An intelligent ticket inventory management system

Abstract

The present invention relates to the field of intelligent ticket management technology, and in particular to an intelligent ticket inventory management system applied to scenic spot ticketing terminal equipment. The system comprises a multi-source data acquisition module that extensively collects multivariate data from inside and outside the scenic spot to support subsequent decision-making. A dynamic inventory allocation engine integrates an LSTM neural network and a random forest algorithm to accurately predict ticket demand and optimize allocation plans. A flexible inventory adjustment module flexibly adjusts inventory based on prediction results using a multi-level inventory buffer pool and generates encrypted credentials to ensure operational security. A conflict detection and arbitration unit detects and resolves cross-platform inventory conflicts through a distributed algorithm. An adaptive learning module continuously optimizes strategies, and a visual decision support interface intuitively displays key information to assist in decision-making. The present invention significantly improves the efficiency and scientific nature of scenic spot ticket management, ensures reasonable inventory allocation consistent with platform data, and enhances system adaptability and decision-making accuracy.

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 booming tourism industry, the number of visitors to scenic spots is increasing, and ticket management faces many challenges. Traditional ticket inventory management methods can no longer meet the complex and ever-changing operational needs of modern scenic spots. This is specifically reflected in the following aspects:

[0003] 1. Limitations of Static Inventory Management

[0004] Lack of dynamic adjustment capabilities: Traditional inventory management is typically based on fixed forecasts and settings, and is unable to dynamically adjust ticket inventory based on real-time factors such as visitor traffic, weather changes, and emergencies. For example, during peak tourist seasons or special events, visitor numbers may far exceed expectations, and static inventory management may not be able to increase ticket supply in a timely manner, making it difficult for tourists to purchase tickets, affecting the visitor experience and scenic spot revenue. Conversely, during off-seasons or during inclement weather, ticket inventory may accumulate, resulting in a waste of resources.

[0005] Difficulty adapting to market changes: The tourism market is influenced by numerous factors, such as holidays, seasons, and social media promotions. Tourists' travel intentions and ticket purchasing behaviors are highly uncertain. Traditional static inventory management struggles to quickly respond to these market changes, hindering timely adjustments to ticket prices and inventory allocation strategies, putting scenic spots at a competitive disadvantage.

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

[0007] Data inconsistency: Nowadays, scenic spot tickets are often sold through multiple channels, including the scenic spot's own platform, online travel agent (OTA) platforms, and travel agency distribution systems. Inaccurate or untimely data synchronization between different platforms can easily lead to inconsistent inventory data. For example, one platform may show that tickets are available, but the actual inventory of the scenic spot is sold out, resulting in overselling and unnecessary trouble for the scenic spot and tourists.

[0008] Lack of a unified coordination mechanism: Each sales platform typically manages its own inventory independently, lacking a unified coordination and management mechanism. When a scenic spot needs to make overall adjustments to its ticket inventory, it is difficult to quickly and accurately communicate and implement them across various platforms, leading to chaotic inventory management and impacting the scenic spot's overall operational efficiency.

[0009] 3. Insufficient Decision-Making Support

[0010] Incomplete information acquisition: When making ticket inventory management decisions, scenic spot managers often rely on limited data and experience, lacking comprehensive access to and analysis of multi-dimensional information both inside and outside the scenic area. For example, they may only consider historical ticket purchase data while ignoring factors such as real-time visitor traffic, weather conditions, and competition from surrounding scenic spots, resulting in unscientific and rational 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 scenic area's inventory status and visitor distribution. Faced with complex data and changing circumstances, it is difficult to make timely and accurate decisions, impacting the scenic area's operational management efficiency.

[0012] IV. Tourist Experience and Resource Utilization

[0013] Impact on visitor experience: 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 their travel experience and reduce the reputation and attractiveness of the scenic spot;

[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 fully utilize the resources of the scenic area, and also increases the management cost and safety risks of the scenic area.

[0015] Therefore, to address 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 an intelligent ticket inventory 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] An intelligent ticket inventory management system, comprising:

[0019] Multi-source data collection module: real-time acquisition of tourist flow data within the scenic area, 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 scenic area cooperation platforms;

[0020] Dynamic inventory allocation engine: Connected to the multi-source data acquisition module, it has a built-in ticket demand forecasting model based on spatiotemporal characteristics. This model couples the LSTM neural network with the random forest algorithm to implement multi-dimensional data fusion analysis and outputs time-segment demand forecast results.

[0021] Flexible Inventory Adjustment Module: This module communicates with the dynamic inventory allocation engine, dynamically generates time-segmented inventory quotas based on demand forecasts, establishes a real-time inventory mapping table, and adjusts inventory allocation ratios based on a multi-level inventory buffer pool.

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

[0023] Adaptive Learning Module: This module connects to the dynamic inventory allocation engine, the flexible inventory adjustment module, and the conflict detection and arbitration unit to continuously optimize inventory allocation strategies through online incremental learning. The module includes a feedback evaluation indicator 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 geofence data, weather characteristics, and holiday distribution. M3D-CNN introduces an attention mechanism, and its output feature map is calculated using the following formula:

[0027] ,in, Indicates 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 the Position in the 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;

[0028] 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:

[0029] ,in, Indicates at time Real-time inventory needs; is the initial inventory capacity; For the moment No. The arrival rate of different types of tourists; For the moment No. Factors affecting inventory demand by different types of tourists; For the moment No. The departure rate of different types of tourists; and denote the number of tourist arrival and departure types respectively;

[0030] 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 limitations ,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 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 condition numbers; For the The available inventory quantity at each inventory location; For the capacity constraints of sales channels.

