Tourism equipment information management system based on star topology

Abstract

The invention relates to the technical field of information management, and particularly discloses a star topology-based travel equipment information management system, which comprises a central server, at least one host node deployed in each scenic spot of a scenic area, a data processing module, a visual interaction module and a plurality of sub-machine equipment connected with the host node through a short-distance wireless communication protocol, and generating a multi-modal navigation interface according to the route recommendation intensity, and displaying the route trajectory in different colors in an interactive display screen according to the route recommendation intensity difference between different scenic spots. On the basis of a star topology network architecture, the problem of data congestion of a traditional network is solved, the real-time synchronization efficiency of the equipment state and tourist data is improved, higher node expansibility and fault-tolerant ability are achieved, and through the technical fusion of star topology, a dynamic algorithm and digital twinning, the real-time synchronization efficiency of the equipment state and tourist data is improved. The defects of a traditional system in the aspects of real-time performance, personalized recommendation and carrying capacity are overcome, and collaborative optimization of scenic spot resource scheduling efficiency, tourist experience and management decision-making capacity is achieved.

Description

Technical Field

[0001] The present invention relates to the field of information management technology, and in particular to a cultural and tourism equipment information management system based on a star topology. Background Art

[0002] With the rapid development of smart cultural tourism, the demand for intelligent scenic spot information management systems is growing. Traditional scenic spot management systems often use centralized or distributed network architectures, which suffer from high communication latency, poor scalability, and insufficient dynamic response. They fail to fully integrate real-time environmental parameters such as crowd density, attraction capacity, and individual visitor behavior. This results in a poor match between recommended routes and actual needs, making it difficult to dynamically optimize the visitor experience.

[0003] At the data collection level, traditional navigation systems often use two-dimensional maps to display route information, lacking three-dimensional dynamic visualization capabilities and unable to intuitively reflect the real-time status of scenic spots, such as congested areas and the popularity distribution of scenic spots. In addition, existing route recommendation strategies are not effectively integrated with scenic spot operation strategies, and the recognition accuracy of tourists' movement preferences is limited, further reducing the flexibility and adaptability of route recommendations.

[0004] Therefore, there is an urgent need for a cultural and tourism equipment information management system based on star topology to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a cultural tourism equipment information management system based on a star topology, comprising: A central server, at least one host node deployed at each scenic spot in the scenic area, a data processing module, a visual interaction module, and multiple slave devices connected to the host node via a short-range wireless communication protocol; The central server establishes a two-way communication link with all host nodes through a star topology network, and is used to receive the scenic spot equipment status and visitor data uploaded by each host node in real time; Dividing the host node into at least two virtual subnets based on service type, each virtual subnet being used for data transmission of a single service; The sub-device is integrated with a positioning unit and an information collection unit. The positioning unit is used to collect the tourist's location coordinates in real time, and the information collection unit is used to collect the scenic spot environmental parameters and trigger information requests, and upload the data to the central server through the host node; The data processing module is deployed on the central server, establishes a data channel with the host node through the data bus, and is used to receive the positioning information and environmental parameters uploaded by each slave device, including: The digital twin construction unit builds a three-dimensional scenic area topology model based on GIS data, dynamically mapping the status of the neutron equipment and the distribution of crowd density at each scenic spot; Route optimization engine, which uses an improved ant colony algorithm to obtain real-time route recommendation strength; The visualization interaction module includes an interactive display screen, which is used to generate a multimodal navigation interface based on the route recommendation strength obtained by the path optimization engine, and display the path trajectories between scenic spots in different colors on the interactive display screen according to the differences in route recommendation strength between different scenic spots.

[0006] Furthermore, the improved ant colony algorithm is obtained by integrating the historical heat decay value and the dynamic recommendation gain, and the dynamic recommendation gain is obtained by integrating the system recommendation weight, the actual tour time of tourists, the path carrying capacity value, the tourist movement preference and the path matching tolerance; The method for obtaining the historical heat decay value includes: Get the basic popularity value of each attraction; Classify and calibrate the popularity of attractions according to their types, where the types of attractions include at least cultural attractions and entertainment attractions, where cultural attractions correspond to lower popularity decay rates and entertainment attractions correspond to higher popularity decay rates; Dynamically adjust the decay process of the basic popularity of scenic spots through time series analysis; The calculation of the historical popularity decay value further includes dynamically modifying the decay rate according to the real-time behavior data of tourists. For example, when it is detected that the length of time a tourist stays at a certain attraction exceeds a preset threshold, the decay rate of the attraction is temporarily reduced to prolong its recommendation strength. Furthermore, the path optimization engine also includes a recommendation weight dynamic control module preset in the central server, including: A policy database storing weight adjustment rules based on time characteristics, including holiday flags, seasonal codes, and special event information; A neural network correction unit, wherein the input layer receives real-time environmental data and current weight parameters, and the output layer generates a revised recommendation weight. The real-time environmental data includes temperature and humidity sensor data, pedestrian flow statistics, and emergency alarm signals. The weight distribution unit sends the normalized recommendation weights to each host node. The recommendation weights are associated with the personalized tags of tourists. For example, higher weights are assigned to quick-sighted tourists to prioritize the shortest path, while lower weights are assigned to in-depth experience tourists to focus on cultural path recommendations.

