Tourism digital intelligence analog simulation system and method

Abstract

The invention belongs to the technical field of tourism exhibition hall people flow regulation and control, and particularly relates to a tourism digital-intelligence analog simulation system and method, and the method achieves the real-time monitoring of the aggregation state of tourists in each exhibition area of an exhibition hall, and combines the analysis of the impact of newly-added tourists on the people flow aggregation and the prediction of the distribution proportion of the newly-added tourists in each exhibition area. And introducing a tourism digital intelligence simulation technology to construct a dynamic simulation model of the people flow gathering trend in the future period. Based on a simulation result of the model, the system can provide exhibition area access recommendation for newly-added tourists, optimize tourist movement line distribution in time, effectively relieve local congestion and improve the space utilization rate and touring comfort, thereby realizing refined, intelligent and prospective regulation and control of exhibition hall people flow management, and enhancing operation management efficiency and tourist experience quality.

Description

Technical Field

[0001] The present invention belongs to the technical field of crowd flow regulation in tourist exhibition halls, and specifically relates to a tourism digital simulation system and method. Background Art

[0002] With the continuous development of the tourism industry and the continuous improvement of the public's cultural consumption demand, the number of tourists visiting exhibition halls is increasing day by day, and the resulting phenomenon of crowd gathering is becoming more and more prominent. The high-density concentration of people in local areas not only affects the passage efficiency in the exhibition hall, but also reduces the visiting comfort and experience quality of tourists. To address this challenge, the current focus of exhibition hall management has gradually shifted towards refined crowd flow regulation.

[0003] In the prior art, there are already relevant solutions for crowd flow regulation in exhibition halls. For example, a method, device, equipment and medium for guiding the crowd flow in an exhibition hall proposed in Chinese Patent Publication No. CN119940706A. This solution customizes explanation videos and their playback strategies (including playback duration and cycle) for each sub-region of the exhibition hall, and dynamically adjusts the video playback strategy by combining the monitoring of the real-time crowd flow in each region. When the crowd flow in a certain region is too high, the video playback frequency in this region is increased or the playback interval is shortened to attract tourists to relatively idle regions, so as to achieve an even distribution of the crowd flow, reduce the dependence on manual guidance, and save operating costs.

[0004] However, this type of regulation method essentially belongs to a post-event reactive regulation mechanism, that is, the regulation measures are only initiated after detecting that the crowd flow exceeds the set threshold, lacking the ability to predict the trend of crowd flow changes in advance. Especially before new tourists enter the exhibition hall, it is unable to effectively predict and divert the exhibition areas that they may choose, resulting in a lag in regulation and being difficult to cope with sudden surges in passenger flow.

[0005] In addition, the monitoring of the crowd flow in the above solution mainly relies on counting the number of tourists in the area, and the evaluation index is relatively single, ignoring the microscopic characteristics of the crowd gathering state in the exhibition area. Under the condition of the same number of people, there are significant differences in the impacts of different crowd gathering patterns on the tourist experience quality, passage efficiency, environmental comfort, and safety risk level. Therefore, only using the number of tourists as the basis for regulation is likely to cause misjudgment or over-intervention in the crowd flow regulation strategy, affecting the accuracy and effectiveness of regulation. Summary of the Invention

[0006] The present invention aims to overcome the problems of lagging and extensive regulation in the existing exhibition hall crowd management, and proposes a tourism digital simulation system and method. By real-time monitoring the crowd gathering state in each exhibition area, and introducing tourism digital simulation technology to predict the crowd gathering in each exhibition area after new tourists enter the exhibition hall, and implementing dynamic guidance accordingly, to achieve refined and forward-looking scheduling of the exhibition hall crowd management.

[0007] The object of the present invention can be achieved by the following technical solutions: In the first aspect of the present invention, a tourism digital simulation system is provided, including: An image acquisition module: using image acquisition devices deployed in each exhibition area of the exhibition hall to collect real-time images of the distribution of tourists.

[0008] A crowd aggregation analysis module: extracting aggregation features from the tourist distribution images, including the number of aggregation clusters, the area covered by the clusters, the number of tourists within the clusters, and the distance between adjacent aggregation clusters, and analyzing the crowd aggregation degree in combination with the exhibition area layout.

[0009] A new aggregation association module: establishing a correspondence between the tourist increment and the increment of the crowd aggregation degree through the comparison of tourist distribution images at adjacent acquisition times, and associatively analyzing the tendency of the increment of the aggregation degree corresponding to the unit tourist increment.

[0010] A distribution prediction module: obtaining the preferred exhibition areas of tourists entering the exhibition hall through path tracking devices deployed at the entrance of the exhibition hall, and predicting the distribution ratio of new tourists in each exhibition area based on this.

[0011] A risk simulation module: docking with the exhibition hall ticket system to obtain the number of new tourists in the future period, and simulating the increment value of the crowd aggregation degree in each exhibition area after the new tourists enter based on the distribution ratio and the tendency of the increment of the aggregation degree corresponding to the unit tourist increment in each exhibition area.

[0012] A risk identification module: identifying safe exhibition areas using the simulated increment values of the crowd aggregation degree in each exhibition area.

[0013] An exhibition area recommendation module: recommending the preferred exhibition areas for new tourists based on the distribution of safe exhibition areas and displaying them at the entrance of the exhibition hall.

[0014] In the second aspect of the present invention, a tourism digital simulation method is proposed, including the following steps: S1: Using image acquisition devices deployed in each exhibition area of the exhibition hall to collect real-time images of the distribution of tourists.

