, and other factors affecting resource usage, thus achieving accurate predictions:

[0050] - Real-time status monitoring: Pay special attention to the venue reservation status, the progress of events, and the monitoring of the audience flow.

[0051] - Equipment status monitoring: Focus on monitoring key facilities such as the sound system, lighting, and scoreboards in the stadium.

[0052] - Environmental parameters: Pay attention to weather conditions, venue humidity, and temperature to ensure the performance of athletes and the experience of the audience.

[0053] This system uses the ARIMA model of time series analysis to predict the reservation trend of venue resources. By considering seasonal variations and special events, the system can achieve accurate predictions and optimize resource allocation. The resource scheduling optimization algorithm takes into account multiple constraints and uses a linear programming model to achieve the optimal allocation of resources, ensuring the highest efficiency.

[0054] This system uses a linear programming model to optimize resource allocation, ensuring the maximization of resource utilization and user satisfaction. The optimization objective of resource allocation can be expressed as:

[0055]

[0056] where i is the expected utility of resource i, i is the quantity allocated to resource i, total is the total resource quantity.

[0057] In the case where resource conflicts are inevitable, the algorithm will provide alternative solutions, such as recommending idle venues with similar functions or adjusting the reservation time. In addition, the algorithm can also continuously learn and optimize based on user feedback and reservation success rates.

[0058] Once the reservation request is accepted by the system, the intelligent scheduling algorithm will generate detailed reservation confirmation information. These information include:

[0059] - Reservation number: Assign a unique identifier to the reservation request for easy management and tracking.

[0060] - Resource Details: List the types, locations, and characteristics of the reserved resources to ensure that users are clear about the resources they have reserved.

[0061] - Time and Location: Specify the exact time and location of the reservation, including start and end times.

[0062] - Special Instructions: If the reservation contains special requirements or notes, the system will provide explanations in this section.

[0063] The reservation interface is the front-end portal for user-system interaction. It provides an intuitive user interface that allows users to select reserved resources as needed:

[0064] - Time Selector: Users can select the desired date and time for the reservation through the calendar view, and the system will display available time slots.

[0065] - Resource Type Filter: The system provides different types of resource options based on the venue layout and functional areas.

[0066] - Special Requirements Form: Users can enter special requirements, such as specific technical equipment needs or venue arrangements.

[0067] The request parsing module uses advanced text analysis techniques to understand the user's natural language input and convert it into reservation parameters that the system can understand. In addition, this module also has the ability to detect conflicts and can immediately prompt users about possible time or resource conflicts.

[0068] The system sends reservation confirmation information through multiple communication channels, including:

[0069] - Email: Send a formatted reservation confirmation email for easy user archiving and reference.

[0070] - SMS: Provide SMS notifications to users, especially for urgent or important reservation updates.

[0071] - In-App Notification: Provide real-time push notifications when users use the venue management application.

[0072] Through these meticulous steps, the resource reservation and scheduling system not only improves resource utilization but also enhances user satisfaction and the overall operational efficiency of the venue.

[0073] III. Data Analysis and Optimization Suggestions

[0074] The system's data integration tools play a core role in data collection. It can extract information from multiple heterogeneous data sources, including but not limited to:

[0075] - Ticketing System: Collect ticket sales data, admission records, and customer preferences.

[0076] - Monitoring system: Captures real-time data related to venue usage, traffic patterns, and security.

[0077] - Online feedback platform: Analyzes customers' online reviews, ratings, and direct feedback.

[0078] This system combines statistical analysis methods (such as ANOVA) and machine learning models (such as SVM) to provide in-depth insights for venue operations. For example, it uses multivariate statistical analysis to evaluate the effects of different operation strategies and machine learning techniques to identify and predict customer behavior patterns.

[0079] This system uses the mean squared error (MSE) and the coefficient of determination (R 2 ) to evaluate the performance of the prediction model:

[0080]

[0081] where, i is the actual value, is the predicted value, is the average of the actual values.

[0082] The data integration tool adopts a standardized process to convert data in different formats and structures into a unified format, ensuring data consistency and integrity. This step is crucial as it provides a high-quality and reliable data foundation for subsequent data analysis.

[0083] The system uses a variety of data analysis algorithms to process the integrated data, including:

[0084] - Statistical analysis: Calculates key metrics such as the average number of visitors, the proportion of repeat visitors, and the sales conversion rate.

