Intelligent scenic spot cross-platform ticket business distribution system fused with big data analysis

Abstract

The invention discloses a smart scenic spot cross-platform ticket distribution system fused with big data analysis, and relates to the technical field of smart tourism and ticket management. According to the invention, the passenger flow monitoring data and the emergency event early warning information in the scenic area are collected in real time, and the emergency event grade division model is constructed by using the multi-dimensional combination weight scoring algorithm, so that the emergency events can be quickly and accurately divided into different grades; according to the grading processing mechanism, a scenic area management party can immediately take corresponding ticket adjustment measures for different grades of emergencies, such as pausing ticket sales in a high-risk area and opening a free refunding / refunding channel for a user who purchases a ticket, so that the emergency response speed and accuracy are remarkably improved, and the safety of tourists is effectively guaranteed.

Description

Technical Field

[0001] This invention relates to the field of smart tourism and ticketing management technology, specifically a smart scenic area cross-platform ticketing allocation system that integrates big data analysis. Background Technology

[0002] With the booming development of the tourism industry, the number of tourists in scenic spots has increased dramatically. How to manage the flow of tourists in scenic spots efficiently and safely has become an urgent problem to be solved. In particular, when faced with emergencies such as extreme weather, equipment failure or safety incidents, traditional manual management methods are often unable to respond quickly, which threatens the safety of tourists and disrupts the operation of scenic spots.

[0003] Traditional scenic area ticketing management and emergency response mechanisms rely primarily on manual monitoring and experience-based judgment, which has the following significant drawbacks: First, data collection is incomplete and lacks timeliness, making it difficult to grasp the real-time visitor flow and emergency information at various attractions within the scenic area, resulting in delayed emergency response. Second, the lack of a scientific tiered processing mechanism leads to a high degree of subjectivity in assessing the severity of emergencies, making it impossible to quickly and accurately classify incident levels and take corresponding countermeasures. Third, ticketing adjustment plans lack data support, often relying on experience rather than real-time data analysis, resulting in unreasonable resource allocation, ineffective visitor flow management, and potentially exacerbating congestion and safety hazards within the scenic area. Finally, the visitor service experience is poor, with cumbersome ticket change and refund processes and long response times, failing to meet the rapid needs of visitors in emergency situations.

[0004] In response to the numerous problems existing in traditional scenic area ticketing management and emergency response mechanisms, a smart scenic area cross-platform ticketing allocation system integrating big data analysis has emerged. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a smart scenic area cross-platform ticketing allocation system that integrates big data analysis. It can collect and analyze visitor flow monitoring data and emergency event early warning information in real time within the scenic area, and construct an emergency event classification model using a multi-dimensional combined weight scoring algorithm. This enables rapid and accurate classification and response to emergencies. This innovative technology not only significantly improves the emergency response speed and accuracy of scenic area management, but also effectively optimizes resource allocation, reduces the pressure on popular scenic areas, and greatly simplifies the ticket change and refund process, thereby improving the overall satisfaction of tourists.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a smart scenic spot cross-platform ticketing allocation system integrating big data analysis, the system comprising the following components: a data acquisition module, a level classification module, an algorithm decision module, an execution module, and an auxiliary module; The data acquisition module collects real-time visitor flow monitoring data and emergency event warning information within the scenic area. The visitor flow monitoring data includes real-time visitor numbers, visitor density, and traffic speed at each attraction. The emergency event warning information includes extreme weather warnings, equipment malfunction information, and safety incident warnings. The classification module: Based on the data acquired by the data acquisition module, it constructs a classification model for emergencies, classifying emergencies into levels I-IV, where level I is an extremely serious event, level II is a serious event, level III is a relatively serious event, and level IV is a general event; The algorithm decision module adopts a deep Q-network reinforcement learning algorithm, taking the level of the emergency, real-time passenger flow distribution, scenic area carrying capacity limit, and cross-platform ticket sales status as inputs to generate corresponding ticket adjustment plans. The execution module, based on the ticketing adjustment plan output by the algorithm decision module, performs operations such as suspending ticket sales in high-risk areas, opening a free rescheduling / refund channel for ticketed users, and linking with surrounding scenic spots to send diversion ticketing recommendations, thus achieving fully automated processing from early warning to response. The auxiliary module includes a data storage and analysis unit and a security protection unit, which are used for data storage and model optimization, data security and operation standard protection, respectively, to ensure the stable and reliable operation of the system.

