A movie ticket online reservation management system

Abstract

This invention belongs to the field of movie ticketing technology, specifically an online movie ticketing reservation and management system. First, it collects users' historical behavior and real-time session data, integrates multi-source heterogeneous features to identify immediate viewing intentions, and outputs contextualized user profiles. Next, it constructs a theater spatial structure model based on theater architectural drawings and on-site verification data, annotates the spatiotemporal attributes of each screening to generate a resource status map, and integrates external circulation resources and weather / road conditions to construct a theater spatiotemporal context map, outputting a screening resource status map with spatiotemporal coordinates. Based on this, the invention achieves structured integration of theater spatial parameters and external circulation resources in the ticketing decision-making process, enabling users to complete the entire decision-making process from seat selection to travel based on a unified data view, eliminating decision fragmentation caused by information silos, and improving the overall efficiency of movie-watching experience planning.

Description

Technical Field

[0001] This invention relates to the field of movie ticketing technology, specifically to an online movie ticketing reservation and management system. Background Technology

[0002] With the rapid development of the digital economy, online movie ticket booking has become the mainstream consumption method for moviegoers. Existing ticketing platforms mainly realize basic functions such as film information aggregation, visual seat selection, online payment, and electronic ticketing, forming a relatively mature transaction loop.

[0003] However, with the increasing sophistication of user needs and the growing demands for cinema operational efficiency, current online movie ticketing management systems may suffer from insufficient structuring of cinema resource descriptions and fragmented core circulation information. Specifically, most existing systems simplify theater seating into a two-dimensional planar matrix, merely marking "available / sold," without providing structured and parameterized descriptions of seat audiovisual quality and spatial attributes. Furthermore, information silos exist between the cinema itself and its directly connected core circulation resources (such as parking facilities and public transportation connections) and the ticketing system. For example, when a user purchases an IMAX seat, the system does not provide a structured indication of the audiovisual quality level relative to the screen; when a user drives there, the system does not link the dynamic availability of parking spaces in the cinema's parking lot in real time, leading to parking difficulties and entry delays. These problems do not stem from a single technological limitation, but rather from the failure to establish a unified, contextualized data association mechanism between theater spatial parameters and external circulation resources within the ticketing decision-making process. Summary of the Invention

[0004] To address the technical problems of insufficient structuring in cinema resource descriptions and fragmented presentation of core circulation information, this invention provides the following technical solution:

[0005] An online movie ticketing management system, comprising:

[0006] The multi-source data acquisition and user intent parsing module collects users' historical behavior and real-time conversation data, integrates multi-source heterogeneous features to identify immediate movie-watching intent, and outputs contextualized user profiles.

[0007] The cinema spatiotemporal resource digital modeling module includes a parametric modeling unit for the auditorium spatial structure, a spatiotemporal annotation unit for screenings, and a cinema spatiotemporal context map construction unit.

[0008] The parametric modeling unit for the spatial structure of the cinema hall performs parametric modeling of the spatial structure of each cinema hall and outputs the spatial structure model of the cinema hall.

[0009] The spatiotemporal annotation unit for each screening session, based on the cinema space structure model output by the cinema space structure parameterized modeling unit, annotates the spatiotemporal attributes of each screening session and outputs a screening session resource status map with spatiotemporal coordinates.

[0010] The cinema spatiotemporal context map construction unit maps the cinema itself and its directly associated core circulation resources based on the spatiotemporal status map with spatiotemporal coordinates output by the screening spatiotemporal annotation unit; and combines external environmental parameters to construct and output the cinema spatiotemporal context map.

[0011] The supply and demand situation prediction and dynamic assessment module retrieves historical ticket sales curves to establish a benchmark prediction model, integrates real-time popularity to calculate real-time demand popularity values, and compares them with the screening resource status map with spatiotemporal coordinates output by the cinema spatiotemporal resource digital modeling module to output a supply and demand situation matrix.

[0012] The personalized matching and contextual recommendation module, based on the contextualized user profile output by the multi-source data collection and user intent parsing module, calls the cinema spatiotemporal context map constructed by the cinema spatiotemporal resource digital modeling module, calculates the spatiotemporal adaptation score to generate a set of candidate solutions, and integrates the peer-friend plan under the condition of two-way authorization to output the final personalized recommendation solution.

[0013] The dynamic pricing and seat resource allocation optimization module generates a dynamic base price based on the supply and demand situation matrix output by the supply and demand situation prediction and dynamic evaluation module. It also classifies seat values ​​based on the theater space structure model constructed by the cinema spatiotemporal resource digital modeling module and the real-time demand heat value calculated by the supply and demand situation prediction and dynamic evaluation module. It then overlays a differentiated pricing generation scheme and executes the optimization of adjacent seat reservation and waiting list to output the optimal seat allocation scheme.

[0014] The booking transaction and intelligent fulfillment module assembles a draft order based on the final personalized recommendation scheme output by the personalized matching and contextual recommendation module and the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module. It then matches the payment channel to generate electronic tickets, pushes fulfillment reminders to ticket-buying users at different nodes, and triggers the waiting queue for immediate rematch when tickets are refunded.

