Intelligent scenic spot ticket dynamic capacity adaptation and verification system perceived by Internet of Things

Abstract

The invention discloses a smart scenic spot ticket dynamic capacity adaptation and verification system sensed by the internet of things, and relates to the field of smart scenic spot internet of things sensing and ticket capacity collaborative intelligent management. The system comprises a multi-modal data acquisition module, a data preprocessing and anonymization module, a feature cluster extraction and dynamic capacity calculation module, a ticket aggregation verification and decision module and a flexible intervention and dynamic optimization module. According to the invention, by deploying the low-power-consumption sensor network, multi-dimensional data acquisition and anonymization processing are realized, and privacy security is guaranteed; static capacity limitation is broken through according to an algorithm, and passenger flow regulation and control accuracy is improved; and meanwhile, the ticketing system is linked to construct a verification mechanism, the influence on tourists is reduced through flexible intervention, effect data is collected, whole-process collaboration is achieved, the operation efficiency of the scenic spot and tourist experience and safety are effectively balanced, and intelligent upgrade of scenic spot management is promoted.

Description

Technical Field

[0001] This invention relates to the field of intelligent management of smart scenic area IoT sensing and ticketing capacity collaboration, specifically to a smart scenic area dynamic ticketing capacity adaptation and verification system based on IoT sensing. Background Technology

[0002] With the rapid development of the smart tourism industry, the scale of scenic area reception continues to expand, and the dynamic fluctuations in tourist flow place higher demands on ticketing management, capacity control, and operational safety. Traditional scenic area management models rely heavily on static capacity standards and manual verification methods, making it difficult to capture real-time patterns of visitor flow changes. This can easily lead to problems such as localized congestion and resource imbalances, affecting both the visitor experience and safety hazards. The widespread adoption of technologies such as the Internet of Things and big data provides technical support for the upgrading of smart scenic areas. There is an urgent need to build an integrated system based on multi-dimensional perception, dynamic capacity adaptation, and accurate ticketing verification to achieve dynamic matching between visitor flow and scenic area capacity, balancing operational efficiency, visitor experience, and safety.

[0003] Existing scenic area management systems have many shortcomings, failing to meet the demands for dynamic and precise management. In terms of data collection, they mostly rely on single-dimensional sensors or identification-based collection devices, resulting in limited data dimensions and a lack of capture of behavioral characteristics such as walking speed and movement trajectory correlations, while also posing a risk of individual privacy leaks. Data processing lacks standardized procedures, and the absence of anonymization and format standardization for group data leads to insufficient data usability and security. Capacity calculations are often based on fixed baseline values, failing to incorporate collaborative characteristics of group behavior, resulting in poor dynamic adaptability. Rigid warning threshold settings cannot respond to real-time changes in visitor flow. Ticket verification and capacity data are disconnected, making it difficult to link authorization validity with real-time visitor capacity verification. Intervention methods are mostly passive controls such as rigid flow restrictions and area closures, lacking flexible guidance mechanisms and closed-loop feedback optimization. This prevents adjustments to core algorithm parameters based on intervention effects, leading to persistently insufficient control accuracy and adaptability, making it difficult to cope with complex and ever-changing scenic area operation scenarios.

