Museum exhibition dynamic tourist calibration and tourist scheduling system and method

Abstract

The invention provides a museum exhibition dynamic tourist calibration and tourist scheduling system and method. The system comprises a tourist calibration device and a central server. The tourist calibration device is installed on a monitoring rod in an exhibition hall and is composed of a sensor module, a main control unit and a communication module. A sensor module monitors carbon dioxide concentration and captures tourist image data. And the main control unit analyzes the image data by using a dynamic target detection algorithm to obtain real-time tourist information. The communication module transmits the data to a central server. The central server is configured with a tourist scheduling model and comprises a data input module, a multi-objective optimization algorithm module, a scheduling strategy generation module and a scheduling execution module. The model determines an optimal tourist scheduling scheme based on real-time data, and converts the optimal tourist scheduling scheme into a specific strategy to be transmitted to tourists. According to the system, through real-time monitoring and intelligent analysis, accurate control and efficient management of the tourist flow of the museum are realized, and the operation efficiency, tourist experience and safety of the exhibition hall are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of museum management, and in particular to a system and method for dynamic visitor calibration and visitor scheduling for museum exhibitions. Background Art

[0002] In museums and heritage sites, dynamic calibration refers to the real-time collection and analysis of data such as visitor location, movement speed, density, etc., in order to effectively manage visitor flow and avoid congestion. Its key technologies include: real-time positioning system, which uses wireless sensor networks, radio frequency identification (RFID) technology or positioning technology based on Bluetooth Low Energy (BLE) to accurately locate the location of visitors. Movement speed prediction, which calculates the real-time movement speed of tourists by analyzing the time and distance of tourists' movement, thereby evaluating the movement status and activity of tourists. Density estimation, which is based on real-time positioning data and uses spatial statistical methods or machine learning algorithms to estimate the density of tourists in different areas, helping to predict and manage congestion.

[0003] The first existing technology adopts a dynamic calibration solution, including image processing technology, RFID technology, and Wi-Fi and Bluetooth positioning. Image processing technology, image recognition technology based on deep learning, can extract tourists' movement information from video streams. These technologies have been applied in some large-scale events and public places, but they need to be further optimized in environments such as museums to deal with problems such as light changes and occlusion. RFID technology can accurately track the location and movement of tourists by wearing RFID tags on them. Although this technology has high accuracy, it is costly and requires tourists to cooperate with wearing the device. Wi-Fi and Bluetooth positioning can accurately locate the location and movement trajectory of tourists by analyzing the Wi-Fi and Bluetooth signals of their mobile phones. This method has been applied in some large shopping malls and airports, but in museum environments, signal interference and accuracy issues still need to be resolved.

[0004] The second prior art adopts a visitor diversion scheme, which includes a rule-based method and an optimization-based method. The rule-based method presets some rules, such as the maximum carrying capacity of each exhibition area. When an exhibition area is close to the maximum carrying capacity, new visitors are restricted from entering. This method is simple and easy to implement, but has poor flexibility and cannot respond to changes in real time. The optimization-based method uses optimization techniques such as linear programming and integer programming, combined with real-time data, to dynamically adjust the flow direction and speed of tourists. This method can better balance the number of tourists in each exhibition area, but the calculation complexity is high and requires strong computing power support.

[0005] The inventors found in the process of implementing the present invention that the disadvantages of the prior art are:

[0006] The accuracy of image processing technology is affected by environmental factors such as light changes and occlusion, which can lead to reduced accuracy and reliability. At the same time, real-time processing of large amounts of video data requires high computing power, resulting in high computational complexity and increased equipment and operating costs. The disadvantage of RFID technology is its high cost. It is necessary to equip each tourist with an RFID tag and deploy a large number of reading and writing devices, resulting in high initial investment and subsequent maintenance costs. In addition, tourists have a low acceptance of wearing RFID devices, which affects their tour experience. The accuracy of Wi-Fi and Bluetooth positioning technologies is easily affected by signal interference and multipath effects, and it is difficult to ensure accuracy, especially in complex indoor environments. At the same time, this technology requires the collection of data from tourists' mobile devices, which involves privacy issues and reduces tourists' acceptance.

[0007] Rule-based methods are less flexible because the preset rules are relatively fixed and cannot effectively respond to real-time changes in tourist flows. In addition, overly strict rules lead to uneven resource utilization, such as some exhibition areas being vacant while other exhibition areas are too crowded, reducing overall scheduling efficiency. Optimization-based methods are usually computationally complex and require powerful computing power to process large-scale, real-time data, resulting in slow response speed and difficulty in responding to sudden changes in tourist flows in a timely manner. Summary of the invention

[0008] In view of this, an embodiment of the present invention provides a museum exhibition dynamic visitor calibration and visitor scheduling system and method to improve the accuracy and real-time performance of museum exhibition dynamic calibration, reduce system costs, and enhance system stability and adaptability, thereby improving museum exhibition management and visitor experience.

[0009] To achieve the above objectives, in a first aspect, a museum exhibition dynamic visitor calibration and visitor scheduling system is provided, which includes:

[0010] The visitor calibration device is installed on a monitoring pole in the exhibition hall of the museum, and the visitor calibration device includes:

[0011] The sensor module includes: a carbon dioxide concentration monitoring module for monitoring real-time carbon dioxide concentration data in the exhibition hall; an image sensor for capturing real-time visitor image data in the exhibition hall;

[0012] A main control unit is used to receive the real-time carbon dioxide concentration data and the real-time tourist image data, and analyze the real-time tourist image data based on a dynamic target detection algorithm to obtain real-time tourist information, wherein the real-time tourist information includes any one of tourist quantity information, tourist location information, tourist flow information, tourist distribution information, tourist stay time information in the exhibition hall, and tourist queuing time information;

[0013] a communication module, electrically connected to the main control unit, for transmitting the real-time visitor information and the real-time carbon dioxide concentration data to a central server;

[0014] The central server is configured with a tourist scheduling model, wherein the tourist scheduling model includes:

[0015] A data input module, used for receiving the real-time visitor information and the real-time carbon dioxide concentration data;

[0016] A multi-objective optimization algorithm module, used to determine an optimal tourist scheduling plan based on the multi-objective optimization algorithm according to the real-time tourist information and the real-time carbon dioxide concentration data;

[0017] A scheduling strategy generation module, used to convert the optimal tourist scheduling plan into a specific scheduling strategy;

[0018] The scheduling execution module is used to convey the scheduling strategy to tourists.

[0019] In a second aspect, a method for the museum exhibition dynamic visitor calibration and visitor scheduling system based on the first aspect is provided, which comprises the following steps:

[0020] The visitor calibration device monitors the real-time carbon dioxide concentration data in the exhibition hall and captures the real-time visitor image data in the exhibition hall; analyzes the real-time visitor image data based on a dynamic target detection algorithm to obtain real-time visitor information, wherein the real-time visitor information includes any one of visitor quantity information, visitor location information, visitor flow information, visitor distribution information, visitor stay time in the exhibition hall and visitor queue time; transmits the real-time visitor information and the real-time carbon dioxide concentration data to a central server;

[0021] The central server receives the real-time tourist information and the real-time carbon dioxide concentration data through a tourist scheduling model; determines an optimal tourist scheduling plan based on the real-time tourist information and the real-time carbon dioxide concentration data based on a multi-objective optimization algorithm; converts the optimal tourist scheduling plan into a specific scheduling strategy; and communicates the scheduling strategy to tourists.

[0022] In a third aspect, an electronic device is provided, comprising:

[0023] one or more processors;

[0024] a storage device for storing one or more programs,

[0025] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in the second aspect.

[0026] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method described in the second aspect is implemented.

