An intelligent tourism scenic spot optimization management system and method based on big data

Abstract

The application discloses an intelligent travel and tourism scenic spot optimization management system and method based on big data, and belongs to the technical field of scenic spot optimization management; an overall layout map of a scenic spot is acquired, and a tour route is marked; an affiliated scenic spot of the tour route is acquired; the tour route is divided into line segments in sequence based on the affiliated scenic spot; average passenger flow data in the line segments is acquired; time tags are attached to the average passenger flow data in the line segments, an experience flow index of the line segments is calculated based on the average passenger flow data at a single time point; the experience flow index of the line segments is analyzed, early warning is output, and the transferred passenger flow is calculated and distributed. The experience flow index is calculated, and the loads of the line segments are balanced; not only is the tour experience of tourists improved, and congestion and queuing time reduced, but also the allocation of scenic spot resources is optimized, the operation efficiency of the scenic spot is improved, and safety and order are ensured.

Description

Technical Field

[0001] This invention relates to the field of scenic area optimization management technology, specifically to an intelligent cultural tourism scenic area optimization management system and method based on big data. Background Technology

[0002] In recent years, with the booming development of technologies such as big data, the Internet of Things, and artificial intelligence, scenic area management has gradually transformed towards intelligence and data-driven approaches. Intelligent scenic area management methods based on big data can predict and dynamically adjust tourist flow trends by collecting real-time tourist behavior data and environmental data. The application of these technologies can not only improve the efficiency of scenic area management but also enhance the tourist experience to a certain extent. However, traditional management methods cannot flexibly cope with fluctuations in tourist flow and changes in tourist demand within the scenic area. Especially during peak hours, some popular attractions within the scenic area may face severe congestion, affecting the tourist experience and the operational safety of the scenic area.

[0003] Existing intelligent cultural tourism scenic area management technologies still suffer from problems such as uneven data distribution, insufficient traffic allocation, and a lack of intelligent emergency dispatch. For example, although some existing systems can acquire tourist traffic data within the scenic area through sensors, these systems often cannot calculate and respond to complex tourist flow patterns in real time, leading to excessive congestion on some sections of the scenic area and an inability to flexibly dispatch tourists to other routes. This limitation means that the mobility of tourists and the optimization space for scenic area management are not fully utilized, and the carrying capacity of the scenic area cannot be dynamically adjusted through intelligent means. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent cultural tourism scenic area optimization management system and method based on big data, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A smart cultural tourism scenic area optimization management method based on big data includes the following steps: Step S1: Obtain the overall layout map of the scenic area and mark the tour route; obtain the ancillary attractions of the tour route; Step S2: Based on the ancillary attractions, divide the tour route into segments in sequence; obtain the average visitor flow data within the segments; Step S3: Add time tags to the average visitor flow data within the segments, and calculate the experience mobility index of the segments based on the average visitor flow data within a single time point; Step S4: If the experience mobility index of a segment is greater than or equal to the mobility index threshold, it is recorded as a segment to be diverted and is given an early warning for maintenance; obtain segments with experience mobility indices less than the mobility index threshold, calculate the transferred visitor flow, and allocate it.

[0007] As a preferred embodiment of the intelligent cultural tourism scenic area optimization management method based on big data described in this invention, a remote sensing survey device is mounted on a drone to obtain an overall layout map of the scenic area. The overall layout map includes tour routes, which are numbered, with the i-th tour route denoted as TR. i The tour route TR is marked on the overall layout map. i .

[0008] Based on the overall layout map, obtain the location information of all attractions in the scenic area; for each attraction, obtain the tour route TR from the attraction. i The straight-line distance from the scenic spot to the tour route TR i The straight-line distance from the attraction to the tourist route TR is less than or equal to the distance from the attraction to the tourist route TR. i The straight-line distance of all other tourist routes, excluding those mentioned above, is used to mark the attractions as tourist routes TR. i Accessible attractions; if the attractions are on the tour route TR i The straight-line distance is greater than the distance from the attraction to the tourist route TR. i If all other routes are not included in the list of routes, the route with the shortest straight-line distance is selected, and the attraction is marked as an accessory attraction of that route. If there are multiple routes with the shortest straight-line distance, the attraction is marked as an accessory attraction of the route with the lowest number.