[0004] In summary, existing smart scenic area ticketing and capacity management systems suffer from several problems, including limited data collection dimensions, lack of privacy protection, low accuracy in dynamic capacity calculation, insufficient verification of ticketing and visitor flow linkage, and rigid intervention methods without feedback or optimization. These issues prevent them from meeting the actual needs of dynamic scenic area operations, potentially leading to resource waste, poor visitor experience, or accumulated safety risks. Therefore, developing a dynamic capacity adaptation and ticketing aggregation verification system that integrates multimodal non-identification perception, anonymized data processing, and collaborative linkage of group behavior is crucial for improving management efficiency through flexible intervention and closed-loop optimization. Addressing the pain points of existing technologies is an urgent requirement for the high-quality development of smart scenic areas. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an IoT-based smart scenic area ticketing dynamic capacity adaptation and verification system. This system can deploy a low-power sensor network to collect multi-dimensional group biological behavior data in real time. After preprocessing and anonymization, feature cluster data is extracted based on algorithms to achieve intelligent adaptation of dynamic capacity thresholds and early warning thresholds. Simultaneously, it links with the ticketing system to build a verification mechanism, implements flexible intervention, and forms a closed-loop feedback loop of collected data to optimize algorithm parameters, achieving full-process collaboration and improving the precision and dynamism of scenic area management.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a smart scenic spot ticketing dynamic capacity adaptation and verification system based on Internet of Things sensing, the system comprising: a multimodal data acquisition module, a data preprocessing and anonymization module, a feature cluster extraction and dynamic capacity calculation module, a ticketing aggregation verification and decision-making module, and a flexible intervention and dynamic optimization module; The multimodal data acquisition module deploys a low-power non-identifying IoT sensor network in the scenic area to collect group biological behavior data in real time, and transmits the collected group biological behavior data to the data preprocessing and anonymization module; the group biological behavior data includes group walking speed, contour compactness, movement trajectory characteristics, and acoustic intensity changes; The data preprocessing and anonymization module sequentially performs abnormal data removal, data filtering, anonymization, and format standardization on the received group biological behavior data to generate group characteristic data, which is then transmitted to the feature cluster extraction and dynamic capacity calculation module. The feature cluster extraction and dynamic capacity calculation module uses a group behavior synergy aggregation algorithm to aggregate the group characteristic data, extract feature cluster data, and generate unique identifiers. Simultaneously, it uses a synergy linkage dynamic capacity algorithm to calculate the dynamic capacity thresholds and warning thresholds for each area of ​​the scenic area, transmitting the feature cluster data and threshold data to the ticketing aggregation verification and decision-making module. The ticketing aggregation verification and decision-making module: links the authorized data of the scenic spot ticketing system, and combines the received feature cluster data and threshold data to complete the ticketing aggregation verification, generate hierarchical decision instructions and transmit them to the flexible intervention and dynamic optimization module; The flexible intervention and dynamic optimization module executes flexible intervention operations according to the received hierarchical decision instructions, while collecting intervention effect data and feeding it back to the feature cluster extraction and dynamic capacity calculation module to optimize the parameters of the two core algorithms.

[0007] Furthermore, the low-power non-identification IoT sensor network in the multimodal data acquisition module adopts a distributed hierarchical deployment structure, including sensing nodes, aggregation nodes, and gateway nodes deployed at the scenic area entrance, core tourist area, passageway, and edge area. The sensing nodes are composed of environmental sensing sensors, used to collect group biological behavior data and transmit the collected data to the aggregation node in the corresponding area. After the aggregation node performs preliminary aggregation of the data from the sensing nodes in its jurisdiction, it uploads the data to the gateway node through low-power wireless transmission. After data format conversion and protocol adaptation, the gateway node transmits the data to the data preprocessing and anonymization module.

[0008] Furthermore, the data preprocessing and anonymization module sequentially performs abnormal data removal, data filtering, anonymization, and format standardization on the received group biological behavior data to generate group characteristic data. The specific steps are as follows: Abnormal data is removed based on the normal fluctuation range of the group biological behavior data, eliminating invalid data that exceeds the reasonable fluctuation range. The normal fluctuation range of group biological behavior data is: group walking speed 0.3-1.5 m / s, contour compactness 0.1-0.8, trajectory offset in movement trajectory features 0-5 m, and sound pressure level corresponding to acoustic intensity changes 40-85 dB. Data exceeding any of these ranges is judged as abnormal data and removed. Data filtering is performed according to the subsequent feature cluster extraction requirements, retaining valid data for group walking speed, contour compactness, movement trajectory features, and acoustic intensity changes. The filtered valid data is anonymized to remove information associated with individual identities. The anonymized valid data is then format-standardized to unify the data storage format and field types, generating standardized group characteristic data, which is then transmitted to the feature cluster extraction and dynamic capacity calculation module.

[0009] Furthermore, the specific steps of the feature cluster extraction and dynamic capacity calculation module, which uses a group behavior coordination degree aggregation algorithm to aggregate group feature data, extract feature cluster data, and generate unique identifiers, are as follows: Group feature data is grouped according to scenic area zones and data collection time slices; data on group walking speed, contour compactness, movement trajectory characteristics, and acoustic intensity changes within the same area and time slice are grouped into a single set of data to be processed; the group behavior coordination degree is calculated based on the grouped data to be processed using the group behavior coordination degree aggregation algorithm; the consistency and correlation of each data dimension are analyzed to obtain an index of the degree of coordination within the group; each group of data to be processed is aggregated according to the group behavior coordination degree index; multiple groups of data whose coordination degree index meets preset conditions are aggregated to form feature cluster data. The preset conditions refer to the coordination degree thresholds set according to the spatial attributes and safety management requirements of different areas of the scenic area; the feature attributes of each feature cluster are extracted, and a unique feature cluster identifier is generated by combining the corresponding area identifier, time identifier, and clustering sequence number, thus completing the extraction of feature cluster data and the assignment of unique identifiers.