[0031] Preferably, the flexible inventory adjustment module specifically includes:

[0032] Multi-level inventory buffer pool: divided into immediate 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:

[0033] ,in, Indicates 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 updates and determines the speed and amplitude of rule updates; is the partial derivative;

[0034] 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.

[0035] Preferably, the conflict detection and arbitration unit includes:

[0036] Distributed inventory status monitors that compare inventory data across various sales channels in real time, including the scenic spot's own platform, OTA platforms, and third-party distribution systems;

[0037] 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. The private key update formula is:

[0038] ,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, used to dynamically adjust the private key according to time and inventory changes;

[0039] A multi-level arbitration rule base with preset solutions for oversold inventory, inconsistent data, and invalid certificates. When node consensus is not reached, a penalty mechanism is triggered:

[0040] , among which, Penalty Indicates the The penalty value of each dissenting node; is the penalty coefficient, which is used to measure the intensity of the penalty; Indicates the The total number of votes in favor of each node; is the total number of nodes.

[0041] Preferably, the adaptive learning module includes:

[0042] The strategy effectiveness evaluation matrix includes 12 indicators, including inventory turnover rate, visitor satisfaction, resource utilization rate and conflict resolution success rate;

[0043] The improved deep reinforcement learning model M-DRL adopts a dual Q network and a priority experience replay mechanism to optimize the strategy. The Q value update formula is:

[0044] ,in, Indicates that the status Take action Time 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 Time value; Adjust the pace factor for the strategy, control The step size of the value update determines the speed of model learning; In state 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 that the status All actions that can be taken under ; the priority of experience playback is determined by error Decide;

[0045] 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:

[0046] ,in, represents the selected subspace partitioning dimension; Indicates the The variance of a variable is used to measure the degree of dispersion of the variable data; Indicates that all variables In the Get the dimension of the maximum value as subspace partitioning dimensions.

[0047] Preferably, the visual decision support interface includes:

[0048] Dynamic heat map generation unit, which displays the tourist density of each area in real time based on geo-fence data;

[0049] The risk warning subsystem triggers three levels of warning signals: mild, moderate, and severe, when the inventory consumption rate exceeds the preset threshold, and buffers the situation by adjusting the inventory at the flexible allocation layer;

[0050] The federated learning interface enables privacy-preserving data sharing across scenic areas, using a differential privacy mechanism to add noise during the sharing process:

[0051] ,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 response to small changes in input data; is a parameter in the differential privacy mechanism, used to adjust the scale of the noise distribution; represents the Laplace distribution.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] Accurate demand forecasting and optimized inventory allocation:

[0054] Multi-dimensional data fusion analysis: The multi-source data collection module collects multi-dimensional information such as tourist flow within the scenic area, historical ticket purchase records, environmental monitoring, and ticketing from external cooperative platforms. The dynamic inventory allocation engine achieves in-depth fusion analysis of this data by coupling the LSTM neural network with the random forest algorithm. This enables the system to accurately capture various factors affecting ticket demand, thereby outputting high-precision demand forecasts by time period, laying the foundation for reasonable inventory allocation.

[0055] Optimization algorithm development: The allocation optimization submodule, based on an improved mixed integer programming algorithm (M-MIP), considers practical constraints such as inventory costs and sales channel capacity limitations. It also introduces 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 stockouts, and achieves efficient allocation of scenic spot ticket resources.

[0056] Flexible inventory adjustment mechanism:

[0057] Multi-level inventory buffering to cope with changes: The flexible inventory adjustment module's multi-level inventory buffer pool 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 based on the deviation between the real-time inventory consumption rate and the demand forecast using 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, enhancing inventory management flexibility and the ability to cope with uncertainty, and ensuring the stability of ticket supply.

[0058] Encrypted credentials ensure security: The encrypted inventory voucher generation unit generates encrypted credentials for each inventory allocation operation, including timestamp, inventory level identifier, and digital signature, for cross-platform verification. This ensures the security, traceability, and data integrity of inventory allocation operations, prevents data tampering and illegal operations, and enhances the credibility of the entire inventory management system.

[0059] Efficient conflict detection and arbitration:

[0060] Real-time monitoring and data synchronization: The distributed inventory status monitor of the conflict detection and arbitration unit compares inventory data from various sales channels in real time to promptly identify inventory conflicts. The blockchain consensus network, composed of nodes from scenic area management, cooperation platforms, and regulatory agencies, 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.

[0061] Multi-level arbitration solves problems: The multi-level arbitration rule base presets solutions for issues such as oversold inventory, inconsistent data, and expired credentials. When node consensus is not reached, a reasonable penalty mechanism is triggered. This ensures the rapid and effective resolution of cross-platform inventory conflicts, maintains the normal order of the ticket sales market, and protects the interests of scenic spots and tourists.

[0062] Smart strategies for continuous optimization:

[0063] Comprehensive Evaluation and Reinforcement Learning: The adaptive learning module comprehensively evaluates the effectiveness of inventory allocation strategies using 12 indicators of the strategy effectiveness 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 prioritized experience replay mechanism to conduct online incremental learning based on the evaluation results, continuously optimizing inventory allocation strategies. This allows the system to continuously adapt to changes in the scenic area's operating environment and improve overall operational efficiency.

[0064] Anomaly Detection and Prevention: The anomaly detection unit uses an improved isolation forest algorithm with dynamic subspace partitioning to identify abnormal inventory consumption patterns, promptly identifying potential problems and issuing early warnings. This helps scenic spots take proactive measures to address anomalies, avoid operational disruptions caused by abnormal events, and ensure the stable operation of the ticket management system.