[0015] The movie viewing feedback and system iteration optimization module collects multi-dimensional feedback data after movie viewing. It combines the final personalized recommendation scheme output by the personalized matching and contextual recommendation module with the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module to conduct satisfaction attribution analysis. Based on this, it updates the parameters of each algorithm model and condenses typical cases into rules for feedback.

[0016] As a preferred embodiment of the online movie ticketing management system described in this invention, the multi-source data acquisition and user intent parsing module includes:

[0017] The behavior log collection unit collects and cleans users' historical behavior data to build a structured user behavior database and outputs it.

[0018] The real-time intent recognition unit, based on the structured user behavior database output by the behavior log collection unit, integrates search keywords, trigger time, GPS location, device type and social relationship status in the current session, and uses a multi-source heterogeneous feature fusion and classification model to identify the user's real-time viewing intent and output intent type labels.

[0019] The profile dynamic update unit, based on the intent type labels output by the real-time intent recognition unit, calls up the user's long-term profile and combines it with the current contextual features to output a contextualized user profile that includes preferred movie types, acceptable time and space range, price sensitivity, intensity of social needs, and commuting mode preferences.

[0020] As a preferred embodiment of the online movie ticketing management system of the present invention, the supply and demand situation prediction and dynamic evaluation module includes:

[0021] The historical box office analysis unit retrieves historical ticket sales curves for similar films, release dates, and cinemas, analyzes the occupancy rate distribution patterns and price elasticity coefficients at different times, and outputs a historical benchmark prediction model.

[0022] The real-time popularity calculation unit, based on the historical benchmark prediction model output by the historical box office analysis unit, integrates the current pre-sale rate, social media sentiment index, film reputation rating change rate, and the popularity of the lead actor's topic to calculate and output the real-time demand popularity value for each screening.

[0023] The supply-demand gap prediction unit, based on the real-time demand heat value of each session output by the real-time heat calculation unit, compares it with the session resource status map with spatiotemporal coordinates output by the cinema spatiotemporal resource digital modeling module, predicts the supply-demand gap ratio and sell-out probability of each time window in the future, and outputs a supply-demand situation matrix.

[0024] As a preferred embodiment of the online movie ticketing management system of the present invention, the personalized matching and contextual recommendation module includes:

[0025] The candidate film selection unit, based on the contextualized user profile output by the multi-source data collection and user intent analysis module, selects a set of candidate films from the film library that meet the user's preference type, duration constraints, and language requirements, and outputs them, while calculating the basic matching score.

[0026] The spatiotemporal adaptation sorting unit, based on the candidate film set output by the candidate film screening, calls the cinema spatiotemporal context map constructed by the cinema spatiotemporal resource digital modeling module, calculates the adaptation score of each candidate solution in the dimensions of user's current location accessibility, departure time rationality, and post-show transportation convenience, sorts them in descending order of spatiotemporal comprehensive convenience, and outputs the candidate solution set.

[0027] The group collaboration and social context integration unit, based on the candidate solution set output by the spatiotemporal adaptation sorting unit, analyzes the movie viewing plans and group preference consistency of authorized friends in the user's social relationship chain, under the premise of obtaining explicit authorization from both the user and friends within the same ticketing platform, and collaboratively recommends unified showtimes and adjacent seats for group movie viewing scenarios, outputting the final personalized recommendation solution.

[0028] As a preferred embodiment of the online movie ticketing management system of the present invention, the dynamic pricing and seat resource allocation optimization module includes:

[0029] The basic price modeling unit, based on the supply and demand situation matrix output by the supply and demand situation prediction and dynamic evaluation module, establishes a flexible dynamic pricing baseline model within the policy framework of the film's minimum release price and platform service fee, and outputs the basic dynamic price for each session.

[0030] The seat value grading unit is based on the cinema space structure model constructed by the cinema spatiotemporal resource digital modeling module. Combined with the real-time demand heat value calculated by the supply and demand situation prediction and dynamic evaluation module, the seat value is graded according to the audiovisual quality and physical convenience attributes, and the seat value grading matrix is ​​output.

[0031] The differentiated pricing generation unit, based on the seat value level matrix output by the seat value grading unit, adds differentiated pricing to the basic dynamic price of each session to output a dynamic pricing scheme.

[0032] The seat association optimization unit, based on the dynamic price scheme output by the differentiated pricing generation unit, optimizes the combination strategy of adjacent empty seats and outputs the optimal seat configuration scheme.

[0033] As a preferred embodiment of the online movie ticketing management system described in this invention, the reservation transaction and intelligent fulfillment module includes:

[0034] The intelligent order assembly unit automatically assembles and outputs a draft order containing movie information, showtime, seat number, dynamic price, and supporting services based on the final personalized recommendation scheme output by the personalized matching and contextual recommendation module and the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module.

[0035] The payment link adaptation unit, based on the draft order output by the intelligent order assembly unit, intelligently matches payment channels, calculates the final amount due, and outputs the payment order;

[0036] The electronic ticketing generation unit, based on the payment order output by the payment link adaptation unit, outputs an electronic ticket containing dynamic anti-counterfeiting marks, encrypted QR codes, and refund and change rule labels after successful payment, and synchronizes it to the user's digital wallet and the cinema ticketing system.