[0010] Furthermore, the formula for the group behavior synergy aggregation algorithm in the feature cluster extraction and dynamic capacity calculation module is as follows: in, For the degree of coordination in group behavior; The number of dimensions in the group characteristic data; For the first Weight coefficients for each feature dimension; For the first Single-dimensional synergy of each feature dimension; For the first Standard deviation of group data under each feature dimension; For the first The mean of group data under each feature dimension; by introducing feature dimension weight coefficients and variation coefficients for normalization, the quantitative fusion of multi-dimensional behavioral data such as group walking speed and contour compactness is realized. It can accurately identify the consistency and correlation of group behavior in the same area and time segment, and adapt to the management needs of different areas in the scenic area through weight allocation, and finally output a standardized coordination index.

[0011] Further, the specific steps of calculating the dynamic capacity threshold and warning threshold of each area in the scenic area by using the synergy-linked dynamic capacity algorithm in the feature cluster extraction and dynamic capacity calculation module are as follows: associate the data of each area in the scenic area with the group feature data, and establish a linkage mapping relationship between the synergy of the group feature data and the area capacity; according to the mapping relationship, combine the group behavior synergy index of the area to determine the adjustment direction of the area capacity, and convert the group behavior synergy index into the basis for adapting and adjusting the area capacity; according to the adjustment basis, combine the synergy-linked dynamic capacity algorithm to perform dynamic adaptation calculation on the benchmark capacity of each area, generate a dynamic capacity threshold that conforms to the group behavior state, and deduce the warning threshold of the corresponding area according to the dynamic capacity threshold, forming a matching relationship between the dynamic capacity threshold and the warning threshold; the dynamic capacity threshold and the warning threshold are determined according to the area type and the group behavior synergy level: when S≥0.7, it is determined as a high synergy level; when 0.3<S<0.7, it is determined as a medium synergy level; when S≤0.3, it is determined as a low synergy level; when the synergy of the core tourist area is high, the dynamic capacity threshold is 30-40 people per 100 square meters, when the synergy is medium, it is 20-30 people per 100 square meters, and when the synergy is low, it is 10-20 people per 100 square meters; when the synergy of the passage area is high, the dynamic capacity threshold is 5-8 people per meter of passage length, when the synergy is medium, it is 3-5 people per meter of passage length, and when the synergy is low, it is 1-3 people per meter of passage length; when the synergy of the edge area is high, the dynamic capacity threshold is 50-60 people per 100 square meters, when the synergy is medium, it is 40-50 people per 100 square meters, and when the synergy is low, it is 30-40 people per 100 square meters; the warning threshold of each area is uniformly set to 80% of the corresponding dynamic capacity threshold.

[0012] Further, the formula of the synergy-linked dynamic capacity algorithm in the feature cluster extraction and dynamic capacity calculation module is as follows: Calculation of dynamic capacity threshold: Calculation of warning threshold: Among them, is the dynamic capacity threshold of the target area in the scenic area; is the warning threshold of the target area; is the benchmark capacity of the target area; is the correction coefficient of the group behavior synergy level; is the area type adaptation coefficient; is the warning threshold proportion coefficient; realizing the scientific calculation of the dynamic capacity threshold and the warning threshold, and ensuring the adaptability of different scenic areas and different areas through parameter modular design.

[0013] Furthermore, the specific steps of the ticketing aggregation verification and decision-making module, which links the authorized data of the scenic area's ticketing system and combines the received feature cluster data and threshold data to complete ticketing aggregation verification and generate hierarchical decision instructions, are as follows: The module links with the scenic area's ticketing system to obtain tourist authorization data for the corresponding time period, including information on the number of authorized visitors, reserved visit areas, and valid entry time periods. Simultaneously, it extracts the area identifier, time identifier, and real-time visitor flow association information from the feature cluster data. Based on the extracted association information, it completes ticketing aggregation verification, verifying the validity of the authorized data, including the validity of the entry time period and the matching of the reserved area. It also compares the real-time visitor flow data associated with the feature cluster data with the dynamic capacity threshold and warning threshold of the corresponding area. Based on the ticketing aggregation verification results, it generates hierarchical decision instructions: when the real-time visitor flow does not reach the warning threshold and all authorized data is valid, a normal passage instruction is generated; when the real-time visitor flow reaches the warning threshold but does not exceed the dynamic capacity threshold, a visitor flow guidance warning instruction is generated; when the real-time visitor flow exceeds the dynamic capacity threshold or invalid authorized data exists, a ticketing control restriction instruction is generated.