[0065] Intuitive decision support and cross-scenic area collaboration:

[0066] Visualization-assisted decision-making: The dynamic heat map generation unit of the visual decision-support interface displays the visitor density of each area in real time based on geo-fence data. The risk warning subsystem monitors inventory consumption rates, triggers warnings, and provides inventory allocation recommendations. These intuitive information presentations help decision-makers quickly understand the scenic area's operating status and make scientific and reasonable decisions, improving decision-making efficiency and accuracy.

[0067] Privacy-preserving data sharing: The federated learning interface enables privacy-preserving data sharing across scenic spots. It uses a differential privacy mechanism to add noise. While protecting the privacy of local data in each scenic spot, it improves the industry's overall ticket management level through joint training of a global model. This promotes cooperation and communication between scenic spots, driving the entire industry towards a more intelligent and efficient direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a schematic diagram of the overall structure of an intelligent ticket inventory management system of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to 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.

[0070] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0071] Example:

[0072] See also Figure 1 , this embodiment provides a technical solution:

[0073] An intelligent ticket inventory 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.

[0074] 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 ticketing information of external cooperation platforms within the scenic area. The external cooperation platforms include online travel agent OTA platforms, travel agency distribution systems and official cooperation platforms of scenic spots.

[0075] The multi-source data acquisition module is described in detail below:

[0076] I. Overview

[0077] The multi-source data acquisition module is a key foundational component of the ticket inventory intelligent management system. Its core function is to collect a wide range of data in real time, covering multiple dimensions such as visitor flow within the scenic area, historical ticket purchases, environmental conditions, and ticket information from external cooperative platforms. By integrating this data, the system can more comprehensively and accurately understand the supply and demand of scenic area tickets, thereby providing strong support for subsequent dynamic inventory allocation and intelligent ticket management.

[0078] 2. Specific data types and acquisition methods

[0079] (1) Tourist flow data within the scenic area

[0080] Data content:

[0081] Real-time visitor numbers: Accurately count the number of visitors to 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;

[0082] Tourist flow direction: Tracking the movement of tourists within the scenic area and understanding their flow from one area to another helps analyze tourists' touring paths and behavior patterns;

[0083] Visitor dwell time: records the length of time visitors stay at each attraction or area, which can be used to evaluate the attractiveness of attractions and points of interest for tourists;

[0084] How to obtain:

[0085] 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 to the scenic area and at 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 tourists are staying and the approximate length of stay based on pressure changes.

[0086] Video surveillance and image recognition: High-definition cameras deployed throughout the scenic area provide real-time monitoring, and advanced image recognition algorithms are used to detect, count, and track visitors in video footage. This technology not only counts the number of visitors but also analyzes their behavior and movement directions.

[0087] Wi-Fi positioning and Bluetooth beacons: Visitors' mobile devices typically 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 interactions between devices and these networks, enabling real-time positioning and tracking of visitor locations and movements.

[0088] (2) Historical ticket purchase records

[0089] Data content:

[0090] 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;

[0091] 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;

[0092] Ticket purchase channel: Identify whether tourists purchase tickets through the scenic spot's own ticket windows, the scenic spot's official website, online travel agent (OTA) platforms, travel agencies, or other channels, and analyze the sales contribution and popularity of each channel;

[0093] Visitor information: such as the visitor's name, age, gender, contact information, and place of origin (some necessary information is collected in compliance with privacy protection laws and regulations). This helps us understand the characteristics and origins of visitors and conduct market segmentation and targeted marketing.

[0094] How to obtain:

[0095] 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 data interface of this system.

[0096] Data sharing on partner platforms: Establish data sharing mechanisms with external partners such as online travel agents (OTAs) and travel agencies, and obtain ticket purchase records generated on these platforms through secure data transmission protocols;

[0097] (3) Environmental monitoring data

[0098] Data content:

[0099] 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;

[0100] Air quality data: such as PM2.5, PM10, sulfur dioxide, nitrogen oxides and other pollutant concentration indicators, reflecting the air quality in the scenic area and having a significant impact on the health and comfort of tourists;

[0101] Geological and ecological data: For example, soil moisture, vegetation cover, and water level changes within a scenic area. For some natural landscape scenic areas, these data can help assess the ecological environment quality and carrying capacity of the scenic area;

[0102] How to obtain:

[0103] Professional weather stations and environmental monitoring equipment: Weather stations and air quality monitors and other professional equipment are installed at appropriate locations within 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 via wired or wireless communication.

[0104] 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 more extensive monitoring networks and more accurate monitoring equipment, and can provide more comprehensive and authoritative environmental information;

[0105] (IV) Ticketing information on external cooperation platforms

[0106] Scope of external cooperation platform:

[0107] Online travel agency (OTA) platforms: Well-known OTA platforms such as Ctrip, Qunar, and Fliggy have a large user base and extensive sales channels, and are an important channel for scenic spot ticket sales;

[0108] 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;

[0109] Official scenic spot cooperation platform: an exclusive ticket sales platform established by the scenic spot in cooperation with other relevant enterprises or institutions. These platforms usually combine the characteristics of the scenic spot and the resources of the partners to carry out customized ticket sales and promotion activities;

[0110] Data content:

[0111] Inventory information: The current ticket inventory of each partner platform, including the remaining number of different ticket types, so as to timely understand the inventory status of each platform and avoid overselling and other problems;

[0112] Sales data: real-time information on ticket sales quantity, sales amount, and sales trends, and analysis of sales performance and market performance on each platform;

[0113] Price information: Ticket pricing on various platforms, including original price, discounted price, promotional price, etc., to understand the pricing strategies and market competition trends of different platforms;

[0114] Booking data: Visitors’ ticket booking status on various platforms, including booking time, booking quantity, booking type, etc., to predict ticket demand and traffic peaks in advance;

[0115] How to obtain:

[0116] API interface connection: Establish API (Application Programming Interface) connections with various external cooperation platforms and obtain real-time ticketing information on the platform through interface calls. 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 ticketing data in a timely manner;

[0117] Data synchronization protocol: Develop a unified data synchronization protocol that specifies data format, transmission frequency, and security mechanisms. Each partner platform will regularly synchronize ticketing information to the multi-source data collection module in accordance with the protocol requirements to ensure data consistency and integrity.