[0037] The performance reminder push unit, based on the electronic ticket output by the electronic ticketing generation unit, calls traffic and weather data in the cinema's spatiotemporal context map to push pre-trip reminder plans to ticketed users at different time points before the start of the show.

[0038] The instant rematching unit for refund and change resources establishes a waiting list queue based on the electronic tickets output by the electronic ticketing generation unit. The waiting list queue adopts a prepayment escrow model, where candidate users prepay the ticket price to the platform's escrow account when registering their waiting list intention. If the match is not successful, the full amount will be refunded to the original payment method.

[0039] As a preferred embodiment of the online movie ticketing management system described in this invention, the movie viewing feedback and system iteration optimization module includes:

[0040] The movie viewing data collection unit collects and outputs multi-dimensional feedback data from users on their movie viewing experience after the movie ends.

[0041] The satisfaction attribution analysis unit, based on the multi-dimensional feedback data output by the movie viewing data collection unit, combined with the final personalized recommendation scheme output by the personalized matching and contextual recommendation module and the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module, uses an attribution model to analyze the key factors affecting user satisfaction and outputs the attribution analysis results.

[0042] The model parameter update unit updates the parameters of each algorithm model based on the attribution analysis results output by the satisfaction attribution analysis unit, and outputs the model update results.

[0043] The knowledge base accumulation unit, based on the model update results output by the model parameter update unit, structurally accumulates typical cases into the system knowledge base, outputs the iterative system rules, and provides feedback.

[0044] Compared with existing technologies:

[0045] 1. The cinema spatial structure parametric modeling unit parametrically models the audiovisual quality and spatial attributes of the cinema, constructing a cinema spatial structure model M that includes seat coordinates, row spacing, elevation angle, distance from the screen, and sound coverage level. At the same time, the cinema spatiotemporal context map construction unit establishes a cinema spatiotemporal context map, which unifies and links the internal resources of the cinema with external circulation resources such as real-time parking space availability and public transportation connections. This enables the structured integration of cinema spatial parameters and external circulation resources in the ticketing decision-making process, allowing users to complete the entire decision-making process from seat selection to travel based on a unified data view, eliminating the fragmented decision-making caused by information silos, and improving the overall efficiency of the movie-watching experience planning.

[0046] 2. By analyzing the consistency between authorized friends' movie-watching plans and group preferences under the premise of two-way authorization through the personalized matching and contextual recommendation modules, the system collaboratively recommends the same showtimes and adjacent seats, enabling automated collaborative matching for group ticket purchases and reducing the cost of coordinating showtimes and seats for multiple moviegoers. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the overall framework of the present invention;

[0048] Figure 2 This is a schematic diagram of the multi-source data acquisition and user intent parsing module framework of the present invention;

[0049] Figure 3 This is a schematic diagram of the framework of the cinema spatiotemporal resource digital modeling module of the present invention;

[0050] Figure 4 This is a schematic diagram of the supply and demand situation prediction and dynamic evaluation module framework of the present invention;

[0051] Figure 5 This is a schematic diagram of the personalized matching and contextual recommendation module framework of the present invention;

[0052] Figure 6 This is a schematic diagram of the dynamic pricing and seat resource allocation optimization module framework of the present invention;

[0053] Figure 7 This is a schematic diagram of the framework of the reservation transaction and intelligent fulfillment module of the present invention;

[0054] Figure 8 This is a schematic diagram of the viewing feedback and system iterative optimization module framework of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0056] This invention provides an online movie ticket booking management system. Please refer to [link / reference]. Figure 1,include:

[0057] The multi-source data acquisition and user intent parsing module collects users' historical behavior and real-time conversation data, integrates multi-source heterogeneous features to identify immediate movie-watching intent, and outputs contextualized user profiles.

[0058] The cinema spatiotemporal resource digital modeling module constructs a cinema spatial structure model based on the cinema building drawings and on-site verification data, marks the spatiotemporal attributes of each screening to generate a resource status map, integrates external circulation resources and weather and road conditions to construct a cinema spatiotemporal context map, and outputs a screening resource status map with spatiotemporal coordinates.

[0059] The supply and demand situation prediction and dynamic assessment module retrieves historical ticket sales curves to establish a benchmark prediction model, integrates real-time popularity to calculate real-time demand popularity values, and compares them with the screening resource status map with spatiotemporal coordinates output by the cinema spatiotemporal resource digital modeling module to output a supply and demand situation matrix.

[0060] The personalized matching and contextual recommendation module, based on the contextualized user profile output by the multi-source data collection and user intent parsing module, calls the cinema spatiotemporal context map constructed by the cinema spatiotemporal resource digital modeling module, calculates the spatiotemporal adaptation score to generate a set of candidate solutions, and integrates the peer-friend plan under the condition of two-way authorization to output the final personalized recommendation solution.

