Guidance navigation method based on fusion of guidance route and road network

Abstract

The present application relates to the technical field of path guiding, and specifically includes a guiding navigation method based on fusion of a guiding route and a road network, which comprises the following steps: connecting a plurality of user terminals in the same tour group, collecting tour coordinates and constructing a road network, analyzing the influence of road network nodes and road network edges to optimize the guiding network; configuring a starting point according to the real-time position and progress of a user, establishing a guiding optimization model, performing multi-directional guiding tracking iteration, obtaining a multi-directional balanced guiding route set, and performing route navigation guiding, which solves the technical problems that the guiding mode of a scenic spot is mainly based on fixed routes, lacks dynamic response capability to the demand of tourists, and the utilization rate of scenic spot resources is low, dynamically plans a guiding route through the influence analysis of road network nodes and road network edges, improves the utilization rate of scenic spot resources, realizes the technical effects that the tourist flow distribution is optimized, the guiding efficiency is optimized through multi-directional guiding tracking iteration under the constraint of time balance, and the tour experience is improved.

Description

Technical Field

[0001] This invention relates to the field of route guidance technology, specifically to a navigation method based on the integration of tour routes and road networks. Background Technology

[0002] With the rapid development of the tourism industry and the continuous improvement of tourists' demands, conventional scenic spot tours can no longer meet the diverse needs of modern tourists. Conventional tour services rely on manual explanations or electronic devices with fixed routes, which makes it difficult to fully meet the actual needs of tourists. In addition, in some large scenic spots, nature reserves or cultural sites, due to the wide distribution of attractions, the large number of tourists and their high mobility, some attractions are congested while other attractions are idle, resulting in low utilization of attraction resources, which affects the tourist experience and the efficiency of scenic spot operation.

[0003] In summary, existing technologies suffer from the problem that scenic area tours rely primarily on fixed routes, lack the ability to dynamically respond to tourist needs, and result in low utilization of scenic resources. Summary of the Invention

[0004] This application provides a navigation method based on the integration of tour routes and road networks, aiming to solve the technical problems of existing scenic spot tour methods that are mainly based on fixed routes, lack dynamic response to tourist needs, and have low utilization of scenic spot resources.

[0005] In view of the above problems, the technical solution to achieve the present application is as follows:

[0006] This application provides a navigation method based on the integration of guided routes and road networks. The method includes: connecting multiple user terminals within the same tour group; collecting multiple basic tour coordinates, each with tour item markers; uploading an internal guided road network based on the multiple basic tour coordinates; drafting multiple basic guided routes, each connecting multiple basic tour coordinates; analyzing the road network nodes and their impact on tourist flow distribution based on the internal guided road network of the multiple basic tour coordinates; obtaining an impact assessment combination; performing tourist behavior pattern correlation analysis based on the impact assessment combination; and establishing a first guided navigation optimization network using the results of the first behavior pattern correlation analysis. The internal guide network of the tour coordinates is analyzed, and the impact of the network edges and their influence on the efficiency of the guide route is analyzed. Two types of impact assessment combinations are obtained, and a tourist behavior pattern correlation analysis is performed based on these combinations. Using the results of the second behavior pattern correlation analysis, a second guide optimization network is established. Guide navigation starting points are configured using the real-time location information and tour progress of multiple user terminals within the same tour group, and a guide optimization model is established based on the first and second guide optimization networks. Based on the guide optimization model, multi-directional guide tracking iterations are performed under time balance constraints, combining the guide navigation starting point and guide navigation demand of any user terminal within the same tour group, to obtain a multi-directional balanced guide route set and provide route navigation guidance.

[0007] In summary, one or more technical solutions provided in this application solve the technical problems of fixed routes in scenic area tours, which lack dynamic response to tourist needs and result in low utilization of scenic resources. By analyzing the impact of road network nodes and road edges, the application dynamically plans tour routes, improves the utilization of scenic resources, optimizes tourist flow distribution, and optimizes tour efficiency and enhances the tour experience through multi-directional tour tracking and iteration under time balance constraints. Attached Figure Description

[0008] Figure 1 This application provides a flowchart illustrating a navigation method based on the integration of tour routes and road networks;

[0009] Figure 2 This application provides a flowchart illustrating the unidirectional navigation locking process in a navigation method based on the fusion of navigation routes and road networks. Detailed Implementation

[0010] Example

[0011] The present application will now be described in detail with reference to the accompanying drawings, such as... Figure 1As shown, this application provides a navigation method based on the fusion of tour routes and road networks, wherein the method includes:

[0012] S1: Connect multiple user terminals in the same tour group, collect multiple basic tour coordinates, and the multiple basic tour coordinates have tour item tags.