[0027] The above technical solution has the following beneficial technical effects:

[0028] The museum exhibition dynamic visitor calibration and visitor scheduling system of the present invention collects carbon dioxide concentration data and visitor image data in real time through a visitor calibration device installed in the exhibition hall, and uses a dynamic target detection algorithm for analysis to obtain comprehensive real-time visitor information. This information is processed by the visitor scheduling model of the central server, and the optimal visitor scheduling plan is generated using a multi-objective optimization algorithm, and converted into a specific scheduling strategy. The implementation of this system can effectively balance the distribution of visitors in the exhibition hall, reduce congestion, optimize visitor tour routes, and improve the carrying capacity and utilization rate of the exhibition hall. At the same time, by monitoring the concentration of carbon dioxide, the system can adjust the exhibition hall environment in time to ensure the health and safety of visitors. This intelligent management method can not only enhance the visitor's visiting experience, but also help museum managers operate the exhibition hall more efficiently and achieve the optimal allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0030] Figure 1 It is a functional block diagram of a museum exhibition dynamic visitor calibration and visitor scheduling system according to an embodiment of the present invention;

[0031] Figure 2 is a functional block diagram of a multi-objective optimization algorithm module according to an embodiment of the present invention;

[0032] Figure 3 is a functional block diagram of a scheduling strategy generation module according to an embodiment of the present invention;

[0033] Figure 4 is a functional block diagram of a scheduling execution module according to an embodiment of the present invention;

[0034] Figure 5 It is a functional block diagram of another museum exhibition dynamic visitor calibration and visitor scheduling system according to an embodiment of the present invention;

[0035] Figure 6 It is a flow chart of a method for dynamic visitor calibration and visitor scheduling system based on museum exhibitions according to an embodiment of the present invention;

[0036] Figure 7 is a specific flow chart of step S160 of an embodiment of the present invention;

[0037] Figure 8It is a schematic diagram of the structure of a computer system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0039] In response to the "museum fever" phenomenon, the embodiments of the present invention calibrate the density and activities of tourists in semi-enclosed sites or museum spaces, and on the premise of ensuring the safety of cultural relics and personnel, realize the scheduling of cultural and museum resources and tourists in the sightseeing space, tap the potential of non-popular exhibits, exhibition areas or idle spaces, improve the rational utilization rate of cultural and tourism resources and space, alleviate the sightseeing pressure in related popular areas, and reduce the impact of carbon dioxide emissions caused by excessive concentration of tourists on the cultural relics themselves.

[0040] Embodiment 1

[0041] like Figure 1 As shown, this embodiment provides a museum exhibition dynamic visitor calibration and visitor scheduling system, including: a visitor calibration device, installed on a monitoring pole in the exhibition hall of the museum, the visitor calibration device includes: a sensor module, which includes a carbon dioxide concentration monitoring module for monitoring real-time carbon dioxide concentration data in the exhibition hall; an image sensor for capturing real-time visitor image data in the exhibition hall; a main control unit for receiving the real-time carbon dioxide concentration data and the real-time visitor image data, and analyzing the real-time visitor image data based on a dynamic target detection algorithm to obtain real-time visitor information, the real-time visitor information including visitor quantity information, visitor location information, visitor flow information, visitor distribution information, and visitor stay time in the exhibition hall. any one of the time information and the tourist queuing time information; a communication module, electrically connected to the main control unit, for transmitting the real-time tourist information and the real-time carbon dioxide concentration data to a central server; the central server is configured with a tourist scheduling model, and the tourist scheduling model includes: a data input module, for receiving the real-time tourist information and the real-time carbon dioxide concentration data; a multi-objective optimization algorithm module, for determining the optimal tourist scheduling plan based on the real-time tourist information and the real-time carbon dioxide concentration data based on a multi-objective optimization algorithm; a scheduling strategy generation module, for converting the optimal tourist scheduling plan into a specific scheduling strategy; a scheduling execution module, for conveying the scheduling strategy to tourists.

[0042] In a specific embodiment, the visitor calibration device adopts a modular design for easy installation and maintenance. The sensor module is integrated in an aluminum alloy housing with an IP67 waterproof rating, which contains an NDIR (non-dispersive infrared) carbon dioxide concentration monitoring module with an accuracy of ±30ppm and a 1080p high-resolution camera as an image sensor with a field of view of up to 120°. The main control unit can use a quad-core processor based on the ARM Cortex-A72 architecture, with a main frequency of 2.0GHz, running a customized Linux operating system, equipped with 4GB RAM and 64GB eMMC storage, and has sufficient computing power to execute the dynamic target detection algorithm based on YOLOv8. The communication module uses a wireless communication module that supports 5GSA / NSA, with an uplink rate of up to 1Gbps to ensure real-time data transmission. The central server is deployed on Alibaba Cloud ECS (Elastic Compute Service) and adopts a distributed architecture orchestrated by Kubernetes containers to improve the scalability and reliability of the system. The visitor dispatch model is implemented using Python 3.9, and the PyTorch 1.9 deep learning framework is used to build and train a multi-objective optimization algorithm based on NSGA-II or NSGA-III. The dispatch strategy is implemented through multiple channels, including the 4K resolution OLED electronic display screen in the exhibition hall, the cross-platform mobile phone APP push developed based on Flutter, and the intelligent voice broadcasting system supporting voice recognition, to ensure that information can be conveyed to tourists in a timely and effective manner.

[0043] In this embodiment, the multi-objective optimization algorithm module adopts an advanced multi-objective optimization algorithm, comprehensively considering real-time visitor information (such as the number of visitors, location, flow, distribution, residence time and queue time) and real-time carbon dioxide concentration data to determine the optimal visitor scheduling plan. The algorithm considers multiple goals at the same time, such as maximizing visitor experience, minimizing crowding, optimizing air quality, etc. For example, when the visitor density in a certain exhibition area is too high and the carbon dioxide concentration increases, the algorithm will generate a visitor scheduling plan, suggesting that some visitors be guided to other relatively idle exhibition areas. Subsequently, the scheduling strategy generation module converts this high-level scheduling plan into specific executable scheduling strategies or instructions. These scheduling strategies may include: adjusting the maximum capacity of a specific exhibition area, changing the opening hours of certain exhibits, allocating different tour routes for different visitor groups, etc. For example, for the above situation, the specific strategy may be: "In the next 30 minutes, limit the visitor capacity of exhibition area A to 50 people, and recommend the tour route of exhibition area B to newly arrived visitors." This method not only ensures the comprehensiveness and optimization of decision-making, but also ensures the feasibility and specificity of execution, thereby realizing the intelligent and dynamic management of museum visitor flow.

[0044] In some alternative embodiments, the sensor module can be supplemented with an infrared thermal imaging camera to more accurately detect visitor locations and flows in low-light environments. The main control unit can use the NVIDIA Jetson Xavier NX module with an integrated GPU acceleration unit to further improve the performance of the target detection algorithm. The communication module can be supplemented with a Wi-Fi 6 module to provide a backup communication method in areas with poor 5G signal coverage. The central server can adopt a hybrid cloud architecture, with key data and core algorithms running on a private cloud and non-sensitive data processing on a public cloud to balance security and cost. The visitor scheduling model can introduce reinforcement learning algorithms, such as PPO (Proximal Policy Optimization), to adapt to dynamically changing environments and continuously optimize scheduling strategies. Scheduling strategy execution can add an AR (Augmented Reality) guide function to provide real-time personalized navigation and exhibit information through visitors' smart glasses or smart watches.

[0045] In some alternative embodiments, air quality sensors and / or sound sensors can be used to monitor the environmental conditions in the exhibition hall. Air quality sensors can detect PM2.5 (fine particulate matter) concentrations and other air quality indicators, such as PM10 (inhalable particulate matter), VOCs (volatile organic compounds), formaldehyde, ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2) and sulfur dioxide (SO2). These indicators can fully reflect the air quality conditions in the exhibition hall, and help to promptly discover and deal with potential air pollution problems. At the same time, sound sensors can be used to monitor the noise level in the exhibition hall, and indirectly reflect the density and activities of people in the exhibition hall through changes in decibel values. The use of these two sensors alone or in combination can provide more comprehensive environmental monitoring data.

[0046] The technical effects of the system include: the system has a high degree of real-time and accuracy. Through high-precision sensors and advanced image processing algorithms, it can obtain the distribution of visitors and environmental data in the exhibition hall in real time and accurately, providing a reliable basis for subsequent scheduling decisions. Secondly, the system has intelligent and adaptive capabilities. The scheduling model based on the multi-objective optimization algorithm can comprehensively consider multiple factors, automatically generate the optimal scheduling plan, and can dynamically adjust according to real-time conditions. In addition, the system can provide personalized and efficient services, and provide personalized visit suggestions and route planning according to the characteristics and needs of different visitors, which improves the visitor experience and optimizes the overall visit efficiency. In terms of safety and comfort, the system effectively ensures the air quality and visitor comfort in the exhibition hall and reduces safety risks through real-time monitoring and adjustment of carbon dioxide concentration and crowd density. The modular design and standardized interface of the system ensure that it has good scalability and compatibility, and can adapt to the needs of museums of different sizes and types.