[0007] Furthermore, the method for calculating the actual time spent on a tourist's tour includes: Continuously collect visitor movement trajectory data through the positioning unit of the slave device, wherein the movement trajectory data includes a position coordinate sequence and a timestamp; Identify visitors' stay events based on trajectory data. The stay events are determined by speed thresholds. When the movement speed of consecutive positioning points is lower than the preset threshold and the duration exceeds the set range, it is marked as a stay. Classify the types of stop events, including photo stops, rest stops, and interactive device stops, and add a time correction factor based on the stop type; The actual tour time is the sum of the movement time and the corrected stay time, and the time data is smoothed by a sliding window algorithm to eliminate positioning errors; The time-consuming calculation result is uploaded to the central server in real time and used to update the dynamic recommendation gain parameters of the path optimization engine.

[0008] Furthermore, the path carrying capacity evaluation method includes: Obtaining physical width data of scenic spots and real-time crowd flow monitoring data, wherein the physical width is obtained through GIS map annotation or on-site measurement; The real-time human traffic is collected through a camera or infrared sensor connected to the host node; The product of the physical width and the passenger flow per unit time is used as the traffic comfort index. When the index value is lower than the first threshold, it is marked as a congested path; when it is higher than the second threshold, it is marked as a smooth path. The path carrying capacity is dynamically graded according to the real-time comfort index and is inversely correlated with the path recommendation strength; The evaluation results are mapped to the star topology model of the three-dimensional scenic area through the digital twin construction unit, forming a visual heat map for administrators to make reference.

[0009] Furthermore, the method for analyzing tourists' mobility preferences includes: Collect historical visitor data through the sub-device, including route selection records, length of stay distribution, and frequency of use of interactive devices; Based on the clustering algorithm, tourists are divided into two categories: fast-sighted tourists and in-depth tourists. The fast-sighted tourists are characterized by an average movement speed higher than the system threshold and fewer than the set number of stops. The in-depth tourists are characterized by a movement speed lower than the threshold and a cultural label matching degree of the stops higher than the set value. Generate dynamic preference tags for each type of tourist and bind the tags to the route recommendation strategy; The preference tags are synchronized to all host nodes through the central server to ensure the continuity of recommendation strategies when tourists move across attractions.

[0010] Furthermore, the configuration method of the path matching tolerance includes: Preset tolerance benchmark parameters according to scenic area types, where different scenic area types correspond to different benchmark parameter ranges; Dynamically adjust the tolerance parameter based on the preset benchmark parameter range, and the adjustment range is constrained by the real-time traffic data of the scenic area; The tolerance parameter is input into the improved ant colony algorithm, and the path recommendation strength is adjusted by controlling the pheromone volatilization rate.

[0011] Furthermore, the synthesis method of the dynamic recommendation gain includes: Input the system recommendation weight, tourists' actual tour time, path carrying capacity, tourists' movement preference and path matching tolerance into the weighted calculation model; The dynamic recommendation gain is a linear combination of each component and is mapped to a preset value range through normalization processing; The gain calculation results are fed back to the path optimization engine in real time to adjust the distribution of path selection probabilities in the improved ant colony algorithm; the historical heat decay value and the dynamic recommendation gain are integrated according to a preset ratio to obtain the route recommendation strength; Finally, the path display effect of the interactive display screen is controlled according to the route recommendation strength; The route recommendation strength data is sent to the slave device through the host node, and the highlight state of the optimal path is automatically switched according to the real-time location of the tourist.

[0012] Furthermore, the dividing of the host node into at least two virtual subnets based on the service type, each of the virtual subnets being used for data transmission of a single service, includes: Establishing at least two virtual subnets in the central server, and binding the host node to the virtual subnet based on the service type name corresponding to each host node; Formulate access control list rules for each virtual subnet and set priorities for the access control list rules according to business needs; Deploy a firewall on the host node, connect the virtual subnet to different interfaces of the firewall, require all cross-virtual subnet and external network access traffic to pass through the firewall, and set access control policies on the firewall; The traffic data between the virtual subnets is monitored in real time by a network traffic analysis tool, and the division of the virtual subnets, the access control list rules and the access control policy are dynamically adjusted according to the traffic monitoring results and changes in business needs.