[0118] 3. Data Processing and Integration

[0119] After acquiring the above-mentioned types of 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 uniformly 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 in the process of data collection, transmission and storage, such as data encryption, access control, identity authentication, etc.

[0120] In this embodiment, the dynamic inventory allocation engine is connected to the multi-source data acquisition module and has 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.

[0121] Furthermore, the dynamic inventory allocation engine is the core component of the ticket inventory intelligent management system. Leveraging advanced algorithms and models, it conducts in-depth analysis of data captured by the multi-source data acquisition module to achieve accurate ticket demand forecasts and optimize inventory allocation plans. Specifically:

[0122] 1.Overall Architecture and Functional Overview

[0123] The dynamic inventory allocation engine is primarily composed of a spatiotemporal feature extraction submodule, a demand forecasting submodule, and an allocation optimization submodule. It receives multi-dimensional information from the multi-source data acquisition module, including tourist flow data within the scenic area, historical ticket purchase records, and environmental monitoring data. By coupling the LSTM neural network with the random forest algorithm, it performs multi-dimensional data fusion analysis and outputs time-segment demand forecasts, ultimately generating the optimal inventory allocation plan.

[0124] 2. Detailed introduction of submodules

[0125] Spatiotemporal feature extraction submodule:

[0126] Functionality: This submodule focuses on extracting key spatiotemporal features from complex multi-source data, which are crucial for accurately predicting ticket demand. It primarily processes geofence data, weather characteristics, and holiday distribution information.

[0127] 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:

[0128] ;

[0129] in, Indicates 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 the Position in the 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;

[0130] Demand forecasting submodule:

[0131] Function: Receives the features output by the spatiotemporal feature extraction submodule, combines them with the dynamic carrying capacity assessment function, and comprehensively calculates real-time inventory demand. This module fully considers the actual carrying capacity of the scenic area and changes in tourist flow, making the prediction results more in line with actual operational needs.

[0132] Technical implementation: The dynamic bearing capacity evaluation function is used for calculation. The evaluation function is:

[0133] ;

[0134] in, Indicates at time Real-time inventory needs; is the initial inventory capacity; For the moment No. The arrival rate of different types of tourists; For the moment No. Factors affecting inventory demand by different types of tourists; For the moment No. The departure rate of different types of tourists; and denote the number of tourist arrival and departure types respectively;

[0135] Allocation optimization submodule:

[0136] Function: Based on the improved mixed integer programming algorithm (M-MIP), it generates the optimal inventory allocation plan. Taking into account actual constraints such as inventory costs and sales channel capacity limitations, this module introduces adaptive penalty factors and dynamically adjusts the weights of slack variables to achieve reasonable inventory allocation and optimize overall benefits.

[0137] Technical implementation: Using an improved mixed integer programming algorithm to solve the problem, the objective function introduces an adaptive penalty factor , dynamically adjust the slack variable weights, and the objective function is:

[0138] ;

[0139] Constraints include the available quantity in stock and sales channel capacity limitations ;

[0140] 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 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 condition numbers; For the The available inventory quantity at each inventory location; For the Capacity limitations of sales channels;

[0141] 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.

[0142] In this embodiment, the flexible inventory adjustment module is in communication with the dynamic inventory allocation engine, dynamically generates time-segmented inventory quotas based on demand forecast results, establishes a real-time inventory mapping table, and adjusts inventory allocation ratios based on a multi-level inventory buffer pool.

[0143] Furthermore, the flexible inventory adjustment module is a key component of the ticket inventory intelligent management system, enabling flexible inventory allocation and efficient management. Based on the demand forecast results provided by the dynamic inventory allocation engine, it dynamically adjusts ticket inventory to adapt to the real-time changes in tourist demand at the scenic spot, ensuring the stability and rationality of ticket supply.

[0144] 1. Module Architecture and Function Overview

[0145] The flexible inventory adjustment module consists primarily of a multi-level inventory buffer pool and an encrypted inventory voucher generation unit. It closely communicates with the dynamic inventory allocation engine, dynamically generating time-segmented inventory quotas based on predicted ticket demand, establishing a real-time inventory mapping table, and flexibly adjusting inventory allocation ratios through the multi-level inventory buffer pool. Furthermore, to ensure the security and traceability of inventory allocation operations, the encrypted inventory voucher generation unit generates encrypted vouchers for each inventory allocation operation.

[0146] 2. Detailed introduction of submodules

[0147] Multi-level inventory buffer pool:

[0148] Function: The multi-level inventory buffer pool manages inventory in layers, enabling rapid response and flexible allocation to varying demand situations. 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 based on the real-time inventory consumption rate and demand forecast deviation to cope with the uncertainty of scenic spot ticket demand.

