Smart text, travel and digital twin interaction system

Abstract

The invention discloses a smart text travel digital twin interaction system, relates to the technical field of data processing, and aims to solve the problem of automatically guiding abnormally gathered tourists so as to improve the experience feeling of the tourists in visiting indoor scenes. Comprising a digital twin model, a monitoring unit and an interaction unit, the monitoring unit is used for collecting a positioning point uploaded by the terminal and a thermal sensing image uploaded by the thermal infrared imager, and sending the positioning point and the thermal sensing image to the digital twinborn model; the digital twin model is used for receiving the positioning point and the thermal sensing graph, simulating the activity of the tourist based on the positioning point and the thermal sensing graph, determining the visiting position of the tourist, and sending a starting instruction to the interaction unit when the visiting position coincides with a preset target visiting area; and the interaction unit is connected with the display equipment and is used for starting the display equipment in response to the received starting instruction.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a smart cultural tourism digital twin interactive system. Background Technology

[0002] During their visits to indoor cultural and tourism venues, tourists mainly rely on textual explanations. For example, when visiting museums, science and technology museums, lava caves, etc., tourists learn about the relevant background by reading the textual explanations on the display boards. However, due to the monotony of the text, many tourists choose not to read it. Due to the lack of background knowledge, visitors cannot gain a deeper understanding of the exhibits, such as cultural relics and landform features, resulting in low visitor interest and a low experience. Furthermore, due to the attractiveness of the exhibits or human guidance, abnormal crowd gatherings often occur in certain areas, affecting the visitor experience and posing safety hazards. Existing crowd gathering monitoring and management methods have the following limitations: (1) Manual monitoring and patrols rely on the passive response of security personnel, which has problems such as lag, human fatigue, and blind spots in monitoring; (2) Visual monitoring systems based on simple threshold alarms lack flexibility, making it difficult to distinguish between normal queuing and abnormal gatherings, and are easily affected by obstruction and changes in light, resulting in a high false alarm rate; (3) Passive sensors (such as infrared or pressure sensors) can only provide rough crowd density data and cannot accurately locate the core of the gathering or analyze dynamic trends. Furthermore, existing technologies lack automated intervention methods, relying solely on manual persuasion and evacuation, which is inefficient and difficult to control in real time, further exacerbating the congestion problem in hotspot areas. Therefore, improving the visitor experience of indoor attractions and automatically managing abnormally large crowds of tourists have become issues that need to be addressed. Summary of the Invention

[0003] In view of this, this application provides a smart cultural tourism digital twin interactive system to improve the visitor experience of indoor scenes and automatically guide abnormally crowded tourists.

[0004] This application provides a smart cultural tourism digital twin interactive system, which includes: a digital twin model, a monitoring unit, and an interaction unit; wherein, The monitoring unit is used to collect the positioning points uploaded by the terminal and the thermal sensing images uploaded by the infrared thermal imager, and send the positioning points and thermal sensing images to the digital twin model. The digital twin model is used to receive location points and thermal sensing maps. Based on the location points and thermal sensing maps, the activities of tourists are simulated to determine the tourist's visiting location. When the visiting location coincides with the pre-set target visiting area, a start command is sent to the interactive unit. The interactive unit connects to the display device and is used to activate the display device in response to a received activation command.

[0005] In one possible implementation of the first aspect, the smart cultural tourism digital twin interactive system further includes a twin model construction unit; the twin model construction unit is used to collect venue structure point cloud data and environmental parameters, perform semantic annotation on key areas in the point cloud data, and construct a digital twin model based on the annotated point cloud data and environmental parameters.

[0006] In one possible implementation of the first aspect, the display device connected to the interactive unit is at least one of the following devices: a display screen, a projector, a lighting device, a sound-generating device, a haptic feedback device, a thermostat, and an odor-generating device.

[0007] In one possible implementation of the first aspect, the digital twin model is also used to identify tourist gathering areas in the site to be monitored, and to send an alert to the host computer when an abnormal tourist gathering area is identified.