[0013] Specifically, the user end refers to the smart devices used by tourists, such as smartphones or dedicated tour guide devices, capable of receiving and sending data; basic tour coordinates refer to the specific location information of each important attraction within the scenic area, and these coordinate points are marked for easy identification and navigation; tour item markers refer to the identifiers associated with the basic tour coordinates, such as attraction names, numbers, or other descriptive information, used for quick identification and location. Furthermore, each tourist's mobile phone has a tour guide application installed. When tourists in a tour group enter the scenic area, the application automatically connects to other tourists' mobile phones via Bluetooth or Wi-Fi and begins collecting the location coordinates of each attraction, such as attraction A, attraction B, etc. These coordinate points are accompanied by tour item markers, including skiing, movie screenings, etc.

[0014] Connecting multiple user terminals within the same tour group can be achieved through wireless network technologies such as Wi-Fi or Bluetooth, ensuring that all user terminals can communicate and exchange data in real time. Subsequently, basic tour coordinates provided by each user terminal are collected. These coordinate points include tour item markers, making each coordinate point uniquely identifiable and providing the necessary location data for subsequent route planning and optimization.

[0015] S2: Based on the multiple basic tour coordinates, upload the internal guide network of the multiple basic tour coordinates, and formulate multiple basic guide routes, each of which connects multiple basic tour coordinates.

[0016] Specifically, the internal guide network refers to the network of paths within the scenic area used for guiding visitors, including roads and trails connecting various attractions. The basic guide routes are routes designed based on the internal guide network that connect multiple basic visitor coordinates. These routes provide basic navigation for visitors from one point to another. Furthermore, based on the collected coordinates of attractions A, B, etc., the corresponding internal guide network data is uploaded. After analyzing this data, multiple basic guide routes can be formulated, such as the route "attraction entrance - attraction A - attraction B - attraction exit". Each route ensures that visitors can smoothly navigate from one attraction to the next. These basic routes will serve as the basis for subsequent dynamic optimization and personalized adjustments.

[0017] After collecting basic tour coordinates, the corresponding internal navigation network data is uploaded. This data includes information such as road connectivity, direction, and length. Based on this data, multiple basic tour routes are proposed. Each route connects multiple basic tour coordinates, providing tourists with a basic navigation path from the starting point to the destination and offering them an initial tour route selection. These routes will serve as the basis for subsequent optimizations.

[0018] S3: Based on the internal guide network of the multiple basic tour coordinates, analyze the network nodes and their impact on tourist flow distribution, obtain a type of impact assessment combination, and conduct tourist behavior pattern correlation analysis based on the type of impact assessment combination. Using the results of the first behavior pattern correlation analysis, establish a first guide optimization network.

[0019] Specifically, road network nodes refer to key points in the internal guide road network of a scenic area, such as intersections and attraction entrances. One type of impact assessment combination is based on the assessment results corresponding to the impact of road network nodes on the distribution of tourist flow. Further, through analysis, it was found that the entrance of attraction A is a key node that frequently experiences tourist congestion. This node is used as an impact assessment point, and the behavioral patterns of tourists at this node are analyzed. For example, most tourists tend to visit in a clockwise direction. Based on this information, the guide route can be optimized to guide tourists to be more evenly distributed among various attractions and reduce congestion.

[0020] This study analyzes the network nodes within the internal guided tour network and their impact on visitor flow distribution. By collecting and analyzing visitor behavior data at each node, it identifies nodes that significantly influence visitor flow distribution, such as those that may cause congestion or diversion of visitors. After obtaining a set of impact assessment combinations, further correlation analysis of visitor behavior patterns is conducted to understand visitors' behavioral habits and preferences at these nodes. These analytical results will be used to establish the first guided tour optimization network, aiming to optimize guided tour routes, reduce congestion, and improve the visitor experience.

[0021] The first tour guide optimization network is an optimization network established based on the results of a combination of impact assessments and the correlation analysis of tourist behavior patterns. Further, utilizing the results of this correlation analysis, the first tour guide optimization network is established. This network comprehensively considers the impact of road network nodes on tourist flow distribution and tourist behavior habits, aiming to optimize tour routes, improve tour efficiency, and enhance the tourist experience. In actual tour services, the first tour guide optimization network will adjust routes, such as increasing or decreasing the recommendation level of certain attractions, or adjusting the opening status of attractions within specific time periods. For example, based on the congestion situation at entrance A of attraction A and tourists' clockwise tour preference, the first tour guide optimization network is established. In this network, combined with the above analysis, a counter-clockwise tour route is used to disperse tourist flow and reduce congestion at entrance A of attraction A. Simultaneously, during peak hours, tourists are recommended to visit other attractions with lower visitor traffic first to balance the tourist distribution throughout the scenic area.