[0061] The dynamic pricing and seat resource allocation optimization module generates a dynamic base price based on the supply and demand situation matrix output by the supply and demand situation prediction and dynamic evaluation module. It also classifies seat values ​​based on the theater space structure model constructed by the cinema spatiotemporal resource digital modeling module and the real-time demand heat value calculated by the supply and demand situation prediction and dynamic evaluation module. It then overlays a differentiated pricing generation scheme and executes the optimization of adjacent seat reservation and waiting list to output the optimal seat allocation scheme.

[0062] The booking transaction and intelligent fulfillment module assembles a draft order based on the final personalized recommendation scheme output by the personalized matching and contextual recommendation module and the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module. It then matches the payment channel to generate electronic tickets, pushes fulfillment reminders to ticket-buying users at different nodes, and triggers the waiting queue for immediate rematch when tickets are refunded.

[0063] The movie viewing feedback and system iteration optimization module collects multi-dimensional feedback data after movie viewing. It combines the final personalized recommendation scheme output by the personalized matching and contextual recommendation module with the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module to conduct satisfaction attribution analysis. Based on this, it updates the parameters of each algorithm model and condenses typical cases into rules for feedback.

[0064] Please see Figure 2 The multi-source data acquisition and user intent parsing module includes:

[0065] The behavior log collection unit collects and cleans users' historical behavior data, including historical ticket purchase records, movie browsing trajectory, favorite / rating operations, refund and change records, device usage habits and commuting mode preferences (such as driving / public transportation / walking, inferred based on historical navigation destination type and travel time), in order to build a structured user behavior database and output it;

[0066] The real-time intent recognition unit, based on the structured user behavior database output by the behavior log collection unit, integrates search keywords, trigger time, GPS location, device type, and social relationship status in the current session. It employs a multi-source heterogeneous feature fusion and classification model to identify the user's immediate viewing intent and outputs intent type labels. These intent type labels use a two-dimensional encoding, including time intent labels and social intent labels: time intent labels include immediate viewing and planned viewing; social intent labels include solo viewing and group viewing.

[0067] The specific method for multi-source heterogeneous feature fusion is as follows: search keywords are converted into semantic vectors by a text encoder; trigger time is mapped into time semantic labels (such as weekday morning, lunch break, weekend evening); GPS location is mapped into business district type labels (such as office building area, residential area, shopping mall); device type is converted into usage scenario preference weight; social relationship status is converted into group demand intensity coefficient; the above multi-dimensional features are concatenated and input into the intent classification model, which outputs time intent labels and social intent labels respectively.

[0068] The profile dynamic update unit, based on the intent type label output by the real-time intent recognition unit, calls the user's long-term profile and combines it with current contextual features (weekday / holiday, geographical location business district attributes, estimated number of people traveling with the user, commuting mode preference) to output a contextualized user profile that includes preferred movie types, acceptable time and space range, price sensitivity, intensity of social needs, and commuting mode preference.

[0069] Please see Figure 3 The cinema spatiotemporal resource digital modeling module includes:

[0070] The cinema space structure parametric modeling unit performs parametric modeling of the space structure of each cinema. Based on the architectural as-built drawings, seating layout diagrams and on-site verification data provided by the cinema, it collects parameters such as seat coordinates, row spacing, elevation angle, straight distance from the screen, sound coverage level, accessibility facility signs and accessibility parameters, and outputs the cinema space structure model.

[0071] The screening time and space annotation unit, based on the cinema space structure model output by the cinema space structure parametric modeling unit, annotates the time and space attributes of each screening session, including the screening time stamp, film duration, screening format (2D / 3D / IMAX), current seat sales status and remaining inventory, and outputs a screening resource status map with time and space coordinates.

[0072] The cinema spatiotemporal context map construction unit, based on the spatiotemporal annotation unit outputting the screening resource status map with spatiotemporal coordinates, maps the cinema itself and its directly supporting core circulation resources, including the real-time availability of parking spaces in the cinema's supporting parking lot and information on public transportation stations within 200 meters; and combines external environmental parameters (weather conditions, road congestion index) to construct and output the cinema spatiotemporal context map.

[0073] Among them, the weather conditions in the external environment parameters are used to assess the convenience of user travel (such as extending the suggested departure time on rainy days and prioritizing screenings with convenient indoor routes on hot days); the road congestion index is used to calculate the estimated navigation time from the user's current location to the cinema; the public transportation station information in the core route resources is only activated when the user profile indicates that their commuting mode preference includes public transportation; the core route resources and external environment parameters are connected in the graph through associated edges, jointly supporting subsequent spatiotemporal adaptation calculations.

[0074] Please see Figure 4 The supply and demand situation prediction and dynamic assessment module includes:

[0075] The historical box office analysis unit retrieves historical ticket sales curves for similar films, release dates, and cinemas, analyzes the occupancy rate distribution patterns and price elasticity coefficients at different times (weekday day / evening, weekends, and holidays), and outputs a historical benchmark prediction model.

[0076] The real-time popularity calculation unit, based on the historical benchmark prediction model output by the historical box office analysis unit, integrates the current pre-sale rate, social media sentiment index, film reputation rating change rate, and the popularity of the lead actor's topic to calculate and output the real-time demand popularity value for each screening.