[0015] S2: Extracting aggregation features from the tourist distribution images, including the number of aggregation clusters, the area covered by the clusters, the number of tourists within the clusters, and the distance between adjacent aggregation clusters, and analyzing the crowd aggregation degree in combination with the exhibition area layout.

[0016] S3: Establishing a correspondence between the tourist increment and the increment of the crowd aggregation degree through the comparison of tourist distribution images at adjacent acquisition times, and associatively analyzing the tendency of the increment of the aggregation degree corresponding to the unit tourist increment.

[0017] S4: Obtaining the preferred exhibition areas of tourists entering the exhibition hall through path tracking devices deployed at the entrance of the exhibition hall, and predicting the distribution ratio of new tourists in each exhibition area based on this.

[0018] S5: Interface with the exhibition hall ticket system to obtain the number of new visitors in future time periods, and simulate the increment value of the crowd aggregation degree in each exhibition area after the new visitors enter based on the allocation ratio and the aggregation degree increment tendency corresponding to the increment of the number of visitors per unit area in each exhibition area.

[0019] S6: Identify the safe exhibition areas by using the simulated increment values of the crowd aggregation degree in each exhibition area.

[0020] S7: Recommend the preferred exhibition areas for new visitors according to the distribution of the safe exhibition areas and display them at the entrance of the exhibition hall.

[0021] Combining all the above technical solutions, the positive effects of the present invention are as follows: 1. Through the real-time monitoring of the crowd aggregation state in each exhibition area of the exhibition hall, combined with the analysis of the impact of new visitors on the crowd aggregation and the prediction of their allocation ratio in each exhibition area, the present invention introduces the tourism digital simulation technology to dynamically simulate the crowd aggregation in future time periods, and accordingly provides exhibition area visit recommendations for new visitors, timely optimizes the distribution of visitor movement lines, effectively alleviates local congestion, and thus realizes the refined and forward-looking control of the exhibition hall crowd management, which is beneficial to enhancing the operation management efficiency and the quality of the visitor experience.

[0022] 2. The monitoring of the aggregation state in each exhibition area of the present invention realizes the detailed presentation of the crowd aggregation behavior of visitors at the micro level in units of aggregation clusters, can more precisely reflect the spatial organization characteristics of the crowd, provides higher-precision data support for subsequent crowd control, and significantly improves the sensitivity and decision-making scientificity of crowd control. Description of the Drawings

[0023] The present invention is further described with reference to the attached drawings, but the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the following drawings without creative efforts.

[0024] Figure 1 It is a schematic diagram of the module connection of a tourism digital simulation system provided in Embodiment 1 of the present invention.

[0025] Figure 2 It is an operation implementation diagram of the crowd aggregation analysis module in the present invention.

[0026] Figure 3 It is a flow chart of a tourism digital simulation method provided in Embodiment 2 of the present invention. Detailed Embodiments

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0028] Embodiment 1

[0029] The present invention provides a tourism digital simulation system, including: an image acquisition module, a crowd aggregation analysis module, a new aggregation association module, a distribution prediction module, a risk simulation module, a risk identification module, and an exhibition area recommendation module.

[0030] See Figure 1 As shown, in the above modules, the image acquisition module is connected to the crowd aggregation analysis module, the crowd aggregation analysis module is connected to the new aggregation association module, both the new aggregation association module and the distribution prediction module are connected to the risk simulation module, the risk simulation module is connected to the risk identification module, and the risk identification module is connected to the exhibition area recommendation module.

[0031] Specifically, the image acquisition module is used to collect the tourist distribution images in real time by using the image acquisition devices deployed in each exhibition area of the exhibition hall.

[0032] In a preferred embodiment, the image acquisition devices include but are not limited to high-definition cameras and infrared thermal imaging devices, which are used to obtain the tourist distribution images in each exhibition area at a fixed frequency.

[0033] See Figure 2 As shown, the crowd aggregation analysis module is used to extract the aggregation features from the tourist distribution images, including the number of aggregation clusters, the cluster coverage area, the number of tourists in the cluster, and the distance between adjacent aggregation clusters, and analyze the crowd aggregation degree in combination with the exhibition area layout.

[0034] It should be noted that the purpose of extracting the aggregation features from the tourist distribution images is to depict the local aggregation state of the crowd from the micro level and break through the traditional coarse-grained analysis method that only depends on the number of people in the overall area.

[0035] The reason for selecting the number of aggregation clusters, the cluster coverage area, the number of tourists in the cluster, and the distance between adjacent aggregation clusters as the aggregation features is that the number of aggregation clusters reflects the dispersion degree of the population distribution in the area and reveals the spatial complexity of the crowd organization.

[0036] The cluster coverage area represents the spatial occupancy range of a specific aggregation area.

[0037] The number of tourists in the cluster reflects the flow of people in the local area.

[0038] The combination of the cluster coverage area and the number of tourists within the cluster can reflect the pedestrian flow density in a local area.

[0039] The distance between adjacent aggregated clusters characterizes the spatial proximity between different aggregation regions.

[0040] The above aggregation characteristics together constitute a multi-dimensional characterization of the pedestrian flow aggregation state in terms of spatial distribution, density level, etc., providing all-round refined data for subsequent pedestrian flow aggregation degree evaluation.

[0041] Among the ways that the above modules can achieve, the extraction of aggregation characteristics from the tourist distribution image is as follows: Segment the background of the collected tourist distribution image to separate the foreground area containing tourist activities.