[0007] Furthermore, the passenger flow monitoring unit of the data acquisition module is implemented according to the following technical steps: infrared sensors and high-definition video surveillance equipment are deployed in the core areas of each scenic spot, major passage turning points, and entrance and exit gates of the scenic area. The deployment density of equipment is increased in narrow passages and areas prone to congestion. All equipment is connected to the scenic area's local area network through a 5G industrial gateway to achieve data synchronization. After the equipment is started, it first performs a 30-second self-test calibration to ensure that the acquisition accuracy meets the standard. Then, it collects data in real time at a preset frequency. During the acquisition process, frame synchronization technology is used to align the timestamps of data from multiple devices to avoid data timing deviations. The emergency early warning unit connects to the meteorological department's early warning platform, equipment operation and maintenance system, and security monitoring center through a RESTful API interface. Before the connection, interface compatibility testing and data format standardization conversion are completed. After receiving the data, abnormal data is first identified through a rule engine and then a rejection operation is performed. Then, weighted average filtering technology is used to denoise the remaining data. During the filtering process, weights are automatically assigned according to the accuracy level of the equipment to ensure the consistency of the input data. The entire data acquisition and preprocessing process does not require manual intervention and is fully automated to ensure data real-time performance.

[0008] Furthermore, the emergency incident classification model of the classification module adopts a multi-dimensional combined weight scoring algorithm, the formula of which is: in A comprehensive score for emergencies. To evaluate the number of indicators, For the first The combined weights of the indicators are calculated using a combination of the analytic hierarchy process (AHP) and the entropy weight method. For the first Quantitative scoring of each indicator.

[0009] Furthermore, the dynamic adjustment function of the grading module is implemented according to the following technical steps: The system activates the event grading dynamic monitoring mode, collecting real-time data of core indicators every 5 minutes, including the spread of the event's impact, the changing trend of the impact level, the extension or shortening of the duration, the mitigation or aggravation of tourist safety threats, and the repair progress of damaged facilities; after the data collection is completed, the grading model is automatically called to recalculate the comprehensive score. The rating is compared with the previous rating. If the rating improves and crosses the level threshold, a stricter ticketing adjustment plan is immediately triggered. If the rating decreases and crosses the level threshold, ticket sales are gradually reopened according to a tiered recovery strategy. Tickets for some time slots in low-risk areas are reopened first, and passenger flow recovery is monitored in real time. If passenger flow does not exceed 60% of the area's capacity within 30 minutes, tickets for more time slots or areas are reopened until the event level drops to level IV and stabilizes for 30 minutes, at which point normal ticket sales are restored. The entire dynamic adjustment process requires no manual intervention, ensuring timely and flexible event response.

[0010] Furthermore, the reward function of the DQN reinforcement learning algorithm in the algorithm decision module is designed as follows: in For the overall reward value, To achieve the optimal passenger flow control volume in emergency scenarios, This refers to the actual passenger flow control volume after the plan is implemented. This is the preset maximum response time for tourist change and refund services. This represents the average response time for changes or cancellations after the solution is implemented. The target value for cross-platform collaborative response rate. To determine the actual cross-platform collaborative response rate, Weighting of passenger flow control effectiveness Prioritizing the protection of tourists' rights Weighting for cross-platform collaboration efficiency. Multiple linear regression analysis was used to determine the optimal weight combination by fitting the reward function value with the implementation effect of the plan using the emergency ticketing adjustment data of scenic spots in the past three years as a sample. During the algorithm training process, the experience replay pool adopts a priority replay mechanism, and the sampling probability is allocated according to the reward value to improve the utilization efficiency of high-value experience. The target network update frequency is determined by a dynamic adjustment strategy. When the training error exceeds the preset threshold, the update frequency is shortened from every 100 steps to every 50 steps to ensure that the algorithm converges quickly and generates the optimal ticketing adjustment plan.

[0011] Furthermore, the emergency resource matching function of the algorithm decision-making module adopts a resource carrying capacity matching algorithm, the formula of which is: in To improve the matching degree of emergency resources, The total capacity of emergency resources in the scenic area. The resource utilization efficiency coefficient is derived from historical emergency response data, with a value ranging from 0.8 to 1.0. The number of affected attractions For the first Real-time visitor numbers for each attraction. For the first The emergency resource demand coefficient for each scenic spot is determined based on the complexity of the terrain and the density of facilities, with a value ranging from 1.0 to 1.5. If the system determines that emergency resources are insufficient, the algorithm automatically strengthens diversion and refund measures, increases the coverage of free refunds, increases the discount rate of diversion packages, and simultaneously coordinates with local emergency management departments to request external support; when When the resource allocation is deemed appropriate, the standard ticketing adjustment plan will be implemented. If resources are deemed sufficient, the scope of the sales suspension can be appropriately narrowed, while ticket sales in some low-risk areas can be retained; in the formula The quantitative calculation is obtained by assigning weights to different types of emergency resources according to their importance and then summing them up. The weights are determined through review by emergency management experts in scenic areas to ensure the scientific nature of the resource carrying capacity calculation.