[0077] The method for quantifying the social media sentiment index is as follows: collect public discussion data of the target film on mainstream social platforms through compliant data interfaces or public data channels, and after denoising and sentiment analysis, calculate the normalized sentiment index by weighting the discussion volume and the proportion of positive sentiment, with a value range of [0,1].

[0078] The supply-demand gap prediction unit, based on the real-time demand heat value of each session output by the real-time heat calculation unit, compares it with the session resource status map (remaining seat capacity) with spatiotemporal coordinates output by the cinema spatiotemporal resource digital modeling module, predicts the supply-demand gap ratio and sell-out probability for each future time window, and outputs a supply-demand situation matrix.

[0079] Please see Figure 5 The personalized matching and contextual recommendation module includes:

[0080] The candidate film selection unit, based on the contextualized user profile output by the multi-source data collection and user intent analysis module, selects a set of candidate films from the film library that meet the user's preference type, duration constraints, and language requirements, and outputs them, while calculating the basic matching score.

[0081] The spatiotemporal adaptation sorting unit, based on the candidate film set output by the candidate film screening, calls the cinema spatiotemporal context map constructed by the cinema spatiotemporal resource digital modeling module, calculates the adaptation score of each candidate solution in the dimensions of user's current location accessibility, departure time rationality, and post-show transportation convenience, sorts them in descending order of spatiotemporal comprehensive convenience, and outputs the candidate solution set.

[0082] The calculation method for the reasonableness of the departure time is as follows: Based on the user's current GPS location and the candidate cinema location, a third-party navigation service is called to obtain the real-time navigation estimated duration T_nav; combined with the road congestion index, the congestion buffer time T_buffer is calculated; according to the show start time T_start, the suggested departure time T_depart is calculated as follows: T_depart = T_start - T_nav - T_buffer - T_buffer_park (parking lot finding buffer time, if the user profile U indicates that the commuting mode is driving) - T_buffer_ticket (ticket collection and entry buffer time, a fixed value); if T_depart is later than the current time and the time difference is within a reasonable decision window (e.g., greater than 30 minutes), the fit score for this dimension is higher; if T_depart is earlier than the current time or the time difference is too short, the score is reduced or the candidate is eliminated.

[0083] The group collaboration and social context integration unit, based on the candidate solution set output by the spatiotemporal adaptation sorting unit, analyzes the movie-watching plans and group preference consistency (such as family-oriented / couple-oriented / friends-oriented) of authorized friends in the user's social relationship chain, under the premise of obtaining explicit authorization from both the user and friends within the same ticketing platform. It then collaboratively recommends unified showtimes and adjacent seat options for group movie-watching scenarios, and outputs the final personalized recommendation solution.

[0084] The scope of data acquisition for friend movie-watching plans includes users who have established a two-way friend relationship within the same ticketing platform and have both enabled the "movie-watching plan sharing" permission. The group preference consistency analysis is calculated based on the intersection of the distribution of film types, average ticket price range, and commonly used movie-watching times in each member's historical ticket purchase records. If a user has not enabled social relationship authorization or the social intent tag is "solo movie-watching," the set of candidate options will be directly used as the final personalized recommendation.

[0085] Please see Figure 6 The dynamic pricing and seat resource allocation optimization module includes:

[0086] The basic price modeling unit, based on the supply and demand situation matrix output by the supply and demand situation prediction and dynamic evaluation module, establishes a flexible dynamic pricing baseline model within the policy framework of the film's minimum distribution price and platform service fees: for screenings with high demand gaps, the issuance of discount coupons is reduced and the weight of targeted points benefits is increased (such as doubling points and increasing the flexibility of refunds and changes); for screenings with low attendance rates, targeted coupons are issued and package discounts are launched, and the basic dynamic price of the screening is output.

[0087] The triggering logic for the adjustment measures is as follows: the supply-demand gap ratio is mapped to the discount coefficient α (α∈[0,1]). When the supply-demand gap ratio is positive (supply is less than demand), α approaches 0 (discount is minimized); when the supply-demand gap ratio is negative (supply exceeds demand), α increases as the absolute value of the gap increases (discount is enhanced), thereby realizing the quantitative linkage between the real-time supply and demand situation and the adjustment measures.

[0088] The seat value grading unit is based on the cinema space structure model constructed by the cinema spatiotemporal resource digital modeling module. Combined with the real-time demand heat value calculated by the supply and demand situation prediction and dynamic evaluation module, the seat value is graded according to the audiovisual quality (golden zone / normal zone / edge zone) and physical convenience attributes (proximity to entrance / exit / accessibility facilities), and the seat value grading matrix is ​​output.

[0089] The differentiated pricing generation unit, based on the seat value level matrix output by the seat value grading unit, adds differentiation to the basic dynamic price of the screening and outputs a dynamic pricing scheme. Specifically, in cinemas that have implemented zoned pricing strategies, it generates a refined dynamic pricing scheme of "screening base price and seat quality premium / discount"; in cinemas that have not implemented zoned pricing, the seat value level matrix is ​​only used for recommendation and sorting display and does not directly trigger price differences.

