Ticket inventory intelligent management system

Abstract

The invention relates to the technical field of ticket intelligent management, in particular to a ticket inventory intelligent management system, which is applied to scenic spot ticket selling terminal equipment, and is characterized in that a multi-source data acquisition module widely collects internal and external multivariate data of a scenic spot to provide support for subsequent decision making; the dynamic inventory distribution engine fuses an LSTM neural network and a random forest algorithm, accurately predicts ticket demands and optimizes a distribution scheme; the elastic inventory adjustment module flexibly adjusts the inventory by means of a multi-stage inventory buffer pool based on the prediction result, and generates an encryption certificate to ensure the operation safety; the conflict detection and arbitration unit detects and solves cross-platform inventory conflicts through a distributed algorithm; the adaptive learning module continuously optimizes the strategy, and the visual decision support interface visually displays key information to assist decision making. The scenic spot ticket management efficiency and scientificity are remarkably improved, reasonable inventory allocation and platform data consistency are guaranteed, and the system adaptability and decision accuracy are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of ticket intelligent management, and in particular to a ticket inventory intelligent management system. Background Art

[0002] With the vigorous development of tourism, the passenger flow of scenic spots is increasing day by day, and ticket management faces many challenges; the traditional ticket inventory management method can no longer meet the complex and changing operational needs of modern scenic spots, which is specifically reflected in the following aspects:

[0003] 1. Limitations of Static Inventory Management

[0004] Lack of dynamic adjustment capabilities: Traditional inventory management is usually based on fixed forecasts and settings, and cannot dynamically adjust ticket inventory based on real-time tourist flow, weather changes, emergencies and other factors. For example, during the peak tourist season or when special events are held, the number of tourists may far exceed expectations, and static inventory management may not be able to increase ticket supply in time, making it difficult for tourists to buy tickets, affecting tourist experience and scenic spot revenue. On the contrary, during the off-season or when encountering bad weather, ticket inventory may be piled up, resulting in a waste of resources.

[0005] Difficulty in adapting to market changes: The tourism market is affected by many factors, such as holidays, seasons, social media promotion, etc., and tourists' travel intentions and ticket purchasing behaviors are highly uncertain. Traditional static inventory management is difficult to respond quickly to these market changes and cannot adjust ticket prices and inventory allocation strategies in a timely manner, which puts scenic spots at a disadvantage in market competition.

[0006] 2. Inventory conflicts caused by multi-platform sales

[0007] Data inconsistency: Nowadays, scenic spot tickets are often sold through multiple channels, including scenic spot platforms, online travel agent (OTA) platforms, travel agency distribution systems, etc.; untimely or inaccurate data synchronization between different platforms can easily lead to inconsistent inventory data; for example, a platform shows that tickets are still available, but the actual scenic spot inventory is sold out, resulting in overselling, which brings unnecessary troubles to the scenic spot and tourists;

[0008] Lack of unified coordination mechanism: Each sales platform usually manages its own inventory independently, lacking a unified coordination and management mechanism. When the scenic spot needs to make overall adjustments to the ticket inventory, it is difficult to quickly and accurately communicate and implement it to each platform, resulting in chaotic inventory management and affecting the overall operational efficiency of the scenic spot.

[0009] 3. Insufficient support for decision making

[0010] Incomplete information acquisition: When making ticket inventory management decisions, scenic spot managers often rely on limited data and experience, and lack comprehensive acquisition and analysis of multi-dimensional information inside and outside the scenic spot. For example, they only consider historical ticket purchase data, but ignore factors such as real-time tourist flow, weather conditions, and competition from surrounding scenic spots, resulting in unscientific and unreasonable decisions.

[0011] Lack of visualization tools: Traditional ticket management systems lack intuitive visualization tools, making it difficult for managers to quickly understand key information such as the inventory status and visitor distribution of scenic spots. When faced with complex data and changing situations, it is difficult to make timely and accurate decisions, which affects the operational management efficiency of scenic spots.

[0012] IV. Tourist Experience and Resource Utilization

[0013] Tourist experience is affected: Due to poor inventory management, tourists may face problems such as inconvenience in purchasing tickets, long waiting times, and crowded scenic spots, which seriously affect tourists' travel experience and reduce the reputation and attractiveness of the scenic spots;

[0014] Unreasonable resource utilization: Unreasonable ticket inventory allocation may lead to excessive concentration of tourists in certain areas of the scenic area, while resources in other areas are idle, which makes it impossible to give full play to the resource utilization efficiency of the scenic area, and also increases the management cost and safety risk of the scenic area.

[0015] Therefore, in order to solve the above problems, an intelligent ticket inventory management system is proposed. Summary of the invention

[0016] The purpose of the present invention is to provide a ticket inventory intelligent management system to solve the problems raised in the above background technology.

[0017] To achieve the above object, the present invention provides the following technical solutions:

[0018] A ticket inventory intelligent management system, comprising:

[0019] Multi-source data collection module: real-time acquisition of tourist flow data, historical ticket purchase records, environmental monitoring data and ticketing information from external cooperation platforms, including online travel agent OTA platforms, travel agency distribution systems and official cooperation platforms of scenic spots;

[0020] Dynamic inventory allocation engine: connected to the multi-source data acquisition module, with a built-in ticket demand forecasting model based on spatiotemporal characteristics. The ticket demand forecasting model achieves multi-dimensional data fusion analysis by coupling the LSTM neural network with the random forest algorithm, and outputs the demand forecast results by time period;

[0021] Flexible inventory adjustment module: communicates with the dynamic inventory allocation engine, dynamically generates time-segment inventory quotas based on demand forecast results, establishes a real-time inventory mapping relationship table, and adjusts inventory allocation ratios based on a multi-level inventory buffer pool;

[0022] Conflict detection and arbitration unit: connected with the flexible inventory adjustment module and external cooperation platform data, detects cross-platform inventory conflicts through distributed node consensus algorithm, and triggers blockchain smart contracts for data synchronization and arbitration;

[0023] Adaptive learning module: connected with the dynamic inventory allocation engine, flexible inventory adjustment module and conflict detection and arbitration unit, it continuously optimizes the inventory allocation strategy through online incremental learning. The adaptive learning module includes a feedback evaluation index system and a policy iteration algorithm driven by deep reinforcement learning.

[0024] Visual decision support interface: dynamically displays inventory heat maps, allocation suggestions and risk warning information.

[0025] Preferably, the dynamic inventory allocation engine includes:

[0026] The spatiotemporal feature extraction submodule uses an improved three-dimensional convolutional network M3D-CNN to process geo-fence data, weather characteristics, and holiday distribution. M3D-CNN introduces an attention mechanism, and its output feature map is calculated by the following formula: ,in, Indicated in Position in the feature map of the layer The eigenvalue at ; is the attention weight, which is obtained by normalizing the score generated by the fully connected layer through Softmax and is used to measure the position The importance of the feature; It is The convolution kernel of the layer is at position The weight value at is used to perform convolution operation on the input data; Indicates Position in layer feature map The input value at ; For the The bias value of the layer is used to adjust the result after the convolution operation;

[0027] The demand forecasting submodule receives the output of the spatiotemporal feature extraction submodule and calculates the real-time inventory demand in combination with the dynamic carrying capacity evaluation function. The evaluation function is: ,in, Indicates at time Real-time inventory needs; is the initial inventory capacity; For at the moment No. The arrival rate of different types of tourists; For at the moment No. The factors affecting the inventory demand of different types of tourists; For at the moment No. The departure rate of different types of tourists; and denote the number of tourist arrival and departure types respectively;

[0028] The allocation optimization submodule generates the optimal inventory allocation plan based on the improved mixed integer programming algorithm M-MIP, and its objective function introduces an adaptive penalty factor , dynamically adjust the slack variable weights: , the constraints include the available inventory quantity and sales channel capacity constraints ,in, represents the cost item for inventory allocation, Is to allocate inventory to Inventory point and The unit cost of each sales channel, It is from The inventory point is assigned to Inventory quantity for each sales channel; is an adaptive penalty factor, which is used to adjust the weight of slack variables to balance the rationality of cost and inventory allocation; represents all slack variables The slack variables are used to handle the flexibility in the constraints. Represents different constraint numbers; For the The available inventory quantity at each inventory location; For the capacity constraints of sales channels.