[0013] The beneficial effects of this application are: The present invention is based on a star topology network architecture. Through the two-way communication link between the central server and the distributed host nodes, it solves the data congestion problem of traditional centralized / distributed networks, significantly improves the real-time synchronization efficiency of device status and visitor data, and has stronger node scalability and fault tolerance. Through the technical integration of star topology + dynamic algorithm + digital twin, it solves the shortcomings of traditional systems in real-time, personalized recommendation, carrying capacity assessment and visual interaction, and realizes the coordinated optimization of scenic area resource scheduling efficiency, visitor experience and management decision-making capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a schematic diagram of a system proposed in an embodiment of the present application.

[0015] Figure 2 This is a flowchart of a control process based on a virtual subnet architecture proposed in another embodiment of the present application.

[0016] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0017] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] Example 1

[0019] This application provides a cultural tourism equipment information management system based on a star topology, including: A central server, at least one host node deployed at each scenic spot in the scenic area, a data processing module, a visual interaction module, and multiple slave devices connected to the host node via a short-range wireless communication protocol; The central server establishes a two-way communication link with all host nodes through a star topology network, and is used to receive the scenic spot equipment status and visitor data uploaded by each host node in real time; Dividing the host node into at least two virtual subnets based on service type, each virtual subnet being used for data transmission of a single service; The sub-device is integrated with a positioning unit and an information collection unit. The positioning unit is used to collect the tourist's location coordinates in real time, and the information collection unit is used to collect the scenic spot environmental parameters and trigger information requests, and upload the data to the central server through the host node; The data processing module is deployed on the central server, establishes a data channel with the host node through the data bus, and is used to receive the positioning information and environmental parameters uploaded by each slave device, including: The digital twin construction unit builds a three-dimensional scenic area topology model based on GIS data, dynamically mapping the status of the neutron equipment and the distribution of crowd density at each scenic spot; The route optimization engine uses an improved ant colony algorithm to obtain real-time route recommendation strength, where the route recommendation strength can be expressed as express; The visualization interaction module includes an interactive display screen, which is used to generate a multimodal navigation interface based on the route recommendation strength obtained by the path optimization engine, and display the path trajectories between scenic spots in different colors on the interactive display screen according to the differences in route recommendation strength between different scenic spots.

[0020] The present invention is based on a star topology network architecture. Through the two-way communication link between the central server and the distributed host nodes, it solves the data congestion problem of traditional centralized / distributed networks, significantly improves the real-time synchronization efficiency of device status and visitor data, and has stronger node scalability and fault tolerance. Through the technical integration of star topology + dynamic algorithm + digital twin, it solves the shortcomings of traditional systems in real-time, personalized recommendation, carrying capacity assessment and visual interaction, and realizes the coordinated optimization of scenic area resource scheduling efficiency, visitor experience and management decision-making capabilities.

[0021] By using technologies such as sliding window algorithms to eliminate positioning errors, time series analysis to dynamically adjust the heat decay rate, and automatic optimization of path matching tolerance, we can achieve closed-loop optimization of the data collection-analysis-decision-making chain, significantly improving the system's anti-interference ability and the robustness of the recommendation results.

[0022] Furthermore, the improved ant colony algorithm is obtained by integrating the historical heat decay value and the dynamic recommendation gain, wherein the historical heat decay value can be represented by α, and the dynamic recommendation gain is obtained by integrating the system recommendation weight, the actual tour time of tourists, the path carrying capacity value, the tourist movement preference and the path matching tolerance; The method for obtaining the historical heat decay value includes: Get the basic heat value of each scenic spot. The basic heat value of the scenic spot can be used express; The popularity of attractions is classified and calibrated according to the type of attraction, and the attraction type includes at least cultural attractions and entertainment attractions; cultural attractions correspond to a lower heat decay rate, and entertainment attractions correspond to a higher heat decay rate, for example, α for cultural attractions is 0.1, and α for entertainment attractions is 0.3.

[0023] Dynamically adjust the decay process of the basic popularity of attractions through time series analysis. Specifically, the decay rate of the basic popularity of attractions is periodically calculated based on preset time intervals, where the decay rate is associated with the type of attraction. For example, the decay rate of cultural attractions is set to a fixed value, and the decay rate of entertainment attractions is set to another fixed value. The calculation of the historical popularity decay value further includes dynamically modifying the decay rate based on the real-time behavior data of tourists. For example, when it is detected that the length of time a tourist stays at a certain attraction exceeds a preset threshold, the decay rate of the attraction is temporarily reduced to prolong its recommendation strength; The initial value of the basic popularity of the attraction is derived from historical tourist flow statistics and attraction attribute labels, and the initial value is optimized through a machine learning model to ensure the consistency of the basic popularity with the actual preferences of tourists.

[0024] By integrating positioning and multimodal sensor sub-devices, combining stop event recognition (determined by speed threshold) and tourist classification (through clustering algorithms, which can be divided into quick tour types and in-depth tour types), we can build a refined tourist behavior portrait, provide high-granularity data support for dynamic recommendations, and enhance personalized service capabilities.