[0008] In one possible implementation of the first aspect, the process by which the digital twin model identifies tourist gathering areas in the site to be monitored is as follows: Adjust the neighborhood radius and minimum neighborhood sample size based on the number of location points; The clustering algorithm is run at unit time steps to obtain clusters in the time series. Compare clusters at adjacent times to determine whether clusters at adjacent times belong to the same cluster; Update the cluster lifetime based on the judgment results of neighboring clusters; The existence of anomalous clustering areas can be identified based on the cluster's lifetime.

[0009] In one possible implementation of the first aspect, the smart cultural tourism digital twin interactive system also includes a prediction unit; The digital twin model is also used to extract multiple location points in the abnormal cluster area and determine the corresponding area information of the abnormal cluster area in the site to be monitored; the area information includes the display cases within the neighborhood of the location point, the equipment within the neighborhood of the location point, and special locations within the neighborhood of the location point. The prediction unit has a built-in crowd diffusion model, which is used to input multiple location points in the target cluster into the crowd diffusion model, retrieve the historical location points uploaded by the corresponding terminals of multiple location points, and input the historical location points of the corresponding terminals of each location point into the driving route prediction model. The driving route prediction model calculates based on the historical location points of the corresponding terminals of each location point to predict the development trend of the target cluster.

[0010] In one possible implementation of the first aspect, the prediction unit sends a warning message to the host computer when the development trend of the target cluster is in a clustering state.

[0011] In one possible implementation of the first aspect, the smart cultural tourism digital twin interactive system further includes an execution unit; The prediction unit is also used to determine the target display case lighting device based on the information of the area corresponding to the abnormal cluster in the site to be monitored when the development trend of the predicted target cluster is to converge, generate an execution instruction for the target display case lighting device, and send the execution instruction to the execution unit. The execution unit is used to respond to execution commands and control the display case lighting device to be powered on.

[0012] In one possible implementation of the first aspect, the monitoring unit is communicatively connected to a terminal and an infrared imager.

[0013] In one possible implementation of the first aspect, the digital twin model is also used to obtain the terminal number of the terminal corresponding to the location point in the abnormal gathering area when an abnormal gathering area of ​​tourists is identified, and to send a prompt message to the terminal.

[0014] Compared with existing technologies, this application provides a smart cultural tourism digital twin interactive system, the beneficial effects of which are as follows: The embodiments of the present invention construct a digital twin model for simulating the tourist activity process in a virtual environment, accurately reproducing and analyzing the tourist activity trajectory in the site to be monitored, and then accurately identifying the tourist location through the digital twin model and determining the matching status of the tourist location with the target area. When the tourist location matches the target area, the interactive device is automatically triggered, realizing intelligent interactive response without human intervention. This not only improves the immediacy and accuracy of the tourist experience, but also optimizes the energy efficiency of equipment operation, providing data-driven automated management capabilities for venue operation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the composition of the smart cultural tourism digital twin interactive system proposed in the embodiments of this application; Figure 2 This is a schematic diagram illustrating the connection relationship of an example interactive unit in this application; Figure 3 This is a schematic diagram illustrating the composition of another smart cultural tourism digital twin interactive system proposed in an embodiment of this application; Figure 4 This is a schematic diagram of an abnormal clustering region in one example of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0018] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0019] Digital twins encompass a technological system that utilizes Internet of Things (IoT) sensors, edge computing, and other sensors to collect real-time operational data from physical entities. A virtual model is then constructed based on physical laws, data-driven approaches, or hybrid modeling methods. This virtual model maps the operational data of the physical entity to the virtual model, driving its dynamic evolution. The virtual model continuously interacts with the physical entity, receiving real-time updates and optimizations from the physical entity's data, thus using simulation predictions to guide the physical entity.

[0020] In cultural and tourism settings, indoor tourist venues (such as museums, science and technology museums, and lava caves) often experience sudden gatherings of people in localized areas due to their enclosed spaces and the attractiveness of their exhibits. These gatherings are typically driven by exhibit highlights, spontaneous visitor lingering, or deliberate manipulation. Furthermore, visitors can only learn about the exhibits through textual descriptions and cannot physically immerse themselves in the exhibits' historical context and stories through multiple senses. Currently, the monitoring and management of indoor crowd gatherings primarily relies on the following methods: Manual monitoring and patrols: Most venues rely on security personnel to view surveillance videos in real time or patrol the site to identify abnormal gatherings. This method has obvious lag and can only respond passively after the gathering scale expands or events such as pushing and conflict occur. Moreover, it is limited by human fatigue and monitoring blind spots, making it difficult to achieve efficient 24 / 7 coverage.