[0029] Preferably, the flexible inventory adjustment module specifically includes:

[0030] Multi-level inventory buffer pool: divided into instant inventory layer, flexible allocation layer and emergency reserve layer. The inventory ratio of each layer is dynamically adjusted through the improved fuzzy control algorithm M-Fuzzy. The algorithm is based on the real-time inventory consumption rate. Deviation from demand forecast Update fuzzy rules: ,in, It means that the updated fuzzy rule is The input variable level and The value of each output variable level; Indicates that the fuzzy rule before updating is The input variable level and The value of each output variable level; is the learning rate, which is used to control the step size of fuzzy rule update and determines the speed and amplitude of rule update; is the partial derivative;

[0031] Encrypted inventory voucher generation unit: Generates an encrypted voucher containing a timestamp, inventory level identifier, and digital signature for each inventory allocation operation for cross-platform verification.

[0032] Preferably, the conflict detection and arbitration unit comprises:

[0033] Distributed inventory status monitor, which compares inventory data of various sales channels in real time, including the scenic spot’s own platform, OTA platform and third-party distribution system;

[0034] The blockchain consensus network consists of scenic area management nodes, cooperation platform nodes and regulatory agency nodes. It uses the improved asymmetric encryption algorithm M-RSA to achieve data synchronization, and the private key update formula is: ,in, is the updated private key; is the private key before the update; is the value of the Euler function, is the modulus in the RSA algorithm, Indicates less than And with The number of mutually prime positive integers; It is a dynamic factor based on timestamp and inventory change, which is used to dynamically adjust the private key according to time and inventory changes;

[0035] The multi-level arbitration rule base has preset solutions for oversold inventory, inconsistent data, and invalid certificates. When node consensus is not reached, a penalty mechanism is triggered: , among which, Penalty Indicates The penalty value of the dissenting node; is the penalty coefficient, which is used to measure the intensity of the penalty; Indicates The total number of votes in favor of each node; is the total number of nodes.

[0036] Preferably, the adaptive learning module includes:

[0037] The strategy effectiveness evaluation matrix includes 12 indicators, including inventory turnover rate, tourist satisfaction, resource utilization rate and conflict resolution success rate;

[0038] The improved deep reinforcement learning model M-DRL adopts the dual Q network and priority experience replay mechanism optimization strategy, and the Q value update formula is: ,in, Indicates in status Take action of Value, which is the value estimated by the current policy for this state-action pair; Indicates that the target network is in state Take action of value; Adjust the pace factor for the strategy, controlling The step size of the value update determines the speed of model learning; For the status Take action The reward value obtained after is the discount factor, which is used to measure the importance of future rewards and has a value range of between; Indicates at time Status; Indicates at time Actions taken; Indicates at time Status; Indicates in status All actions that can be taken under ; the priority of experience playback is determined by error Decide;

[0039] The anomaly detection unit uses the improved isolation forest algorithm with dynamic subspace partitioning to identify abnormal inventory consumption patterns. The formula for selecting the subspace partitioning dimension is: ,in, represents the selected subspace partitioning dimension; Indicates The variance of a variable is used to measure the degree of dispersion of the variable data; Indicates that in all variables , select Get the maximum dimension As subspace partitioning dimension.

[0040] Preferably, the visual decision support interface includes:

[0041] Dynamic heat map generation unit, which displays the density of tourists in each area in real time based on geo-fence data;

[0042] The risk warning subsystem triggers mild, moderate, and severe warning signals when the inventory consumption rate exceeds the preset threshold, and buffers by adjusting the flexible allocation layer inventory;

[0043] Federated learning interface, which enables privacy-preserving data sharing across scenic spots. Differential privacy mechanism is used to add noise during the sharing process: ,in, represents the data after adding noise; Indicates that the original data passes through the function The calculation results of The privacy budget is used to control the intensity of added noise; For function The global sensitivity of the function The maximum degree of response to small changes in input data; It is a parameter in the differential privacy mechanism, used to adjust the scale of the noise distribution; represents the Laplace distribution.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] Accurate demand forecasting and optimized inventory allocation:

[0046] Multi-dimensional data fusion analysis: The multi-source data acquisition module collects multi-dimensional information such as tourist flow in the scenic area, historical ticket purchase records, environmental monitoring, and ticketing on external cooperation platforms. The dynamic inventory allocation engine achieves in-depth fusion analysis of these data by coupling the LSTM neural network and the random forest algorithm. This enables the system to accurately capture various factors that affect ticket demand, thereby outputting high-precision demand forecast results by time period, laying the foundation for reasonable inventory allocation;

[0047] Optimization algorithm formulation: The allocation optimization submodule is based on the improved mixed integer programming algorithm (M-MIP), taking into account actual constraints such as inventory costs and sales channel capacity limitations, and introducing an adaptive penalty factor to dynamically adjust the weights of slack variables to generate the optimal inventory allocation plan; this effectively improves inventory utilization, reduces inventory costs, avoids inventory backlogs or out-of-stock phenomena, and achieves efficient allocation of scenic spot ticket resources;

[0048] Flexible inventory adjustment mechanism:

[0049] Multi-level inventory buffer to cope with changes: The multi-level inventory buffer pool of the flexible inventory adjustment module is divided into an immediate inventory layer, a flexible allocation layer and an emergency reserve layer. The inventory ratio of each layer is dynamically adjusted according to the real-time inventory consumption rate and demand forecast deviation through an improved fuzzy control algorithm (M-Fuzzy). This design enables the system to quickly respond to short-term fluctuations and sudden increases in ticket demand, enhances the flexibility of inventory management and the ability to cope with uncertainty, and ensures the stability of ticket supply.

[0050] Encrypted credentials ensure security: The encrypted inventory voucher generation unit generates encrypted credentials containing timestamp, inventory level identification and digital signature for each inventory allocation operation for cross-platform verification; this ensures the security, traceability and data integrity of inventory allocation operations, prevents data tampering and illegal operations, and improves the credibility of the entire inventory management system;

[0051] Efficient conflict detection and arbitration:

[0052] Real-time monitoring and data synchronization: The distributed inventory status monitor of the conflict detection and arbitration unit compares the inventory data of each sales channel in real time to detect inventory conflicts in a timely manner; the blockchain consensus network is composed of scenic area management, cooperation platform and regulatory agency nodes, and uses an improved asymmetric encryption algorithm (M-RSA) to achieve data synchronization, ensuring the consistency and security of data at each node, making the entire ticket sales system more transparent and reliable;

[0053] Multi-level arbitration solves problems: The multi-level arbitration rule base presets solutions to problems such as oversold inventory, inconsistent data, and invalid vouchers. When node consensus is not reached, a reasonable penalty mechanism is triggered; this provides a guarantee for quickly and effectively resolving cross-platform inventory conflicts, maintaining the normal order of the ticket sales market, and protecting the interests of scenic spots and tourists;

[0054] Smart strategies for continuous optimization:

[0055] Comprehensive evaluation and reinforcement learning: The adaptive learning module comprehensively evaluates the effectiveness of inventory allocation strategies through 12 indicators of the strategy effect evaluation matrix, such as inventory turnover rate and visitor satisfaction. The improved deep reinforcement learning model (M-DRL) uses a dual Q network and a priority experience playback mechanism to conduct online incremental learning based on the evaluation results, continuously optimize the inventory allocation strategy, and enable the system to continuously adapt to changes in the scenic area's operating environment and improve overall operational efficiency.