[0149] Hierarchical division and function:

[0150] Real-time inventory: As the most front-end inventory layer, it is mainly used to meet the scenic spot's short-term, predictable regular ticket demand. The inventory at this layer is usually kept relatively low, but it must ensure a rapid response to immediate ticket purchase requests to provide an efficient service experience. For example, during the scenic spot's daily operating hours, the real-time inventory layer can promptly meet the needs of tourists for on-site ticket purchases.

[0151] Flexible Allocation Layer: This layer acts as a buffer and regulator to address short-term fluctuations in ticket demand. When the immediate inventory layer's inventory is depleted faster or when demand is predicted to increase, the flexible allocation layer can quickly replenish inventory to avoid a supply shortage. For example, during weekends or minor holidays, when visitor numbers may increase significantly, the flexible allocation layer's inventory can be promptly allocated to meet the additional demand.

[0152] 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 hosts a large 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 purchasing needs and maintain the stability of the scenic spot's operations.

[0153] Dynamic adjustment mechanism: The inventory ratio of each level is dynamically adjusted through an improved fuzzy control algorithm (M-Fuzzy); this algorithm is based on the real-time inventory consumption rate Deviation from demand forecast Update the fuzzy rules. The specific formula is:

[0154] ;

[0155] in, Indicates that the updated fuzzy rule is The input variable level and The value of each output variable level determines the adjustment direction and magnitude 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 updates and determines the speed and amplitude of rule updates; is the partial derivative, the smaller The value makes the adjustment process smoother and more precise, while larger A value of 0 enables the system to respond more quickly to changes in demand, but may lead to over-adjustment;

[0156] Encrypted inventory voucher generation unit:

[0157] Function: Generates encrypted credentials for each inventory allocation operation. These credentials contain key information such as timestamp, inventory level identification, and digital signature. These credentials are used to verify the authenticity, integrity, and accuracy of inventory allocations across platforms, ensuring the security and reliability of the entire inventory management system in a multi-platform collaborative environment.

[0158] Generation logic:

[0159] Timestamps: Record the precise time when inventory allocation operations occur, accurate to the second or even millisecond level. Timestamps are not only used to trace the chronological order of inventory allocations, but also serve as an important basis for determining data sequence and timeliness during cross-platform data synchronization and consistency verification. For example, when comparing inventory data across different platforms, timestamps can help determine which operation is the most recent, thereby avoiding inventory conflicts caused by untimely data updates.

[0160] Inventory Tier Identification: This identifies the inventory tier involved in the inventory allocation operation, i.e., immediate inventory tier, flexible allocation tier, or emergency reserve tier. This helps to clearly understand the source and nature of inventory during cross-platform verification, and also facilitates statistics and analysis of inventory usage at different tiers. For example, scenic area managers can use inventory tier identification to understand the allocation frequency and usage of inventory at each tier within a specific time period, thereby better optimizing inventory structure.

[0161] Digital Signature: Using an asymmetric encryption algorithm, a private key is used to encrypt information related to inventory allocation operations (such as allocation quantity and involved sales channels) to generate a digital signature. During cross-platform verification, the recipient can use the corresponding public key to decrypt and verify the digital signature, ensuring 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 complex network environments.

[0162] 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.

[0163] In this embodiment, the conflict detection and arbitration unit is connected to 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;

[0164] Furthermore, the conflict detection and arbitration unit plays a crucial role in the intelligent ticket inventory management system. It is responsible for maintaining the consistency of inventory data across sales channels, ensuring a fair and orderly ticket sales process. When cross-platform inventory conflicts arise, the unit can quickly detect them and resolve them through a reasonable arbitration mechanism.

[0165] 1. Overview of unit architecture and functions

[0166] The conflict detection and arbitration unit consists of three main components: a distributed inventory status monitor, a blockchain consensus network, and a multi-level arbitration rule library. It is closely connected to the flexible inventory adjustment module and external collaboration platform, comparing inventory data across sales channels in real time, synchronizing data using blockchain technology, and resolving inventory conflicts based on pre-set arbitration rules.

[0167] 2. Detailed introduction of submodules

[0168] Distributed Inventory Status Monitor:

[0169] Function: Real-time monitoring and comparison of inventory data across various sales channels, including scenic spots' own platforms, online travel agencies (OTAs), and third-party distribution systems. Through continuous monitoring, discrepancies in inventory data between different platforms can be promptly identified, providing a basis for subsequent conflict detection.

[0170] How it works: Using a distributed architecture, monitoring nodes are deployed in the systems of various sales channels. These nodes regularly collect inventory data from their respective channels, including key information such as current ticket inventory, number of reservations, and number of sales, and aggregate this data to a central monitoring module. The central monitoring module uses a specific algorithm to compare and analyze data from various channels. If any data inconsistency is found, a conflict detection process is immediately triggered. For example, if a scenic spot's own platform shows a stock of 50 tickets of a certain type, while the OTA platform shows 45, the monitor will identify this discrepancy and determine that there may be an inventory conflict.

[0171] Blockchain consensus network:

[0172] Function: It is composed of scenic area management nodes, cooperation platform nodes, and regulatory agency nodes. It aims to achieve secure and reliable data synchronization 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.

[0173] Node composition and function:

[0174] Scenic Area Management Node: As the core node, it is responsible for maintaining the overall data of the scenic area's ticket inventory and verifying and processing data synchronization requests from other nodes. The scenic area management publishes the latest inventory information through this node and monitors the data synchronization status of other nodes to ensure that the entire system operates based on the actual inventory status of the scenic area.

[0175] Collaboration Platform Nodes: These represent external collaboration platforms, such as online travel agencies (OTAs) and travel agency distribution systems. These nodes synchronize their own platform's inventory data to the blockchain network and obtain the latest data from other nodes to ensure consistency between their own platform's inventory information and the overall system. Furthermore, collaboration platform nodes participate in the consensus process to verify and confirm changes to inventory data.