[0042] In the example of the above embodiment, background segmentation can be implemented by background modeling. Through background segmentation, background interference can be effectively removed, focusing on the tourist activity area, and improving the accuracy and efficiency of subsequent processing steps.

[0043] Identify individual tourists within the extracted foreground area and label the position of each tourist in the image in the form of a bounding box.

[0044] The above identification and position annotation of individual tourists provide a reliable data basis for subsequent calculation of the distance between adjacent tourists and clustering, contributing to a more refined analysis of the pedestrian flow aggregation state.

[0045] Obtain the distance between adjacent individual tourists based on the position of each tourist in the image, and use a clustering algorithm in combination with a preset distance proximity threshold to divide tourists who are spatially close into the same aggregation cluster.

[0046] The above-mentioned clustering algorithm can effectively divide aggregation clusters automatically according to the spatial distribution of tourists, discover naturally formed aggregation patterns, and help understand the spatial structure of crowd aggregation.

[0047] Obtain the coverage area of each aggregation cluster by extracting the boundary contour of each aggregation cluster, and at the same time count the number of tourists within each aggregation cluster.

[0048] The above extraction of the coverage area and the number of tourists of the aggregation cluster can quantify the scale and density of each aggregation cluster.

[0049] Obtain the distance between adjacent aggregation clusters based on the geometric center point positions of the corresponding boundary contours of adjacent aggregation clusters.

[0050] As an example of the above solution, the distance between adjacent aggregated clusters can be calculated using the Euclidean distance formula based on the positions of the geometric center points of the corresponding boundary contours of adjacent aggregated clusters. The geometric center point is used as the representative position of the aggregated cluster, which is convenient for quantitative analysis of spatial relationships and helps to understand the relative position relationships between different aggregated clusters.

[0051] In another implementable way of the above module, the analysis of the crowd aggregation degree in combination with the exhibition area layout is as follows: The total number of tourists is obtained by accumulating the number of tourists in each aggregated cluster in the tourist distribution image, and the basic crowd density is calculated by the ratio to the physical area of the exhibition area in the exhibition area layout. It can represent the overall crowd density and is a basic indicator for measuring the crowded state at the macroscopic level.

[0052] For each independent aggregated cluster, calculate the ratio of the number of tourists it contains to the area covered by the aggregated cluster to obtain the local crowd density of the cluster, which reveals the differences in the crowd density in different regions and reflects the uneven phenomenon of spatial utilization at the microscopic level.

[0053] It should be understood that there may be a situation where multiple high-density aggregation points and relatively empty areas coexist inside the exhibition area. Relying solely on the overall crowd density cannot accurately reflect the local risks.

[0054] Extract the maximum local crowd density and the minimum local crowd density among all aggregated clusters and calculate the ratio to construct the aggregation crowd difference degree.

[0055] The above constructed aggregation crowd difference degree is used to quantify the uneven degree of the crowd density distribution between different regions in the exhibition area, reflecting the coordination and utilization efficiency of spatial resources in carrying the crowd. When the local crowd density in a certain region is significantly higher than that in other regions, that is, the gap between the maximum local crowd density and the minimum local crowd density is larger, the aggregation crowd difference degree is correspondingly higher, indicating that there is an over-concentration phenomenon in some regions and it is prone to aggregation risks.

[0056] Construct an aggregated cluster distance matrix from the distances between adjacent aggregated clusters, and calculate the standard deviation by standardizing the distance data in the matrix to construct the aggregation space dispersion degree.

[0057] It should be noted that the above standardization process of the matrix distance data is to eliminate the dimension and unify the data dimension, so as to ensure the accuracy and comparability of subsequent statistical analysis.

[0058] The above constructed aggregation space dispersion degree is used to describe the degree of concentration of the distribution of aggregated clusters in space. The larger the value of this index, the more significant the difference in the distances between adjacent aggregated clusters, that is, there is a phenomenon that some aggregated clusters are too close to each other while others are farther apart. Among them, the too small adjacent distance means that the local high-density areas may be merging to form a larger area of dense crowd, thus bringing higher safety risks.

[0059] Taking the basic pedestrian flow density as the benchmark quantity, the aggregation difference degree of pedestrian flow and the dispersion degree of aggregation space are introduced as microscopic supplementary factors to construct a fusion model to obtain the pedestrian flow aggregation degree.

[0060] It should be noted that the basic pedestrian flow density only reflects the average relationship between the number of tourists in the area and the spatial area, which belongs to the macroscopic coarse-grained description and is difficult to depict the non-uniform distribution state of the crowd in the local area and the potential spatial conflict risk. Therefore, relying solely on this indicator cannot comprehensively and accurately evaluate the pedestrian flow aggregation degree in complex scenarios. For this reason, the fusion model supplements and corrects the basic density by introducing the above two microscopic additional factors, and can more scientifically quantify the pedestrian flow aggregation degree in detail.

[0061] An example of the fusion model is , in this model represents the pedestrian flow aggregation degree, represents the basic pedestrian flow density, 、 represent the aggregation difference degree of pedestrian flow and the dispersion degree of aggregation space respectively.

[0062] It should be noted that in the above fusion model plays the role of the crowd total magnification factor, making the model output have practical physical significance. The greater the basic pedestrian flow density, the greater the bearing pressure of the exhibition area, and the higher the aggregation risk naturally.

[0063] 、 respectively and independently reflect the uneven degree and diffusion characteristics of the pedestrian flow distribution in space.