[0012] Furthermore, the ticketing sales control unit of the execution module is implemented according to the following technical steps: First, an API interface docking agreement is signed with each cross-platform sales channel to clarify data transmission specifications and security requirements. After docking, a 7-day joint debugging test is conducted to verify the real-time performance and accuracy of ticketing status synchronization. In emergency scenarios, after receiving the suspension sales instruction from the algorithm decision module, a control signal is immediately sent to each platform through an encrypted communication channel. The signal contains key information such as high-risk area codes, suspension sales periods, and locked identifiers for sold but unverified tickets. After receiving the signal, each platform first verifies the validity of the signal signature, then executes the ticket removal operation, and simultaneously locks the secondary transfer function of sold tickets in the corresponding area. After the operation is completed, the platform reports the execution result to the system. If a platform fails to report within 10 seconds or reports execution failure, the system automatically starts a backup communication channel to resend the instruction and triggers an alarm notification from the maintenance personnel. Throughout the process, the system monitors the ticketing sales status of each platform in real time to ensure that ticketing in high-risk areas is completely suspended without any omissions or delays in execution.

[0013] Furthermore, the user service unit of the execution module implements the following technical steps: After receiving the ticketing adjustment plan, it first classifies and filters users who have purchased tickets, prioritizing them according to three dimensions: whether the ticket purchase area belongs to a high-risk area, whether the departure time from the scenic spot is less than 48 hours, and whether they are group ticket users. The priorities from high to low are: high-risk area + departure time < 24 hours + group ticket users, high-risk area + departure time < 24 hours + individual users, high-risk area + departure time 24-48 hours + group ticket users, high-risk area + departure time 24-48 hours + individual users, and users in non-high-risk areas but affected by the event; and then, in order of priority, it sends notifications via SMS and AP. Service notifications are sent simultaneously through three channels: push notifications, WeChat official account notifications, and include event descriptions, access to the refund / modification channel, referral information, and contact details for intelligent customer service. An online refund / modification channel is available; users can directly access the operation page by clicking the link in the notification without login verification. The refund / modification process is simplified to three steps: selecting the operation type, confirming information, and submitting the application. The system automatically reviews and provides real-time feedback. Intelligent customer service utilizes voice recognition and semantic understanding technology to quickly respond to user inquiries. It automatically generates standard answers for common questions and automatically transfers complex questions to human customer service, ensuring that user inquiry response time does not exceed 10 seconds and refund / modification application processing time does not exceed 1 minute, thus improving user experience.

[0014] Furthermore, the data storage and analysis unit of the auxiliary module is implemented according to the following technical steps: A storage architecture is constructed using the Hadoop Distributed File System, dividing data into three categories: real-time data streams, historical business data, and model training data, which are stored in different node clusters. Real-time data stream nodes use SSDs to ensure read / write speeds, while historical business data nodes use mechanical hard drives to reduce storage costs. Data is desensitized before storage, with irreversible encryption algorithms used to process sensitive information such as tourist ID numbers and mobile phone numbers, retaining only necessary ticket-related identification information. Data backup employs a local incremental backup + off-site full backup strategy, performing an incremental backup every hour locally and a full backup every day at 3 AM, synchronizing with the off-site disaster recovery center. In the data analysis phase, historical data is first cleaned, transformed, and integrated using the Spark framework. Then, an association rule mining algorithm is used to analyze the matching relationship between different types of emergency events and ticket adjustment plans, extracting the optimal execution strategy. Finally, the analysis results are output to the level classification module and the algorithm decision module for model parameter updates and algorithm iteration optimization, forming a closed-loop mechanism of data-analysis-optimization.

[0015] Furthermore, the security protection unit of the auxiliary module is implemented according to the following technical steps: cross-platform data transmission adopts the SSL / TLS 1.3 encryption protocol to establish an end-to-end encrypted communication channel to prevent data from being stolen or tampered with during transmission; system access adopts a role-based access control mechanism, dividing operation permissions into three levels: system administrator, maintenance personnel, and ordinary operators, with different levels corresponding to different operation permission ranges; an operation log recording function is set up to record all system operations in their entirety, with log content including the operator, operation time, operation content, operation result, and device IP address information, and log data is retained for one year and cannot be tampered with; regular security vulnerability scanning and penetration testing are conducted, with system vulnerabilities scanned monthly and a third-party security organization invited to conduct penetration testing quarterly, and an emergency remediation process is immediately initiated upon discovery of vulnerabilities, completing vulnerability patching within 24 hours, while updating security protection strategies to ensure the system's ability to resist security risks such as network attacks and data leaks.

[0016] Compared with existing technologies, this smart scenic area cross-platform ticketing allocation system that integrates big data analytics has the following beneficial effects: I. This system collects real-time visitor flow monitoring data and emergency warning information within the scenic area. Utilizing a multi-dimensional weighted scoring algorithm, it constructs a model for classifying emergencies into different levels, enabling rapid and accurate categorization. This tiered processing mechanism allows scenic area management to immediately implement corresponding ticketing adjustments for different levels of emergencies, such as suspending ticket sales in high-risk areas or opening free rescheduling / refund channels for already purchased tickets. This significantly improves the speed and accuracy of emergency response, effectively ensuring visitor safety.