[0047] Embodiment 2

[0048] In some embodiments, the dynamic target detection algorithm includes: a dynamic target detection algorithm based on the YOLOv8 architecture; the multi-objective optimization algorithm includes: an NSGA-II multi-objective genetic optimization algorithm; the optimization objectives of the multi-objective optimization algorithm module include: maximizing visitor satisfaction, minimizing passenger flow congestion, and minimizing carbon dioxide concentration; the constraints of the multi-objective optimization algorithm module include: the maximum carrying capacity of the exhibition hall and the speed limit of visitor flow. In this embodiment, the dynamic target detection algorithm adopts the latest YOLOv8 architecture. Compared with the previous version, YOLOv8 has improved detection speed and accuracy. For example, on a computer vision dataset, the YOLOv8-l model can achieve 52.9% mAP (mean average precision) while maintaining high real-time performance and can process more than 60 frames of 1080p video streams per second. The multi-objective optimization algorithm adopts NSGA-II (Non-dominated Sorting Genetic Algorithm II), which is an efficient multi-objective optimization algorithm that can quickly converge to the Pareto optimal solution set while maintaining population diversity. The optimization objectives cover three key aspects: visitor experience, exhibition hall management, and environmental control. They maximize visitor satisfaction to improve the visitor experience, minimize crowding to ensure safety and comfort, and minimize carbon dioxide concentration to maintain good air quality. The constraints take into account the physical limitations and safety requirements of the exhibition hall to ensure that the optimization results are feasible in actual operations. This multi-objective optimization method can find a balance between multiple conflicting objectives and provide comprehensive decision support for museum managers.

[0049] like Figure 2As shown, in some embodiments, the multi-objective optimization algorithm module specifically includes: an exhibition area optimization target definition submodule, which is used to define the exhibition area optimization target, which includes the following multiple indicators: average walking path length, calculated by the following formula: Among them, m is the total number of visitors in the exhibition hall, L i is the walking path length of the i-th tourist; the average stay time is calculated by the following formula: Among them, T i is the stay time of the i-th tourist; the average queuing time is calculated by the following formula: Where Wi is the queuing time of the i-th tourist; the chaos index is calculated by the following formula: When the average walking speed of tourists is greater than 2m / s, the chaos index is 1, and when the average walking speed of tourists is less than or equal to 2m / s, the chaos index is 0. i is the walking speed of the ith visitor; the passenger flow density of the exhibition area is calculated by the following formula: ρ = m / S, where S is the exhibition area and ρ is the passenger flow density of the exhibition area; the passenger flow crowding index is calculated by the following formula: crowding = ∑(ρ*t i ) / ∑(t i ), where t i It represents the time weight coefficient per hour, which is positively correlated with the number of tourists; the tourist satisfaction is calculated by the following formula:

[0050]

[0051] Among them, d1, d2, d3, and d4 are preset weights, d1 and d2 are positive weight values, and d3 and d4 are negative weight values.

[0052] In this embodiment, the exhibition area optimization target definition submodule quantifies the key indicators that affect the visitor experience and exhibition hall operation through a series of mathematical formulas. These indicators include average walking path length, average stay time, average queuing time, chaos index, exhibition area passenger flow density, passenger flow congestion index and visitor satisfaction. Each indicator has its own specific calculation formula, taking into account different factors and weights. For example, the chaos index is judged based on the average walking speed of tourists, the passenger flow congestion index takes into account the time weight coefficient, and the visitor satisfaction combines multiple factors and introduces positive and negative weights. This multi-dimensional optimization target definition enables the system to comprehensively evaluate and optimize the operation of the exhibition hall.

[0053] like Figure 2As shown, in some embodiments, the multi-objective optimization algorithm module specifically includes: a constraint definition submodule, which is used to define the constraints that need to be met in the optimization process, and the constraints include: all tourists in the queue outside the exhibition hall will eventually enter the venue; the average walking speed of tourists in the exhibition hall shall not exceed 2 meters per second; the maximum carrying capacity of each exhibition area shall not exceed the safe capacity of the exhibition area; the tourist density per unit area of ​​the exhibition area shall not exceed the design limit of the exhibition area; the chaos index of the exhibition area shall not exceed the preset threshold; the carbon dioxide concentration in the exhibition hall shall not exceed the preset concentration upper limit; an optimization algorithm processing submodule, which is used to use a non-dominated sorting genetic algorithm II (NSGA-II) combined with a gray wolf genetic algorithm for multi-objective optimization, with tourist satisfaction, passenger flow congestion and carbon dioxide concentration as optimization targets, and through initialization, optimization, non-dominated sorting and crowding distance calculation steps, determine the priority of the exhibition area and generate a set of recommended exhibition areas, and finally determine the optimal tourist scheduling plan based on the Pareto optimal solution set, the comprehensive ranking of the exhibition areas, the most recommended destination exhibition areas and the specific index values ​​of each exhibition area; it specifically includes: executing the solution set initialization step, which The method includes initializing N initial solutions, wherein N is the number of pavilions; obtaining the satisfaction, passenger flow congestion and carbon dioxide concentration value of the corresponding pavilion for each initial solution; optimizing the N initial solutions using a gray wolf genetic algorithm to improve the satisfaction, reduce the passenger flow congestion and carbon dioxide concentration, and obtain N optimized new solutions; performing a non-dominated sorting step, including merging the N initial solutions and the N optimized new solutions to form a set of 2N solutions; performing non-dominated sorting on the 2N solutions to obtain a Pareto optimal solution set; performing a crowding distance calculation step, including calculating the crowding distance of each solution in the Pareto optimal solution set; determining the most recommended destination exhibition area based on the crowding distance; generating a comprehensive ranking of the exhibition area according to the non-dominated level and crowding distance of each solution in the Pareto optimal solution set, wherein the non-dominated level is determined by comparing the pros and cons of each solution in terms of the three objectives of satisfaction, passenger flow congestion and carbon dioxide concentration, and the lower the non-dominated level, the higher the priority of the exhibition area; determining the optimal visitor scheduling plan according to the most recommended destination exhibition area, the comprehensive ranking of the exhibition area, and the satisfaction, passenger flow congestion and carbon dioxide concentration values ​​of each exhibition area.

[0054] Specifically, the optimization algorithm processing submodule uses the gray wolf genetic algorithm to optimize the initial solution to improve satisfaction, reduce passenger congestion and carbon dioxide concentration, and obtain N optimized new solutions. In specific implementation, the algorithm simulates the social hierarchy and hunting behavior of wolves, regards the optimal solution as prey, and guides the ω wolf (other solutions) to update their positions through α wolf (optimal solution), β wolf (second-best solution) and δ wolf (third-best solution). At the same time, the crossover and mutation operations of the genetic algorithm are combined to enhance population diversity and search capabilities. In each iteration, the algorithm evaluates the performance of each solution on the three objectives, and updates the wolf position according to the predefined fitness function, and finally obtains a set of optimized new solutions, which have improved on the three objectives.

[0055] Specifically, the crowding distance calculation is used to evaluate the distribution of each solution in the Pareto optimal solution set. The calculation process is as follows: First, for each objective function (satisfaction, passenger flow congestion, carbon dioxide concentration), the solutions are sorted according to the value of the objective function. Then, for the sorted solutions, the normalized difference between two adjacent solutions on the objective function is calculated. For boundary solutions (that is, solutions that achieve the maximum or minimum value on a certain objective function), an infinite crowding distance is assigned to ensure that they are retained. Finally, the differences on all objective functions are added to obtain the crowding distance of each solution. This distance reflects the density of the solution in the target space. The larger the distance, the sparser the solutions around the solution, which is conducive to maintaining the diversity of the population.

[0056] Specifically, the process of determining the most recommended destination exhibition area based on crowding distance is as follows: In the Pareto optimal solution set, select the solution with the largest crowding distance. The first exhibition area in the exhibition area sequence corresponding to this solution is the most recommended destination exhibition area. The solution with the largest crowding distance is selected to ensure the diversity of recommendations and avoid all visitors being directed to the same exhibition area, thereby reducing the possible overcrowding in some exhibition areas. This method not only takes into account the excellence of the exhibition area (because it is in the Pareto optimal solution set), but also its uniqueness (because it has the largest crowding distance). In addition, the system can set up a dynamic update mechanism, and when the real-time status of the most recommended exhibition area changes significantly (for example, it suddenly becomes crowded), the recommendation can be recalculated and updated.

