An ai-based reading room seat intelligent allocation and management system and method

Abstract

The application discloses an AI-based reading room seat intelligent allocation and management system and method. The system comprises a seat condition detection module, a seat allocation analysis module and a seat real-time feedback module. After the data update frequency of the seat allocation optimization model is confirmed, the application real-time monitors the seat state change of each seat area in the seat management cycle of the reading room to determine whether the model performs the recalculation of seat allocation. If yes, the time consumption of the recalculation of the seat allocation optimization model is analyzed, and the real-time influence strategy is executed. Otherwise, the seat allocation local optimization analysis and adjustment are performed. Finally, based on the dynamic adjustment of the seat state change of the seat allocation analysis module, the application automatically feeds back the real-time updated reading room seat intelligent allocation scheme, realizes the improvement of the dynamic adjustment real-time of the seat intelligent allocation, and solves the problem of the insufficient dynamic adjustment real-time of the seat intelligent allocation in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computing model, and particularly relates to an AI-based reading room seat intelligent allocation and management system and method. BACKGROUND

[0002] In order to realize a seat intelligent allocation system covering seat allocation, management, monitoring, user experience and the like, in combination with the reading room scene, the implementation of the system is refined into multiple function modules, specifically including intelligent seat allocation function, user service function, management and operation function, and AI intelligent analysis and optimization; wherein the intelligent seat allocation function is used to realize reservation allocation, dynamic adjustment, personalized recommendation and fairness optimization, the reservation allocation means that the user reserves a seat through the system, the AI recommends the optimal seat according to the preferences (window / power / quiet area), historical behavior and use time prediction, the dynamic adjustment means that when temporary cancellation, no-show, seat damage and the like occur, the AI can adjust and reallocate the empty seats in real time, the personalized recommendation means recommending appropriate seats based on user portrait and learning habits (for example, recommending quiet areas for users who learn for a long time, and recommending seats near the exit for short-time use), and the fairness optimization means avoiding that the positions with higher functions are always allocated to the same part of people through scheduling algorithms; the user service function is used to realize multi-channel reservation, real-time state query, check-in and check-out management, temporary leaving function and learning data feedback, the multi-channel reservation is used to support mobile APP (Application, application program), WeChat mini-program and Web (World Wide Web, World Wide Web) reservation, the real-time state query means that the user can check the seat usage (idle / occupied / about to release), the check-in and check-out management means checking in through scanning, facial recognition or NFC (Near Field Communication, near field communication), and automatically releasing if not checked in for a certain period of time, the temporary leaving function is used to support the user to leave for a short time (such as going to the toilet / taking books), the system automatically times, releases when overtime, the learning data feedback is used to count the user learning time, seat usage habits, and form a personal learning file; the management and operation function is used to realize global seat monitoring, abnormality detection, statistics and report, and flexible rule configuration, the global seat monitoring is used for the management end to view the seat state, usage rate, reservation rate, no-show rate and the like in real time, the abnormality detection is used to detect long-time seat occupation without learning, illegal proxy check-in, malicious occupation and the like, the system automatically alarms or releases the seat, the statistics and report are used to generate seat utilization rate, user satisfaction, reservation peak period and the like data report, assist management optimization, and the flexible rule configuration means configuring the upper limit of reservation time, advance reservation time, no-show punishment mechanism; the AI intelligent analysis and optimization is used to realize demand prediction, user behavior modeling, abnormal behavior identification and dynamic optimization model, the demand prediction means predicting the future seat demand peak by using historical data, making allocation optimization in advance, the user behavior modeling is based on the user's arrival time, stay time, preferred area, continuously optimizing the recommendation strategy, the abnormal behavior identification identifies whether there are illegal situations such as "occupying seats without using" or "proxy occupying seats" through image recognition or sensor detection, and the dynamic optimization model uses reinforcement learning or heuristic algorithm to realize rapid adjustment, and still can give a better scheme when the resources are limited.

[0003] The above technology at least has the following technical problems:

[0004] In the intelligent seat allocation function, the dynamic adjustment may have deficiencies. In actual application, when temporary cancellation, no-show, etc. occurs, the AI needs to recalculate the allocation scheme. If the algorithm is time-consuming, it may not respond in time, and there is a problem of insufficient real-time performance of dynamic adjustment of seat intelligent allocation. SUMMARY

[0005] The present application provides an AI-based reading room seat intelligent allocation and management system and method, which solves the problem of insufficient real-time performance of dynamic adjustment of seat intelligent allocation in the prior art, and improves the real-time performance of dynamic adjustment of seat intelligent allocation.

[0006] To solve the above-mentioned invention purposes, the technical solutions provided by the present application are as follows: an AI-based reading room seat intelligent allocation and management system comprises a seat condition detection module, a seat allocation analysis module and a seat real-time feedback module; wherein the seat condition detection module is used to: after confirming the data update frequency of the seat allocation optimization model used to generate the reading room seat allocation scheme, real-time monitor the seat state change of each seat area in the reading room in the seat management cycle to determine whether to perform recalculation of seat allocation by the model; the seat allocation analysis module is used to: if the recalculation of seat allocation by the model is performed, analyze the recalculation time consumption of the seat allocation optimization model, and execute the adaptive real-time performance influence strategy for ensuring seat intelligent allocation, otherwise, perform seat allocation local optimization analysis and adjustment to reduce the real-time performance influence degree of local optimization on seat intelligent allocation; the seat real-time feedback module is used to: based on the dynamic adjustment of seat state change by the seat allocation analysis module, automatically feedback the real-time updated reading room seat intelligent allocation scheme to improve the response ability of the seat allocation optimization model to the dynamic change of seat state.

[0007] The present application also provides an AI-based reading room seat intelligent allocation and management method, and the specific steps are as follows: S1, after confirming the data update frequency of the seat allocation optimization model used to generate the reading room seat allocation scheme, real-time monitor the seat state change of each seat area in the reading room in the seat management cycle to determine whether to perform recalculation of seat allocation by the model; S2, if the recalculation of seat allocation by the model is performed, analyze the recalculation time consumption of the seat allocation optimization model, and execute the adaptive real-time performance influence strategy for ensuring seat intelligent allocation, otherwise, perform seat allocation local optimization analysis and adjustment to reduce the real-time performance influence degree of local optimization on seat intelligent allocation; S3, based on the dynamic adjustment of seat state change by S2, automatically feedback the real-time updated reading room seat intelligent allocation scheme to improve the response ability of the seat allocation optimization model to the dynamic change of seat state.

[0008] Compared with the prior art, the technical scheme has at least the following beneficial effects:

[0009] 1、The above scheme confirms the data update frequency of the seat allocation optimization model for generating the reading room seat allocation scheme, ensures the stability of the update frequency of the system itself, then monitors the seat state change of each seat area in the reading room in the seat management cycle in real time to determine whether to perform re-computation of seat allocation by the model, improves the real-time response of the seat allocation scheme to the change of seat state, if the re-computation of seat allocation by the model is performed, analyzes the time consumption of the re-computation of the seat allocation optimization model, more accurately quantifies the time consumption of the re-computation of the seat allocation scheme, and then more accurately determines the influence degree of the re-computation of the seat allocation scheme on the dynamic adjustment real-time, and executes the adaptive real-time influence strategy to ensure the timeliness of the intelligent seat allocation, otherwise, performs local optimization analysis and adjustment of the seat allocation, thereby reducing the influence degree of the local optimization on the real-time of the intelligent seat allocation, and then improving the real-time of the local optimization of the seat allocation, finally, based on the dynamic adjustment of the seat state change by the seat allocation analysis module, automatically feeds back the real-time updated intelligent seat allocation scheme of the reading room, thereby improving the response capability of the seat allocation optimization model to the dynamic change of the seat state, and then improving the dynamic adjustment real-time of the intelligent seat allocation, and solving the problem of insufficient dynamic adjustment real-time of the intelligent seat allocation in the prior art.