[0090] The seat association optimization unit, based on the dynamic price scheme output by the differentiated pricing generation unit, optimizes the combination strategy of adjacent empty seats: it enables a seat reservation protection mechanism for potential multi-person ticket purchase demand (based on the group ticket purchase probability model, it sets a temporary lock timer for adjacent empty seats, and prioritizes recommending them to group ticket purchase users during the lock period; if they are not sold after the timeout, they are automatically released), it enables a waiting list mechanism for high-value seats, and it performs proximity aggregation recommendation for scattered empty seats, outputting the optimal seat configuration scheme;

[0091] The technical implementation of the adjacent seat reservation protection mechanism is as follows: When the system detects that there are N consecutive (N≥2) empty seats for a certain show, it calculates the probability of group ticket purchase based on the real-time demand popularity value of the show and the historical group ticket purchase ratio; if the probability of group ticket purchase exceeds the threshold θ, a temporary lock is initiated for the adjacent seat area, and the lock duration is negatively correlated with the show's start time (i.e., the closer to the start time, the shorter the lock duration, to avoid resource idleness); during the lock period, the adjacent seat area is preferentially displayed to users with the social intent tag of group viewing, and non-group users can only purchase seats outside the area; if the lock expires without being sold, it is automatically unlocked and included in the normal sales pool; if the lock expires without being sold for M consecutive times (M≥3), the system automatically increases the threshold θ for the show, raising the trigger threshold for subsequent adjacent seat locks and reducing the risk of excessive locking;

[0092] It should be noted that the seat reservation protection mechanism achieves compatibility management through inventory status marking in scenarios with multiple sales channels: seats that are temporarily locked are marked as "reserved within the platform" in the platform's own channels, while remaining available for sale in external channels (if any), or locked synchronously according to the channel agreement, depending on the channel management rules of the cinema and the platform.

[0093] Please see Figure 7 The reservation transaction and smart fulfillment module includes:

[0094] The intelligent order assembly unit automatically assembles and outputs a draft order containing movie information, showtime, seat number, dynamic price, and supporting services based on the final personalized recommendation scheme output by the personalized matching and contextual recommendation module and the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module.

[0095] The payment link adaptation unit, based on the order draft output by the intelligent order assembly unit, intelligently matches payment channels (according to the user's historical payment preferences, current bank promotional activities, and platform points deduction strategies), calculates the final amount payable, and outputs the payment order;

[0096] The electronic ticketing generation unit, based on the payment order output by the payment link adaptation unit, outputs an electronic ticket containing dynamic anti-counterfeiting marks, encrypted QR codes, and refund and change rule labels after successful payment, and synchronizes it to the user's digital wallet and the cinema ticketing system.

[0097] The performance reminder push unit, based on the electronic ticket output by the electronic ticketing generation unit, calls traffic and weather data in the cinema's spatiotemporal context map to push pre-trip reminder plans to ticketed users at different time points before the start of the show, including suggested departure time, parking entrance navigation, and quick access to ticket collection code;

[0098] The push notification times are as follows: 24 hours before the start (long-term planning reminder), 2 hours before the start (travel preparation reminder), and 30 minutes before the start (emergency departure reminder). These reminders only trigger for users who have already purchased tickets and generated an electronic ticket (T); users who have not purchased tickets will not be affected by the fulfillment reminder process.

[0099] The instant rematching unit for refund and change resources establishes a waiting list queue based on the electronic tickets output by the electronic ticketing generation unit. The waiting list queue adopts a prepayment escrow model, where candidate users prepay the ticket price to the platform's escrow account when registering their waiting list intentions. If the match is unsuccessful, the full amount will be refunded to the original payment method.

[0100] When a user initiates a ticket refund or reschedule to release seat resources, the system immediately queries the matching degree of candidate users in the waiting list for that show (including film preferences, acceptable time slot range, and spatial distance constraints), and pushes an instant resource release notification to highly matched candidate users; if the candidate user confirms the purchase within a preset time window (e.g., 15 minutes), the system completes the order assembly and generates an electronic ticket, and transfers the escrow funds to the formal payment; if no confirmation is made within the time limit, the seat lock is released, the escrow funds are automatically refunded, and the seat returns to the regular sales pool, realizing the proactive rematching and rapid circulation of refund and reschedule resources.

[0101] Please see Figure 8 The viewing feedback and system iteration optimization module includes:

[0102] The movie viewing data collection unit collects multi-dimensional feedback data on users' movie viewing experience after the movie is watched, through in-app reviews, ratings, comment text analysis, refund and rescheduling behavior tracking, and social sharing content capture, and outputs the data.

[0103] The satisfaction attribution analysis unit, based on the multi-dimensional feedback data output by the movie viewing data collection unit, combined with the final personalized recommendation scheme output by the personalized matching and contextual recommendation module and the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module, uses an attribution model to analyze the key factors affecting user satisfaction (such as recommendation accuracy, seat comfort, price acceptance, and convenience of surrounding facilities), and outputs the attribution analysis results.