[0085] - Trend analysis: Identifies long-term trends, such as seasonal fluctuations, growth trends, or decline trends.

[0086] - Cluster analysis: Groups customers or events into clusters with similar characteristics to identify market segments.

[0087] In addition, the system also integrates advanced machine learning models that can:

[0088] - Identify patterns: Discover complex non-linear relationships from large amounts of data.

[0089] - Predict future trends: Predict future visitor traffic, resource requirements, etc., based on historical data.

[0090] - Anomaly detection: Identify outliers in the data, such as potential system failures or service quality issues.

[0091] Based on in-depth data analysis, the system generates detailed optimization recommendation reports. These reports contain the following content:

[0092] - Optimization of operational efficiency: Analyze event arrangements, propose optimization plans, reduce venue idle time, and improve utilization rate.

[0093] - Enhancement of user satisfaction: Improve the viewing experience and venue services based on audience feedback.

[0094] - Optimization of resource allocation: Based on predictions of event popularity and audience flow, optimize the allocation of venues and facilities.

[0095] The reports are presented through visualization techniques, including charts, graphs, and heatmaps, enabling managers to intuitively understand the data and recommendations. Visualization not only enhances the readability of the reports but also helps managers quickly identify key issues and opportunities.

[0096] The system provides the optimization recommendation reports to venue managers through the report distribution module. This module supports multiple report formats to adapt to different usage scenarios and preferences:

[0097] - PDF reports: Provide detailed, formatted documents suitable for official record-keeping and archiving.

[0098] - HTML reports: Support interactive elements and multimedia content, suitable for online browsing and sharing.

[0099] - Interactive dashboards: Provide a real-time updated view, allowing managers to deeply explore the data and analysis results.

[0100] Managers can adjust operational strategies and resource allocation according to the recommendations in the reports, combined with their own experience and intuition. For example, if the report suggests adding specific types of activities on weekends, managers can rearrange the event schedule to attract more visitors.

[0101] Through this comprehensive method of data analysis and optimization recommendations, more informed decisions can be made, operational efficiency can be improved, user satisfaction can be increased, and the venue can stand out in the highly competitive market.

[0102] IV. Customer Service

[0103] The customer service interface is the front line for customers to communicate with the venue. This interface design focuses on user experience and provides the following features:

[0104] - Support for multimodal input: Provide fast feedback channels, such as real-time game comments and venue service feedback.

[0105] - User-friendly navigation: Set up quick navigation to venue maps, seat arrangements, food and beverage services, etc.

[0106] - Emergency response mechanism: Quickly respond to the urgent needs of the audience, such as medical assistance or safe evacuation.

[0107] By introducing natural language processing (NLP) technology, this system automatically processes and analyzes customer feedback, extracting sentiment tendencies and keywords. Based on behavior pattern recognition, the system uses a Markov chain model to predict customers' service needs, thus providing more personalized and timely customer service.

[0108] Request processing module:

[0109] - This module is responsible for receiving and recording customer requests, ensuring that each request can be responded to in a timely manner.

[0110] - Use automated tools to screen and classify requests for quick assignment to appropriate service personnel.

[0111] The customer analysis module is the core of the system's intelligence. It uses the following technologies to deeply understand customer needs:

[0112] - Natural language processing (NLP): Apply NLP technology to extract sentiment tendencies, keywords, and topics from customers' text and voice feedback.

[0113] - Data correlation analysis: Correlate customers' feedback with other data in the customer profile, such as historical interactions, preference settings, and service history.

[0114] - Behavior pattern recognition: Analyze customers' behavior patterns to predict their possible needs and preferences.

[0115] Based on the results of customer analysis, the system can automatically adjust the service process to meet the needs of different customers:

[0116] - Personalized service priority: The system automatically adjusts the priority of requests according to customers' historical preferences and behavior patterns.

[0117] - Customized service path: Customize a service path for each customer to ensure that they can quickly obtain the required service.

[0118] - Dynamic service strategy adjustment: The system dynamically adjusts the service strategy according to customers' real-time feedback and satisfaction ratings.

[0119] This system uses the Customer Satisfaction Index (CSI) as a quantitative indicator of service effectiveness. The calculation formula of CSI is as follows:

[0120] CSI = α·S quality + β·S time + γ·S cost

[0121] Where, S quality 、Stime and S cost represent the scores for quality of service, response time, and cost respectively, and α, β, and γ are the corresponding weight parameters.