[0057] Specifically, generating a comprehensive ranking of exhibition areas is a multi-step process that takes into account both non-dominated rank and crowding distance. First, the solutions are sorted according to the non-dominated rank, with lower ranks ranking higher, which ensures that solutions that perform well on multiple objectives get higher rankings. For solutions of the same non-dominated rank, they are sorted according to crowding distance, with larger distances ranking higher, which helps to maintain the diversity of solutions. Then, based on the above sorting, a comprehensive ranking value is assigned to each solution. Next, the position and frequency of each exhibition area in different solutions are counted, and the weighted average ranking of each exhibition area is calculated based on the comprehensive ranking of the solutions. Finally, the exhibition areas are finally sorted according to this weighted average ranking to obtain the comprehensive ranking of the exhibition areas. This method not only takes into account the comprehensive performance of the exhibition area on multiple objectives, but also takes into account its importance among different excellent solutions.

[0058] Specifically, determining the optimal visitor scheduling plan is a dynamic and personalized process that takes into account multiple factors. First, the most recommended destination exhibition area is used as the starting point. This exhibition area has the best overall performance in terms of satisfaction, crowding, and air quality. Then, based on the comprehensive ranking of the exhibition areas, the approximate order of visitor visits is determined, which provides an overall optimal tour route. At the same time, the system monitors the satisfaction, crowding, and carbon dioxide concentration values ​​of each exhibition area in real time, and dynamically adjusts the visit order accordingly. For example, when the crowding or carbon dioxide concentration of an exhibition area exceeds the preset threshold, the system will temporarily remove it from the recommended sequence to avoid a decline in the visitor experience. In addition, the system will provide a personalized next recommended exhibition area based on the actual location, interest preferences, and conditions of the surrounding exhibition areas of the visitor. In this way, the scheduling plan can flexibly respond to real-time situations while ensuring the overall optimality, providing each visitor with the best visit experience.

[0059] In one example, the implementation process of the optimization algorithm processing submodule is as follows: First, the sorting schemes of the five exhibition areas (A, B, C, D, E) are initialized, such as [A, B, C, D, E] and [B, A, E, C, D], etc. Then, the gray wolf genetic algorithm is used to optimize these initial solutions to improve satisfaction, reduce passenger flow congestion and carbon dioxide concentration. The algorithm simulates the hunting behavior of wolves, guides other wolves to update their positions through α, β, and δ wolves, and combines the crossover and mutation operations of the genetic algorithm. After optimization, a new sorting scheme is obtained, such as [C, A, E, B, D]. Then, the initial solution and the optimized solution are merged, and non-dominated sorting is performed to obtain the Pareto optimal solution set. The crowding distance is calculated for these solutions to reflect the distribution of the solutions in the target space. Based on the crowding distance, the solution with the largest distance is selected, and its first exhibition area is used as the most recommended destination exhibition area. Then, a comprehensive ranking of the exhibition areas is generated based on the non-dominated level and crowding distance. Finally, the optimal visitor scheduling plan is determined. Starting from the most recommended exhibition area, the visiting order is determined based on the comprehensive ranking. The system will monitor the status of each exhibition area in real time, dynamically adjust the order, and provide personalized suggestions based on the individual situation of the visitor. For example, the final suggestion is: visit exhibition area C first, then exhibition area A, exhibition area E, exhibition area B, and exhibition area D. However, if exhibition area A is suddenly crowded, the system recommends going to exhibition area E first, and then returning after the situation in exhibition area A improves, ensuring that every visitor gets the best experience.

[0060] like Figure 2 As shown, in this embodiment, the constraint definition submodule sets a series of specific constraints to ensure the feasibility and safety of the optimization results in actual operation. These constraints cover multiple aspects such as visitor flow, exhibition area capacity, and safety indicators. The optimization algorithm processing submodule adopts an innovative algorithm combination, namely the non-dominated sorting genetic algorithm II (NSGA-II) combined with the gray wolf genetic algorithm. This module goes through a series of steps, including initializing the solution set, gray wolf genetic algorithm optimization, non-dominated sorting, crowding distance calculation, etc., to finally determine the optimal visitor scheduling plan. This method makes full use of the advantages of NSGA-II in multi-objective optimization and the ability of the gray wolf algorithm in global search, and can quickly find high-quality solutions in complex multi-objective spaces, providing scientific and reasonable scheduling suggestions for museum managers.

[0061] The technical effects of the above-mentioned embodiments mainly include the following aspects: First, the dynamic target detection algorithm based on YOLOv8 greatly improves the system's recognition accuracy and speed of visitor behavior and location, providing a reliable data basis for real-time scheduling. Secondly, the multi-objective optimization method of NSGA-II combined with the gray wolf genetic algorithm can quickly find a high-quality solution that balances multiple objectives in a complex decision space, thereby improving the overall effect of the scheduling scheme. Thirdly, through the refined definition of indicators and setting of constraints, the system can comprehensively consider various factors that affect the operation of the exhibition hall and the experience of visitors, so that the optimization results are closer to actual needs. For example, the calculation formula for visitor satisfaction comprehensively considers multiple factors such as walking path length, stay time, queuing time and chaos, and adjusts the importance of each factor through different weights. Finally, this intelligent scheduling system can not only improve visitor satisfaction, but also effectively control the congestion and air quality in the exhibition hall.

[0062] Embodiment 3

[0063] like Figure 3As shown, in some embodiments, the scheduling strategy generation module specifically includes: a first scheduling strategy generation submodule, which is used to guide the movement path of tourists in the exhibition hall based on real-time real-time tourist flow information and the information of tourists' stay time in the exhibition hall; a second scheduling strategy generation submodule, which is used to dynamically adjust the maximum number of tourists that can be accommodated at the same time in each exhibition area according to the current tourist distribution information; a third scheduling strategy generation submodule, which is used to adjust the number of open rest areas and the open and closed states of evacuation passages according to the number of tourists, the distribution of tourists and the flow of tourists. The specific scheduling strategy includes: tourist tour route planning, which is used to guide the movement path of tourists in the exhibition hall based on real-time real-time tourist flow information and the information of tourists' stay time in the exhibition hall; the access restriction setting of each exhibition area in the exhibition hall, which is used to dynamically adjust the maximum number of tourists that can be accommodated at the same time in each exhibition area according to the current tourist distribution information; public area management, which is used to adjust the number of open rest areas and the open and closed states of evacuation passages according to the number of tourists, the distribution of tourists and the flow of tourists. In this embodiment, the scheduling strategy covers three key aspects: First, the visitor route planning uses real-time data to dynamically optimize the tour path. For example, when a certain exhibition area is crowded, the system can guide newly arrived visitors to visit other relatively idle exhibition areas first, thereby balancing the overall passenger flow. Secondly, the exhibition area access restriction setting can adjust the capacity of each exhibition area according to real-time conditions. For example, in a particularly popular temporary exhibition, the system will reduce the maximum number of people that can be accommodated at the same time in the exhibition area to ensure the quality and safety of the visit. Finally, the public area management effectively responds to changes in passenger flow at different times by flexibly adjusting the status of rest areas and evacuation passages. For example, during peak passenger flow periods, the system will increase the number of open rest areas while keeping more evacuation passages open to improve the overall evacuation capacity. This comprehensive scheduling strategy can not only improve the operating efficiency of the exhibition hall, but also improve the visitor experience while ensuring the safe operation of the exhibition hall.

[0064] like Figure 4As shown, in some embodiments, the scheduling execution module specifically includes: a first control submodule, used to control multiple display screens to display visual guidance information for all tourists in real time, and the multiple display screens are installed at different locations in the exhibition hall as devices independent of the central server and the tourist calibration device; a second control submodule, used to control the broadcasting system to play audio scheduling information for all tourists, and the broadcasting system is installed in the exhibition hall as a device independent of the central server and the tourist calibration device; a third control submodule, used to control the server application running on the central server to send personalized guidance information to the client application of the tourist's mobile phone through a wireless network; wherein the guidance information for all tourists, the scheduling information for all tourists and the personalized guidance information are all generated by the scheduling execution module according to the scheduling strategy, and the personalized guidance information is determined accordingly according to the specific situation of each tourist.