[0009] As a preferred embodiment of the intelligent cultural tourism scenic area optimization management method based on big data described in this invention, based on the tour route TR i All the ancillary attractions on the site, and the tour route marked on the overall layout map TR i The text appears to be a mix of unrelated phrases and sentences, making it impossible to translate coherently. It seems to be a collection of fragments from various sources, possibly related to a tour route (TR), a tour route, ... i All secondary attractions are marked on the map; based on these secondary attractions, the tour route TR will be developed. i The sequence is divided into J-1 line segments, where the first end of the j-th line segment is the j-th affiliated scenic spot, and the last end is the (j+1)-th affiliated scenic spot. The j-th line segment is denoted as XD[A]. j (TR i → A j+1 (TR i )], where A j (TR i ) indicates the tour route TR i The j-th affiliated attraction, A j+1 (TR i ) indicates the tour route TR i The (j+1)th affiliated attraction is given, where J represents the total number of affiliated attractions.

[0010] In line segment XD[A j (TRi → A j+1 (TR i A laser sensor is installed within the line segment XD[A] to monitor pedestrian flow data and acquire the data of the line segment. j (TR i → A j+1 (TR i Average passenger flow data within [ )].

[0011] As a preferred embodiment of the intelligent cultural tourism scenic area optimization management method based on big data described in this invention, for line segment XD[A j (TR i → A j+1 (TR i The average passenger flow data within the range is appended with a time label, and the average passenger flow data at time point t is recorded as AP. t [A j (TR i → A j+1 (TR i )).

[0012] AP based on average passenger flow data at time point t t [A j (TR i → A j+1 (TR i )], calculate line segment XD[A j (TR i → A j+1 (TR i The liquidity index at time point t is calculated using the following formula:

[0013]

[0014] Among them, EMI t [A j (TR i → A j+1 (TR i )] represents line segment XD[A j (TR i → A j+1 (TR i The liquidity index at time point t, C cap (XD[A j (TR i → A j+1 (TR i )]) represents the preset line segment XD[A j (TR i → A j+1 (TRi The maximum passenger capacity.

[0015] It should be noted that the experience mobility index is a key technical means of this invention. It is a core indicator that can help scenic area managers understand the real-time distribution of visitor flow within the scenic area. By monitoring the experience mobility index of each route segment, managers can quickly identify which route segments may experience congestion.

[0016] As a preferred embodiment of the intelligent cultural tourism scenic area optimization management method based on big data described in this invention, if line segment XD[A j (TR i → A j+1 (TR i The EMI (Experience Liquidity Index) at time point t. t [A j (TR i → A j+1 (TR i If the value of XD[A] is greater than or equal to the liquidity index threshold, then the line segment XD[A] will be... j (TR i → A j+1 (TR i This section is marked as the section to be diverted, and an early warning is issued to remind staff to perform infrastructure maintenance.

[0017] Based on the overall layout diagram, obtain the distance from line segment XD[A] j (TR i → A j+1 (TR i Recently, for line segments where the experience liquidity index is less than the liquidity index threshold, passenger flow is transferred, and the calculation formula for the passenger flow transfer is as follows:

[0018]

[0019] in, Indicates the flow transfer coefficient. β represents the preset initial flow transfer coefficient, and β represents the preset adjustment factor. EMI t [A k (TR i → A k+1 (TR i )] represents the distance from line segment XD[A j (TR i → A j+1 (TR i Recently, and the line segment with an experience liquidity index less than the liquidity index threshold, and k≠j, F i,j,t This indicates the passenger flow that needs to be delivered.

[0020] The passenger flow F that needs to be transferred i,j,t From line segment XD[A j (TR i → A j+1 (TR i EMI transmitted to the line segment t [A k (TR i → A k+1 (TR i )]middle.