[0064] reflects the synergistic interaction effect between the pedestrian flow difference degree and the spatial dispersion degree. When high difference degree and high dispersion degree exist simultaneously, it means that the pedestrian flow is not only highly concentrated locally, but also its distribution shows a multi-point dispersion characteristic, which may lead to the simultaneous emergence of multiple potential congestion areas, thus significantly increasing the overall risk level.

[0065] This modeling method quantifies the spatial heterogeneity and diffusion characteristics of the pedestrian flow distribution, combines the magnification effect of the crowd total, and considers the enhancement effect of the synergistic interaction between multiple factors on the pedestrian flow aggregation risk, which is more in line with the actual evolution law and risk conduction mechanism of the pedestrian flow aggregation behavior in complex spaces, and improves the adaptability and prediction accuracy of the model in a dynamic environment.

[0066] The newly added aggregation correlation module establishes the corresponding relationship between the tourist increment and the pedestrian flow aggregation degree increment through the comparison of the tourist distribution images at adjacent acquisition times, and conducts correlation analysis on the aggregation degree increment tendency corresponding to the unit tourist increment.

[0067] In an alternative implementation, the corresponding relationship between the tourist increment and the increment of the crowd concentration degree is established by comparing the tourist distribution images at adjacent acquisition times as follows: The total number of tourists at adjacent acquisition times is compared using the tourist distribution images collected in real time for each exhibition area to identify whether there is an increase in the number of tourists. If there is no significant increase in the number of tourists at the current adjacent acquisition time, the time window is slid backward until the time interval with a tourist increment is identified, which is recorded as the increment time interval.

[0068] When no tourist increment is detected in the current time window, the sliding window mechanism is adopted to continue the backward retrieval until a meaningful increment interval is identified. This mechanism conforms to the discontinuous and sudden characteristics of the crowd flow change and can effectively avoid missing key increment events.

[0069] For each increment time interval, calculate the difference in the number of tourists between the two acquisition moments before and after as the tourist increment in this time interval, and synchronously calculate the difference in the crowd concentration degree corresponding to the two acquisition moments within this increment time interval to form the increment of the crowd concentration degree in this time interval.

[0070] It should be understood that when there is a tourist increment in the exhibition area, the basic crowd density in the crowd concentration degree fusion model will increase accordingly, which will lead to an increasing trend in the overall crowd concentration degree. The specific increase amplitude has dynamic characteristics and depends on the spatial organization of the crowd flow in the exhibition area after the new tourists arrive and its impact on the local density and spatial structure, that is, the difference degree of the aggregated crowd and the dispersion degree of the aggregated space. Therefore, when there is a tourist increment, there will inevitably be an increase in the crowd concentration degree.

[0071] Pair the tourist increment corresponding to each increment time interval with the corresponding increment of the crowd concentration degree to form a corresponding relationship.

[0072] In a further alternative implementation, the process of associatively analyzing the increment tendency of the concentration degree corresponding to the unit tourist increment is as follows: For each increment time interval, calculate the ratio between the increment of the crowd concentration degree and the tourist increment in this interval to obtain the increment of the crowd concentration degree caused by the unit tourist increment in this interval.

[0073] It should be understood that the increment of the crowd concentration degree caused by the unit tourist increment corresponding to each increment time interval represents the marginal contribution of each new tourist to the crowd concentration degree in the exhibition area under specific time periods and spatial states.

[0074] Perform clustering analysis on the increment of the crowd concentration degree corresponding to the unit tourist increment in each increment time interval to divide into multiple clustering groups, count the number of increment time intervals included in each clustering group, and identify the dominant clustering group with the highest sample proportion.

[0075] It should be noted that due to the significant differences in the behavioral characteristics and activity patterns of tourists at different times, the marginal contribution of each tourist to the degree of crowd gathering in the exhibition area is not consistent at different times. Through cluster analysis, time intervals with similar marginal contribution characteristics can be automatically identified, and these time periods with similar response characteristics are grouped into the same category. This process helps to reveal the patterned rules of the marginal contribution of new tourists to the crowd gathering boundary at different times.

[0076] Calculate the average value of the increment of the degree of crowd gathering increment per tourist in the dominant clustering group to obtain the tendency of the increment of the degree of crowd gathering corresponding to the increment of tourists per unit.

[0077] It should be explained that selecting the clustering group with the largest sample size as the dominant mode can reflect the marginal impact of the increment of tourists per unit on the degree of crowd gathering in most cases. This method avoids the deviation caused by individual special situations and ensures that the analysis results are more representative and stable. The relationship between the increment of tourists per unit and the increment of the degree of crowd gathering in the dominant clustering group represents the normal behavior pattern in the system operation. Therefore, using the average value within this group as the tendency of the increment of the degree of crowd gathering corresponding to the increment of tourists per unit has higher statistical credibility and practical application value.

[0078] The present invention provides key parameter support for simulating the crowd gathering state after new tourists enter the exhibition hall by quantitatively analyzing the increment of the degree of crowd gathering caused by the increment of tourists per unit in each exhibition area.

[0079] The said allocation prediction module obtains the preferred exhibition area of tourists entering the exhibition hall through the path tracking device deployed at the entrance of the exhibition hall, and accordingly predicts the allocation ratio of new tourists in each exhibition area.

