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CN-121998339-A - Intelligent scenic spot cross-platform ticket distribution system integrating big data analysis

CN121998339ACN 121998339 ACN121998339 ACN 121998339ACN-121998339-A

Abstract

The invention discloses a cross-platform ticket distribution system for intelligent scenic spots integrating big data analysis, and relates to the technical field of intelligent travel and ticket management; according to the invention, passenger flow monitoring data and emergency early warning information in a scenic spot are collected in real time, the emergency grade division model is constructed by utilizing a multidimensional combination weight scoring algorithm, and the emergency can be rapidly and accurately divided into different grades, and the grading processing mechanism enables scenic spot management parties to immediately take corresponding ticket adjusting measures aiming at the emergency of different grades, such as suspending ticket sales in a high risk area, opening free diversion/ticket returning channels of ticket-purchased users and the like, so that the speed and the accuracy of emergency response are remarkably improved, and the safety of tourists is effectively ensured.

Inventors

  • ZHOU LILI

Assignees

  • 上海卓磐网络科技有限公司

Dates

Publication Date
20260508
Application Date
20260124

Claims (10)

  1. 1. The intelligent scenic spot cross-platform ticket distribution system integrating big data analysis is characterized by comprising a data acquisition module, a grading module, an algorithm decision module, an execution module and an auxiliary module; The data acquisition module acquires passenger flow monitoring data and emergency early warning information in scenic spots in real time, wherein the passenger flow monitoring data comprises the number of people in real time, the density of people and the passing speed of each scenic spot, and the emergency early warning information comprises extreme weather early warning, equipment fault information and safety event early warning; the grading module is used for constructing an emergency grading model based on the data acquired by the data acquisition module and grading the emergency into I-IV grades, wherein the I grade is a special major event, the II grade is a major event, the III grade is a major event and the IV grade is a general event; The algorithm decision module adopts a deep Q network reinforcement learning algorithm, takes the sudden event grade, real-time passenger flow distribution, scenic spot bearing limit and cross-platform ticket sale state as input, and generates a corresponding ticket adjustment scheme; The execution module executes operations of suspending ticket sales in a high risk area, opening free diversion/ticket return channels of the ticket purchasing users and linking surrounding scenic spots to send shunting ticket recommendation information according to the ticket adjustment scheme output by the algorithm decision module; The auxiliary module comprises a data storage and analysis unit and a safety protection unit, and is respectively used for data storage and model optimization, data safety and operation standard guarantee.
  2. 2. The intelligent scenic spot cross-platform ticket distribution system integrating big data analysis according to claim 1 is characterized in that a passenger flow monitoring unit of the data acquisition module is implemented according to the following technical steps of deploying infrared sensors and high-definition video monitoring equipment at core areas, main channel inflection points and entrance gates of all scenic spots of the scenic spot, encrypting equipment deployment density in narrow channels and areas prone to congestion, enabling all equipment to access a scenic area network through a 5G industrial gateway to achieve data synchronization, performing self-checking calibration for 30 seconds after equipment is started, then acquiring data in real time according to preset frequency, achieving multi-equipment data time stamp alignment in the acquisition process through a frame synchronization technology, abutting a weather department early warning platform, an equipment operation and maintenance system and a safety monitoring center through RESTfulAPI interfaces, completing interface compatibility test and data format standardized conversion before abutting, identifying abnormal data through a rule engine after receiving data, then performing rejection operation, performing noise reduction filtering technology on the residual data, and automatically distributing noise reduction weight according to equipment precision level in the filtering process.
  3. 3. The intelligent scenic spot cross-platform ticketing distribution system integrated with big data analysis according to claim 1, wherein the emergency grading model of the grading module adopts a multidimensional combination weight scoring algorithm, and the formula is: Wherein For the comprehensive scoring of the emergency event, In order to evaluate the number of indicators, Is the first The combining weights of the individual indicators are used, Is the first Quantitative scoring of the individual indicators.
  4. 4. The intelligent scenic spot cross-platform ticket distribution system integrating big data analysis according to claim 1, wherein the dynamic adjustment function of the grading module is implemented by the following technical steps that the system starts an event grade dynamic monitoring mode, and real-time data of a core index is collected every 5 minutes, wherein the real-time data comprise the diffusion condition of an event influence range, the change trend of influence degree, the extension or shortening condition of duration, the alleviation or aggravation state of tourist security threat and the repair progress of facility damage, and the grading module is automatically called to recalculate comprehensive scores after the collection is completed And if the score is increased and the grade threshold value is crossed, immediately triggering a stricter ticket adjustment scheme, if the score is reduced and the grade threshold value is crossed, gradually opening ticket sales according to a stepwise recovery strategy, recovering partial time period ticket of a low risk area, monitoring the recovery condition of the passenger flow in real time, and if the passenger flow does not exceed 60% of the bearing capacity of the area within 30 minutes, opening ticket of more time periods or areas until the event grade is reduced to IV grade and is stable for 30 minutes, and recovering a normal ticket sales mode.
  5. 5. The intelligent scenic spot cross-platform ticketing distribution system integrated with big data analysis according to claim 1, wherein the DQN reinforcement learning algorithm of the algorithm decision module designs a reward function with the formula: Wherein In order to integrate the prize values, For optimal passenger flow control in emergency scenarios, For the actual passenger flow control amount after the scheme execution, The longest response time is serviced for the preset guest, For the average change response time after execution of the scheme, To coordinate the response rate target value across platforms, For an actual cross-platform co-response rate, For the purpose of the effect weight of passenger flow control, The weight is guaranteed for the rights and interests of the tourist, Is a cross-platform collaborative efficiency weight.
