Method for prompting scenic spot passenger flow hotspots in metropolitan area based on big data

Abstract

The invention relates to the technical field of passenger flow monitoring, and discloses a method for prompting scenic spot passenger flow hotspots in a metropolitan area based on big data, and the method comprises the steps: determining a touring path of a user in a city according to user identification information obtained by a service platform; combining all the sightseeing paths obtained on the day by taking the day as a cycle to obtain a tourist flow diagram in the city area range; determining weights of sightseeing path points in the graph; determining the matching between the weight between the touring path points in the graph and the planned path points; and prompting the scenic spots needing attention based on the difference part of the matching result. According to the method, through a big data technology, the passenger flow dynamic state of the scenic spot is accurately analyzed based on the actual sightseeing path, the flow direction and the scenic spot weight of the tourist, so that accurate passenger flow hotspot prompt is provided for the user.

Description

Technical Field

[0001] The present invention relates to the technical field of passenger flow monitoring, and in particular to a method for prompting passenger flow hot spots of scenic spots within a city based on big data. Background Art

[0002] With the rapid development of the global tourism industry, urban tourism has become an important choice for many tourists. Especially during holidays, popular attractions in the city often attract a large number of tourists, resulting in increased traffic pressure inside and outside the scenic area and frequent tourist congestion. At present, most scenic spot passenger flow management and recommendation systems are mainly based on historical data analysis, using the past tourists' tour behavior and the fixed weights of scenic spots for prediction and recommendation. However, this recommendation method based on static data cannot cope with the real-time changes in tourist flow. With the development of science and technology, big data and intelligent algorithms are increasingly used in various industries. Tourist passenger flow management based on big data analysis has become an important means to solve scenic spot congestion and optimize the tourist experience. By analyzing the real-time data of tourists' tour paths and dynamically providing passenger flow hotspot prompts for scenic spots, tourists can avoid crowded attractions and improve their tour experience.

[0003] Existing scenic spot recommendation systems generally have technical limitations. First, traditional systems cannot obtain tourists' tour trajectories and directions in real time, and cannot provide tourists with the best tour suggestions based on actual conditions, which may cause tourists to mistakenly enter high-traffic areas. Secondly, most existing systems are based on static weight calculation models and cannot flexibly respond to complex changes in passenger flow, especially when there are correlations between multiple scenic spots within the city, it is difficult to plan the best path for tourists. In addition, the current passenger flow management system also lacks sufficiently refined analysis methods and cannot provide accurate passenger flow hotspot prompts, especially for those scenic spots that are greatly affected by passenger flow fluctuations, the prompt effect is often delayed. For this reason, there is an urgent need for a scenic spot passenger flow hotspot prompt method based on big data analysis and real-time path adjustment to solve the above-mentioned problems in the prior art. Summary of the invention

[0004] At least one aspect or advantage of the invention will be set forth in part in the following description, or may be obvious from the description, or may be learned by practice of the disclosed subject matter.

[0005] According to a first aspect of the present invention, a method for prompting tourist flow hotspots of scenic spots in a city based on big data comprises:

[0006] Determine the user's tour path within the city based on the user identification information obtained by the service platform;

[0007] All the tourist routes obtained on the day are merged on a daily basis to obtain a tourist flow map within the city.

[0008] Determine the weights of the tour path points in the graph;

[0009] Determine the matching between the weights of the tour path points in the graph and the planned path points;

[0010] Based on the differences in the matching results, some scenic spots that need attention are prompted.

[0011] According to one embodiment of the present invention, the tourist flow diagram is obtained as follows:

[0012] A directed graph is constructed based on the scenic spots in the city, where the nodes of the directed graph are the scenic spots and the edges of the directed graph contain the duration of tourist movement and the time when the movement starts;

[0013] Determine directly connected tour path points and indirectly connected tour path points in the subgraph according to the directed graph and the nodes included in the tour path;

[0014] The directed graph is updated based on directly connected tour path points, and the directed graph is updated based on the shortest path algorithm and indirectly connected tour path points.