[0122] The service delivery module is the final link in the system's interaction with customers and provides the following customized services:

[0123] - Multi-channel service support: Provide services through multiple channels such as online chat, phone, email, and social media.

[0124] - Customer preference matching: Select the most suitable service channel for response according to the customer's communication preferences.

[0125] - Emergency response mechanism: For emergency requests, the system can quickly trigger a high-priority response process.

[0126] The system continuously innovates in technology integration to provide the best customer service experience:

[0127] - Artificial intelligence integration: Utilize AI technologies such as chatbots to provide 24 / 7 customer support.

[0128] - Big data analysis: Through big data analysis, deeply understand customer behavior and market trends.

[0129] - Cloud services: Use cloud services to provide a scalable and highly available customer service platform.

[0130] Through these meticulous steps and functions, the customer service system not only enhances the customer experience but also strengthens the venue's operation ability, enabling it to flexibly respond to various customer needs and market changes.

[0131] V. Multi-tenant Architecture Design

[0132] The data model design of the multi-tenant architecture focuses on flexibility and scalability to adapt to the specific needs of different venues:

[0133] - Data isolation: Ensure the data independence of different sports events or venue operators.

[0134] - Cross-venue data analysis: Analyze the usage trends of different venues and provide decision support for joint marketing and event scheduling.

[0135] - Schema design: Adopt a shared database architecture and use schemas at the database level to distinguish the data of different tenants.

[0136] - Data consistency: Maintain data consistency and integrity through a unified data definition language (DDL) and data manipulation language (DML).

[0137] This system uses the AES algorithm to encrypt data with a key length of at least 256 bits to ensure data security during storage and transmission. In addition, by implementing role-based access control (RBAC) and multi-factor authentication (MFA), this system provides fine-grained permission control for users with different roles.

[0138] At the database level, this system uses a schema to distinguish data of different tenants and maintains data consistency and integrity through a unified data definition language (DDL) and data manipulation language (DML). The implementation of data isolation can be represented by the following formula:

[0139]

[0140] The system provides powerful cross-venue data analysis capabilities to support centralized data reporting and decision-making support:

[0141] - Data aggregation: It can collect data from multiple venues and perform aggregation analysis to identify overall trends and patterns.

[0142] - Multidimensional data analysis: Supports multidimensional data models, allowing data to be analyzed from different perspectives and dimensions to provide a comprehensive view for decision-making.

[0143] - Real-time reporting: Provides real-time data reporting functions, enabling managers to quickly respond to market changes and customer needs.

[0144] The system implements a user authentication and authorization mechanism based on role-based access control (RBAC):

[0145] - Fine-grained permission control: Defines detailed permissions for different roles to precisely control users' access to data and functions.

[0146] - Multi-factor authentication: Provides multi-factor authentication (MFA) to enhance security, such as combining passwords with mobile verification codes or biometrics.

[0147] - Single sign-on (SSO): Supports SSO, allowing users to access services and resources of all relevant venues through a single authentication.

[0148] The multi-tenant adapter is the core component of the system, used to handle the specific needs of different venues:

[0149] - Dynamic configuration adjustment: The adapter can dynamically adjust system configurations according to the operation mode and requirements of the venue.

[0150] - Resource allocation optimization: Intellectually allocates computing resources, storage resources, and network resources to meet the load requirements of different venues.

[0151] - Customized services: Provide customized services and interfaces for each venue to adapt to its unique business processes and user needs.

[0152] The multi-tenant architecture design of the system also takes into account scalability and maintainability:

[0153] - Modular design: Adopt modular design principles to facilitate the addition of new functions or the adjustment of existing functions as needed.

[0154] - Automated deployment: Support automated deployment and expansion, simplifying the system maintenance and upgrade process.

[0155] - Continuous Integration / Continuous Deployment (CI / CD): Integrate the CI / CD process to ensure that code changes are deployed to the production environment quickly and securely.

[0156] Through these meticulous design considerations, the multi-tenant SaaS management software can provide safe, reliable, and personalized services for each venue while maintaining efficient operation and maintenance.

[0157] VI. Data Security and Privacy Protection

[0158] The system implements comprehensive data encryption measures to ensure data security during storage and transmission:

[0159] - Data encryption: Focus on protecting the personal information of audiences, transaction data, and member information.