[0017] Second, this system employs a deep Q-network reinforcement learning algorithm to comprehensively consider the level of emergencies, real-time passenger flow distribution, scenic area capacity limits, and cross-platform ticket sales status to generate an optimal ticketing adjustment plan. This plan not only helps to rationally allocate scenic area resources and avoid safety hazards caused by excessive passenger flow, but also effectively guides tourists to divert their flow by sending diversion ticketing recommendations to surrounding scenic areas, thus reducing pressure on popular scenic spots. At the same time, the system provides an online exclusive change and refund channel and intelligent customer service, which greatly simplifies the change and refund process, improves the tourist experience, and ensures that tourists can quickly obtain assistance in case of emergencies, thereby improving overall tourist satisfaction.

[0018] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0020] Figure 1 A flowchart for a smart scenic area cross-platform ticketing allocation system integrating big data analytics; Figure 2 A flowchart illustrating the specific implementation process of the data acquisition module in a smart scenic area cross-platform ticketing allocation system that integrates big data analytics; Figure 3 A flowchart illustrating the dynamic adjustment function of the grade classification module in a smart scenic area cross-platform ticketing allocation system that integrates big data analytics. Detailed Implementation

[0021] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation methods, structures, features, and effects of the present invention. Embodiment 1: Infrared sensors and high-definition video monitoring equipment are deployed in the core areas of each scenic spot, at major turning points of passageways, and at entrance and exit gates. For areas prone to congestion and high risk during heavy rain, such as narrow mountain roads and open-air viewing platforms, the deployment density of the equipment is further increased. All equipment is connected to the scenic area's local area network via a 5G industrial gateway to ensure synchronous data transmission. After startup, the equipment performs a 30-second self-test calibration, and then collects real-time visitor flow monitoring data such as the number of people, visitor density, and passage speed at each scenic spot at a preset frequency. Simultaneously, the emergency early warning unit connects to the meteorological department's early warning platform via a RESTful API interface; interface compatibility testing and data format standardization conversion were completed before the connection. Upon receiving an orange rainstorm warning, the system first identifies and removes abnormal data using a rule engine. Then, it employs a weighted average filtering technique to reduce noise in the remaining data. During the filtering process, weights are automatically assigned according to the device's accuracy level. Finally, the processed rainstorm warning information, along with passenger flow monitoring data, is synchronously transmitted to the level classification module. Figure 2 As shown.

[0022] The rating module calls a multi-dimensional combined weighted scoring algorithm, the formula of which is: in A comprehensive score for emergencies. To evaluate the number of indicators, For the first The combined weights of the indicators For the first The system calculates a comprehensive score for the emergency by combining quantitative scores of various indicators with evaluation indicators such as the intensity of the rainstorm warning, the scope of impact, real-time visitor flow distribution in the scenic area, and the resilience of facilities. Ultimately, the rainstorm event is classified as a Level II major event. The system then activates a dynamic event level monitoring mode, collecting real-time data on core indicators every 5 minutes, including the spread of the rainstorm's impact, the changing trend of its intensity, the extension of its duration, the escalation of the threat to visitor safety, and the damage to facilities such as scenic trails and guardrails. After each data collection, the system automatically calls the level classification model to recalculate the comprehensive score and compares it with the previous score, tracking changes in the event level in real time. Figure 3 As shown.

[0023] The algorithm decision module uses a deep Q-network reinforcement learning algorithm, and the formula is: ,in For the overall reward value, To achieve the optimal passenger flow control volume in emergency scenarios, This refers to the actual passenger flow control volume after the plan is implemented. This is the preset maximum response time for tourist change and refund services. This represents the average response time for changes or cancellations after the solution is implemented. The target value for cross-platform collaborative response rate. To determine the actual cross-platform collaborative response rate, Weighting of passenger flow control effectiveness Prioritizing the protection of tourists' rights To optimize cross-platform collaboration efficiency, the core input data includes Level II emergency response level, real-time visitor flow distribution data in various areas of the scenic area, the overall carrying capacity of the scenic area and its sub-areas, and the real-time ticket sales status across different platforms. Simultaneously, a comprehensive reward function and a resource carrying capacity matching algorithm are introduced, with the following formula: in To improve the matching degree of emergency resources, The total capacity of emergency resources in the scenic area. The resource utilization efficiency coefficient. The number of affected attractions For the first Real-time visitor numbers for each attraction. For the first The emergency resource demand coefficient of each scenic spot was determined by comprehensively considering the effectiveness of visitor flow control, the level of protection of tourists' rights, the efficiency of cross-platform collaboration, and the matching of emergency resources of the scenic spot with the affected areas. This resulted in a targeted ticketing adjustment plan, explicitly requiring the suspension of ticket sales in high-risk mountainous areas, opening a free rescheduling / refund channel for already purchased tickets, and linking with surrounding low-altitude plain scenic spots to send diversion ticketing recommendations. Figure 1 As shown.