[0065] In this embodiment, the scheduling execution module realizes all-round information transmission and scheduling execution through three submodules. The first control submodule is responsible for managing the display screens in the exhibition hall. These display screens are distributed in key locations, such as entrances, intersections of main exhibition areas, etc., and are used to display real-time visual guidance information. For example, it can display the real-time congestion level of each exhibition area, recommended tour routes, etc. The second control submodule manages the broadcasting system, which is used to play audio scheduling information, such as reminding visitors to pay attention to safety, notifying temporary exhibition opening hours, etc. The third control submodule provides personalized guidance services for each visitor through mobile applications, such as recommending the next most suitable visiting point based on the visitor's interest preferences and the exhibits visited. These three submodules work together to ensure the comprehensiveness and pertinence of information transmission. For example, when a certain exhibition area is about to reach its maximum capacity, the system can simultaneously display warning information through the display screen, play reminders through the broadcasting system, and send personalized suggestions to the mobile phones of visitors who are heading to the exhibition area, guiding them to visit other exhibition areas first. This multi-level, multi-channel information transmission and scheduling execution mechanism improves the efficiency and flexibility of the system and can better cope with various complex exhibition hall operation scenarios.

[0066] By comprehensively considering visitor flow, exhibition area capacity and public area management, the system can achieve optimal utilization of exhibition hall resources. For example, dynamically adjusting exhibition area access restrictions can effectively prevent overcrowding in certain popular exhibition areas, while guiding visitors to other relatively idle exhibition areas, thereby balancing the overall passenger flow. Secondly, the multi-level scheduling execution mechanism (display screen, broadcasting system and personal mobile phone application) ensures the comprehensiveness and timeliness of information transmission. This not only improves the visitor's experience, but also enhances the emergency response capability of the exhibition hall. For example, when emergency evacuation is required, the system can release information through multiple channels at the same time to improve evacuation efficiency. Thirdly, the provision of personalized guidance information enables each visitor to obtain a customized visit experience. This not only improves visitor satisfaction, but also helps to disperse passenger flow and avoid local congestion. Finally, this intelligent scheduling system improves the operational efficiency of the exhibition hall.

[0067] Embodiment 4

[0068] like Figure 5 As shown, in some embodiments, the visitor scheduling model also includes a feedback and adjustment module for real-time monitoring of scheduling effects, the scheduling effects including visitor distribution information, changes in carbon dioxide concentration and emergency information, and dynamically adjusting the scheduling strategy according to the visitor distribution information, changes in carbon dioxide concentration and emergency information; wherein the emergency information is obtained by at least one of the following ways: emergency reports uploaded by visitors through mobile applications; abnormal behaviors associated with emergency information detected based on image analysis. In this embodiment, the feedback and adjustment module continuously monitors and optimizes the scheduling effect. The module analyzes visitor distribution information in real time, such as whether certain exhibition areas are congested; monitors changes in carbon dioxide concentration to ensure the air quality in the exhibition hall; and responds to emergencies in a timely manner. For example, when the carbon dioxide concentration in a certain exhibition area suddenly rises, the system will reduce the maximum capacity of the area and guide some visitors to other areas. For monitoring emergencies, the system adopts a dual protection mechanism: on the one hand, visitors can directly report emergencies through mobile applications, such as discovering safety hazards; on the other hand, the system uses advanced image analysis technology to automatically detect abnormal behaviors, such as sudden gathering or rapid movement of visitors. This all-round monitoring and rapid response mechanism improves the safety and operational efficiency of the exhibition hall, and can take timely measures before problems escalate, ensuring the safety and good experience of visitors.

[0069] In some embodiments, the visitor calibration device further includes: a power module, which is electrically connected to the main control unit, the sensor module and the communication module, and is used to provide power for the visitor calibration device; the carbon dioxide concentration monitoring module, the image sensor and the wireless communication receiving module are all electrically connected to the main control unit; the sensor module also includes a wireless communication receiving module, which is used to receive wireless signal data sent by a visitor's mobile device; the main control unit is also used to: process the wireless signal data to obtain visitor location information; fuse the visitor location information with the real-time visitor image data to obtain enhanced real-time visitor information; wherein the enhanced real-time The tourist information also includes tourist interest data; wherein, based on the continuous tourist location information of a single tourist, the movement trajectory data of a single tourist is generated through time series analysis; the movement trajectory data of multiple tourists are aggregated and analyzed to obtain tourist flow information representing the overall tourist movement trend; wherein, the tourist interest data is obtained through a variety of methods, including: obtaining a multi-dimensional data source, which includes tourist stay time, visit frequency, movement trajectory pattern, interactive behavior, social media activities, questionnaire feedback, consumption behavior, and reservation data; based on the multi-dimensional data source, combined with a deep learning algorithm, a comprehensive tourist interest model is generated to output tourist interest data for different exhibition areas.

[0070] Specifically, in order to obtain accurate data on tourists' interest, this system uses a method that combines multi-dimensional data sources with advanced deep learning algorithms. Specifically, the system first collects multiple types of data: stay time, which refers to the length of time tourists stay in each exhibition area, such as staying in the dinosaur exhibition area for 45 minutes; visit frequency, which is the number of times tourists repeatedly visit an exhibition area, such as returning to the aquarium many times; movement trajectory pattern, which records the routes and stopovers of tourists in the park, such as visiting the giant panda pavilion first and then the butterfly garden; interactive behavior, including the number and method of interaction with exhibits, such as frequent use of interactive devices in the science and technology museum; social media activities, analyzing tourists' posts, likes and comments on the exhibition area on various platforms; questionnaire feedback, directly collecting tourists' evaluation and suggestions on the exhibition area; consumption behavior, recording tourists' shopping and dining in the park, such as buying related souvenirs in the tropical rainforest area; reservation data, analyzing the exhibition area or activities pre-selected by tourists. Subsequently, the system uses deep learning algorithms, such as multi-layer neural networks or recurrent neural networks, to process and analyze these complex multi-dimensional data. Deep learning models can automatically learn the underlying features and patterns in the data, for example, finding that exhibition areas with long stays, multiple interactions, and frequent social sharing tend to correspond to higher interest. The model can also identify associations between different data sources, such as positive questionnaire feedback but short stay time indicates that the exhibition area is attractive but small in scale. In this way, the system generates a comprehensive visitor interest model that takes into account the complex relationships and mutual influences between various factors. Ultimately, the model outputs each visitor's interest data in different exhibition areas. These data are continuous values ​​(for example, between 0 and 1) that accurately reflect the visitor's interest in each exhibition area. For example, the model shows that a visitor's interest in the oceanarium is 0.9, while his interest in the insectarium is 0.3. Through this comprehensive and precise approach, the system is able to capture subtle differences in visitors' interest preferences, thereby improving the visitor experience.

[0071] In this embodiment, the visitor calibration device is a highly integrated intelligent system that integrates multiple functional modules. The power module ensures the continuous operation of the device, while the wireless communication receiving module achieves accurate location tracking by receiving signals from the visitor's mobile device. The main control unit, as a data processing center, not only processes wireless signal data to obtain location information, but also fuses this information with image data to generate enhanced real-time visitor information. This data fusion technology improves the accuracy and reliability of the system. For example, when the Wi-Fi signal is unstable, the system can rely on image data to supplement the positioning information. In addition, the system generates individual movement trajectories through time series analysis and obtains the overall tourist flow trend through aggregation analysis. The method for obtaining tourist interest data integrates multi-dimensional data sources, including behavioral data (stay time, visit frequency), social data, questionnaire feedback, etc., and combines deep learning algorithms to generate a comprehensive interest model. This comprehensive data collection and analysis method enables the system to accurately grasp the preferences of tourists, thereby providing more personalized services. For example, the system finds that a tourist is particularly interested in modern art exhibits, so it will give priority to relevant exhibition areas when recommending routes. This deep personalization not only improves visitor satisfaction, but also helps optimize the allocation of exhibition hall resources, such as adjusting exhibit layouts or developing new themed exhibitions based on visitor interests.