[0056] Anomaly detection and prevention: The anomaly detection unit uses the improved isolation forest algorithm with dynamic subspace partitioning to identify abnormal inventory consumption patterns, discover potential problems in a timely manner and issue warnings; this helps scenic spots take measures in advance to deal with abnormal situations, avoid operational chaos caused by abnormal events, and ensure the stable operation of the ticket management system;

[0057] Intuitive decision support and cross-scenic area collaboration:

[0058] Visualization aids decision-making: The dynamic heat map generation unit of the visual decision support interface displays the tourist density of each area in real time based on the geographic fence data. The risk warning subsystem monitors the inventory consumption rate and triggers warnings, while providing inventory allocation suggestions. These intuitive information presentation methods help decision makers quickly understand the operation status of the scenic spot and make scientific and reasonable decisions, thereby improving decision-making efficiency and accuracy.

[0059] Privacy-preserving data sharing: The federated learning interface realizes privacy-preserving data sharing across scenic spots, adopts differential privacy mechanism to add noise, and improves the overall ticket management level of the industry by jointly training the global model while protecting the privacy of local data in each scenic spot. This promotes cooperation and communication between scenic spots and drives the entire industry to develop in a more intelligent and efficient direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The figure is a schematic diagram of the overall structure of an intelligent ticket inventory management system of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0063] Example:

[0064] See also Figure 1 , this embodiment provides a technical solution:

[0065] A ticket inventory intelligent management system includes a multi-source data acquisition module, a dynamic inventory allocation engine, a flexible inventory adjustment module, a conflict detection and arbitration unit, an adaptive learning module and a visual decision support interface.

[0066] In this embodiment, the multi-source data acquisition module obtains in real time the tourist flow data, historical ticket purchase records, environmental monitoring data and ticket information of external cooperation platforms in the scenic area. The external cooperation platforms include online travel agent OTA platforms, travel agency distribution systems and official cooperation platforms of scenic spots;

[0067] The multi-source data acquisition module is described in detail below:

[0068] 1. Overview

[0069] The multi-source data acquisition module is an important basic part of the ticket inventory intelligent management system. Its core function is to collect multi-faceted data in real time. These data cover multiple dimensions such as tourist flow in the scenic area, historical ticket purchases, environmental conditions, and ticket information on external cooperation platforms. By integrating these data, the system can understand the supply and demand of scenic spot tickets more comprehensively and accurately, thereby providing strong support for subsequent dynamic inventory allocation and ticket intelligent management.

[0070] 2. Specific data types and acquisition methods

[0071] 1. Tourist flow data in scenic spots

[0072] Data content:

[0073] Real-time number of tourists: Accurately count the number of tourists in various areas of the scenic area (such as entrances, popular attractions, rest areas, etc.) at different time points to reflect the distribution and concentration of people in the scenic area;

[0074] Tourist flow direction: Tracking the movement of tourists in the scenic area and understanding their flow from one area to another helps analyze tourists' travel paths and behavior patterns;

[0075] Visitors’ residence time: records the length of time that visitors stay at each attraction or area, which can be used to evaluate the attractiveness of attractions and points of interest for visitors;

[0076] How to obtain:

[0077] Sensor technology: infrared sensors, laser sensors or pressure sensors are installed at key locations in scenic areas. For example, infrared sensors are installed at the entrance of scenic areas and the entrances and exits of various scenic spots. When tourists pass through, the sensors will sense the infrared signals of human bodies and record the number of people passing through. Pressure sensors are installed on the observation decks of some popular scenic spots to determine whether there are tourists staying and the approximate length of stay based on pressure changes.

[0078] Video surveillance and image recognition: Real-time monitoring is carried out using high-definition cameras installed in the scenic area, and advanced image recognition algorithms are used to detect, count and track tourists in the video images; this technology can not only count the number of tourists, but also analyze their behavior and movement direction;

[0079] Wi-Fi positioning and Bluetooth beacons: Visitors’ mobile devices usually automatically search for nearby Wi-Fi signals or Bluetooth beacons. Scenic spots can deploy Wi-Fi access points and Bluetooth beacon networks to collect information about the interaction between devices and these networks, thereby locating and tracking visitors’ locations and movement trajectories in real time.

[0080] (II) Ticket purchase history

[0081] Data content:

[0082] Ticket purchase time: record the specific date and time of each ticket purchase transaction, so as to analyze the peak and trough of ticket purchases in different time periods;

[0083] Ticket purchase type: including different types of tickets such as adult tickets, children's tickets, senior tickets, group tickets, etc., to understand the sales ratio and demand of different types of tickets;

[0084] Ticket purchase channel: clarify whether tourists purchase tickets through the scenic spot’s own ticket window, the scenic spot’s official website, online travel agent (OTA) platform, travel agency or other channels, and analyze the sales contribution and popularity of each channel;

[0085] Tourist information: such as the name, age, gender, contact information, place of origin, etc. of the tourist (some necessary information is collected in compliance with privacy protection laws and regulations), which helps to understand the characteristics and origins of tourists and conduct market segmentation and precision marketing;

[0086] How to obtain:

[0087] Scenic spot ticketing system: The scenic spot's own ticket sales system will record the relevant information of each ticket purchase transaction in detail. The multi-source data acquisition module can obtain historical ticket purchase records regularly or in real time by connecting to the system through a data interface;

[0088] Data sharing on cooperative platforms: Establish data sharing mechanisms with external partners such as online travel agent (OTA) platforms and travel agencies, and obtain ticket purchase records generated on these platforms through secure data transmission protocols;

[0089] 3. Environmental monitoring data

[0090] Data content:

[0091] Meteorological data: including temperature, humidity, air pressure, wind speed, wind direction, precipitation and other meteorological factors. These data will directly affect tourists' travel intention and sightseeing experience;

[0092] Air quality data: such as PM2.5, PM10, sulfur dioxide, nitrogen oxides and other pollutant concentration indicators, which reflect the air quality in the scenic area and have an important impact on the health and comfort of tourists;

[0093] Geological and ecological data: such as soil moisture, vegetation coverage, water level changes, etc. in the scenic area. For some natural landscape scenic spots, these data are helpful in evaluating the ecological environment quality and carrying capacity of the scenic area;

[0094] How to obtain:

[0095] Professional weather stations and environmental monitoring equipment: Install weather stations and air quality monitors and other professional equipment at appropriate locations in or around the scenic area to collect meteorological and environmental data in real time; these devices will transmit the collected data to the multi-source data acquisition module through wired or wireless communication;

[0096] Third-party data interface: Cooperate with local meteorological departments, environmental protection departments or professional environmental monitoring agencies to obtain relevant environmental monitoring data through data interfaces; these agencies have a wider monitoring network and more accurate monitoring equipment, and can provide more comprehensive and authoritative environmental information;

[0097] (IV) Ticketing information of external cooperation platforms

[0098] Scope of external cooperation platform:

[0099] Online travel agency (OTA) platforms: well-known OTA platforms such as Ctrip, Qunar, and Fliggy. They have a large user base and extensive sales channels, and are one of the important ways to sell scenic spot tickets;

[0100] Travel agency distribution system: the internal ticket distribution system of major travel agencies. Through cooperation with travel agencies, scenic spot tickets can be sold through travel agencies' offline stores and online channels;

[0101] Official cooperation platform of scenic spots: an exclusive ticket sales platform established by scenic spots in cooperation with other relevant enterprises or institutions. These platforms usually combine the characteristics of scenic spots and the resources of partners to carry out some customized ticket sales and promotion activities;

[0102] Data content:

[0103] Inventory information: the current ticket inventory of each partner platform, including the remaining number of different types of tickets, so as to timely understand the inventory status of each platform and avoid overselling and other problems;