[0022] S4: Based on the internal guide network of the multiple basic tour coordinates, analyze the influence of the network edge and the network edge on the efficiency of the guide route, obtain two types of influence assessment combinations, and conduct a tourist behavior pattern association analysis based on the two types of influence assessment combinations. Using the results of the second behavior pattern association analysis, establish a second guide optimization network.

[0023] Specifically, the road network edge refers to the path or section within the scenic area's internal guide road network that connects various nodes (such as attractions or intersections). The second type of impact assessment combination is based on the assessment results corresponding to the impact of the road network edge on the efficiency of the guide route. Further, through analysis, it was found that the path connecting attractions A and B has slow tourist travel during a specific time period. This section is used as an impact assessment point, and the behavioral patterns of tourists on this section are analyzed, such as most tourists tending to stop at a certain viewpoint along the path. Based on this information, the guide route is optimized, for example, by providing more rest areas near viewpoints to reduce congestion on the guide route and improve guide efficiency.

[0024] This study analyzes the roadside edges within the internal guided tour network and their impact on the efficiency of the guided tour route. By collecting and analyzing visitor movement data across various road segments, it identifies road segments that significantly affect the efficiency of the guided tour route, such as those that may cause delays or congestion. After obtaining a combination of two types of impact assessments, further correlation analysis of visitor behavior patterns is conducted to understand visitor habits and preferences on these road segments. These analytical results will be used to establish a second guided tour optimization network, aiming to optimize the guided tour route, improve guided tour efficiency, and enhance the visitor experience.

[0025] The second tour guide optimization network is an optimization network established based on the results of the correlation analysis between the two types of impact assessment combinations and tourist behavior patterns. Furthermore, utilizing the results of the correlation analysis between the two types of impact assessment combinations and tourist behavior patterns, the second tour guide optimization network is established. This network comprehensively considers the impact of roadside access on tour route efficiency and tourist behavior habits, aiming to optimize tour routes, improve tour efficiency, and enhance the tourist experience. The second tour guide optimization network will adjust routes in actual tour services, such as increasing or decreasing the recommendation level of certain routes, or adjusting the opening status of routes within specific time periods. For example, based on the congestion situation of the path connecting attractions A and B and tourist dwell preferences, the second tour guide optimization network is established. Combining the above analysis, the second tour guide optimization network recommends more alternative routes to disperse tourist flow and reduce congestion on main paths. Simultaneously, during peak hours, tourists are recommended to use fast lanes to improve the efficiency of the tour routes.

[0026] S5: Configure the starting point for the tour guide and navigation by using the real-time location information and tour progress of multiple user terminals in the same tour group, and establish a tour guide optimization model based on the first tour guide optimization network and the second tour guide optimization network.

[0027] Specifically, real-time location information refers to data about the user's current location provided by the user's terminal in real time; tour progress refers to the user's progress during the tour, including the attractions visited and the estimated tour time; the navigation starting point refers to the starting point of the tour service determined based on the user's real-time location and tour progress. The tour optimization model is used to optimize the tour route based on the first tour optimization network and the second tour optimization network. Furthermore, by collecting real-time location information and tour progress, if it is found that most tourists have already visited attraction A and are heading to attraction B, attraction B is set as the new starting point for the tour navigation for subsequent tour route planning and optimization. For example, during peak hours, tourists are recommended to visit other attractions with less traffic first, and then visit popular attractions when there are fewer people, in order to balance the distribution of tourists in the entire scenic area and improve tour efficiency.

[0028] By collecting real-time location information and tour progress from multiple users within the same tour group to configure the starting point of the guided tour, the starting point of the guided tour service can be dynamically adjusted according to the actual location and tour progress of the tourists. This ensures that the guided tour service can be dynamically adjusted according to the actual location and tour progress of the tourists, providing a more personalized and flexible guided tour service. In this way, a more personalized and flexible guided tour service is provided, ensuring that the guided tour route can meet the actual needs and preferences of tourists, thereby improving the responsiveness and adaptability of the guided tour service to adapt to the tour pace of different tourists. A comprehensive guided tour optimization model is established based on the first guided tour optimization network (focusing on tourist flow distribution optimization) and the second guided tour optimization network (focusing on guided tour route efficiency optimization). The guided tour optimization model will be used for subsequent guided tour route planning and optimization to ensure that the guided tour service is both efficient and meets the actual needs of tourists, providing users with a more efficient and personalized guided tour service.

[0029] S6: Based on the tour guide optimization model, and combining the tour guide starting point and tour guide requirements of any one of the multiple user terminals in the same tour group, perform multi-directional tour guide tracking iteration under time balance constraints to obtain a multi-directional balanced tour guide route set, and provide route navigation guidance.