[0072] The advantages of the above technical solution are: the introduction of the feedback and adjustment module enables the system to have adaptive capabilities and can dynamically adjust the scheduling strategy according to the real-time situation. This not only improves the flexibility of the system, but also enhances the ability of the exhibition hall to respond to emergencies. For example, when an abnormal increase in the carbon dioxide concentration in a certain area is detected, the system can immediately adjust the maximum capacity of the area and guide visitors to other areas, thereby ensuring the safety of the exhibition hall environment. Secondly, the multifunctional integrated design of the visitor calibration device improves the comprehensiveness and accuracy of data collection. By integrating Wi-Fi signal positioning and image analysis, the system can track visitor locations and behaviors more accurately, and maintain a high degree of accuracy even in complex indoor environments. Thirdly, the visitor interest model based on multi-dimensional data sources and deep learning algorithms enables the system to deeply understand the preferences of each visitor. This not only provides a highly personalized visiting experience, but also helps exhibition hall managers better understand visitor needs, thereby optimizing exhibition design and resource allocation. For example, if the system finds that most visitors are particularly interested in a certain type of exhibits, the exhibition hall will increase exhibitions of related themes or expand the display area of ​​such exhibits.

[0073] Embodiment 5

[0074] like Figure 6 As shown, this embodiment provides a method based on the museum exhibition dynamic visitor calibration and visitor scheduling system, which includes the following steps:

[0075] S110: Visitor calibration device monitors real-time carbon dioxide concentration data in the exhibition hall;

[0076] S120: The visitor calibration device captures real-time visitor image data in the exhibition hall;

[0077] S130: The tourist calibration device analyzes the real-time tourist image data based on a dynamic target detection algorithm to obtain real-time tourist information, wherein the real-time tourist information includes any one of tourist quantity information, tourist location information, tourist flow information, tourist distribution information, tourist stay time in the exhibition hall, and tourist queue time;

[0078] S140: The tourist calibration device transmits the real-time tourist information and the real-time carbon dioxide concentration data to a central server;

[0079] S150: The central server receives the real-time tourist information and the real-time carbon dioxide concentration data through a tourist scheduling model;

[0080] S160: The central server determines an optimal tourist scheduling plan based on a multi-objective optimization algorithm according to the real-time tourist information and the real-time carbon dioxide concentration data;

[0081] S170: The central server converts the optimal tourist scheduling plan into a specific scheduling strategy;

[0082] S180: The central server communicates the scheduling strategy to the tourists.

[0083] Specifically, the visitor calibration device is a small hardware containing an algorithm and a navigation system. It is directly installed on the existing three-proof monitoring pole in the venue space. At the same time, it is necessary to set up the three-dimensional spatial functional area of ​​the entire site / museum to calibrate the guided route, rest route, emergency evacuation route and its tourist carrying capacity. The visitor calibration device is a small hardware device, including the following main parts: a main control unit, which is responsible for data processing and controlling the operation of the entire system. A sensor module, which includes a carbon dioxide concentration monitoring module and a camera module, is used to monitor the number of tourists, movement speed, residence time, queue time, etc., as well as the carbon dioxide concentration in the venue in real time. A communication module is used to exchange data with the central server. A power module is used to provide the power required for the operation of the equipment. A mounting bracket is used to fix the calibration device on the existing three-proof monitoring pole in the venue space. The visitor calibration device obtains the carbon dioxide concentration in the venue through the sensor module, and the camera module captures the situation of tourists in the venue. These data are transmitted to the central server through the communication module, and the main control unit uses dynamic target detection based on the YOLOv8 architecture for processing to obtain quantitative information such as the number of tourists, movement speed, residence time, queue time, etc.

[0084] The sensor module collects the location and movement information of tourists in real time, as well as the real-time carbon dioxide concentration in the venue. The main control unit analyzes and processes the data, calibrates the distribution of tourists and carbon dioxide. Data transmission transmits the data to the central server through the communication module. The central server analyzes the results and guides tourists to move along the preset route through the navigation system.

[0085] The optimization scheduling model is a system that integrates the NSGA-II genetic optimization algorithm and real-time scheduling strategy, which is used to reasonably allocate tourist flows in spaces such as museums or heritage sites to improve tourist experience, protect cultural relics and optimize space utilization.

[0086] The optimization scheduling model mainly includes the following parts:

[0087] The data input module is used to receive real-time data from the visitor calibration device, including the number of visitors, the time spent in the venue, the length of the movement trajectory, the queuing time and the carbon dioxide concentration.

[0088] The multi-objective optimization algorithm module is used to initialize the initial quantities involved in the evaluation (carbon dioxide concentration, exhibition area passenger flow density, and visitor satisfaction) based on the received data and calculate the optimal visitor scheduling plan. This multi-objective optimization algorithm module uses the advanced NSGA-II genetic optimization algorithm to handle complex multi-objective optimization problems.

[0089] The scheduling strategy generation module is used to convert the solution calculated by the optimization algorithm into a specific scheduling strategy.

[0090] The scheduling execution module is used to convey the scheduling strategy to tourists and guide them to move along the preset route through display screens, broadcasts, mobile phone applications, etc.

[0091] The feedback and adjustment module is used to monitor the scheduling effect in real time and dynamically adjust the scheduling strategy according to the feedback information to ensure the effectiveness and flexibility of the scheduling.

[0092] This multi-objective optimization algorithm module uses the NSGA-II multi-objective genetic optimization algorithm to optimize multiple objectives simultaneously, including: maximizing visitor satisfaction, minimizing passenger flow congestion, and minimizing carbon dioxide concentration. Maximizing visitor satisfaction, comprehensively considering visitor tour time, walking route length, average stay time, and average queue time, and giving priority to recommending venues that tourists are more interested in and more satisfied with. Minimizing passenger flow congestion, by integrating the density of passenger flow at different time periods of the day to reduce congestion. Minimizing carbon dioxide concentration, by real-time monitoring of carbon dioxide concentration, to reduce damage to cultural relics in the exhibition hall.

[0093] like Figure 7 As shown, the specific steps are as follows:

[0094] S161: Construct an objective function and define exhibition area optimization objectives.

[0095] Maximize visitor satisfaction, which comprehensively considers visitor visiting time, distance, queuing time and whether there is riot, and recommends the venues with the highest visitor satisfaction. Minimize the crowdedness of the exhibition area, which controls the visitor density of each exhibition area to prevent overcrowding. Minimize carbon dioxide emissions, which controls the concentration of carbon dioxide and reduces the environmental impact on cultural relics.

[0096] Chaos Index or Disturbance:

[0097] Average walking distance:

[0098] Average stay time:

[0099] Average waiting time:

[0100] Customer Satisfaction:

[0101]

[0102] Among them, L i is the trajectory length, T i is the residence time, W i is the queuing time, m is the number of visitors in the exhibition hall, S is the exhibition area, d1, d2, d3, d4 are preset weights, d1 and d2 are positive weights, and d3 and d4 are negative weights;

[0103] Visitor flow density in the exhibition area: ρ = m / S;

[0104] Passenger flow crowding index: crowding = ∑(ρ*t i ) / ∑(t i );

[0105] Among them, t i is the daily time weight coefficient (time interval is 1h), which is positively correlated with the number of tourists, and the CO2 concentration ρ co2 .

[0106] S162: Define the constraints that the optimization process must satisfy.

[0107] The assumptions and constraints of the model are as follows: tourists in the queue outside the exhibition hall will definitely enter the venue; tourists in the exhibition hall with good order will walk slowly, with a walking speed of <2m / s; the longer the tourists stay and the longer the distance they visit, the more interested they are in the exhibition hall, and the queuing time will reduce the attraction of the exhibition hall to tourists; the maximum carrying capacity of each exhibition area must not exceed the safe capacity of the exhibition area; the flow speed of tourists must be limited to prevent riots and other safety accidents.

[0108] S163: A non-dominated sorting genetic algorithm II combined with a grey wolf genetic algorithm is used for multi-objective optimization to determine the optimal tourist scheduling solution.

[0109] The NSGA-II genetic optimization algorithm is used to evaluate the priority of exhibition areas and obtain the recommended exhibition areas (sets) with the highest comprehensive scores, thereby giving suggestions for visitor scheduling.

[0110] Initialize N initial solutions (N is the number of exhibition halls) and obtain the satisfaction and congestion of these N exhibition halls.