[0015] According to one embodiment of the present invention, updating the directed graph based on the shortest path algorithm and indirectly connecting the tour path points includes:

[0016] According to the starting point and end point of the tourist's browsing path, the shortest path algorithm is used to obtain the first shortest reachable path from the starting point and end point of the tour;

[0017] Based on the ranking of the flow of people on the nodes in the shortest reachable path, the node with the maximum flow on the shortest reachable path is selected as the mutation point;

[0018] Recalculate the second shortest reachable path from the starting point to the end point, where the second shortest reachable path does not contain the variation point;

[0019] Determine a coefficient of passing the first shortest reachable path and a coefficient of passing the second shortest reachable path based on the travel costs corresponding to the first shortest reachable path and the second shortest reachable path;

[0020] The directed graph is updated based on the coefficient of the first shortest reachable path and the first shortest reachable path, and the directed graph is updated based on the coefficient of the second shortest reachable path and the second shortest reachable path.

[0021] According to one embodiment of the present invention, the travel cost is the number of passengers traveling between the sightseeing route points.

[0022] According to one embodiment of the present invention, the passage cost is the number of passengers passing through obtained based on image acquisition devices arranged between sightseeing route points.

[0023] According to one embodiment of the present invention, the process of obtaining the weights between the tour path points in the graph includes:

[0024] Filter the edges of the directed graph according to the first duration to obtain several subgraphs;

[0025] The ratio of the net inflow of tourists to the calculation benchmark of each path point in each subgraph is calculated as the weight of the path point. The net inflow of tourists to each path point is obtained based on the edge set corresponding to the tourist tour path in the flow state in the directed graph;

[0026] The calculation basis is the inflow of the browsing path point with the maximum inflow determined according to the edge set;

[0027] There is an overlapping time domain between the departure time from the starting point to the arrival time at the end point of the browsing path of the tourists in the mobile state and the calculated time interval.

[0028] According to one embodiment of the present invention, the matching between the weights of the tour path points in the graph and the planned path points is determined as follows:

[0029] Obtain all edges in the graph and determine the difference between the actual weight of the edge and the preset weight value, where the actual weight of the edge is the difference between the starting weight and the ending weight of the edge;

[0030] In response to a difference between an actual weight of the edge and a preset weight value being greater than a first threshold, determining the matching is a positive match;

[0031] In response to a difference between an actual weight of the edge and a preset weight value being a value between a first threshold and a second threshold, determining the matching as a match;

[0032] In response to a difference between the actual weight of the edge and the preset weight value being less than a second threshold, the matching is determined to be a negative match.

[0033] According to an embodiment of the present invention, in response to the matching behavior being a negative match, adjacent edges involved in the start point and end point of the edge corresponding to the negative match are determined, and the adjacent edges are used as required prompt objects.

[0034] According to one embodiment of the present invention, in response to a matching behavior being a negative match, adjacent tour path points involved in the start and end points of the edge corresponding to the negative match are determined, and the adjacent tour path points and the edges associated with the adjacent tour path points are used as required prompt objects.

[0035] According to a second aspect of the present invention, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for prompting tourist flow hotspots of scenic spots within a city based on big data.

[0036] The technical solution of the present invention has the following technical effects:

[0037] The present invention uses big data technology to accurately analyze the passenger flow dynamics of scenic spots based on tourists' actual touring routes, flow directions and scenic spot weights, thereby providing users with accurate passenger flow hotspot prompts.

[0038] The present invention utilizes big data analysis technology, combined with a directed graph model, to calculate the weights and travel costs of different tour paths to identify hot spots that need special attention, and provides suggestions based on the passenger flow of different attractions, helping tourists avoid high passenger flow areas and optimize tour paths, thereby improving the user's travel experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A flow chart of a method for prompting tourist flow hotspots of scenic spots within a city based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present disclosure will now be discussed with reference to several exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and thus implement the present disclosure, rather than implying any limitation on the scope of the present disclosure.