[0021] It should be noted that the main function of this formula in this invention is to achieve dynamic optimization of visitor flow within the scenic area. When a certain route segment experiences congestion (i.e., a high experience mobility index), by calculating the flow transfer coefficient and the required flow of visitors, some tourists can be reasonably guided to other, less congested routes. For example, if the visitor flow of a popular attraction route segment is too high, these formulas can determine how many tourists should be guided to nearby attractions to balance the visitor flow across different routes. This visitor flow allocation method is highly flexible and adaptable, and can be dynamically adjusted according to the real-time visitor flow within the scenic area, avoiding the rigidity of a fixed management model. By rationally guiding visitor flow, not only can the overall visitor experience be improved and queuing time reduced, but also the various attractions and facilities within the scenic area can be fully utilized, avoiding situations where some route segments are idle while others are overused. At the same time, this dynamic management method helps the scenic area react quickly to sudden peak visitor flows, ensuring the orderly operation of the scenic area.

[0022] A smart cultural tourism scenic area optimization management system based on big data includes: a data collection and scenic spot association module, an auxiliary scenic spot labeling and visitor flow monitoring module, an experience mobility index calculation module, and a visitor flow dynamic allocation module.

[0023] The data collection and attraction association module: obtains the overall layout map of the scenic area and marks the tour route; obtains the auxiliary attractions along the tour route.

[0024] The attached attraction labeling and visitor flow monitoring module: Based on the attached attractions, the tour route is divided into segments in sequence; and the average visitor flow data within the segments is obtained.

[0025] The experience mobility index calculation module adds a time label to the average passenger flow data within the line segment, and calculates the experience mobility index of the line segment based on the average passenger flow data within a single time point.

[0026] The dynamic passenger flow allocation module: if the experience flow index of the line segment is greater than or equal to the flow index threshold, it is recorded as a line segment to be diverted and an early warning is issued for maintenance; it obtains line segments with experience flow index less than the flow index threshold, calculates the transferred passenger flow, and allocates it.

[0027] Furthermore, the data acquisition and attraction association module includes a data acquisition unit and an attraction association unit.

[0028] The data acquisition unit acquires an overall layout map of the scenic area by mounting remote sensing survey equipment on a drone. The overall layout map includes tour routes, which are numbered and marked on the overall layout map.

[0029] The scenic spot association unit: Based on the overall layout map, obtains the location information of all scenic spots in the scenic area; for each scenic spot, obtains the straight-line distance from the scenic spot to the tour route; if the straight-line distance from the scenic spot to the tour route is less than or equal to the straight-line distance from the scenic spot to all tour routes other than the tour route, then the scenic spot is marked as an affiliated scenic spot of the tour route; if the straight-line distance from the scenic spot to the tour route is greater than the straight-line distance from the scenic spot to all tour routes other than the tour route, then the tour route with the smallest straight-line distance is obtained, and the scenic spot is marked as an affiliated scenic spot of that tour route; if there are multiple tour routes with the smallest straight-line distance, then the scenic spot is marked as an affiliated scenic spot of the tour route with the smallest number.

[0030] Furthermore, the auxiliary attraction labeling and visitor flow monitoring module includes an auxiliary attraction labeling unit and a visitor flow monitoring unit.

[0031] The auxiliary attraction labeling unit: Based on all the auxiliary attractions on the tour route, a secondary labeling is performed on the tour route marked on the overall layout map. The secondary labeling indicates that all the auxiliary attractions are marked on the tour route. Based on the marked auxiliary attractions, the tour route is sequentially divided into multiple line segments.

[0032] The passenger flow monitoring unit: laser sensors are deployed within the line segment. The laser sensors are used to monitor the passenger flow data within the line segment and obtain the average passenger flow data within the line segment.

[0033] Furthermore, the experience liquidity index calculation module includes an experience liquidity index calculation unit.

[0034] The experience mobility index calculation unit: adds a time label to the average passenger flow data within the line segment; and calculates the experience mobility index of the line segment at time point t based on the average passenger flow data at a single time point.

[0035] Furthermore, the dynamic passenger flow allocation module includes an early warning and maintenance unit and a dynamic passenger flow allocation unit.

[0036] The early warning and maintenance unit: if the experience liquidity index of a line segment at a single point in time is greater than or equal to the liquidity index threshold, the line segment is marked as a line segment to be diverted and an early warning is issued to remind staff to carry out infrastructure maintenance.

[0037] The dynamic passenger flow allocation unit: based on the overall layout diagram, obtains the line segment closest to the line segment and whose experience flow index is less than the flow index threshold, and calculates the passenger flow that needs to be transferred; and transfers the passenger flow that needs to be transferred between line segments.