[0024] The ticketing sales control unit of the execution module had pre-signed API interface integration agreements with various cross-platform sales channels and completed a 7-day joint debugging test, fully verifying the real-time performance and accuracy of ticketing status synchronization. Upon receiving the suspension sales instruction from the algorithm decision module, the system immediately sends control signals to each platform through an encrypted communication channel. These signals contain key information such as high-risk area codes, suspension periods, and locked indicators for sold but unverified tickets. Each platform, upon receiving the signal, first verifies the validity of the signal signature before executing the ticket removal operation. Simultaneously, it locks the secondary transfer function for sold tickets in the corresponding area and promptly reports the execution result to the system after completion. If any platform fails to report the execution result within 10 seconds, the system automatically activates a backup communication channel to resend the instruction and triggers an alarm notification for maintenance personnel. The system monitors the ticketing sales status of each platform in real time throughout the process, ensuring that all tickets in high-risk areas are suspended without omission or delay.

[0025] Meanwhile, the user service unit categorizes and filters ticketed users, prioritizing them according to a preset priority: group ticket holders from high-risk areas with departure times less than 24 hours ago, individual ticket holders from high-risk areas with departure times less than 24 hours ago, group ticket holders from high-risk areas with departure times 24-48 hours ago, individual ticket holders from high-risk areas with departure times 24-48 hours ago, and users from non-high-risk areas affected by the rainstorm. Service notifications are simultaneously sent via SMS, app push notifications, and WeChat official account notifications according to this priority. The notifications include an explanation of the rainstorm warning, access to the refund / change channel, recommended nearby attractions, and contact information for intelligent customer service. An online refund / change channel is available; users can directly access the operation page by clicking the link in the notification without login verification. The refund / change process is simplified to three steps: selecting the operation type, confirming information, and submitting the application. The system automatically reviews and provides real-time feedback. Intelligent customer service uses voice recognition and semantic understanding technology to quickly respond to user inquiries and automatically generate standard answers for common questions.

[0026] The auxiliary module's data storage and analysis unit employs a Hadoop distributed file system to construct its storage architecture. Real-time data streams related to this rainstorm event, historical business data from similar rainstorm events, and model training data are stored separately in different node clusters according to their type. Real-time data stream nodes use SSDs to ensure fast read / write speeds, while historical business data nodes use mechanical hard drives to reduce storage costs. Data is anonymized before storage; sensitive information such as tourist ID numbers and mobile phone numbers is processed using irreversible encryption algorithms, retaining only necessary ticket-related identification information. Data backup employs a strategy of local incremental backup plus off-site full backup.

[0027] During the data analysis phase, historical rainstorm event data is cleaned, transformed, and integrated using the Spark framework. Then, association rule mining algorithms are employed to analyze the matching relationship between this type of emergency event and ticketing adjustment plans, extracting the optimal execution strategy. The analysis results are output to the level classification module and algorithm decision module for model parameter updates and algorithm iteration optimization. The security protection unit uses SSL / TLS 1.3 encryption protocols during cross-platform data transmission to establish an end-to-end encrypted communication channel, preventing data theft or tampering during transmission. System access employs a role-based access control mechanism, dividing operation permissions into three levels: system administrator, maintenance personnel, and ordinary operators, each with different operation permission ranges. An operation log recording function is implemented to record all system operations, including the operator, operation time, operation content, operation result, and device IP address information. System vulnerability scans are conducted monthly as planned, and a third-party security organization is invited to conduct penetration testing quarterly. Upon discovering vulnerabilities, an emergency remediation process is immediately initiated, with vulnerability patching completed within 24 hours, and security protection strategies updated simultaneously. Example 2: The scenic area has deployed infrared sensors and high-definition video surveillance equipment in the core sightseeing areas, turning points of entrance and exit channels, sightseeing bus pick-up and drop-off points, and entrance and exit gates. For narrow and congested areas such as sightseeing bus transfer channels, the equipment deployment density has been increased. All equipment is connected to the scenic area's local area network via a 5G industrial gateway for synchronized data transmission. After startup, the equipment performs a 30-second self-test calibration, and then collects real-time visitor flow monitoring data, such as the number of people, visitor density, and passage speed, in the lake island scenic area and surrounding areas at a preset frequency. The emergency warning unit connects to the scenic area's equipment operation and maintenance system via a RESTful API interface. Interface compatibility testing and data format standardization conversion were completed before the connection. Upon receiving equipment fault information from the core control module of the sightseeing bus dispatch system, the system first identifies and removes abnormal data using a rule engine, then uses weighted average filtering technology to denoise the remaining data. During the filtering process, weights are automatically assigned according to the equipment accuracy level. The processed equipment fault information and visitor flow monitoring data are then synchronously transmitted to the level classification module. Figure 2 As shown.