[0041] As used herein, the term "including" and its variants are to be interpreted as open-ended terms meaning "including but not limited to". The term "based on" is to be interpreted as "based at least in part on". The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment". The term "another embodiment" is to be interpreted as "at least one other embodiment". The orientation or position relationship indicated by the terms "upper", "lower", "left", "right", "front", "back", "top", "bottom", "inner", "outer", "vertical", "horizontal", "lateral", "longitudinal" and the like is based on the orientation or position relationship shown in the accompanying drawings. These terms are mainly for better describing the present application and its embodiments, and are not used to limit the indicated devices, elements or components to have a specific orientation, or to be constructed and operated in a specific orientation. In addition, in addition to being used to indicate an orientation or position relationship, some of the above terms may also be used to indicate other meanings, such as the term "upper" may also be used to indicate a certain dependency or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in this application can be understood according to the specific circumstances. In addition, the terms "install", "set", "provided with", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection, or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be an internal connection between two devices, elements or components. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to the specific circumstances. In addition, the terms "first", "second", etc. are mainly used to distinguish different devices, elements or components (the specific types and structures may be the same or different), and are not used to indicate or imply the relative importance and quantity of the indicated devices, elements or components. Unless otherwise specified, "plurality" means two or more.

[0042] Current scenic spot recommendation and passenger flow management systems mainly rely on historical data and fixed scenic spot weights for analysis and prompts. However, these systems have some significant technical problems. First, they lack real-time performance and cannot adjust the recommendation plan according to the tourists' immediate tour path and current passenger flow, which may cause tourists to enter overcrowded attractions and reduce the tour experience. Secondly, most existing systems use fixed algorithms to process scenic spot recommendations and cannot flexibly respond to dynamic changes in passenger flow, especially during holidays or peak tourist seasons. The system's recommendations are often not accurate enough. Finally, the existing prompt system lacks refined analysis and cannot provide personalized and accurate passenger flow prompts based on the complex flow relationship between scenic spots, resulting in tourists being unable to effectively avoid high passenger flow areas, affecting the overall tour effect.

[0043] In this embodiment, refer to Figure 1As shown, a method for prompting tourist flow hotspots of scenic spots in a city based on big data is provided, including:

[0044] Determine the user's tour path within the city based on the user identification information obtained by the service platform;

[0045] All the tour routes obtained on the day are merged on a daily basis to obtain a tourist flow map within the city.

[0046] The present invention identifies user identification information, such as a tourist's account or mobile phone location information, and then determines the user's tour path in the city. With one day as a cycle, the tour trajectories of all tourists on that day are collected, and these data are merged to generate a tourist flow map within the city. By establishing a directed graph of scenic spots, the actual flow trajectory of tourists in the city can be clearly described, providing intuitive path data, and providing a more dynamic and accurate analysis and prompt basis for the tour path of the tour.

[0047] Specifically, the tourist flow diagram is obtained as follows:

[0048] A directed graph is constructed based on the scenic spots in the city, where the nodes of the directed graph are the scenic spots and the edges of the directed graph contain the duration of tourist movement and the time when the movement starts;

[0049] Determine directly connected tour path points and indirectly connected tour path points in the subgraph according to the directed graph and the nodes included in the tour path;

[0050] The directed graph is updated based on directly connected tour path points, and the directed graph is updated based on the shortest path algorithm and indirectly connected tour path points.

[0051] The tourist flow graph in the present invention is a directed graph model constructed based on various scenic spots within the city, wherein the nodes in the graph represent various scenic spots, the edges represent the movement of tourists between scenic spots, and the edges contain the duration information of the tourist movement and the start time of the movement. For example, a tourist departs from scenic spot A at 10:00 and arrives at scenic spot B 30 minutes later. In the directed graph, the edge from A to B indicates that the movement duration is 30 minutes and the start time of the movement is 10:00. The directed graph is updated by identifying directly connected tour path points or indirectly connected tour path points. Directly connected tour path points refer to tourists going directly from one scenic spot to another, for example, from scenic spot A directly to scenic spot B; indirectly connected tour path points are based on directly connected tour path points, and the shortest path algorithm is used to calculate the shortest path through the intermediate nodes, for example, from scenic spot A through scenic spot C and then to scenic spot B.