[0021] Monitoring systems based on simple threshold alarms: Some venues use visual models to analyze video and trigger alarms by identifying targets or setting thresholds for the number of people in a given area. However, such systems lack flexibility, cannot distinguish between normal queuing and abnormal gatherings (such as thieves deliberately blocking the way), and are easily affected by factors such as obstruction and changes in lighting, resulting in a high false alarm rate.

[0022] Passive sensor detection: Infrared or pressure sensors are deployed in a few places to count the density of people, but they can only provide rough data on the number of people in the area and cannot accurately locate the core location of the crowd or analyze the dynamic trend of the crowd.

[0023] None of the above methods can implement automatic intervention or guidance; the only way to disperse the crowd is through manual persuasion.

[0024] In view of this, this application provides a smart cultural tourism digital twin interactive system, which establishes a digital twin model of the site to be monitored through digital twin technology. The digital twin model receives data collected by other sensing devices, simulates the scene of crowds in the site and the interaction between crowds and site equipment, and controls the site to execute equipment response commands based on the simulation results, adjusts the state of the site to be monitored, and guides the crowd with different state of the site to be monitored.

[0025] Figure 1 This is a schematic diagram illustrating the composition of the smart cultural tourism digital twin interactive system proposed in the embodiments of this application, as shown below. Figure 1 As shown, the smart cultural tourism digital twin interactive system includes a digital twin model, a monitoring unit, and an interaction unit.

[0026] The digital twin model is set up on a central server, but local servers, cloud servers, edge servers, etc., can all serve as the central server for this application. The smart cultural tourism digital twin interactive system also includes a twin model construction unit, which is used to collect point cloud data of venue structure and environmental parameters, perform semantic annotation on key areas in the point cloud data, and construct a digital twin model based on the annotated point cloud data and environmental parameters.

[0027] In one example of this application, the process of constructing a digital twin model may include: The data acquisition unit is used to execute K1 and acquire point cloud data of the site to be monitored using LiDAR scanning, including the structural dimensions of walls, columns, stairs, display cases, seats, interactive devices, etc. The site to be monitored generally refers to the exhibition hall of a museum, the visiting rooms of a science and technology museum, etc.

[0028] The data acquisition unit is also used to execute K2 and collect environmental data through sensors; it uses a photoresistor to collect the light intensity of the site under monitoring at different times, a temperature sensor to collect the temperature of the site under monitoring at different times, and a humidity sensor to collect the humidity of the site under monitoring at different times.

[0029] The data processing unit is used to execute K3: to annotate key areas in the point cloud data. Key areas include exits, entrances, emergency exits, and popular display areas of the site to be monitored.

[0030] The model building unit executes K4: it imports labeled point cloud data and environmental data into the 3D engine (3DEngine) to create an initial digital twin model of the site to be monitored. Links are set for key locations, such as popular display areas, which can redirect to a database storage location. This storage location stores the correspondence between key locations and the set programs. The digital twin model reserves a terminal data interface to integrate real-time pedestrian flow data during actual operation.

[0031] The data acquisition unit is also used to perform K5: collecting pedestrian flow samples: heat map analysis of tourist resident areas, using the average dwell time of tourists in front of each display case, and statistically analyzing the passenger flow within a specific time period from the ticketing system, ticket gates, and video data, such as statistically analyzing the hourly passenger flow of the site to be monitored.

[0032] The data processing unit is also used to execute K6: setting virtual human attributes, including movement parameters, such as an adult's movement speed of 1.2 m / s and a child's movement speed of 0.8 m / s; setting virtual human behavior logic, such as selecting and setting the passenger flow ratio, dwelling behavior, and dwelling time for each route based on the passenger flow history path; setting group interaction modes, such as tour guide group follow mode, family group follow mode, and group gathering; and setting visitor behavior modes, such as moving to the display case, dwelling, and taking photos.