[0104] Sales data: real-time information on ticket sales quantity, sales amount, sales trends, etc., and analysis of sales performance and market performance on each platform;

[0105] Price information: Ticket pricing on each platform, including original price, discount price, promotional price, etc., to understand the pricing strategies and market competition trends of different platforms;

[0106] Booking data: tourists’ ticket bookings on various platforms, including booking time, booking quantity, booking type, etc., to predict ticket demand and traffic peaks in advance;

[0107] How to obtain:

[0108] API interface connection: Establish API (application programming interface) connection with each external cooperation platform, and obtain ticket information on the platform in real time through interface call; this method has the advantages of fast data transmission speed and high accuracy, and can ensure that the multi-source data collection module obtains the latest ticket data in time;

[0109] Data synchronization protocol: formulate a unified data synchronization protocol to specify the data format, transmission frequency and security mechanism, etc. Each cooperation platform shall regularly synchronize ticketing information to the multi-source data collection module in accordance with the protocol requirements to ensure data consistency and integrity;

[0110] 3. Data Processing and Integration

[0111] After acquiring the above-mentioned data, the multi-source data acquisition module needs to perform pre-processing operations such as cleaning, conversion and integration on the data to eliminate noise, errors and inconsistencies in the data, and convert data from different sources and in different formats into a standard format that the system can recognize and process, ultimately forming a complete and accurate data set to provide high-quality data input for subsequent modules such as the dynamic inventory allocation engine. At the same time, in order to ensure the security and privacy of the data, a series of security technologies and measures will be adopted during the data collection, transmission and storage process, such as data encryption, access control, identity authentication, etc.

[0112] In this embodiment, the dynamic inventory allocation engine is connected to the multi-source data acquisition module, and a ticket demand prediction model based on spatiotemporal characteristics is built in. The ticket demand prediction model realizes multi-dimensional data fusion analysis by coupling the LSTM neural network and the random forest algorithm, and outputs the demand prediction results by time period;

[0113] Furthermore, the dynamic inventory allocation engine is the core component of the ticket inventory intelligent management system. It uses advanced algorithms and models to conduct in-depth analysis of the data obtained by the multi-source data acquisition module to achieve accurate ticket demand forecasting and optimized inventory allocation plan formulation. Specifically:

[0114] 1.Overall architecture and functional overview

[0115] The dynamic inventory allocation engine is mainly composed of the spatiotemporal feature extraction submodule, the demand forecasting submodule and the allocation optimization submodule. It receives multi-dimensional information such as tourist flow data, historical ticket purchase records, and environmental monitoring data in the scenic area from the multi-source data acquisition module, realizes multi-dimensional data fusion analysis by coupling the LSTM neural network with the random forest algorithm, and outputs the demand forecast results for different time periods, and finally generates the optimal inventory allocation plan.

[0116] 2. Detailed introduction of submodules

[0117] Spatiotemporal feature extraction submodule:

[0118] Functionality: This submodule focuses on extracting key time-space related features from complex multi-source data, which are essential for accurately predicting ticket demand; it mainly processes information such as geo-fence data, weather characteristics, and holiday distribution;

[0119] Technical implementation: An improved three-dimensional convolutional network (M3D-CNN) is used, and an attention mechanism is introduced to enhance the ability to capture important features; its output feature map is calculated using the following formula: ;

[0120] in, Indicated in Position in the feature map of the layer The eigenvalue at ; is the attention weight, which is obtained by normalizing the score generated by the fully connected layer through Softmax and is used to measure the position The importance of the feature; It is The convolution kernel of the layer is at position The weight value at is used to perform convolution operation on the input data; Indicates Position in layer feature map The input value at ; For the The bias value of the layer is used to adjust the result after the convolution operation;

[0121] Demand forecasting submodule:

[0122] Function: Receive the features output by the spatiotemporal feature extraction submodule, combine it with the dynamic carrying capacity evaluation function, and comprehensively calculate the real-time inventory demand; this module fully considers the actual carrying capacity of the scenic area and the changes in tourist flow, so that the prediction results are more in line with actual operational needs;

[0123] Technical implementation: The dynamic bearing capacity evaluation function is used for calculation, and the evaluation function is: ;

[0124] in, Indicates at time Real-time inventory needs; is the initial inventory capacity; For at the moment No. The arrival rate of different types of tourists; For at the moment No. The factors affecting the inventory demand of different types of tourists; For at the moment No. The departure rate of different types of tourists; and denote the number of tourist arrival and departure types respectively;

[0125] Allocation optimization submodule:

[0126] Function: Based on the improved mixed integer programming algorithm (M-MIP), the optimal inventory allocation plan is generated. This module introduces adaptive penalty factors and dynamically adjusts the weights of slack variables under actual constraints such as inventory costs and sales channel capacity constraints to achieve reasonable inventory allocation and optimize overall benefits.

[0127] Technical implementation: The improved mixed integer programming algorithm is used to solve the problem, and the objective function introduces an adaptive penalty factor , dynamically adjust the slack variable weights, and the objective function is: ;

[0128] Constraints include the available quantity in stock and sales channel capacity constraints ;

[0129] in, represents the cost item for inventory allocation, Is to allocate inventory to Inventory point and The unit cost of each sales channel, It is from The inventory point is assigned to Inventory quantity for each sales channel; is an adaptive penalty factor, which is used to adjust the weight of slack variables to balance the rationality of cost and inventory allocation; represents all slack variables The slack variables are used to handle the flexibility in the constraints. Represents different constraint numbers; For the The available inventory quantity at each inventory location; For the Capacity constraints of sales channels;

[0130] Through the collaborative work of the above three sub-modules, the dynamic inventory allocation engine can make full use of multi-source data to achieve accurate prediction of ticket demand and optimal allocation of inventory, providing strong support for the intelligent management of scenic spot tickets.

[0131] In this embodiment, the flexible inventory adjustment module is in communication with the dynamic inventory allocation engine, dynamically generates time-divided inventory quotas according to demand forecast results, establishes a real-time inventory mapping relationship table, and adjusts the inventory allocation ratio based on the multi-level inventory buffer pool;

[0132] Furthermore, the flexible inventory adjustment module is a key part of the ticket inventory intelligent management system to achieve flexible inventory allocation and efficient management; it dynamically adjusts the ticket inventory based on the demand forecast results provided by the dynamic inventory allocation engine to adapt to the real-time changing tourist demand of the scenic spot and ensure the stability and rationality of ticket supply;

[0133] 1. Module architecture and function overview

[0134] The flexible inventory adjustment module is mainly composed of two parts: a multi-level inventory buffer pool and an encrypted inventory voucher generation unit. It communicates closely with the dynamic inventory allocation engine, dynamically generates time-segment inventory quotas based on predicted ticket demand, establishes a real-time inventory mapping relationship table, and flexibly adjusts the inventory allocation ratio through the multi-level inventory buffer pool. At the same time, in order to ensure the security and traceability of inventory allocation operations, the encrypted inventory voucher generation unit generates encrypted vouchers for each inventory allocation operation.

[0135] 2. Detailed introduction of submodules

[0136] Multi-level inventory buffer pool:

[0137] Function: The multi-level inventory buffer pool can achieve rapid response and flexible allocation to different demand situations through hierarchical inventory management; it is divided into an immediate inventory layer, a flexible allocation layer, and an emergency reserve layer. The inventory ratio of each layer can be dynamically adjusted according to the real-time inventory consumption rate and demand forecast deviation to cope with the uncertainty of scenic spot ticket demand;

[0138] Level division and function:

[0139] Real-time inventory layer: As the most front-end inventory layer, it is mainly used to meet the short-term predictable regular ticket demand of scenic spots. The inventory at this layer is usually kept at a relatively low level, but it must ensure that it can quickly respond to instant ticket purchase requests to provide an efficient service experience. For example, during the daily operation period of the scenic spot, the real-time inventory layer can promptly meet the needs of tourists to purchase tickets on site.