[0052] According to one embodiment of the present invention, updating a directed graph based on a shortest path algorithm and indirectly connecting tour path points includes:

[0053] According to the starting point and end point of the tourist's browsing path, the shortest path algorithm is used to obtain the first shortest reachable path from the starting point and end point of the tour;

[0054] Based on the ranking of the flow of people on the nodes in the shortest reachable path, the node with the maximum flow on the shortest reachable path is selected as the mutation point;

[0055] Recalculate the second shortest reachable path from the starting point to the end point, where the second shortest reachable path does not contain the variation point;

[0056] Determine a coefficient of passing the first shortest reachable path and a coefficient of passing the second shortest reachable path based on the travel costs corresponding to the first shortest reachable path and the second shortest reachable path;

[0057] The directed graph is updated based on the coefficient of the first shortest reachable path and the first shortest reachable path, and the directed graph is updated based on the coefficient of the second shortest reachable path and the second shortest reachable path.

[0058] The passage cost is the number of passengers passing between the sightseeing route points or the number of passengers passing through obtained based on the image acquisition device set between the sightseeing route points.

[0059] Specifically, when the passage cost is the number of tourists passing through the tour path points, the specific implementation method is as follows:

[0060] The system obtains the actual starting and ending point information of tourists through the service platform and determines the movement trajectory of tourists. In each time period, the system can construct a tour path between the starting point and the end point based on the location information of tourists.

[0061] Based on the obtained tour path information, the system uses the shortest path algorithm to calculate the first shortest reachable path from the tourist's tour starting point to the end point. For example, if a tourist goes from attraction A to attraction B, the system calculates A→C→B as the first shortest reachable path through a directed graph model.

[0062] Based on historical data, the system sorts the tourist flow of each node in the first shortest reachable path. Assuming that in the path A→C→B, the tourist flow of scenic spot C is the largest, the system marks scenic spot C as a mutation point. The acquisition of tourist flow can be achieved by real-time counting of the number of people passing through each scenic spot through image acquisition devices set at the entrance and exit of the scenic spot.

[0063] To prevent tourists from entering the variation point, the system recalculates the second shortest reachable path from the starting point A to the end point B based on the historical tour data, and this path does not include the variation point. Assuming the path is A→D→B, this path avoids the attraction C with a large tourist flow.

[0064] The system predicts the number of tourists passing through the tour route based on historical data. The pass cost is the tourist flow of each connecting path between attractions in the path from the starting point to the end point. For example, the pass cost of the first shortest path A→C→B is 1,000 people, and the pass cost of the second shortest path A→D→B is 300 people.

[0065] The system calculates the pass coefficients of the first shortest reachable path and the second shortest reachable path based on their pass costs. Assuming that the pass cost of the first path is 1,000 people and the pass cost of the second path is 300 people, the pass coefficients are proportionally allocated according to these values. The higher the coefficient of the first path, the greater the pressure on the passage.

[0066] Based on the traffic coefficient, the system updates the relevant edges in the directed graph. The edge weight of the first shortest path A→C→B will increase due to high traffic pressure, while the edge weight of the second shortest path A→D→B will decrease due to low traffic pressure. This dynamic adjustment enables the directed graph to more accurately reflect the passenger flow between scenic spots. By dynamically calculating the traffic cost, it is possible to combine historical data and automatically adjust the edge weight of the directed graph according to the tourist flow in different time periods, thereby continuously optimizing the recommendation of scenic spots.