[0033] The model building unit is also used to execute K7: using built-in tools such as MetaHuman Creator and StableDiffusion, it generates simulated crowds based on pre-set virtual human attributes.

[0034] The model building unit is also used to execute K8: using the crowd simulation tool AnyLogic / NetLogo, based on pre-set information such as tourist behavior patterns and virtual human behavior logic, it simulates crowd behavior and obtains a digital twin model that can simulate crowd movement based on parameters such as dwelling behavior, dwelling time, and group aggregation.

[0035] In one embodiment of this application, the smart cultural tourism digital twin interactive system further includes a twin model construction unit, which is used to perform the above-mentioned steps K1-K8, construct a virtual model corresponding to the site to be monitored, synchronize the site to be monitored and the status and behavior of tourists in the site to be monitored in real time, simulate the tourist activity process in the virtual environment, and accurately reproduce and analyze the tourist activity trajectory in the site to be monitored.

[0036] The aforementioned digital twin model integrated into the central server has a reserved API interface, which can obtain real-time pedestrian data collected by the monitoring unit. Based on the real-time pedestrian data collected by the monitoring unit, the physical world pedestrian data is mapped to the digital model of the site to be monitored, and the dynamic trajectory of tourists is monitored through the digital model.

[0037] The monitoring unit collects location points uploaded by the terminal and thermal imagery uploaded by the infrared thermal imager, and sends the location points and thermal imagery to the digital twin model. The location points and thermal imagery can represent real-time pedestrian data.

[0038] The monitoring unit includes a receiver and monitoring equipment located on a central server. The monitoring equipment includes an infrared thermal imager and a terminal. The monitoring equipment, such as a wristband, mobile phone, or infrared thermal imager, sends its own identifier to the receiver via the TCP / IP protocol. After verifying the identity of the terminal, the receiver establishes a connection with the terminal, thereby receiving the location points uploaded by the terminal.

[0039] For example, when visitors enter a museum, staff distribute wristbands with built-in Global Navigation Satellite System (GNSS) and Radio Frequency Identification (RFID) modules. The RFID system collects data such as visitor dwell time and route. Simultaneously, each artifact is associated with a unique RFID tag. When a visitor wearing the RFID wristband approaches an exhibit, it automatically triggers voice or video explanations or interactive content, controls access to the artifact, and activates an alarm if the artifact is moved unauthorizedly to prevent theft. The wristband is pre-connected to a receiver, uploading its location via the GNSS module. The receiver then inputs this location into a digital twin model, recreating the visitor's real-time location.

[0040] The digital twin model receives location points and thermal imaging data. Based on these, it simulates visitor activities to determine the visitor's location. When the visitor's location coincides with a pre-defined target visit area, it sends a start command to the interactive unit. The start command carries a tag corresponding to a predetermined procedure. For example, the digital twin model determines that the visitor's location matches the target visit area 1 and generates a start command carrying a tag corresponding to the predetermined procedure 1 for visit area 1.

[0041] Staff can also use the digital twin model to view the distribution of visitors in the museum in real time.

[0042] The interactive unit connects to the display equipment and responds to the received start command to activate the display equipment and enable interaction with visitors.

[0043] Figure 2This is a schematic diagram illustrating the connection relationship of an example interactive unit in this application, such as... Figure 2 As shown, displays, projectors, lighting equipment, sound-generating devices, haptic feedback devices, temperature controllers, and odor generators can all be used as display equipment. These display devices are configured according to the interactive needs of different visitor areas.

[0044] For example, the digital twin model identifies the visitor's location in real time. When the visitor's location overlaps with the Dunhuang mural exhibition area, a trigger signal is generated according to a predetermined procedure 1. The trigger signal is sent to the projector at the corresponding location, controlling the projector to start displaying customized content. The activation command carries a tag from the predetermined procedure.