[0010] 2、The scheme obtains the re-computation data scale parameter of the seat allocation optimization model to obtain a model computation time consumption influence coefficient, thereby more accurately quantifying the time consumption degree of the seat allocation optimization model for re-computation, and providing a data basis for whether to re-compute the seat allocation scheme, then matching the model computation time consumption influence coefficient with a time consumption mapping table to output a corresponding computation time consumption estimation value, to more accurately evaluate the influence of re-computation time consumption on dynamic adjustment real-time performance, then extracting a feedback time limit value and a feedback time error value for corresponding determination, if the computation time consumption estimation value is less than the feedback time limit value, indicating that re-computation does not affect the real-time performance of dynamic adjustment, then the model continues to perform re-computation of seat allocation, if the computation time consumption estimation value is not less than the feedback time limit value, indicating that re-computation may affect the real-time performance of dynamic adjustment, further determination is required, then the difference between the computation time consumption estimation value and the feedback time limit value is recorded as a corresponding feedback time difference value, which helps to improve the real-time performance of subsequent determination, finally, the feedback time difference value is compared with the feedback time error value, if the feedback time difference value is less than the feedback time error value, indicating that it does not affect the real-time performance of dynamic adjustment, then the model continues to perform re-computation of seat allocation, otherwise, a real-time performance influence strategy is executed to reduce the computation time consumption of the model re-computing the seat allocation scheme, thereby improving the real-time performance of the dynamic adjustment of the seat intelligent allocation scheme.

[0011] 3、The scheme obtains the seat state change quantity of each seat area where the seat state changes occur to obtain a seat state change quantity sequence, to facilitate processing of local optimization of each seat area, then determining a corresponding seat local optimization scheme based on the seat state change quantity of each seat area in the seat state change quantity sequence and a seat state change quantity processing value, thereby ensuring the accuracy of seat allocation optimization, if the seat state change quantity of the seat area is greater than the seat state change quantity processing value, the seat area is subjected to seat local optimization based on the seat state change quantity, to ensure the integrity of the seat allocation scheme, and the parallel optimization time saving is determined based on the seat state change difference value query, the influence of the current computation on real-time performance, then the seat area whose seat state change quantity is not greater than the seat state change quantity processing value is subjected to parallel optimization, and each seat area whose seat state change quantity sum is not greater than the seat state change quantity processing value is combined for parallel optimization, thereby improving the corresponding local optimization efficiency, and saving the time of local optimization, while obtaining the corresponding parallel optimization time in real time, which helps subsequent analysis, finally, the local optimization real-time performance is determined based on the seat management time, further analyzing the effectiveness of the local optimization scheme on real-time performance adjustment, to select the corresponding optimization scheme in time, and ensuring the scientificity of seat re-allocation. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0013] Figure 1 A structural schematic diagram of an AI-based reading room seat intelligent allocation and management system provided for an embodiment of the present application;

[0014] Figure 2 A structural schematic diagram of a long short-term memory network model provided for an embodiment of the present application;

[0015] Figure 3 A structural schematic diagram of a decision tree model provided for an embodiment of the present application;

[0016] Figure 4 A flowchart of a recalculation time consumption analysis provided for an embodiment of the present application;

[0017] Figure 5 A flowchart of seat allocation local optimization analysis and adjustment provided for an embodiment of the present application;

[0018] Figure 6 A flowchart of an AI-based reading room seat intelligent allocation and management method provided for an embodiment of the present application. DETAILED DESCRIPTION

[0019] The present application provides an AI-based reading room seat intelligent allocation and management system and method in view of the problem of insufficient real-time dynamic adjustment in seat intelligent allocation in the prior art. After confirming the data update frequency of the seat allocation optimization model, the seat state change of each seat area in the seat management cycle of the reading room is monitored in real time to determine whether to perform recalculation of seat allocation by the model. If yes, the recalculation time consumption of the seat allocation optimization model is analyzed, and a real-time impact strategy is executed. Otherwise, seat allocation local optimization analysis and adjustment are performed. Finally, based on the dynamic adjustment of the seat state change by the seat allocation analysis module, the real-time updated reading room seat intelligent allocation scheme is automatically fed back, thereby improving the real-time dynamic adjustment effect in seat intelligent allocation.

[0020] As shown in Figure 1 A structural schematic diagram of an AI-based reading room seat intelligent allocation and management system provided for an embodiment of the present application, an AI-based reading room seat intelligent allocation and management system includes a seat condition detection module, a seat allocation analysis module and a seat real-time feedback module.

[0021] The seat condition detection module is configured to, after confirming the data update frequency of the seat allocation optimization model used to generate the reading room seat allocation scheme, monitor the seat state changes of each seat area in the reading room in a seat management cycle in real time to determine whether to perform re-computation of seat allocation by the model.

[0022] It should be noted that the seat allocation optimization model involves the following AI technologies according to different targets:

[0023] 1. Optimization and planning model: Integer Linear Programming (ILP) / Constraint Programming (CP), which is used for optimal allocation problem that strictly satisfies constraints. When a heuristic algorithm (greedy algorithm, local search) is adopted, a fast approximate solution is achieved, which is suitable for real-time allocation. When a meta-heuristic algorithm (genetic algorithm, simulated annealing, ant colony algorithm) is adopted, it is suitable for large-scale complex optimization problems.

[0024] 2. Prediction and recommendation model: demand prediction model includes time series model, LSTM (Long Short-Term Memory) and Transformer, which is used to predict the seat demand in a certain period, help to reserve resources, and if the seat preference modeling (collaborative filtering / recommendation system) is needed, the seat preference is predicted by learning the user's historical behavior, and the satisfaction is improved.

[0025] As shown in Figure 2 , a structure diagram of a long short-term memory network model provided by an embodiment of the present application is shown, which shows a recurrent neural network model based on a long short-term memory network (LSTM) for processing serialized seat allocation and optimization problems; wherein the top part of the picture is a general recurrent neural network unit, x t (input) represents the current input at time step t, A (recurrent unit) represents a general recurrent neural network unit, which receives the current input x t and the hidden state (or internal state) from the previous time step, and performs calculation, h t (output) represents the output (usually the hidden state) at time step t; the self-loop arrow from A to A and the arrow from A to h t indicate that the unit has a "memory" function, that is, the calculation at the current time step depends not only on the current input x t but also on the information of the previous time step; the bottom picture is a long short-term memory network (LSTM) sequence model, which specifically shows the general A unit at the top as an LSTM unit and shows its expansion in time series.

[0026] Specifically, the time series expansion: illustrates three consecutive time steps: t-1 (last period), t (current period) and t+1 (next period), each time step has an LSTM unit A; its input (x): x t-1 is the user reservation information, seat status of the last period, x t is the current user reservation information, seat status, x t+1 is the user reservation information, seat status of the next period, the above input data represents the user demand (such as new reservation, cancellation, user preference, etc.) and the real-time state (such as idle, occupied, failure, cleaning, etc.) of the seat itself in a specific time point (or time period) in the local seat area; its output (h): h t-1 is the seat allocation result, optimization index and user feedback of the last period, h t is the seat allocation result, optimization index and user feedback of the current period, h t+1 is the seat allocation result, optimization index, user feedback of the next period, the above output is the decision and evaluation given by the LSTM unit after processing the input at each time step, which specifically includes: seat allocation result: specific seat allocation scheme (for example, which user is allocated to which seat), optimization index: index to measure the quality of allocation (for example, seat utilization rate, user satisfaction, conflict rate, etc.); user feedback: can be explicit (user's evaluation of the allocation) or implicit (whether the user accepts the allocation, whether to sign in on time, etc.), which is used for further optimization of the model.