[0080] In the specific implementation of the above solution, obtaining the preferred exhibition area of tourists entering the exhibition area through the path tracking device deployed at the entrance of the exhibition hall is implemented as follows: Use the infrared sensor deployed at the entrance of the exhibition hall to sense tourists. When a tourist is sensed to enter the entrance of the exhibition hall, the path tracking device deployed in the entrance area is linked to track the position of the tourist, and its movement trajectory after leaving the entrance is constructed in the time dimension.

[0081] It should be noted that by deploying an infrared sensor at the entrance of the exhibition hall to start the path tracking mechanism only after detecting the actual entry of tourists, it can effectively avoid continuous tracking of non-target areas, thereby reducing the waste of system resources and the redundant burden of data processing.

[0082] In the example of the above operation, the path tracking device can be a camera, a Wi-Fi probe, an RFID reader, etc.

[0083] Based on the residence time series of tourists at each location in the movement trajectory, identify the residence duration of tourists at each location. If the residence duration of tourists at a certain location exceeds the set value, it indicates that the tourists have a clear visiting intention for the exhibition area where the location is located, then stop tracking, which means the initial visit itinerary after the tourists enter the exhibition hall ends.

[0084] It should be understood that by identifying and analyzing the residence duration of tourists at each location after entering the exhibition hall, it can effectively reflect their visiting behaviors and interest tendencies. Comparing the actual residence time with the set value, for example, the set value can be 2 minutes, which helps to distinguish between passing by briefly and staying behaviors with clear visiting intentions.

[0085] Reconstruct the complete movement path using the location information of tourists at multiple consecutive acquisition time points, and extract the exhibition area where the path end point is located as the tourists' preferred exhibition area.

[0086] It should be noted that the tourists' preferred exhibition area refers to the exhibition area where the tourists' first behavior intention is clear after entering the exhibition hall, which reflects the content direction that the tourists are most concerned about within a limited time and is a direct manifestation of their interest orientation.

[0087] In a further specific implementation of the above solution, the prediction of the distribution ratio of newly added tourists in each exhibition area is achieved as follows: Count the number of tourists sensed entering the exhibition hall within the set duration, and classify and summarize the preferred exhibition areas of the tourists each time they are sensed entering to obtain the corresponding number of newly added tourists in each exhibition area.

[0088] The above set duration can be dynamically adjusted according to the frequency of tourists entering the exhibition hall. Specifically: When tourists enter the exhibition hall more frequently, such as during peak hours or special events, the set duration can be shorter, such as 30 minutes. When the frequency of tourists entering the exhibition hall is lower, such as during off-peak hours or non-popular time periods, the set duration can be appropriately extended, such as 1 hour.

[0089] The main purpose of the set duration is to collect a certain number of tourist sample data to classify and summarize the corresponding number of newly added tourists in each exhibition area, and calculate the distribution ratio of newly added tourists in each exhibition area.

[0090] Calculate the ratio of the number of newly added tourists in each exhibition area to obtain the distribution ratio of newly added tourists in each exhibition area.

[0091] As an example of the above operation, assume there are three exhibition areas in the exhibition hall: Area A, Area B, and Area C. Within the set duration, for example, 1 hour, through path tracking equipment and data analysis, the newly added tourists entering the exhibition hall and their preferred exhibition areas are counted as follows: Area A: 25 newly added tourists, Area B: 40 newly added tourists, Area C: 35 newly added tourists.

[0092] Calculate the ratio of the number of newly added tourists in each exhibition area to obtain the distribution ratio of newly added tourists in each exhibition area, and the ratio of Area A is , the proportion of area B is , the proportion of area C is .

[0093] The risk simulation module is used to dock with the exhibition hall ticket system to obtain the number of new visitors in the future period, and simulate the increment value of the crowd aggregation degree in each exhibition area after the new visitors enter based on the allocation ratio and the aggregation degree increment tendency corresponding to the increment of unit visitors in each exhibition area.

[0094] In the preferred implementation of the above module, the process of docking with the exhibition hall ticket system to obtain the number of new visitors in the future period is as follows: The exhibition hall ticket system has the function of displaying ticket purchase information, including key data such as the identity of the ticket purchaser and the planned exhibition time period. When the visitor actually enters the exhibition hall and completes the admission verification, the system will record the cancellation status of the ticket.

[0095] By counting the number of tickets that have not been cancelled currently and combining the corresponding planned visit time intervals, the number of new visitors expected to enter the exhibition hall in the future period can be estimated.

[0096] In the further preferred implementation of the above module, in the process of simulating the increment value of the crowd aggregation degree in each exhibition area after the new visitors enter based on the allocation ratio and the aggregation degree increment tendency corresponding to the increment of unit visitors in each exhibition area, simulation technology is required. Exemplarily, a computer simulation model can be used.

[0097] In the example simulation, assume that an exhibition hall contains three exhibition areas A, B, and C, and the data of 100 new visitors expected in the next 1 hour has been obtained.

[0098] According to historical data analysis, area A accounts for 25%, area B accounts for 40%, and area C accounts for 35%.

[0099] Allocate the 100 new visitors to each exhibition area according to the above ratio, which are 25 people in area A, 40 people in area B, and 35 people in area C respectively.

[0100] Assume that the aggregation degree increment tendency corresponding to the increment of unit visitors is +0.1 for area A, +0.15 for area B, and +0.12 for area C respectively. Then the aggregation degree increments brought by the new visitors are +2.5 for area A, +6 for area B, and +4.2 for area C respectively.

[0101] Use the computer simulation model to simulate the distribution and aggregation degree change of these 100 new visitors in each exhibition area after entering the exhibition hall.