[0104] The model parameter update unit updates the parameters of each algorithm model based on the attribution analysis results output by the satisfaction attribution analysis unit, and outputs the model update results.

[0105] Specifically, for the intent recognition model, feature weights are updated based on intent classification accuracy and confusion matrix; for the popularity prediction model, time window parameters are corrected based on pre-sale curve fitting error; for the recommendation ranking model, ranking factors are adjusted based on explicit user ratings and implicit feedback (stay time, collection behavior, click conversion rate and ticket purchase completion rate); and for the pricing model, elasticity coefficients are optimized based on occupancy rate achievement rate and ticket refund rate.

[0106] The knowledge base accumulation unit, based on the model update results output by the model parameter update unit, structurally accumulates typical cases (such as high satisfaction combination strategies, refund and change early warning modes, and supply and demand mismatch scenarios) into the system knowledge base, outputs iterated system rules, and feeds them back to the behavior log collection unit to optimize the feature engineering strategy of subsequent user behavior logs, forming a closed loop across the entire chain.

[0107] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A movie ticketing online reservation management system, characterized by, include: The multi-source data acquisition and user intent parsing module collects users' historical behavior and real-time conversation data, integrates multi-source heterogeneous features to identify immediate movie-watching intent, and outputs contextualized user profiles. The cinema spatiotemporal resource digital modeling module includes a parametric modeling unit for the auditorium spatial structure, a spatiotemporal annotation unit for screenings, and a cinema spatiotemporal context map construction unit. The parametric modeling unit for the spatial structure of the cinema hall performs parametric modeling of the spatial structure of each cinema hall and outputs the spatial structure model of the cinema hall. The spatiotemporal annotation unit for each screening session, based on the cinema space structure model output by the cinema space structure parameterized modeling unit, annotates the spatiotemporal attributes of each screening session and outputs a screening session resource status map with spatiotemporal coordinates. The cinema spatiotemporal context map construction unit maps the cinema itself and its directly associated core circulation resources based on the spatiotemporal status map with spatiotemporal coordinates output by the screening spatiotemporal annotation unit; and combines external environmental parameters to construct and output the cinema spatiotemporal context map. The supply and demand situation prediction and dynamic assessment module retrieves historical ticket sales curves to establish a benchmark prediction model, integrates real-time popularity to calculate real-time demand popularity values, and compares them with the screening resource status map with spatiotemporal coordinates output by the cinema spatiotemporal resource digital modeling module to output a supply and demand situation matrix. The personalized matching and contextual recommendation module, based on the contextualized user profile output by the multi-source data collection and user intent parsing module, calls the cinema spatiotemporal context map constructed by the cinema spatiotemporal resource digital modeling module, calculates the spatiotemporal adaptation score to generate a set of candidate solutions, and integrates the peer-friend plan under the condition of two-way authorization to output the final personalized recommendation solution. The dynamic pricing and seat resource allocation optimization module generates a dynamic base price based on the supply and demand situation matrix output by the supply and demand situation prediction and dynamic evaluation module. It also classifies seat values ​​based on the theater space structure model constructed by the cinema spatiotemporal resource digital modeling module and the real-time demand heat value calculated by the supply and demand situation prediction and dynamic evaluation module. It then overlays a differentiated pricing generation scheme and executes the optimization of adjacent seat reservation and waiting list to output the optimal seat allocation scheme. The booking transaction and intelligent fulfillment module assembles a draft order based on the final personalized recommendation scheme output by the personalized matching and contextual recommendation module and the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module. It then matches the payment channel to generate electronic tickets, pushes fulfillment reminders to ticket-buying users at different nodes, and triggers the waiting queue for immediate rematch when tickets are refunded. The movie viewing feedback and system iteration optimization module collects multi-dimensional feedback data after movie viewing. It combines the final personalized recommendation scheme output by the personalized matching and contextual recommendation module with the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module to conduct satisfaction attribution analysis. Based on this, it updates the parameters of each algorithm model and condenses typical cases into rules for feedback.

2. The online movie ticket reservation management system as claimed in claim 1 wherein, The multi-source data acquisition and user intent parsing module includes: The behavior log collection unit collects and cleans users' historical behavior data to build a structured user behavior database and outputs it. The real-time intent recognition unit, based on the structured user behavior database output by the behavior log collection unit, integrates search keywords, trigger time, GPS location, device type and social relationship status in the current session, and uses a multi-source heterogeneous feature fusion and classification model to identify the user's real-time viewing intent and output intent type labels. The profile dynamic update unit, based on the intent type labels output by the real-time intent recognition unit, calls up the user's long-term profile and combines it with the current contextual features to output a contextualized user profile that includes preferred movie types, acceptable time and space range, price sensitivity, intensity of social needs, and commuting mode preferences.