[0027] Note that the internal structure of LSTM unit A (take the middle A unit as an example): LSTM unit solves the gradient vanishing / explosion problem of traditional RNN (Recurrent Neural Network) by introducing "gate" structure, so that it can better learn and remember long-term dependencies. Forget gate: controlled by a sigma (sigmoid) activation function, decides which information to discard from the cell state (not explicitly marked in the figure, but passed through the loop connection) of the previous time step. For example, if a reservation has expired, the forget gate will decide to "forget" the old state of the reservation. Input gate: jointly controlled by a sigma activation function and a tanh activation function. Sigma decides which new information needs to be updated to the cell state, and tanh is responsible for generating new candidate values. For example, when new user reservation information comes in, the input gate decides how to add these new information to the memory. Cell state update: through the calculation of forget gate and input gate, as well as addition and multiplication operations, the cell state is updated. This is the core of LSTM, which carries the network's "memory". Output gate: jointly controlled by a sigma activation function and a tanh activation function. Sigma decides which part of the cell state will be output to the hidden state h t of the current time step, and tanh processes the cell state. For example, according to the current memory, the output gate decides the final seat allocation result and optimization index. In summary, this figure represents a learning and adaptive time series decision system that can dynamically allocate and optimize local seat areas based on historical and current seat and user data.

[0028] 3. Reinforcement Learning (RL): model seat allocation as a sequential decision problem, e.g., if the state is the current seat occupancy and user request, then perform action as user allocation to a certain seat, and get corresponding reward to improve utilization, user satisfaction, and reduce conflicts; RL is suitable for dynamic scenarios.

[0029] For example, Figure 3As shown, the structural diagram of the decision tree model provided by the embodiment of the application is shown, which shows a multi-decision tree-based intelligent seat allocation system architecture. DataSet is the input layer, which is the original input data layer of the whole system. DataSet includes seat-related data, user demand data, and constraint conditions. Specifically, the seat-related data includes the physical attributes of each seat (such as location, type, whether it is near the window, whether it has power supply), current state (idle, occupied, in maintenance), historical use, etc.; the user demand data includes the user's reservation information (required time, duration), preferences (such as quiet area, proximity to specific facilities), user type (ordinary user, VIP user), historical behavior, etc.; the constraint conditions include system-level rules (such as maximum reservation duration, same user can only reserve one seat at the same time, social distancing restrictions), regional capacity restrictions, and operation strategies. Then, the parallel decision tree analysis, i.e., the processing layer, is performed. The system distributes the input data to three parallel decision trees (all marked as "Tree-1, Tree-2, and Tree-3" in the figure). Each decision tree focuses on a specific analysis dimension: Tree-1 is used to analyze seat attributes. This decision tree focuses on evaluating and understanding the inherent characteristics of each seat and its impact on allocation. Its input mainly uses "seat-related data" in DataSet, and its output may include scores or classifications of seat comfort, functionality, and popularity, for example, a seat near the window with power supply may be assigned a higher attribute score. Tree-2 is used to analyze user demand and preferences. This decision tree focuses on understanding the user's individual needs and potential preferences to achieve more accurate matching. Its input mainly uses "user demand data" in DataSet, and its output may include user's inclination score for specific seat attributes, or prediction of the most likely acceptable seat type based on user's historical behavior, for example, a user who often reserves a quiet area will have a higher preference score for quiet seats. Tree-3 is used to analyze time constraints. This decision tree focuses on handling various time-related restrictions and optimizations to ensure the rationality and feasibility of the allocation time. Its input mainly uses "user demand data" (reservation time, duration) and "constraint conditions" (seat available time period, reservation rules) in DataSet, and its output may include judgments or scores such as whether a seat is available in a specific time period, whether it meets the user's duration requirement, whether there is a time conflict, etc. For example, if a seat has been reserved for the user's required period, the decision tree will give an unavailable judgment.

[0030] Secondly, the aggregation mechanism (decision layer) of the decision tree output, which integrates the analysis results (possibly scores, classifications, feasibility judgments, etc.) of the three parallel decision trees, can be implemented through weighted summation, voting mechanism, rule engine, or another meta-learner; this mechanism is the core decision-making part of the system, responsible for weighing all factors, resolving potential conflicts, and finding the best allocation solution. For example, weighted summation assigns weights to the output of each decision tree according to the importance of different dimensions (e.g., user preferences may be more important than seat attributes, or vice versa), and then sums them up to get a comprehensive score; the rule engine defines a series of rules, such as "if the seat attribute score is high and the time is available, then prioritize user preferences"; the optimization algorithm takes the outputs of the decision trees as objective functions or constraints, and finds the best allocation through optimization algorithms.

[0031] Figure 3 The Final Result is the output layer, used to output the final result of seat allocation, which is the final output of the entire system, i.e., the specific seat allocation plan, which specifies which user is allocated to which specific seat at which time period. This result is the optimized product after considering seat attributes, user demand preferences, and time constraints; as described above, this diagram depicts a multi-dimensional, parallel processing intelligent seat allocation system that breaks down complex allocation problems into multiple sub-problems (seat attributes, user demand, time constraints), analyzes them using specialized decision trees, and then integrates these analysis results through an aggregation mechanism to achieve efficient, intelligent, and multi-demand seat allocation, thereby improving the accuracy of seat allocation and user satisfaction.

[0032] The seat allocation analysis module is used to analyze the time-consuming situation of the re-computation of the seat allocation optimization model if the re-computation of the seat allocation by the model is performed, and to perform an adaptive real-time impact strategy for ensuring the real-time performance of the seat intelligent allocation, otherwise to perform local optimization analysis and adjustment of the seat allocation to reduce the degree of impact of local optimization on the real-time performance of the seat intelligent allocation.

[0033] The seat real-time feedback module is used to automatically feedback the real-time updated intelligent seat allocation scheme of the reading room based on the dynamic adjustment of the seat allocation analysis module to the seat state changes to improve the response capability of the seat allocation optimization model to the dynamic changes of the seat state. Among them, automatic feedback means real-time feedback on multiple terminals, for mobile terminals, push notifications or automatically refresh the seat reservation page to display the new scheme, for WeChat mini programs, through service messages or page automatic update, remind users of seat changes, while the web page automatically refreshes the seat distribution interface and prompts the user to update the scheme on the user reservation management page, and the NFC check-in device synchronously updates the local cache seat allocation state to ensure that the information is consistent when the user checks in.

[0034] In the embodiment, the seat state is monitored in real time by the seat condition detection module, and combined with the data update frequency confirmation of the model, it can be determined in time whether the model needs to be recalculated or locally optimized, so as to avoid unreasonable seat allocation caused by delay; and when the recalculation is needed, the seat allocation analysis module analyzes the calculation time of the model, and selects the appropriate influence strategy, and when the recalculation is not needed, the local optimization is adopted, which reduces the overall calculation complexity and resource consumption, realizes the balance between global calculation and local optimization; the seat real-time feedback module automatically outputs the latest seat allocation scheme based on the result of the analysis module, so that the system can quickly respond to the dynamic changes of the seat state, improve the adaptability and accuracy of the scheme, and when it is detected that the overall recalculation is not needed, the system is adjusted through local optimization analysis and adjustment, which is helpful to control the influence degree of the local optimization process on the overall real-time allocation, and further ensures the real-time performance of the dynamic adjustment of the seat intelligent allocation.