[0067] When the passage cost is the number of passengers passing through obtained by the image acquisition device set between the sightseeing route points, the specific implementation method is as follows:

[0068] The service platform obtains the actual tour path of tourists through their user representation information. The system extracts the tour trajectory from the starting point to the end point of the tourist's tour. For example, if a tourist starts from attraction A and ends at attraction B, the system records and tracks his tour path. Through the real-time positioning information of tourists, the actual tour path of tourists can be accurately obtained, avoiding the errors of relying on historical data prediction and improving the accuracy of path information.

[0069] Based on the starting point and end point of the tourist, the system uses the shortest path algorithm to calculate the first shortest accessible path. For example, the tourist's tour path is from attraction A→C→B, and the system calculates this path as the first shortest accessible path from starting point A to end point B. Through the shortest path algorithm, the system can quickly and effectively find the shortest tour path under the current circumstances, thereby providing a basis for subsequent path optimization and improving the efficiency of path planning.

[0070] The system sorts all nodes (i.e., scenic spots) in the first shortest reachable path by tourist flow. Assuming that the tourist flow of scenic spot C is the largest, the system marks it as a variation point. At this time, the tourist flow data is acquired in real time through image acquisition devices installed at the entrance and exit of the scenic spot. By using the image acquisition device to count the number of passengers passing through each scenic spot in real time, the system can accurately identify congested scenic spots as variation points, help prevent tourists from entering areas with too much traffic, and enhance the tourists' sightseeing experience.

[0071] In order to avoid the variation point, the system recalculates the second shortest reachable path from the starting point A to the end point B without the variation point. For example, A→D→B becomes the new path, avoiding the tourist attraction C with a large flow of tourists. By recalculating the second shortest path without the variation point, the system can provide tourists with a more reasonable path selection, help tourists avoid areas with large flow of people, effectively disperse passenger flow, and reduce congestion pressure at certain attractions.

[0072] The system uses image acquisition devices installed between scenic spots to count the number of passengers passing through each path in real time. For example, the number of passengers passing through path A→C→B is 1,200, and the number of passengers passing through path A→D→B is 450. The system calculates the travel costs of the two paths based on these data. Through real-time data acquisition by image acquisition devices, the system can accurately calculate the travel costs of each path, reflect the flow of tourists in real time, and provide accurate data support for path optimization.

[0073] The system calculates the pass coefficient based on the pass costs of the two paths. For example, the pass cost of A→C→B is higher, and the pass coefficient increases accordingly, while the pass cost of A→D→B is lower, and the pass coefficient is smaller. In this way, the system can effectively identify paths with high and low traffic. By calculating the pass costs of the paths, the system can accurately identify which paths have greater traffic pressure, so as to better plan tourist diversion strategies, reduce congestion, and improve the management efficiency of scenic spots.

[0074] The system dynamically updates the edge weights of the directed graph based on the travel coefficients of the first shortest reachable path and the second shortest reachable path. For example, since the travel cost of A→C→B is high, the system will increase its weight, while the travel cost of A→D→B is low, the system will reduce its weight. Dynamically updating the edge weights of the directed graph enables the system to more flexibly reflect the current flow of tourists, realize intelligent path optimization and recommendation, and help tourists make more reasonable tour decisions.

[0075] According to one embodiment of the present invention, after obtaining the directed graph, the method further includes:

[0076] Determine the weights of the tour path points in the graph;

[0077] Determine the matching between the weights of the tour path points in the graph and the planned path points;

[0078] Based on the differences in the matching results, some scenic spots that need attention are prompted.

[0079] Specifically, the process of obtaining the weights between the tour path points in the graph includes:

[0080] The edges of the directed graph are filtered according to the first duration to obtain several subgraphs. The system filters the edges in the directed graph according to the set time period to obtain multiple subgraphs. Each subgraph represents the path situation under the flow state of a specific tourist in a period of time. In this way, the tourist flow characteristics in different time periods can be distinguished, including peak and non-peak periods. By filtering the edges of the directed graph, the tourist flow situation in different time periods can be separated more accurately, providing a more accurate data basis for subsequent weight calculations, thereby improving the dynamic influence ability of the system.