[0102] Generate a report or chart to show the expected aggregation degree change in each exhibition area in the next 1 hour.

[0103] The present invention obtains the state of crowd gathering after new visitors enter the exhibition hall by introducing simulation technology, can more accurately predict the actual impact of new visitors entering the exhibition hall on the crowd gathering degree and environmental state of each exhibition area, and at the same time, the simulation results can be visually presented through visualization means such as charts and heat maps, making the state of crowd gathering after new visitors enter the exhibition hall more clear, dynamic and easy to understand.

[0104] The risk identification module is used to identify safe exhibition areas by using the increment values of crowd gathering degrees of each exhibition area obtained by simulation. The specific implementation process is as follows: The increment values of crowd gathering degrees corresponding to each exhibition area obtained by the risk simulation module are combined with the actual crowd gathering degree values obtained by analyzing the visitor distribution images at the current moment for superposition operation to obtain the simulated crowd gathering degrees of each exhibition area.

[0105] The environmental perception units deployed in each exhibition area perform air quality detection at the same frequency as the image acquisition devices.

[0106] It should be noted that the environmental perception unit includes various sensing devices, such as CO2 concentration monitoring sensors, etc., which are used to collect air quality data such as CO2 concentration while collecting visitor distribution images in each exhibition area.

[0107] Perform a correlation analysis on the crowd gathering degree and air quality collected in real time for each exhibition area, and construct a mapping relationship between the two.

[0108] It should be added that since the visitor distribution images and air quality data are collected synchronously, and the crowd gathering degree information can be extracted through image analysis, the two are synchronous in the time dimension. As the degree of crowd gathering increases, the personnel density in the exhibition area increases, and the impacts of human behaviors such as breathing and activities on the air environment are enhanced, resulting in an increase in the CO2 concentration, which is manifested as a downward trend in the local air quality.

[0109] It can be seen from this that there is a significant dynamic correlation between the crowd gathering degree and the air quality. In order to quantify this relationship, a correlation model can be established, such as a statistical regression model, to analyze the mapping relationship between the crowd gathering degree and air quality parameters.

[0110] The construction of the specific mapping relationship belongs to the category of existing technologies and will not be elaborated in detail here.

[0111] According to the preset safe air quality standard, the corresponding critical crowd gathering degree is deduced from the above mapping relationship as the crowd gathering degree warning threshold for each exhibition area.

[0112] The above-mentioned safe air quality standards usually refer to the indoor air quality specification documents issued by the country or the industry. These documents stipulate the concentration limits of CO2 in closed spaces, reflecting the basic environmental quality requirements for ensuring human health and comfort.

[0113] The above-mentioned crowd concentration warning threshold obtained by reverse deduction represents the maximum reasonable concentration that the exhibition area can bear without causing the air quality to exceed the safety boundary.

[0114] Compare the simulated crowd concentration of each current exhibition area with the crowd concentration warning threshold of the corresponding exhibition area, and thus mark the exhibition areas that do not reach the warning threshold as safe exhibition areas.

[0115] In the present invention, the setting of the crowd concentration warning threshold is not based on subjective experience or artificial preset, but is scientifically deduced based on the dynamic correlation between air quality and crowd concentration. By establishing a mapping model between crowd concentration and air quality and combining the established safe air quality standard, the corresponding crowd concentration critical value is deduced by reverse, so as to determine the warning threshold of each exhibition area. The warning threshold determined in this way comes from the internal connection between actual environmental parameters and crowd behavior, avoiding the uncertainty brought by subjective setting.

[0116] The exhibition area recommendation module is used to recommend the preferred exhibition areas for newly added visitors according to the distribution of safe exhibition areas and display them at the entrance of the exhibition hall.

[0117] In an optional implementation of the above solution, the operation of recommending the preferred exhibition areas for newly added visitors according to the distribution of safe exhibition areas is as follows: calculate the difference between the current simulated crowd concentration and the warning threshold for each identified safe exhibition area to obtain the crowd concentration surplus of the exhibition area. This parameter can represent the crowd concentration that the exhibition area can still accommodate without exceeding the safety boundary.

[0118] Based on the predicted distribution ratio of newly added visitors among the exhibition areas, extract the tendency selection ratio of newly added visitors corresponding to each safe exhibition area. This parameter reflects the probability that visitors naturally select a certain safe exhibition area without intervention and can represent the potential preference intensity of visitors for each exhibition area.

[0119] Perform a weighted average calculation on the crowd concentration surplus and the tendency selection ratio of each safe exhibition area to obtain the recommendation index of each safe exhibition area, and select the exhibition area with the maximum recommendation index as the preferred exhibition area for newly added visitors.

[0120] It should be noted that in the weighted calculation process, the weight distribution of the crowd concentration surplus and the tendency selection ratio can be flexibly set according to the focus of the exhibition hall management objectives. If the management strategy pays more attention to the safety and balance of the crowd distribution, a higher weight can be given to the crowd concentration surplus; if it pays more attention to the visitor experience and willingness to visit, the weight of the tendency selection ratio can be increased.

[0121] When selecting the preferred exhibition areas for newly added visitors from the identified safe exhibition areas, the present invention comprehensively considers two key factors: the aggregation surplus degree and the tendency selection ratio of the safe exhibition areas, so that the recommended results can not only meet the requirements of space carrying capacity but also take into account the behavior preferences of visitors, thus realizing an intelligent guidance strategy that emphasizes both safety and serviceability.