[0038] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: The intelligent cultural tourism scenic area optimization management system and method based on big data provided by this invention accurately acquires the overall layout map and tour routes of the scenic area, marks auxiliary attractions, and divides the tour routes into multiple segments. Laser sensors are used to monitor the visitor flow data of each segment in real time, providing a reliable foundation for subsequent visitor flow analysis. Based on this, the system calculates the experience mobility index to objectively assess the congestion level of each segment, helping scenic area managers accurately identify potential congestion areas. If the experience mobility index of a certain segment is greater than or equal to the mobility index threshold, the system automatically issues a flow guidance warning, promptly guiding tourists to segments with lower flow. Furthermore, the system optimizes visitor flow distribution and balances the load on each segment through a dynamic flow transfer formula. This method not only improves the visitor experience and reduces congestion and queuing time, but also optimizes the allocation of scenic area resources, improves the operational efficiency of the scenic area, and ensures safety and order. Attached Figure Description

[0039] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0040] Figure 1 This is a schematic diagram illustrating the steps of an intelligent cultural tourism scenic area optimization management method based on big data according to the present invention;

[0041] Figure 2 This is a schematic diagram of the structure of an intelligent cultural tourism scenic area optimization management system based on big data according to the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Please see Figure 1 In this first embodiment, a smart cultural tourism scenic area optimization management method based on big data is provided. The method includes the following steps:

[0044] Step S1: Obtain the overall layout map of the scenic area and mark the tour route; obtain the auxiliary attractions along the tour route.

[0045] Specifically, by equipping a drone with remote sensing surveying equipment, an overall layout map of the scenic area is obtained. This overall layout map includes tour routes, which are then numbered, with the i-th tour route denoted as TR. i The tour route TR is marked on the overall layout map. i .

[0046] Furthermore, based on the overall layout map, the location information of all attractions in the scenic area is obtained; for each attraction, the route TR from the attraction to the tour is obtained. i The straight-line distance from the scenic spot to the tour route TR i The straight-line distance from the attraction to the tourist route TR is less than or equal to the distance from the attraction to the tourist route TR. i The straight-line distance of all other tourist routes, excluding those mentioned above, is used to mark the attractions as tourist routes TR. i Accessible attractions; if the attractions are on the tour route TR i The straight-line distance is greater than the distance from the attraction to the tourist route TR. i If all other routes are not included in the list of routes, the route with the shortest straight-line distance is selected, and the attraction is marked as an accessory attraction of that route. If there are multiple routes with the shortest straight-line distance, the attraction is marked as an accessory attraction of the route with the lowest number.

[0047] Step S2: Based on the affiliated attractions, divide the tour route into segments in sequence; obtain the average visitor flow data within the segments.

[0048] Specifically, based on the tour route TR i All the ancillary attractions on the site, and the tour route marked on the overall layout map TR i The text appears to be a mix of unrelated phrases and sentences, making it impossible to translate coherently. It seems to be a collection of fragments from various sources, possibly related to a tour route (TR), a tour route, ... i All secondary attractions are marked on the map; based on these secondary attractions, the tour route TR will be developed. i The sequence is divided into J-1 line segments, where the first end of the j-th line segment is the j-th affiliated scenic spot, and the last end is the (j+1)-th affiliated scenic spot. The j-th line segment is denoted as XD[A]. j (TR i → A j+1 (TR i )], where A j (TR i) indicates the tour route TR i The j-th affiliated attraction, A j+1 (TR i ) indicates the tour route TR i The (j+1)th affiliated attraction is given, where J represents the total number of affiliated attractions.

[0049] Furthermore, in the line segment XD[A j (TR i → A j+1 (TR i A laser sensor is installed within the line segment XD[A] to monitor pedestrian flow data and acquire the data of the line segment. j (TR i → A j+1 (TR i Average passenger flow data within [ )].

[0050] Step S3: Add time labels to the average passenger flow data within the line segment, and calculate the experience mobility index of the line segment based on the average passenger flow data within a single time point.

[0051] Specifically, for line segment XD[A j (TR i → A j+1 (TR i The average passenger flow data within the range is appended with a time label, and the average passenger flow data at time point t is recorded as AP. t [A j (TR i → A j+1 (TR i )).