[0081] The ratio of the net inflow of tourists to the calculation benchmark of each path point in each subgraph is calculated as the weight of the path point. The net inflow of tourists to each path point is obtained based on the edge set corresponding to the tourist tour path in the flow state in the directed graph. Among them, the calculation benchmark is the inflow of the browsing path point with the maximum inflow determined according to the edge set; the departure time from the starting point to the arrival time at the end of the tourist browsing path in the flow state and the calculated time interval have overlapping time domains.

[0082] For each subgraph, the system calculates the net inflow of tourists for each tour path point. The net inflow of tourists refers to the difference between the number of tourists entering the path point and the number of tourists leaving the path point. Based on the ratio of the net inflow to the calculation benchmark, the system assigns a weight to each path point. The calculation benchmark is determined based on the edge set, and the inflow of the path point with the largest inflow is usually selected as the benchmark. By calculating the net inflow of the path point and comparing it with the maximum inflow, it is possible to more clearly identify which path points are the main gathering points of passenger flow, helping the system focus on high-traffic areas and provide accurate prompts.

[0083] Specifically, the matching between the weights of the tour path points in the graph and the planned path points is determined as follows:

[0084] Obtain all edges in the graph and determine the difference between the actual weight of the edge and the preset weight value, where the actual weight of the edge is the difference between the starting weight and the ending weight of the edge;

[0085] In response to a difference between an actual weight of the edge and a preset weight value being greater than a first threshold, determining the matching is a positive match;

[0086] In response to a difference between an actual weight of the edge and a preset weight value being a value between a first threshold and a second threshold, determining the matching as a match;

[0087] In response to a difference between the actual weight of the edge and the preset weight value being less than a second threshold, the matching is determined to be a negative match.

[0088] In response to the matching behavior being a negative match, adjacent edges involved in the start point and the end point of the edge corresponding to the negative match are determined, and the adjacent edges are used as required prompt objects.

[0089] In response to the matching behavior being a negative match, adjacent tour path points involved in the start point and the end point of the edge corresponding to the negative match are determined, and the adjacent tour path points and the edges associated with the adjacent tour path points are used as required prompt objects.

[0090] After the weights are determined, the system then analyzes the weights between the tour path points and the matching between the planned path points. Specifically, the system first obtains all the edges in the graph and calculates the difference between the actual weight of each edge (the difference between the starting weight and the end weight of the edge) and the preset weight value.

[0091] If the difference value is greater than the set first threshold, the system determines that the path matching is a "positive match", indicating that the path conforms to the planned path and can proceed as planned.

[0092] If the difference value is between the first threshold and the second threshold, it is determined to be a "match", indicating that the path conforms to a certain plan, but there may be some deviations.

[0093] If the difference value is less than the second threshold, the system will determine it as a "negative match", that is, the path deviates from the planned path, and there may be problems with tourists being stranded or deviated, which requires special prompts.

[0094] By calculating the weight differences of the edges, the system can quickly identify the deviation between the planned path and the actual tourist flow path, ensuring that scenic area operators or tourists can promptly understand path deviations and make adjustments or avoid congestion.

[0095] When the match is "negative match", the system will further analyze the adjacent edges involved in the start and end points of the edge corresponding to the negative match. The system will take these adjacent edges as objects that need to be prompted, and remind tourists that these path points may have problems, such as excessive flow of people, detention, etc. By timely prompting the adjacent paths and attractions of negative matches, the system can effectively remind tourists which attractions or paths may need to be avoided, prevent tourists from entering overly crowded areas, and optimize the tour experience.

[0096] The system also identifies the adjacent tour path points of the negative matching path points and uses these path points and their associated edges as key prompts. These prompts help tourists not only avoid the current problematic path, but also choose a more suitable alternative route to ensure a smooth tour. This step ensures that the system can provide tourists with more comprehensive suggestions, not only for the current problematic path, but also foresee possible problems in the paths connected to it, thereby further improving tourists' decision-making efficiency and tour experience.