[0140] Flexible allocation layer: This layer plays a role of buffering and regulation to cope with short-term fluctuations in ticket demand. When the inventory consumption rate of the immediate inventory layer accelerates, or when it is predicted that the demand will increase to a certain extent, the flexible allocation layer can quickly replenish the inventory to avoid the situation of supply exceeding demand. For example, during weekends or small holidays, the number of tourists may increase to a certain extent, and the inventory of the flexible allocation layer can be allocated in time to meet the additional demand.

[0141] Emergency reserve layer: As the last line of defense for inventory, the emergency reserve layer is used to cope with sudden, large-scale increases in ticket demand or other emergencies; for example, when a scenic spot holds a large-scale event that attracts a large number of tourists, or encounters extreme weather that causes tourists to change their itineraries and purchase tickets in batches, the inventory in the emergency reserve layer can ensure that the scenic spot can still meet tourists' ticket purchase needs and maintain the stability of scenic spot operations;

[0142] Dynamic adjustment mechanism: The inventory ratio of each level is dynamically adjusted through an improved fuzzy control algorithm (M-Fuzzy); the algorithm is based on the real-time inventory consumption rate Deviation from demand forecast Update the fuzzy rules. The specific formula is: ;

[0143] in, Indicates that the updated fuzzy rule is The input variable level and The value of the output variable level, which determines the adjustment direction and amplitude of the inventory ratio at each level; Indicates that the fuzzy rule before updating is The input variable level and The value of each output variable level; is the learning rate, which is used to control the step size of fuzzy rule update and determines the speed and amplitude of rule update; is the partial derivative, the smaller The larger the value, the smoother and more precise the adjustment process will be. A value of 0 enables the system to respond more quickly to changes in demand, but may lead to over-adjustment;

[0144] Encrypted inventory voucher generation unit:

[0145] Function: Generate encrypted credentials for each inventory allocation operation. These credentials contain key information such as timestamp, inventory level identification and digital signature, which are used to verify the authenticity, integrity and accuracy of inventory allocation across platforms, ensuring the security and reliability of the entire inventory management system in a multi-platform collaborative environment;

[0146] Generation logic:

[0147] Timestamp: records the exact time when the inventory allocation operation occurs, accurate to the second or even millisecond level. Timestamps are not only used to trace the time sequence of inventory allocation, but also serve as an important basis for judging the sequence and timeliness of data in cross-platform data synchronization and consistency verification. For example, when comparing inventory data between different platforms, timestamps can help determine which operation is the latest, thereby avoiding inventory conflicts caused by untimely data updates.

[0148] Inventory level identification: It specifies the inventory level involved in the inventory allocation operation, i.e., the immediate inventory level, the flexible allocation level, or the emergency reserve level. This helps to clearly understand the source and nature of the inventory during cross-platform verification, and also facilitates statistics and analysis of the usage of inventory at different levels. For example, scenic area management can use inventory level identification to understand the allocation frequency and usage of inventory at each level within a certain period of time, so as to better optimize the inventory structure.

[0149] Digital signature: Adopting asymmetric encryption algorithm, the private key is used to encrypt the relevant information of inventory allocation operation (such as allocation quantity, sales channels involved, etc.) to generate a digital signature; during cross-platform verification, the recipient can use the corresponding public key to decrypt and verify the digital signature to ensure that the inventory allocation operation has not been tampered with and is indeed issued by the authorized system module; digital signature technology provides a high degree of security and non-repudiation for inventory allocation operations, ensuring the stable operation of the system in a complex network environment;

[0150] Through the collaborative work of multi-level inventory buffer pools and encrypted inventory voucher generation units, the flexible inventory adjustment module can flexibly and efficiently adjust inventory allocation according to the dynamic changes in scenic spot ticket demand, and ensure the security and traceability of inventory management operations, providing strong support for the intelligent management of scenic spot tickets.

[0151] In this embodiment, the conflict detection and arbitration unit is connected with the flexible inventory adjustment module and the external cooperation platform data, detects cross-platform inventory conflicts through a distributed node consensus algorithm, and triggers the blockchain smart contract for data synchronization and arbitration;

[0152] Furthermore, the conflict detection and arbitration unit plays a vital role in the intelligent ticket inventory management system. It is responsible for maintaining the consistency of inventory data across sales channels and ensuring that the ticket sales process is fair and orderly. When cross-platform inventory conflicts occur, the unit can quickly detect them and resolve them through a reasonable arbitration mechanism.

[0153] 1. Overview of unit architecture and functions

[0154] The conflict detection and arbitration unit is mainly composed of three parts: distributed inventory status monitor, blockchain consensus network and multi-level arbitration rule library. It is closely connected with the flexible inventory adjustment module and external cooperation platform, and compares the inventory data of each sales channel in real time, synchronizes data with the help of blockchain technology, and handles inventory conflicts according to preset arbitration rules.

[0155] 2. Detailed introduction of submodules

[0156] Distributed Inventory Status Monitor:

[0157] Function: Real-time monitoring and comparison of inventory data of various sales channels, including scenic spots’ own platforms, OTA platforms, and third-party distribution systems; through continuous monitoring, timely discover differences in inventory data between different platforms, providing a basis for subsequent conflict detection;

[0158] Working principle: A distributed architecture is adopted to deploy monitoring nodes in the systems of various sales channels. These nodes regularly collect inventory data of the channels, including key information such as the current ticket inventory, the number of reservations, and the number of sales, and summarize the data to the central monitoring module. The central monitoring module uses a specific algorithm to compare and analyze the data of each channel. Once the data inconsistency is found, the conflict detection process is immediately triggered. For example, if the scenic spot’s own platform shows that the inventory of a certain type of ticket is 50, while the OTA platform shows that it is 45, the monitor will identify this difference and determine that there may be an inventory conflict.

[0159] Blockchain consensus network:

[0160] Function: It is composed of scenic area management nodes, cooperation platform nodes and regulatory agency nodes, aiming to achieve safe and reliable synchronization of data between nodes, and ensure the consistency of inventory data among all nodes through a consensus mechanism; it uses an improved asymmetric encryption algorithm (M-RSA) to ensure the security of data transmission and storage;

[0161] Node composition and function:

[0162] Scenic spot management node: As the core node, it is responsible for maintaining the overall data of the scenic spot ticket inventory and verifying and processing the data synchronization requests of other nodes. The scenic spot management party publishes the latest inventory information through this node, and supervises the data synchronization of other nodes to ensure that the entire system operates around the actual inventory situation of the scenic spot;

[0163] Cooperation platform nodes: represent various external cooperation platforms, such as OTA platforms, travel agency distribution systems, etc.; these nodes synchronize the inventory data of their own platforms to the blockchain network, and obtain the latest data of other nodes from the network to ensure the consistency of their own platform inventory information with the overall system; at the same time, the cooperation platform nodes participate in the consensus process to verify and confirm changes in inventory data;

[0164] Regulatory agency node: mainly plays the role of supervision and notarization; the regulatory agency uses this node to monitor the flow of ticket inventory data in real time to ensure that all parties comply with relevant regulations and agreements; when disputes arise, the regulatory agency node assists in data verification and the execution of arbitration decisions based on its credibility;

[0165] Data synchronization and encryption: The improved asymmetric encryption algorithm (M-RSA) is used to achieve data synchronization; the private key update formula is: ;

[0166] in, The updated private key is used to encrypt and decrypt data to ensure data security and privacy. The generation of the new private key is based on the old private key and factors related to time and inventory changes, so that the private key can be dynamically updated as the system runs and data changes, enhancing encryption security. The private key before the update, which is the basic value for the private key update; is the value of the Euler function, is the modulus in the RSA algorithm, Indicates less than And with The number of mutually prime positive integers; It is a dynamic factor based on timestamp and inventory change. Timestamp ensures the timeliness of private key update, while inventory change reflects the changes of key data in the system. The combination of these two factors enables the private key to be dynamically adjusted according to the actual operation status of the system, better adapting to the ever-changing security needs;

[0167] Multi-level arbitration rules library:

[0168] Function: Preset solutions for various inventory conflict situations, including common problems such as oversold inventory, inconsistent data, and invalid vouchers. When nodes in the blockchain consensus network fail to reach a consensus, the penalty mechanism is triggered to impose corresponding penalties on the offending nodes to maintain the normal operation of the system and data consistency.