[0030] Specifically, multi-directional tour tracking iteration refers to the process of optimizing the tour route by performing multiple iterative calculations based on the user's starting point and needs under time balance constraints; multi-directional balanced tour route set is a set of optimal tour routes obtained through iterative calculations, taking into account factors such as time, efficiency, and tourist flow; route navigation guidance is to provide real-time navigation guidance to users based on the optimized tour route.

[0031] Based on the tour guide optimization model, and combining the starting point and needs of tour guides from any user terminal within the same tour group, multi-directional tour guide tracking and iteration are performed under time balance constraints. This involves real-time analysis of user location, tour progress, and tour guide needs, and iterative calculation to optimize tour routes through preset time windows. This yields a set of multi-directional balanced tour routes that comprehensively consider factors such as time, efficiency, and tourist flow to provide the best tour guide service. Real-time navigation guidance is then provided based on these optimized routes to ensure that tourists can smoothly tour along the predetermined routes, improving tour efficiency and experience.

[0032] Based on the above example, multi-directional tour tracking iterations are performed according to the tour guide optimization model and each tourist's real-time location (e.g., starting from attraction B) and tour needs (e.g., hoping to visit the main attractions within two hours). Each tourist's tour time window is considered, such as the planned time spent in the Forbidden City, as well as tour guide navigation needs, such as attractions of particular interest. Through iterative calculations, a set of balanced tour routes is obtained. This multi-directional balanced tour route set considers both tourists' individual interests and tourist flow and route efficiency. Navigation guidance is provided based on these routes to achieve orderly tourist flow, avoiding congestion and improving both the efficiency of the tour guide service and the tourist experience.

[0033] Furthermore, based on the aforementioned tour guide optimization model, and combining the tour guide starting point and tour guide requirements of any one of the multiple user terminals belonging to the same tour group, this application's method performs multi-way tour guide tracking iteration under time balance constraints, including:

[0034] S61: Collect the historical tour records of any one of the multiple user terminals in the same tour group; S62: Identify tour points of interest through the historical tour records; S63: Add a matching score to each alternative route during the guided tour tracking iteration process based on the tour points of interest.

[0035] Specifically, historical travel records refer to data records generated by users during past travels, including attractions visited, stay times, and travel routes; points of interest refer to attractions or areas that users show particular interest or preference for during their travels; and matching score refers to the process by which the system scores alternative routes based on the user's points of interest and travel needs to determine which routes better match the user's interests and needs.

[0036] The system collects historical tour records from any user within the same tour group, including attractions visited, time spent at each attraction, and tour routes. Analyzing this historical data identifies users' points of interest (POIs), i.e., attractions where users spend more time or visit more frequently. After identifying POIs, a matching score is added to each alternative route during the iterative guided tour tracking process. This score assesses the degree to which each route matches the user's POIs and assigns a score accordingly. The matching score is used to determine the route that best meets the user's personalized needs, thereby providing a more personalized and optimized guided tour service.

[0037] If a user's travel history shows that they have visited natural landscape attractions multiple times during previous visits and spent a significant amount of time on each visit, analyzing this history can identify natural landscape attractions as points of interest for the user. When planning a guided tour route starting from attraction A, this user's interest in natural landscape attractions will be taken into account, and routes that include natural landscape attractions will be given a higher matching score. Furthermore, given the user's limited time, routes that cover the attractions the user is most interested in can be prioritized based on the user's travel interests and time constraints. In this way, not only can the efficiency of the guided tour service be improved, but the user's travel experience can also be enhanced.

[0038] Furthermore, based on the aforementioned points of interest, a matching score is added to each alternative route during the guided tour tracking iteration process. The method of this application includes:

[0039] S631: Based on the historical tour records, construct a tour information database, which is used to store historical tour routes and historical stay times; S632: Through the tour information database, combine social network information to mine potential points of interest; S633: Classify the tour points of interest and potential points of interest, and formulate a matching degree evaluation strategy.

[0040] Specifically, the tour information database refers to the storage medium used to store users' historical tour data, including tour routes and the time spent at each attraction; historical tour routes are records of the paths taken by users during past tours; historical time spent is records of the duration of time users stayed at each attraction.

[0041] Users' historical travel data, including the order in which they visited attractions and the time spent at each attraction, is stored in a travel information database to analyze their travel habits. For example, it can be found that users consistently spend more time at natural landscape attractions than at other attractions, indicating a particular interest in natural landscapes. Based on these collected historical travel records, a travel information database is built. This database not only stores users' historical travel routes and the time spent at each attraction but also serves as the foundation for analyzing and mining user travel behavior. Through this database, we can gain a deeper understanding of users' travel habits and preferences, providing data support for subsequent guided tour services.