[0035] Preferably, the specific process of data update frequency confirmation is as follows:

[0036] Firstly, the update frequencies of the timing batch data and the dynamic update data of the seat allocation optimization model are obtained respectively, to obtain the corresponding timing batch data update frequency and dynamic update data update frequency. The timing batch data represents data with a set fixed update period, and the dynamic update data represents data with a variable update frequency. The timing batch data is usually updated at a fixed time point every day, and is used to affect the seat allocation of the next batch or the next day, such as user reservation information, blacklist, rule adjustment, etc. The dynamic update data is data that may change in real time, such as seat usage status, user real-time arrival and departure, cancellation and reservation request, etc. The update frequencies of the timing batch data and the dynamic update data are read through the log of the reading room seat intelligent allocation and management system.

[0037] In the second step, the timing batch data update frequency and the dynamic update data update frequency are compared with the corresponding timing batch data update frequency determination value and the dynamic update data update frequency determination value, including two cases. Specifically, the timing batch data update frequency determination value and the dynamic update data update frequency determination value are obtained from the preset database, which are usually set by the preset staff based on the requirements of seat intelligent allocation and historical experience, and are stored in the preset database in advance. In the first case, if the timing batch data update frequency and the dynamic update data update frequency are not less than the corresponding timing batch data update frequency determination value and the dynamic update data update frequency determination value, the seat state of each seat area is continuously detected. In the second case, if the timing batch data update frequency and the dynamic update data update frequency are less than the corresponding timing batch data update frequency determination value and the dynamic update data update frequency determination value, the timing batch data update frequency is adjusted to the timing batch data update frequency determination value, and the dynamic update data update frequency is adjusted to the dynamic update data update frequency determination value, otherwise, if the timing batch data update frequency is less than the corresponding timing batch data update frequency determination value, the timing batch data update frequency is adjusted to the timing batch data update frequency determination value, and if the dynamic update data update frequency is less than the corresponding dynamic update data update frequency determination value, the dynamic update data update frequency is adjusted to the dynamic update data update frequency determination value.

[0038] In the embodiment, by obtaining the update frequency of the timing batch data and the dynamic update data respectively and comparing them with the corresponding determination values, the timeliness and accuracy of the seat allocation optimization model in data driving can be ensured, and the distortion of the allocation result caused by data update lag can be avoided; at the same time, when the update frequency of the timing batch data or the dynamic update data is detected to be lower than the set determination value, the system will automatically adjust it to the determination value level, so as to realize the dynamic regulation of the data update frequency, and thus improve the stability and reliability of the model operation; and only when it is confirmed that the data update frequency meets the determination condition, the seat state detection link is continued, so as to ensure that the detection and subsequent optimization are based on the latest effective data, and the credibility of the detection and analysis result is improved; the automatic judgment and adjustment mechanism of the data update frequency enables the system to dynamically switch according to the actual data update situation, so as to better adapt to the data change demand in different scenarios in the reading room, and improve the real-time response ability of the seat allocation optimization model.

[0039] Preferably, the seat state changes of each seat area in the reading room during the seat management period are monitored in real time to determine whether to perform re-computation of seat allocation by the model, and the specific process is as follows: Step one, detecting the seat state of each seat area in the seat management period and counting the number of changed seats to obtain the seat state change quantity, and accumulating and summing the seat state change quantity of each seat area to obtain the total seat state change quantity, the seat state includes reserved, cancelled reservation, modified reservation, no-show, no check-in, early departure, and new reservation, etc. Step two, comparing the total seat state change quantity with the pre-set seat state change limit value that can trigger the re-computation of the seat allocation optimization model: if the total seat state change quantity is greater than the seat state change limit value, the seat allocation optimization model is executed to re-compute the seat allocation scheme, otherwise, the seat allocation local optimization analysis and adjustment are performed; wherein the seat state change limit value is obtained from the pre-set database, and is specifically determined by the pre-set staff based on the specific calculation performance of the model, and is set and stored in the pre-set database in advance.

[0040] In the embodiment, by detecting the state change of each seat area in the seat management period and counting the seat state change quantity, it is ensured that the dynamic change of each seat can be monitored in real time, and then accurate basis is provided for subsequent optimization decision; the system compares the total seat state change quantity with the pre-set trigger limit value, automatically judges whether the re-computation of the seat allocation optimization model needs to be triggered, avoids manual intervention, and thus improves the processing efficiency and system response speed; if the seat state change does not exceed the limit value, local optimization analysis and adjustment are selected, avoiding global re-computation for each small state change, thereby effectively saving computing resources and improving computing efficiency; the process can adjust the model calculation strategy according to the actual situation of seat state change, improve the flexibility and real-time response ability of the seat allocation optimization scheme, thereby enhancing the adaptability of the system to actual scene changes, and thus improving the real-time performance of the dynamic adjustment of the seat allocation optimization model.

[0041] As Figure 4As shown, the flowchart of the re-computation time consumption analysis provided by the embodiment of the application, the specific logic is: obtaining the re-computation data scale parameter of the seat allocation optimization model to obtain the model computation time consumption influence coefficient; matching the model computation time consumption influence coefficient with the time consumption mapping table, and outputting the corresponding computation time consumption estimation value; extracting the feedback time limit value and the feedback time error value; if the computation time consumption estimation value is less than the feedback time limit value, continue to perform the re-computation of seat allocation by the model; if the computation time consumption estimation value is not less than the feedback time limit value, the difference between the computation time consumption estimation value and the feedback time limit value is recorded as the corresponding feedback time difference value; comparing the feedback time difference value with the pre-set feedback time error value, if the feedback time difference value is less than the feedback time error value, continue to perform the re-computation of seat allocation by the model; if the feedback time difference value is not less than the feedback time error value, execute the real-time influence strategy for reducing the computation time consumption of the model re-computing the seat allocation scheme; through the above process, the intelligent prediction of the seat computation re-allocation time consumption length is realized, so as to take corresponding optimization measures accordingly, and thus the real-time performance of the seat intelligent allocation is improved.

[0042] Preferably, the re-computation time consumption of the seat allocation optimization model is analyzed, and the specific process is as follows:

[0043] Firstly, the re-computation data scale parameter of the seat allocation optimization model is obtained to obtain the model computation time consumption influence coefficient for quantifying the re-computation time consumption degree of the seat allocation optimization model, the re-computation data scale parameter includes the number of reservation users, the number of seats, the number of idle seats, and the user modification frequency for quantifying the frequency of the corresponding seat state being modified. It should be noted that the number of reservation users, the number of seats, and the number of idle seats can be directly obtained from the system, the number of seats represents the total number of reading room seats, and the user modification frequency for quantifying the frequency of the corresponding seat state being modified is obtained by ratio operation of the number of seats whose state is modified in the seat management period and the length of the seat management period.