[0176] Regulatory Agency Node: This node primarily serves as a supervisory and notarization entity. The regulatory agency uses this node to monitor the flow of ticket inventory data in real time, ensuring that all parties involved comply with relevant regulations and agreements. In the event of a dispute, the regulatory agency node, relying on its credibility, assists in data verification and the execution of arbitration decisions.

[0177] Data synchronization and encryption: An improved asymmetric encryption algorithm (M-RSA) is used to achieve data synchronization; the private key update formula is:

[0178] ;

[0179] in, The updated private key is used to encrypt and decrypt data, ensuring data security and privacy. The new private key is generated based on the old private key and factors related to time and inventory changes, allowing the private key to be dynamically updated as the system operates and data changes, enhancing encryption security. The private key before the update, which is the base 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; This is a dynamic factor based on timestamps and inventory changes. Timestamps ensure the timeliness of private key updates, while inventory changes reflect changes in key data in the system. The combination of these two factors allows private keys to be dynamically adjusted based on the actual operating status of the system, better adapting to changing security needs.

[0180] Multi-level arbitration rules library:

[0181] Functionality: Pre-set solutions for various inventory conflict situations, including common issues such as oversold inventory, inconsistent data, and expired vouchers. When nodes in the blockchain consensus network fail to reach consensus, a penalty mechanism is triggered to penalize offending nodes accordingly, maintaining the normal operation of the system and data consistency.

[0182] Arbitration Rules and Penalty Mechanism:

[0183] Oversold Inventory: If the number of tickets sold by a sales channel exceeds its actual inventory, it is considered oversold. In this case, 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 oversold tickets, such as providing coupons or upgrading ticket levels. At the same time, a certain degree of financial penalty may be imposed on the channel to serve as a warning.

[0184] Data inconsistency: When discrepancies arise in inventory data between different nodes, a data verification process is initiated. Each node rechecks its own data and provides detailed data records and operation logs to other nodes. If data on a particular node is confirmed to be incorrect, the node must correct it based on the correct data and address any data update issues that may have occurred at other nodes due to data inconsistencies. Nodes with repeated data inconsistencies will be subject to increased penalties, such as limiting their inventory adjustment permissions for a certain period of time.

[0185] Credential Expiration: If an encrypted inventory credential fails verification, it is deemed invalid. Any inventory allocation operations involving that credential will be deemed invalid, and the relevant inventory will be restored to its pre-operation state. Furthermore, the node that generated the invalid credential will be investigated. If it is found that the invalidation was caused by illegal operations by the node, the node will be severely punished, including but not limited to financial fines and suspension of cooperation.

[0186] Penalty mechanism formula: When the node consensus is not reached, the penalty mechanism is triggered:

[0187] ;

[0188] Among them, Penalty Indicates the The penalty value of each 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 The total number of votes a node receives reflects the degree of recognition of the node's behavior or data by other nodes. is the total number of nodes, serving as the base number for calculations and measuring the proportion of affirmative votes in the entire network. According to this formula, when a node receives a low proportion of affirmative votes compared to the total number of nodes, it indicates that the node's behavior or data is inconsistent with the majority of nodes, and will be subject to a correspondingly heavier penalty.

[0189] 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, maintaining the stable operation of the ticket intelligent management system and data consistency.

[0190] In this embodiment, the adaptive learning module is connected to the dynamic inventory allocation engine, the flexible inventory adjustment module, and the conflict detection and arbitration unit to continuously optimize the inventory allocation strategy through online incremental learning. The adaptive learning module includes a feedback evaluation indicator system and a policy iteration algorithm driven by deep reinforcement learning.

[0191] 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 inventory allocation strategies according to the ever-changing internal and external environment, thereby achieving more efficient ticket management and better operational results;

[0192] 1. Module Architecture and Function Overview

[0193] The adaptive learning module consists of three main parts: a strategy effectiveness evaluation matrix, an improved deep reinforcement learning model (M-DRL), and an 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. It collects real-time feedback data generated by each module and continuously optimizes the inventory allocation strategy through online incremental learning.

[0194] 2. Detailed introduction of submodules

[0195] Strategy effectiveness evaluation matrix:

[0196] Function: The strategy effectiveness 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 advantages and disadvantages of the strategy from different dimensions, and provides a clear direction and basis for subsequent strategy optimization.

[0197] Indicator composition:

[0198] Inventory turnover rate: This metric measures how quickly ticket inventory is turned over. It's determined by calculating the ratio of ticket sales to average inventory over a specific period. A higher inventory turnover rate means tickets are turning over quickly, reducing inventory overstock and improving capital efficiency. For example, if a scenic spot has an average inventory of 1,000 tickets in a month and sells 5,000 tickets, the inventory turnover rate for that month is 5 times (5,000 ÷ 1,000).

[0199] Visitor satisfaction: By collecting visitor feedback, such as online reviews and questionnaires, we can obtain visitor satisfaction ratings on aspects such as ticket purchasing experience and scenic spot tour arrangements. Improving visitor satisfaction helps enhance the scenic spot's reputation and brand image, thereby attracting more tourists.

[0200] Resource Utilization: This assesses the degree to which various resources within a scenic area (such as venues and facilities) match ticket sales and visitor reception. Reasonable inventory allocation should ensure that resources are fully utilized and avoid idle or overused resources. For example, tickets should be allocated rationally based on the carrying capacity and visitor flow of different areas in the scenic area to ensure that resources in each area are effectively utilized.