[0042] Social network information refers to posts, comments, photos, and other information shared by users on social networks regarding their travel experiences. Potential points of interest (POIs) are new attractions or areas that users might be interested in, discovered through analysis of historical travel records and social network information. After establishing a travel information database, POIs are mined by combining this database with social network information. Social network information can provide users' emotional feedback and recommendations regarding attractions, which is helpful in discovering new attractions that users might be interested in. By mining users' shares and comments on social networks, it was found that users showed a strong interest in calligraphy and painting exhibitions. These are considered potential POIs. When planning tour routes, routes including natural landscape attractions will be prioritized and given higher matching scores. Routes including calligraphy and painting exhibitions will also be considered, especially when users have sufficient time. In this way, new attractions that users might like can be predicted and recommended, thus enriching the user's travel experience.

[0043] Tiered processing refers to classifying points of interest into different levels based on the user's level of interest in each point. Matching evaluation strategy is an evaluation system used to assess the degree of match between alternative routes and the user's points of interest, and to score them accordingly. After identifying points of interest and potential points of interest, these points are tiered, and a matching evaluation strategy is formulated. Tiered processing helps the system rank attractions according to the user's level of interest, while the matching evaluation strategy is used to assess the degree of match between alternative routes and the user's points of interest. Through this evaluation, each alternative route can be scored, thereby selecting the route that best matches the user's interests and needs.

[0044] Furthermore, based on the internal guide network of the multiple basic visitor coordinates, the network nodes and their impact on visitor flow distribution are analyzed to obtain a class of impact assessment combinations. The method of this application includes:

[0045] S31: Collect visitor flow data for each scenic spot through IoT devices; S32: Based on the visitor flow data for each scenic spot, identify peak and off-peak periods and visitor flow trends, and formulate a visitor flow distribution map; S33: Based on the internal guide network of the multiple basic tour coordinates and the impact degree of the visitor flow distribution map, determine the impact assessment information, including the degree of node congestion and visitor waiting time; S34: Based on the internal guide network of the multiple basic tour coordinates and the impact assessment information, traverse the network nodes to obtain a type of impact assessment combination.

[0046] Specifically, IoT devices refer to smart devices deployed within scenic areas to collect data, such as sensors and cameras; tourist flow data refers to data collected at various attractions regarding the number and distribution of tourists; tourist flow distribution maps are used to display the distribution of tourist flow at different times and at different attractions; the aforementioned type of impact assessment combination is a combination formed by optimizing road network nodes based on impact assessment information, wherein the impact assessment information refers to information obtained by assessing the degree of impact on the tour route and road network nodes according to the tourist flow distribution, such as the degree of node congestion and tourist waiting time.

[0047] By deploying IoT devices within the scenic area, visitor flow data for each attraction is collected, including the number of visitors and their entry and exit times. Analyzing this data identifies peak and off-peak periods and visitor flow trends, providing real-time data support for subsequent visitor flow management and tour route optimization. After collecting visitor flow data for each attraction, peak and off-peak periods and visitor flow trends are identified based on this data, and a visitor flow distribution map is created. This map visually displays the distribution of visitors within the scenic area, allowing for a better understanding of visitor behavior and enabling flow management and tour route optimization accordingly.

[0048] After developing a visitor flow distribution map, impact assessment information is determined based on the map and the internal guide network. This includes the congestion level at each network node and visitor waiting time. These factors support the assessment of the efficiency of the guide routes and the visitor experience, thereby identifying guide routes and network nodes that require optimization and proposing improvement measures. After determining the impact assessment information, this information is used to traverse all nodes of the internal guide network to obtain a set of impact assessments. This set of assessments includes optimization suggestions for each network node, such as measures to reduce congestion and optimize visitor waiting time. Its purpose is to provide specific improvement plans for guide route optimization, thereby improving guide efficiency and visitor satisfaction.

[0049] Furthermore, the method of this application also includes:

[0050] Using GIS technology and remote sensing data, the ecological environment of the nature reserve is assessed to identify sensitive areas. Based on these sensitive areas and a preset safety distance, alternative routes within the first time window are selected and incorporated into the tour guide optimization model.

[0051] Specifically, GIS technology refers to Geographic Information System technology, which is used to capture, store, analyze, and manage geospatial data; remote sensing data is data acquired through remote sensing technologies such as satellites or aerial photography, used to monitor and assess the environment; ecological environment assessment refers to the analysis and evaluation of the natural environment status of nature reserves in order to understand ecological health and changes; sensitive areas refer to areas with fragile ecological environments or with special protection value.

[0052] The ecological environment of nature reserves is assessed using GIS technology and remote sensing data. By analyzing this data, sensitive areas in the ecological environment can be identified. These areas may require special protection due to their fragile ecosystems, rich biodiversity, or special scientific research value. The purpose of these steps is to ensure that guided tours do not negatively impact the ecological environment of nature reserves, and also to provide a basis for ecological protection in subsequent route planning. For example, when planning a guided tour route in a nature reserve near attraction A, GIS technology and remote sensing data analysis may identify an area that is an important habitat for migratory birds. This area will be marked as a sensitive area and taken into consideration in subsequent guided tour route planning.