[0169] Arbitration rules and penalty mechanism:

[0170] Inventory oversold: If the number of tickets sold by a sales channel exceeds its actual inventory, it is considered to be oversold. At this time, the arbitration rule base may require the channel to immediately stop selling the relevant tickets and take measures to compensate tourists for losses caused by overselling, such as providing coupons, upgrading ticket levels, etc. At the same time, a certain degree of economic penalties will be imposed on the channel to serve as a warning.

[0171] Data inconsistency: When there are discrepancies in inventory data between different nodes, the data verification process is initiated first; each node rechecks its own data and provides detailed data records and operation logs to other nodes; if it is confirmed that the data of a node is incorrect, the node needs to correct it according to the correct data and handle data update issues of other nodes that may be caused by data inconsistency; for nodes that have multiple data inconsistencies, the penalty will be increased, such as limiting their inventory adjustment authority within a certain period of time;

[0172] Voucher expiration: If the encrypted inventory voucher fails to pass verification, it is considered invalid. At this time, the inventory allocation operation involving the voucher will be deemed invalid, and the relevant inventory will be restored to the state before the operation. At the same time, the node that generated the invalid voucher will be investigated. If it is found that the voucher is invalid due to illegal operation of the node, the node will be severely punished, including but not limited to economic fines, suspension of cooperation and other measures;

[0173] Penalty mechanism formula: When the node consensus is not reached, the penalty mechanism is triggered: ;

[0174] Among them, Penalty Indicates The penalty value of the dissenting node; is the penalty coefficient, which is used to measure the intensity of the penalty. The larger the value, the more severe the punishment for dissenting nodes, and vice versa. The value is usually set according to the actual needs of the system and the tolerance for violations. Indicates The total number of votes a node receives reflects the degree of recognition of the behavior or data of this node by other nodes. is the total number of nodes, which is used as the calculation base to measure the proportion of affirmative votes in the entire network. According to this formula, when the proportion of affirmative votes obtained by a node to the total number of nodes is low, it means that the behavior or data of the node is inconsistent with the majority of nodes, and it will be subject to a corresponding heavier penalty.

[0175] Through the collaborative work of distributed inventory status monitors, blockchain consensus networks, and multi-level arbitration rule bases, the conflict detection and arbitration unit can effectively detect and resolve cross-platform inventory conflicts and maintain the stable operation of the ticket intelligent management system and data consistency.

[0176] In this embodiment, the adaptive learning module is connected with the dynamic inventory allocation engine, the flexible inventory adjustment module and the conflict detection and arbitration unit, and the inventory allocation strategy is continuously optimized through online incremental learning. The adaptive learning module includes a feedback evaluation index system and a policy iteration algorithm driven by deep reinforcement learning;

[0177] Furthermore, the adaptive learning module is a key component of the ticket inventory intelligent management system. Through continuous learning and optimization, it enables the system to automatically adjust the inventory allocation strategy according to the changing internal and external environment to achieve more efficient ticket management and better operational results;

[0178] 1. Module architecture and function overview

[0179] The adaptive learning module is mainly composed of three parts: the strategy effect evaluation matrix, the improved deep reinforcement learning model (M-DRL) and the anomaly detection unit. It is closely connected with the dynamic inventory allocation engine, the elastic inventory adjustment module and the conflict detection and arbitration unit, collects the real-time feedback data generated by each module, and continuously optimizes the inventory allocation strategy through online incremental learning.

[0180] 2. Detailed introduction of submodules

[0181] Strategy effectiveness evaluation matrix:

[0182] Function: The strategy effect evaluation matrix is ​​used to comprehensively and systematically evaluate the implementation effect of the current inventory allocation strategy; it contains multiple key indicators, reflecting the pros and cons of the strategy from different dimensions, and providing a clear direction and basis for subsequent strategy optimization;

[0183] Indicator composition:

[0184] Inventory turnover rate: This indicator measures the turnover speed of ticket inventory, which is determined by calculating the ratio of the number of tickets sold in a certain period to the average inventory number; a higher inventory turnover rate means that tickets can be circulated quickly, reducing inventory backlogs and improving the efficiency of capital use; for example, if a scenic spot has an average inventory of 1,000 tickets in a month and sells a total of 5,000 tickets, the inventory turnover rate for that month is 5 times (5,000 ÷ 1,000);

[0185] Tourist satisfaction: By collecting tourists' feedback, such as online comments and questionnaires, we can obtain tourists' satisfaction scores on ticket purchase experience, scenic spot tour arrangements, etc. Improving tourist satisfaction will help improve the reputation and brand image of the scenic spot and attract more tourists;

[0186] Resource utilization rate: evaluate the matching degree between various resources (such as venues, facilities, etc.) in the scenic area and ticket sales and tourist reception; reasonable inventory allocation should ensure that resources are fully utilized and avoid idle or overused resources; for example, tickets should be reasonably allocated according to the carrying capacity and tourist flow of different areas in the scenic area so that resources in each area can be effectively utilized;

[0187] Conflict resolution success rate: Statistics on the proportion of successful conflict resolution when dealing with cross-platform inventory conflicts and other issues; a high conflict resolution success rate indicates that the system is stable and effective in dealing with complex situations and can ensure the normal order of ticket sales;

[0188] Other indicators: In addition to the above main indicators, the strategy effectiveness evaluation matrix may also include 12 indicators such as sales channel balance and revenue growth rate, which comprehensively evaluate the effectiveness of inventory allocation strategies from multiple perspectives;

[0189] Improved Deep Reinforcement Learning Model (M-DRL):

[0190] Function: The M-DRL model is based on the feedback of the strategy effect evaluation matrix and explores the optimal inventory allocation strategy through continuous learning and trial and error. It uses a dual Q network and a priority experience replay mechanism to accelerate the learning process and improve the stability and accuracy of the strategy.

[0191] Learning mechanism:

[0192] Dual Q network: Traditional Q learning algorithms have the problem of overestimation when estimating Q values. Dual Q network solves this problem by introducing two Q networks (main network and target network); the main network is used to select actions, and the target network is used to calculate the target Q value; in each iteration, the main network selects actions based on the current state. Select Action , and then get rewards by interacting with the environment and the new state ; The target network is based on the new state Choose the action that maximizes the Q value , and calculate the target Q value ; The Q value update formula of the main network is: ;

[0193] in, Indicates in status Take action of Value, which is the value estimated by the current policy for this state-action pair; Indicates that the target network is in state Take action of value; Adjust the pace factor for the strategy, controlling The step size of the value update determines the speed of model learning; For the status Take action The reward value obtained after is the discount factor, which is used to measure the importance of future rewards and has a value range of between; Indicates at time Status; Indicates at time Actions taken; Indicates at time Status; Indicates in status All actions that can be taken under ; the priority of experience playback is determined by error Decide;

[0194] Prioritized experience replay mechanism: In traditional reinforcement learning, experience replay is to store the experience samples generated by the interaction between the agent and the environment in the experience replay pool, and then randomly extract samples for learning; the priority experience replay mechanism is based on the TD error Assign a priority to each experience sample. The larger the TD error, the higher the priority. When learning, give priority to replaying and learning samples with high priority. This allows the model to pay more attention to samples that have a greater impact on policy improvement and accelerates learning convergence. For example, when a certain inventory allocation decision leads to a large change in revenue (i.e., a large TD error), the experience sample corresponding to the decision will have a higher priority and is more likely to be learned by the model again to strengthen the understanding and application of the decision.