[0025] The improved ant colony algorithm in this invention integrates historical heat decay values (which can dynamically correct the popularity of scenic spots), real-time environmental parameters (including path carrying capacity and passenger flow), and tourists' personalized preferences. Compared with the limitations of traditional static path planning, it can achieve dynamic adjustment of route recommendation intensity according to the scenario, thereby improving the matching degree between recommendation results and actual needs.

[0026] The path optimization engine also includes a recommendation weight dynamic control module pre-installed in the central server, including: A policy database storing weight adjustment rules based on time characteristics, including holiday flags, seasonal codes, and special event information; A neural network correction unit, wherein the input layer receives real-time environmental data and current weight parameters, and the output layer generates a revised recommendation weight. The real-time environmental data includes temperature and humidity sensor data, pedestrian flow statistics, and emergency alarm signals. The weight distribution unit sends the normalized recommendation weight to each host node. The recommendation weight is associated with the visitor's personalized tag. The normalization process uses the Min-Max standardization algorithm, and the processed weight value is mapped to the interval [0.5, 1.8].

[0027] The recommendation weight is expressed as β, for example (β = 1.2 on holidays, β = 0.8 on weekdays) The strategy database, combined with the neural network correction unit, can dynamically adjust and obtain recommendation weights based on the weight adjustment rules of time characteristics, real-time environmental data, and current weight parameters. For example, during holidays, the recommendation weight can be increased to enhance the route recommendation strength of popular attractions, while on weekdays, the recommendation weight can be reduced to balance the traffic of various attractions. The calculation of the recommendation weights also incorporates real-time environmental data, such as weather conditions, emergencies, or temporary park closures, and uses a neural network model to modify the weights in real time. The adjustment result of the recommendation weight is sent to each host node through the central server to ensure that the path recommendation strategy of all slave devices is updated synchronously; The value range of the recommendation weight is normalized and associated with the personalized tags of tourists. For example, a higher weight is assigned to fast-sighted tourists to prioritize the shortest path, and a lower weight is assigned to in-depth experience tourists to focus on cultural path recommendations.

[0028] The present invention also supports administrators to dynamically adjust path strategies through recommendation weights (such as holiday diversion and priority recommendation of cultural attractions), and introduces a neural network model to correct weight parameters in real time, taking into account the scenic area's operational goals and the personalized needs of tourists, and enhancing the scalability and scenario adaptability of the system strategy.

[0029] Based on the traffic comfort index of the product of physical width and passenger flow, combined with real-time sensor data, the path congestion status is dynamically classified to achieve the inverse correlation between path recommendation intensity and carrying capacity, effectively alleviating the local congestion problem in scenic spots. Furthermore, the method for calculating the actual time spent on a tourist's tour includes: Continuously collect visitor movement trajectory data through the positioning unit of the slave device, wherein the movement trajectory data includes a position coordinate sequence and a timestamp; Identify visitors' stay events based on trajectory data. The stay events are determined by speed thresholds. When the movement speed of consecutive positioning points is lower than the preset threshold and the duration exceeds the set range, it is marked as a stay. Classify the types of stop events, including photo stops, rest stops, and interactive device stops, and add a time correction factor based on the stop type; It should be noted that It can represent the actual visiting time of tourist k, which can be calculated through the movement trajectory data collected by the positioning unit of the sub-machine (including behavioral corrections such as stopping and taking photos).

[0030] The actual tour time is the sum of the movement time and the corrected stay time, and the time data is smoothed by a sliding window algorithm to eliminate positioning errors; The time-consuming calculation result is uploaded to the central server in real time and used to update the dynamic recommendation gain parameters of the path optimization engine.

[0031] Furthermore, the path carrying capacity evaluation method includes: Obtaining physical width data of scenic spots and real-time crowd flow monitoring data, wherein the physical width is obtained through GIS map annotation or on-site measurement; The real-time human traffic is collected through a camera or infrared sensor connected to the host node; The product of the physical width and the passenger flow per unit time is used as the traffic comfort index. When the index value is lower than the first threshold, it is marked as a congested path; when it is higher than the second threshold, it is marked as a smooth path. The path carrying capacity is dynamically graded according to the real-time comfort index and is inversely correlated with the path recommendation strength, i.e., the lower the carrying capacity, the greater the attenuation of the recommendation strength; The evaluation results are mapped to the star topology model of the three-dimensional scenic area through the digital twin construction unit, forming a visual heat map for administrators to make reference.