[0052] Furthermore, based on the average passenger flow data at time point t, AP t [A j (TR i → A j+1 (TR i )], calculate line segment XD[A j (TR i → A j+1 (TR i The liquidity index at time point t is calculated using the following formula:

[0053]

[0054] Among them, EMI t [A j (TR i → A j+1 (TR i )] represents line segment XD[Aj (TR i → A j+1 (TR i The liquidity index at time point t, C cap (XD[A j (TR i → A j+1 (TR i )]) represents the preset line segment XD[A j (TR i → A j+1 (TR i The maximum passenger capacity.

[0055] For example, suppose at time point 1, line segment XD[A1(TR i1 The average passenger flow data AP1[A1(TR1)→A2(TR1)] is 100, and the preset maximum passenger flow capacity C of line segment XD[A1(TR1)→A2(TR1)] is 100. cap If (XD[A1(TR1)→A2(TR1)]) is 98, then the experiential liquidity index EMI1[A1(TR1)→A2(TR1)] = 1.02 is obtained by substituting it into the formula.

[0056] Step S4: If the experience mobility index of the line segment is greater than or equal to the mobility index threshold, it is recorded as a line segment to be guided and an early warning maintenance is issued; obtain the line segments with experience mobility index less than the mobility index threshold, calculate the transmitted passenger flow and allocate it.

[0057] Specifically, if line segment XD[A j (TR i → A j+1 (TR i The EMI (Experience Liquidity Index) at time point t. t [A j (TR i → A j+1 (TR i If the value of XD[A] is greater than or equal to the liquidity index threshold, then the line segment XD[A] will be... j (TR i → A j+1 (TR i This section is marked as the section to be diverted, and an early warning is issued to remind staff to perform infrastructure maintenance.

[0058] Furthermore, based on the overall layout diagram, the distance from line segment XD[A] is obtained. j (TR i → A j+1 (TR iRecently, for line segments where the experience liquidity index is less than the liquidity index threshold, passenger flow is transferred, and the calculation formula for the passenger flow transfer is as follows:

[0059]

[0060]

[0061] in, Indicates the flow transfer coefficient. β represents the preset initial flow transfer coefficient, and β represents the preset adjustment factor. EMI t [A k (TR i → A k+1 (TR i )] represents the distance from line segment XD[A j (TR i → A j+1 (TR i Recently, and the line segment with an experience liquidity index less than the liquidity index threshold, and k≠j, F i,j,t This indicates the passenger flow that needs to be delivered.

[0062] The passenger flow F that needs to be transferred i,j,t From line segment XD[A j (TR i → A j+1 (TR i EMI transmitted to the line segment t [A k (TR i → A k+1 (TR i )]middle.

[0063] For example, the mobility index threshold can typically be set to 1. When the mobility index of a certain route segment approaches or exceeds 1, it means that the visitor flow of that route segment has approached or reached its maximum capacity, which may lead to overcrowding and a decline in the visitor experience. The mobility index is highly objective and accurate, allowing scenic area managers to overcome the limitations of subjective judgment and manage in a more scientific way. By monitoring the mobility index in real time, managers can take timely and effective optimization measures, such as guiding tourists to other less congested routes or adding temporary facilities when necessary to alleviate visitor pressure. This not only improves visitor comfort but also ensures the operational safety and order of the scenic area. When the mobility index EMI... t [A j (TR i → A j+1 (TR iWhen the EMI (Experience Mobility Index) is 0, it indicates that there are no tourists on the line segment at time t, resulting in extremely poor visitor mobility; when the EMI is 0, it indicates that there are no tourists on the line segment at time t, resulting in extremely poor visitor mobility. t [A j (TR i → A j+1 (TR i When the EMI (Experience Liquidity Index) is 1, it indicates that the line segment has been at full load throughout time t, and the liquidity has reached its limit. t [A j (TR i → A j+1 (TR i A value greater than 1 indicates that, at time t, the actual passenger flow on the line segment has exceeded the maximum passenger flow that the line segment can handle, which may lead to a sharp decline in the visitor experience, such as overcrowding and excessively long queues; furthermore, this affects the Experience Mobility Index (EMI). t [A j (TR i → A j+1 (TR i A line segment with a value greater than or equal to 1 can indicate that the attractions at the beginning and end of the line segment are popular and attractive. However, for the attractions, this may lead to increased wear and tear on the infrastructure and a risk of malfunction.