[0044] Then, the model calculates the time-consuming influence coefficient and matches it with the time-consuming mapping table set in advance to reflect the mapping relationship between the time-consuming influence coefficient and the corresponding time-consuming estimation value, and outputs the corresponding time-consuming estimation value. Specifically, the time-consuming mapping table is pre-constructed in the preset database. The time-consuming influence coefficient is input into the trained time-consuming mapping table to output the corresponding time-consuming estimation value, that is, the estimation value of the time-consuming influence coefficient on the time-consuming. The training data used in the mapping table comes from the time-consuming influence coefficient obtained in the historical period and the time-consuming estimation value set by professional technicians according to the experience rule, which is used to fit the mapping relationship between the time-consuming influence coefficient and the time-consuming estimation value, so as to more accurately evaluate the time-consuming. At the same time, the feedback time limit value and the feedback time error value used to ensure the real-time feedback effect of the dynamic adjustment of the seat allocation optimization model are extracted. It needs to be explained that the feedback time limit value and the feedback time error value are read from the preset database and are set in advance by the preset staff.

[0045] Then, if the time-consuming estimation value is less than the feedback time limit value, the model continues to perform the recalculation of seat allocation, otherwise the difference between the time-consuming estimation value and the feedback time limit value is recorded as the corresponding feedback time difference value, that is, the difference between the time-consuming estimation value and the feedback time limit value.

[0046] Finally, the feedback time difference value is compared with the feedback time error value. If the feedback time difference value is less than the feedback time error value, the model continues to perform the recalculation of seat allocation, otherwise the real-time influence strategy for reducing the time-consuming of the model to recalculate the seat allocation scheme is executed.

[0047] In the embodiment, by introducing the re-computation data scale parameters including the number of pre-booking users, the number of seats, the number of idle seats and the user modification frequency, the model computation time consumption influence coefficient is calculated, the time consumption of the seat allocation optimization model re-computation is more accurately quantitatively analyzed, the model computation time consumption influence coefficient is matched with the time consumption mapping table, the corresponding computation time consumption estimation value is obtained, the system can predict the time consumption level in advance, and a scientific basis is provided for subsequent dynamic feedback regulation; at the same time, by introducing the feedback time limit value and the feedback time error value, it is ensured that the model re-computation time consumption will not exceed the expected feedback time, so as to ensure that the seat allocation optimization system has the ability of timely dynamic response; and in the case of long computation time, the system will automatically trigger the real-time influence strategy according to the comparison result of the feedback time difference value and the error value, so as to reduce the time consumption pressure of re-computation, and further improve the utilization efficiency of computing resources; by dynamically adjusting the model computation process under the premise of ensuring the feedback timeliness, both full re-computation within the controllable time consumption range and optimization strategy selection when the time consumption is too long are realized, and the stability of the system is further improved.

[0048] Preferably, the specific content of the real-time influence strategy is as follows:

[0049] X1, the difference between the feedback time difference value and the feedback time error value is the feedback time deviation, that is, the feedback time difference value and the feedback time error value are subjected to difference operation to obtain the feedback time deviation, and according to the feedback time deviation, a projection mapping sequence for reflecting the mapping relationship between the feedback time deviation and the model constraint number reduction ratio is queried to obtain the corresponding model constraint number reduction ratio. It should be noted that the projection mapping sequence is pre-constructed in the preset database, the feedback time deviation is input into the trained projection mapping sequence to output the corresponding model constraint number reduction ratio, that is, the reduction degree of the model constraint number by the feedback time deviation. The training data of the projection mapping sequence comes from the feedback time deviation obtained in the historical period and the model constraint number reduction ratio set by the professional and technical personnel according to the experience rule, which is used to fit the mapping relationship between the feedback time deviation and the model constraint number reduction ratio, so as to more accurately adjust the projection mapping sequence.

[0050] X2, the model constraint number reduction ratio is compared with the constraint number reduction ratio limit value of the set limit constraint number reduction degree, wherein the constraint number reduction ratio limit value is usually set in advance by the preset professional and technical personnel based on the specification requirements and experience of the constraint number, and stored in the preset database.

[0051] X3, if the model constraint quantity reduction ratio is lower than the constraint quantity reduction ratio limit value, reducing the number of constraints with low-to-high constraint priority in the seat allocation optimization model by the model constraint quantity reduction ratio. It should be noted that the constraint is a rule and limit that must be followed when performing seat allocation, and reducing the number of constraints with low-to-high constraint priority means removing some low-priority constraints to reduce the complexity of constraints, for example, if the seat preferences of users (such as window seats, aisle seats, etc.) do not need to be considered, it can be simplified to seat area priority, or some complex priority sorting is removed and replaced with a simple "first come, first served" or allocation according to a preset area.

[0052] X4, if the model constraint quantity reduction ratio is not lower than the constraint quantity reduction ratio limit value, reducing the number of constraints with low-to-high constraint priority in the seat allocation optimization model by the constraint quantity reduction ratio limit value.

[0053] In this embodiment, by calculating the feedback time deviation and combining the projection mapping sequence, the feedback time deviation can be mapped with the model constraint quantity reduction ratio, so as to realize fine regulation and control of the model calculation complexity. The system selectively reduces the constraints in the seat allocation optimization model according to different model constraint quantity reduction ratios, and the reduction process gradually reduces according to the constraint priority from low to high, ensuring efficient implementation of the optimization goal within a certain range. By setting the constraint quantity reduction ratio limit value, the distortion or unavailability of the allocation result caused by too high model constraint reduction ratio is avoided, and the executability of the optimization model is ensured while reducing the time consumption. And dynamically adjusting the number of constraints according to different feedback time deviations can make the seat allocation optimization system better adapt to the time consumption requirements in different scales and different scenarios, and realize more intelligent seat allocation regulation and control.

[0054] As a further embodiment, the method for obtaining the model calculation time consumption influence coefficient includes two steps:

[0055] The first step involves normalizing the recalculated data scale parameters and matching them with corresponding recalculation scale parameter influence factors. These factors reflect the impact of the recalculated data scale parameters on the model's computation time. These influence factors include the number of booked users, the number of seats, the number of available seats, and the frequency of user modifications. It's important to note that these influence factors are obtained by querying an influence factor matching table in a pre-defined database. This table reflects the mapping relationship between the recalculated data scale parameters and their corresponding influence factors. By inputting the real-time recalculated data scale parameters into this table, the corresponding influence factors for the number of booked users, the number of seats, the number of available seats, and the frequency of user modifications are output. These factors are used to assess the impact of the number of booked users, the number of seats, the number of available seats, and the frequency of user modifications on the model's computation time, respectively, resulting in a more accurate coefficient for evaluating the model's computation time.

[0056] The second step involves weighting the recalculated data size parameters based on the impact factor of the recalculated size parameters and then coupling them to obtain the impact coefficient of model computation time. The specific constraint expression is as follows:

[0057] ;

[0058] In the formula, x1 represents the number of users making reservations, x2 represents the number of seats, x3 represents the number of available seats, x4 represents the frequency of user modifications, a represents the influence factor of the number of users making reservations, b represents the influence factor of the number of seats, c represents the influence factor of the number of available seats, d represents the influence factor of the frequency of user modifications, and y represents the influence coefficient of model calculation time.