[0032] Furthermore, the method for analyzing tourists' mobility preferences includes: Collect historical visitor data through the sub-device, including route selection records, length of stay distribution, and frequency of use of interactive devices; Based on the clustering algorithm, tourists are divided into two categories: fast-sighted tourists and in-depth tourists. The fast-sighted tourists are characterized by an average movement speed higher than the system threshold and fewer than the set number of stops. The in-depth tourists are characterized by a movement speed lower than the threshold and a cultural label matching degree of the stops higher than the set value. Generate dynamic preference labels for each type of tourist and bind the labels to route recommendation strategies. For example, prioritize the shortest route for quick-sightseeing tourists and prioritize routes with dense cultural nodes for in-depth experience tourists. The preference tags are synchronized to all host nodes through the central server to ensure the continuity of recommendation strategies when tourists move across attractions.

[0033] It can be expressed as the travel preference of tourist k, which can be analyzed and obtained based on the preference label; Furthermore, the configuration method of the path matching tolerance includes: Preset tolerance benchmark parameters according to scenic area types, where different scenic area types correspond to different benchmark parameter ranges; Ancient city attractions are set to a higher tolerance to allow detour recommendations, while amusement park attractions are set to a lower tolerance to enforce the shortest path. The tolerance parameters are dynamically adjusted through the administrator interface and can be automatically optimized based on real-time crowd density. For example, the tolerance can be temporarily lowered during peak hours to reduce the risk of path intersections. Dynamically adjust the tolerance parameter based on the preset benchmark parameter range, and the adjustment range is constrained by the real-time traffic data of the scenic area; For example, path matching tolerance can be used It means that the system sets parameters according to the type of scenic spot: σ=2 for ancient city (detours are allowed), and σ=0.5 for amusement park (strict shortest path).

[0034] The tolerance parameter is input into the improved ant colony algorithm, and the path recommendation strength is adjusted by controlling the pheromone volatilization rate.

[0035] Furthermore, the synthesis method of the dynamic recommendation gain includes: The recommendation weight, actual visitor duration, route carrying capacity, visitor mobility preference, and route matching tolerance are input into a weighted calculation model, where the weight coefficients of each parameter are obtained through historical data training. The visitor duration is inversely transformed and multiplied by the recommendation weight to generate a basic gain component. The difference between the route carrying capacity and the visitor preference is input into an exponential function, and the route matching component is calculated in combination with the tolerance parameter. The dynamic recommendation gain is a linear combination of each component and is mapped to a preset value range through normalization processing; The gain calculation result is fed back to the path optimization engine in real time to adjust the distribution of path selection probability in the improved ant colony algorithm.

[0036] Furthermore, the historical popularity decay value and the dynamic recommendation gain are fused at a preset ratio to obtain the route recommendation strength. The historical popularity decay value reflects the long-term attraction decay trend of the attraction, and the dynamic recommendation gain reflects the matching degree between the real-time environment and the personalized needs of tourists. The fusion result is smoothed by the time decay factor to avoid route jumps caused by sudden changes in recommendation strength. Finally, the path display effect of the interactive display screen is controlled according to the route recommendation strength, where paths with high recommendation strength are highlighted with high saturation colors, and paths with low recommendation strength are faded with a gradual fade effect; The route recommendation strength data is sent to the slave device through the host node, and the highlight state of the optimal path is automatically switched according to the real-time location of the tourist.

[0037] It should be noted that the calculation formula for route recommendation strength is: ; in It is the strength of route recommendation, dynamically reflects the value of traveling from attraction a to attraction b, and can be used to highlight the path on the interactive display screen. It is the basic popularity of the attraction; α is the popularity decay rate of the attraction, which automatically reduces its popularity over time. For example, α=0.1 for cultural attractions and α=0.3 for entertainment attractions.

[0038] β is the recommendation weight, for example (β=1.2 on holidays, β=0.8 on weekdays); is the actual visiting time of tourist k, which can be calculated based on the movement trajectory data collected by the positioning unit of the slave unit (including behavioral corrections such as stopping and taking photos). It is the carrying capacity of the path, which can be combined with the physical width (meters) × real-time passenger flow (people / minute) to calculate the traffic comfort index. is the mobile preference of visitor k, for example, the type label based on historical behavior analysis: It is the path matching tolerance, which is a parameter set by the system according to the scenic area type: σ=2 for ancient city type (detours are allowed), and σ=0.5 for amusement park type (strict shortest path).

[0039] The calculation process and numerical values of the above formula are obtained after normalization or dimension processing.

[0040] For example, there are 4 scenic spots in the current scenic area. The current user is at scenic spot a. A path navigation can be formed between scenic spot a and the remaining scenic spots bcd respectively, and according to the recommendation strength between scenic spots calculated above ,The navigation path is displayed with different colors of path trajectories. For example, the route from scenic spot a to scenic spot b is currently the most recommended, and is displayed with a green trajectory on the interactive display screen observed by the current user. The route from scenic spot a to scenic spot c is currently recommended, and is displayed with a yellow trajectory. The route from scenic spot a to scenic spot d is currently not recommended, and is displayed with a red trajectory for the user to choose and go to the next scenic spot, which can provide the user with sufficient reference.