  6. 6. The intelligent scenic spot cross-platform ticketing distribution system integrated with big data analysis according to claim 1, wherein the emergency resource matching function of the algorithm decision module adopts a resource bearing capacity matching algorithm, and the formula is: Wherein the method comprises the steps of For the degree of matching of the emergency resources, For the total bearing capacity of emergency resources in scenic spots, As a coefficient of the efficiency of the utilization of the resources, In order to be able to influence the number of attractions, Is the first The number of real-time guests at each attraction, Is the first Emergency resource demand coefficients for individual attractions.
  7. 7. The intelligent scenic spot cross-platform ticket distribution system integrating big data analysis according to claim 1 is characterized in that ticket sales control units of the execution modules are implemented according to the following technical steps of firstly signing an API interface docking protocol with each cross-platform sales channel, defining data transmission standards and safety requirements, conducting joint debugging test for 7 days after docking is completed, verifying real-time performance and accuracy of ticket state synchronization, immediately sending control signals to each platform through an encryption communication channel after receiving a suspension sales instruction of an algorithm decision module in an emergency scene, wherein the signals comprise key information such as high risk area codes, suspension sales time periods, sold and un-verified ticket locking identification, and the like, after each platform receives the signals, carrying out prior certificate signal signature validity, then executing ticket shelving operation, simultaneously locking a secondary transfer function of the sold ticket in the corresponding area, feeding back an execution result to a system after the operation is completed, and automatically starting a standby communication channel to resend the instruction and sending an alarm notification to an operator if a certain platform does not feed back or feed back the execution failure within 10 seconds, and monitoring the ticket sales state of each platform in real time in the whole process, so that ticket sales state of each platform is monitored in real time, and ticket sales is completely delayed or the ticket is not suspended, and the ticket is guaranteed.
  8. 8. The intelligent scenic spot cross-platform ticket distribution system integrating big data analysis according to claim 1 is characterized in that a user service unit of the execution module is implemented according to the following technical steps that after receiving a ticket adjustment scheme, classified screening is firstly carried out on ticket purchased users, priority ordering is carried out on three dimensions of a team ticket user according to whether the ticket purchased areas belong to high risk areas, whether the departure time of the scenic spot is smaller than 48 hours or not, the priority ordering is carried out on the basis of priority order, the priority ordering is carried out on the basis of high risk areas, the departure time is smaller than 24 hours, the team ticket users, the high risk areas, the departure time is smaller than 24 hours, the scattered passenger users, the departure time is 24-48 hours, the group ticket users, the high risk areas, the scattered users and the users which are affected by events are, three channel synchronous sending service notices are notified through short messages, APP pushing and micro-message public numbers, notification content includes event notices, a diversion recommendation information and intelligent customer service contact mode, the priority order is from high risk area, the high risk area is changed into a page, the user is required to enter a link, the user is required to be directly read by a user, the user is required to enter a real-time operation is required to be automatically to be verified, a user is required to enter a real-time operation, and a user is required to be automatically read, and a real-time operation is required to be automatically to be verified, and a user is required to be automatically to be read, and a user is required to be automatically to be read.
  9. 9. The intelligent scenic spot cross-platform ticket distribution system integrating big data analysis according to claim 1 is characterized in that a data storage and analysis unit of the auxiliary module is implemented according to the following technical steps of adopting a Hadoop distributed file system to construct a storage architecture, dividing data into three types of real-time data streams, historical service data and model training data according to types, respectively storing the three types of data into different node clusters, enabling real-time data stream nodes to guarantee read-write speed by adopting SSD hard disks, enabling the historical service data nodes to reduce storage cost by adopting mechanical hard disks, conducting desensitization processing before data storage, processing tourist identity card number and mobile phone number sensitive information by adopting an irreversible encryption algorithm, only preserving necessary identification information related to ticket, adopting a local incremental backup and remote full-volume backup strategy for data backup, conducting cleaning, conversion and integrated processing on historical data through a Spark framework, analyzing matching relations between different types of emergency events and ticket adjustment schemes by adopting a correlation rule mining algorithm, extracting an optimal execution strategy, and finally outputting analysis results to a grading module and a decision module for parameter updating and iterative optimization algorithm of the model.
  10. 10. The intelligent scenic spot cross-platform ticket distribution system integrating big data analysis according to claim 1 is characterized in that a security protection unit of the auxiliary module is implemented according to the following technical steps of adopting an SSL/TLS1.3 encryption protocol for cross-platform data transmission, establishing an end-to-end encryption communication channel to prevent data from being stolen or tampered in the transmission process, adopting a role-based access control mechanism for system access, dividing operation authorities into three levels of system administrators, operation and maintenance personnel and common operators, enabling the different levels to correspond to different operation authority ranges, setting an operation log recording function, performing whole-course recording on all system operations, wherein the log contents comprise operators, operation time, operation content, operation results and equipment IP address information, periodically performing security scanning and penetration testing, scanning once per month, inviting a third party security mechanism to perform penetration testing once per quarter, immediately starting an emergency repair flow after finding out the system holes, completing repairing the holes within 24 hours, and updating security protection strategies.