[0028] The grading module utilizes a multi-dimensional weighted scoring algorithm, combining evaluation indicators such as the impact range and severity of the sightseeing bus dispatch system malfunction, real-time passenger flow data, and the difficulty of repair, to calculate a comprehensive score for the emergency, ultimately classifying the event as a Level III major event. The system activates a dynamic event grading monitoring mode, collecting real-time data on core indicators every 5 minutes, including the spread of the malfunction's impact, the changing trend of its severity, the extension of the malfunction's duration, changes in the threat to tourist safety, and the progress of the sightseeing bus dispatch system's repair. After each data collection, the system automatically calls the grading model to recalculate the comprehensive score, comparing it with the previous score to dynamically track changes in the event grading level. Figure 3 As shown.

[0029] The algorithm decision-making module employs a deep Q-network reinforcement learning algorithm, using the Level III emergency level, real-time visitor flow distribution in the lake island scenic area and surrounding areas, the carrying capacity limit of the scenic area, and the real-time ticket sales status of various cross-platforms as core input data. Simultaneously, it introduces a comprehensive reward function and resource carrying capacity matching algorithm to comprehensively consider the effectiveness of visitor flow control, the level of protection of tourists' rights, cross-platform collaboration efficiency, and the matching of the scenic area's existing emergency resources with the needs of tourists in the affected area. This generates a corresponding ticketing adjustment plan, explicitly requiring the suspension of ticket sales for the lake island scenic area and related tour routes, opening a free rescheduling / refund channel for ticketed users, and linking with other independent attractions within the scenic area to send diversion ticketing recommendations, such as... Figure 1 As shown.

[0030] The ticketing sales control unit of the execution module has pre-signed API interface integration agreements with various cross-platform sales channels and completed a 7-day joint debugging test to ensure the real-time and accurate synchronization of ticketing status. Upon receiving a suspension order from the algorithm decision module, the system sends control signals to each platform via an encrypted communication channel. These signals include key information such as the area code for the Lake Island scenic area and related routes, the suspension period, and the lockout identifier for sold but unverified tickets. Each platform, upon receiving the signal, first verifies the validity of the signal signature, then executes the ticket removal operation, simultaneously locking the secondary transfer function of sold tickets in the corresponding area. After the operation is completed, it reports the execution result back to the system. If any platform reports execution failure, the system immediately activates a backup communication channel to resend the instruction and triggers an alarm notification for maintenance personnel. The system monitors the ticketing sales status of each platform in real time throughout the process to ensure a complete suspension of ticket sales in the target area.

[0031] The user service unit categorizes and filters ticketed users, prioritizing them according to a preset priority: group ticket holders for the Lake Island Scenic Area and related routes with departure times less than 24 hours, individual ticket holders for the Lake Island Scenic Area and related routes with departure times less than 24 hours, group ticket holders for the Lake Island Scenic Area and related routes with departure times between 24 and 48 hours, individual ticket holders for the Lake Island Scenic Area and related routes with departure times between 24 and 48 hours, and users outside the target area affected by the malfunction. Service notifications are simultaneously sent via SMS, app push notifications, and WeChat official account notifications according to this priority. The notifications include a description of the sightseeing bus dispatch system malfunction, access to the refund / change channel, recommended diversion attractions within the scenic area, and contact information for intelligent customer service. An online refund / change channel is available; users can directly access the operation page by clicking the link in the notification without login verification. The refund / change process is simplified to three steps: selecting the operation type, confirming information, and submitting the application. The system automatically reviews and provides real-time feedback. Intelligent customer service uses voice recognition and semantic understanding technology to quickly respond to user inquiries and automatically generate standard answers for common questions.

[0032] The auxiliary module's data storage and analysis unit employs a Hadoop distributed file system to construct its storage architecture. Real-time data streams from the current equipment failure event, historical equipment failure-related business data, and model training data are stored separately in different node clusters according to their type. Real-time data stream nodes use SSDs to ensure fast read / write speeds, while historical business data nodes use mechanical hard drives to reduce storage costs. Data is anonymized before storage; sensitive information such as tourist ID numbers and mobile phone numbers is processed using irreversible encryption algorithms, retaining only necessary ticketing-related identification information. Data backup employs a combination of local incremental backups and off-site full backups. During the data analysis phase, the Spark framework is used to clean, transform, and integrate historical equipment failure-related emergency event data. Then, association rule mining algorithms are used to analyze the matching relationship between these events and ticketing adjustment plans, extracting the optimal execution strategy. The analysis results are output to the level classification module and algorithm decision module for model parameter updates and algorithm iteration optimization.

[0033] The security protection unit employs SSL / TLS 1.3 encryption protocol in cross-platform data transmission to establish an end-to-end encrypted communication channel; system access adopts a role-based access control mechanism, dividing access into three permission levels: system administrator, maintenance personnel, and ordinary operators; an operation log recording function is set up to record all system operations in their entirety, including the operator, operation time, operation content, operation result, and device IP address information; a system vulnerability scan is performed monthly, and a penetration test is conducted quarterly by a third-party security organization. After a vulnerability is discovered, it is repaired and the security protection strategy is updated within 24 hours to ensure the secure and stable operation of the system.