[0045] For example, the digital twin model identifies the location of tourists in real time. When the tourist's location coincides with the cultural relics exhibition area of ​​the Silk Road, a trigger signal is generated according to the predetermined procedure 3. The trigger signal is sent to the sound device and the odor generating device at the corresponding location. The sound device can simulate the sound of carriages and horses, and the odor generating device can release the aroma of substances such as tea polyphenols and theanine to imitate the taste of tea.

[0046] In the above process, the information flow is as follows: the monitoring unit receives the location points uploaded by the terminal and the thermal sensing maps uploaded by the infrared thermal imager, and sends the location points and thermal sensing maps to the digital twin model; the digital twin model simulates the visitor's activities based on the location points and thermal sensing maps, determines the visitor's location, and when the obtained location coincides with the pre-set target visit area, sends a start command to the interactive unit. The interactive unit responds to the received start command, activates the display equipment, and realizes interaction with the visitor.

[0047] This invention constructs a digital twin model to accurately reproduce and analyze the activity trajectories of tourists in a virtual environment, simulating their activities. The digital twin model then accurately identifies tourist locations and determines the matching status between tourist locations and target areas. When a tourist's location matches the target area, interactive devices are automatically triggered, achieving intelligent interactive responses without human intervention. This improves the immediacy and accuracy of the tourist experience while optimizing equipment operating efficiency, providing data-driven automated management capabilities for venue operations.

[0048] In simulating visitor activities and determining visitor locations, digital twin models periodically perform clustering calculations on location points uploaded from different terminals to identify visitor gathering areas in the monitored area, promptly detecting abnormal gathering phenomena such as localized overcrowding or suspicious lingering, and providing data support for safety warnings, personnel evacuation, and interactive resource scheduling.

[0049] The engine of the digital twin model can integrate machine learning libraries, such as scikit-learn and TensorFlow, and call the density clustering algorithm (DBSCAN) to calculate the location points: by setting the parameters of neighborhood radius (eps) and minimum number of neighborhood samples (min_samples), it can automatically identify dense clusters and anomalous outliers, and identify tourist gathering areas in the site to be monitored.

[0050] The process of using a digital twin model to identify tourist gathering areas in a monitored site is as follows: S1: Adjust the neighborhood radius and minimum number of neighborhood samples based on the number of location points.

[0051] For example, if the number of location points received by the digital twin model in detection time T is greater than the threshold Q, the neighborhood radius is set to 0.5 and the minimum number of neighborhood samples is 6; if the number of location points received in detection time T is less than the threshold Q, the neighborhood radius is set to 0.3 and the minimum number of neighborhood samples is 3.

[0052] S2: Run the clustering algorithm at unit time steps to obtain the clustering results on the time series.

[0053] The clustering structure is the clusters obtained from clustering.

[0054] For example, assuming a unit time step of t, the clustering structure obtained by performing clustering calculations k times can be represented as: (1); Each calculation starts from a certain location point and continuously expands to the density-accessible region, thus obtaining a maximized region containing the core point and boundary points, where any two points in the region are density-connected.

[0055] S3: Compare clusters at adjacent times to determine whether the corresponding clusters at adjacent times are the same cluster.

[0056] Specifically, similarity calculations can be used to determine whether clusters corresponding to adjacent time points belong to the same cluster: For example, comparing using the Jaccard index. and Similarity, The clusters are obtained by time i clustering. The clusters obtained by clustering at time i+t are calculated using the following formula: (2); like and The similarity is greater than θ, indicating that yes The continuation cluster, if and If the similarity is less than or equal to θ, it means... and They are different clusters.

[0057] Understandably, if and If they are different clusters, then the population cluster corresponding to the cluster existing at time i has already disappeared at time i+t. yes If a cluster is a continuation cluster, then the cluster that exists at time i corresponds to a population cluster that continues to exist until time i+t.

[0058] S4: Update the cluster lifetime based on the judgment results of neighboring clusters.

[0059] For example, if for Calculations are performed on every two adjacent clusters to obtain the newly generated time 2. Continue until It still exists. The survival time is k-2. If k-2 is greater than the time threshold, then... There is a risk of abnormal clustering of the corresponding groups of people.

[0060] The time threshold can be set according to the actual exhibits in the monitoring site corresponding to the crowd gathering area.