[0053] The preset safety distance refers to a certain range within which entry or approach is prohibited to protect sensitive areas, such as within 100 meters of migratory bird habitats. The first time window refers to the time range for route planning and optimization determined based on visitor flow and environmental conditions within a specific period. The alternative routes are those that meet ecological protection requirements and are selected within the first time window based on the preset safety distance. After identifying sensitive areas, the alternative routes within the first time window are screened based on these areas and the preset safety distance. This means that the system will exclude routes that are too close to sensitive areas to reduce the impact on the ecological environment. The screened alternative routes will be incorporated into the tour guide optimization model, which will comprehensively consider factors such as ecological protection, visitor flow, and tour guide efficiency to optimize the final tour route.

[0054] Furthermore, by incorporating the alternative routes selected during the first time window into the tour guide optimization model, the method of this application also includes:

[0055] Based on the alternative routes selected in the first time window, and combined with the preset safety distance, the first negative impact on the sensitive area is assessed; according to the preset safety distance and the first negative impact, the remaining routes are adjusted to avoid the sensitive area as a limiting condition, and alternative adjusted routes in the first time window are determined; a second time window is set, and in the overlapping part between the second time window and the first time window, the alternative adjusted routes in the first time window are incorporated into the tour guide optimization model.

[0056] Specifically, the first negative impact refers to the adverse effects that the alternative routes may have on sensitive areas within the first time window, such as disturbing wildlife and damaging vegetation; the alternative adjusted routes refer to the new routes after adjusting the original alternative routes after considering sensitive areas and the first negative impact; the second time window refers to the time range for planning and optimizing another route, determined according to changes in environmental conditions and tourist flow, at different time periods.

[0057] Based on the alternative routes selected within the first time window and the preset safety distance, the first potential negative impact of these routes on sensitive areas is assessed. The purpose of the assessment is to quantify and identify the potential environmental risks of the routes so that measures can be taken to reduce these impacts in subsequent route planning. Its role is to ensure that the guided tour route planning not only takes into account the visitor experience but also the needs of ecological protection. Furthermore, in conjunction with the above example, the potential impact of alternative routes on migratory bird breeding, such as noise interference, is assessed in the area surrounding the habitat with a preset safety distance as the radius, and the first negative impact of these routes is determined accordingly.

[0058] After assessing the primary negative impacts, the remaining routes will be adjusted based on preset safety distances and these impacts. The adjustment aims to avoid sensitive areas and reduce interference with the ecological environment. Alternative adjusted routes for the first time window will be determined, meeting both tourism needs and ecological protection requirements. After identifying these alternative routes for the first time window, they will be incorporated into the tour guide optimization model. Subsequently, a second time window will be established, and overlaps between the second and first time windows will be identified and incorporated into the model. The goal is to provide flexible tour guide services across different time periods while ensuring that route planning adapts to changes in the environment and tourist flow.

[0059] For example, if the system finds that some alternative routes are too close to the breeding grounds of migratory birds and may cause noise pollution, it will adjust these routes to move them away from the breeding grounds, or restrict the use of these routes during specific periods to reduce the impact on migratory birds. For example, morning and evening are the most active times for migratory birds, so morning and evening are set as sensitive times, and routes are adjusted during these times to avoid sensitive areas. At the same time, a second time window is set at noon, and the routes adjusted in the morning are included in the model so that guided tours can be provided during noon while protecting sensitive areas. In this way, highly adaptable and eco-friendly guided tours can be provided at different times.

[0060] Furthermore, such as Figure 2As shown, based on the tour guide optimization model, and combining the tour guide starting point and tour guide requirements of any one of the multiple user terminals belonging to the same tour group, the method of this application further includes multi-way tour tracking iteration under time balance constraints:

[0061] S64: Establish a sliding time window mechanism, integrate environmental change data and resource change data corresponding to the multiple basic tour coordinates, and update the open status bit of the tour item marker; S65: If the updated open status bit is 1, activate one-way tour; S66: Traverse the open status bits associated with multiple basic tour coordinates and activate one-way tour multiple times.

[0062] Specifically, the sliding time window mechanism is a dynamic time management technology used to adjust the time range based on real-time data to adapt to changes in the environment and resources. Environmental change data refers to changes in environmental factors within the scenic area, such as weather, temperature, and humidity. Resource change data refers to changes in resources within the scenic area, such as visitor flow and attraction maintenance status. The open status bit refers to the open status indicator of an attraction or tour project, used to indicate whether the attraction is open to visitors. One-way tour activation refers to activating the one-way tour mode under specific conditions to optimize visitor flow management and resource protection.