[0059] In this embodiment, the algorithm combines the recalculated data scale parameter with the corresponding recalculation scale parameter influence factor to obtain the model calculation time influence coefficient. Specifically, as the recalculated data scale parameter increases, the corresponding model calculation time influence coefficient also increases. As the number of reserved users, seats, and available seats gradually increases, the computational requirements on the seat allocation optimization model become higher, the computational complexity increases, and the calculation time increases accordingly, resulting in a larger model calculation time influence coefficient. Similarly, when the frequency of user modifications is higher, the seat status that the model needs to handle may become more complex, the computational requirements on the model become higher, and the calculation time may be longer, resulting in a larger model calculation time influence coefficient. Furthermore, the recalculated data scale parameters are related to... The systems are interconnected. For example, when the number of users making reservations is no greater than the number of seats, it indicates that there are plenty of seats; otherwise, seat allocation optimization is needed. These two factors determine whether there is competition for seats in the reading room. The number of vacant seats reflects resource utilization. Fewer vacant seats indicate resource scarcity, while higher seat utilization and more vacant seats suggest potential uneven distribution. Furthermore, if the number of users making reservations is close to the total number of seats, the number of vacant seats approaches zero. If users modify their reservations more frequently, the number of effective reservations may become unstable, i.e., the number of user reservations may be unstable. Through the above analysis, we can better understand the time consumption of model recalculation, thereby more accurately assessing the impact of recalculation time on the real-time performance of dynamic adjustments, and then taking corresponding optimization measures in a timely manner to improve the real-time performance of dynamic adjustments during intelligent seat allocation.

[0060] like Figure 5 The diagram illustrates the process of local optimization analysis and adjustment of seat allocation provided in this application embodiment. The specific logic is as follows: The number of seat status changes in each seat area that has undergone a change is statistically analyzed to obtain a sequence of seat status change counts; a judgment is made based on the number of seat status changes in each seat area within the sequence and the processed value of the number of seat status changes; if the number of seat status changes in a seat area is greater than the processed value, local seat optimization is performed on the seat area based on the number of seat status changes, and the seat status change difference is calculated based on the deviation between the number of seat status changes and the processed value. The time saved by parallel optimization is then retrieved from the time-saving mapping table; parallel optimization is performed on seat areas where the number of seat status changes is not greater than the processed value, and seat areas whose sum of seat status changes is not greater than the processed value are combined for parallel optimization, while the corresponding parallel optimization time is obtained in real time; the real-time performance of local optimization is determined based on the seat management time. The above analysis helps reduce the time consumption of local parallel optimization of seat allocation and improves the real-time performance of dynamic adjustment of seat allocation optimization.

[0061] Preferably, the specific steps for local optimization analysis and adjustment of seat allocation are as follows: P1, statistically analyze the number of seat status changes in each seat area where seat status changes occur to obtain a sequence of seat status change counts. P2, based on the number of seat status changes in each seat area in the sequence of seat status change counts, compare it with a pre-set processing value for the number of seat status changes used to limit the scale of parallel optimization. This processing value is usually pre-set by a designated staff member. P3, if the number of seat status changes in a seat area is greater than the processing value, perform local optimization of the seat area based on the number of seat status changes. Calculate the seat status change difference based on the deviation between the number of seat status changes and the processing value. This difference is calculated by ratioing the difference to the processing value. The parallel optimization time saving is then retrieved from a time-saving mapping table that establishes a mapping relationship between the seat status change difference and the time saved in parallel optimization. It should be explained that local seat optimization refers to optimization through a local area allocation algorithm. Local seat optimization includes dynamic adjustment, priority allocation, and conflict detection. Dynamic adjustment is based on the seat status of the current seat area, combined with the availability of seats in surrounding seat areas, user demand, etc., to calculate the best seat allocation scheme for that seat area. Priority allocation is adjusted according to the current reservation status or user behavior (such as prioritizing seats for users with frequent reservations or those who have already checked in). Conflict detection checks whether there are seat conflicts or duplicate allocations in the current seat area. If a conflict is found, the seat arrangement in that seat area is readjusted. It should be added that the local area allocation algorithm includes, but is not limited to, priority-based allocation algorithms, proximity optimization algorithms, time-slice optimization algorithms, conflict resolution algorithms, and regional load balancing algorithms, etc., specifically set according to the corresponding reading room seat allocation rules and requirements. Among them, the priority-based allocation algorithm is used to prioritize and match available seats and users to be allocated in a local area; the proximity optimization algorithm quantifies the physical distance between the user's current or target location and available seats, and prioritizes the seat with the lowest cost during allocation; the time-slice optimization algorithm divides the usage time of the seat into segments, and matches the user's required usage time with the remaining time of the seat, prioritizing the matching of users with the highest "time demand fit"; the conflict resolution algorithm resolves conflicts by setting rules (such as reservation order, user credit score, usage time, etc.) or by "secondary allocation" to reassign conflicting users to other available seats in the same seating area; the regional load balancing algorithm is used to randomly or evenly distribute users within the seating area to ensure balanced overall seat utilization.

[0062] It should be added that the time-saving mapping table is pre-built in a preset database. The seat status change difference is input into the trained time-saving mapping table to output the corresponding parallel optimization time saving, i.e., the time saving to be optimized as reflected by the seat status change difference. The training data used in this mapping table comes from seat status change differences obtained within historical time periods, and parallel optimization time saving parameters set by technical personnel based on empirical rules, used to fit the mapping relationship between seat status change differences and parallel optimization time saving.

[0063] P4. Parallel optimization is performed on seat areas where the number of seat status changes is no greater than the processing value for seat status change counts. Seat areas whose sum of seat status change counts is no greater than the processing value are combined for parallel optimization, while the corresponding parallel optimization time is acquired in real time. Parallel optimization means that the combined seat areas are simultaneously optimized locally. For example, assuming the processing value for seat status change counts is 100, and the number of seat status changes for each seat area in the sequence is 15, 8, 26, 9, 2, 10, 25, 14, 5, 12…, the number of seat status changes for each area is added sequentially: 15 + 8 + 26 + ... + 25 = 95. The corresponding seat area can be combined for parallel optimization. If 14 is added, the value becomes 109, exceeding the processing value of 100. Therefore, the seat area with 14 seat status changes is included in the next combination, and the addition continues from 14, i.e., “14 + 5 + 12 + …”.

[0064] P5. Real-time determination of local optimization based on seat management time. Seat management time includes total parallel optimization time, total optimization saving time, and total local optimization time. Total parallel optimization time is the sum of the time of each parallel optimization, total optimization saving time is the sum of the time saved by each parallel optimization, and total local optimization time is the time for each seat area to perform local optimization of reallocation.

[0065] In this embodiment, by statistically analyzing and determining the number of changes in the seat area's state, areas with large changes can be optimized in a focused manner, achieving localization of the optimization scope and improving the targeting and precision of the optimization. For areas where the number of seat state changes does not exceed the processing value, parallel optimization is performed through a combination approach, which can fully utilize parallel computing resources while ensuring reasonable allocation and shortening the overall optimization time. Furthermore, based on the mapping table between the seat state change difference and the time saved by parallel optimization, the time-saving effect of optimization can be dynamically estimated, thereby flexibly selecting appropriate optimization strategies under different change scales. By comprehensively considering the total parallel optimization time, the total optimization saving time, and the total local optimization time, a real-time judgment mechanism for seat management time is established, which helps to avoid the feedback effect of excessive optimization time affecting the dynamic adjustment of the system. While ensuring that large-scale change areas can respond quickly, it also ensures that small-scale change areas can be processed through combined parallel optimization, thereby balancing the overall optimization effect and system stability.