[0160] - Secure communication protocol: Ensure the security of data transmission for online ticket purchases and venue service reservations.

[0161] - Static data encryption: All sensitive data, including user personal information and venue operation data, is encrypted using the Advanced Encryption Standard (AES) during storage to ensure the security of data in a static state.

[0162] - Dynamic data encryption: Data is encrypted using the SSL / TLS protocol during transmission to protect it from being intercepted or tampered with during network transmission.

[0163] - End-to-end encryption: For particularly sensitive information, such as payment information or personal authentication data, the system uses end-to-end encryption to ensure that only the sender and receiver can decrypt and access the data content.

[0164] This system uses the Advanced Encryption Standard (AES) for data encryption, with a key length of at least 256 bits. The SSL / TLS protocol is used for data transmission encryption to ensure data security during network transmission. The system conducts regular security audits and vulnerability scans, and adopts the HTTPS protocol and API security measures to further enhance the security of data transmission.

[0165] The encryption strength can be evaluated by the following formula:

[0166]

[0167] where K length is the key length.

[0168] The system implements the following secure communication protocols to further enhance the security of data transmission:

[0169] - HTTPS: All interactions through the web interface are carried out via the HTTPS protocol, ensuring the protection of data during transmission between the client and the server.

[0170] - API Security: The API interfaces provided by the system adopt authentication mechanisms such as OAuth 2.0 to ensure the security and legality of API calls.

[0171] - Data Integrity Verification: The system uses a hash algorithm to perform integrity verification on the transmitted data to ensure that the data has not been tampered with during transmission.

[0172] The system regularly conducts the following security audits and vulnerability scanning activities:

[0173] - Internal Security Audit: The internal security team regularly conducts security audits on the system to check system configurations, permission settings, and potential security vulnerabilities.

[0174] - External Penetration Testing: External security experts are regularly invited to conduct penetration testing, simulating the attack methods that an attacker might use to discover and fix security vulnerabilities.

[0175] - Vulnerability Response Plan: The system implements a vulnerability management system to track, report, and fix security issues, ensuring a timely response to security incidents.

[0176] The system strictly adheres to the following data protection regulations to ensure the legality of data processing and the protection of user privacy:

[0177] - GDPR Compliance: The system follows the General Data Protection Regulation (GDPR) of the European Union, providing EU users with the right to data protection, including the right to access, correct, and delete data, etc.

[0178] - CCPA Compliance: The system complies with the California Consumer Privacy Act (CCPA), providing additional privacy rights protection for users in the state of California.

[0179] - Privacy Policy and Transparency: The system designs a transparent data processing process and publishes a privacy policy, allowing users to understand how their personal information is collected, used, and protected.

[0180] The system provides users with the right to control their personal information:

[0181] - User access and control: Users can easily access and manage their personal information through the user interface, including viewing, correcting, and deleting personal information.

[0182] - Right to data portability: Users can request to export their personal information in a structured, common, and machine-readable format to exercise the right to data portability.

[0183] - Withdrawal of consent: Users can withdraw their consent to data processing at any time, and the system will stop processing or delete their personal information according to the user's wishes.

[0184] The system also focuses on enhancing the data security awareness of employees and users:

[0185] - Employee training: Regularly train employees on data security and privacy protection to improve their awareness and skills of data protection.

[0186] - User education: Through user guides and educational resources, educate users on how to protect their personal information and improve users' awareness of data security.

[0187] The system continuously pays attention to and adopts the latest data security technologies:

[0188] - Follow-up on security technologies: Keep up with the latest developments in data security technologies, such as quantum encryption, biometric technologies, etc., to improve the security of the system.

[0189] - Regular updates and patch management: The system regularly updates security patches to fix known security vulnerabilities and ensure the security of the system software.

[0190] Through these meticulous measures, the multi-tenant SaaS management software ensures the security of data and the protection of user privacy, enhances users' trust in the system, and provides a safe and reliable operating environment for the venue.

[0191] VII. User Feedback Integration and Service Iteration

[0192] The user feedback collection system is the starting point for service iteration, and the system has designed the following multiple feedback channels:

[0193] - Online surveys: Through embedded online questionnaires, collect users' satisfaction with the venue services and facilities and improvement suggestions.