[0063] A sliding time window mechanism is established to integrate and process environmental and resource change data corresponding to multiple basic tour coordinates. This mechanism allows for dynamic adjustment of the time window to adapt to real-time changes in the environment and resources. In this way, the open status bits of tour item markers can be updated in real time, ensuring the accuracy and timeliness of the guide information. If the updated open status bit is 1, it indicates that the attraction is open, and one-way tours will be activated. This means that the one-way tour mode will be activated based on the current visitor flow, environmental conditions, and resource status to optimize visitor flow management and protect scenic area resources. By traversing the open status bits associated with multiple basic tour coordinates and activating one-way tours multiple times, the guide strategy can be dynamically adjusted based on real-time data within different tour coordinates and time windows to adapt to the constantly changing environment and visitor needs. This ensures effective management of visitor flow at each attraction within the entire scenic area while protecting the resources of each attraction. In this way, effective guide services are provided while ensuring visitor safety and protecting scenic area resources.

[0064] Furthermore, in addition to establishing a sliding time window mechanism, the method of this application also includes:

[0065] S641: Based on the sliding time window mechanism, an overlap constraint strategy is introduced, which is used to adjust the overlap between the second time window and the first time window; S642: According to the overlap constraint strategy, a smooth transition mechanism is configured, which is used to handle data interruption during time window switching; S643: According to the smooth transition mechanism, the window switching logic of the sliding time window mechanism is adjusted by feedback.

[0066] Specifically, the overlap constraint strategy is a time management strategy used to adjust the degree of overlap between different time windows to ensure data continuity; the overlap degree refers to the proportion of the overlapping part of two time windows on the time axis; the smooth transition mechanism is a mechanism to handle data interruption when switching time windows, ensuring the smooth operation of the system during the time window switching process.

[0067] Based on the established sliding time window mechanism, an overlap constraint strategy is introduced to adjust the overlap between the second and first time windows. The aim is to ensure sufficient data to support decision-making during the switching process between the two time windows, thereby improving the stability and reliability of the tour guide service. By appropriately setting the overlap, sufficient data can be ensured to support decision-making during time window switching, thus enhancing the stability and reliability of the tour guide service. According to the overlap constraint strategy, a smooth transition mechanism is configured to handle potential data interruptions during time window switching. By gradually adjusting data weights within the overlapping time period, it ensures that data does not undergo sudden changes or interruptions during time window switching. The smooth transition mechanism improves the fluency of the tour guide service and user experience, achieving a smooth data transition and avoiding service interruptions or erroneous decisions due to data interruptions, ensuring the continuity and stability of the tour guide service. Based on the implementation effect of the smooth transition mechanism, feedback adjustments are made to optimize the window switching logic of the sliding time window mechanism. This feedback adjustment is based on actual operating data and user experience feedback to optimize the sliding time window mechanism. Through continuous adjustment and optimization, it can better adapt to environmental changes and user needs, continuously ensuring the quality of the tour guide service and improving its adaptability and efficiency.

[0068] In summary, the beneficial effects of the embodiments of this application are:

[0069] This system connects multiple user terminals within the same tour group, collects multiple basic tour coordinates, uploads an internal tour guide network, and proposes multiple basic tour guide routes. Based on the internal tour guide network, it analyzes the network nodes and their impact on tourist flow distribution, obtains a first-class impact assessment combination, conducts tourist behavior pattern correlation analysis, and establishes a first-class tour guide optimization network. Based on the internal tour guide network, it analyzes the network edges and their impact on tour route efficiency, obtains a second-class impact assessment combination, conducts tourist behavior pattern correlation analysis, and establishes a second-class tour guide optimization network. It configures tour navigation starting points based on real-time location information and tour progress, and establishes a tour guide optimization model based on the first and second-class optimization networks. It then performs multi-directional tour tracking iterations under time balance constraints, combining the tour navigation starting point and navigation needs of any user terminal within the same tour group, to obtain a multi-directional balanced tour route set and provide route navigation guidance. By dynamically planning guided routes through the influence analysis of road network nodes and road edges, the utilization rate of scenic spot resources is improved, and the distribution of tourist flow is optimized. At the same time, through multi-guided tracking and iteration under time balance constraints, the guided tour efficiency is optimized, and the technical effect of improving the tour experience is enhanced.

[0070] In summary, any step can be stored as a computer instruction or program in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor; no further restrictions are imposed here.

[0071] Furthermore, the above technical solutions only embody the preferred technical solutions of the embodiments of this application. Any changes that those skilled in the art may make to certain parts of these solutions embody the novel principles of the embodiments of this application. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application.