[0061] S5: Identify the existence of abnormal clustering areas based on the cluster's lifetime and send a warning message to the host computer.

[0062] The host computer can be the computer terminal in the monitoring room, which can promptly detect areas where people gather and issue alarm signals.

[0063] For example, the digital twin model determines the cluster's location as exhibition hall 1 based on location information, obtains the average dwell time of visitors in each exhibition area of ​​exhibition hall 1 as U, and sets a time threshold of U. Clusters with a lifespan greater than U are identified as anomalous clustering regions.

[0064] Clusters whose survival time exceeds the time threshold are designated as target clusters, and the terminal holders corresponding to the location points within the target clusters are considered abnormally clustered groups.

[0065] The digital twin model is also used to obtain the terminal number of the terminal corresponding to the location point in the abnormal gathering area when an abnormal gathering area is identified, and send a prompt message to the terminal to guide the crowd to disperse.

[0066] The message could be: There is relevant information to be displayed ahead.

[0067] As can be seen from the above, the digital twin model is specifically used to periodically perform clustering calculations on the location points uploaded by the terminal, calculate the similarity of clustering results at adjacent times, determine whether there are clusters that have not dissipated for a long time, thereby determining whether there are abnormal crowds. If there are abnormal crowds, an alarm is triggered, thus realizing real-time identification and early warning of abnormal crowds gathering in the venue (such as prolonged stays or suspicious gatherings), providing accurate decision support for safety management.

[0068] Figure 3 This is a schematic diagram illustrating the composition of another smart cultural tourism digital twin interactive system proposed in this application embodiment, with reference to... Figure 3 The smart cultural tourism digital twin interactive system also includes a prediction unit; The digital twin model is also used to extract multiple location points in the abnormal cluster area and determine the corresponding area information of the abnormal cluster area in the site to be monitored; the area information includes display cases within the neighborhood of the location point, equipment within the neighborhood of the location point, and special locations within the neighborhood of the location point.

[0069] Assuming the location point is (x, y), then the neighborhood of this location point is (R + r, w), where R = w=arttan For example, fire exits and restroom entrances can be designated as special locations.

[0070] The neighborhood range can usually be calculated based on the center point of the cluster corresponding to the anomalous clustering area.

[0071] The prediction unit has a built-in crowd diffusion model. Multiple location points in the target cluster are input into the crowd diffusion model. The prediction unit retrieves the historical location points uploaded by the terminals corresponding to the multiple location points. The historical location points of the terminals corresponding to each location point are input into the driving route prediction model. The driving route prediction model calculates based on the historical location points of the terminals corresponding to each location point to predict the driving trend of the terminals corresponding to each location point at future times.

[0072] The driving route prediction model is built based on a hidden Markov model (HMM), which captures the temporal dependency between location points and historical time to obtain the historical driving process of the terminal. The historical driving process of the terminal is modeled as a sequence of hidden states. The state transition matrix of adjacent hidden states in the hidden state sequence is statistically analyzed. The state transition matrix is ​​used to calculate the location points in the target cluster and predict the position of the location points in the target cluster in the future time, thereby predicting the development trend of the target cluster, that is, predicting the driving trend of the terminal corresponding to the location points in the target cluster.

[0073] If the future position of the terminal corresponding to the location point in the target cluster moves away from the center of the target cluster compared to the original position of the location point in the target cluster, the development trend of the target cluster is determined to be dispersion; if the future position of the terminal corresponding to the location point in the target cluster moves closer to the center of the target cluster compared to the original position of the location point in the target cluster, or remains unchanged or approximately unchanged compared to the original position of the location point in the target cluster, the development trend of the target cluster is determined to be convergence.

[0074] Understandably, if the trend of the target cluster is to move closer together, it means that the abnormal gathering of people will continue, and it is necessary to take timely measures to guide the crowd to disperse. If the trend of the target cluster is to move away, then it is not necessary to spend too many resources to excessively interfere with tourists.

[0075] The prediction unit is used to obtain the historical driving routes of the terminals corresponding to the location points in the target cluster based on the location point information in the target cluster, and predict the development trend of the target cluster based on the historical driving routes.