[0014] Furthermore, the specific process of collecting intervention effect data and feeding it back to the feature cluster extraction and dynamic capacity calculation module in the flexible intervention and dynamic optimization module is as follows: The dimensions for collecting intervention effect data are defined, including real-time passenger flow data of the target area after intervention, data on changes in group behavior coordination, data on the compatibility between dynamic capacity threshold and actual passenger flow, and data on tourist passage efficiency; The collected intervention effect data is filtered and deduplicated using authorized data collected in real-time by the low-power non-identification IoT sensor network deployed in the scenic area and the ticketing system, eliminating interfering data and retaining data that truly reflects the intervention effect; The processed intervention effect data is fed back to the feature cluster extraction and dynamic capacity calculation module through the data transmission channel within the ticketing system.

[0015] Compared with existing technologies, this IoT-based smart scenic area ticketing dynamic capacity adaptation and verification system has the following advantages: I. This invention utilizes a low-power, non-identifiable IoT sensor network to achieve real-time collection of multi-dimensional group biological behavior data. Combined with a standardized data preprocessing workflow, anonymization is achieved by removing invalid data and filtering key information, enriching the dimensions and effectiveness of data collection while ensuring individual privacy from the source. Based on a group behavior synergy aggregation algorithm, group characteristic data is precisely aggregated to extract uniquely identified feature clusters. Simultaneously, a synergy-linked dynamic capacity algorithm establishes a correlation mapping between group behavior and regional capacity, enabling intelligent adaptation of dynamic capacity thresholds and warning thresholds. This breaks the limitations of traditional static capacity standards, making capacity management more aligned with the real-time operational status of scenic areas, effectively improving the accuracy and timeliness of visitor flow control, and providing scientific and reliable data support for scenic area operational decisions.

[0016] Second, this invention constructs an integrated ticketing aggregation and verification mechanism by linking authorized data, feature cluster data, and threshold data from the scenic area ticketing system. While verifying the validity of authorizations, it also achieves real-time linkage verification of passenger flow and regional capacity, ensuring coordinated and unified ticketing management and capacity control. Flexible intervention operations are executed according to hierarchical decision-making instructions, replacing the traditional rigid control model and reducing the impact on the tourist experience. Simultaneously, by collecting intervention effect data to form a closed-loop feedback, core algorithm parameters are continuously optimized, constantly improving the system's adaptability and control efficiency. Overall, it achieves full-process collaboration of data perception, processing, calculation, verification, intervention, and optimization, effectively balancing scenic area operational efficiency, tourist experience, and safety, and promoting the upgrading of smart scenic area management towards refinement, dynamism, and humanization.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A block diagram of the module composition of a smart scenic area ticketing dynamic capacity adaptation and verification system based on Internet of Things (IoT) sensing. Figure 2 Flowchart for a smart scenic area ticketing dynamic capacity adaptation and verification system based on Internet of Things (IoT) sensing; Figure 3 A schematic diagram of the dual-algorithm data transmission for the feature cluster extraction and dynamic capacity calculation module of the IoT-sensing smart scenic area ticketing dynamic capacity adaptation and verification system. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the specific implementation methods, structures, features, and effects of the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. Embodiment 1: As shown in the accompanying drawings... Figure 1As shown, a distributed, hierarchical deployment structure is used throughout the scenic area. Sensing nodes, aggregation nodes, and gateway nodes are deployed at the entrance, core tourist areas, passageways, and edge areas, respectively. The sensing nodes consist of environmental sensors that can comprehensively cover the main activity areas of tourists, collecting real-time data on the group's biological behavior, including walking speed, contour compactness, movement trajectory characteristics, and changes in acoustic intensity, ensuring the integrity and timeliness of data collection. The collected data is then transmitted to the aggregation node in the corresponding area. The aggregation node performs preliminary aggregation of data from the sensing nodes within its jurisdiction to reduce data transmission redundancy, and then uploads the data to the gateway node via low-power wireless transmission to reduce transmission energy consumption. The gateway node completes data format conversion and protocol adaptation to achieve unified compatibility of different types of data, and transmits the data uniformly to the data preprocessing and anonymization module.