Claims

1. A navigation method based on the integration of tour routes and road networks, characterized in that, The method includes: Connect multiple user terminals belonging to the same tour group, collect multiple basic tour coordinates, and the multiple basic tour coordinates have tour item tags; Based on the multiple basic tour coordinates, an internal guide network of the multiple basic tour coordinates is uploaded, and multiple basic guide routes are proposed. Each basic guide route connects multiple basic tour coordinates. Based on the internal guide network of the multiple basic tour coordinates, the network nodes and their impact on tourist flow distribution are analyzed to obtain a type of impact assessment combination. Based on the type of impact assessment combination, a tourist behavior pattern correlation analysis is performed. Using the results of the first behavior pattern correlation analysis, a first guide optimization network is established. Based on the internal guide network of the multiple basic tour coordinates, the influence of the network edge and the network edge on the efficiency of the guide route is analyzed, two types of influence assessment combinations are obtained, and a tourist behavior pattern association analysis is performed based on the two types of influence assessment combinations. Using the results of the second behavior pattern association analysis, a second guide optimization network is established. The starting point for the tour guide is configured by using the real-time location information and tour progress of multiple users in the same tour group, and a tour guide optimization model is established based on the first tour guide optimization network and the second tour guide optimization network. Based on the tour guide optimization model, and combined with the tour guide starting point and tour guide requirements of any one of the multiple user terminals in the same tour group, multi-directional tour guide tracking iteration under time balance constraints is performed to obtain a multi-directional balanced tour guide route set and provide route navigation guidance. Using GIS technology and remote sensing data, the ecological environment of nature reserves is assessed, and sensitive areas are identified; Based on the sensitive area, the alternative routes for the first time window are filtered in combination with the preset safety distance, and the alternative routes for the first time window are incorporated into the tour guide optimization model. The method further includes incorporating alternative routes selected in the first time window into the navigation optimization model: Based on the alternative routes selected within the first time window, and combined with the preset safety distance, the first negative impact on the sensitive area is assessed; Based on the preset safety distance and the first negative impact, the remaining routes are adjusted to avoid the sensitive area as a limiting condition, and the alternative adjustment routes for the first time window are determined. A second time window is set, and in the overlapping part between the second time window and the first time window, the alternative adjustment routes of the first time window are incorporated into the tour guide optimization model; Based on the aforementioned tour guide optimization model, and combining the tour guide starting point and tour guide requirements of any one of the multiple user terminals belonging to the same tour group, the method performs multi-way tour guide tracking iteration under time balance constraints. The method further includes: Establish a sliding time window mechanism to integrate environmental change data and resource change data corresponding to the multiple basic tour coordinates, and update the open status bit of the tour item marker; If the updated open status bit is 1, activate one-way navigation; Iterate through multiple open status points associated with basic tour coordinates and activate one-way tour multiple times; The method further includes establishing a sliding time window mechanism: Based on the sliding time window mechanism, an overlap constraint strategy is introduced, which is used to adjust the degree of overlap between the second time window and the first time window. Based on the overlapping constraint strategy, a smooth transition mechanism is configured to handle data interruptions that occur during time window switching. Based on the smooth transition mechanism, the window switching logic of the sliding time window mechanism is adjusted accordingly; Based on the aforementioned tour guide optimization model, and combining the tour guide starting point and tour guide requirements of any one of the multiple user terminals belonging to the same tour group, a multi-way tour guide tracking iteration is performed under time balance constraints. The method includes: Collect the historical travel records of any one of the multiple user terminals belonging to the same tour group; Identify points of interest during visits by analyzing the historical visit records; Based on the points of interest, a matching score is added to each alternative route during the guided tour tracking iteration process; Based on the points of interest, a matching score is added to each alternative route during the guided tour tracking iteration process. The method includes: Based on the historical visit records, a visit information database is constructed, which is used to store historical visit routes and historical stay times; By combining the aforementioned visitor information database with social network information, potential points of interest can be identified. The points of interest and potential points of interest are classified and processed, and a matching evaluation strategy is formulated.

2. The navigation method based on the fusion of tour routes and road networks as described in claim 1, characterized in that, Based on the internal guide network of the multiple basic visitor coordinates, the network nodes and their impact on visitor flow distribution are analyzed to obtain a class of impact assessment combinations. The method includes: Collect visitor flow data for various scenic spots through IoT devices; Based on the visitor flow data of each scenic spot, peak and off-peak periods of visitor flow and visitor flow trends are identified, and a visitor flow distribution map is proposed. Based on the internal guide network of the multiple basic tour coordinates and the impact degree of the tourist flow distribution map, impact assessment information is determined, including node congestion level and tourist waiting time. Based on the internal navigation network of the multiple basic tour coordinates, and combined with the impact assessment information, the network nodes are traversed to obtain a type of impact assessment combination.

Images