[0194] - Live chat: Provide live chat support to enable users to instantly feedback problems and needs.

[0195] - Feedback forms: Integrate feedback forms on the website and mobile applications to facilitate users to submit detailed feedback.

[0196] - On-site feedback: Collection points for on-site feedback of the competition, such as suggestion boxes and on-site investigators.

[0197] This system establishes a user feedback analysis engine that uses text and sentiment analysis technologies to extract key information. Based on the results of the feedback analysis, the system conducts A / B tests and quickly deploys new service processes and functions to respond to user needs and improve service quality.

[0198] This system uses key performance indicators (KPIs) to monitor the effectiveness of service iteration. The calculation formula of KPI is as follows:

[0199]

[0200] The data collected through these channels is stored in a central database, providing a basis for subsequent analysis. The collected user feedback data is stored in the central database and undergoes preliminary processing:

[0201] - Data cleaning: Remove irrelevant information, such as formatting errors and duplicate submissions, to ensure data quality.

[0202] - Classification and tagging: Automatically or manually classify according to the feedback content, such as services, facilities, prices, etc., for subsequent analysis.

[0203] The user feedback analysis module utilizes advanced text analysis and sentiment analysis technologies:

[0204] - Text analysis: Extract key information from the feedback and identify specific problems and needs of users.

[0205] - Sentiment analysis: Judge the sentiment tendency of the feedback, such as positive, negative or neutral, to understand user emotions.

[0206] The analysis results are used to identify service improvement points and changes in user needs:

[0207] - Identification of service improvement points: Identify specific feedback from the audience on the event experience, venue facilities and services, and respond quickly with adjustments.

[0208] - Hotspot identification: Identify common problems and frequent feedback to determine the priority of service improvement.

[0209] - Trend analysis: Analyze the changing trend of user feedback over time to predict future needs.

[0210] The service iteration engine is the core of the system for updating service processes and functions:

[0211] - Quick deployment: Support the quick deployment of new service processes and functions to respond to user feedback.

[0212] - A / B Testing: Conduct A / B testing to compare the effects of different service strategies and select the best solution.

[0213] Based on the results of feedback analysis, the system functions and service processes are continuously optimized:

[0214] - Personalized Service: Provide customized services according to user preferences to enhance the user experience.

[0215] - Process Optimization: Simplify the service process, reduce user waiting time, and improve service efficiency.

[0216] The system has established a mechanism for continuously integrating user feedback:

[0217] - Regular Feedback Solicitation: Regularly solicit user feedback via email, app push, etc.

[0218] - Feedback Response: Establish a rapid response mechanism to promptly respond to and handle user feedback.

[0219] Encourage users to participate in the service innovation process:

[0220] - Innovation Workshops: Invite sports fans to participate in innovation workshops for venue services and event experiences to jointly design a better viewing experience.

[0221] - User Co-creation: Collaborate with users to develop new services and optimize products using users' ideas and feedback.

[0222] The effects of service iteration are monitored and evaluated through the following methods:

[0223] - Key Performance Indicators (KPIs): Set and monitor KPIs related to service iteration, such as user satisfaction, service efficiency, etc.

[0224] - User Feedback Loop: Establish a feedback loop mechanism to ensure that user feedback is incorporated into every aspect of service iteration.

[0225] Through these meticulous measures, the multi-tenant SaaS management software can continuously integrate user feedback and quickly respond for service iteration, ensuring that the service always meets user needs and market changes.