[0195] Anomaly Detection Unit:

[0196] Function: The anomaly detection unit is responsible for identifying abnormal patterns in the inventory consumption process, timely discovering potential problems that may affect the normal operation of the system, such as abnormal ticket rush, sudden changes in tourist flow, etc., and providing early warning information for strategy adjustment;

[0197] Detection method: An improved isolation forest algorithm with dynamic subspace partitioning is used. The algorithm performs dynamic subspace partitioning on inventory consumption data and projects the data into different subspaces for analysis. The formula for selecting the subspace partitioning dimension is: ;

[0198] in, represents the selected subspace partitioning dimension; Indicates The variance of a variable is used to measure the degree of dispersion of the variable data. The larger the variance, the more dispersed the data distribution on this dimension is, and the more likely it is to contain abnormal information. Indicates that in all variables , select Get the maximum dimension As the subspace division dimension; in this way, the algorithm can focus on the dimension with the largest data change and more effectively identify abnormal data points, that is, abnormal inventory consumption patterns; for example, when analyzing ticket sales data, if it is found that the variance of the number of tickets sold on a certain day in the time dimension is much larger than that in other dimensions, then the time dimension may be the key dimension containing abnormal information, and the algorithm will focus on analyzing whether there is abnormal sales behavior from the time dimension;

[0199] Through the collaborative work of the strategy effect evaluation matrix, the improved deep reinforcement learning model and the anomaly detection unit, the adaptive learning module can continuously optimize the inventory allocation strategy, improve the system's adaptability to complex and changing environments, and ensure the efficiency and stability of scenic spot ticket management.

[0200] In this embodiment, the visual decision support interface dynamically displays inventory heat maps, allocation suggestions, and risk warning information;

[0201] Furthermore, the visual decision support interface is a key part of the interaction between the ticket inventory intelligent management system and managers and relevant decision makers; it presents various important information in an intuitive and easy-to-understand way, helping decision makers to quickly understand the ticket inventory status of scenic spots, the distribution of tourist flow, etc., and assisting them in making scientific and reasonable decisions. At the same time, it has functions such as risk warning and cross-scenic area data sharing, which improves the overall operation and management level of scenic spots;

[0202] 1. Interface architecture and function overview

[0203] The visual decision support interface is mainly composed of three parts: dynamic heat map generation unit, risk warning subsystem and federated learning interface. It integrates data from other modules of the system, displays key information in a graphical and visual way, and provides interactive functions to facilitate decision makers' operation and analysis.

[0204] 2. Detailed introduction of submodules

[0205] Dynamic heat map generation unit:

[0206] Function: Based on the geographic fence data, the tourist density of each area of ​​the scenic spot is displayed in real time; through intuitive graphic display, decision makers can clearly understand the distribution of tourists in the scenic spot, find the areas where people gather and the areas where people flow sparsely, and provide important basis for the resource allocation and safety management of the scenic spot;

[0207] Implementation method: Use geographic information system (GIS) technology to digitize the scenic area map and delineate different areas in combination with geo-fence data; the system obtains the location information of tourists in real time (through technologies such as Wi-Fi positioning and Bluetooth beacons) and counts the number of tourists in each geo-fence area; according to the number of tourists, it is presented on the heat map with different colors or transparency. Generally, the darker the color or the higher the transparency, the greater the density of tourists; for example, red areas may indicate a high density of tourists, yellow areas indicate a moderate density, and green areas indicate a low density of tourists; this visualization method allows decision makers to quickly and intuitively grasp the real-time distribution dynamics of tourists in the scenic area, so as to take corresponding measures in a timely manner, such as adding security personnel or guides in areas with dense tourist populations, optimizing tour routes, etc.

[0208] Risk early warning subsystem:

[0209] Function: Real-time monitoring of ticket inventory status. When the inventory consumption rate exceeds the preset threshold, different levels of warning signals are triggered to alert decision makers of possible inventory shortage risks. At the same time, the system automatically adjusts the flexible allocation layer inventory for buffering to ease inventory pressure and ensure the normal supply of tickets.

[0210] Early warning mechanism:

[0211] Threshold setting: Based on the scenic spot's historical data, expected tourist flow, ticket sales patterns and other factors, different levels of inventory consumption rate thresholds are pre-set; for example, the mild warning threshold may be set at 120% of the normal inventory consumption rate, the moderate warning threshold at 150%, and the severe warning threshold at 200%;

[0212] Warning signal triggering: When the system detects that the inventory consumption rate reaches or exceeds a certain level of threshold, the corresponding level of warning signal is immediately triggered; the warning signal can be presented to decision makers through a variety of methods such as eye-catching color changes on the interface, flashing prompts, and sound alarms; for example, in the case of a mild warning, the inventory data area on the interface may turn yellow and flash, and a slight warning sound will be emitted; in the case of a moderate warning, it will turn orange and the prompt sound will be strengthened; in the case of a severe warning, it will turn red and a strong alarm sound will be emitted;

[0213] Inventory buffer adjustment: Once the warning signal is triggered, the system automatically starts the inventory adjustment mechanism of the flexible allocation layer; according to the warning level and the current inventory situation, the inventory is reasonably allocated from the flexible allocation layer to the instant inventory layer to meet the possible increase in ticket demand; for example, when a mild warning is triggered, a certain proportion (such as 10%) of the inventory is transferred from the flexible allocation layer to the instant inventory layer; when a moderate warning is triggered, the transfer ratio may increase to 30%; when a severe warning is triggered, the transfer ratio is increased to 50%, so as to avoid the situation where tourists are blocked from buying tickets due to inventory shortages;

[0214] Federated learning interface:

[0215] Function: It can realize privacy-protected data sharing among scenic spots. Under the premise of not leaking local sensitive data, each scenic spot can jointly train the global model through federated learning technology to improve the ticket management level of the entire industry. At the same time, the differential privacy mechanism is used to add noise in the data sharing process to further protect data privacy.

[0216] Data sharing and privacy protection mechanism:

[0217] Federated learning: Each scenic spot retains the original data locally and only uploads the model parameters or gradient information to the federated learning server; the server aggregates the information uploaded by each scenic spot, updates the global model, and sends the updated model to each scenic spot; each scenic spot uses the updated global model to predict and analyze local data, and continues to train the model with local data, and then uploads the model parameters again, and iterates in this way to continuously optimize the global model; for example, scenic spots A, B, and C train local models based on their own historical ticket purchase records, tourist flow and other data, and then upload the model's gradient information to the federated learning server. The server aggregates this gradient information to update the global model, and then sends the global model to each scenic spot. Each scenic spot uses the new global model to process local data, and further trains the model with local new data to continuously improve the accuracy and generalization ability of the model;

[0218] Differential privacy: In the process of data sharing, in order to prevent attackers from inferring sensitive information of each scenic spot by analyzing the shared data, a differential privacy mechanism is used to add noise; the specific formula is: ;

[0219] in, represents the data after adding noise; Indicates that the original data passes through the function The calculation results of is the privacy budget, which is used to control the intensity of added noise. The smaller the value, the higher the privacy protection, but the data availability may be lower; For function The global sensitivity of the function The maximum degree of response to small changes in input data; It is a parameter in the differential privacy mechanism, used to adjust the scale of the noise distribution; represents the Laplace distribution,

[0220] In this way, the local data privacy of each scenic spot is protected to the greatest extent while ensuring data availability. For example, when uploading model parameters, noise that conforms to the above formula is added to the parameter value, making it difficult for attackers to obtain real scenic spot data from the shared parameter information.