[0034] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A smart scenic area cross-platform ticketing allocation system integrating big data analytics, characterized in that: The system comprises the following components: a data acquisition module, a classification module, an algorithm decision-making module, an execution module, and an auxiliary module; The data acquisition module collects real-time visitor flow monitoring data and emergency event warning information within the scenic area. The visitor flow monitoring data includes real-time visitor numbers, visitor density, and traffic speed at each attraction. The emergency event warning information includes extreme weather warnings, equipment malfunction information, and safety incident warnings. The classification module: Based on the data acquired by the data acquisition module, it constructs a classification model for emergencies, classifying emergencies into levels I-IV, where level I is an extremely serious event, level II is a serious event, level III is a relatively serious event, and level IV is a general event; The algorithm decision module adopts a deep Q-network reinforcement learning algorithm, taking the level of the emergency, real-time passenger flow distribution, scenic area carrying capacity limit, and cross-platform ticket sales status as inputs to generate corresponding ticket adjustment plans. The execution module, based on the ticketing adjustment plan output by the algorithm decision module, performs the following operations: suspending ticket sales in high-risk areas, opening a free rescheduling / refund channel for users who have already purchased tickets, and coordinating with surrounding scenic spots to send diversion ticketing recommendations. The auxiliary module includes a data storage and analysis unit and a security protection unit, which are used for data storage and model optimization, and data security and operation standard protection, respectively.

2. The smart scenic area cross-platform ticketing allocation system integrating big data analysis as described in claim 1, characterized in that, The passenger flow monitoring unit of the data acquisition module is implemented according to the following technical steps: Infrared sensors and high-definition video surveillance equipment are deployed in the core areas of each scenic spot, major passage turning points, and entrance and exit gates of the scenic area. The deployment density of equipment is increased in narrow passages and areas prone to congestion. All equipment is connected to the scenic area's local area network through a 5G industrial gateway to achieve data synchronization. After the equipment is started, it first performs a 30-second self-test calibration, and then collects data in real time at a preset frequency. During the collection process, frame synchronization technology is used to align the timestamps of data from multiple devices. The emergency early warning unit connects to the meteorological department's early warning platform, equipment operation and maintenance system, and safety monitoring center through a RESTful API interface. Before the connection, interface compatibility testing and data format standardization conversion are completed. After receiving the data, abnormal data is first identified through a rule engine, and then a rejection operation is performed. Subsequently, weighted average filtering technology is used to denoise the remaining data. During the filtering process, weights are automatically assigned according to the accuracy level of the equipment.

3. The smart scenic area cross-platform ticketing allocation system integrating big data analysis as described in claim 1, characterized in that, The emergency level classification model of the classification module adopts a multi-dimensional combined weight scoring algorithm, the formula of which is: ,in A comprehensive score for emergencies. To evaluate the number of indicators, For the first The combined weights of the indicators For the first Quantitative scoring of each indicator.

4. The smart scenic area cross-platform ticketing allocation system integrating big data analysis as described in claim 1, characterized in that, The dynamic adjustment function of the rating module is implemented according to the following technical steps: The system starts the event rating dynamic monitoring mode, and collects real-time data of core indicators every 5 minutes, including the spread of the event's impact, the changing trend of the impact degree, the extension or shortening of the duration, the mitigation or aggravation of tourist safety threats, and the repair progress of damaged facilities; after the data collection is completed, the rating model is automatically called to recalculate the comprehensive score. The results are compared with the previous rating. If the rating improves and exceeds the level threshold, a stricter ticketing adjustment plan is immediately triggered. If the rating decreases and exceeds the level threshold, ticket sales are gradually reopened according to a tiered recovery strategy. Tickets for some time slots in low-risk areas are reopened first, and the recovery of passenger flow is monitored in real time. If the passenger flow does not exceed 60% of the area's capacity within 30 minutes, tickets for more time slots or areas are reopened until the event level drops to level IV and stabilizes for 30 minutes, at which point normal ticket sales are restored.

5. The smart scenic area cross-platform ticketing allocation system integrating big data analysis as described in claim 1, characterized in that, The reward function for the DQN reinforcement learning algorithm in the algorithm decision module is designed as follows: ,in For the overall reward value, To achieve the optimal passenger flow control volume in emergency scenarios, This refers to the actual passenger flow control volume after the plan is implemented. This is the preset maximum response time for tourist change and refund services. This represents the average response time for changes or cancellations after the solution is implemented. The target value for cross-platform collaborative response rate. To determine the actual cross-platform collaborative response rate, Weighting of passenger flow control effectiveness Prioritizing the protection of tourists' rights Weighting for cross-platform collaboration efficiency.