[0122] Embodiment 2

[0123] See Figure 3 As shown, the present invention proposes a tourism digital simulation method, which includes the following steps: S1: Use image acquisition devices deployed in each exhibition area of the exhibition hall to collect real-time images of visitor distribution.

[0124] S2: Extract aggregation features from the visitor distribution images, including the number of aggregation clusters, the cluster coverage area, the number of visitors within the cluster, and the distance between adjacent aggregation clusters, and analyze the crowd aggregation degree in combination with the exhibition area layout.

[0125] S3: Establish the corresponding relationship between the visitor increment and the increment of the crowd aggregation degree through the comparison of the visitor distribution images at adjacent acquisition times, and conduct correlation analysis on the tendency of the increment of the aggregation degree corresponding to the unit visitor increment.

[0126] S4: Obtain the preferred exhibition areas of the visitors entering the exhibition hall through the path tracking devices deployed at the entrance of the exhibition hall, and predict the distribution ratio of newly added visitors in each exhibition area accordingly.

[0127] S5: Connect with the exhibition hall ticket system to obtain the number of newly added visitors in the future period, and simulate the increment value of the crowd aggregation degree in each exhibition area after the newly added visitors enter based on the distribution ratio and the tendency of the increment of the aggregation degree corresponding to the unit visitor increment in each exhibition area.

[0128] S6: Identify the safe exhibition areas by using the simulated increment values of the crowd aggregation degree in each exhibition area.

[0129] S7: Recommend the preferred exhibition areas for newly added visitors according to the distribution of the safe exhibition areas and display them at the entrance of the exhibition hall.

[0130] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0131] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0132] In addition, each functional module in various embodiments of the present application may be integrated into one processing module, may exist physically alone for each module, or two or more modules may be integrated into one module.

[0133] As mentioned above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0134] Finally, the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A tourism digital simulation system, characterized in that, Including: Image acquisition module: Utilize image acquisition devices deployed in each exhibition area of the exhibition hall to collect real-time images of the distribution of visitors; Crowd aggregation analysis module: Extract aggregation features from the visitor distribution images, including the number of aggregation clusters, the coverage area of the clusters, the number of visitors within the clusters, and the distance between adjacent aggregation clusters, and analyze the crowd aggregation degree in combination with the exhibition area layout; New aggregation association module: Establish the corresponding relationship between the visitor increment and the increment of the crowd aggregation degree through the comparison of the visitor distribution images at adjacent acquisition times, and conduct correlation analysis on the tendency of the aggregation degree increment corresponding to the unit visitor increment; Allocation prediction module: Obtain the preferred exhibition area of the visitors entering the exhibition hall through the path tracking device deployed at the entrance of the exhibition hall, and predict the distribution ratio of the new visitors in each exhibition area accordingly; Risk simulation module: Connect with the exhibition hall ticket system to obtain the number of new visitors in the future period, and simulate the increment value of the crowd aggregation degree in each exhibition area after the new visitors enter based on the distribution ratio and the tendency of the aggregation degree increment corresponding to the unit visitor increment in each exhibition area; Risk identification module: Identify the safe exhibition areas using the simulated increment values of the crowd aggregation degree in each exhibition area; Exhibition area recommendation module: Recommend the preferred exhibition areas for the new visitors according to the distribution of the safe exhibition areas and display them at the entrance of the exhibition hall.

2. The tourism digital simulation system according to claim 1, wherein: The process of extracting the aggregation features from the visitor distribution images is as follows: Perform background segmentation on the collected visitor distribution images to separate the foreground area containing visitor activities; Identify individual visitors within the extracted foreground area and mark the position of each visitor in the image in the form of a bounding box; Obtain the distance between adjacent individual visitors based on their positions in the image, and use a clustering algorithm to divide spatially close visitors into the same aggregation cluster in combination with a preset distance proximity threshold; Obtain the coverage area of each aggregation cluster by extracting the boundary contour of each aggregation cluster, and simultaneously count the number of visitors within each aggregation cluster; Obtain the distance between adjacent aggregation clusters based on the geometric center point positions of the corresponding boundary contours of adjacent aggregation clusters.

3. The digital intelligent simulation system for tourism according to claim 1, wherein: The content of analyzing the crowd aggregation degree in combination with the exhibition area layout is as follows: Accumulate the number of visitors within each aggregation cluster in the visitor distribution image to obtain the total number of visitors, and calculate the basic crowd density through the ratio to the physical area of the exhibition area in the exhibition area layout; Calculate the ratio of the number of visitors contained in each independent aggregation cluster to the coverage area of the cluster to obtain the local crowd density of the cluster; Extract the maximum local crowd density and the minimum local crowd density among all aggregation clusters for ratio calculation to construct the aggregation crowd difference degree; Form an aggregation cluster distance matrix with the distances between adjacent aggregation clusters, and perform standardization processing on the distance data in the matrix and calculate the standard deviation to form the aggregation space dispersion degree; Take the basic crowd density as the benchmark quantity, introduce the aggregation crowd difference degree and the aggregation space dispersion degree as microscopic supplementary factors to construct a fusion model to obtain the crowd aggregation degree.