[0021] Based on the normal fluctuation range of group biological behavior data, invalid and abnormal data exceeding the reasonable fluctuation range are eliminated to ensure the reliability of subsequent analysis data. Then, according to the requirements of subsequent feature cluster extraction, valid data such as group walking speed, contour compactness, movement trajectory characteristics, and acoustic intensity changes are selected and retained to focus on core analysis dimensions. The selected valid data is anonymized to remove all information that may be associated with individual identities, ensuring the privacy and security of tourists. The anonymized valid data is then standardized in format to unify the data storage format and field types, ensuring that the data can be smoothly read and processed by subsequent modules, generating standardized group feature data, which is then transmitted to the feature cluster extraction and dynamic capacity calculation module.

[0022] The group characteristic data is grouped and associated according to scenic area zones and data collection time slices, grouping related data within the same area and time slice into a single group for processing, making the data more targeted; subsequently, a group behavior synergy aggregation algorithm is used, with the formula: in, For the degree of coordination in group behavior; The number of dimensions in the group characteristic data; For the first Weight coefficients for each feature dimension; For the first Single-dimensional synergy of each feature dimension; For the first Standard deviation of group data under each feature dimension; For the first The mean of group data under each feature dimension; calculate the group behavior coordination degree based on the grouped data to be processed; obtain the group behavior coordination degree index by analyzing the consistency and correlation of each data dimension to accurately capture the group behavior pattern; then aggregate the data to be processed in each group according to the index; aggregate multiple groups of data that meet the preset conditions to form feature cluster data; extract key group behavior features; extract the feature attributes of each feature cluster data; generate a unique feature cluster identifier by combining the corresponding area identifier, time identifier and clustering number of the feature cluster for easy subsequent data traceability and correlation; at the same time, associate the data of each area in the scenic area with the group feature data to establish a linkage mapping relationship between the coordination degree of group feature data and the area capacity, so that the capacity calculation fits the actual group state; determine the adjustment direction of the area capacity according to the mapping relationship; transform the group behavior coordination degree index into the basis for area capacity adaptation adjustment; then use the coordination degree linkage dynamic capacity algorithm to dynamically adapt and calculate the baseline capacity of each area to generate a dynamic capacity threshold that conforms to the current group behavior state, ensuring the scientific nature of the capacity threshold; at the same time, derive the warning threshold of the corresponding area according to the dynamic capacity threshold to form a matching relationship between the two; dynamic capacity threshold calculation: Early warning threshold calculation: ,in, The dynamic capacity threshold for the target area of ​​the scenic spot; The warning threshold for the target area; The baseline capacity for the target area; This is a correction coefficient for the level of group behavior coordination. For region type adaptation coefficients; This serves as the early warning threshold ratio coefficient, providing a clear standard for subsequent early warnings. Finally, the feature cluster data and threshold data are transmitted to the ticketing aggregation verification and decision-making module. Figure 3 As shown.

[0023] The system integrates with the scenic area's ticketing system to obtain authorized visitor data for peak periods, ensuring the data source's authority. Simultaneously, it extracts regional identifiers, time identifiers, and corresponding real-time visitor flow information from the received feature cluster data, establishing a bridge between ticketing and visitor flow data. Based on this extracted information, it performs ticket aggregation verification to confirm the validity of authorized data and prevent invalid tickets from entering the scenic area. Subsequently, it compares the real-time visitor flow data associated with the feature cluster data with the corresponding area's dynamic capacity threshold and warning threshold to clearly determine whether the current visitor flow exceeds a reasonable range. Based on the ticket aggregation verification results and the comparison between visitor flow and thresholds, it generates tiered decision-making instructions to provide precise guidance for subsequent intervention operations and transmits these instructions to the flexible intervention and dynamic optimization module.