[0097] According to one embodiment of the present invention, a computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, a method for prompting tourist flow hotspots of scenic spots within a city based on big data is implemented.

[0098] The beneficial effects of the present invention are:

[0099] The user identification information obtained by the system can track the tourist's tour path in real time and generate a dynamically updated tourist flow map within the city. This method is more real-time and accurate than the traditional prediction based on historical data, allowing the system to reflect the current tourist flow in a timely manner, thereby providing more reasonable route recommendations and prompts.

[0100] Through the shortest path algorithm and dynamic analysis of tourist flow, nodes with high passenger flow (variation points) are identified, and alternative routes that avoid congested areas are recommended for tourists, significantly improving the tourists' sightseeing experience. By calculating the travel costs of the shortest path and the second shortest path, the system can effectively divert tourists, reduce the pressure of crowded attractions, and help tourists arrange their itineraries reasonably to avoid peak hours and crowded attractions.

[0101] Through the weight calculation and matching analysis of the path points in the directed graph, the system can accurately analyze the passenger flow of each tour path in the scenic area. Based on the weight difference, the system can not only identify the difference between the actual flow of tourists and the planned path, but also promptly remind tourists to pay attention to certain potential problem attractions to prevent tourists from being stranded or entering risky areas. This precise reminder mechanism helps scenic area managers and tourists make more informed decisions.

[0102] By calculating the travel cost of each path and updating the edge weights in the directed graph in real time, intelligent path recommendations can be achieved. The system can automatically adjust the recommended tour routes based on the actual flow of tourists, thereby continuously optimizing scenic spot recommendations in different time periods. This mechanism ensures the accuracy and dynamic adaptability of the recommended paths and improves the overall operating efficiency of the scenic spot.

[0103] By analyzing passenger flow in different time periods (such as peak and non-peak periods), the system can distinguish the characteristics of tourist flow in different time periods and provide more flexible route adjustment and optimization solutions. In addition, based on the real-time data feedback of the image acquisition device, the system has high-precision tourist flow monitoring capabilities and can quickly respond to sudden changes in passenger flow and special events.

[0104] The system of the present invention not only identifies matching by calculating the weight difference of each tour path point, but also prompts the negative matching path and its adjacent path points. This comprehensive prompt mechanism helps tourists not only avoid the current problematic path when making decisions, but also predict the problems that may be encountered in the future in advance, ensuring the continuity and smoothness of the tour.

[0105] Those of ordinary skill in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0106] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0108] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0109] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0110] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the energy-saving signal sending / receiving method of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0111] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the present application.

[0112] It should be understood that the size of the sequence number of each step in the content of the invention and the embodiments of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. For the purpose of example and description, the foregoing description of the implementation of the present disclosure has been given. The foregoing description is not exhaustive and is not intended to limit the present disclosure to the exact form disclosed. Various deformations and modifications may exist according to the above teachings, or various deformations and modifications may be obtained from the practice of the present disclosure. These embodiments are selected and described to illustrate the principles of the present disclosure and its practical application, so that those skilled in the art can use the present disclosure in various embodiments and various modifications suitable for the specific purpose conceived.

Claims

1. A method for prompting tourist flow hotspots of scenic spots in a city based on big data, characterized in that: include: Determine the user's tour path within the city based on the user identification information obtained by the service platform; All the tourist routes obtained on the day are merged on a daily basis to obtain a tourist flow map within the city. Determine the weights of the tour path points in the graph; Determine the matching between the weights of the tour path points in the graph and the planned path points; Based on the differences in the matching results, some scenic spots that need attention are prompted.