[0064] For example, assuming a preset initial flow transfer coefficient Given a value of 0.3, a preset adjustment factor β of 0.5, and EMI1[A2(TR1)→A3(TR1)] of 0.65, the flow transfer coefficient can be calculated by substituting these values ​​into the formula.

[0065] It should be noted that this method, by monitoring the visitor flow of each tour route within the scenic area in real time and calculating the experience mobility index in conjunction with time tags, can accurately reflect the carrying capacity of each route segment. Through dynamic adjustment and rational diversion of visitor flow, it avoids the static constraints of traditional management models, providing a more flexible and real-time responsive scenic area management solution. In particular, through the calculation of the experience mobility index and the flow transmission mechanism, this invention can intelligently adjust visitor routes, reduce congestion, improve the overall visitor experience, and enhance the safety and efficiency of scenic area management. Therefore, this invention provides a more innovative and practically valuable optimization solution for existing intelligent scenic area management technologies, filling the gap in emergency dispatch and dynamic adjustment in traditional management methods.

[0066] Please see Figure 2In this second embodiment, a smart cultural tourism scenic area optimization management system based on big data is provided. The system includes: a data collection and scenic spot association module, an auxiliary scenic spot labeling and visitor flow monitoring module, an experience mobility index calculation module, and a visitor flow dynamic allocation module.

[0067] The data collection and attraction association module: obtains the overall layout map of the scenic area and marks the tour route; obtains the auxiliary attractions along the tour route.

[0068] The attached attraction labeling and visitor flow monitoring module: Based on the attached attractions, the tour route is divided into segments in sequence; and the average visitor flow data within the segments is obtained.

[0069] The experience mobility index calculation module adds a time label to the average passenger flow data within the line segment, and calculates the experience mobility index of the line segment based on the average passenger flow data within a single time point.

[0070] The dynamic passenger flow allocation module: if the experience flow index of the line segment is greater than or equal to the flow index threshold, it is recorded as a line segment to be diverted and an early warning is issued for maintenance; it obtains line segments with experience flow index less than the flow index threshold, calculates the transferred passenger flow, and allocates it.

[0071] Furthermore, the data acquisition and attraction association module includes a data acquisition unit and an attraction association unit.

[0072] The data acquisition unit acquires an overall layout map of the scenic area by mounting remote sensing survey equipment on a drone. The overall layout map includes tour routes, which are numbered and marked on the overall layout map.

[0073] The scenic spot association unit: Based on the overall layout map, obtains the location information of all scenic spots in the scenic area; for each scenic spot, obtains the straight-line distance from the scenic spot to the tour route; if the straight-line distance from the scenic spot to the tour route is less than or equal to the straight-line distance from the scenic spot to all tour routes other than the tour route, then the scenic spot is marked as an affiliated scenic spot of the tour route; if the straight-line distance from the scenic spot to the tour route is greater than the straight-line distance from the scenic spot to all tour routes other than the tour route, then the tour route with the smallest straight-line distance is obtained, and the scenic spot is marked as an affiliated scenic spot of that tour route; if there are multiple tour routes with the smallest straight-line distance, then the scenic spot is marked as an affiliated scenic spot of the tour route with the smallest number.

[0074] Furthermore, the auxiliary attraction labeling and visitor flow monitoring module includes an auxiliary attraction labeling unit and a visitor flow monitoring unit.

[0075] The auxiliary attraction labeling unit: Based on all the auxiliary attractions on the tour route, a secondary labeling is performed on the tour route marked on the overall layout map. The secondary labeling indicates that all the auxiliary attractions are marked on the tour route. Based on the marked auxiliary attractions, the tour route is sequentially divided into multiple line segments.

[0076] The passenger flow monitoring unit: laser sensors are deployed within the line segment. The laser sensors are used to monitor the passenger flow data within the line segment and obtain the average passenger flow data within the line segment.

[0077] Furthermore, the experience liquidity index calculation module includes an experience liquidity index calculation unit.

[0078] The experience mobility index calculation unit: adds a time label to the average passenger flow data within the line segment; and calculates the experience mobility index of the line segment at time point t based on the average passenger flow data at a single time point.