4. A tourism digital simulation system according to claim 1, characterized in that: The implementation of establishing the corresponding relationship between the visitor increment and the increment of the crowd aggregation degree through the comparison of the visitor distribution images at adjacent acquisition times is as follows: The real-time visitor distribution images collected in each exhibition area are used to compare the total number of visitors at adjacent collection times to identify whether there is an increase in the number of visitors. If there is no significant increase in the number of visitors during the current adjacent collection time, the time window is continuously slid backward until a time interval with an increase in visitors is identified and recorded as the incremental time interval. For each incremental time interval, the difference in the number of tourists between the two collection moments is calculated as the tourist increment of the time interval. The difference in the crowd concentration corresponding to the two collection moments in the incremental time interval is simultaneously calculated to form the crowd concentration increment of the time interval. The tourist increment corresponding to each incremental time interval is paired with the corresponding crowd concentration increment to form a corresponding relationship.

5. The digital intelligent simulation system for tourism according to claim 4, characterized in that: The trend of the increase in aggregation degree corresponding to the increase in tourist per unit in the correlation analysis is as follows: For each incremental time interval, the ratio between the crowd concentration increment and the tourist increment is calculated to obtain the crowd concentration increment caused by the unit tourist increment in the interval; The increase in crowd concentration caused by the increase in tourist volume per unit in each incremental time interval is clustered and divided into multiple cluster groups. The number of incremental time intervals contained in each cluster group is counted, and the dominant cluster group with the highest sample proportion is identified. The mean of the aggregation increment per unit tourist increment in the dominant cluster group is calculated to obtain the aggregation increment tendency corresponding to the unit tourist increment.

6. The tourism digital simulation system according to claim 1, wherein: The method of obtaining the preferred exhibition area of visitors entering the exhibition hall by using the path tracking device deployed at the entrance of the exhibition hall is implemented as follows: Infrared sensors deployed at the entrance of the exhibition hall are used to detect visitors. When a visitor enters the exhibition hall, the path tracking device deployed at the entrance area is linked to track the visitor's location and construct the visitor's movement trajectory from the entrance in the time dimension. Based on the tourist's stay time sequence at each location in the movement trajectory, the length of time the tourist stays at each location is identified. If the tourist's stay time at a certain location exceeds the set value, the tracking is stopped. The complete movement path is reconstructed using the visitor location information at multiple consecutive collection time points, and the exhibition area at the end of the path is extracted as the visitor's preferred exhibition area.

7. The tourism digital simulation system according to claim 6, characterized in that: The predicted distribution ratio of new visitors to each exhibition area is as follows: The number of visitors entering the exhibition hall is counted within a set time period, and the preferred exhibition area when a visitor enters each time is classified and summarized to obtain the number of new visitors corresponding to each exhibition area; The number of new visitors in each exhibition area is proportionally calculated to obtain the distribution ratio of the new visitors in each exhibition area.

8. The tourism digital simulation system according to claim 3, characterized in that: The risk identification module implementation process is as follows: The incremental crowd concentration values corresponding to each exhibition area obtained by the risk simulation module are combined with the actual crowd concentration values obtained by analyzing the visitor distribution image at the current time to obtain the simulated crowd concentration of each exhibition area; Use environmental sensing units deployed in each exhibition area to detect air quality at a frequency consistent with that of the image acquisition equipment; Conduct a correlation analysis on the real-time crowd concentration and air quality of each exhibition area, and establish a mapping relationship between the two; According to the preset safe air quality standards, the corresponding critical crowd concentration is inferred from the above mapping relationship as the crowd concentration warning threshold of each exhibition area; Compare the simulated crowd concentration of each current exhibition area with the crowd concentration warning threshold of the corresponding exhibition area, and thus mark the exhibition areas that do not reach the warning threshold as safe exhibition areas.

9. The tourism digital simulation system according to claim 8, wherein: The implementation of recommending the preferred exhibition areas for newly added visitors based on the distribution of safe exhibition areas is as follows: For each identified safe exhibition area, calculate the difference between its current simulated crowd concentration and the warning threshold to obtain the crowd concentration surplus of this exhibition area; Based on the distribution ratio of newly added visitors in each exhibition area predicted, extract the tendency selection ratio of newly added visitors corresponding to each safe exhibition area; Perform a weighted average calculation on the crowd concentration surplus and the tendency selection ratio of each safe exhibition area to obtain the recommendation index of each safe exhibition area, and select the exhibition area with the largest recommendation index as the preferred exhibition area for newly added visitors.

10. A digital simulation method for tourism, characterized in that: It includes the following steps: S1: Use the image acquisition devices deployed in each exhibition area of the exhibition hall to collect the visitor distribution images in real time; S2: Extract the aggregation features from the visitor distribution images, including the number of aggregation clusters, the cluster coverage area, the number of visitors in the cluster, and the distance between adjacent aggregation clusters, and analyze the crowd concentration in combination with the exhibition area layout; S3: Establish the corresponding relationship between the visitor increment and the crowd concentration increment through the comparison of the visitor distribution images at adjacent acquisition times, and conduct a correlation analysis on the tendency of the crowd concentration increment corresponding to the unit visitor increment; S4: Obtain the preferred exhibition areas of the visitors entering the exhibition hall through the path tracking devices deployed at the entrance of the exhibition hall, and predict the distribution ratio of newly added visitors in each exhibition area based on this; S5: Connect with the exhibition hall ticket system to obtain the number of newly added visitors in the future period, and simulate the crowd concentration increment value of each exhibition area after the newly added visitors enter based on the distribution ratio and the tendency of the crowd concentration increment corresponding to the unit visitor increment in each exhibition area; S6: Use the simulated crowd concentration increment values of each exhibition area to identify safe exhibition areas; S7: Recommend the preferred exhibition areas for newly added visitors according to the distribution of safe exhibition areas and display them at the entrance of the exhibition hall.

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