[0111] The three initial parameters are optimized using the Grey Wolf Genetic Algorithm to obtain N optimized new solutions.

[0112] The 2N solutions are sorted by non-dominated order to obtain the Pareto solution set, which is the recommended exhibition hall list.

[0113] The crowding distance of each solution in the Pareto solution set is calculated to further obtain the most recommended destination exhibition area and the comprehensive ranking of the exhibition areas.

[0114] The scheduling strategy generation module is used to convert the plan calculated by the optimization algorithm into a specific scheduling strategy, including: guiding the movement route planning of tourists; setting dynamic entry restrictions to the exhibition area; and adjusting the opening of rest areas and evacuation passages.

[0115] The scheduling execution module is used to convey the scheduling strategy to tourists through the following methods: display screens, which are set at key locations to display tourist guidance information in real time; broadcasting system, which releases scheduling information through the in-hall broadcasting system; mobile phone applications, which push personalized guidance information to tourists through the mobile phone applications in the museum via wifi or Bluetooth services.

[0116] The feedback and adjustment module is used to monitor the scheduling effect in real time and collect the following feedback information: emergencies and abnormal events, which are used to automatically alarm when a riot occurs, that is, when the Chaos index = 1. According to the feedback information, the three parameter values ​​of each exhibition hall are changed in real time, and the scheduling strategy is adjusted dynamically to ensure the effectiveness and flexibility of the scheduling.

[0117] The workflow is described as follows:

[0118] The sensor module collects data and transmits it to the central server. The data input module of the central server receives real-time data, including visitor location, density and movement information. The multi-objective optimization algorithm module calculates the optimal scheduling plan based on real-time data. The scheduling strategy generation module converts the optimization results into specific scheduling strategies. The scheduling execution module communicates the scheduling strategy to visitors through display screens, broadcasts and mobile phone applications. The feedback and adjustment module dynamically adjusts the scheduling strategy based on actual conditions and feedback information to ensure the reasonable distribution of each exhibition area and the satisfaction of visitors.

[0119] In this embodiment, the visitor calibration system combines deep learning and embeds real-time target detection based on the YOLOv8 architecture to detect visitor information in real time in the camera, thereby calculating visitor density, residence time, trajectory length, etc. In this embodiment, the visitor calibration device combines the carbon dioxide detection device with the camera, which can not only perform real-time data calibration, but also exchange data with the remote server, and send recommended scheduling information to tourists. In this embodiment, the visitor scheduling model adopts the NSGA-II algorithm, and makes a comprehensive evaluation of the three factors through non-dominated sorting to obtain the optimized Pareto solution set as the optimized solution set, that is, the recommended exhibition hall list. At the same time, the optimal solution set can be further sorted by congestion to obtain the best recommended exhibition area.

[0120] The beneficial technical effects of the above technical solution of the embodiment of the present invention are as follows:

[0121] The embodiments of the present invention are conducive to improving the accuracy and real-time performance of dynamic calibration. Through multi-source data fusion technology, combined with sensor, video analysis and mobile device data, the shortcomings of a single data source are effectively reduced and the calibration accuracy is improved. At the same time, efficient data processing algorithms and edge computing technologies are adopted, and algorithms are optimized to reduce computational complexity, thereby improving data processing efficiency and achieving more accurate and real-time dynamic calibration.

[0122] The embodiments of the present invention are conducive to reducing costs and improving the visitor experience. By reducing the reliance on high-cost RFID tags and devices and adopting low-cost, high-precision positioning technologies such as Wi-Fi, Bluetooth, and sensor fusion, the overall cost of the system is effectively reduced. In addition, the embodiments of the present invention also use anonymization and privacy protection technologies to improve the system's acceptance while protecting the privacy of tourists, thereby improving the visitor experience.

[0123] The embodiments of the present invention are conducive to improving the flexibility and efficiency of the visitor scheduling model. By constructing a dynamic multi-objective optimization model, multiple objectives such as visitor satisfaction, exhibition area utilization, and cultural relics protection are comprehensively considered to achieve more flexible scheduling. At the same time, based on real-time data and feedback information, the scheduling strategy is dynamically adjusted to improve the response speed and scheduling efficiency of the system to adapt to the ever-changing exhibition hall environment and visitor needs.

[0124] The embodiments of the present invention are conducive to improving the stability and adaptability of the system. By improving the intelligent algorithm, the convergence speed and the quality of the solution are improved, and the sensitivity to the initial conditions is reduced, thereby enhancing the robustness of the system. In addition, the embodiments of the present invention also introduce adaptive control technology, so that the system can autonomously adjust the scheduling strategy according to real-time environmental changes, further enhancing the stability and adaptability of the system to cope with various complex exhibition hall situations.

[0125] In terms of dynamic calibration, the YOLOv8 computer vision model of the embodiment of the present invention has been trained with a large number of data sets, and is fast, accurate and easy to use for object detection (human detection in this model), which improves the accuracy of image recognition. The model is mature and easy to use, and data training and prediction are both performed on the cloud server, which effectively reduces costs. The model samples the camera video, which does not require the cooperation of tourists and will not affect the tourists' experience.

[0126] In the tourist scheduling model, the gray wolf genetic algorithm is used to optimize the initial solution set, which has better convergence and can provide more accurate tourist scheduling suggestions. The optimization based on the genetic algorithm reduces the computational complexity.

[0127] In terms of the calibration device, the embodiment of the present invention integrates multiple modules, has high integration and good systematization, and can autonomously adjust the scheduling strategy according to real-time environmental changes, thereby enhancing the stability and adaptability of the system.

[0128] The tourist scheduling model of the embodiment of the present invention optimizes the distribution and activities of tourists in space based on dynamically calibrated data to achieve the goal of improving resource utilization and tourist experience. Multi-objective optimization algorithms, such as genetic algorithms, particle swarm algorithms, or multi-objective evolutionary algorithms, are used to balance and optimize multiple goals (such as minimizing crowding, maximizing the utilization of resources such as non-popular exhibits / exhibition areas / idle spaces, and maximizing tourist satisfaction); real-time decision support systems, which combine real-time data and predictive analysis to automatically adjust tourist guided routes, recommend non-popular areas, reduce tourist concentration and overcrowding, and upload tourist crowding phenomena to the management center server in real time. The management center makes a decision on relief and notifies the audience through broadcasting; artificial intelligence and machine learning, which analyze big data, learn tourists' preferences and behavior patterns, provide personalized guided tours and recommendation services, and adjust the utilization of space resources at the same time.

[0129] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0130] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the above methods is implemented.

[0131] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. Of course, there are other ways of readable storage media, such as quantum memory, graphene memory, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0132] The present invention also provides an electronic device. The electronic device of an embodiment of the present invention comprises: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of the present invention.

[0133] Reference below Figure 8 , which shows a schematic diagram of the structure of a computer system 800 of an electronic device suitable for implementing an embodiment of the present invention. Figure 8 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0134] like Figure 8 As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the computer system 800 are also stored. The CPU 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0135] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read therefrom is installed into the storage section 808 as needed.

[0136] In particular, according to the embodiments disclosed in the present invention, the process described in the main step diagram above can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the main step diagram. In the above embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit 801, the above functions defined in the system of the present invention are executed.

[0137] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0138] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0139] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A museum exhibition dynamic visitor calibration and visitor scheduling system, characterized in that: include: The visitor calibration device is installed on a monitoring pole in the exhibition hall of the museum, and the visitor calibration device includes: The sensor module includes: a carbon dioxide concentration monitoring module for monitoring real-time carbon dioxide concentration data in the exhibition hall; an image sensor for capturing real-time visitor image data in the exhibition hall; A main control unit is used to receive the real-time carbon dioxide concentration data and the real-time tourist image data, and analyze the real-time tourist image data based on a dynamic target detection algorithm to obtain real-time tourist information, wherein the real-time tourist information includes any one of tourist quantity information, tourist location information, tourist flow information, tourist distribution information, tourist stay time information in the exhibition hall, and tourist queuing time information; a communication module, electrically connected to the main control unit, for transmitting the real-time visitor information and the real-time carbon dioxide concentration data to a central server; The central server is configured with a tourist scheduling model, wherein the tourist scheduling model includes: A data input module, used for receiving the real-time visitor information and the real-time carbon dioxide concentration data; A multi-objective optimization algorithm module, used to determine an optimal tourist scheduling plan based on the multi-objective optimization algorithm according to the real-time tourist information and the real-time carbon dioxide concentration data; A scheduling strategy generation module, used to convert the optimal tourist scheduling plan into a specific scheduling strategy; The scheduling execution module is used to convey the scheduling strategy to tourists.