[0076] If the development trend of the target cluster is to cluster together, the prediction unit sends a warning message to the host computer.

[0077] The first alarm message is sent by the digital twin model when it identifies an abnormal clustering area. The second alarm message is sent by the prediction unit when it determines that the development trend of the target cluster is to converge. The two alarm messages correspond to the development trend of the abnormal clustering area becoming unfavorable. Based on the two alarms from the digital twin model and the prediction unit, the monitoring results of the development trend of the abnormal clustering area are displayed to the host computer.

[0078] Continue to refer to Figure 3 The smart cultural tourism digital twin interactive system also includes an execution unit, which includes a display case lighting device. The display case lighting device can be a high color rendering index LED module, a 4K laser projection module, or other lighting devices used to create an atmosphere.

[0079] When the predicted development trend of the target cluster is clustering, the prediction unit determines the target display case lighting device based on the information of the corresponding area of ​​the abnormal clustering area in the site to be monitored, generates an execution command for the target display case lighting device, and sends the execution command to the execution unit.

[0080] Figure 4 This is a schematic diagram of an abnormal clustering region in one example of this application, such as... Figure 4 As shown, a neighborhood range of 1 is calculated based on the center position point of the cluster corresponding to the abnormal clustering area. The display cases within the neighborhood range of 1 are designated display cases, the display cases adjacent to the designated display cases are target display cases, and the lighting devices installed in the target display cases are target display case lighting devices.

[0081] The execution unit responds to execution commands by energizing the display case lighting to attract crowds to other display cases. For example... Figure 4 As shown, the lighting fixtures of the target display case are powered on to create a special display effect, which can guide the crowd to disperse and solve the problem of people gathering for a long time in abnormally crowded areas.

[0082] This application embodiment uses a digital twin model to simulate abnormal gathering areas. A prediction unit retrieves historical trajectories from relevant terminals within the abnormal gathering area to predict its development trend. Finally, based on this trend, an execution unit is notified to execute instructions. This process links prediction and execution mechanisms. When the prediction unit detects that an abnormal gathering of visitors in a specific display case will not disperse in the future, it generates an execution instruction and sends it to the execution unit. This triggers the target display case's lighting device to activate special lighting effects, using visual appeal to guide the gathered visitors to other display cases, thereby achieving balanced visitor flow and safe crowd control. This function, through a closed-loop control of "monitoring-prediction-intervention," optimizes visitor distribution while ensuring a pleasant viewing experience.

[0083] Based on the smart cultural tourism digital twin interactive system provided in Embodiment 1 of this application, correspondingly, Embodiment 2 of this application also provides a smart cultural tourism digital twin interactive method, applied to the smart cultural tourism digital twin interactive system, the method including: S101: The monitoring unit collects the positioning points uploaded by the terminal and the thermal sensing images uploaded by the infrared thermal imager, and sends the positioning points and thermal sensing images to the digital twin model. S102: Receives location points and thermal sensing maps through a digital twin model, simulates visitor activities based on location points and thermal sensing maps, determines visitor's location, and sends a start command to the interactive unit when the visitor's location coincides with a pre-set target visit area. S103: The interactive unit connects to the display device and starts the display device by responding to the received start command through the interactive unit.

[0084] In another embodiment, the smart cultural tourism digital twin interaction method further includes: S104: Extract multiple location points in the abnormal cluster area through a digital twin model to determine the corresponding area information of the abnormal cluster area in the site to be monitored; the area information includes display cases within the neighborhood of the location point, equipment within the neighborhood of the location point, and special locations within the neighborhood of the location point.

[0085] S105: The prediction unit has a built-in crowd diffusion model. The prediction unit inputs multiple location points in the target cluster into the crowd diffusion model, retrieves the historical location points uploaded by the terminals corresponding to the multiple location points, and inputs the historical location points of the terminals corresponding to each location point into the driving route prediction model. The driving route prediction model calculates based on the historical location points of the terminals corresponding to each location point to predict the development trend of the target cluster.

[0086] S106: When the prediction unit predicts that the development trend of the target cluster is to converge, it determines the target display case lighting device based on the information of the corresponding area of ​​the abnormal clustering area in the site to be monitored, generates an execution command for the target display case lighting device, and sends the execution command to the execution unit. S107: The execution unit responds to the execution command and controls the display case lighting device to be powered on.