[0221] Through the collaborative work of the dynamic heat map generation unit, the risk warning subsystem and the federated learning interface, the visual decision support interface provides comprehensive, intuitive and secure data support and decision-making assistance for scenic spot ticket management, helping scenic spots to achieve efficient and intelligent operation management.

[0222] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent ticket inventory management system, applied to scenic spot ticket terminal equipment, characterized by: include: Multi-source data collection module: real-time acquisition of tourist flow data, historical ticket purchase records, environmental monitoring data and ticketing information of external cooperation platforms in the scenic area, including online travel agency OTA platforms, travel agency distribution systems and official cooperation platforms of scenic spots; Dynamic inventory allocation engine: connected to the multi-source data acquisition module, with a built-in ticket demand forecasting model based on spatiotemporal characteristics. The ticket demand forecasting model achieves multi-dimensional data fusion analysis by coupling the LSTM neural network with the random forest algorithm, and outputs the demand forecast results by time period; Flexible inventory adjustment module: communicates with the dynamic inventory allocation engine, dynamically generates time-segment inventory quotas based on demand forecast results, establishes a real-time inventory mapping relationship table, and adjusts inventory allocation ratios based on a multi-level inventory buffer pool; Conflict detection and arbitration unit: connected with the flexible inventory adjustment module and external cooperation platform data, detects cross-platform inventory conflicts through distributed node consensus algorithm, and triggers blockchain smart contracts for data synchronization and arbitration; Adaptive learning module: connected with the dynamic inventory allocation engine, flexible inventory adjustment module and conflict detection and arbitration unit, it continuously optimizes the inventory allocation strategy through online incremental learning. The adaptive learning module includes a feedback evaluation index system and a policy iteration algorithm driven by deep reinforcement learning. Visual decision support interface: dynamically displays inventory heat maps, allocation suggestions and risk warning information.

2. The ticket inventory intelligent management system according to claim 1, characterized in that: The dynamic inventory allocation engine includes: The spatiotemporal feature extraction submodule uses an improved three-dimensional convolutional network M3D-CNN to process geo-fence data, weather characteristics, and holiday distribution. The M3D-CNN introduces an attention mechanism, and its output feature map is calculated by the following formula: ,in, Indicated in Position in the feature map of the layer The eigenvalue at ; is the attention weight, which is obtained by normalizing the score generated by the fully connected layer through Softmax and is used to measure the position The importance of the feature; It is The convolution kernel of the layer is at position The weight value at is used to perform convolution operation on the input data; Indicates Position in layer feature map The input value at ; For the The bias value of the layer is used to adjust the result after the convolution operation; The demand forecasting submodule receives the output of the spatiotemporal feature extraction submodule and calculates the real-time inventory demand in combination with the dynamic carrying capacity evaluation function. The evaluation function is: ,in, Indicates at time Real-time inventory needs; is the initial inventory capacity; For at the moment No. The arrival rate of different types of tourists; For at the moment No. The factors affecting the inventory demand of different types of tourists; For at the moment No. The departure rate of different types of tourists; and denote the number of tourist arrival and departure types respectively; The allocation optimization submodule generates the optimal inventory allocation plan based on the improved mixed integer programming algorithm M-MIP, and its objective function introduces an adaptive penalty factor , dynamically adjust the slack variable weights: , the constraints include the available inventory quantity and sales channel capacity constraints ,in, represents the cost item for inventory allocation, Is to allocate inventory to Inventory point and The unit cost of each sales channel, It is from The inventory point is assigned to Inventory quantity for each sales channel; is an adaptive penalty factor, which is used to adjust the weight of slack variables to balance the rationality of cost and inventory allocation; represents all slack variables The slack variables are used to handle the flexibility in the constraints. Represents different constraint numbers; For the The available inventory quantity at each inventory location; For the capacity constraints of each sales channel.

3. The ticket inventory intelligent management system according to claim 1, characterized in that: The flexible inventory adjustment module specifically includes: Multi-level inventory buffer pool: divided into instant inventory layer, flexible allocation layer and emergency reserve layer. The inventory ratio of each layer is dynamically adjusted through the improved fuzzy control algorithm M-Fuzzy, which is based on the real-time inventory consumption rate. Deviation from demand forecast Update fuzzy rules: ,in, It means that the updated fuzzy rule is The input variable level and The value of each output variable level; Indicates that the fuzzy rule before updating is The input variable level and The value of each output variable level; is the learning rate, which is used to control the step size of fuzzy rule update and determines the speed and amplitude of rule update; is the partial derivative; Encrypted inventory voucher generation unit: Generates an encrypted voucher containing a timestamp, inventory level identifier, and digital signature for each inventory allocation operation for cross-platform verification.

4. The ticket inventory intelligent management system according to claim 1, characterized in that: The conflict detection and arbitration unit comprises: Distributed inventory status monitor, which compares inventory data of various sales channels in real time, including the scenic spot’s own platform, OTA platform and third-party distribution system; The blockchain consensus network consists of scenic area management nodes, cooperation platform nodes and regulatory agency nodes. It uses the improved asymmetric encryption algorithm M-RSA to achieve data synchronization, and the private key update formula is: ,in, is the updated private key; is the private key before the update; is the value of the Euler function, is the modulus in the RSA algorithm, Indicates less than And with The number of mutually prime positive integers; It is a dynamic factor based on timestamp and inventory change, which is used to dynamically adjust the private key according to time and inventory changes; The multi-level arbitration rule base has preset solutions for oversold inventory, inconsistent data, and invalid certificates. When node consensus is not reached, a penalty mechanism is triggered: , among which, Penalty Expressing the The penalty value of the dissenting node; is the penalty coefficient, which is used to measure the intensity of the penalty; Indicates The total number of votes in favor of each node; is the total number of nodes.

5. The ticket inventory intelligent management system according to claim 1, characterized in that: The adaptive learning module includes: The strategy effectiveness evaluation matrix includes 12 indicators, including inventory turnover rate, tourist satisfaction, resource utilization rate and conflict resolution success rate; The improved deep reinforcement learning model M-DRL adopts the dual Q network and priority experience replay mechanism optimization strategy, and the Q value update formula is: ,in, Indicates in status Take action of Value, which is the value estimated by the current policy for this state-action pair; Indicates that the target network is in status Take action of value; Adjust the pace factor for the strategy, controlling The step size of the value update determines the speed of model learning; For the status Take action The reward value obtained after is the discount factor, which is used to measure the importance of future rewards and has a value range of between; Indicates at time Status; Indicates at time Actions taken; Indicates at time Status; Indicates in status All actions that can be taken under ; the priority of experience playback is determined by error Decide; The anomaly detection unit uses the improved isolation forest algorithm with dynamic subspace partitioning to identify abnormal inventory consumption patterns. The formula for selecting the subspace partitioning dimension is: ,in, represents the selected subspace partitioning dimension; Indicates The variance of a variable is used to measure the degree of dispersion of the variable data; Indicates that in all variables , select Get the maximum dimension As subspace partitioning dimension.

6. The ticket inventory intelligent management system according to claim 1, characterized in that: The visual decision support interface includes: Dynamic heat map generation unit, which displays the density of tourists in each area in real time based on geo-fence data; The risk warning subsystem triggers mild, moderate, and severe warning signals when the inventory consumption rate exceeds the preset threshold, and buffers by adjusting the flexible allocation layer inventory; Federated learning interface, which enables privacy-preserving data sharing across scenic spots. Differential privacy mechanism is used to add noise during the sharing process: ,in, represents the data after adding noise; Indicates that the original data passes through the function The calculation results of The privacy budget is used to control the intensity of added noise; For function The global sensitivity of the function The maximum degree of response to small changes in input data; It is a parameter in the differential privacy mechanism, used to adjust the scale of the noise distribution; represents the Laplace distribution.

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