2. The method for prompting tourist flow hot spots in a metropolitan area based on big data as claimed in claim 1, characterized in that: The tourist flow diagram is obtained as follows: A directed graph is constructed based on the scenic spots in the city, where the nodes of the directed graph are the scenic spots and the edges of the directed graph contain the duration of tourist movement and the time when the movement starts; Determine directly connected tour path points and indirectly connected tour path points in the subgraph according to the directed graph and the nodes included in the tour path; The directed graph is updated based on directly connected tour path points, and the directed graph is updated based on the shortest path algorithm and indirectly connected tour path points.

3. The method for prompting tourist flow hot spots in a metropolitan area based on big data as claimed in claim 2, characterized in that: The updating of the directed graph based on the shortest path algorithm and the indirect connection of the tour path points includes: According to the starting point and end point of the tourist's browsing path, the shortest path algorithm is used to obtain the first shortest reachable path from the starting point and end point of the tour; Based on the ranking of the flow of people on the nodes in the shortest reachable path, the node with the maximum flow on the shortest reachable path is selected as the mutation point; Recalculate the second shortest reachable path from the starting point to the end point, where the second shortest reachable path does not contain the variation point; Determine a coefficient of passing the first shortest reachable path and a coefficient of passing the second shortest reachable path based on the travel costs corresponding to the first shortest reachable path and the second shortest reachable path; The directed graph is updated based on the coefficient of the first shortest reachable path and the first shortest reachable path, and the directed graph is updated based on the coefficient of the second shortest reachable path and the second shortest reachable path.

4. The method for prompting tourist flow hot spots in a metropolitan area based on big data as claimed in claim 3, characterized in that: The travel cost is the number of passengers passing through the tour path points.

5. The method for prompting tourist flow hot spots in a metropolitan area based on big data as claimed in claim 3, characterized in that: The passage cost is the number of passengers passing through obtained based on the image acquisition devices set between the sightseeing route points.

6. The method for prompting tourist flow hot spots in a metropolitan area based on big data as claimed in claim 1, characterized in that: The process of obtaining the weights between the tour path points in the graph includes: Filter the edges of the directed graph according to the first duration to obtain several subgraphs; The ratio of the net inflow of tourists to the calculation benchmark of each path point in each subgraph is calculated as the weight of the path point. The net inflow of tourists to each path point is obtained based on the edge set corresponding to the tourist tour path in the flow state in the directed graph; The calculation basis is the inflow of the browsing path point with the maximum inflow determined according to the edge set; There is an overlapping time domain between the departure time from the starting point to the arrival time at the end point of the browsing path of the tourists in the mobile state and the calculated time interval.

7. The method for prompting tourist flow hot spots in a metropolitan area based on big data as claimed in claim 6, characterized in that: The matching between the weights of the tour path points in the graph and the planned path points is determined as follows: Obtain all edges in the graph and determine the difference between the actual weight of the edge and the preset weight value, where the actual weight of the edge is the difference between the starting weight and the ending weight of the edge; In response to a difference between an actual weight of the edge and a preset weight value being greater than a first threshold, determining the matching is a positive match; In response to a difference between an actual weight of the edge and a preset weight value being a value between a first threshold and a second threshold, determining the matching as a match; In response to a difference between the actual weight of the edge and the preset weight value being less than a second threshold, the matching is determined to be a negative match.

8. The method for prompting tourist flow hot spots in a metropolitan area based on big data as claimed in claim 7, characterized in that: In response to the matching behavior being a negative match, adjacent edges involved in the start point and the end point of the edge corresponding to the negative match are determined, and the adjacent edges are used as required prompt objects.

9. The method for prompting tourist flow hot spots in a metropolitan area based on big data as claimed in claim 7, characterized in that: In response to the matching behavior being a negative match, adjacent tour path points involved in the start point and the end point of the edge corresponding to the negative match are determined, and the adjacent tour path points and the edges associated with the adjacent tour path points are used as required prompt objects.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the method for prompting tourist flow hotspots of scenic spots within a city based on big data as described in any one of claims 1 to 9 is implemented.

Images