[0079] Furthermore, the dynamic passenger flow allocation module includes an early warning and maintenance unit and a dynamic passenger flow allocation unit.

[0080] The early warning and maintenance unit: if the experience liquidity index of a line segment at a single point in time is greater than or equal to the liquidity index threshold, the line segment is marked as a line segment to be diverted and an early warning is issued to remind staff to carry out infrastructure maintenance.

[0081] The dynamic passenger flow allocation unit: based on the overall layout diagram, obtains the line segment closest to the line segment and whose experience flow index is less than the flow index threshold, and calculates the passenger flow that needs to be transferred; and transfers the passenger flow that needs to be transferred between line segments.

[0082] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0083] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the management of intelligent cultural tourism scenic spots based on big data, characterized in that: The method includes the following steps: Step S1: Obtain the overall layout map of the scenic area and mark the tour route; obtain the auxiliary attractions along the tour route; Step S2: Based on the aforementioned attractions, divide the tour route into segments in sequence; obtain the average visitor flow data within each segment; Step S3: Add time tags to the average passenger flow data within the line segment, and calculate the experience mobility index of the line segment based on the average passenger flow data within a single time point; Step S4: If the experience mobility index of the line segment is greater than or equal to the mobility index threshold, it is recorded as a line segment to be guided and an early warning maintenance is issued; obtain the line segments with experience mobility index less than the mobility index threshold, calculate the transmitted customer flow and allocate it. The specific implementation process of step S2 includes: Based on the i-th tour route All the ancillary attractions on the site, and the tour route marked on the overall layout map. The above is marked with secondary annotations, which indicate the location of the tour route. All secondary attractions are marked on the map; the tour route is then planned based on these attractions. The sequence is divided into J-1 line segments, where the first end of the j-th line segment is the j-th affiliated scenic spot, and the last end is the (j+1)-th affiliated scenic spot. Let the j-th line segment be denoted as... ,in, Indicates tour route The j-th affiliated attraction, Indicates tour route The (j+1)th affiliated attraction on the list, where J represents the total number of affiliated attractions; Online segment Laser sensors are installed inside the line segment to monitor pedestrian flow data and acquire line segment data. Average passenger flow data within the premises; The specific implementation process of step S3 includes: For line segments The average passenger flow data within the time frame is appended with a time label, and the average passenger flow data at time point t is recorded as follows: ; Average passenger flow data at time point t Calculate line segments The liquidity index at time point t is calculated using the following formula: ； in, Represents line segment The liquidity index at time point t. Represents a preset line segment Maximum passenger capacity; The specific implementation process of step S4 includes: If line segment Experience liquidity index at time point t If the value is greater than or equal to the liquidity index threshold, then the line segment will be... The section to be diverted is marked and an early warning is issued to remind staff to perform infrastructure maintenance; Based on the overall layout diagram, obtain the distance from the line segment. Recently, for line segments where the experience liquidity index is less than the liquidity index threshold, passenger flow is transferred. The calculation formula for the passenger flow transfer is as follows: ； ； in, Indicates the flow transfer coefficient. This represents the preset initial flow transfer coefficient. This indicates the preset adjustment factor. Indicates distance from line segment Recently, for line segments where the liquidity index is less than the liquidity index threshold, and k≠j, This indicates the passenger flow that needs to be delivered; Passenger traffic that needs to be transferred From line segment Transfer to line segment middle.

2. The intelligent cultural tourism scenic area optimization management method based on big data according to claim 1, characterized in that, The specific implementation process of step S1 includes: By equipping a drone with remote sensing surveying equipment, an overall layout map of the scenic area is obtained. This overall layout map includes tour routes, which are then numbered, with the i-th tour route denoted as... Mark the tour route on the overall layout map. ; Based on the overall layout map, obtain the location information of all attractions in the scenic area; for each attraction, obtain the tour route from the attraction. The straight-line distance from the scenic spot to the tour route The straight-line distance is less than or equal to the distance from the attraction to the tourist route. The straight-line distance of all other tourist routes, excluding those mentioned above, will be used to mark the attractions as tourist routes. Accessible attractions; if the attractions are on the tour route The straight-line distance is greater than the distance from the attraction to the tourist route. If all other routes are not included in the list of routes, the route with the shortest straight-line distance is selected, and the attraction is marked as an accessory attraction of that route. If there are multiple routes with the shortest straight-line distance, the attraction is marked as an accessory attraction of the route with the lowest number.