Description

Intelligent scenic spot cross-platform ticket distribution system integrating big data analysis Technical Field The invention relates to the technical field of intelligent travel and ticket management, in particular to a cross-platform ticket distribution system for intelligent scenic spots integrating big data analysis. Background With the rapid development of the tourist industry, the number of tourists in the scenic spot is rapidly increased, and how to efficiently and safely manage the passenger flow in the scenic spot becomes a problem to be solved, especially when sudden events such as extreme weather, equipment failure or safety events are faced, the traditional manual management mode is difficult to rapidly respond, so that the safety of the tourists is threatened, and the operation order of the scenic spot is disordered. The conventional scenic spot ticket management and emergency response mechanism mainly relies on manual monitoring and experience judgment, and has the obvious defects that firstly, data acquisition is incomplete and timeliness is poor, passenger flow conditions and emergency information of all scenic spots in a scenic spot are difficult to master in real time, emergency response is lagged, secondly, a scientific hierarchical processing mechanism is lacking, the severity evaluation of the emergency is high in subjectivity, event grades cannot be rapidly and accurately classified and corresponding countermeasures cannot be adopted, and thirdly, a ticket adjustment scheme is lack of data support and is often based on experience rather than real-time data analysis, so that resource allocation is unreasonable, passenger flow cannot be effectively dredged, and even scenic spot congestion and potential safety hazards are possibly aggravated. Aiming at a plurality of problems existing in the conventional scenic spot ticket management and emergency response mechanism, the intelligent scenic spot cross-platform ticket distribution system integrating big data analysis is generated. Disclosure of Invention The invention aims to make up the defects of the prior art and provides an intelligent scenic spot cross-platform ticket distribution system integrating big data analysis, which can construct an emergency grade division model by collecting and analyzing passenger flow monitoring data and emergency early warning information in scenic spots in real time and utilizing a multi-dimensional combined weight scoring algorithm, thereby realizing rapid and accurate grading and response of emergency. The intelligent scenic spot cross-platform ticket distribution system integrating big data analysis comprises a data acquisition module, a grading module, an algorithm decision module, an execution module and an auxiliary module; The data acquisition module acquires passenger flow monitoring data and emergency early warning information in scenic spots in real time, wherein the passenger flow monitoring data comprises the number of people in real time, the density of people and the passing speed of each scenic spot, and the emergency early warning information comprises extreme weather early warning, equipment fault information and safety event early warning; the grading module is used for constructing an emergency grading model based on the data acquired by the data acquisition module and grading the emergency into I-IV grades, wherein the I grade is a special major event, the II grade is a major event, the III grade is a major event and the IV grade is a general event; The algorithm decision module adopts a deep Q network reinforcement learning algorithm, takes the sudden event grade, real-time passenger flow distribution, scenic spot bearing limit and cross-platform ticket sale state as input, and generates a corresponding ticket adjustment scheme; the execution module executes operations of suspending ticket sales in a high risk area, opening free diversion/ticket returning channels of the ticket purchasing users and transmitting diversion ticket recommendation information in linkage surrounding scenic spots according to the ticket adjustment scheme output by the algorithm decision module, so that full-flow automatic processing from early warning to response is realized; The auxiliary module comprises a data storage and analysis unit and a safety protection unit, and is respectively used for data storage and model optimization, data safety and operation standard guarantee, and the stable and reliable operation of the system is ensured. The passenger flow monitoring unit of the data acquisition module is implemented according to the following technical steps that infrared sensors and high-definition video monitoring equipment are deployed in the core area, the main channel inflection point and the entrance gate of each scenic spot, the density of deployment of encryption equipment in a narrow channel and an area prone to congestion is achieved, all the equipment is connected to a scenic area network thro