3. The online movie ticket booking management system as claimed in claim 1 wherein, The supply and demand situation forecasting and dynamic assessment module includes: The historical box office analysis unit retrieves historical ticket sales curves for similar films, release dates, and cinemas, analyzes the occupancy rate distribution patterns and price elasticity coefficients at different times, and outputs a historical benchmark prediction model. The real-time popularity calculation unit, based on the historical benchmark prediction model output by the historical box office analysis unit, integrates the current pre-sale rate, social media sentiment index, film reputation rating change rate, and the popularity of the lead actor's topic to calculate and output the real-time demand popularity value for each screening. The supply-demand gap prediction unit, based on the real-time demand heat value of each session output by the real-time heat calculation unit, compares it with the session resource status map with spatiotemporal coordinates output by the cinema spatiotemporal resource digital modeling module, predicts the supply-demand gap ratio and sell-out probability of each time window in the future, and outputs a supply-demand situation matrix.

4. The online movie ticketing management system according to claim 1, characterized in that, The personalized matching and contextual recommendation module includes: The candidate film selection unit, based on the contextualized user profile output by the multi-source data collection and user intent analysis module, selects a set of candidate films from the film library that meet the user's preference type, duration constraints, and language requirements, and outputs them, while calculating the basic matching score. The spatiotemporal adaptation sorting unit, based on the candidate film set output by the candidate film screening, calls the cinema spatiotemporal context map constructed by the cinema spatiotemporal resource digital modeling module, calculates the adaptation score of each candidate solution in the dimensions of user's current location accessibility, departure time rationality, and post-show transportation convenience, sorts them in descending order of spatiotemporal comprehensive convenience, and outputs the candidate solution set. The group collaboration and social context integration unit, based on the candidate solution set output by the spatiotemporal adaptation sorting unit, analyzes the movie viewing plans and group preference consistency of authorized friends in the user's social relationship chain, under the premise of obtaining explicit authorization from both the user and friends within the same ticketing platform, and collaboratively recommends unified showtimes and adjacent seats for group movie viewing scenarios, outputting the final personalized recommendation solution.

5. The online movie ticket reservation management system as claimed in claim 1 wherein, The dynamic pricing and seat resource allocation optimization module includes: The basic price modeling unit, based on the supply and demand situation matrix output by the supply and demand situation prediction and dynamic evaluation module, establishes a flexible dynamic pricing baseline model within the policy framework of the film's minimum release price and platform service fee, and outputs the basic dynamic price for each session. The seat value grading unit is based on the cinema space structure model constructed by the cinema spatiotemporal resource digital modeling module. Combined with the real-time demand heat value calculated by the supply and demand situation prediction and dynamic evaluation module, the seat value is graded according to the audiovisual quality and physical convenience attributes, and the seat value grading matrix is ​​output. The differentiated pricing generation unit, based on the seat value level matrix output by the seat value grading unit, adds differentiated pricing to the basic dynamic price of each session to output a dynamic pricing scheme. The seat association optimization unit, based on the dynamic price scheme output by the differentiated pricing generation unit, optimizes the combination strategy of adjacent empty seats and outputs the optimal seat configuration scheme.

6. The online movie ticket reservation management system as claimed in claim 1 wherein, The reservation transaction and smart fulfillment module includes: The intelligent order assembly unit automatically assembles and outputs a draft order containing movie information, showtime, seat number, dynamic price, and supporting services based on the final personalized recommendation scheme output by the personalized matching and contextual recommendation module and the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module. The payment link adaptation unit, based on the order draft output by the intelligent order assembly unit, intelligently matches payment channels, calculates the final amount due, and outputs the payment order; The electronic ticketing generation unit, based on the payment order output by the payment link adaptation unit, outputs an electronic ticket containing dynamic anti-counterfeiting marks, encrypted QR codes, and refund and change rule labels after successful payment, and synchronizes it to the user's digital wallet and the cinema ticketing system. The performance reminder push unit, based on the electronic ticket output by the electronic ticketing generation unit, calls traffic and weather data in the cinema's spatiotemporal context map to push pre-trip reminder plans to ticketed users at different time points before the start of the show. The instant rematching unit for refund and change resources establishes a waiting list queue based on the electronic tickets output by the electronic ticketing generation unit. The waiting list queue adopts a prepayment escrow model, where candidate users prepay the ticket price to the platform's escrow account when registering their waiting list intention. If the match is not successful, the full amount will be refunded to the original payment method.

7. The online movie ticket reservation management system as claimed in claim 1 wherein, The viewing feedback and system iteration optimization module includes: The movie viewing data collection unit collects and outputs multi-dimensional feedback data from users on their movie viewing experience after the movie ends. The satisfaction attribution analysis unit, based on the multi-dimensional feedback data output by the movie viewing data collection unit, combined with the final personalized recommendation scheme output by the personalized matching and contextual recommendation module and the optimal seat configuration scheme output by the dynamic pricing and seat resource allocation optimization module, uses an attribution model to analyze the key factors affecting user satisfaction and outputs the attribution analysis results. The model parameter update unit updates the parameters of each algorithm model based on the attribution analysis results output by the satisfaction attribution analysis unit, and outputs the model update results. The knowledge base accumulation unit, based on the model update results output by the model parameter update unit, structurally accumulates typical cases into the system knowledge base, outputs the iterative system rules, and provides feedback.

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