[0024] Based on the received hierarchical decision-making instructions, flexible intervention operations are executed, such as adjusting the display content of guidance signs within the scenic area and issuing diversion prompts via broadcasts, to gently guide the flow of tourists and avoid the negative experience caused by mandatory control. Simultaneously, the dimensions for collecting intervention effect data are clearly defined to ensure the targeted nature of data collection. Intervention effect data is collected in real time through a low-power non-identification IoT sensor network deployed within the scenic area and the ticketing system. The collected data is filtered and deduplicated to remove interfering data, retaining only data that truly reflects the intervention effect and ensuring the accuracy of the feedback data. The processed intervention effect data is fed back to the feature cluster extraction and dynamic capacity calculation module through the data transmission channel within the ticketing system. This module optimizes the parameters of the group behavior coordination degree aggregation algorithm and the coordination degree linkage dynamic capacity algorithm based on the feedback data, making the subsequent system operation more closely aligned with the actual situation of the scenic area and improving overall adaptability and verification accuracy. Example 2: Based on the low-power non-identification IoT sensor network already deployed in the scenic area, this network adopts a distributed, hierarchical deployment structure, including sensing nodes, aggregation nodes, and gateway nodes distributed in the core activity area, surrounding passages, and entrances / exits of activity participants. It can accurately cover key areas related to the activity. The sensing nodes collect real-time group biometric data of participants during the activity, covering group walking speed, contour compactness, movement trajectory characteristics, and acoustic intensity changes, meeting the data collection needs of concentrated visitor flow during the activity. After collection, the data is transmitted to the aggregation node in the corresponding area. The aggregation node performs preliminary aggregation of data from the sensing nodes in its jurisdiction, integrates the scattered data, and then uploads it to the gateway node through low-power wireless transmission, ensuring the stability and low energy consumption of data transmission. The gateway node performs data format conversion and protocol adaptation to achieve unified compatibility and transmission of data, and transmits the data uniformly to the data preprocessing and anonymization module.

[0025] Based on the normal fluctuation range of group biological behavior data, invalid and abnormal data exceeding the reasonable range are removed, and data interference caused by special environmental factors during the activity is excluded to ensure data quality. According to the requirements of feature cluster extraction, effective data such as group walking speed, contour compactness, movement trajectory features, and acoustic intensity changes are selected and retained to focus on the core analysis dimensions of group behavior during the activity. The selected effective data is anonymized to remove all information that may be associated with individual identities, fully protecting the privacy and security of the participants. The anonymized effective data is then standardized in format to unify the data storage format and field types, ensuring that the data can be efficiently processed by subsequent modules to generate standardized group feature data, which is then transmitted to the feature cluster extraction and dynamic capacity calculation module.

[0026] Group characteristic data is grouped and correlated based on activity-related areas and data collection time slots within the scenic area. Related data within the same activity-related area and time slot are grouped together to focus on the activity scenario. A group behavior synergy aggregation algorithm is used to calculate the group behavior synergy based on the grouped data. By analyzing the consistency and correlation of various data dimensions, an index of the degree of synergy within the group is obtained, accurately grasping the specificity of group behavior during the activity. This index is then used to aggregate the data from each group. Multiple groups of data meeting the preset synergy index are aggregated to form feature clusters. Key group behavior characteristics within the activity scenario are extracted, and the feature attributes of each feature cluster are extracted. A unique feature cluster is generated by combining the corresponding area identifier, time identifier, and clustering sequence number. The system uses identification to facilitate data traceability; it also links data from various activity-related areas within the scenic area with group characteristic data, establishing a linkage mapping relationship between the synergy of group characteristic data and regional capacity. This allows capacity calculation to adapt to the activity scenario. Based on the mapping relationship and the specific characteristics of group behavior during the activity, the system determines the direction of regional capacity adjustment, transforming the group behavior synergy index into a basis for regional capacity adaptation adjustment. A synergy-linked dynamic capacity algorithm is used to dynamically adapt and calculate the baseline capacity of each activity-related area, generating a dynamic capacity threshold that matches the group behavior status during the activity. This ensures that the capacity threshold aligns with the characteristics of visitor flow during the activity. Simultaneously, the system derives the warning threshold for the corresponding area based on the dynamic capacity threshold, forming a matching relationship to proactively avoid visitor flow safety risks during the activity. Finally, the feature cluster data and threshold data are transmitted to the ticketing aggregation verification and decision-making module.

[0027] The system integrates with the scenic area's ticketing system to obtain exclusive visitor authorization data for this special event, ensuring the uniqueness and compliance of participation eligibility. Simultaneously, it extracts regional identifiers, time identifiers, and corresponding real-time visitor flow information from the feature cluster data, establishing a link between ticketing data and event visitor flow data. Based on this information, it performs ticket aggregation verification to validate the validity of the event-specific authorization data, preventing unauthorized personnel from entering the event area. Then, it compares the real-time visitor flow data linked to the feature cluster data with the corresponding area's dynamic capacity threshold and warning threshold to promptly grasp the visitor flow carrying capacity of the event area. Combining the ticket aggregation verification results and visitor flow comparison, it generates tiered decision-making instructions to adapt to the intervention needs of the event scenario and transmits these instructions to the flexible intervention and dynamic optimization module.