[0041] The model dynamically generates the optimal tour route by simulating the dual mechanisms of "heat decay" and "real-time feedback". Its core calculation is divided into two stages: The system first implements a natural decay of the existing base popularity of an attraction. The extent of this decay is determined by the attraction type: cultural attractions (such as museums) use a light decay coefficient of 0.1 to ensure the continued appeal of historical attractions; entertainment attractions (such as roller coasters) use a higher decay coefficient of 0.3 to reflect the declining popularity of time-sensitive attractions. This design allows new route recommendations to maintain the heritage of classic attractions while promptly reflecting the time-sensitive value of emerging attractions.

[0042] In addition to the baseline popularity, the system also adds dynamic recommendation values generated by real-time visitor behavior data. This process is intelligently adjusted across three dimensions, and administrators can also set a global adjustment coefficient based on operational needs (e.g., 1.2 for holidays, 0.8 for weekdays). Adjusting this parameter can increase or decrease the overall recommendation intensity, achieving macro-control of scenic area traffic.

[0043] By collecting each tourist's actual movement trajectory through smart devices, the system can calculate the actual tour duration (including behavioral corrections such as stopping to take photos), analyze individual preference characteristics (such as distinguishing between quick tours and in-depth experiences), and evaluate the comfort of the route (combining the product of road width and real-time pedestrian flow). For each tourist's individual data, the system calculates the feature matching degree: The path carrying capacity is compared with tourist preferences (for example, short paths are matched for fast-paced tourists, and dense paths are matched for deep-paced tourists). The matching tolerance is controlled by the scenic area type parameter (ancient cities allow 2 times the detour, and amusement parks only allow 0.5 times the tolerance). Finally, the system processes the real-time data of all tourists according to the following rules: the shorter the visit, the greater the weight of the tourist data; the higher the match between the path characteristics and the tourist preferences, the greater the contribution value; the scenic area type parameter controls the flexibility of the matching results.

[0044] Through continuous iterative updates, this system not only ensures the stability of classic routes, but also responds to changes in tourist behavior in real time. The final recommendation strength value will be directly reflected in the highlighted path on the interactive display screen, which is more intuitive and forms a dynamically optimized guide planning plan.

[0045] Example 2 In addition to the technical solutions proposed in Example 1, this example also proposes to optimize the star topology used in scenic spots and add "traffic isolation and access control" content to ensure the security and stable operation of the star topology network. The following is a detailed technical solution from the four steps of dividing virtual network VLANs, configuring access control lists (ACLs), deploying firewalls, and dynamic policy adjustment. Figure 2 As shown: S21, establishing at least two virtual subnets in the central server, and binding the host node to the virtual subnet based on the service type name corresponding to each host node.

[0046] (1) VLAN division and subnet isolation Logical subnet division: Multiple VLANs are created on the central switch based on departmental functions, business types, or security requirements. For example, the finance department, R&D department, and general office areas can each be assigned their own VLAN. By default, direct communication between different VLANs is prevented, achieving physical isolation of traffic.

[0047] Port assignment: Bind switch ports to corresponding VLANs. For example, assign the port connected to the finance department's office computers to the "Finance VLAN" to ensure that the port only receives and forwards traffic belonging to that VLAN. For wireless access, use SSID-to-VLAN mapping to allow wireless users with different SSIDs to access the corresponding VLAN subnet.

[0048] S22, formulate access control list rules for each virtual subnet, and set priorities for the access control list rules according to business requirements.

[0049] (2) Access Control List (ACL) policy configuration Rule development: Develop refined ACL rules for each VLAN. For example, the finance VLAN can be allowed to access specific ports on the company's finance server (such as the ERP system port), but prohibited from accessing server resources on the R&D VLAN. Alternatively, the R&D VLAN can be allowed to access the code repository server, but restricted from accessing ports on internet entertainment websites.

[0050] Rule priority setting: Set the priority of ACL rules based on business needs. Generally, rules with high security requirements and business-criticality are placed first to ensure priority matching and execution, avoiding access confusion caused by rule conflicts.

[0051] In practice, ACL is a network security technology that filters and controls data packets entering and leaving network interfaces by setting rules on network devices such as routers, switches, or firewalls, enabling refined management of access to network resources. In a star topology network, it can be used to isolate traffic between different VLANs.

[0052] S23, deploy a firewall on the host node, connect the virtual subnet to different interfaces of the firewall, make all cross-virtual subnet and external network access traffic must pass through the firewall detection, and set access control policy on the firewall.

[0053] (3) Firewall deployment and in-depth protection Firewall access: Deploy hardware firewalls or virtual firewalls at the core nodes of the star network, connect different VLAN subnets to different interfaces of the firewall, and ensure that all cross-VLAN and external network access traffic must pass through the firewall inspection.