[0066] Preferably, the specific circumstances for determining the real-time performance of local optimization based on seat management time are as follows: First, if the total local optimization time is less than the feedback time limit, the current optimization is considered effective, and the seat status of each seat area in the next seat management cycle continues to be monitored. Second, if the total local optimization time is not less than the feedback time limit, the difference between the total local optimization time and the total parallel optimization time is recorded as the time to be verified for saving optimization, specifically the difference between the total local optimization time and the total parallel optimization time, and the time to be verified for saving optimization is compared with the total optimization saving time. Third, if the time to be verified for saving optimization is higher than the total optimization saving time, the local optimization is considered effective, and a computing resource alarm for the seat allocation optimization model is issued, requesting the pre-set staff to publish a delayed public announcement of the seat reassignment results. Fourth, if the time to be verified for saving optimization is not higher than the total optimization saving time, the local optimization is considered invalid, and adjustments are made to the local optimization.

[0067] In this embodiment, by determining whether the total local optimization time is less than the feedback time limit, dynamic monitoring of the optimization real-time performance is achieved, ensuring that the optimization results can be output within the time limit, thereby avoiding allocation delays and improving real-time performance. Furthermore, through a dual judgment mechanism (time comparison and time-saving comparison), the effectiveness of the optimization can be more accurately distinguished, avoiding deviations caused by a single indicator. When it is found that the local optimization is within the time limit but the resource pressure is high, the system can automatically issue a computing resource alarm and request staff to publish a delay notice, thereby reducing the user expectation gap caused by excessive optimization time. By taking delay notice or optimization adjustment measures in scenarios where the time limit is exceeded, a balance can be achieved between user experience and computing resources.

[0068] Preferably, the specific adjustments to the local optimization are as follows:

[0069] The difference between the time saved in the verification optimization and the total time saved is quantified to obtain the time-saving difference. This difference is represented by subtracting the time saved in the verification optimization from the total time saved. This difference is then matched against a mapping set constructed to fit the relationship between the time-saving difference and the amplification of seat status change processing values. It should be noted that the amplification of seat status change processing values ​​is obtained by querying a mapping set in a pre-defined database. This mapping set reflects the mapping relationship between the time-saving difference and the corresponding amplification of seat status change processing values. Inputting the real-time time-saving difference into the mapping set outputs the corresponding amplification of seat status change processing values, reflecting the degree of amplification of the seat status change processing values, thus obtaining more accurate seat status change processing values.

[0070] The seat status change quantity processing value is amplified by multiplying it. If the time to be verified for optimization savings in the next seat management cycle is still not higher than the total optimization time savings, the amplification process continues until it exceeds the set amplification limit. At this point, the amplification process stops. The amplification limit is pre-stored in a preset database and is usually set by preset staff. If the time to be verified for optimization savings exceeds the total optimization time savings within the corresponding time period of the cycle, a computing resource alarm is issued for the seat allocation optimization model; otherwise, the seat allocation optimization model is triggered to recalculate the seat allocation scheme.

[0071] In this embodiment, by quantifying the difference between the time saved in optimization to be verified and the total time saved in optimization, and constructing a mapping relationship, the adjustment of the number of seat status changes can be more precisely controlled, thereby ensuring the efficiency and accuracy of the optimization process. Furthermore, based on the mapping result of the amplification of the number of seat status changes, the system can dynamically amplify the processing value, enabling continuous optimization and addressing different optimization needs, thus improving the system's adaptability in a changing environment. Setting a limit on the amplification range and stopping the cyclic optimization helps prevent waste of system resources or reduced optimization efficiency due to excessive amplification, ensuring stable system operation. During the cyclic optimization process, if the optimization does not meet expectations, the system can automatically trigger a recalculation of the optimization scheme to ensure that the final optimization result meets actual needs, thereby enhancing the system's adaptive and self-adjusting capabilities. By mapping and adjusting the difference in time saved during optimization, the system can more accurately allocate appropriate computing resources for each cycle, thereby improving resource utilization efficiency and reducing resource waste. Simultaneously, during the cyclic optimization process, if the time saved in optimization to be verified is higher than the total time saved in optimization, the system can promptly issue a computing resource alarm to prevent potential bottlenecks and resource overload during the execution of the optimization scheme.

[0072] like Figure 6 The diagram shows a flowchart of an AI-based intelligent seating allocation and management method for a reading room, as provided in an embodiment of this application. The specific steps are as follows: S1, After confirming the data update frequency of the seating allocation optimization model used to generate the reading room seating allocation scheme, the changes in the seat status of each seat area in the reading room during the seating management cycle are monitored in real time to determine whether the model should recalculate the seating allocation. S2, If the model recalculates the seating allocation, the recalculation time of the seating allocation optimization model is analyzed, and an appropriate strategy to ensure the real-time impact of intelligent seating allocation is implemented; otherwise, local optimization analysis and adjustment of seating allocation are performed to reduce the impact of local optimization on the real-time performance of intelligent seating allocation. S3, Based on the dynamic adjustment of seat status changes in S2, the real-time updated intelligent seating allocation scheme for the reading room is automatically fed back to improve the responsiveness of the seating allocation optimization model to dynamic changes in seat status.

Claims

1. An AI-based reading room seat intelligent allocation and management system, characterized in that, The seat condition detection module, the seat allocation analysis module, and the seat real-time feedback module are included. The seat condition detection module is configured to monitor the seat state changes of each seat area in the reading room within a seat management cycle to determine whether to perform re-computation of seat allocation by the model after confirming the data update frequency of the seat allocation optimization model used to generate the reading room seat allocation scheme. The seat allocation analysis module is configured to analyze the re-computation time consumption of the seat allocation optimization model and perform an adaptive real-time influence strategy for ensuring the real-time seat intelligent allocation if the re-computation of seat allocation by the model is performed, or to perform local optimization analysis and adjustment of seat allocation to reduce the real-time influence degree of local optimization on seat intelligent allocation. The seat real-time feedback module is configured to automatically feed back the real-time updated reading room seat intelligent allocation scheme based on the dynamic adjustment of seat state changes by the seat allocation analysis module to improve the response capability of the seat allocation optimization model to dynamic changes in seat state. The real-time monitoring of seat state changes of each seat area in the reading room within a seat management cycle to determine whether to perform re-computation of seat allocation by the model includes the following steps: Step one: detecting the seat state of each seat area within a seat management cycle and counting the number of changed seats to obtain the seat state change quantity, and accumulating and summing the seat state change quantity of each seat area to obtain the total seat state change quantity. Step two: comparing the total seat state change quantity with the pre-set seat state change limit value that triggers the re-computation of seat allocation optimization model for seat allocation scheme: If the total seat state change quantity is greater than the seat state change limit value, the seat allocation optimization model re-computes the seat allocation scheme, otherwise, local optimization analysis and adjustment of seat allocation are performed. The analysis of the re-computation time consumption of the seat allocation optimization model includes the following steps: Obtain the re-computation data size parameters of the seat allocation optimization model to obtain the model computation time influence coefficient for quantifying the re-computation time consumption of the seat allocation optimization model, the re-computation data size parameters including the number of reservation users, the number of seats, the number of idle seats, and the user modification frequency for quantifying the frequency of corresponding seat state modification. Match the model computation time influence coefficient with the pre-set time consumption mapping table reflecting the mapping relationship between the computation time influence coefficient and the corresponding computation time estimate value, and output the corresponding computation time estimate value. Extract the feedback time limit value for ensuring the real-time feedback effect of dynamic adjustment of the seat allocation optimization model and set the feedback time error value. If the computation time estimate value is less than the feedback time limit value, continue to perform re-computation of seat allocation by the model. If the computation time estimate value is not less than the feedback time limit value, the difference between the computation time estimate value and the feedback time limit value is recorded as the corresponding feedback time difference value. Compare the feedback time difference value with the pre-set feedback time error value: If the feedback time difference value is greater than the feedback time error value, the feedback time limit value is adjusted to the feedback time difference value, otherwise, the feedback time limit value remains unchanged. If the feedback time difference value is less than the set feedback time error value, continue to perform the recalculation of the seat allocation by the model; If the feedback time difference value is not less than the set feedback time error value, execute a real-time impact strategy for reducing the calculation time of the model to recalculate the seat allocation scheme.