[0028] Based on the received hierarchical decision-making instructions, flexible intervention operations are executed, adjusting the guidance rhythm of the activity process, opening temporary channels, and issuing visitor flow reminders through dedicated activity notification channels to quickly alleviate concentrated visitor flow during the activity and ensure its orderly conduct. Simultaneously, the dimensions for collecting intervention effect data are clearly defined to ensure data collection aligns with the activity intervention scenario. Intervention effect data is collected in real time through the scenic area's low-power non-identification IoT sensor network and ticketing system. The collected data is filtered and deduplicated to remove interfering data, retaining only the true and valid intervention effect data to provide a reliable basis for algorithm optimization. The processed intervention effect data is fed back to the feature cluster extraction and dynamic capacity calculation module through the internal data transmission channel of the ticketing system. This module optimizes the parameters of the group behavior synergy aggregation algorithm and the synergy linkage dynamic capacity algorithm based on the feedback data. Figure 2 As shown, this allows the system to more accurately achieve capacity adaptation and ticket verification in subsequent similar activities, thereby improving the system's scenario adaptability.

[0029] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A smart scenic area ticketing dynamic capacity adaptation and verification system based on Internet of Things (IoT) sensing, characterized in that: The system includes: a multimodal data acquisition module, a data preprocessing and anonymization module, a feature cluster extraction and dynamic capacity calculation module, a ticket aggregation verification and decision-making module, and a flexible intervention and dynamic optimization module; The multimodal data acquisition module deploys a low-power non-identifying IoT sensor network in the scenic area to collect group biological behavior data in real time, and transmits the collected group biological behavior data to the data preprocessing and anonymization module; the group biological behavior data includes group walking speed, contour compactness, movement trajectory characteristics, and acoustic intensity changes; The data preprocessing and anonymization module performs abnormal data removal, data filtering, anonymization, and format standardization on the received group biological behavior data in sequence, generates group feature data, and transmits it to the feature cluster extraction and dynamic capacity calculation module. The feature cluster extraction and dynamic capacity calculation module uses a group behavior synergy aggregation algorithm to aggregate group feature data, extract feature cluster data and generate unique identifiers; at the same time, it uses a synergy linkage dynamic capacity algorithm to calculate the dynamic capacity threshold and early warning threshold of each area of ​​the scenic spot, and transmits the feature cluster data and threshold data to the ticketing aggregation verification and decision module. The ticketing aggregation verification and decision-making module: links the authorized data of the scenic spot ticketing system, and combines the received feature cluster data and threshold data to complete the ticketing aggregation verification, generate hierarchical decision instructions and transmit them to the flexible intervention and dynamic optimization module; The flexible intervention and dynamic optimization module executes flexible intervention operations according to the received hierarchical decision instructions, while collecting intervention effect data and feeding it back to the feature cluster extraction and dynamic capacity calculation module to optimize the parameters of the two core algorithms.

2. The IoT-based smart scenic area ticketing dynamic capacity adaptation and verification system according to claim 1, characterized in that, The low-power non-identification IoT sensor network in the multimodal data acquisition module adopts a distributed, hierarchical deployment structure, including sensing nodes, aggregation nodes, and gateway nodes deployed at the scenic area entrance, core tourist area, passageways, and edge areas. The sensing nodes consist of environmental sensing sensors used to collect group biological behavior data and transmit the collected data to the aggregation nodes in the corresponding areas. After the aggregation nodes perform preliminary aggregation of the data from the sensing nodes within their jurisdiction, they upload the data to the gateway nodes via low-power wireless transmission. After data format conversion and protocol adaptation, the gateway nodes transmit the data uniformly to the data preprocessing and anonymization module.

3. The IoT-based smart scenic area ticketing dynamic capacity adaptation and verification system according to claim 1, characterized in that, The data preprocessing and anonymization module sequentially performs abnormal data removal, data filtering, anonymization, and format standardization on the received group biological behavior data to generate group characteristic data. The specific steps are as follows: Abnormal data is removed based on the normal fluctuation range of the group biological behavior data, eliminating invalid data exceeding the reasonable fluctuation range; data filtering is performed according to the subsequent feature cluster extraction requirements, retaining valid data such as group walking speed, contour compactness, movement trajectory features, and acoustic intensity changes; the filtered valid data is anonymized to remove information associated with individual identities; the anonymized valid data is then format-standardized to unify the data storage format and field types, generating standardized group characteristic data, which is then transmitted to the feature cluster extraction and dynamic capacity calculation module.