[0087] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computing software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0088] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0089] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A smart cultural tourism digital twin interactive system, characterized in that, The smart cultural tourism digital twin interactive system includes: a digital twin model, a monitoring unit, and an interaction unit; wherein, The monitoring unit is used to collect the positioning points uploaded by the terminal and the thermal sensing images uploaded by the infrared thermal imager, and send the positioning points and thermal sensing images to the digital twin model. The digital twin model is used to receive location points and thermal sensing maps. Based on the location points and thermal sensing maps, the activities of tourists are simulated to determine the tourist's visiting location. When the visiting location coincides with the pre-set target visiting area, a start command is sent to the interactive unit. The interactive unit connects to the display device and is used to activate the display device in response to a received activation command.

2. The smart cultural tourism digital twin interactive system according to claim 1, characterized in that, The smart cultural tourism digital twin interactive system also includes a twin model construction unit; the twin model construction unit is used to collect venue structure point cloud data and environmental parameters, perform semantic annotation on key areas in the point cloud data, and construct a digital twin model based on the annotated point cloud data and environmental parameters.

3. The smart cultural tourism digital twin interactive system according to claim 1, characterized in that, The interactive unit is connected to at least one of the following display devices: display screen, projector, lighting equipment, sound-emitting device, haptic feedback device, thermostat, and odor-generating device.

4. The smart cultural tourism digital twin interactive system according to claim 1, characterized in that, The digital twin model is also used to identify tourist gathering areas in the monitored site, and when an abnormal tourist gathering area is identified, an alert message is sent to the host computer.

5. The smart cultural tourism digital twin interactive system according to claim 4, characterized in that, The process by which the digital twin model identifies tourist gathering areas in the monitored site is as follows: Adjust the neighborhood radius and minimum neighborhood sample size based on the number of location points; The clustering algorithm is run at unit time steps to obtain clusters in the time series. Compare clusters at adjacent times to determine whether clusters at adjacent times belong to the same cluster; Update the cluster lifetime based on the judgment results of neighboring clusters; The existence of anomalous clustering areas can be identified based on the cluster's lifetime.

6. The smart cultural tourism digital twin interactive system according to claim 1, characterized in that, The smart cultural tourism digital twin interactive system also includes a prediction unit; The digital twin model is also used to extract multiple location points in the abnormal cluster area and determine the corresponding area information of the abnormal cluster area in the site to be monitored; the area information includes the display cases within the neighborhood of the location point, the equipment within the neighborhood of the location point, and special locations within the neighborhood of the location point. The prediction unit has a built-in crowd diffusion model, which is used to input multiple location points in the target cluster into the crowd diffusion model, retrieve the historical location points uploaded by the corresponding terminals of multiple location points, and input the historical location points of the corresponding terminals of each location point into the driving route prediction model. The driving route prediction model calculates based on the historical location points of the corresponding terminals of each location point to predict the development trend of the target cluster.

7. The smart cultural tourism digital twin interactive system according to claim 1, characterized in that, When the development trend of the target cluster is in a clustering state, the prediction unit sends a warning message to the host computer.

8. The smart cultural tourism digital twin interactive system according to claim 1, characterized in that, The intelligent cultural tourism digital twin interactive system also includes an execution unit; The prediction unit is also used to determine the target display case lighting device based on the information of the area corresponding to the abnormal cluster in the site to be monitored when the development trend of the predicted target cluster is to converge, generate an execution instruction for the target display case lighting device, and send the execution instruction to the execution unit. The execution unit is used to respond to execution commands and control the display case lighting device to be powered on.

9. The smart cultural tourism digital twin interactive system according to claim 1, characterized in that, The monitoring unit is communicatively connected to the terminal and the infrared imager.

10. The smart cultural tourism digital twin interactive system according to claim 4, characterized in that, The digital twin model is also used to obtain the terminal number of the terminal corresponding to the location point in the abnormal gathering area when an abnormal gathering area is identified, and to send a prompt message to the terminal.

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