[0054] Policy Configuration: Set access control policies on the firewall based on application-layer protocols (such as HTTP, HTTPS, and SMTP), user identities (e.g., employee and guest accounts), and time policies (e.g., weekdays and non-workdays). For example, employees can access the enterprise OA system only via HTTPS during weekday working hours, blocking access requests from unauthorized devices and during unusual hours. Additionally, enable the firewall's intrusion detection and prevention (IDS / IPS) features to monitor and block malicious attack traffic in real time.

[0055] S24, using network traffic analysis tools to monitor traffic data between virtual subnets in real time, and dynamically adjust virtual subnet divisions, access control list rules, and access control policies based on traffic monitoring results and changes in business needs.

[0056] (4) Dynamic strategy adjustment and audit Traffic monitoring and analysis: Use network traffic analysis tools (such as Wireshark or SolarWinds) to monitor traffic data between VLANs in real time, analyzing traffic patterns, the source of abnormal traffic, and its destination. For example, if a VLAN is found to be transmitting a large amount of data outside of business hours, further investigation is required to determine whether there is a risk of data leakage.

[0057] Dynamic Policy Optimization: VLAN divisions, ACL rules, and firewall policies are dynamically adjusted based on traffic monitoring results and changing business needs. For example, when a new business system is added, a separate VLAN is assigned to it and corresponding access policies are added to the ACL and firewall. If a security vulnerability is discovered in a department, access rights to that department's VLAN are promptly tightened. Access control policies are regularly audited, and redundant or outdated rules are removed to ensure policy effectiveness and security.

[0058] Based on the above four steps, first, VLAN division divides the network into logical subnets, physically isolates traffic between different VLANs, and blocks illegal access across subnets; second, ACL rules fine-tune traffic flow based on IP, port, etc., allowing legitimate business flows and blocking illegal access; third, the firewall deeply detects cross-VLAN and extranet traffic, and combines it with intrusion prevention to block malicious attacks; finally, it dynamically monitors traffic anomalies, adjusts policies in real time, and deletes redundant rules to ensure that isolation and control policies remain effective, forming a complete security chain from logical isolation to dynamic protection to ensure network security and stability.

[0059] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, value library or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM).

[0060] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0061] The above description is only a preferred embodiment of the present invention and does not limit the scope of this application. Any equivalent results or equivalent process transformations made using the contents of the description and drawings of the present invention, or directly or indirectly applied in other related technical fields, are also included in the scope of protection of this application.

Claims

1. A cultural tourism equipment information management system based on star topology, characterized by: include: A central server, at least one host node deployed at each scenic spot in the scenic area, a data processing module, a visual interaction module, and multiple slave devices connected to the host node via a short-range wireless communication protocol; The central server establishes a two-way communication link with all host nodes through a star topology network, and is used to receive the scenic spot equipment status and visitor data uploaded by each host node in real time; Dividing the host node into at least two virtual subnets based on service type, each virtual subnet being used for data transmission of a single service; The sub-device integrates positioning and information collection units to collect visitor locations and environmental parameters, which are then uploaded to the central server via a host node trigger request. The data processing module includes: The digital twin construction unit builds a three-dimensional scenic area topology model based on GIS data, dynamically mapping the status of the neutron equipment and the distribution of crowd density at each scenic spot; Route optimization engine, which uses an improved ant colony algorithm to obtain real-time route recommendation strength; The visualization interaction module includes an interactive display screen, which is used to generate a multimodal navigation interface according to the route recommendation strength output by the path optimization engine, and display the path trajectory between scenic spots in different colors according to the difference in recommendation strength.

2. The star topology-based cultural tourism equipment information management system according to claim 1, characterized in that: The improved ant colony algorithm is obtained by integrating the historical heat decay value and the dynamic recommendation gain, and the dynamic recommendation gain is obtained by integrating the system recommendation weight, the actual tour time of tourists, the path carrying capacity value, the tourist movement preference and the path matching tolerance.

3. The star topology-based cultural tourism equipment information management system according to claim 2, characterized in that: The method for obtaining the historical heat decay value includes: Get the basic popularity value of each attraction; Classify and calibrate the popularity of attractions according to their types, where the types of attractions include at least cultural attractions and entertainment attractions; Dynamically adjust the decay process of the basic popularity of scenic spots through time series analysis; The calculation of the historical heat decay value further includes dynamically correcting the decay rate based on the real-time behavior data of tourists.

4. The star topology-based cultural tourism equipment information management system according to claim 1, characterized in that: The path optimization engine also includes a recommendation weight dynamic control module pre-installed in the central server, including: A policy database storing weight adjustment rules based on time characteristics, including holiday flags, seasonal codes, and special event information; A neural network correction unit, wherein the input layer receives real-time environmental data and current weight parameters, and the output layer generates a revised recommendation weight. The real-time environmental data includes temperature and humidity sensor data, pedestrian flow statistics, and emergency alarm signals. The weight distribution unit sends the normalized recommendation weight to each host node, where the recommendation weight is associated with the visitor's personalized tag.