3. A big data-based intelligent cultural tourism scenic area optimization management system, implementing the big data-based intelligent cultural tourism scenic area optimization management method as described in any one of claims 1-2, characterized in that, The system includes: a data collection and attraction association module, an auxiliary attraction labeling and visitor flow monitoring module, an experience mobility index calculation module, and a visitor flow dynamic allocation module; The data collection and attraction association module: obtains the overall layout map of the scenic area and marks the tour route; obtains the auxiliary attractions along the tour route; The auxiliary attraction labeling and visitor flow monitoring module: Based on the auxiliary attractions, divides the tour route into segments in sequence; and obtains the average visitor flow data within the segments; The experience mobility index calculation module adds a time label to the average passenger flow data within the line segment, and calculates the experience mobility index of the line segment based on the average passenger flow data within a single time point. The dynamic passenger flow allocation module: if the experience flow index of the line segment is greater than or equal to the flow index threshold, it is recorded as a line segment to be diverted and an early warning is issued for maintenance; it obtains line segments with experience flow index less than the flow index threshold, calculates the transferred passenger flow, and allocates it.

4. The intelligent cultural tourism scenic area optimization management system based on big data according to claim 3, characterized in that: The data acquisition and attraction association module includes a data acquisition unit and an attraction association unit; The data acquisition unit: acquires an overall layout map of the scenic area by mounting remote sensing survey equipment on a drone. The overall layout map includes tour routes, which are numbered and marked on the overall layout map. The scenic spot association unit: Based on the overall layout map, obtain the location information of all scenic spots in the scenic area; for each scenic spot, obtain the straight-line distance from the scenic spot to the tour route; if the straight-line distance from the scenic spot to the tour route is less than or equal to the straight-line distance from the scenic spot to all tour routes other than the tour route, then mark the scenic spot as an auxiliary scenic spot of the tour route. If the straight-line distance from the scenic spot to the tour route is greater than the straight-line distance from the scenic spot to all other tour routes except the tour route, then the tour route with the smallest straight-line distance is obtained, and the scenic spot is marked as an affiliated scenic spot of that tour route. If there are multiple tour routes with the smallest straight-line distance, then the scenic spot is marked as an affiliated scenic spot of the tour route with the smallest number.

5. The intelligent cultural tourism scenic area optimization management system based on big data according to claim 4, characterized in that: The auxiliary attraction labeling and visitor flow monitoring module includes an auxiliary attraction labeling unit and a visitor flow monitoring unit; The auxiliary attraction labeling unit: Based on all the auxiliary attractions on the tour route, a secondary labeling is performed on the tour route marked on the overall layout map. The secondary labeling indicates that all the auxiliary attractions are marked on the tour route. Based on the marked attractions, the tour route is divided into multiple segments. The passenger flow monitoring unit: laser sensors are deployed within the line segment. The laser sensors are used to monitor the passenger flow data within the line segment and obtain the average passenger flow data within the line segment.

6. The intelligent cultural tourism scenic area optimization management system based on big data according to claim 5, characterized in that: The experience liquidity index calculation module includes an experience liquidity index calculation unit; The experience mobility index calculation unit: adds a time label to the average passenger flow data within the line segment; and calculates the experience mobility index of the line segment at time point t based on the average passenger flow data at a single time point.

7. The intelligent cultural tourism scenic area optimization management system based on big data according to claim 6, characterized in that: The dynamic passenger flow allocation module includes an early warning and maintenance unit and a dynamic passenger flow allocation unit; The early warning and maintenance unit: if the experience liquidity index of a line segment at a single point in time is greater than or equal to the liquidity index threshold, then the line segment is recorded as a line segment to be diverted and an early warning is issued to remind staff to carry out infrastructure maintenance; The dynamic passenger flow allocation unit: Based on the overall layout diagram, it obtains the line segment closest to the line segment and whose experience flow index is less than the flow index threshold, and calculates the passenger flow that needs to be transferred. The passenger flow that needs to be transmitted is transferred between line segments.

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