2. The system according to claim 1, characterized in that The dynamic target detection algorithm includes: a dynamic target detection algorithm based on the YOLOv8 architecture; the multi-objective optimization algorithm includes: NSGA-II multi-objective genetic optimization algorithm; The optimization objectives of the multi-objective optimization algorithm module include: maximizing tourist satisfaction, minimizing passenger flow congestion, and minimizing carbon dioxide concentration; The constraints of the multi-objective optimization algorithm module include: the maximum carrying capacity of the exhibition hall and the speed limit of visitor flow.

3. The system according to claim 2, characterized in that The multi-objective optimization algorithm module specifically includes: The exhibition area optimization target definition submodule is used to define the exhibition area optimization target, which includes the following multiple indicators: The average walking path length is calculated by the following formula: Among them, m is the total number of visitors in the exhibition hall, L i is the walking path length of the i-th tourist; The average residence time is calculated by the following formula: Among them, T i is the stay time of the i-th tourist; The average waiting time is calculated by the following formula: Among them, W i is the queuing time of the i-th tourist; The chaos index is calculated by the following formula: When the average walking speed of tourists is greater than 2m / s, the chaos index is 1, and when the average walking speed of tourists is less than or equal to 2m / s, the chaos index is 0. i is the walking speed of the i-th tourist; The passenger flow density of the exhibition area is calculated by the following formula: ρ = m / S, where S is the exhibition area and ρ is the passenger flow density of the exhibition area; The passenger flow crowding index is calculated by the following formula: crowding = ∑(ρ*t i ) / ∑(t i ), where t i represents the time weight coefficient per hour, which is positively correlated with the number of tourists; Tourist satisfaction is calculated using the following formula: Among them, d1, d2, d3, and d4 are preset weights, d1 and d2 are positive weight values, and d3 and d4 are negative weight values; The constraint definition submodule is used to define the constraint conditions that must be met in the optimization process. The constraint conditions include: all visitors in the queue outside the exhibition hall will eventually enter the venue; the average walking speed of visitors in the exhibition hall shall not exceed 2 meters per second; the maximum carrying capacity of each exhibition area shall not exceed the safe capacity of the exhibition area; the visitor density per unit area of ​​the exhibition area shall not exceed the design limit of the exhibition area; the chaos index of the exhibition area shall not exceed the preset threshold; the carbon dioxide concentration in the exhibition hall shall not exceed the preset concentration limit; The optimization algorithm processing submodule is used to use the non-dominated sorting genetic algorithm II combined with the gray wolf genetic algorithm for multi-objective optimization to determine the optimal tourist scheduling plan; it specifically includes: executing the solution set initialization step, which includes initializing N initial solutions, where N is the number of pavilions; for each initial solution, obtaining the satisfaction, passenger flow congestion and carbon dioxide concentration value of the corresponding pavilion; using the gray wolf genetic algorithm to optimize the N initial solutions to improve satisfaction, reduce passenger flow congestion and carbon dioxide concentration, and obtain N optimized new solutions; executing the non-dominated sorting step, including merging the N initial solutions and the N optimized new solutions to form a set of 2N solutions; The method comprises the following steps: performing non-dominated sorting to obtain a Pareto optimal solution set; executing a crowding distance calculation step, which includes calculating the crowding distance of each solution in the Pareto optimal solution set; determining the most recommended destination exhibition area based on the crowding distance; generating a comprehensive ranking of the exhibition areas according to the non-dominated level and crowding distance of each solution in the Pareto optimal solution set, wherein the non-dominated level is determined by comparing the pros and cons of each solution in terms of satisfaction, passenger flow crowding and carbon dioxide concentration, and the lower the non-dominated level, the higher the priority of the exhibition area; determining the optimal visitor scheduling plan according to the most recommended destination exhibition area, the comprehensive ranking of the exhibition areas, and the satisfaction, passenger flow crowding and carbon dioxide concentration values ​​of each exhibition area.

4. The system according to claim 1, characterized in that The scheduling strategy generation module specifically includes: The first scheduling strategy generation submodule is used to guide the movement path of tourists in the exhibition hall based on the real-time information of tourist flow and the information of tourists' stay time in the exhibition hall; The second scheduling strategy generation submodule is used to dynamically adjust the maximum number of visitors that can be accommodated at the same time in each exhibition area according to the current visitor distribution information; The third scheduling strategy generation submodule is used to adjust the number of open rest areas and the open and closed states of evacuation passages according to the information on the number of tourists, the distribution of tourists and the flow of tourists.

5. The system according to claim 4, characterized in that The scheduling execution module specifically includes: A first control submodule, used for controlling a plurality of display screens to display visual guidance information for all visitors in real time, wherein the plurality of display screens are installed at different locations in the exhibition hall as devices independent of the central server and the visitor calibration device; A second control submodule is used to control a broadcasting system to play audio scheduling information for all visitors, wherein the broadcasting system is installed in the exhibition hall as a device independent of the central server and the visitor calibration device; A third control submodule is used to control the server application running on the central server to send personalized guidance information to the client application on the tourist's mobile phone through a wireless network; Among them, the guidance information for all tourists, the scheduling information for all tourists and the personalized guidance information are all generated by the scheduling execution module according to the scheduling strategy, and the personalized guidance information is determined accordingly according to the specific situation of each tourist.

6. The system according to claim 1, characterized in that The tourist scheduling model also includes a feedback and adjustment module for real-time monitoring of scheduling effects, which include tourist distribution information, changes in carbon dioxide concentrations, and emergency information, and dynamically adjusting scheduling strategies based on the tourist distribution information, changes in carbon dioxide concentrations, and emergency information; wherein the emergency information is obtained in at least one of the following ways: emergency reports uploaded by tourists through mobile applications; abnormal behaviors associated with emergency information detected based on image analysis.

7. The system according to claim 1, characterized in that The visitor calibration device further includes: a power module, which is electrically connected to the main control unit, the sensor module and the communication module, and is used to provide power for the visitor calibration device; the carbon dioxide concentration monitoring module, the image sensor and the wireless communication receiving module are all electrically connected to the main control unit; the sensor module also includes a wireless communication receiving module, which is used to receive wireless signal data sent by a mobile device of a visitor; The main control unit is also used to: process the wireless signal data to obtain tourist location information; fuse the tourist location information with the real-time tourist image data to obtain enhanced real-time tourist information; wherein the enhanced real-time tourist information also includes tourist interest data; Among them, based on the continuous tourist location information of a single tourist, the movement trajectory data of a single tourist is generated through time series analysis; the movement trajectory data of multiple tourists are aggregated and analyzed to obtain the tourist flow information representing the overall tourist movement trend; Among them, the tourist interest data is obtained through a variety of methods, including: obtaining a multi-dimensional data source, which includes tourist stay time, visit frequency, movement trajectory pattern, interactive behavior, social media activities, questionnaire feedback, consumption behavior, and reservation data; based on the multi-dimensional data source, combined with a deep learning algorithm, a comprehensive tourist interest model is generated to output tourist interest data for different exhibition areas.

8. A method for the museum exhibition dynamic visitor calibration and visitor scheduling system based on any one of claims 1-7, characterized in that: The following steps are involved: The visitor calibration device monitors the real-time carbon dioxide concentration data in the exhibition hall and captures the real-time visitor image data in the exhibition hall; analyzes the real-time visitor image data based on a dynamic target detection algorithm to obtain real-time visitor information, wherein the real-time visitor information includes any one of visitor quantity information, visitor location information, visitor flow information, visitor distribution information, visitor stay time in the exhibition hall and visitor queue time; transmits the real-time visitor information and the real-time carbon dioxide concentration data to a central server; The central server receives the real-time tourist information and the real-time carbon dioxide concentration data through a tourist scheduling model; determines an optimal tourist scheduling plan based on the real-time tourist information and the real-time carbon dioxide concentration data based on a multi-objective optimization algorithm; converts the optimal tourist scheduling plan into a specific scheduling strategy; and communicates the scheduling strategy to tourists.

9. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to claim 8 is implemented.

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