5. The star topology-based cultural and tourism equipment information management system according to claim 2, characterized in that: The calculation method of the actual time spent on the tourist tour includes: Continuously collect visitor movement trajectory data through the positioning unit of the slave device, wherein the movement trajectory data includes a position coordinate sequence and a timestamp; Identify visitors' stay events based on trajectory data. The stay events are determined by speed thresholds. When the movement speed of consecutive positioning points is lower than the preset threshold and the duration exceeds the set range, it is marked as a stay. Classify the types of stop events, including photo stops, rest stops, and interactive device stops, and add a time correction factor based on the stop type; The actual tour time is the sum of the movement time and the corrected stay time, and the time data is smoothed by a sliding window algorithm to eliminate positioning errors; The time-consuming calculation result is uploaded to the central server in real time and used to update the dynamic recommendation gain parameters of the path optimization engine.

6. The star topology-based cultural and tourism equipment information management system according to claim 2, characterized in that: The evaluation method of the path carrying capacity includes: Obtaining physical width data of scenic spots and real-time crowd flow monitoring data, wherein the physical width is obtained through GIS map annotation or on-site measurement; The real-time human traffic is collected through a camera or infrared sensor connected to the host node; The product of the physical width and the passenger flow per unit time is used as the traffic comfort index. When the index value is lower than the first threshold, it is marked as a congested path; when it is higher than the second threshold, it is marked as a smooth path. The path carrying capacity is dynamically graded according to the real-time comfort index and is inversely correlated with the path recommendation strength; The evaluation results are mapped to the star topology model of the three-dimensional scenic area through the digital twin construction unit, forming a visual heat map for administrators to make reference.

7. The star topology-based cultural and tourism equipment information management system according to claim 2, characterized in that: The method for analyzing tourists' mobility preferences includes: Collect historical visitor data through the sub-device, including route selection records, length of stay distribution, and frequency of use of interactive devices; Based on the clustering algorithm, tourists are divided into two categories: fast-sighted tourists and in-depth tourists. The fast-sighted tourists are characterized by an average movement speed higher than the system threshold and fewer than the set number of stops. The in-depth tourists are characterized by a movement speed lower than the threshold and a cultural label matching degree of the stops higher than the set value. Generate dynamic preference tags for each type of tourist and bind the tags to the route recommendation strategy; The preference tags are synchronized to all host nodes through the central server to ensure the continuity of recommendation strategies when tourists move across attractions.

8. The star topology-based cultural and tourism equipment information management system according to claim 2, characterized in that: The method for configuring the path matching tolerance includes: Preset tolerance benchmark parameters according to scenic area types, where different scenic area types correspond to different benchmark parameter ranges; Dynamically adjust the tolerance parameter based on the preset benchmark parameter range, and the adjustment range is constrained by the real-time traffic data of the scenic area; The tolerance parameter is input into the improved ant colony algorithm, and the path recommendation strength is adjusted by controlling the pheromone volatilization rate.

9. The star topology-based cultural and tourism equipment information management system according to claim 8, characterized in that: The synthesis method of the dynamic recommendation gain includes: Input the system recommendation weight, tourists' actual tour time, path carrying capacity, tourists' movement preference and path matching tolerance into the weighted calculation model; The dynamic recommendation gain is a linear combination of each component and is mapped to a preset value range through normalization processing; The gain calculation result is fed back to the path optimization engine in real time to adjust the distribution of path selection probability in the improved ant colony algorithm; The historical heat decay value and the dynamic recommendation gain are integrated according to a preset ratio to obtain the route recommendation strength. Finally, the path display effect of the interactive display screen is controlled according to the route recommendation strength. The route recommendation strength data is sent to the slave device through the host node, and the highlight status of the optimal path is automatically switched according to the real-time location of the tourist.

10. The star topology-based cultural and tourism equipment information management system according to claim 1, characterized in that: The dividing the host node into at least two virtual subnets based on the service type, each virtual subnet being used for data transmission of a single service, includes: Establishing at least two virtual subnets in the central server, and binding the host node to the virtual subnet based on the service type name corresponding to each host node; Formulate access control list rules for each virtual subnet and set priorities for the access control list rules according to business needs; Deploy a firewall on the host node, connect the virtual subnet to different interfaces of the firewall, require all cross-virtual subnet and external network access traffic to pass through the firewall, and set access control policies on the firewall; The traffic data between the virtual subnets is monitored in real time by a network traffic analysis tool, and the division of the virtual subnets, the access control list rules and the access control policy are dynamically adjusted according to the traffic monitoring results and changes in business needs.

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