[0201] Conflict Resolution Success Rate: This measures the percentage of conflicts successfully resolved when dealing with issues like cross-platform inventory conflicts. A high conflict resolution success rate indicates the system's stability and effectiveness in handling complex situations, ensuring the smooth flow of ticket sales.

[0202] Other indicators: In addition to the above key indicators, the strategy effectiveness evaluation matrix may also include 12 indicators, such as sales channel balance and revenue growth rate, to comprehensively evaluate the effectiveness of inventory allocation strategies from multiple perspectives.

[0203] Improved Deep Reinforcement Learning Model (M-DRL):

[0204] Function: The M-DRL model uses feedback from a strategy effectiveness evaluation matrix to explore the optimal inventory allocation strategy through continuous learning and trial and error. It uses a dual-Q network and a prioritized experience replay mechanism to accelerate the learning process and improve the stability and accuracy of the strategy.

[0205] Learning mechanism:

[0206] 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 is used to select actions according to 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:

[0207] ;

[0208] in, Indicates that the status Take action Time 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 Time value; Adjust the pace factor for the strategy, control The step size of the value update determines the speed of model learning; In state 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 that the status All actions that can be taken under ; the priority of experience playback is determined by error Decide;

[0209] 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. During learning, high-priority samples are preferentially selected for replay and learning. This allows the model to focus more on samples that have a greater impact on policy improvement, accelerating learning convergence. For example, when an inventory allocation decision results in a large change in revenue (i.e., a large TD error), the experience sample corresponding to this decision will be given a higher priority and is more likely to be relearned by the model, thereby strengthening the model's understanding and application of the decision.

[0210] Anomaly detection unit:

[0211] Function: The anomaly detection unit is responsible for identifying abnormal patterns in inventory consumption and promptly discovering potential issues that may affect the normal operation of the system, such as abnormal ticket rush and sudden changes in tourist flow, providing early warning information for strategic adjustments.

[0212] Detection method: An improved isolation forest algorithm with dynamic subspace partitioning is used. This algorithm dynamically partitions inventory consumption data into subspaces and projects the data into different subspaces for analysis. The formula for selecting the subspace partitioning dimension is:

[0213] ;

[0214] in, represents the selected subspace partitioning dimension; Indicates the 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 all variables In the Get the dimension of the maximum value As a subspace division dimension; this way, the algorithm can focus on the dimension with the greatest data variation and more effectively identify anomalous data points, that is, abnormal inventory consumption patterns. For example, when analyzing ticket sales data, if the variance of ticket sales in the time dimension on a particular day is found to be much greater 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 in the time dimension.

[0215] Through the collaborative work of the strategy effectiveness 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.

[0216] In this embodiment, the visual decision support interface dynamically displays inventory heat maps, allocation suggestions, and risk warning information;

[0217] 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 manner, helping decision-makers quickly understand the status of scenic spot ticket inventory, visitor flow distribution, and other situations, assisting them in making scientific and reasonable decisions. It also has functions such as risk warning and cross-scenic spot data sharing, improving the overall operation and management level of scenic spots.

[0218] 1. Interface architecture and function overview

[0219] The visual decision support interface consists of three main parts: a dynamic heat map generation unit, a risk warning subsystem, and a federated learning interface. It integrates data from other modules of the system, presents key information in a graphical and visual manner, and provides interactive functions to facilitate operation and analysis by decision makers.

[0220] 2. Detailed introduction of submodules

[0221] Dynamic heat map generation unit:

[0222] Function: Based on geo-fence data, it displays the visitor density of each area of the scenic area in real time. Through intuitive graphical display, decision makers can clearly understand the distribution of tourists within the scenic area, identify areas with large and small crowds, and provide important basis for resource allocation and safety management of the scenic area.

[0223] Implementation: Utilizing Geographic Information System (GIS) technology, scenic area maps are digitized and delineated using geofence data. The system acquires real-time visitor location information (via technologies such as Wi-Fi positioning and Bluetooth beacons) and counts the number of visitors within each geofenced area. This information is then presented on a heat map using varying colors or levels of transparency, depending on the number of visitors. Generally, darker colors or higher levels of transparency indicate greater visitor density. For example, red areas may indicate high visitor density, yellow areas moderate density, and green areas low density. This visualization allows decision makers to quickly and intuitively understand the real-time distribution of visitors within a scenic area, enabling them to take timely action, such as adding security personnel or guides to densely populated areas and optimizing tour routes.

[0224] Risk early warning subsystem:

[0225] Function: Real-time monitoring of ticket inventory status. When the inventory consumption rate exceeds a preset threshold, different levels of warning signals are triggered to alert decision makers of potential inventory shortage risks. At the same time, the system automatically adjusts the flexible allocation layer inventory to buffer inventory, alleviate inventory pressure, and ensure the normal supply of tickets.

[0226] Early warning mechanism:

[0227] Threshold setting: Based on historical data of the scenic spot, expected visitor 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%;

[0228] Warning signal triggering: When the system detects that the inventory consumption rate reaches or exceeds a certain threshold, it immediately triggers a warning signal of the corresponding level. 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 a mild warning, the inventory data area on the interface may turn yellow and flash, and a slight warning tone will be emitted; in a moderate warning, it will turn orange and the warning tone will be intensified; in a severe warning, it will turn red and a strong alarm will be emitted.