4. The IoT-based smart scenic area ticketing dynamic capacity adaptation and verification system according to claim 1, characterized in that, The specific steps of using the group behavior coordination degree aggregation algorithm in the feature cluster extraction and dynamic capacity calculation module to aggregate group feature data, extract feature cluster data and generate unique identifiers are as follows: group feature data are associated and grouped according to scenic area zoning and data collection time slices, and group walking speed, contour compactness, movement trajectory features and acoustic intensity change data of the same area and the same time slice into a group of data to be processed. A group behavior coordination degree aggregation algorithm is adopted, and the group behavior coordination degree is calculated based on the grouped data to be processed. By analyzing the consistency and correlation of each data dimension, the group behavior coordination degree index is obtained. Based on the group behavior coordination degree index, the data to be processed in each group is aggregated, and multiple groups of data that meet the preset conditions are aggregated to form feature cluster data. Extract the feature attributes of each feature cluster data, and generate a unique feature cluster identifier by combining the region identifier, time identifier and clustering number corresponding to the feature cluster, thus completing the extraction of feature cluster data and the assignment of unique identifier.

5. The IoT-sensing smart scenic area ticketing dynamic capacity adaptation and verification system according to claim 4, characterized in that, The formula for the group behavior synergy aggregation algorithm in the feature cluster extraction and dynamic capacity calculation module is as follows: in, For the degree of coordination in group behavior; The number of dimensions in the group characteristic data; For the first Weight coefficients for each feature dimension; For the first Single-dimensional synergy of each feature dimension; For the first Standard deviation of group data under each feature dimension; For the first The mean of the group data under each feature dimension.

6. The IoT-sensing smart scenic area ticketing dynamic capacity adaptation and verification system according to claim 1, characterized in that, The specific steps of calculating the dynamic capacity thresholds and early warning thresholds for each area of ​​the scenic area using the synergy-linked dynamic capacity algorithm in the feature cluster extraction and dynamic capacity calculation module are as follows: Associate the data of each area within the scenic area with the group characteristic data to establish a linkage mapping relationship between the synergy of the group characteristic data and the area capacity; based on the mapping relationship, determine the adjustment direction of the area capacity by combining the group behavior synergy index of the area, and transform the group behavior synergy index into a basis for area capacity adaptation adjustment; based on the adjustment basis, perform dynamic adaptation calculation of the baseline capacity of each area using the synergy-linked dynamic capacity algorithm to generate a dynamic capacity threshold that conforms to the group behavior state, and derive the early warning threshold for the corresponding area based on the dynamic capacity threshold, forming a matching relationship between the dynamic capacity threshold and the early warning threshold.

7. The IoT-sensing smart scenic area ticketing dynamic capacity adaptation and verification system according to claim 6, characterized in that, The formula for the collaborative dynamic capacity algorithm in the feature cluster extraction and dynamic capacity calculation module is as follows: Dynamic capacity threshold calculation: Early warning threshold calculation: ,in, The dynamic capacity threshold for the target area of ​​the scenic spot; The warning threshold for the target area; The baseline capacity for the target area; This is a correction coefficient for the level of group behavior coordination. For region type adaptation coefficients; This is the proportional coefficient for the early warning threshold.

8. The IoT-sensing smart scenic area ticketing dynamic capacity adaptation and verification system according to claim 1, characterized in that, The specific steps of the ticketing aggregation verification and decision-making module, which links the authorized data of the scenic area's ticketing system and combines the received feature cluster data and threshold data to complete ticketing aggregation verification and generate hierarchical decision instructions, are as follows: The module links the scenic area's ticketing system to obtain tourist authorization data for the corresponding time period, and simultaneously extracts the area identifier, time identifier, and real-time passenger flow association information of the corresponding area from the feature cluster data; it completes ticketing aggregation verification based on the extracted association information, verifies the validity of the authorized data, and compares the real-time passenger flow data associated with the feature cluster data with the dynamic capacity threshold and early warning threshold of the corresponding area; and it generates hierarchical decision instructions based on the ticketing aggregation verification results.

9. The IoT-based smart scenic area ticketing dynamic capacity adaptation and verification system according to claim 1, characterized in that, The specific process of collecting intervention effect data and feeding it back to the feature cluster extraction and dynamic capacity calculation module in the flexible intervention and dynamic optimization module is as follows: The dimensions for collecting intervention effect data are defined; authorized data collected in real time through the low-power non-identification IoT sensor network deployed in the scenic area and the ticketing system is used to filter and deduplicate the collected intervention effect data, removing interfering data and retaining data that truly reflects the intervention effect; the processed intervention effect data is then fed back to the feature cluster extraction and dynamic capacity calculation module through the data transmission channel within the ticketing system.

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