Claims

1. A venue SaaS management system based on a multi-tenant architecture, characterized in that, Including: - A multi-tenant environment module that uses a distributed database management system to achieve data isolation and resource pool sharing, supporting multiple venue managers to operate through independent instances without interference from each other; - Personalized venue management module, integrating machine learning algorithms, using K-means clustering to analyze the user interface customization process, and a dynamic layout engine to adjust the layout of interface elements in real time according to device size S screen and user preferences, and an intelligent user behavior tracking component to predict user behavior patterns through a Markov model and recommend frequently used functional areas; Among them, the adjustment of the layout of the interface elements is based on the following formula L new : L new = f(C clicks , T duration , S screen ) = ω1·log(C clicks + 1)+ ω2·T duration ·S screen Among them, C clicks is the number of clicks on the interface element within a specific time period, T diration is the average residence time of the user in the same functional area, S screen is the device screen size, and ω1 and ω2 are weight parameters; - A resource reservation and scheduling module that integrates advanced sensors, data collection tools, and intelligent scheduling algorithm components. Among them, the intelligent scheduling algorithm component uses the ARIMA model of time series analysis to predict the reservation trend of venue resources by considering seasonal changes and special events, and adopts a linear programming model to achieve the optimal allocation of resources; - A data analysis and optimization suggestion module that integrates data integration components, multi-data analysis algorithm components, and machine learning model components. Among them, the analysis performed by the multi-data analysis algorithm components includes: Trend analysis to identify seasonal fluctuations, growth trends, or decline trends; Cluster analysis to group customers or events into clusters with similar characteristics to identify market segments; - A customer service module that supports multi-modal input, user navigation, and emergency response components; - A multi-tenant adapter module that realizes dynamic configuration adjustment, resource allocation optimization, and customized services.

2. The management system according to claim 1, wherein The personalized venue management module further includes: - An interactive setup wizard unit that uses a decision tree algorithm to guide the manager to select customized interface options through logical branches; - A template library unit that stores preset interface templates analyzed according to venue types and user preferences; - An intelligent matching algorithm unit that uses the cosine similarity calculation method to match the similarity between user input and preset templates, and automatically selects or creates customized templates.

3. The management system according to claim 1, characterized in that, The customer service module further includes: - An automated customer feedback processing unit that uses sentiment analysis algorithms to identify the sentiment tendency in the text based on dictionaries and machine learning models; - A customer behavior pattern analysis unit that uses association rule mining technology algorithms to discover customer behavior patterns; - A service process automatic adjustment mechanism unit that uses reinforcement learning algorithms to dynamically optimize the service process according to real-time feedback.

4. The management system according to claim 1, wherein It also includes a data security and privacy protection module, including: - A data encryption technology unit that uses the AES-256 algorithm to encrypt sensitive data statically and encrypts data transmission through the TLS1.3 protocol; - A secure communication protocol unit that realizes two-way authentication and data integrity verification, and enhances communication security through digital certificates and elliptic curve encryption algorithms (ECC); - A regularly executed security audit and vulnerability scanning unit that uses automated tools to detect system vulnerabilities and generate audit reports.

5. The management system according to claim 1, characterized in that It also includes a user feedback integration and service iteration module, including: - A comprehensive user feedback channel unit that parses user feedback through natural language processing technology, and uses sentiment analysis and topic modeling to extract user opinions and requirements; - A user feedback comprehensive analysis unit that uses text clustering technology to classify and analyze the trend of feedback content; - A service iteration engine unit that combines the KPI evaluation results and user feedback, and uses genetic algorithms to optimize the service process configuration.

6. The management system according to claim 1, characterized in that It also includes a continuous integration / continuous deployment (CI / CD) process unit that uses containerization technology and automated deployment tools to achieve automated testing and deployment, ensuring that code changes are deployed to the production environment.

7. The management system according to claim 1, characterized in that, The customer service module uses the Customer Satisfaction Index (CSI) as a quantitative indicator of service effectiveness. The calculation formula of CSI is as follows: CSI = α·S quality + β·S time + γ·S cost Among them, S quality , S time and S cost represent the scores of service quality, response time, and cost respectively, and α, β, and γ are the corresponding weight parameters.

8. The management system according to claim 1, characterized in that, The multi-tenant adapter module includes: - A dynamic configuration adjustment unit that uses a configuration management database to store and manage configuration parameters of different venues; - A resource allocation optimization unit that dynamically adjusts resource allocation by adopting a resource demand prediction algorithm and load balancing technology.

9. The management system according to claim 1, wherein The multi-tenant environment module is also used for: - Ensuring the isolation of data of different tenants through an attribute-based access control method, implementing fine-grained permission management by defining permissions and roles using the role engineering method, and using data fusion technology to combine data from different venues for comprehensive analysis and decision support.

10. The management system according to claim 1, wherein The resource reservation and scheduling module further includes: - A real-time monitoring unit that uses an exponential smoothing state space model to predict the usage status of venue resources; - A reservation management unit that uses a weight-based resource allocation algorithm to schedule resources considering reservation priorities and user historical behaviors; - A machine learning algorithm unit that integrates a support vector machine classifier to classify user reservation behaviors and optimize resource allocation.

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