2. The AI-based reading room seat intelligent allocation and management system according to claim 1, wherein, The specific process of the data update frequency confirmation is as follows: Respectively acquire the update frequency of the timing batch data and the dynamic update data of the seat allocation optimization model, to obtain the corresponding timing batch data update frequency and dynamic update data update frequency, the timing batch data represents data with a set fixed update period, and the dynamic update data represents data with a dynamic change in update frequency; If the timing batch data update frequency and the dynamic update data update frequency are both not less than the corresponding timing batch data update frequency judgment value and the dynamic update data update frequency judgment value, continue to detect the seat state of each seat area; If the timing batch data update frequency and the dynamic update data update frequency are both less than the corresponding timing batch data update frequency judgment value and the dynamic update data update frequency judgment value, adjust the timing batch data update frequency to the timing batch data update frequency judgment value, and adjust the dynamic update data update frequency to the dynamic update data update frequency judgment value; If only the timing batch data update frequency is less than the corresponding timing batch data update frequency judgment value, adjust the timing batch data update frequency to the timing batch data update frequency judgment value; If only the dynamic update data update frequency is less than the corresponding dynamic update data update frequency judgment value, adjust the dynamic update data update frequency to the dynamic update data update frequency judgment value.

3. The AI-based reading room seat intelligent allocation and management system according to claim 1, wherein, The specific content of the real-time impact strategy is as follows: Record the difference between the feedback time difference value and the set feedback time error value as the feedback time deviation, and perform a query operation according to the projection mapping sequence stored in advance for reflecting the mapping relationship between the feedback time deviation and the model constraint quantity reduction ratio, to obtain the corresponding model constraint quantity reduction ratio; Compare the model constraint quantity reduction ratio with the constraint quantity reduction ratio limit value set for limiting the reduction degree of the constraint quantity: If the model constraint quantity reduction ratio is lower than the constraint quantity reduction ratio limit value, reduce the number of constraint conditions with a low-to-high constraint condition priority in the seat allocation optimization model through the model constraint quantity reduction ratio; If the model constraint quantity reduction ratio is not lower than the constraint quantity reduction ratio limit value, reduce the number of constraint conditions with a low-to-high constraint condition priority in the seat allocation optimization model through the constraint quantity reduction ratio limit value.

4. The AI-based reading room seat intelligent allocation and management system according to claim 1, wherein, The specific acquisition method of the model calculation time consumption influence coefficient is as follows: Perform data normalization processing on the recalculation data size parameter, and match the corresponding recalculation size parameter influence factor for reflecting the influence degree of the recalculation data size parameter on the model calculation time consumption influence coefficient, the recalculation size parameter influence factor includes the number of pre-booking users influence factor, the number of seats influence factor, the number of idle seats influence factor, and the user modification frequency influence factor; The model calculation time consumption influence coefficient is obtained by coupling the weighting operation on the re-calculation data scale parameter based on the re-calculation scale parameter influence factor.

5. The AI-based reading room seat intelligent allocation and management system according to claim 1, wherein, The specific steps of performing the seat allocation local optimization analysis and adjustment are as follows: The seat state change quantity of each seat area in which the seat state change occurs is counted to obtain a seat state change quantity sequence; It is determined whether the seat state change quantity of each seat area in the seat state change quantity sequence is greater than a seat state change quantity processing value preset for limiting the parallel optimization scale; If the seat state change quantity of a seat area is greater than the seat state change quantity processing value, the seat of the seat area is locally optimized based on the seat state change quantity, and a seat state change difference value is obtained based on the deviation operation result between the seat state change quantity and the seat state change quantity processing value, and the parallel optimization time saving is obtained by querying a time saving mapping table in which a mapping relationship between the seat state change difference value and the parallel optimization time saving is established; The seat areas whose seat state change quantity is not greater than the seat state change quantity processing value are optimized in parallel, and each seat area whose seat state change quantity sum is not greater than the seat state change quantity processing value is combined for parallel optimization, while the corresponding parallel optimization time is obtained in real time; The local optimization real-time determination is performed based on the seat management time, which includes a total parallel optimization time length, a total optimization time saving length, and a total local optimization time length, the total parallel optimization time length is the sum of each parallel optimization time, the total optimization time saving length is the sum of each parallel optimization time saving, and the total local optimization time length is the time length for executing the re-allocation local optimization of each seat area.

6. The AI-based reading room seat intelligent allocation and management system according to claim 5, wherein, The specific process of the local optimization real-time determination based on the seat management time is as follows: If the total local optimization time length is less than a feedback time limit value, it indicates that the current optimization is effective, and the seat state of each seat area in the next seat management period is continuously detected; If the total local optimization time length is not less than the feedback time limit value, the difference between the total local optimization time length and the total parallel optimization time length is recorded as a to-be-verified optimization time saving length, and the to-be-verified optimization time saving length is compared with the total optimization time saving length: If the to-be-verified optimization time saving length is higher than the total optimization time saving length, it indicates that the local optimization is effective, a seat allocation optimization model computing resource warning prompt is issued, and a pre-set staff is requested to release a seat re-allocation result delay public announcement; If the to-be-verified optimization time saving length is not higher than the total optimization time saving length, it indicates that the local optimization is ineffective, and the local optimization is adjusted.

7. The AI-based reading room seat intelligent allocation and management system according to claim 6, wherein, The specific content of the adjustment of the local optimization is as follows: The to-be-verified optimization time saving length and the total optimization time saving length are quantified to obtain a time saving length difference, and a mapping set for fitting the time saving length difference and a seat state change quantity processing value release amount is constructed, and the seat state change quantity processing value release amount corresponding to the time saving length difference is matched. The seat state change quantity processing value is amplified, and if the to-be-verified saving optimization time length of the next seat management cycle is still not higher than the total optimization saving time length, the amplification of the seat state change quantity processing value is continued until the amplification is higher than the set amplification range limit value, and the loop optimization of the amplification of the seat state change quantity processing value is stopped; If the to-be-verified saving optimization time length is higher than the total optimization saving time length in the time period corresponding to the loop optimization, a seat allocation optimization model computing resource alarm prompt is issued, otherwise the seat allocation optimization model is triggered to recalculate the seat allocation scheme.

8. An AI-based reading room seat intelligent allocation and management method applied to the AI-based reading room seat intelligent allocation and management system of any one of claims 1-7, characterized in that, The specific steps are as follows: S1, after confirming the data update frequency of the seat allocation optimization model used to generate the reading room seat allocation scheme, the seat state change of each seat area in the reading room in the seat management cycle is monitored in real time to determine whether the seat allocation is recalculated by the model; S2, if the seat allocation is recalculated by the model, the recalculation time consumption of the seat allocation optimization model is analyzed, and the real-time influence strategy for ensuring seat intelligent allocation is executed, otherwise the seat allocation local optimization analysis and adjustment are performed to reduce the real-time influence degree of local optimization on seat intelligent allocation; S3, based on the dynamic adjustment of the seat state change in S2, the real-time updated reading room seat intelligent allocation scheme is automatically fed back to improve the response ability of the seat allocation optimization model to the dynamic change of the seat state.

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