[0229] Inventory buffer adjustment: Once an early warning signal is triggered, the system automatically activates the flexible allocation layer inventory adjustment mechanism. Based on the warning level and current inventory conditions, inventory is rationally allocated from the flexible allocation layer to the immediate inventory layer to meet the potential increase in ticket demand. For example, when a mild early warning is triggered, a certain proportion (such as 10%) of inventory from the flexible allocation layer is transferred to the immediate inventory layer. With a moderate early warning, the transfer ratio may increase to 30%; with a severe early warning, the transfer ratio is increased to 50%, minimizing the possibility of tourists being hindered from purchasing tickets due to inventory shortages.

[0230] Federated learning interface:

[0231] Function: Enables privacy-preserving data sharing across scenic spots. Without leaking local sensitive data, each scenic spot can jointly train a global model through federated learning technology, improving ticket management across the industry. Furthermore, a differential privacy mechanism is used to add noise during data sharing to further protect data privacy.

[0232] Data sharing and privacy protection mechanism:

[0233] 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, while continuing to train the model with local data, and then uploads the model parameters again. This cycle iterates to continuously optimize the global model. For example, scenic spots A, B, and C each 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 train the model with new local data, continuously improving the model's accuracy and generalization ability.

[0234] Differential privacy: During data sharing, a differential privacy mechanism is used to add noise to prevent attackers from inferring sensitive information about each scenic spot by analyzing shared data. The specific formula is:

[0235] ;

[0236] 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 response to small changes in input data; is a parameter in the differential privacy mechanism, used to adjust the scale of the noise distribution; represents the Laplace distribution,

[0237] In this way, while ensuring data availability, the privacy of local data in each scenic spot is protected to the greatest extent possible. For example, when uploading model parameters, noise that conforms to the above formula is added to the parameter values, making it difficult for attackers to obtain real scenic spot data from the shared parameter information.

[0238] 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 achieve efficient and intelligent operation and management.

[0239] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent ticket inventory management system, applied to scenic spot ticket sales terminal equipment, characterized by: include: Multi-source data acquisition module: real-time acquisition of tourist flow data within the scenic area, 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 scenic area cooperation platforms; Dynamic Inventory Allocation Engine: Connects to multi-source data acquisition modules, including: The spatiotemporal feature extraction submodule uses an improved three-dimensional convolutional network (M3D-CNN) to process geofence data, weather characteristics, and holiday distribution. The M3D-CNN introduces an attention mechanism. A demand forecasting submodule receives the output of the spatiotemporal feature extraction submodule and calculates real-time inventory demand in combination with a dynamic carrying capacity evaluation function; Allocation optimization submodule generates the optimal inventory allocation plan based on the improved mixed integer programming algorithm M-MIP; Flexible Inventory Adjustment Module: This module communicates with the dynamic inventory allocation engine, dynamically generates time-segmented inventory quotas based on demand forecasts, establishes a real-time inventory mapping table, and adjusts inventory allocation ratios based on a multi-level inventory buffer pool. Conflict Detection and Arbitration Unit: Connects with the flexible inventory adjustment module and external cooperation platform data, detects cross-platform inventory conflicts through a distributed node consensus algorithm, and triggers blockchain smart contracts for data synchronization and arbitration; Adaptive Learning Module: This module connects to the dynamic inventory allocation engine, the flexible inventory adjustment module, and the conflict detection and arbitration unit to continuously optimize inventory allocation strategies through online incremental learning. The module includes a feedback evaluation indicator 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 output feature map of the M3D-CNN after the introduction of the attention mechanism is calculated by the following formula: ,in, Indicates 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 the Position in the 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 evaluation function of the demand forecast submodule is: ,in, Indicates at time Real-time inventory needs; is the initial inventory capacity; For the moment No. The arrival rate of different types of tourists; For the moment No. Factors affecting inventory demand by different types of tourists; For the moment No. The departure rate of different types of tourists; and denote the number of tourist arrival and departure types respectively; In the allocation optimization submodule, the objective function introduces an adaptive penalty factor , dynamically adjust the slack variable weights: , the constraints include the available inventory quantity and sales channel capacity limitations ,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 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 condition numbers; For the The available inventory quantity at each inventory location; For the capacity constraints of sales channels.

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 immediate inventory layer, flexible allocation layer and emergency reserve layer. The inventory ratio of each layer is dynamically adjusted by 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, Indicates 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 updates and determines the speed and amplitude of rule updates; 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 includes: Distributed inventory status monitors that compare inventory data across various sales channels in real time, including the resort's own platform, OTA platforms, and third-party distribution systems; 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. 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; A dynamic factor based on timestamp and inventory change, used to dynamically adjust the private key according to time and inventory changes; A multi-level arbitration rule base with 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 The penalty value of each dissenting node; is the penalty coefficient, which is used to measure the intensity of the penalty; Indicates the 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 four indicators: inventory turnover rate, tourist satisfaction, resource utilization rate, and conflict resolution success rate; The improved deep reinforcement learning model M-DRL adopts a dual Q network and a priority experience replay mechanism to optimize the strategy. The Q value update formula is: ,in, Indicates that the status Take action Time 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 Time value; Adjust the pace factor for the strategy, control The step size of the value update determines the speed of model learning; In state 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 that the 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 The variance of a variable is used to measure the degree of dispersion of the variable data; Indicates that all variables In the Get the dimension of the maximum value as subspace partitioning dimensions.

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 tourist density of each area in real time based on geo-fence data; The risk warning subsystem triggers three levels of warning signals: mild, moderate, and severe, when the inventory consumption rate exceeds the preset threshold, and buffers the situation by adjusting the inventory at the flexible allocation layer; The federated learning interface enables privacy-preserving data sharing across scenic areas, using a differential privacy mechanism 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 response to small changes in input data; 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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