6. The smart scenic area cross-platform ticketing allocation system integrating big data analysis as described in claim 1, characterized in that, The emergency resource matching function of the algorithm decision module adopts a resource carrying capacity matching algorithm, the formula of which is: in To improve the matching degree of emergency resources, The total capacity of emergency resources in the scenic area. The resource utilization efficiency coefficient. The number of affected attractions For the first Real-time visitor numbers for each attraction. For the first Emergency resource demand coefficient for each scenic spot.

7. The smart scenic area cross-platform ticketing allocation system integrating big data analysis as described in claim 1, characterized in that, The ticketing sales control unit of the execution module is implemented according to the following technical steps: First, an API interface docking agreement is signed with each cross-platform sales channel to clarify the data transmission specifications and security requirements. After docking is completed, a 7-day joint debugging test is conducted to verify the real-time performance and accuracy of ticketing status synchronization. In emergency scenarios, after receiving the suspension sales instruction from the algorithm decision module, the system immediately sends control signals to each platform through an encrypted communication channel. The signals contain key information such as high-risk area codes, suspension sales periods, and locked-up sold but unverified tickets. After receiving the signal, each platform first verifies the validity of the signal signature, then executes the ticket removal operation, and simultaneously locks the secondary transfer function of the sold tickets in the corresponding area. After the operation is completed, the platform reports the execution result to the system. If a platform fails to report within 10 seconds or reports execution failure, the system automatically starts the backup communication channel to resend the instruction and triggers an alarm notification to the maintenance personnel. Throughout the process, the system monitors the ticket sales status of each platform in real time to ensure that ticket sales in high-risk areas are completely suspended without any omissions or delays.

8. The smart scenic area cross-platform ticketing allocation system integrating big data analysis as described in claim 1, characterized in that, The user service unit of the execution module is implemented according to the following technical steps: After receiving the ticketing adjustment plan, users who have purchased tickets are first classified and screened. Priority is then assigned based on three dimensions: whether the purchased area is a high-risk area, whether the departure time from the scenic spot is less than 48 hours, and whether they are group ticket users. The priority from highest to lowest is as follows: high-risk area + departure time < 24 hours + group ticket users, high-risk area + departure time < 24 hours + individual users, high-risk area + departure time 24-48 hours + group ticket users, high-risk area + departure time 24-48 hours + individual users, and non-high-risk area users. For users affected by the event, service notifications will be sent simultaneously via SMS, app push notifications, and WeChat official account notifications, prioritizing the notifications. The notifications will include an event description, access to the refund / modification channel, referral recommendations, and contact information for intelligent customer service. An online refund / modification channel will be opened, allowing users to directly access the operation page by clicking the link in the notification. No login verification is required, and the refund / modification process is simplified to three steps: selecting the operation type, confirming information, and submitting the application. The system will automatically review the application and provide real-time feedback. Intelligent customer service utilizes voice recognition and semantic understanding technology to quickly respond to user inquiries and automatically generate standard answers for common questions.

9. The smart scenic area cross-platform ticketing allocation system integrating big data analysis as described in claim 1, characterized in that, The data storage and analysis unit of the auxiliary module is implemented according to the following technical steps: A storage architecture is constructed using the Hadoop Distributed File System. Data is divided into three categories: real-time data streams, historical business data, and model training data, which are stored in different node clusters. Real-time data stream nodes use SSDs to ensure read / write speeds, while historical business data nodes use mechanical hard drives to reduce storage costs. Data is desensitized before storage, and sensitive information such as tourist ID numbers and mobile phone numbers is processed using irreversible encryption algorithms, retaining only necessary identification information associated with ticketing. Data backup employs a strategy of local incremental backup + off-site full backup. In the data analysis phase, historical data is first cleaned, transformed, and integrated using the Spark framework. Then, association rule mining algorithms are used to analyze the matching relationship between different types of emergency events and ticketing adjustment plans, extract the optimal execution strategy, and finally output the analysis results to the level classification module and algorithm decision module for model parameter updates and algorithm iteration optimization.

10. The smart scenic area cross-platform ticketing allocation system integrating big data analysis as described in claim 1, characterized in that, The security protection unit of the auxiliary module is implemented according to the following technical steps: cross-platform data transmission adopts the SSL / TLS 1.3 encryption protocol to establish an end-to-end encrypted communication channel to prevent data from being stolen or tampered with during transmission; system access adopts a role-based access control mechanism, dividing operation permissions into three levels: system administrator, maintenance personnel, and ordinary operators, with different levels corresponding to different operation permission ranges; an operation log recording function is set up to record all system operations in their entirety, and the log content includes the operator, operation time, operation content, operation result, and device IP address information; Regularly conduct security vulnerability scans and penetration tests, scanning for system vulnerabilities monthly and inviting third-party security organizations to conduct penetration tests quarterly. Upon discovering vulnerabilities, immediately initiate emergency remediation procedures, complete vulnerability patching within 24 hours, and update security protection strategies.

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